Conference Agenda

Floods are natural hazards triggered by intense rainfall and are particularly destructive in low-lying areas such as floodplains. In flood-prone regions, effective disaster management relies on prevention, monitoring, and emergency response strategies. In this context, remote sensing, especially Synthetic Aperture Radar (SAR), has become indispensable for flood mapping and monitoring due to its ability to acquire data under adverse weather conditions and persistent cloud cover. Multi-temporal SAR imagery processed into RGB composites allows rapid visualization of inundation patterns, while the Geographic Object-Based Image Analysis (GEOBIA) approach improves the classification of flooded areas through the integration of backscatter thresholds and terrain elevation data. This study investigates the spatial and temporal dynamics of flooding in the Lower Tubarão River Sub-basin (LTRSb), southern Brazil, following an extreme precipitation event that produced 260 mm of accumulated rainfall between May 24 and 25, 2019. The Sentinel-1B SAR images, acquired pre- and post-event, were used to map flooded areas with an overall classification accuracy of 88%. The results indicate that three days after the event, flooding covered 140 km² (29%) of the LTRSb, predominantly affecting agricultural (86.3 km²) and pasture areas (47.6 km²). The flooded extent decreased to 62 km² after 15 days and to 15 km² after two months, with agricultural land consistently accounting for 97% of the flooded area. Urbanized areas (≈1 km²) were also impacted, indicating significant risks to infrastructure and public health. These findings highlight the importance of SAR-based flood monitoring for risk assessment and disaster management in hydrographic basins.

Post-seismic deformation reflects the crustal adjustment to stress perturbations induced by earthquakes and may lead to modifications of ongoing deformation patterns. While such effects are well documented for moderate-to-large events, their detectability and significance after low-magnitude earthquakes remain poorly understood. Here, possible deformation pattern modifications associated with the February 2022 ML 3.5 Brentonico earthquake in Northearn Italy are investigated using ground-deformation time series derived from the European Ground Motion Service (EGMS) Ortho products. The earthquake occurrence is treated as a temporal discontinuity, enabling the estimation of pre- and post-event vertical/horizontal differential velocities during 2019-2023. A dual-weighted interpolation scheme, accounting for both spatial proximity and time-series reliability, is applied to derive spatially coherent maps of kinematic modifications. The results reveal measurable and spatially organized changes in deformation patterns, including localized accelerations/decelerations and direction reversals. A clear spatial correspondence between differential velocity anomalies and mapped fault systems suggests that the earthquake acted as a localized stress perturbation, modulating pre-existing tectonic structures. This study demonstrates the capability of EGMS datasets to capture post-seismic deformation signals and highlights the importance of considering low-magnitude events in long-term deformation analyses.

Underground transportation networks are essential for mobility in densely populated cities, addressing urbanization challenges such as traffic congestion, noise, and air pollution. Ensuring the safety and reliability of these infrastructures requires structural health monitoring (SHM) systems, which detect faults, deterioration, and damage. While traditional in-situ monitoring techniques provide real-time data, they are often economically restrictive. Synthetic Aperture Radar Interferometry (InSAR) offers advantages for large-area, long-term monitoring and has been successfully applied to various infrastructures, including dams, bridges, highways, and subways.

This study investigates surface displacement along a 15.4 km metro line with 11 stations in the Gebze district of Kocaeli, Türkiye, using multi-temporal InSAR. Sentinel-1 SLC IW data acquired between January 2019 and October 2025 were processed using MiaplPy, generating 504 interferograms with a 6-day temporal baseline. The phase-linking workflow utilized Persistent Scatterers (PS), Distributed Scatterers (DS), and Statistically Homogeneous Pixels (SHP), combined with Combined Phase Linking (CPL) algorithm and SNAPHU for phase unwrapping, to obtain reliable displacement time series and mean deformation velocities.

Results indicate line-of-sight displacements ranging from –10 to 10 mm/year, with the highest movements near the first station. Time series analysis shows stability from 2019 to 2021, a sudden displacement from 2021 to mid-2022, and stabilization until 2025. Monitoring these deformations provides insights into construction-induced dynamics and enables early detection of potential risks. Incorporating additional data, such as lithological, soil, and geotechnical information, can enhance data-driven monitoring. Long-term deformation monitoring ultimately supports the development of sustainable urbanization strategies and contributes to safe, resilient underground infrastructure management.

While polarimetric interferometric SAR techniques provide a strong theoretical framework for forest height retrieval, their application using C-band Sentinel-1 data is challenging due to repeat-pass acquisition geometry and strong temporal decorrelation. In this study, we assemble a globally distributed dataset combining Sentinel-1 interferometric observations with spaceborne LiDAR forest height measurements from the GEDI and ICESat-2 missions. More than 1800 Sentinel-1 interferometric image pairs were processed and spatially matched with LiDAR observations across tropical, temperate, and boreal forest regions. Sentinel-1 Single Look Complex data were used to derive interferometric coherence and polarimetric–interferometric observables, enabling statistical analysis of their relationship with forest structural properties. The results reveal physical relationships between Sentinel-1 coherence and canopy height across multiple forest biomes, indicating that Sentinel-1 interferometric measurements, under near-zero spatial baseline conditions, retain measurable sensitivity to vegetation structure despite temporal decorrelation effects. These findings provide a conceptual basis for exploiting similar repeat-pass interferometric observations from new low-frequency SAR missions such as NISAR and and upcoming ROSE-L for forest height mapping. In addition, the assembled dataset provides a global benchmark for developing and evaluating data-driven approaches for forest height estimation using Sentinel-1 observation.

This contribution presents an integrated monitoring framework that combines Sentinel-1 multi-temporal InSAR (MTInSAR) with geological, hydrological, and operational datasets to analyse long-term ground deformation in the Epe cavern storage field, Germany. Using the SBAS approach, vertical and horizontal deformation components are derived for the 30 km² storage area, revealing a bowl-shaped subsidence feature and distinct east–west deformation patterns associated with cyclic cavern operation. The analysis incorporates geoscientific information from GeoBasis NRW, AGSI+, ELWAS, and BÜK/BK50 into a harmonized GIS environment. This enables correlation of SAR-derived deformation fields with cavern-pressure cycles, soil characteristics, and groundwater variations, providing process-oriented interpretation of subsurface–surface interactions. Deformation results will be updated to include data through October 2025. The study demonstrates how MTInSAR, combined with geoscientific knowledge, supports transparent deformation assessment and contributes to the development of geospatial digital twins for subsurface infrastructure, improving operational resilience and environmental risk management.

Accurate land-cover mapping in heterogeneous metropolitan regions requires robust methods capable of overcoming limitations of optical imagery, particularly under persistent cloud cover. This study investigates the potential of L-band ALOS-2 PALSAR-2 SAR data for operational land-cover classification over Istanbul, Türkiye, by integrating advanced machine-learning algorithms, feature-selection strategies, and hyperparameter optimization. Four classifiers Random Forest (RF), XGBoost, LightGBM, and a shallow Artificial Neural Network (ANN) were evaluated using full-polarization SAR observations and a reference dataset derived from Sentinel-2 composites, orthophotos, and LPIS parcel boundaries.

Pre-processing included radiometric calibration, Lee filtering, terrain correction, and extraction of GLCM texture metrics from HH and HV channels, yielding an initial 20-feature set reduced to 18 through correlation and variance filtering. A LightGBM-driven Recursive Feature Elimination (RFE) procedure identified an optimal subset of ten features. Model optimization employed Bayesian hyperparameter tuning (TPE) under stratified 5-fold cross-validation to ensure reproducibility and generalization.

Results show that LightGBM achieved the highest accuracy (OA = 85.1%, κ = 0.81), followed by XGBoost (83.6%), RF (81.4%), and ANN (78.9%). Water surfaces were consistently the most accurately classified class, while confusion primarily occurred between urban and bare surfaces. Hyperparameter tuning improved F1-scores across all models, and reducing the feature stack to ten variables enhanced performance without loss of class separability. HV-derived texture features, particularly Entropy and Contrast, provided the highest discriminative power.

The study demonstrates that optimized feature selection and systematic hyperparameter tuning significantly enhance SAR-based land-cover classification, offering a transferable workflow for large-scale metropolitan mapping.

Mining induced deformation in underground metal mines poses a threat to surface infrastructure, underground access and environmental safety, and needs reliable spatially continuous monitoring. In this contribution, a long-term interferometric SAR analysis over an underground hard rock metal mines (Mine-B) in Khetri Copper Belt, India using TSX/TDX high resolution SAR data is carried out. Coherent small baseline DInSAR time series (CSB-DTS), stacking DInSAR, and single reference PSI chain are implemented. Stacking DInSAR derived average LOS deformation velocity and single-reference PSI derived velocity are obtained for mine-B for dataset of January 2023- December 2024. The obtained results verify that Mine-B is substantially stable while having a persistent fast and slow deformation concentrated inside and around the trough in SoZ-2 in Mine-B. The workflow shows how the combination of CSB-DTS, stacked DInSAR and PSI can facilitate the operational subsidence monitoring and long-term stability evaluation in complex mining environments and gives and gives an indication for the future integration with in-situ measurements and numerical models.

Spatially accurate fire danger information is critical for reliably predicting fire ignition probability, spread potential, and behaviour. However, Canadian fire management agencies mainly predict fire danger using weather stations, which only collect observations at explicit spatial points and cannot accurately model the fine-scale spatial variability of moisture across large and remote areas. This study predicts and maps the drought code, a variable representative of the moisture of deep, slow drying, compact organic matter across the landscape of British Columbia using ALOS-2 polarimetric SAR. A random forest model predicted the drought code of target areas with high accuracy to values derived from weather stations. The model was applied to forested areas across a time-series of ALOS-2 images on a grid-by-grid basis at a one square kilometer resolution and predicted the occurrence of fine-scale differences in drought code associated with differences in topography and elevation. The development of this drought code prediction model will allow fire management agencies to predict spatially accurate, fine-scale differences in drought code across the densely forested and highly mountainous landscape of British Columbia, improving fire behaviour and fire prediction systems.

Monitoring water levels in small and remote reservoirs is critical due to the climate crisis and rising water demands. Traditional in-situ gauge networks often provide sparse or inconsistent coverage, especially in remote regions. Satellite altimetry provides a global alternative, but it is frequently limited by long revisit times or coarse footprints unsuitable for smaller water bodies. Existing SAR-based methods face inherent limitations: amplitude-based approaches rely on accurate external Digital Elevation Models, whereas interferometric techniques are affected by coherence loss and phase-unwrapping ambiguities. To address these limitations, this research introduces a novel approach for estimating water level changes using SAR amplitude data without relying on prior morphological information. By modeling the coastal zone as a set of distinct planar slopes, the method relates the vertical water level change to the horizontal shoreline shift specific to each slope, observed as changes in the satellite range direction. The stack's standard deviation image is used to identify low-slope areas, where the horizontal response to water level variations is most pronounced. In these regions, area-based image matching is applied to quantify displacements within the coregistered stack. Finally, a least-squares estimation is used to determine temporal water level changes and local coastal slopes. The method was validated on Lake Trasimeno, Italy, using a stack of 30 Sentinel-1 images acquired in 2022. Comparisons with in-situ gauge data demonstrated high reliability, achieving an accuracy of 4 cm and a Normalised Median Absolute Deviation of 9 cm. The preliminary results are promising, while further experiments are currently underway.

This paper investigates wavelength-adaptive baseline design for spaceborne repeat-pass SAR tomography (TomoSAR) through a controlled simulation framework comparing representative X-band and C-band configurations. The study focuses on how radar wavelength influences the trade-off among vertical resolution, temporal decorrelation sensitivity, sidelobe behaviour, and baseline sampling efficiency. Using a discrete TomoSAR forward model, several experiments are conducted to analyse reconstruction performance under identical aperture, varying coherence conditions, different baseline sampling strategies, joint aperture-spacing design scans, and noise perturbations. Quantitative results show that X-band provides a clear intrinsic resolution advantage under coherent conditions, particularly for closely spaced scatterers, but this advantage degrades more rapidly under temporal decorrelation. C-band, while offering lower nominal resolution, exhibits more stable performance across coherence loss, wider design-space tolerance, and stronger robustness in noisy conditions. The comparison of uniform, minimum-redundancy, and irregular baseline patterns further indicates that baseline optimization is more critical for X-band than for C-band. The study moves beyond the general statement that “X-band is higher resolution whereas C-band is more robust” by providing experiment-based and frequency-dependent baseline design guidance. The findings support practical acquisition planning for future repeat-pass TomoSAR missions and contribute to a more quantitative understanding of wavelength-dependent sampling design.

In order to preserve their quality, SAR data are usually used in their native plane, the slant range. However, it is sometimes necessary to link ground data and radar data. Today, ground range or terrain-corrected data are frequently used for this purpose. An alternative to this approach is to project the data into slant geometry, which allows both the superimposition and co-registration of data from different sensors and the preservation of the resolution and phase of the SAR data.

An Observational Definition of the Absolute Phase in Radar Interferometry

The absolute phase in radar interferometry is required for topography and displacement estimation but lacks a general definition. The conventional definition states that absolute phase is proportional to the range difference between primary and secondary acquisitions. This definition is appropriate for simple targets such as point targets, but it cannot be directly applied to general targets.

Here, a universal observational definition of the absolute phase is proposed. It applies to any mode and does not require any assumptions about scattering mechanisms. For differential interferometry, the absolute phase is obtained by temporally unwrapping the phase as the intermediate secondary acquisition time progresses from the primary to the secondary acquisition time. This definition requires a continuous series of interferometric phase measurements along a pre-specified absolute path. The absolute phase matches the wrapped phase modulo 2π and agrees with the conventional definition for point targets.

This contribution discusses several implications of this general definition. The absolute phase of a complex target need not, and sometimes cannot, be proportional to the range difference. An example involving a permafrost landform demonstrates that the absolute phase following a cyclic change is nonzero and cannot be interpreted as a range difference. Another consequence is that the absolute phase in an interferogram can show 2π discontinuities, even when the interferogram itself is continuous and the coherence high.

This general definition enables thorough evaluation of InSAR processing chains and supports interpretation of observations.

Because of its high reproductivity, PolSAR is a suitable sensor for change detection in urban areas. Although many methods of change detection have been proposed, methods focused on polarimetric states transformation are rarely adapted. Through eigenvalue calculations, polarimetric basis which maximizes the polarimetric sensitivity can be calculated, and if this basis is fixed, quantitative change detection is available. The result from this method shows the obvious change in the target area, which is ‘Umekita project 2nd’ in Osaka city. However, changes outside the target area were also larger so that the change detection was not very effective from a relative viewpoint. To solve this problem, algorithms which surpass unnecessary changes in urban areas should be developed, and deeper understanding of scattering mechanisms in urban areas is needed.

The Amazon region has experienced increasing pressure due to the expansion of mining, especially illegal alluvial mining, driven by rising gold prices and a lack of economic opportunities. In Ecuador, this activity has contributed significantly to deforestation and the alteration of water systems, affecting river stability and water quality. The increase in suspended sediments and the modification of river channels generate ecological, economic and social impacts, including production losses and increased vulnerability of riverside communities. In this context, monitoring through remote sensing and Geographic Information Systems has become an essential tool for assessing river dynamics and the effects of illegal mining in the Amazon biome. This study analyses changes in land use and land cover in the Nangaritza River, considering an intense rainfall event that occurred in 2025. Cloud-free mosaics were generated using Sentinel-2 images, spectral indices were calculated, and supervised classification using Random Forest was applied to establish seven coverage categories. The results show a notable expansion of mining areas and sand deposits, accompanied by a reduction in forest cover. The transition matrix revealed significant losses of forest transformed into mining soil and turbid water, as well as an increase in sedimented areas downstream. The analysis of river dynamics identified five critical areas of mining expansion associated with increased sedimentation, turbidity, and morphological alterations to the riverbed. These changes reflect the growing anthropogenic pressure on the river and the need to strengthen monitoring systems to mitigate environmental impacts.

This study investigates the rapid transformations in land use and land cover (LULC) within Lahore District, a phenomenon with profound implications for ecological sustainability and land-use governance. Analysing these dynamics is crucial for minimizing adverse environmental impacts and promoting sustainable urban development. The primary objective is to assess historical LULC patterns over 30 years (1994–2024) and to simulate potential changes for the years 2034 and 2044 using an integrated CA-Markov modeling approach supported by GIS techniques. Landsat imagery from multiple sensors (TM and OLI) was processed through supervised classification methods, achieving classification accuracies exceeding 90%. The temporal analysis revealed marked changes, notably a substantial increase in built-up areas by 359.8 km², alongside reductions in vegetation cover (198.7 km²) and barren land (158.5 km²). Water bodies exhibited minimal variation throughout the study period. Future LULC scenarios generated via the CA-Markov hybrid model demonstrated strong predictive performance, as evidenced by a kappa coefficient of 0.92. The projections indicate continued urban Expansion primarily at the expense of green and undeveloped areas. These findings emphasize the pressing need for sustainable land management practices and provide a robust decision-support framework for urban planners. By integrating predictive modeling into planning policies, this research helps align developmental objectives with environmental conservation in rapidly urbanizing regions like Lahore.

This study evaluates the residential expansion of Guayaquil between 2016–2020 and 2020–2024 using Sentinel-2 imagery and supervised classification with the Support Vector Machine (SVM) algorithm. Given the rapid land transformation in tropical coastal cities, the research applied the Built-up Area Extraction Index (BAEI) to enhance the detection of built-up surfaces and distinguish them from vegetation and bare soil. The integration of BAEI and SVM allowed the development of an accurate, replicable, and low-cost approach to monitor the city’s urban growth.

Three periods of Sentinel-2 images with low cloud coverage were processed, re-projected, classified, and validated. Classification included four thematic classes (residential, vegetation, bare soil, and water bodies) using 1,000 training samples distributed across the city. All classifications achieved over 85% overall accuracy and a Kappa index of 1.0, confirming the model’s robustness in heterogeneous urban environments.

Spatial analysis of land-use transitions revealed that residential growth is concentrated in peripheral sectors such as Ciudad Santiago, Mucho Lote 2, Mi Lote, Trinipuerto, and areas near Narcisa de Jesús Avenue. Results indicate a strong tendency toward contiguous expansion, forming residential corridors along major road networks and the Guayas River. However, dispersed peripheral nuclei highlight challenges for service provision and environmental sustainability.

Overall, the combination of Sentinel-2 imagery, BAEI, and SVM proved highly effective for detecting built-up areas in tropical contexts, offering a scalable methodology for monitoring urban expansion in Latin American cities.

Hedgerows, a traditional agroforestry practice, are declining in Europe, threatening biodiversity and climate control. To support high-quality agricultural carbon credit certification, a method for automatic hedge detection using Pleiades Neo 30cm satellite imagery was developed.

Two methodological approaches were tested in three French study areas with varied landscapes: (i) a classic image segmentation using NDVI, Green Cover Fraction, and LiDAR-derived Digital Height Model, and (ii) a foundation model retrained on 150 annotated tiles with diverse landscape and satellite acquisition configurations. The methods were compared using quantitative (Intersection Over Union, Omission & Commission errors) and qualitative indicators.

The foundation model demonstrated superior hedge detection and robustness across different landscapes. A ground truth dataset based on stratified random sampling and equal allocation was created to allow the quantification of its accuracy using standard accuracy metrics. It achieved a precision of 0.89 and a recall of 0.83 for the hedge class. It effectively adapted to the morphological and ecological diversity of hedges, with few commission errors primarily due to confusion with isolated trees or linear vegetation, and omissions mainly in discontinuous or degraded hedges.

The study confirms the relevance of Pleiades Neo for detecting thin-scale elements like hedges, the effectiveness of foundation models with limited reference data, and their potential for large-scale hedge mapping. Future work aims to incorporate more spectral bands and expand the model's training to detect hedgerows across the European Union under various satellite acquisition contexts, paving the way for operational tools in agricultural carbon credit valuation.

This study focuses on county-level areas with high-density wind farm distribution in the Xing'an League of Inner Mongolia, China. Using high-resolution satellite imagery from 2016 to 2024, land cover information within and around wind farms was extracted through visual interpretation, and the spatiotemporal dynamics of land cover in these areas were analyzed. The results indicate that: (1) From 2016 to 2024, land cover change in the study area was primarily driven by wind farm expansion, which increased cumulatively by 130.87 km² (+260.35%) and exhibited the highest dynamic degree among all land cover categories (LK = +32.54%/yr). (2) Grassland was the most severely impacted land cover type, with 78.74 km² converted to wind farm land, accounting for 59.66% of the total newly established wind farm area, while cultivated land and forest land contributed 20.06% and 18.33%, respectively. (3) As wind power expanded, the land cover composition within wind farms shifted from a cultivated land–grassland balance toward grassland dominance. (4) Areas subjected to temporary disturbance from wind farm construction activities tended to recover progressively, with cultivated land exhibiting a faster recovery rate than grassland.

Addressing the United Nations Sustainable Development Goals, particularly sustainable cities and communities (SDG 11), and

the protection of terrestrial ecosystems (SDG 15), is closely linked to understanding patterns of urbanization. Rapid urban growth

significantly influences ecosystem functions, including transportation, housing, and economic development. Monitoring this growth

and analyzing performance patterns are essential for supporting decision-making and guiding urban planning and management. This

study presents an automatic approach for monitoring urban expansion by applying the Random Forest machine learning classifier

from 2015 to 2025 using Google Earth Engine. The method exploits spectral indices not only for unsupervised classification but

also for training the Random Forest classifier, thereby ensuring a fully automated workflow. The proposed approach is applied

to Gharbia Governorate, a region which lacks surrounding desert margins and is instead entirely composed of fertile agricultural

land, to monitor urban expansion in three-year intervals. The proposed study, which achieved a kappa coefficient exceeding 0.96

across all study periods, revealed a gradual decline in agricultural land from 75.5% in 2015 to 72.7% in 2025. These outcomes offer

valuable insights to support evidence-based planning and promote sustainable land use management.

Cropland abandonment (CA) is a major land-use change with important environmental and socio-economic implications. This study evaluates cropland abandonment detection using multi-temporal Landsat features and a spatially independent validation framework, comparing the performance and spatial behaviour of Random Forest and XGBoost classifiers. A set of temporally aggregated spectral indices (NDVI, BSI, NDBI, and MNDWI), including multi-year trends and variability measures, was integrated into a 56-band composite dataset. Training and validation samples were generated using 100 × 100-pixel windows centred on land-use parcels, with overlapping areas between different reference classes explicitly excluded to avoid label ambiguity. To reduce spatial autocorrelation, the data were split into separated training (1,582.6 km²) and testing (719.2 km²) areas within the Savinjska statistical region in Slovenia. Random Forest (RF) and XGBoost (XGB) classifiers were trained and evaluated using spatially separated validation data. Classification performance was assessed using overall accuracy, user’s and producer’s accuracy, and F1-score. Results indicate that XGB achieved a higher overall accuracy (0.705) compared to RF (0.670) and exhibited strong sensitivity in detecting cropland abandonment, while RF produced more conservative and spatially stable estimates of abandoned cropland area. Spatial error maps and area-based comparisons reveal systematic differences between the two classifiers, particularly in their tendency to overestimate abandonment extent. The findings highlight the importance of spatially explicit validation strategies, careful reference data preparation, and multi-temporal feature design for robust cropland abandonment mapping. The main contribution lies in the systematic assessment of model behaviour, spatial error patterns, and area estimates under strict spatial separation of training and testing data.

Land Use/Land Cover (LULC) classification is essential for understanding the spatial distribution of Earth’s surface and for supporting sustainable environmental and economic development. Recent extreme events in Central Europe have emphasized the link between LULC change and disaster vulnerability, highlighting the need for accurate monitoring. Advances in satellite technologies, particularly Sentinel-1 and Sentinel-2, combined with deep learning (DL) methods, have significantly improved LULC mapping. Convolutional Neural Networks (CNNs) excel at spatial feature extraction, Long Short-Term Memory (LSTM) networks capture temporal dependencies, and Convolutional LSTM (ConvLSTM) models integrate both spatial and temporal information.

This study evaluates the comparative performance of DL architectures for LULC classification in the Harz Mountains, Central Germany—a region experiencing notable forest cover loss. We assess 2D-UNet using two temporal processing approaches, examine the effect of attention mechanisms in 3D-UNets, and explore multiple integrations of ConvLSTM layers within UNet architectures. Our goal is to identify the most effective strategy for capturing spatio-temporal dynamics in LULC datasets, contributing to improved monitoring and management of vulnerable landscapes.

Monitoring land-use and land-cover (LULC) dynamics in rapidly urbanizing regions is critical for sustainable environmental planning. Dynamic metropolitan areas with rapid urbanization, such as Istanbul, Türkiye, are experiencing significant land-cover changes, among which deforestation is one of the most critical. This study presents a Google Earth Engine (GEE)-based framework to monitor LULC changes in Istanbul from 2016 to 2025 by fusing Sentinel-2 optical imagery, Sentinel-1 SAR and topographic data. From these datasets, a feature set—including spectral bands, vegetation indices, SAR backscatter metrics, and topographic variables—was derived and used to train a Random Forest (RF) baseline model on 2016 Land Parcel Identification System (LPIS) reference data. The baseline model was then applied across the time series to assess its temporal transferability, overcoming the limitation of up-to-date ground-truth data. The baseline model achieved an overall accuracy of 72%, calculated using a validation dataset derived from the LPIS reference data. Feature importance analysis revealed that structural variables—particularly DEM and SAR metrics—were the primary contributors to the classification, used in combination with optical features. Time-series results indicate a cumulative decline of 231 km² in agriculture and 379 km² in forest cover during the study period, inversely corresponding to urban growth. The results of the study highlight that, although applying a single-year model without independent annual validation data causes certain uncertainties—arising from methods, sensors, or topography (e.g., misclassifications)—the proposed framework is highly practical for monitoring deforestation and urbanization trends in complex landscapes.

Accurate and efficient land-use mapping provides intuitive spatial information, which helps to rationalize the planning and deployment of land resources, and provides a basis for urban planning, agricultural development, environmental protection and other aspects. This study utilizes the Google Earth Engine platform and the Resnet-50 method to explore the spatial distribution of land use in Daxing District, Beijing in 2023, by combining point of interest (POI) data, nighttime light data, Sentinel-1 data, and Sentinel-2 data. The results of the study show that the accuracy of land use mapping using different data is different, and the accuracy of the Resnet-50 method is better than that of the Random Forest method. Making full use of the band features and index features of Sentienl-1 data and Sentinel-2 data, nighttime light data and POI data can improve the accuracy of land use mapping results. Among them, the land use mapping accuracy of the proposed method is the highest, with an OA of 88.11% and a Kappa coefficient of 0.83. Ranking the importance of different features found that VH band in January-March has the most important effect on the land use mapping results and the residential land in the POI data has the least important effect on the land use mapping results. This study provides a feasible reference program for efficiently and accurately obtaining land use mapping data for a large study area.

Reliable urban risk assessment requires accurate and up-to-date information on building characteristics, particularly construction year and height, which are often incomplete or unavailable in existing databases. This study presents a cloud-based methodology for the automatic estimation of these parameters using multispectral and very high-resolution Earth Observation (EO) data. The proposed approach integrates temporal analysis of multispectral satellite imagery (Sentinel-2 and Landsat) with photogrammetric processing of very high-resolution stereo imagery (Pléiades). Building construction year is estimated by detecting temporal changes in spectral indices using spline-based modeling and discrete-difference analysis, achieving an accuracy of better than ±3 years. Building height is derived from digital surface models generated from satellite stereo imagery, with a mean accuracy of less than 2 m relative to LiDAR reference data (~1.40 m). The methodology was implemented in a cloud computing environment (Google Earth Engine and Google Colab) and tested in the City of Zagreb, Croatia. Validation results show robust performance, with an F1-score of 0.819 for construction year estimation and strong agreement between EO-derived and LiDAR-based height values. The results demonstrate the potential of EO-based methods for scalable, reliable extraction of building information, thereby supporting improved urban risk assessment and decision-making.

Zhejiang Province is experiencing rapid land use/land cover (LULC) transitions driven by urban expansion, infrastructure development, and increasing environmental pressures. Understanding historical dynamics and future trajectories of these changes is essential for informed regional planning and ecological management. This study analyzes land use changes from 2000 to 2020 and forecasts future patterns for 2025 to 2040 by integrating multi-temporal land use data with key spatial drivers, including elevation, slope, aspect, Normalized Difference Vegetation Index (NDVI), and proximity to roads and built-up areas. Change detection results reveal substantial declines in croplands and green spaces alongside rapid urban expansion, particularly around Hangzhou and Shaoxing and along major transportation corridors, reflecting an early phase of accelerated urbanization from a relatively small baseline. Future land-use dynamics were simulated using a hybrid Convolutional Neural Network - Long Short Term Memory (CNN-LSTM)-Cellular Automata (CA)-Markov framework that captures complex spatiotemporal dependencies and neighbourhood interactions under physical and anthropogenic constraints. Model projections indicate a more moderate growth regime from 2025 to 2040, with urban land increasing by 1.7%, croplands decreasing by 2.2%, and modest gains in water bodies (1.9%) and forest cover (1.1%), suggesting landscape saturation and policy-influenced land management. Validation using the observed 2025 land use map demonstrates strong predictive performance, achieving an overall accuracy of 86% and a Kappa coefficient of 79%. The results provide spatially explicit insights to support balanced development and enhanced ecological resilience.

Land use/land cover change (LULCC) analysis is essential for understanding environmental transformation and guiding sustainable territorial planning. Remote sensing offers a valuable source of information for monitoring these changes, but the accuracy of thematic maps depends heavily on the classification algorithm applied. This study compares the performance of three widely used supervised Machine Learning (ML) algorithms (Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN)) for LULC mapping in the Greater Guayaquil region, a tropical area with persistent cloud cover.

A mosaic of Landsat-9 images from 2023 was processed in Google Earth Engine, followed by the selection of representative training and validation samples. The algorithms were implemented in R Studio, and accuracy was evaluated through confusion matrices and external comparison with MapBiomas Ecuador.

Four LULC classes were defined: Forest, Crops, Vegetation-free areas (urban/bare soil), and Water. Results indicate that SVM achieved the highest performance, with 93% overall accuracy and a Kappa coefficient of 0.91, followed by RF (92%; κ = 0.89) and ANN (90%; κ = 0.86). SVM also showed the highest spatial agreement with MapBiomas (>90%). Discrepancies were concentrated in rapidly changing urban–agricultural boundaries.

The superior performance of SVM is attributed to its capacity to model non-linear class boundaries in complex tropical landscapes. Despite expectations that RF would perform best based on previous literature, SVM proved more effective for this specific AOI. The study confirms that Landsat-9 combined with supervised ML models, particularly SVM, offers a robust and cost-effective approach for environmental monitoring and land-use planning in data-limited regions.

This study analyzed the advance of deforestation associated with artisanal mining in the Tapajós Environmental Protection Area (APA), north of Jacareacanga, Pará State, Brazil, for 2017 and 2024. The National Institute for Space Research (INPE) monitors deforestation in the Amazon using remote sensing, and the Tapajós APA stands out among protected areas for high rates of mining-related deforestation. Sentinel-2A images were used to generate the Normalized Difference Vegetation Index (NDVI) and RGB composites, assigning the 2017 NDVI to the red channel and the 2024 NDVI to the green and blue channels. Auxiliary data, including active mining processes in 2024 and the hydrographic network, were integrated for analysis. This approach enabled the identification of two distinct spectral responses: (i) cyan areas corresponding to regions that were non-vegetated in 2017 but exhibited regenerated vegetation in 2024; and (ii) red areas corresponding to regions that were non-vegetated in 2024. The results enabled visualization of the spatial progression of deforestation, particularly along drainage networks and in relation to active mining areas, revealing a pronounced expansion associated with artisanal mining across multiple waterways, with an upstream progression consistent with alluvial gold and cassiterite deposits. The data corroborate deforestation alerts issued by the Real-Time Deforestation Detection System (DETER) and the Deforestation Alert System (SAD)/Imazon, indicating the continuity of artisanal mining pressure in the Tapajós APA. The methodology demonstrated efficiency in detecting environmental changes and can be replicated in other areas under mining pressure, contributing to territorial monitoring and environmental management

Wildfire activity in Canada has increased significantly in recent decades, shifting to larger, more frequent fires and the emergence of megafires (>10,000 ha) across various ecozones. These events typically exhibit complex spatial patterns of burn severity, including larger and more homogeneous patches of high severity. The burn severity patterns and their drivers in megafires remain unclear, in particular, across diverse ecozones. Remote sensing indices such as the Relativized Burn Ratio (RBR) provide an effective means of quantifying burn severity at large spatial scales. This study uses RBR to evaluate nine megafires (each >50,000 ha) representing the 95th percentile and above of fire size within varying ecozones between 2016 and 2022. These fires were used to develop two random forest models: one predicting RBR and another predicting the within-fire z-score of RBR. Within-fire standardization of RBR was conducted to see whether it alters the relative importance of environmental drivers. In the RBR model (OOB R² = 0.75), regional variables such as ecozone and fire ID, along with drought code, were dominant predictors. In contrast, the z-score model (OOB R² = 0.68) emphasized fuel characteristics, including biomass and canopy closure, with additional contributions from elevation and drought-related variables. These results suggest that broad regional and fire-regime controls exert a stronger influence on burn severity than local fuel conditions at the megafire scale. Standardizing burn severity within fires reduces this regional signal but does not improve predictive performance, highlighting the importance of accounting for regional variability in large-fire dynamics.

Urban Heat Islands (UHIs) represent one of the most pervasive manifestations of human-induced modification of the land surface. They arise from the replacement of natural surfaces with impervious materials, reduced evapotranspiration, waste heat emissions, and altered aerodynamic roughness, collectively causing cities to exhibit elevated temperatures relative to surrounding rural areas.

The Local Climate Zone (LCZ) framework introduced by Stewart & Oke (2012) provides a standardized, physically based classification system for describing urban and natural landscape types according to their surface structure, cover, and thermal properties. Unlike traditional land-use/land-cover schemes, LCZs are explicitly designed for urban climate studies and allow for consistent comparison of urban form, function, and thermal behaviour across cities worldwide.

While LCZ maps of Bologna already exist within the WUDAPT protocol, they are characterized by a declared not high level of accuracy. So, the present work aims to produce the first detailed and reliable LCZ thematic map for the Municipality of Bologna, using higher quality, remotely sensed, input data.

To assess the impact of multi and hyperspectral imagery on the classification results, a Sentinel-2 and a PRISMA image were considered for the study.

Overall, this study provides for a first time a detailed and accurate LCZ map for the Municipality of Bologna and confirms the value of combining UCPs with both multispectral and hyperspectral data.

This study was carried out within the Space It Up project funded by the Italian Space Agency, ASI, and the Ministry of University and Research, MUR, under contract n. 2024-5-E.0 - CUP n. I53D24000060005.

This study focuses on predicting the evolution of internal urban spatial features, a dimension often overlooked in research that prioritizes urban expansion. Using the Yangtze River Delta as the case study, the work integrates multimodal remote sensing data—including high-resolution optical imagery and SAR data—to capture detailed land-use patterns, structural textures, and functional–transportation relationships. These fused datasets support the Futureland model, which applies parallel computing and Generalized Logistic Regression to simulate future spatial configurations with high efficiency and accuracy.

The results suggest that from 2030 to 2050, major cities such as Shanghai, Suzhou, Wuxi, Changzhou, Jiaxing, and Hangzhou will develop into more connected and compact urban clusters. Transportation networks and functional areas are expected to evolve in general alignment, while localized deviations reflect the complexity of internal urban dynamics. Land-use types are projected to undergo new spatial combinations and reorganizations, indicating improved continuity and diversity within the urban structure.

By systematically revealing trends in land-use evolution, transportation–function coupling, and urban form transformation, this research provides a clearer understanding of future urban spatial development. The proposed predictive framework offers valuable guidance for urban planning, governance, and sustainable regional development.

Global shifts in vegetation patterns highlight the need for effective monitoring as climate conditions intensify. Remote sensing provides valuable tools for detecting stress across landscapes. This study examines whether thermal satellite and airborne data can detect early stress linked to temperature, drought, and fire history. Within Australia, eucalypt species are increasingly vulnerable to climate-driven canopy dieback. Prolonged drought and extreme temperatures increase the risk of dieback. As eucalypts regulate leaf temperature through transpiration, when water is limited, leaf temperatures rise and can be detected using thermal imagery. Therefore, our research questions are:

• Can temperature-related stress patterns be identified?

• Does fire history affect long-term stress?

• Can thermal changes indicate dieback events?

• Does topography shape canopy stress?

Study Area:

Scott Creek Conservation Park in South Australia contains diverse native vegetation and steep terrain. The canopy is dominated by two stringybark eucalypt species, with areas burnt in the 2021 fire and unburnt controls.

Data and Methods:

Thermal satellite data from 2019–2025, including land surface temperature and water stress indicators, were analysed alongside local climate records. Airborne hyperspectral, LiDAR, RGB, and thermal imagery (50 cm) were processed to derive canopy structure, topography, and thermal patterns. A supervised classification was used to assess canopy condition.

Preliminary results:

Indicate that after a fire, high and moderate levels of vegetation stress increased and persisted into the following year. Vegetation in fire-affected areas showed no significant improvement in WUE during the recovery period. suggesting that fire-affected vegetation remained physiologically stressed despite visible regrowth.

The study investigates the long-term environmental and social impacts of asbestos mining in the Troodos Mountains of Cyprus, where chrysotile extraction between 1907 and 1988 left a lasting legacy of contamination and landscape degradation. Using multi-temporal aerial photographs, Sentinel-2 satellite data, and field observations, the research analyses land use transformations and vegetation recovery processes in the Amiandos mine area. A land use transfer matrix and Normalized Difference Vegetation Index (NDVI) were applied to assess ecological regeneration and detect spatial patterns of recovery.

To address ongoing environmental health risks, pre-trained deep learning models based on convolutional neural networks (CNNs) were used to identify asbestos-cement roofing in high-resolution aerial imagery. The results indicate measurable reforestation since the 1990s, but also reveal remaining asbestos waste deposits and deteriorated roofing materials posing persistent hazards to local communities.

The integration of remote sensing, vegetation indices, and deep learning methods provides a comprehensive approach to understanding environmental inequality in post-industrial landscapes. This framework supports the development of inclusive and data-driven restoration strategies consistent with the European Union’s environmental health goals. By combining spatial intelligence with machine learning, the study demonstrates the potential of remote sensing to monitor ecological recovery and mitigate asbestos-related risks in Cyprus and similar post-mining environments.

Urban heat and fine particulate matter (PM₂.₅) pollution are critical challenges for sustainable cities, but their high-resolution spatial and temporal patterns are not well understood. This study develops a multi-year 250 m downscaling framework to map the synergy between land surface temperature (LST) and PM₂.₅ in Wuhan, China. Using machine learning–based residual correction, annual, summer, and winter mean PM₂.₅ concentrations in 2015 and 2020 were downscaled from 1 km TAP data to 250 m grids. Correlation and spatial autocorrelation analyses were applied to reveal the spatial patterns of LST–PM₂.₅ interactions. The downscaled PM₂.₅ achieved high accuracy (R² > 0.80), and the heat–pollution relationship showed strong spatial heterogeneity. From 2015 to 2020, synergistic zones changed in the Urban area, consistent with the growth of impervious surfaces. These results provide a fine-scale spatial basis for understanding the coupled dynamics of urban heat and air pollution, supporting integrated strategies for climate adaptation and urban air quality management.

The coastal waters of Shanghai, situated at the confluence of the Yangtze River Estuary and the northern Hangzhou Bay, form a typical high-turbidity aquatic environment influenced by sediment discharge from the Yangtze River and strong tidal dynamics. Extensive urbanization and coastal development have led to the proliferation of hardened shoreline structures in this region, which have altered natural hydrodynamic conditions and sediment transport patterns, contributing to ecological issues such as wetland degradation. In recent years, Shanghai has initiated ecological restoration projects aimed at rehabilitating healthy coastal ecosystems. These restoration efforts, involving geomorphic reshaping, may directly disturb and modify sedimentary environments. However, their impact on suspended sediment concentration (SSC)—a key environmental parameter—across large spatiotemporal scales remains unclear. Traditional in-situ monitoring methods are inadequate for capturing such large-scale dynamic variations, whereas satellite remote sensing provides an effective alternative. This study utilizes multi-source remote sensing data to develop an inversion model for SSC suitable for Shanghai's coastal waters, systematically analyzing the influence of different shoreline types on sediment distribution. The findings illustrate how ecological restructuring of shorelines affects the spatial and temporal variations of SSC, thereby providing a scientific basis for optimizing coastal management strategies and assessing the effectiveness of ecological restoration efforts.

This study investigates the critical role of Urban Morphological Blocks (UMBs) in shaping Land Surface Temperature (LST) patterns across seasons and cities. Through a comparative analysis of Beijing, Wuhan, and Fuzhou, China, we integrated multi-source remote sensing and 3D building data to define UMBs based on building height and density. Employing robust statistical models (Geographical Detector and Random Forest Regression), we quantified the driving forces behind LST variations. Our results consistently identified Low-Rise, High-Density blocks as the primary heat contributors, while High-Rise blocks exhibited cooling effects. Crucially, we found a strong seasonality in dominant drivers: surface biophysical parameters (e.g., vegetation, impervious surfaces) governed LST in warm seasons, whereas 3D architectural morphology (especially building height) became paramount in winter. Furthermore, factor interactions revealed synergistic effects, with the combination of block type and vegetation yielding the highest explanatory power. These findings underscore the UMB as a vital unit for urban climate analysis. The study provides actionable insights for planners, recommending targeted mitigation in high-risk blocks, promotion of thermally efficient building forms, and the adoption of season-specific strategies to enhance urban resilience against heat stress.

Accurate, long-term precipitation data is essential for reliable drought monitoring. This study addresses heterogeneous uncertainties in existing precipitation datasets across mainland China by developing two modifiable weighting schemes: a Cheng-Kling-Gupta Efficiency weighted-ensemble model (CWEM) and Bayesian Model Averaging (BMA). These methods were used to merge seven monthly precipitation datasets into new weighted products (BMAEP and CWEP). The precision and drought monitoring utility of these fused products were evaluated against the benchmark Multi-Source Weighted-Ensemble Precipitation (MSWEP) product using gauge data. Results show that the new weighted schemes outperform individual datasets and MSWEP. Specifically, BMAEP-2P achieved a superior composite CKGE index of 0.828, CWEP-4P attained a higher correlation coefficient (CC) of 0.905, and CWEP-2P excelled in relative bias (0.579%) and root mean square error (20.755 mm). Furthermore, BMAEP or CWEP performed optimally for drought monitoring across all sub-regions of China at multiple time scales (1-24 months), with average highest CC and probability of detection values reaching 0.919 and 0.844, respectively. Contribution analysis identified CPC as the dominant factor enhancing model performance. The study demonstrates that CWEM and BMA methods effectively generate superior precipitation datasets for drought monitoring applications.

Fine particulate matter (PM₂.₅) poses serious risks to environmental quality, human health, and sustainable development. However, existing studies seldom achieve long-term, pixel-level PM₂.₅ projections or comprehensively evaluate scenario-based predictions under different development pathways. This study proposes an integrated method for projecting PM₂.₅ distribution at a pixel-level under multiple scenarios by incorporating land-use simulations, land surface indices, and spatial dependence effects. Using multi-temporal Landsat series, ground-based PM₂.₅ observations, and socio-economic data, we generated land-use projections under the Shared Socioeconomic Pathways (SSPs) using the Futureland model. Corresponding land surface indices (NDVI, NDBI, NDWI) were derived and used within a spatial lag model to predict PM₂.₅ concentrations for the Yangtze River Delta (YRD) from 2010 to 2030. Results indicate that under SSP1, characterized by sustainability, forest area and NDVI increase, leading to a significant decline in PM₂.₅ levels. Conversely, under the fossil-fueled SSP5 scenario, urban expansion drives up NDBI and PM₂.₅ concentrations. These findings demonstrate that increased green space and reduced fossil fuel reliance are crucial for improving air quality. The proposed method provides decision-makers with actionable insights for policy formulation and highlights the environmental importance of land-use planning. Future work will integrate dynamic meteorological models and conduct uncertainty assessments. This scenario-based projection framework can be applied to support sustainable urban and environmental management in rapidly developing regions.

The upper troposphere and lower stratosphere (UTLS) plays a vital role in the global climate system. Central China, particularly Hubei Province, is located directly downstream of the Tibetan Plateau, making it a key region for observing long-range aerosol transport. In this study, the vertical structure and transport mechanisms of UTLS aerosols over Hubei are investigated using a multi-year (2016-2018) satellite dataset. We applied rigorous Cloud-Aerosol Discrimination (CAD) scores and quality control procedures to nighttime CALIOP profiles to minimize cirrus cloud contamination. Our results show that the UTLS background over this region exhibits low aerosol loading during non-monsoon seasons. However, a substantial aerosol enhancement occurs in summer. The monthly mean extinction coefficient at 14-16 km reaches a peak of 8.05 × 10-3 km-1 in August, with a particulate depolarization ratio of ~0.25, indicating the presence of non-spherical particles related to the Asian Tropopause Aerosol Layer (ATAL). To investigate the physical drivers of this seasonal variation, we integrated MERRA-2 meteorological fields and HYSPLIT backward trajectories. The analysis reveals a dual transport mechanism: First, intense local deep convection driven by the East Asian Summer Monsoon (EASM) vertically pumps boundary-layer pollutants into the upper atmosphere. Second, the Asian Summer Monsoon Anticyclone (ASMA) and the westerly jet stream advect aged aerosols horizontally from the Tibetan Plateau to Central China. These findings provide direct observational evidence of how regional monsoon systems synergistically modulate stratospheric aerosol loading.

All living organisms significantly impact air quality, which is vital for the Earth's ecosystems. Air pollution has increased in the Indian subcontinent, mainly due to harmful gases and particles from industrialization and urban development. CNN Health reported that as of February 25, 2020, 21 out of the 30 cities with the worst air quality globally are in this region, with six in the top ten. Urgent research and pollution control measures are needed, especially in urban areas where human health and the environment are most affected.

The Air Quality Index (AQI) measures pollution levels from key pollutants like PM10, PM2.5, ozone, sulfur dioxide, and others, using a scale from 0 to 500. While pollution control boards collect ground data, the limited number of sensors in large cities can be a challenge. Satellite imagery enhances coverage, although in situ data remains essential in many areas.

This research aims to connect satellite remote sensing with air quality monitoring by determining air quality indices for pollutants in Dobson units. In situ sensors measure concentrations in micrograms per cubic meter, while satellites use molecules per square meter (Dobson units). Different techniques, like regression analysis, are used to develop location-specific indices for urban and suburban areas.

The focus of this research is on methodology rather than final conclusions, highlighting the importance of accurate real-world representations through reliable data and atmospheric models.

Air pollution remains a major environmental challenge in Kathmandu District, driven by rapid urbanization, increasing emissions, and meteorological influences. This study examines the spatial and temporal variability of PM₂.₅ and PM₁₀ from 2019 to 2024 by integrating satellite-derived Aerosol Optical Depth (AOD), ground-based measurements, and advanced statistical and machine learning techniques. Two regression approaches—a simple linear model using AOD and a multivariate model incorporating temperature, relative humidity, wind speed, wind direction, and planetary boundary layer height (BLH)—were evaluated using R² and RMSE metrics. The multivariate model consistently outperformed the simple linear regression, demonstrating improved predictive capability and was validated using PM data from the US Embassy monitoring station at Phora Durbar. Seasonal analysis showed pronounced pollution peaks in winter, with PM₂.₅ levels ranging from approximately 165–167 µg/m³, while summer exhibited the lowest concentrations (~51 µg/m³). PM₁₀ showed moderate seasonal variability with a notable decline during spring. The study also identified the influence of wildfire events and meteorological conditions on episodic pollution spikes. Despite limitations related to satellite resolution and uneven ground monitoring coverage, the integration of remote sensing, meteorological parameters, and machine learning proved effective for estimating particulate matter concentrations. Overall, the results highlight distinct seasonal pollution patterns and underscore the value of combined observational and modeling approaches for improving air quality assessment in Kathmandu District.

This study aims to systematically investigate the influence of spatial extent and spatial resolution on the estimation of soybean LAI and AGB and model transferability during the peak of the growing season. The research objectives are to: 1) assess and compare the predictive performance of Stepwise Multiple Linear Regression (SMLR) and Random Forest (RF) models for estimating LAI and AGB across different spatial extents and spatial resolutions; 2) evaluate the transferability of these models across spatial extents and resolutions to determine their robustness under varying scale conditions. Our results demonstrate that RF model outperformed SMLR and presented the highest LAI estimation accuracies across the three nested spatial extents with RMSE of 0.52m2/m2, 0.33m2/m2, and 0.31m2/m2, respectively, explaining 86%, 91%, and 91% of LAI variability at 1m2, 25m2, and 100m2 extents, respectively. Similarly, the RF model had the overall best accuracies with RMSE of 67.13g/m2, 76.98g/m2, and 58.03g/m2, respectively, explaining 83%, 86%, and 84% of soybean AGB variability at 1m2, 25m2, and 100m2 extents, respectively. Moreover, the results showed that the accuracies of both models increased for both LAI and AGB estimation at larger scales. We found that RF models outperformed SMLR in estimating soybean LAI and AGB at 3m resolution (LAI: R2=0.86, RMSE=0.39m2/m2, rRMSE=6.19%; AGB: R2=0.82, RMSE=59.09g/m2, rRMSE=18.09%) and 10m resolution (LAI: R2=0.92, RMSE=0.28m2/m2, rRMSE=4.36%; AGB: R2=0.80, RMSE=59.95 g/m2, rRMSE=18.35%), respectively. Further, the transferability of RF models showed weaker performance when applied to estimate soybean LAI and AGB at higher (or smaller) spatial extents and coarser (or finer) image resolutions.

This study looks to use fine resolution Synthetic Aperture Radar (SAR) for crop classification of small scale fields. The study site is the University of Guelph's Elora Research Station and looks to conduct crop classification with a random forest on four plots divided into 28 fields of 7 x 14 m in size. Four crop types are planted in each field which include, alfalfa, corn, soybeans, and winter wheat. The datasets used for the analysis are 4 SAR scenes taken during the May to July growing season with two of the plots used as training sets, and the other two as testing. The dataset is provided by the Finnish microsatellite company, ICEYE, with the data products being 0.5-meter resolution VV images. Additional textural information known as Grey Level Co-occurrence Matrix (GLCM) are processed from the SAR scenes and added to the random forest. The analysis was conduced at the pixel level and a 70-30 training and test split is used, with the final output map being aggregated to display the most populated classes present in each separate field. Results of the study show that only 6 out of 56 fields were wrongly classified. Corn had a producer accuracy (PA) of 0.93 and a user accuracy (UA) of 0.97, and oats with a PA of 0.85 and a UA of 0.88. Soybeans had a moderate performance with a PA of 0.87 and a UA of 0.63, and alfalfa performed the worst with a PA of 0.54 and a UA of 0.88.

The study of marine macrophytes is becoming increasingly important due to the threat of climate change to intertidal environments and the potential of macrophytes as nature-based climate solutions. Laminaria digitata (finger kelp), Saccharina latissima (sugar kelp), and Zostera marina (eelgrass) are three marine macrophytes whose habitats are known as blue carbon ecosystems due to their outstanding carbon sequestration capabilities. These species are found throughout the Eastern Shore Islands, Nova Scotia, an Area of Interest (AOI) for ecological and biological importance identified by Fisheries and Oceans Canada. Hyperspectral satellite imagery has been little explored as a solution to mapping marine macrophytes in comparison to other remote sensing data, including multispectral imagery and airborne hyperspectral imagery. To test the efficacy of hyperspectral satellite imagery for mapping marine macrophytes in cold temperate regions, we mapped finger kelp, sugar kelp, and eelgrass using a PRISMA image, near Sheet Harbour, NS, within the Eastern Shore Islands AOI. The results show that machine learning classifiers can use hyperspectral imagery to differentiate marine macrophytes, but it is more challenging to differentiate between species with very similar reflectance spectra, such as finger kelp and sugar kelp. The classification accuracy also decreases at deeper depths, where the benthos-reflected signal is diminished. Further investigation is needed to determine the value of narrow hyperspectral bands for species level mapping; initial results suggest that hyperspectral imagery can achieve improved discrimination of spectrally similar species of submerged aquatic vegetation compared to multispectral imagery of the same spatial resolution.

Forest damage is an increasing global concern, particularly as climate change intensifies the frequency and severity of both abiotic and biotic stressors. Early detection of stress-induced damage is essential for effective forest management, yet conventional methods remain labour-intensive and slow. A significant knowledge gap persists regarding how abiotic stress, such as drought, interacts with latent fungal pathogens that can shift from asymptomatic to aggressive under unfavourable conditions.

Multispectral imaging has demonstrated strong potential for detecting physiological disturbances in tree canopies, including pest outbreaks, but its capacity to identify pathogen-specific damage remains poorly explored.

In this study, we investigate whether UAV multispectral drone imagery can detect canopy damage linked to fungal pathogens in Norway spruce (Picea abies). Research was conducted in two contrasting trials in southern Sweden, representing optimal versus drought-prone growth conditions. Across the 2023–2024 growing sea-sons, tree vitality, needle condition, and phenology were monitored and paired with fungal community data to classify reference trees by pathogen type and stress response.

Weekly drone flights provided multispectral imagery that was radiometrically corrected, canopy-segmented, and processed to derive vegetation indices and individual-tree crowns. Using reference trees as training data, statistical models will assess damage patterns and vitality loss.

We expect to detect and distinguish stress signatures arising from combined biotic–abiotic interactions. And validate the Eurich et al. damage model in older trees.

Early- and in-season crop mapping provides vital information for precision agriculture. It is still a challenge for early- and in-season crop mapping because of the limited available images and similar spectral information. This study aims to enhance early- and in-season crop mapping by developing a Genetic Programming (GP) method to construct customized crop features. GP automatically generated candidate features for the target-crop using early- or in-season images, selected programs with substantial value disparities between target and non-target crops through the fitness function, and finally outputted the customized feature after the evolutionary process. These customized features were then compared with commonly used spectral bands and vegetation indices to evaluate their effectiveness for early- and in-season crop mapping. The results proved that the customized crop features had significant advantages in both early- and in-season crop mapping. The early-season accuracy in April after crop planting was 3.97% to 9.53% higher than spectral features and vegetation indices. Based on the classification for the in-season crop mapping, the customized crop features maintained the best performance. Advantages of customized crop features include the ability to automatically select effective bands of useful months without requiring expert knowledge, the ability to catch and enlarge the subtle spectral differences with the early- and in-season images, and the little information redundancy compared with spectral features and vegetation indices. It can be concluded that the customized crop features are outstanding for early- and in-season crop mapping.

Efficient nitrogen (N) management is a key factor for sustainable potato production, as over- or under-fertilization can significantly affect yield, quality, and environmental outcomes. This study explores the potential of unmanned aerial vehicle (UAV) multispectral imagery and machine learning (ML) to predict in-season potato nitrogen status under field conditions in Manitoba, Canada. A DJI Mavic 3M equipped with four spectral bands (green, red, red-edge, and near-infrared) was used to capture canopy reflectance at 15 m altitude during the 2023 and 2024 growing seasons. Vegetation indices (VIs) such as NDVI, GNDVI, CIgreen, TCARI, and SRRE were extracted from orthomosaics and evaluated for their relationships with petiole nitrogen concentration (PNC). Feature selection methods including Recursive Feature Elimination (RFE), Boruta, and Partial Least Squares Regression (PLSR) were applied to enhance model efficiency. Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Regression (GBR) algorithms were compared for prediction accuracy. RF combined with RFE achieved the highest performance (R² = 0.57), confirming its robustness to multicollinearity and nonlinear relationships. The results highlight the strong relevance of CIgreen and red-edge indices to N variability and demonstrate the potential of UAV-based spectral sensing integrated with ML for precision nitrogen management in potato systems.

Satellite remote sensing remains central to global mangrove forest mapping, yet the effectiveness of existing products is often limited by coarse spatial resolution and insufficient locally representative training data. These constraints are particularly evident in many African coastal regions, where access to very high-resolution satellite imagery and field observations is scarce. Deep learning–based super-resolution offers a promising alternative by enhancing the effective resolution of freely available imagery. This study evaluates the utility of applying semantic segmentation to super-resolved Sentinel-2 and Sentinel-1 data for mangrove mapping in two ecologically distinct regions: Tanzania and Ghana. Using analysis-ready data from Digital Earth Africa, temporal median composites of Sentinel-2 VNIR, red-edge, and SWIR bands, together with Sentinel-1 VH and VV images, were generated at 10 m resolution. A modified ESRGAN model was trained to produce imagery with a five-fold increase in spatial resolution. Both the original and super-resolved datasets were used to train a U-Net–based binary segmentation model, supported by training labels derived from Global Mangrove Watch data, Google Earth imagery, drone surveys, and fieldwork.

Results indicate that super-resolved imagery substantially improves the accuracy and precision of mangrove classifications relative to the original-resolution images. The enhanced spatial detail supports the detection of small mangrove patches, complex shoreline features, and local degradation patterns, yielding more complete estimates of mangrove extent. Incorporating Sentinel-1 backscatter further improves mapping accuracy. The study demonstrates that deep learning–based super-resolution can overcome key limitations of open-access satellite data, enabling more reliable, fine-scale mangrove mapping.

Operational quantification of forest aboveground biomass remains one of the most demanding components of Verified Carbon Standard (VCS) project monitoring, largely due to the need for repeated large-scale field inventories. To reduce costs and enable automated updates, CERFO developed for Ecotierra a hierarchical modelling system integrating field measurements, drone photogrammetry, and Sentinel-1/2 imagery. The system is designed to generate biomass updates autonomously every 1–3 years.

In 2024, seventy-two field plots were installed, and plot-level biomass was computed using regional allometric equations. Drone acquisitions from RGB and multispectral sensors produced high-resolution structural and spectral predictors (>300 variables). A machine learning ensemble (AutoGluon), trained on a stratified split of 68 plots, achieved strong accuracy (R² = 78.5%, relative bias = 1.7%). Bias-corrected drone predictions (via empirical quantile mapping) were then used as pseudo-observations for the satellite modelling stage. The Sentinel-based model, combining optical and radar indices, reached R² = 67.8% with a relative bias of 0.79%, demonstrating the value of multi-sensor integration.

A comprehensive uncertainty analysis using one million Monte Carlo simulations confirmed the stability of aggregated results (T² = 84.2%, mean bias = –0.22%), meeting MRV requirements for carbon reporting. The final operational product is a fully automated processing chain that retrieves satellite images, performs preprocessing, computes predictors, applies the trained model, and exports biomass maps.

This approach provides a robust and scalable solution for continuous biomass mapping and forest carbon monitoring. Future improvements include expanding field sampling across broader ecological gradients to reduce uncertainty in underrepresented environments and further strengthen model generalization.

Accurate estimation of aboveground biomass (AGB) is critical for understanding carbon dynamics and forest structure at regional and global scales. Waveform LiDAR, with its ability to capture detailed vertical profiles of vegetation, has proven highly effective for AGB estimation. However, spaceborne waveform LiDAR missions such as NASA’s GEDI face limitations due to sparse sampling, necessitating integration with complementary remote sensing datasets for continuous coverage. This study develops a comprehensive framework to evaluate the contribution of multispectral optical imagery (Sentinel-2) and dual-polarized SAR data (Sentinel-1 C-band and ALOS PALSAR L-band) in reconstructing forest structure across multiple canopy layers in a tropical forest in French Guiana. Using LVIS waveform LiDAR as a reference and an AGB map derived from LiDAR–SAR fusion, Random Forest models were trained to predict LiDAR waveform metrics at relative heights (RH10–RH98), followed by SHAP analysis to quantify feature importance. Results reveal that satellite data exhibit greatest sensitivity at mid-canopy levels (RH55–RH85), with SWIR bands outperforming other optical features, particularly during the dry season when canopy moisture is reduced. SAR features, especially cross-polarized channels, provide consistent contributions across biomass ranges, though their effectiveness declines in very dense forests (>350 Mg/ha).

Forest structural complexity is a key component of forest ecosystems and generally reflects the combined characteristics of tree height, diameter at breast height (DBH), canopy cover, tree spacing, and species composition. While spaceborne LiDAR systems such as GEDI provide near-global full-waveform observations for deriving the Waveform Structural Complexity Index (WSCI), the discrete distribution of GEDI footprints limits their spatial continuity. This study addresses this challenge by integrating GEDI-derived WSCI samples with multisource remote sensing data to enable large-scale mapping of forest structural complexity. We developed and compared machine learning models (RF, SVR) and a deep learning architecture (ConvNeXt) to evaluate their ability to predict WSCI from multisource remote sensing data. The results show that the deep learning framework, supported by multisource remote sensing data, effectively overcomes the discrete footprint limitation of GEDI and enables spatially continuous mapping of forest structural complexity at the regional scale. The ConvNeXt model demonstrated clear advantages, reducing RMSE and MAE to 0.55 and 0.43 (compared with 0.60/0.49 for RF) and improving IoA and correlation to 0.73 and 0.61, thereby enhancing the reliability of regional-scale complexity mapping despite the sparse GEDI footprint distribution. This provides a practical and scalable pathway for large-scale forest structure characterization and supports regional forest monitoring and management. Future work will include expanding the study area, incorporating additional field measurements, integrating terrain-related variables for improved modeling under complex topography, and exploring multi-year datasets to assess temporal dynamics in forest structural complexity.

By integrating remote sensing observations, topography, and crop growth parameters, a multimodal deep learning model named Soil Spectral–Spatial Cross-Attention Network (Soil-SSNet) is proposed. Soil-SSNet includes a spectral sequence convolution module to capture dynamic spectral features, a spatial attention module to address surface heterogeneity and uncover the response relationship between salinity and natural environmental variables, and a multi-head cross-attention mechanism that uses spatial features as Query to guide the selection of spectral-index responses. Compared to traditional machine learning models such as Random Forest (RF) and Support Vector Regression (SVR), the overall accuracy of Soil-SSNet improves by approximately 40%. After incorporating multi-source covariates (water conditions, crop growth status, and topographic factors), the model’s accuracy further increases by about 25%. With the addition of the cross-attention mechanism, accuracy improves by another 35%, significantly enhancing the fusion capability of spectral and environmental information and achieving soil salinity inversion with higher accuracy and stronger generalization. Finally, spectral sensitivity analysis reveals that the 705–750 nm and 1580–2350 nm bands contribute the most to salinity inversion. Mechanism analysis further uncovers a significant coupling effect among salinity, crop growth, and topography: vegetation growth characteristics reflect the intensity of salt stress, topographic factors dominate the redistribution pattern of water and salt, and soil moisture dynamics determine the accumulation and dispersion patterns of salinity. In summary, Soil-SSNet not only improves the accuracy and interpretability of soil salinity inversion in saline-alkali farmland but also provides quantitative evidence for understanding the environmental processes and mechanisms of salinization.

Tanzania hosts one of Africa’s largest Protected Areas (PA), yet deforestation remains widespread in surrounding unprotected landscapes. Assessing the effectiveness of PAs requires analytical approaches that account for environmental and accessibility biases inherent in PA placement. This study presents a remote-sensing-based spatial modelling workflow that integrates Global Forest Change (GFC) forest-loss time series (2012–2022) with terrain, accessibility, and demographic covariates to quantify avoided deforestation attributable to protection.

Biophysical and anthropogenic variables influencing forest-cover change, including elevation, slope, distance to roads, settlement density, and population distribution, were harmonised to a 30 m grid and combined with protected area boundaries from the World Database on Protected Areas. To address spatial biases, Propensity Score Matching (PSM) was applied to match protected forest pixels with statistically similar unprotected pixels, reducing confounding effects and enabling a credible counterfactual baseline. A binomial logistic regression model was then fitted to the matched dataset to estimate the likelihood of deforestation under different conservation categories.

Results show that protected forests were, on average, about three times more likely to avoid deforestation than comparable unprotected forests. National Parks and Game Reserves demonstrated the strongest outcomes, being nearly ten times more effective at avoiding deforestation. Nature Forest Reserves were around three times more effective, while Forest Reserves and Game Controlled Areas showed more modest effects, being roughly twice as likely to avoid deforestation.

The analysis is transparent, reproducible, and scalable, demonstrating how Earth observation and spatial causal inference can strengthen national forest monitoring, support conservation planning, and inform policy processes.

Climate change is increasingly disrupting agricultural ecosystems, particularly in Canada, where rising temperatures and altered precipitation patterns are impacting crop resilience. Alfalfa (Medicago sativa L.), a key forage crop valued for its productivity and nutritional quality, is especially vulnerable to winter stress due to reduced cold tolerance and increased damage from thaw cycles. This study presents a remote sensing-based framework for estimating fractional vegetation cover (FVC), a critical indicator of crop health and ecosystem stability. By integrating Sentinel-2 satellite imagery with high-resolution UAV data, the approach leverages machine learning algorithms, including random forest (RF) and gradient boosting (GB), to efficiently predict alfalfa FVC. UAV-derived RGB orthoimages provide detailed spatial reference data, minimizing the need for extensive field surveys. The proposed method demonstrates the potential of combining multi-source remote sensing with ML to capture complex vegetation dynamics and improve monitoring accuracy. Although both models showed high potential in estimating the alfalfa FVC, RF outperformed GB in terms of all evaluation criteria. The resulting FVC maps provide actionable insights for early spring field assessments, enabling the timely identification of damaged areas and supporting informed crop rotation decisions. The proposed framework was tested across multiple alfalfa fields in 4 provinces of Canada, including Quebec, Ontario, Nova Scotia, and Manitoba, demonstrating robust performance under varying environmental conditions. Its scalability and adaptability make it suitable for broader applications in precision agriculture and climate-resilient crop monitoring.

The Chambal–Yamuna Badland Zone (CYBZ) is among the most severely degraded semi-arid landscapes in India, where persistent gully development reduces soil productivity and threatens long-term environmental stability. This study examines how vegetation dynamics and surface deformation have evolved in the CYBZ over the past 25 years by integrating long-term optical and SAR-based remote sensing observations. MODIS NDVI data (2000–2024) processed through Google Earth Engine were used to track vegetation greening and browning patterns, while Sentinel-1A/B SAR datasets (2017–2024) were analysed using Persistent Scatterer Interferometry (PSI) and the Small Baseline Subset (SBAS) approach to quantify deformation linked to active erosion.

The NDVI time series shows a clear and statistically significant rise in vegetation cover across the region, with the strongest greening occurring in the eastern badlands, particularly after 2015. This widespread improvement aligns with increasing rainfall and indicates a gradual transition from highly eroded terrain to more vegetated and potentially stabilised surfaces. InSAR results reveal minimal ground deformation within major gullies, suggesting that gully erosion during the study period has been low. Seasonal fluctuations observed in PS displacement curves correspond to vegetation cycles rather than ongoing surface lowering. Negative deformation signals are predominantly associated with agricultural zones adjacent to the badlands.

Overall, the combined use of MODIS NDVI and Sentinel-1 InSAR provides a robust framework for monitoring ecological recovery and erosion dynamics in geomorphically fragile landscapes. The findings highlight increasing vegetation stability and reduced gully activity, offering new insights into the contemporary evolution of the Chambal badlands

Accurate prediction of wheat yield is crucial for ensuring food security through the use of machine learning techniques. This research aims to forecast wheat yield in Pakistan by integrating five remote sensing indices, including Green Normalized Difference Vegetation Index, Normalized Difference Vegetation Index, Enhanced Vegetation Index, Soil Adjusted Vegetation Index, and Atmospherically Resistant Vegetation Index, with five climatic variables: maximum Temperature, Minimum Temperature, Rainfall, Soil Moisture, and Windspeed alongside the drought index, Standardized Precipitation Evapotranspiration Index. Ten model combinations are created within two wheat season scenarios: Full Seasonal Mean and Peak Seasonal Mean. Employing two nonlinear ML algorithms, Random Forest and Support Vector Machines, as well as two linear models, LASSO and Ridge, the study aims to determine the most effective combination and ML algorithm in both scenarios. Results indicate that in SC1, the RF model combination (GNDVI + SPEI + WS + SM) outperformed other models (R2 = 0.75, RMSE = 2.40, MAE = 1.98). Similarly, in SC2, the RF regression surpassed SVM, with the model combination demonstrating the highest performance, achieving R2 = 0.78, RMSE = 2.25, and MAE = 1.88, followed by (NDVI + Tmax + Tmin + PPT + PET + WS + SM; R2 = 0.75). Notably, the linear LASSO model also exhibited comparable performance to RF, achieving R² values of 0.74–0.69 in both scenarios. The findings support SC2 for yield prediction, underscoring the significance and potential of ML methodologies in timely crop yield prediction, establishing a robust foundation for ensuring regional food security.

This study evaluates the utility of AlphaEarth Foundation (AEF) embeddings, a pre-trained geospatial foundation model, for regional wetland mapping in Stockholm County, Sweden. Accurate and up-to-date spatial information is crucial for planning, but traditional methods are challenged by the heterogeneity and variability of wetland environments.

Our research assesses how AEF's 64-dimensional feature vectors, summarizing multi-sensor satellite time series (Sentinel-1, Sentinel-2, Landsat) at 10m resolution, perform when integrated with established remote sensing variables (topographic, hydrological, and LiDAR derivatives) within standard machine-learning workflows (MLP). The methodology employs a two-step hierarchical classification based on the BIOTOP SE inventory: a Level-1 land-cover prediction (Huvudklass) followed by binary wetland identification within suitable classes.

Preliminary results demonstrate the potential of this approach. The Level-1 classification showed strong performance for certain classes (e.g., klass6 F1-score: 0.98). For the binary classification within klass4, the model achieved a robust F1-score of 0.87 for the target Wetland subclass (Precision: 0.90, Recall: 0.88). This work highlights the possibility of adapting global pre-trained satellite embeddings with traditional remote sensing inputs using light machine learning models for practical, policy-relevant environmental applications, such as updating national biotope inventories.

Food insecurity remains a major challenge in Rwanda, particularly in rural regions where stunting and anemia rates remain high. Because agriculture is dominated by smallholder farms (0.1–0.5 ha) with fragmented fields and frequent intercropping, accurate crop type mapping is both essential and difficult. Traditional machine learning approaches struggle to model the spatial–temporal variability of such landscapes, whereas CNN-based models require large annotated datasets that are costly to obtain.

We introduce GLSTM-MLP, a hybrid framework that integrates LSTM and MLP classifiers with precomputed Haralick descriptors to efficiently encode spatial context. By combining spectral bands (SB), radiometric indices (RI), and elevation data, the model decouples spatial and temporal dependencies, enabling robust crop type classification even with limited training samples.

Using 3 m PlanetScope time series imagery and drone-based ground-truth data from two Rwandan villages, we evaluated GLSTM-MLP against MLP, RF, and SVM across three feature scenarios: (i) SB + RI; (ii) SB + Haralick features; and (iii) SB + RI + Haralick + elevation. We further compared performance with 2DCNN-LSTM and 3DCNN.

GLSTM-MLP consistently outperformed all baselines, achieving F1-scores of 91%, 91%, and 93%, compared with 87–91% for MLP, 89–91% for RF, and 86–89% for SVM. While 2DCNN-LSTM and 3DCNN underperformed in this data-scarce setting (F1 < 85%), highlighting the advantage of integrating domain-driven feature engineering with sequential modeling.

These results demonstrate that combining temporal dynamics with engineered spatial context provides a practical, data-efficient pathway for accurate crop type classification in heterogeneous, smallholder-dominated farms in SSA, even under limited ground-truth availability.

Accurate monitoring of tree health is important for ensuring sustainable and efficient orchard management in precision agriculture. We evaluated a modified Mask R-CNN deep learning framework for assessing apple tree health using multispectral UAV imagery. The model was tested with four backbone architectures (ResNet-50, ResNet-101, ResNeXt-101, and Swin Transformer) on three image combinations: RGB, 5-band multispectral imagery, and three principal components (3PCs) derived from five spectral bands and twelve vegetation indices. Among all configurations, the Mask R-CNN with a ResNeXt-101 backbone trained on 5-band multispectral imagery achieved the highest performance, reaching an F1-score of 85.70%. In comparison, PCA-based 3-component inputs performed lower (F1 = 82.75%), indicating that while dimensionality reduction reduces computational cost, it may also discard critical information relevant to vegetation health.

Holm oaks (Quercus ilex) are a keystone species of the Mediterranean savannas in the southwest of the Iberian Peninsul, which are of high ecological and socioeconomic value. This ecosystem is increasingly threatened by Seca, a decline process of oaks driven by abiotic factors and the pathogen Phytophthora cinnamomi. Monitoring tree vitality is therefore essential, and structural traits such as diameter at breast height (DBH) provide early indicators of stress-related growth reduction.

LiDAR remote sensing enables efficient derivation of these metrics, but existing methods involve trade-offs: terrestrial laser scanning offers high detail but limited coverage, while airborne and UAS-LiDAR cover larger areas but often lack sufficient point density. Portable SLAM (Simultaneous Localization and Mapping) LiDAR systems offer a promising alternative, providing flexible, high-resolution data collection across broad areas.

This study assesses the potential of a portable SLAM system to derive holm oak structural attributes. In July 2025, approximately 450 trees across 17 ha in Majadas de Tiétar (Spain) were scanned. In a first attempt, based on an Outer Hull Model, DBH was derived for 9 trees by fitting convex hulls to point cloud stem slices extracted at 1.3m height. Initial validation against field measurements showed strong agreement (R² = 0.971; RMSE = 3.33 cm).

These first results demonstrate that portable SLAM LiDAR can reliably capture stem structure and support large-scale monitoring.

A study focusing on investigating how hyperspectral data can be used towards enhancing landcover mapping accuracy in the Drylands ecosystem, so as to support evidence-based decision-making, strengthen restoration planning, promote conservation efforts and enhance national and international reporting frameworks.

The study looks at spectral separability analysis to quantitatively show how best PRISMA hyperspectral data can distinguish land-cover classes being mapped in the study area as compared to Sentnel-2.

The study also presents supervised classification analysis results performed using Random Forest classifier on Sentinel-2 and PRISMA original image, PCA transformed image and MFN transformed image and comparing their accuracy levels.

Finally, the study looks at spectral un-mixing to be able to quantify in terms of abundance, which landcover class is present in each pixel and to what proportion.

This study advances precision viticulture by developing a scalable hyperspectral and GeoAI framework for grapevine cultivar discrimination. Using proximal spectrometry and satellite hyperspectral imagery, the research demonstrates the methodological and feature-level transferability of spectral information from in-field spectrometry to spaceborne data. Ten machine learning and deep learning algorithms were evaluated, with Support Vector Machines (SVM) and a 1D Convolutional Neural Network (1D CNN) achieving the highest accuracies. A novel Partial Least Squares (PLS) ensemble feature selection approach reduced data dimensionality by 95%, identifying key red–NIR, Green and SWIR spectral regions for cultivar mapping. Transferring these features to pansharpened PRISMA hyperspectral satellite imagery yielded high classification accuracies (>80%) at 5 m resolution, confirming the operational potential of hyperspectral GeoAI for vineyard characterisation. The findings establish a foundation for scalable, satellite-driven cultivar mapping to support site-specific management and digital viticulture practices.

TBA ...

The Ecuadorian Amazon faces growing socio-environmental pressure from gold mining, which threatens biodiversity and ecosystem integrity. In Zamora Chinchipe, a province that hosts more than 600,000 ha under protection (18% of Ecuador’s continental protected areas), mining expansion reveals a critical tension between conservation and extraction. This study evaluates the spatial distribution and intensity of gold mining in the buffer zones of six protected areas, using data from MapBiomas Ecuador and Geographic Information System (GIS) techniques.

Mining areas within 5 km of each protected area were extracted from MapBiomas LULC maps and analysed through Kernel Density Estimation (Epanechnikov function, 500 m cell size, 2500 m radius). The results reveal heterogeneous mining pressure, with hotspots concentrated in Cerro Plateado, Podocarpus, and El Zarza, often within 1.6–5 km of official boundaries. Spatial correlation shows that 89% of hotspots lie within 500 m of watercourses and 78% in slopes between 15°–35°, highlighting the geomorphological and hydrological dependency of mining activities.

Conversely, areas such as Yacuambi and Tiwi Nunka show minimal pressure, where local governance and indigenous territorial control have effectively limited extractive expansion. These results demonstrate that governance factors are as critical as physical conditions in determining conservation outcomes.

The integration of MapBiomas data and KDE offers a replicable, low-cost tool for monitoring mining dynamics, providing spatial evidence to strengthen protected area management and inform sustainable territorial planning in the Amazon region.

We are presenting an atmospheric modeling and inversion pipeline for the FINCH (Field Imaging Nanosatellite for Crop residue Hyperspectral mapping) hyperspectral imaging CubeSat, built by the University of Toronto Aerospace Team Space Systems division. Such a pipeline will allow us to validate our spectral unmixing pipeline under possible FINCH imaging conditions, and permit scientifically useful data collection. FINCH, through innovations in crop residue cover mapping, will further enable sustainable agricultural practices.

Tree hydraulic functioning varies strongly over the diurnal cycle as transpiration, stomatal conductance, and xylem tension shift with radiation and vapor pressure deficit (VPD). Capturing these within-day dynamics remotely is essential for interpreting stress signals from airborne or satellite sensors, yet most remote-sensing studies treat forest condition as static. For conifers such as Norway spruce (Picea abies), even brief midday hydraulic limitation can contribute to long-term drought vulnerability and bark-beetle susceptibility, underscoring the need for diurnal monitoring.

Optical indicators such as the Photochemical Reflectance Index (PRI) track rapid photosynthetic-efficiency changes via xanthophyll-cycle activity but often saturate in dense canopies and are sensitive to geometry and structural effects. PRI’s two-band design also underrepresents the broader green-shoulder region (520–550 nm), where carotenoid–chlorophyll interactions more reliably reflect stress in evergreen species.

To address these limitations, we apply a family of green-shoulder indices (GSCR) that integrate information across multiple narrow bands in the 520–550 nm plateau. These indices capture both rapid xanthophyll dynamics and slower pigment adjustments linked to declining water status. Recent UAV studies show that GSCR metrics are highly sensitive to early physiological stress in spruce.

This study extends GSCR use from static health detection to tracking diurnal hydraulic processes. We (1) test whether spectral indices can reproduce the within-day trajectory of spruce physiological activity, including midday depression, and (2) quantify relationships between spectral indices, leaf water potential, and sap-flow velocity. By combining drone-based hyperspectral imaging with high-frequency hydraulic measurements, we establish a framework linking pigment dynamics to diurnal hydraulic status at the crown scale.

Ground-level air temperature (Tair) is an essential variable for climate monitoring, agricultural management, and hazard prevention. Conventional ground-based measurements often fail to capture the fine-scale spatial variability, especially in regions with complex terrain. Land Surface Temperature (LST) remote sensing offers a complementary solution, providing spatially continuous and temporally frequent observations. This study evaluates the potential of MODIS and Landsat-8 LST products to estimate Tair in a heterogeneous agricultural landscape. We developed spatiotemporal regression models linking satellite-derived LST to ground observations from meteorological stations over five years (2018–2022). MODIS data provided high temporal coverage through 8-day composites, while Landsat-8 offered higher spatial resolution LST via the Statistical Mono-Window algorithm. The models were validated using Leave-One-Out Cross-Validation, achieving high predictive accuracy for MODIS-based Tair estimation (R² = 0.981, RMSE = 1.1 °C), whereas Landsat-8 captured finer spatial variability (R² = 0.859, RMSE = 3.4 °C). Our results demonstrate that integrating multi-resolution LST products enables accurate, dense mapping of Tair, supporting operational forecasting for precision agriculture. The study also discusses limitations related to land-cover heterogeneity, temporal representativeness, and potential extensions using spatial correlation methods or radar-derived crop-structure information.

This study uses UAV imagery and videos to monitor cattle behaviour in a rotational grazing system in Thunder Bay, Ontario, Canada.

Heat waves and droughts are recurring global phenomena that profoundly influence terrestrial ecosystems. This study assessed the impacts of drought and heat waves on the functioning of tropical evergreen (Kerela, South India) and moist deciduous (Barkot, North India) forest sites using meteorological and satellite vegetation products, including the FAPAR, SIF, GPP, and ET during 2007– 2018. The SPI, calculated from ERA5 daily data, was used to identify drought occurrences in space and time, while the Mann–Kendall test detected historical trends. Heat waves were characterized using hourly maximum temperature data from ERA5, following the criteria of IMD. Temporal anomalies were quantified using Z-scores, along with the Mann–Kendall trend test and Theil–Sen’s slope analysis. Moist deciduous showed consistent and pronounced declines in productivity and moisture related variables during droughts, particularly during monsoon season, indicating strong sensitivity to water stress. Evergreen forests exhibited more variable responses, with weaker and less consistent drought signals during the pre-monsoon season and mixed responses in vegetation variables even during monsoon drought conditions. Heat wave impacts also varied across forest types. Evergreen forests showed contrasting responses depending on the timing of heatwave events, with early and mid-heatwave phases associated with reductions in productivity, while late heatwave events showed relatively positive or mixed responses among vegetation indicators. In moist deciduous, heatwaves resulted in more consistent negative anomalies in productivity-related variables, although some years exhibited contrasting behaviour in SIF relative to other indicators. The findings underscore the heterogeneous response of forest ecosystems to extreme climatic events.

Accurate prediction of soil organic matter (SOM) in saline semi-arid regions is vital for sustainable land management. This study evaluates EnMAP hyperspectral imagery combined with machine learning (ML) for SOM estimation using Random Forest (RF) regression applied with three feature selection (FS) algorithms. Embedding RF-FS, recursive feature elimination (RFE), and Competitive Adaptive Reweighted Sampling (CARS), alongside five spectral pre-processing techniques have been analysed. The first derivative (FD) transformation significantly enhanced model performance, outperforming other processing methods. FD combined with RF-FS demonstrated the highest accuracy (PCCC = 0.697, R² = 0.446, RMSE = 0.825) compared to other FS methods. In contrast, all feature-selection approaches applied to the original reflectance showed substantially lower performance, with OR-RFE, OR-RFFS, and OR-CARS yielding PCCC values of only 0.617, 0.610, and 0.547, respectively, and consistently higher RMSE values near 0.90. Frequency analysis identified key informative bands in the SWIR-2 (2207–2445 nm) and visible–NIR (418–801 nm) regions, aligning with known organic matter absorption features. These results demonstrate that integrating derivative spectroscopy with robust feature selection substantially improves SOM prediction in challenging semi-arid environments, providing a effective framework for operational remote sensing of soil fertility.

UAV-based lidar systems operate at relatively low power compared to airborne platforms, making them especially sensitive to environmental conditions that influence return strength, canopy penetration, and ground-surface detection. Daytime acquisitions are affected by solar illumination, which introduces photon noise and reduces signal-to-noise ratio, while nighttime missions eliminate this interference and should theoretically enhance intensity and penetration. However, nighttime humidity, dew, and shallow fog can attenuate or scatter the laser pulse, complicating the expected benefits. This study evaluates the combined influence of illumination and atmospheric moisture on UAV-lidar performance across dense and wet vegetation.

We conducted paired day and night flights (Hesai XT32M2X lidar) at 40 m, 75 m, and 100 m over two contrasting peatland sites in eastern Canada: the humid, densely vegetated Alfred Bog (Ontario) and the drier Quinces Bog (Nova Scotia). Independent GNSS checkpoints were used to assess positional accuracy without constraining point-cloud processing.

Nighttime flights at the dry site yielded the clearest benefits, including increased third-return frequencies, stronger intensity values, and slightly improved ground-surface accuracy. In contrast, nighttime flights at Alfred Bog were affected by fog and canopy-level moisture, which produced increased scattering, duplicate points, and reduced ground detection, particularly at lower altitudes. These results show that while nighttime acquisition can improve data quality, its effectiveness depends strongly on humidity and surface wetness.

Overall, both illumination and environmental conditions must be considered when planning UAV-lidar missions. Nighttime flights offer advantages under dry conditions but may degrade outcomes in humid environments where fog or dew is present.

Koshalya‑Jhajhara (K-J) watershed in north India, tributaries of Ghaggar River, covering an area of 134.92 km2 in north India was assessed using multisource geospatial datasets. Satellite imagery, ASTER digital elevation model, Survey of India topographic maps and ancillary thematic data were processed to derive key maps including land use and land cover, geology, geomorphology, drainage density and slope. A temporal comparison of land use for 1999–2000 and 2015–2016 reveals rapid urbanization and shrinking forest cover. Built‑up area expanded from 7.12 km2 to 24.84 km2 while forest area contracted from 109.35 km2 to 96.78 km2, indicating substantially increased water demand and reduced natural recharge capacity. Groundwater potential was assessed by integrating geology, geomorphology, drainage density, slope and land use/land cover through an Analytic Hierarchy Process based multi‑criteria evaluation. The resulting Groundwater Potential Zone map shows that the majority of the watershed is stressed: 61.83 km2 falls in the poor category and 37.87 km2 in the very poor category for groundwater availability. These findings highlight critical zones where recharge and demand‑management interventions are urgently required. Surface water enhancement options were identified through geospatial suitability analysis. Fourteen sites were delineated for check dams and fifteen sites for percolation tanks, selected to maximize recharge potential, minimize sedimentation risk, and complement existing drainage patterns. When implemented, these structures will increase local infiltration, raise groundwater tables, and reduce peak surface runoff, thereby improving water security for domestic, agricultural and ecological needs. Measures have been recommended for sustainable land and water management in the watershed.

The spectral range of the Field Imaging Nanosatellite for Crop residue Hyperspectral Mapping (FINCH), developed by the University of Toronto Aerospace Team’s Space System Division, poses significant challenges for hyperspectral unmixing to determine crop residue cover fractional abundances. The severe lack of standard indices necessitates the use of complex, data-driven unmixing models. Complex unmixing models require dense, well-distributed manifolds, whereas ground-truth datasets are sparse and expensive. To better leverage the information content in existing ground-truth datasets, this study presents a framework that decouples interpolation and abundance-mapping estimation via an intermediary conditional data generator, contrasting with traditional single-model unmixing pipelines. This decoupling enables the inclusion of physical prior knowledge about spectra, which is not natively accessible to unmixing models, thereby artificially expanding the dataset to serve as a Monte Carlo approximation of the population risk. To test this hypothesis, we have proposed two generator models: a 1D Conditional U-Net with Conformer Layers (GD-Streamline) and a Dual-Path Transformer Conditional Variational AutoEncoder (TCVAE), and two unmixing models: Multi-Layered Perceptrons (MLP) and Fourier Neural Operators (FNO). The results of the study indicate a need for rigorous integration and introduction of spectral priors within the scope of the proposed decoupling framework to prevent domain and mode collapse and hallucination by the generator models; otherwise, this leads to falsely approximated data manifolds, ultimately resulting in unsatisfactory and out-of-distribution unmixing performance.

Savannahs are vital ecosystems whose sustainability is endangered by the spread of woody plants. This research targets the accurate mapping of fractional woody cover (FWC) at the species level in a South African savannah, using EnMAP hyperspectral data combined with Sentinel-2 multispectral imagery. Field annotations were intergrated with very high-resolution multispectral drone data to produce land cover maps that included three woody species. The high-resolution labelled maps were then used to generate FWC samples for each woody species class at the 30-m spatial resolution of EnMAP. Several machine learning regression algorithms were tested for FWC mapping on multi-seasonal and/or multi-annual EnMAP and Sentinel-2 imagery. Highest accuracy rates were achieved when incorporating data from both the dry and wet seasons, and for most experiments, data acquired across more than one year. The achieved results demonstrated the suitability of our approach for accurately mapping FWC at the species level and highlighted the synergistic potential of EnMAP and Sentinel-2 data for monitoring savannah ecosystems.

This study investigates the sensitivity of LiDAR, optical, and L-band SAR features for estimating forest above-ground biomass (AGB) in Istanbul’s Belgrade Forest by integrating GEDI lidar measurements with Sentinel-2 and SAOCOM 1A data. Using GEDI L4A AGBD footprints as reference, the authors derived 27 multisource predictors, including spectral indices, SAR backscatter, and polarimetric decomposition parameters. Four machine-learning models—MLP, Kernel Ridge, Lasso, and Elastic Net—were trained and evaluated using a 70/30 train–test split and repeated k-fold cross-validation. Results show limited but notable predictive capability, with R² values ranging from 0.15 to 0.20. The MLP achieved the highest accuracy (R² = 0.20), which can be attributed to its ability to model nonlinear relationships between biomass and multispectral features. Feature-selection experiments reveal that Sentinel-2 red-edge and SWIR bands, along with vegetation indices such as NDVI_red, LSWI, and Cire, consistently provide the strongest contribution to biomass estimation. SAR features contributed less significantly. The study concludes that optical features currently outperform L-band SAR for biomass modelling in this environment and recommends incorporating topography, multitemporal SAR, and additional machine-learning approaches to improve AGB prediction accuracy in future work.

Rapid urbanization in the plain river network of Changshu City has led to prominent water quality degradation and eutrophication risks. Traditional in-situ monitoring is constrained by sparse sampling and high costs, while conventional remote sensing approaches struggle with accurate water body extraction and stable parameter inversion in turbid, fragmented rivers. This study establishes a targeted remote sensing monitoring framework using 2024 multi-source data (Gaofen-2, Sentinel-2) and field measurements. An optimized modified Normalized Difference Water Index (mNDWI) combined with a spatially weighted adaptive threshold algorithm is adopted to precisely extract complex river networks. Based on Pearson correlation analysis, sensitive spectral bands and band combinations are screened for four key indicators: Chlorophyll-a (CHL-a), Total Nitrogen (TN), Total Phosphorus (TP), and Secchi Depth (SD). Statistical regression and weighted Principal Component Analysis-Random Forest (PCA-RF) models are developed for quantitative inversion, and their accuracy is verified using cross-validation with R² and RMSE. A weighted modified Carlson Trophic State Index suitable for plain river networks is applied for eutrophication assessment, and the single-factor pollution index method combined with the worst-factor principle is adopted to conduct comprehensive water quality evaluation in accordance with the national surface water environmental quality standards. The integrated inversion–evaluation–mapping workflow realizes spatially continuous water quality analysis, providing a reliable and region-adapted technical solution for remote sensing monitoring and scientific management of water environment in plain river network areas.

Mowing event temporal localization in dense satellite time series is crucial for monitoring agricultural practices and supporting sustainable land use policies. This study presents an innovative approach using a foundational model (FM), Prithvi-EO-2.0, tailored for Earth Observation time series, to precisely detect and temporally localize mowing events in grassland parcels. Unlike traditional methods that primarily identify mowing occurrence or frequency, this work advances the temporal pinpointing of individual mowing events, addressing challenges related to sparse annotations and diverse agro-climatic contexts. The methodology leverages high-resolution Harmonized Landsat and Sentinel-2 (HLS) optical data, treating time series as sliding temporal windows to capture rapid vegetation changes. The FM backbone is combined with a trainable localization head to predict the precise timing of mowing events, supported by a postprocessing step to reduce false detections. The dataset used includes over 450 newly annotated parcels from Central Greece, enabling robust training and comprehensive evaluation with metrics such as F1-Score, Precision, Recall, and Temporal Distance between predicted and actual events. Preliminary results demonstrate a significant improvement in localization performance, with a 6% increase in F1-score and an average temporal deviation of 2.7 time steps from ground truth. Ablation studies validate the impact of temporal window length, model architecture, and postprocessing on performance. The study highlights the strong generalization capabilities of FM-based approaches despite limited fine-tuning data, paving the way for enhanced agricultural monitoring using multi-temporal satellite data.

Macadamias are the fastest-growing fruit tree crop in South Africa, but the industry is met with challenges due to changing environmental conditions exacerbated by climate change. One of the challenges increasing in frequency and intensity is out-of-season flowering events. These events result in serious problems for orchard management, harvesting practices, and orchard sanitation. Understanding macadamia phenology is, therefore, important and should be investigated as timely phenology changes are crucial in the agricultural sector, particularly the timing of flowering phenology.

Multispectral remote sensing has been successful in quantifying flowering using conventional vegetation indices. However, based on the canopy distribution of macadamia flowers occurring predominantly below the dense evergreen canopy, the use of multi-spectral vegetation indices needs to be complemented to ensure the dependability of the phenology assessments. Synthetic aperture radar data could potentially address these limitations by facilitating the monitoring of within-canopy structural changes throughout the phenology evolution. Furthermore, to ensure validation of flowering phenology signals captured by satellite sensors, the use of unmanned aerial vehicles offers an intermediate level of observation. Therefore, this study aimed to investigate macadamia phenology through the integration of multi-sensor, multi-scale remote sensing data to advance the detection of flowering dynamics in macadamia, located in Barberton, Mpumalanga, South Africa.

This study highlights that macadamia flowering can be detected from a remote sensing perspective, despite the limitation of the flowers being inconspicuous, underscoring the value of integrating optical and synthetic aperture radar data to improve flower detection.

Mapping perennial crops in tropical regions remains challenging due to high spectral complexity, frequent cloud cover, and phenological overlap between different types of vegetation. This study evaluated the potential of the Harmonized Landsat-Sentinel (HLS) dataset to identify perennial crops in the municipality of Jacupiranga, São Paulo, Brazil, an area representative of the Atlantic Forest mosaic. A hierarchical classification was applied using the Random Forest (RF) algorithm on temporal compositions of 2024 NDVI, NDWI, and BSI indices, structured into three analytical levels: (1) natural vegetation versus anthropic areas, (2) perennial crops versus other uses, and (3) banana versus peach palm. Accuracies ranged from 0.86 to 0.98 and F1 ranked between 0.86 and 0.95. The most influential variables were concentrated in transitional periods of the annual cycle, reflecting subtle changes in canopy moisture and vegetative vigor rather than a clear distinction between dry and wet seasons, which are not well-defined in this tropical humid environment. The final maps indicate that approximately 25% of Jacupiranga is agricultural land, of which 4,320 ha correspond to perennial crops, with 80% occupied by banana plantations. The results demonstrate the potential of HLS open data to generate accurate multiscale mapping of perennial crops in complex tropical landscapes, supporting digital agriculture and sustainable management in family farming regions.

This study identifies the primary drivers of natural land conversion in Chile's 343 municipalities from 1999 to 2024. Using comprehensive spatial data, the analysis reveals that urban and road density are the most significant drivers of this change. In contrast, higher human development acts as a mitigating factor. The strength of these drivers is also shown to be highly dependent on local precipitation patterns. These findings provide a critical, evidence-based foundation for targeted land-use policy and ecosystem conservation in Chile.

To address the limitations of traditional fixed-point sampling for monitoring water chlorophyll concentration and improve the inversion accuracy of complex inland waters, this study took the Naoli River Nature Reserve as the research area and conducted research based on Sentinel-2 images and measured chlorophyll point data. First, the study performed combined calculations on multispectral bands to generate multiple derived bands, and selected "B4+B5+B6" as the optimal band combination through the coefficient of determination (R²). Then, using this combination as input, various empirical models were constructed and evaluated using multiple indicators. The results showed that the univariate cubic function model had the highest R² and the smallest multiple error indicators, which was significantly better than other models, and successfully realized the spatial inversion of chlorophyll concentration in the study area. This study reveals the complex nonlinear relationship between chlorophyll and band combinations, provides a high-precision inversion technical scheme for water chlorophyll, offers data support for algal bloom early warning and water quality fluctuation tracking, and provides scientific references for the optimization of river basin management measures and ecological protection decisions.

As climate change is increasingly affecting marine and terrestrial ecosystems, researchers, resource managers, and coastal communities are using satellite-based remote sensing, such as Sentinel-2 multispectral imagery, to monitor coastal environments at large scales. The ability to automatically define the position of the coastline from imagery, referred to as “coastline extraction”, is a valuable tool in extending monitoring of coastal ecosystems, such as kelp forests and eelgrass meadows, to regional and provincial scales. In this work, we present a new dataset for water segmentation, and thus coastline extraction, consisting of manually annotated Sentinel-2 images acquired at low tide, specific to the Pacific coast of British Columbia (BC), Canada. We then evaluate three methods for coastline extraction: an adaptive thresholding method, and two convolutional neural networks trained on firstly, a global dataset and secondly, our newly created BC dataset. The model trained on the BC dataset achieved the highest accuracy across standard image segmentation metrics and in coastline positional error measured relative to a manually defined reference coastline. Additionally, very-high resolution unmanned aerial vehicle data collected at validation sites with comparable tide levels to the Sentinel-2 dataset imagery showed that training on BC specific data decreases pixel misclassification, and therefore coastline positional error, due to the presence of subtidal and intertidal algae and vegetation at various validation sites in the study area.

Climate fluctuations influence many aspects of agriculture including crop growth, soil conditions, distribution of fertilizer and water resources. These climatic fluctuations thereby pose significant challenges for agricultural productivity worldwide. However, the availability of agricultural datasets to study the impact of various adverse climatic conditions on different crops remains limited. To address the data availability limitation for agro-climatic impact study, this paper introduces an Explainable Climate-Aware Generative AI framework (XCA-GenAI). The framework combines a Conditional Tabular GAN (CTGAN) to generate realistic synthetic datasets, a Random Forest (RF) regressor to predict crop yield and stress-related parameters, and a SHAP-enabled “what-if” simulation module that evaluates and explains crop responses under varying temperature and rainfall conditions. The proposed framework is employed to generate synthetic representations of ten climatic variations ranging from Very Hot–Dry to Very Cool–Wet using SF24 dataset. Crop-specific predictive models then estimate how change in climatic condition alters crop density, pest pressure, and frost risk. Further, explainability analysis provides interpretable insights of climate impact across multiple crops represented in the dataset. Comprehensively, this work introduces a climate-aware agricultural decision-support framework to aid farmers and agronomists for informed decision making under varying climatic conditions.

Effective monitoring of cotton fields, especially at the regional level, while also detecting diseases in individual plants, remains a significant problem in precision agriculture. This paper presents a combined framework for monitoring cotton in Pakistan, using satellite remote sensing and artificial intelligence-based leaf image classification. Multi-temporal Sentinel-2 imagery from the 2022 kharif season was used to map cotton fields and evaluate canopy condition during the growing season.

Cotton fields were mapped using a Random Forest classifier with an overall accuracy of 93% and a Kappa coefficient of 0.82. The estimated cotton acreage of 65,269 ha nearly matched official figures. The crop state inside the mapped cotton area was then evaluated using a Fused Health Index constructed from NDVI, EVI, NDMI, NDRE, and SAVI. The results showed geographic variability in canopy condition, with 24.5% of the region falling into the low-health class, 50.9% in the moderate-health class, and 24.6% in the high-health class. A Vision Transformer model achieves 97% accuracy in classifying RGB images of cotton leaves into eight diseases and conditions. The satellite analysis identifies where stress is concentrated at the district scale, while the image-based model gives symptom-level diagnostic help. Together, these results suggest that combining remote sensing and artificial intelligence can improve timely cotton monitoring and allow more targeted field management.

Sand mining is the practice of removing sand from lakes, rivers, and streams using various techniques, including dry and wet pool mining, bar excavations, skimming, and scalping. Excessive and illegal mining activities can lead to severe environmental hazards, including deforestation, water pollution, soil pollution, and air pollution. Fallacious mining practices can also lead to the exhaustion of resources on open lands and riverbeds. In this study, geospatial techniques have been utilised to investigate and audit sand mining practices surrounding a city in North India, where resources are extracted for the city's development. The present study was conducted with the objective of identifying unauthorised mining activities in allotted sites as well as nearby areas, mapping and verifying mining operations in relation to approved mining plans and environmental clearances, and estimating the minor minerals extracted at the mining site. Satellite images from Sentinel-2 and Google Earth, along with the coordinates of the lease area and EIA reports for the site, served as the data sources for the research. Violations of the rules, such as flow obstruction, mining along riverbanks, and mining outside the lease area, were observed through the use of remote sensing images. Furthermore, it can be concluded from the present study that satellite-derived analysis offers a time and cost-effective means of inspecting mining areas.

Repeated airborne laser scanning (ALS) offers new opportunities to quantify individual-tree structural dynamics over time. In this study, we analysed annual to multi-year changes in height growth and crown structure of mature Norway spruce in southern Sweden using repeated ALS acquired in 2016, 2017, 2019, and 2022. Individual trees were delineated from normalized point clouds, and tree height increment, maximum crown radial extension, crown projection area, and crown-boundary metrics were derived to evaluate temporal structural change. To interpret the results in relation to recent climate extremes, the observation period was divided into pre-drought (2016–2017), during-drought (2017–2019), and post-drought (2019–2022) phases, and structural changes were expressed on a yearly basis. Tree height increased significantly in all periods. Annualized median height growth was 24.9 cm yr⁻¹ before drought, 26.5 cm yr⁻¹ during drought, and 16.8 cm yr⁻¹ after drought. In contrast, annualized maximum crown radial expansion was limited before drought (2.4 cm yr⁻¹), peaked during drought (19.0 cm yr⁻¹), and was nearly absent after drought (0.17 cm yr⁻¹). Crown-boundary metrics further suggested an upward shift of the upper crown and a reorganization of the middle crown over time, although lower-crown estimates were more uncertain. Driver analyses showed that height growth was mainly related to tree size before and during drought, whereas post-drought growth became more influenced by stand competition. Overall, this study shows that repeated ALS can be a useful tool for analysing crown structural dynamics during and after drought, providing a promising basis for monitoring how tree architecture responds to stress.

The objective of this study is to build an assessment model of nitrogen balance from reservoir to valley fields linked to reservoirs by combining spatial information processing, chemical analysis and bioanalysis. To this end, the biomass of rice and weeds was detected from hyperspectral images mounted on UAVs, the growth processes of rice and weeds were understood with GIS, and the biomass of rice and weeds was estimated separately to estimate the amount of nitrogen fixation in rice.

This study showed the possibility of determining the distribution of humus from the distribution of carbon and nitrogen in a paddy field by using a UAV hyperspectral sensor, a random forest. Furthermore, by qualitatively assessing the contribution of soil micro-organisms to nitrogen fixation in rice using soil microbial diversity and activity values (BIOTREX), a model was constructed to enable an assessment of the nitrogen cycle derived from organic nitrogen supplied by the reservoir, and the nitrogen balance was estimated.

We showed that the nitrogen balance can be evaluated from the balance of soil-derived nitrogen and humus-derived edible nitrogen in reservoirs, rainfall, and rice paddies by chemical analysis. By combining this with BIOTREX, it was shown that when humus-derived edible nitrogen is high and BIOTRX values are high, the change of organic nitrogen in humus to inorganic nitrogen is promoted. It was shown that this could be used as an indicator of the need for fertilizer inputs and as a method for assessing the potential of agricultural land.

Accurate crop maps are important for agricultural monitoring in water-limited regions because they provide spatial information for crop inventory assessment, land management, and resource planning. In the Navajo Nation, crop classification is challenging because agriculture is influenced by arid environmental conditions, limited water availability, and unevenly distributed cultivated land. This study evaluates a crop-classification workflow for a selected agricultural Region of Interest (ROI) within the Navajo Nation using Sentinel-2 imagery, the USDA/NASS Cropland Data Layer (CDL), and the CDL confidence layer in Google Earth Engine. High-confidence CDL pixels (confidence ≥ 95%) were used to construct pseudo-reference samples for the 2017 and 2022 growing seasons, and a 3 × 3 neighborhood homogeneity filter was applied to reduce local label uncertainty. Spectral predictors derived from Sentinel-2 imagery included the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Green Chlorophyll Vegetation Index (GCVI), and Land Surface Water Index (LSWI). A Random Forest classifier was implemented separately for each year using an 80% training and 20% testing split. The resulting classifications achieved overall accuracies of 87.30% for 2017 and 90.88% for 2022. These results show that confidence-screened CDL samples combined with multi-temporal Sentinel-2 features can support reliable crop classification within the selected ROI under limited reference-data conditions and provide a practical basis for agricultural monitoring in the Navajo Nation.

The remote sensing (RS) and geographic information systems (GIS) technologies combined with artificial intelligence (AI) enable more efficient and sustainable agricultural ecosystems. In recent years, the use of the machine learning and the deep learning models trained over the geospatial data have emerged as a pivotal catalyst for sustainable and smart agriculture initiatives. The unmanned aerial vehicle (UAVs; drones) has become as a transformative force in the context of crop health monitoring, disease detection and yield predictions combined with supervised and unsupervised machine learning that helps to revolutionizing the motoring, deep analysis and timely decision making.

This study presents the integration of remote sensing technologies (e.g., UAVs, drones) combined with data-driven artificial technologies to help the farmers in precision agriculture for cotton framing for plant health monitoring against pest infestation, micro irrigation and crop yield prediction. A UAV-based dataset for cotton crops is prepared from a region in Pakistan. The prepared dataset is evaluated through multiple experiments using classical supervised machine learning algorithms for the classification; Naïve Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF). These classification algorithms helped to classify the cotton crop health; healthy or unhealthy. The experimental results indicate that the RF algorithm outperforms the other applied machine learning methods, in terms of its accuracy and precision.

The research focuses on the use of advanced remote sensing techniques to fight the effects of climate change in urban areas, with particular reference to heat islands. The proposed methodology, applied to the metropolitan city of Naples, is based on the analysis of very high-resolution satellite images from the WorldView-3 constellation, combining panchromatic and multispectral data through pan-sharpening to obtain detailed maps of urban vegetation, including smaller green spaces such as flower beds, tree-lined avenues, private gardens, green roofs, which are often overlooked because they are difficult to map in a sustainable and widespread manner. Through the calculation of spectral indices (NDVI, MSAVI2, GNDVI, NDRE), the study has enabled not only the precise geolocation of photosynthetically active areas, but also the monitoring of their health status by comparing satellite datasets acquired in June and September 2023. The results highlight marked water stress during the summer period, manifested by a reduction in the average values of the indices. These results constitute a valuable decision-making tool for resilient urban planning and the implementation of Nature-based Solutions, and demonstrate the sustainability and replicability of the methodology in other territorial contexts.

Accurate estimation of wildfire-driven carbon loss in boreal forests requires spatially explicit prediction of both aboveground and belowground combustion, yet most existing approaches remain region-specific and are rarely evaluated for transfer across fires or geographic domains. Here, we develop and evaluate a transferable modelling framework for plot-level aboveground and belowground carbon combustion using field observations linked to remotely sensed burn severity, vegetation structure, biomass, climate, terrain, peat occurrence, and fire-weather predictors.

Models were trained in western Canada and evaluated using fire-wise hold-out data within the training region and independent transfer domains in Alaska and Québec. To avoid optimistic performance estimates, all tuning and validation were conducted using grouped cross-validation at the fire-event level. Predictor formulations were defined a priori to represent alternative ecological hypotheses about combustion controls. Predictors included Canadian Fire Weather Index components (FFMC, DMC, DC, BUI and FWI), calculated using MODIS-derived burn dates and 7-day antecedent means. After recursive feature elimination, compact non-collinear predictor subsets were retained for modelling.

Predictive performance varied more strongly among predictor formulations than among model families, indicating that ecological representation exerts greater influence on transferable combustion modelling than algorithm choice. For aboveground combustion, the strongest model achieved R² = 0.31 and RMSE = 682.7 g C m⁻². Belowground combustion was more difficult to predict and was best represented by a climate-augmented nonlinear structure. Transfer to Alaska was weakest for both responses, and high-combustion observations were systematically underpredicted, highlighting uncertainty associated with rare extreme burns.

FINCH is a student-led satellite mission whose novel sensor and cost effective form seek to provide crop residue mapping at a much lower cost and aid in smart-agriculture initiatives. To achieve this, crop residue must be accurately quantified using the limited reflectance range of 900 nm to 1700 nm. Hence, novel unmixing methods must be developed. Two datasets were evaluated: a laboratory-acquired dataset and a simulated, atmospherically propagated dataset. Multiple unmixing methods were tested, including Linear Regression, a Bayesian Linear Dirichlet model, K-Nearest Neighbors, Random Forest, and deep learning approaches such as a Multi-Layered Perceptron. Strong performance was achieved on the laboratory dataset, with the Multi-Layered Perceptron achieving an R2 for crop residue of 0.8436, total R2 of 0.8935, and an RMSE of 0.0909 when plotting true to predicted abundances, demonstrating the feasibility of accurate unmixing in controlled conditions. However, performance decreased substantially on the atmospherically propagated dataset, likely due to nonlinearities and other stark differences between datasets that limit transfer learning. These findings indicate that while the lab results are highly promising, additional atmospheric measurements and model adaptations are necessary to achieve full confidence in FINCH’s predictions. Further testing and validation will be critical to establish robustness and guide the development of operational unmixing methods for determination of optical design and imaging requirements.

Understanding vegetation phenology in high-altitude regions is critical for assessing ecosystem responses to climate variability (Cleland et al., 2007). The Indus River Watershed in Ladakh (69,548 sq.km) spans elevations from 953m to 8,546m with diverse vegetation types adapted to extreme conditions. This study addresses the research question: How does vegetation phenology vary annually across 2018–2023? We employ satellite-based NDVI analysis to quantify phenological patterns, map spatiotemporal vegetation dynamics, and identify climate-driven changes in this data-sparse, high-altitude region.

The harvest mouse, Micromys minutus (Pallas, 1771) is the smallest rodent in Japan and now listed in the Red Data Books of Tokyo, 2 prefectural capitals, and 28 prefectures in Japan due to drastic decline of grasslands. For the harvest mouse, the height and density of the tall grass species where nesting occurs are considered particularly important. However, it has been difficult to continuously and extensively acquire information on the three-dimensional structure of herbaceous vegetation. With recent development of UAV technology, UAV data are beginning to be applied to the analysis of herbaceous vegetation. For acquiring three-dimensional information via UAV, methods include using LiDAR sensors or generating 3D point cloud data from aerial photographs using SfM. This study evaluates whether UAV LiDAR or UAV SfM is more suitable for estimating the height of tall grass species such as Japanese silver grass (Miscanthus sinensis), which serve as important nesting sites for the harvest mouse. As a result of analysis, the proposed method was found to be effective to estimate grass height regardless of whether UAV LiDAR or UAV SfM is used. However, when comparing the accuracy of canopy height estimation using UAV LiDAR data alone, UAV SfM data alone, and combined UAV LiDAR and SfM data, combined UAV LiDAR and SfM data found to perform best. Maximum canopy height was found to be best estimated using the combination of median of hand-measured five maximum canopy height values and maximum height calculated using the combined UAV LiDAR and SfM data.

Light pollution is an emerging global environmental issue driven by the intensified use of artificial light at night, with emissions increasing by nearly 50% in the last decades. In Ecuador, the rapid urban and industrial expansion of Guayaquil has led to a significant rise in nighttime radiance, raising concerns about its effects on nearby protected forests such as Cerro Blanco, Papagayo, Estero Salado, and Prosperina.

This study evaluates the impact of light pollution on local flora and fauna using VIIRS nighttime satellite imagery for 2014, 2019, and 2024. Average radiance values were processed in Google Earth Engine and classified into low, medium, and high pollution levels. Species occurrence data from iNaturalist and GBIF were integrated to identify taxa exposed to elevated light levels.

Results reveal a marked increase in light radiance, especially in areas adjacent to urban growth. In Cerro Blanco, radiance has intensified since 2017, disrupting natural light–dark cycles. Nocturnal and endemic species (such as Engystomops guayaco and Sylvilagus dauleensis) were identified among the most exposed, with potential alterations in reproductive, foraging, and behavioural patterns.

The study demonstrates that artificial light is encroaching upon protected ecosystems, threatening biodiversity and compromising ecological processes. The findings underscore the urgent need for conservation strategies that reduce light emissions, promote sustainable lighting technologies, and preserve natural darkness in nocturnal habitats. This work provides critical insights for the management of protected areas in Guayaquil and contributes to the broader understanding of light pollution impacts in megadiverse regions.

With the continuous intensification of marine plastic pollution, monitoring the transport and dispersion of microplastics has become a critical global concern. However, predicting microplastic concentrations remains highly challenging due to the lack of direct satellite signatures and the complex non-linear physical mechanisms governing their dispersion. This study develops an interpretable machine learning framework to monitor surface microplastic concentrations in the West Pacific from 2018 to 2020. We profoundly integrated Japanese AOMI in-situ microplastic observations, ERA5 meteorological/wave reanalysis, and Euro-Argo subsurface profile data utilizing a 3D Inverse Distance Weighting (3D-IDW) spatiotemporal interpolation algorithm. A Random Forest (RF) model was subsequently trained, achieving robust predictive accuracy (R² = 0.64, 0.76, and 0.87 for 2018, 2019, and 2020, respectively). Crucially, we incorporated SHapley Additive exPlanations (SHAP) to overcome the "black-box" limitations of traditional ensemble models. The SHAP analysis explicitly revealed a distinct, year-by-year regime shift in dominant environmental drivers: microplastic distribution was primarily governed by stable hydrographic and biological conditions in 2018; by dynamic wave forcing (e.g., long-period swells and Stokes drift) in 2019; and by extreme meteorological events (e.g., typhoon-induced terrestrial flushing) in 2020. Ultimately, this physics-informed framework successfully elucidates the dynamic transition of microplastic transport mechanisms between hydrographic–biological dominance and meteorological–physical forcing, providing vital scientific support for targeted pollution mitigation and coastal resilience planning.

Satellite data is crucial in regions lacking ground monitoring stations and is helpful in identifying areas likely to have high concentrations of pollutants harmful to human health. As these cities expand and grow, the quality of life and conditions will also be changing. The study seeks to determine the correlation between land surface temperature (LST), elevation, enhanced vegetation index (EVI), rainfall, particulate matter (PM2.5), nitrogen dioxide (NO2) and ozone (O3) both day and night during the months of October-December, January-February, June-August in the year 2019, 2020, 2024 and 2025. The results indicate a varied strength in relationship between variables in each season, day and night. During the day the highest negative correlation is obtained between elevation and carbon monoxide, while during the night the highest negative correlation is obtained between elevation and LST in all periods analysed. Results from the study thus indicate that it is critical to study the spatial and temporal variations of aerosols and temperature over varied conditions in a geographical region.

This study analyses the spatio-temporal dynamics of urban heat island (UHI) in Pune, focusing on the impact of Land use land cover (LULC) changes on the thermal environment. Using satellite imagery from the summer and winter seasons of 2015 and 2024, LULC, land surface temperature (LST), and geospatial indices were analysed at citywide and ward levels. Results indicate that from 2015 to 2024, the urban area increased by 12.77 km2, with the highest urbanization over Hadapsar. Between 2015 and 2024, Pune's mean LST and UHI increased by 8.18°C and 2.65°C in summer but dropped by 5.19°C and 0.54°C in winter. At a ward scale, during both seasons, the highest alterations in LST (UHI) were experienced at Sangam wadi ward. Among geospatial indices, NDMI was the most significant regulating LST across both seasons and years. Ward-level analysis for 2024 shows that a 1% rise in latent heat can lower UHI by 0.3°C in summer and 1°C in winter. Human thermal discomfort in the city is in the less comfortable category, with wards like Sangam wadi showing an increased discomfort across both seasons. The outcomes of this research can serve as a basis for decision-making to improve the resilience and sustainability of the region.

Wetlands are among the most dynamic and ecologically important ecosystems, yet they remain one of the least temporally monitored environments globally. Existing wetland datasets provide only static or low-frequency snapshots, making it impossible to track rapid hydrological fluctuations, disturbance events, and long-term degradation processes. To bridge this gap, we introduce GWD30, the first-ever global wetland dynamics dataset with near-daily temporal frequency (4-day interval) and 30-m spatial resolution, covering the period 2000–2024.

GWD30 is generated using a seamless data cube and a dynamic sample generation approach that converts static training labels into full time-series dynamic labels using temporal–spectral pattern embedding. A two-stage classification system combining machine learning and knowledge-guided refinement produces a globally consistent wetland taxonomy with 14 detailed classes.

This dataset enables unprecedented monitoring of wetland ecosystem behaviour across regions, timescales, and climate zones. GWD30 opens new opportunities for ecological modelling, biodiversity monitoring, hydrological analysis, climate research, and global conservation planning.

Deep-space exploration depends heavily on remote sensing as its primary data source, with the Moon and Mars serving as the main targets for scientific investigation and future human expansion. In these harsh planetary environments, rovers have become the essential platforms for surface exploration and sample acquisition. To support the development of next-generation rover systems, high-fidelity simulation environments are crucial. They enable safe, efficient, and repeatable testing of rover mechanics, perception, localization, and mapping algorithms under realistic planetary conditions, reducing mission risks and accelerating system development. This paper provides a comprehensive comparative analysis of existing lunar and Martian simulation environments, assessing them in terms of scene fidelity, rendering engines, supported robotic platforms, and intended application tasks. Building on this analysis, we introduce a generalized and reproducible workflow for constructing high-fidelity planetary simulation environments grounded in authentic remote sensing data products. Finally, we demonstrate the fidelity and practical utility of a state-of-the-art planetary simulation environment through a set of targeted validation experiments, followed by a discussion of key findings and future directions for the development of next-generation planetary simulation platforms.

The ability to perceptually interpret complex planetary surface environments is essential for successful robotic or crewed exploration. In this research, we present an online semantically-rich 3D information system that offers an immersive, high-fidelity simulation environment, accurately reproducing lighting and terrain conditions to support multi-disciplinary investigation of planetary surfaces. Built for both desktop and VR environments, it allows users to transition from conventional desktop analysis to fully immersive exploration, where spatial perception and cognitive engagement are significantly enhanced. Using candidate landing sites at the lunar south pole as case studies, we evaluate the performance of the proposed online semantically-rich 3D information system. Preliminary results indicate that the system enables users to interpret complex surface data more efficiently and intuitively than conventional observation methods.

Forage lichen fractional cover mapping using multi-spectral remote sensing (RS) data is challenging, especially over rocky landscapes where there is a high spectral correlation between lichens and non-lichen features. Given this, it is deemed that the use of airborne or satellite hyperspectral imagery may improve lichen mapping. In this study, we report the first results of using AVIRIS-NG hyperspectral imagery and UAV images to estimate forage lichen fractional cover (Cladonia spp.) in a rocky Canadian shield landscape where non-lichen bright features were prevalent. To estimate forage lichen fractional cover, we conducted a regression approach based on deep multi-layer perceptron (MLP) models whose number of hidden layers and neurons were determined using exhaustive grid search procedures. The three MLP models were trained and tested on four scenarios with different hyperspectral compression AVIRIS-NG band images and WorldView-3 (WV3) data of three sites. Our experiments showed that mapping lichen fractional cover using the 5 m AVIRIS-NG surface reflectance imagery was more accurate (i.e., higher R2 and lower RMSE values) than the one using a 4-band WorldView-3 (WV3) image with a spatial resolution of 2 m in most cases.

The catastrophic failure of the Córrego do Feijão Tailings Dam I in Minas Gerais, Brazil, on January 25, 2019, resulted in approximately 270 fatalities, underscoring the potential risks posed by tailings dams and the necessity for stringent monitoring of these structures. We utilized Sentinel-1 SAR data to generate deformation time series via Interferometric Synthetic Aperture Radar (InSAR) and to retrieve surface soil moisture (SSM), enabling pre- and post-failure analyses. InSAR analysis revealed significant pre-failure deformation within the tailings impoundment behind the dam crest, with a line-of-sight velocity reaching −69 mm/yr. In contrast, the post-failure period showed no significant ongoing deformation, indicating a relative stabilization. SSM showed a progressive increase in near-surface moisture, peaking on January 22, 2019, three days before the collapse. Although increased near-surface moisture alone cannot be a sign for liquefaction, continuous saturation might foster conditions prone to failure. We also proposed a spatiotemporal Graph Attention Network–Gated Recurrent Unit (GAT-GRU) technique to predict deformation time series derived from InSAR. The proposed GAT-GRU technique exhibited efficacy in predicting deformation trends by modeling spatial and temporal dependencies within the InSAR-derived time series. Overall, this study emphasizes the potential of InSAR, soil moisture analysis, and predictive models as reliable and complementary tools for managing tailings dam safety.

This paper presents a method and results for detecting, mapping and modelling the progression of wildfire in Australasia, Europe and North America within seconds. Initial detections are achieved using the BRIGHT algorithm (Engel et al., 2020, 2021). BRIGHT uses 10-minute Geostationary satellite observations, to dynamically threshold the satellite observation time stamp comparing it to the 28-day bioregion average at each respective timestamp. This produces a wildfire location (hotspot) and an estimate of FRP (Fire Radiative Power), Engel et al., 2022; Chatzopoulos-Vouzoglanis et al., 2022, within 20-45 seconds. Once detected, grouped fire locations are passed onto a fire behaviour simulator Spark / Inferno (Miller et al., 2015), to deliver a comprehensive bushfire analytics model framework which predicts fire behaviour.

At present this product is available in real time for Australia and is available on-demand for Europe and North America.

Low-lying and densely populated coastal cities are not only crucial areas for human survival and rapid development but also highly vulnerable regions sensitive to climate change. In recent years, tropical cyclone-induced flooding has emerged as a major hazard threatening the sustainable development of coastal cities. At the same time, rapid land use changes in these urban areas have significantly altered the original landscape structures and land use patterns, becoming key drivers of escalating flood risks. Therefore, when mapping and assessing urban flood risks, it is essential to comprehensively account for the combined effects of climate change and urbanization. This study uses Shanghai, a typical coastal city, as a case study to propose an integrated framework for simulating and evaluating coastal flood hazards while incorporating land use changes. The framework realizes the numerical simulation of flood disasters in coastal cities based on physical processes by coupling the SFINCS fast flood inundation model, the land use change model and the Delft 3D storm surge numerical nested model. The results indicate that by 2100, urban land use changes will expand the inundation area of a 1,000-year tropical cyclone flood by 4.91% to 34.00%. Neglecting future urban land use changes would underestimate the inundation extent of storm surges. Moreover, the findings highlight the critical need to account for the long-term impacts of land use changes on urban flood risks in coastal areas. The proposed methodology is applicable to coastal regions worldwide that are susceptible to tropical cyclones.

Advances in artificial intelligence have opened new frontiers in Earth observation, particularly in modeling complex geodynamic phenomena such as Vertical Ground Displacement (VGD). VGD is driven by numerous environmental and hydro-climatic factors, making prediction inherently challenging. This study develops a novel CNN-ConvLSTM hybrid deep-learning architecture that seamlessly integrates static soil characteristics and dynamic spatio-temporal features to predict VGD across the Italian territory. The model achieved an R2 value of 0.59 and a Mean Absolute Error (MAE) of 4.35 mm on the validation dataset, effectively capturing approximately 60% of the VGD variations. Additionally, Explainable AI (XAI) using SHAP (SHapley Additive exPlanations) values was incorporated to interpret the model's predictions. The analysis confirms that while hydro-climatic factors (such as drought and temperature) are the primary drivers of VGD temporal variability, static soil properties (including bulk density and volumetric water content) are the most globally influential predictors, dictating the overall spatial susceptibility of the medium. These findings provide a framework for identifying the key environmental drivers of VGD, which is essential for resource allocation, hazard management, and the development of effective early warning systems in geologically sensitive regions.

This study presents an integrated GIS-based and hydrological modelling assessment of flood vulnerability in the Yola South watershed of Adamawa State, Nigeria—an area experiencing increasingly severe flood events due to rapid urban expansion, land degradation, and intense rainfall. Using high-resolution spatial datasets, the watershed was delineated from a 30-m DEM, and land-use and soil information were utilized to compute Curve Numbers (CN) using the SCS-CN method. A composite CN of 65.33 was derived, indicating moderate infiltration capacity and substantial susceptibility to runoff generation during heavy storms.

The HEC-HMS hydrological model was used to simulate the July 31, 2025, rainfall event across delineated sub-basins. Model outputs revealed peak discharges ranging from 5.3 to 6.0 m³/s, direct runoff volumes of approximately 19 mm, and lag times between 205 and 266 minutes. Sub-basins with increased imperviousness and exposed soils generated faster and higher runoff responses, identifying hydrological hotspots that contribute disproportionately to downstream flooding.

The study demonstrates the utility of combining GIS with HEC-HMS simulation to evaluate watershed behaviour under current land-use conditions. Findings provide actionable guidance for flood risk planning, including targeted drainage improvements, land-use regulation, and nature-based solutions such as vegetation restoration. This research highlights the value of geospatial technologies in supporting climate resilience and aligns with ISPRS priorities on sustainable environmental management and hazard mitigation

This study presents a Transformer-based framework for estimating landslide occurrence dates using fused Sentinel-1 SAR and Sentinel-2 optical time-series data. By leveraging multivariate temporal features and long-range attention, the model substantially improves dating accuracy compared with single-sensor methods, with over 80% of events dated within a 0–15-day offset. The derived occurrence dates enable the creation of landslide activity maps at daily temporal resolution, offering a major advancement over conventional annual assessments. The resulting landslide activity index highlights spatial and temporal variations in slope activity, supporting more precise identification of highly dynamic landslides. The framework offers a valuable tool for monitoring slope hazards and enhancing landslide risk assessment at regional scales.

Floods remain one of the most frequent and disruptive natural hazards in Sri Lanka, particularly within low-lying monsoon-driven basins where cloud-covered conditions hinder optical monitoring. This study develops a multi-temporal flood-mapping framework that integrates Sentinel-1 C-band SAR data with DEM-derived terrain information to assess flood dynamics and identify recurrent inundation zones in the Attanagalu Oya Basin. Three major flood events in 2016, 2017, and 2018 were analysed using pre- and peak-flood SAR acquisitions processed through a standard workflow of orbit correction, radiometric calibration, speckle filtering, and terrain correction. Threshold-based segmentation of VV backscatter (−15 to −13 dB) was applied to delineate inundation, followed by hydrologically guided refinement using slope (<1–3°) and elevation constraints to reduce false positives associated with built-up areas, vegetation, and radar shadow. The results illustrate distinct spatial variations across the three years, with 2016 showing the most extensive inundation and 2018 presenting spatially concentrated flooding. DEM integration significantly improved classification accuracy by eliminating physically implausible detections. Validation against the Survey Department–Sri Lanka Navy inundation map for 2016 produced a spatial agreement of 72.18%, demonstrating the reliability of the SAR–DEM fusion approach. Multi-year overlay of flood layers revealed a persistent high-risk corridor stretching from Gampaha to Katunayake, reflecting entrenched drainage limitations and ongoing floodplain encroachment. The proposed framework provides an operational, scalable method for flood monitoring in cloud-prone environments and offers essential insights for risk-sensitive land-use planning, hazard zoning, and infrastructure design. The approach also forms a basis for future automated, machine-learning–enhanced flood early-warning systems.

Climate change has intensified the variability and frequency of extreme events, particularly affecting vulnerable coastal urban

areas. In Caraguatatuba, located on the north coast of S˜ao Paulo, the impacts are exacerbated by social inequalities and inadequate

infrastructure. However, detailed analyses of climate indices in the region are still scarce. This study analyzes precipitation patterns

in Caraguatatuba, calculating climate indices to identify trends and support risk mitigation. CHIRPS daily precipitation data at

0.05° spatial resolution were processed using Google Earth Engine and R to compute the ETCCDI indices PRCPTOT, CDD, CWD,

R95p, R99p, Rx1day, and Rx5day. Statistical tests were applied to detect significant trends. The results indicate an increase in

consecutive dry periods (CDD, p=0.0065) and at the intensity of daily extreme rainfall (Rx1day, p=0.0044) in the southwestern

area, while other indices did not show significant trends. These findings highlight the city’s increasing climate vulnerability and the

urgent need for adaptation strategies. By offering a replicable framework based on open-access remote sensing and cloud platforms,

this study supports policy development to enhance urban resilience and monitoring climate-related disasters in coastal cities.

Agricultural drought is a recurring and complex hazard that significantly impacts food security, water resources, and rural livelihoods, particularly in vulnerable regions such as the southern agro-climatic region of Tamil Nadu, India. Over the past few decades, the region has experienced numerous drought-related phenomena. In this context, the primary objective of this study is to monitor the dynamics of agricultural drought across the region between 2015 and 2023, with the aim of promoting sustainable agricultural management and climate-resilient practices.

MODIS NDVI and LST products were utilized to derive drought-related indices. Rainfall data from CHIRPS and temperature data from TerraClimate (1982–2023) were used to calculate the SPEI, enabling the identification of representative dry and wet years. The analysis uses the Normalized Vegetation Supply Water Index (NVSWI), derived from remote-sensing time-series datasets. Finally, a correlation analysis was conducted between monthly NVSWI, one-month SPEI, and VHI during the primary growing season of Rabi crops (October to December).

Results reveal that NVSWI identified spatial and temporal patterns of acute water stress in vegetation. Ramanathapuram, Sivaganga, Virudhunagar, Thoothukudi, and Pudukkottai were consistently drought-prone, experiencing moderate to severe drought intensity in multiple years. In 2021, drought conditions were minimal. A strong positive correlation was observed between monthly NVSWI and both SPEI and VHI, confirming its suitability for drought monitoring.

The findings highlight the effectiveness of NVSWI and multi-source satellite data for drought detection, supporting the development of early warning systems and climate-resilient agricultural planning in drought-prone regions.

The deglaciation due to global warming and changing climate has given rise to the formation and expansion of numerous glacial lakes, particularly in the High Mountain Asia region. Many of these glacial lakes are susceptible to experiencing Glacial Lake Outburst Floods (GLOFs) events which can release millions of cubic meters of water and debris, leading to widespread impacts on lives, property, infrastructure, agriculture and livelihoods amongst remote downstream communities. The research investigates the potential of multi-source data, focusing on District Chitral in Northern Pakistan, with elevated potential implications for GLOF and associated risk. A total of 12 vulnerable sites are identified, out of which 5 are highly susceptible to GLOF. A spatio-temporal analysis of the vulnerable sites have been carried out in Google Earth Engine (GEE). The maps were generated in the GIS environment of ArcMap considering key contributing factors with high impact potential including, lake area change, elevation, slope, aspect, temperature and precipitation, LULC, change in snow and glacier cover area, distance from fault line, and proximity to impact area, among others. A pronounced decline in the snow and glacial cover, and an increase in land surface temperature (LST) retrieved from satellite data could be responsible snow/glacial melting resulting to higher frequency of GLOFs and flash floods. The potential implications on population, infrastructure, schools, forest and agriculture, and water quality of Chitral have been estimated. The findings are of great significance for policymakers and disaster management authorities, providing valuable insights to formulate efficient and effective measures for mitigating the risks.

Remote sensing and GIS-based geomorphometric mapping are powerful tools for analyzing neotectonic activity. This study focuses on the Nanga Parbat Syntax (NPS) and its adjoining regions, among the fastest uplifting zones of the Himalayas, rising at 8–10 mm/year. Using SRTM DEM, Trend Analysis of Polynomial Surfaces (TAPS), Local Base Level (LBL), and Vertical Dissection (VD) maps were produced to interpret surface dynamics. The study area, located along the Main Mantle Thrust (MMT) and below the Main Karakoram Thrust (MKT) in Gilgit-Baltistan, encompasses five key geomorphometric zones—two defined by drainage dissection, two by relative relief, and the expansive, relatively flat Deosai plateau in the southeast with prominent VD signatures. A residual elevation map was derived by subtracting a 12th-order polynomial trend surface from the DEM, highlighting spatial elevation anomalies. This trend surface effectively captures the NE–SW and NW–SE uplift patterns across the Nanga Parbat Haramosh Massif Zone (NPHMZ). Elevated anomalies align with the Sassi Raikot Fault Zone (SRFZ), NPHMZ, MKT, and northwest of Jaglot toward the Hindukush. In contrast, areas such as Deosai Plateau, Skardu, Kachura, Gorikot (Astore Valley), Jaglot, and Gunar exhibit negative elevation anomalies. LBL maps generated from 2nd- and 3rd-order Strahler streams yielded insightful correlations with tectonic structures. Both isobase and VD analyses indicate significant dissection and elevation in regions near the NPHMZ, MKT, and upper Astore Valley. The spatial alignment of high residuals with these structural features underscores active tectonic uplift, reaffirming ongoing neotectonic processes across the NPHMZ and its surroundings.

The frequency of wildland fires are increasing due to warmer and drier conditions resulting from climate change. Identifying fire prone areas is essential for planning and mitigating potential impacts. This study aims to create a wildland fire danger map using Random Forest (RF) and competing classification methods for Ontario’s Managed Forest (MF). The critical role of the classification method in wildland fire danger mapping motivated us to evaluate and compare the effectiveness of three classification methods, including Natural Break, Geometric Interval, and Standard Deviation Interval. A total of 42 key static and dynamic variables were analyzed, covering the period from 2020 to 2022. The static variables: distance from roads, railways, settlements, rivers, and water bodies and topographic features like elevation and various indices derived from the NASA Digital Elevation Model (NASADEM). To capture these dynamic environmental conditions, several environmental variables and indices as well as key meteorological parameters were incorporated into the modelling from MODIS and ERA5 land. We used Recursive Feature Elimination and Cross-Validation (RFECV) to select optimize features for the model. To address the opacity inherent in machine learning models, SHapley Additive exPlanations (SHAP) were utilized to quantify the marginal contribution of each variable to the predicted distance from fire. Our results showed that the GI classifier provided the most consistent and well-balanced performance and reliable predictions across all evaluation metrics. The resulting fire danger map highlights high-risk areas, supporting targeted management, prevention, and resource allocation to reduce future wildfire impacts.

Dharali, a Himalayan village in Uttarkashi, Uttarakhand, lies along the narrow Kheer Ganga valley, a terrain marked by steep slopes, high relief, and complex topography- conditions that render it highly susceptible to geomorphological and hydrological hazards. On August 5, 2025, a catastrophic flash flood and debris flow, triggered by a sudden cloudburst or upstream slope failure, caused extensive destruction across 0.54 km² of the settlement. Despite the increasing frequency of such high-magnitude events in the Himalaya, quantitative assessments of recent localized geomorphic and hydrological impacts remain limited, particularly in small, high-altitude villages like Dharali. This study addresses that gap by employing high-resolution PlanetScope imagery (3 m) from pre-event (July 19, 2025) and post-event (August 22, 2025) periods to detect and quantify surface alterations using spectral thresholding and spatial change detection methods. The analysis revealed pronounced spectral shifts, a 39% increase in surface water extent, and topography-driven hydrological redistribution. The statistical association (𝜑 = 0.35; ϗ = 0.34) indicates moderate spatial agreement between the temporal datasets. The findings demonstrate the utility of fine-resolution satellite data in capturing rapid, small-scale landscape transformations and emphasize the urgent need for systematic, event-based monitoring frameworks to improve disaster preparedness and resilience in fragile Himalayan environments.

This paper presents PICANTEO, a modular and multi-modal change detection framework designed for remote sensing applications in natural disaster response. The framework aims to support damage assessment during both the rapid mapping phase, which occurs in the immediate aftermath of a disaster, and the longer recovery phase. PICANTEO provides automated, reliable disaster-related change detection maps and associated impacted areas to support a wide range of disaster monitoring activities. The integration of uncertainty and ambiguity concepts ensures reliable and qualified results.

PICANTEO handles multi-modal remote sensing data, including very high-resolution optical imagery, Digital Surface Models, and Synthetic Aperture Radar (SAR) data. Its modular architecture enables users to apply ready-to-use pipelines or implement their own workflows. The provided scalable components can be combined or extended by custom methods to define new applied pipelines. Several real-world case studies demonstrate PICANTEO’s ability to address various disaster scenarios across diverse geographic contexts. Source code is available at: https://github.com/CNES/picanteo.

Coastal regions are the foci of intense economic activity, but, these dynamic and ecologically sensitive low-lying lands are increasingly threatened due to climate change induced eustatic sea level rise, and anthropogenic activities at regional and local scale leading to relative sea-level rise, thereby necessitating to understand the vulnerability of a coast for their protection and sustainable development. In this background, the present study is focused along the densely populated ~590 km long coastal stretch of Kerala state, India to build an Integrated Coastal Vulnerability Index (ICVI) by using the i) physical vulnerability index (PVI) variables such as a) Geomorphology, b) Coastal Slope, c) Bathymetry, d) Shoreline change history, e) Spring tide range, and f) Significant wave height, and ii) socio-economic vulnerability index (SVI) variables like a) population density, b) land use/land cover, c) number of household, d) fisher-folk population density, e) literacy rate, f) occupation, g) road density, h) railway network and i) tourist spots integrated in Geographic Information System (GIS) environment, and through Analytic Hierarchy Process (APH) technique. Kerala state is located on the south-western margin of the Indian Peninsula, with nine administrative districts i.e., Kasargod, Kannur, Kozhikode, Malappuram, Thrissur, Ernakulam, Allapuzha, Kollam and Thiruvananthapuram districts (from North to South). The Integrated Coastal Vulnerability Index (ICVI) along the Kerala coast revealed that 96.82 km (16.4%) under very low vulnerability, 105 km (18%) under low vulnerability, 145.75 km (24.6%) under moderately vulnerable, 123 km (20.9%) under highly vulnerable and the remaining 119.27 km (20.2) under very highly vulnerable.

This study examines how wildfire disturbance changes urban runoff behaviour in a small Mediterranean basin. The research uses high-resolution UAV images together with GIS processing to create detailed surface models and land-cover maps after the fire event. These spatial datasets were then integrated with SWMM and HEC-RAS 2D to simulate different rainfall scenarios and to understand how the basin responds during storms.

The results show that burned areas have faster runoff, higher peak flow, and stronger surface connectivity even under moderate rainfall. The modelling framework was able to map sensitive zones where water tends to accumulate and where the risk of local flooding increases. The study also demonstrates that UAV-based photogrammetry can improve hydrological and hydraulic simulations by providing more accurate information on terrain shape and surface conditions.

Overall, this contribution presents a practical workflow that combines remote sensing and process-based modelling to support flood-risk assessment in fire-affected urban environments. The approach is suitable for other regions with similar challenges and contributes to ISPRS goals of using geospatial technologies for climate-resilient and sustainable water-management planning.

Digital elevation models are the primary data used in remote sensing and geographic information systems (GIS) for terrain analysis and three-dimensional spatial data processing [1]. In surveying and mapping investigations, SRTM1 (Shuttle Radar Topography Mission) DEM, ASTER GDEMV3 (Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model), and AW3D30 (ALOS World 3D-30m) DEM have become key data sources. This study compares the elevation accuracy of three open-source DEM datasets to high-precision ICESat-2/ATLAS altimetry data. GIS statistical analysis, error correlation analysis, and mathematical statistical approaches are used to compare elevation accuracy in DEM.

Four error assessment metrics are utilized: Mean Error (ME), Standard Deviation of Error (SDE), Root Mean Square Error (RMSE), and Correlation Coefficient (CORR). Furthermore, error correlation analysis is conducted to visually characterize the spatial distribution patterns and error features of the three open-source DEMs in relation to ICESat-2/ATL08 observations.

The AW3D30 DEM has the highest accuracy in plains, with the SRTM1 DEM coming in second. In mountainous terrain, SRTM1 DEM was the most accurate, followed by AW3D30 DEM. Although ASTER GDEMV3 fared less well in the two study locations listed above than previous studies in plateau regions, its accuracy in mountainous and plateau areas is comparable to that of SRTM1 DEM and AW3D30 DEM.The RMSE for all three DEM datasets is roughly 15 m in wooded mountainous regions, around 5 m in artificial surfaces and barren areas, and exhibits the greatest inaccuracy in forested and grassland regions on plains, with the least error occurring in wetlands.

The radiometric adjustment of aerial imagery is a process of very high importance considering the influences this step can generate not only on the look of the image data (white balance), but even more importantly on derived information like indices (NDVI). In comparison to the Aerial Triangulation where it is relatively straight forward to set up thresholds that need to be met to achieve a high-quality result, the world of radiometric adjustment is dramatically different. There is no single standard or guideline that dictates what a high-quality radiometric result will look like. Apart from these challenges there is also a rather big gap between the rich and longstanding academic work done in the field of radiometry and actual application in real-life projects. The by far biggest discrepancy is the usage of single images, especially when dealing with absolute radiometry approaches versus multiple thousand color balanced images in a single block in an actual production environment. In this paper we present the advantages of utilizing reflectance measurements as a method to stabilize radiometric adjustments, as well as utilizing them as anchor to create indices like the NDVI that actually correspond to the value range given by literature.

Urban Spatial Monitoring (USM) data is a new form of fundamental geographic information product. As an important component of the natural resources survey and monitoring system, USM provides strong support for the construction of the China Spatial Planning Observation Network (CSPON). The quality of USM data results is related to the accuracy of statistical analysis outcomes, the scientific decision-making for national economy, people's livelihood and social development, as well as the reliability of natural resources management applications. Based on the analysis of the characteristics of USM results, this paper proposes an inspection process including overall general inspection, detailed inspection, and out-of-sample general inspection; analyzes inspection parameters and items, and determines the inspection methods for different items; further identifies quality elements that can be automatically inspected by programs, and realizes batch automatic inspection of some items by establishing a rule base; then, studies the implementation methods of other quality inspection items, clarifies common problems, and improves the human-computer interactive inspection for quality items. Finally, the quality inspection results of urban space monitoring data products covering 170 prefecture-level city survey areas spanning approximately 6 million square kilometers demonstrate that the technical route proposed in this paper is feasible and the quality evaluation results are objective.

Seagrasses are ecologically-important yet highly threatened blue carbon ecosystems that play a critical role in environmental protection, biodiversity conservation, and carbon sequestration. However, their spatial heterogeneity and dynamic temporal behavior pose challenges to accurate mapping and long-term monitoring. The availability of publicly accessible satellite images with high spatial and temporal resolution, and advances in machine learning, have gradually expanded seagrass geospatial research and led to more accurate and robust image classifications. This study evaluated the performance of traditional and machine learning methods for seagrass and benthic habitat mapping using clear and sun-glinted 3-meter resolution PlanetScope imagery. Classification accuracy metrics were compared across multiple algorithms and varying image quality, using two different reference datasets. Results indicate that the Maximum Likelihood Classification and Support Vector Machine Classification achieved the highest overall accuracy and kappa statistics for the clearest image used, the 8-band PlanetScope image acquired on February 16. As expected, the application of the sun glint correction procedure improved classification accuracies for lower-quality images, particularly for the Random Forest Classification which showed consistent and pronounced gains after deglinting. These findings demonstrate the potential of PlanetScope images for seagrass and benthic mapping, keeping in mind that careful image selection remains essential due to the imagery’s inherent sensitivity to sun glint and other radiometric inconsistencies affecting classification performance. In the absence of optimal or clear images, scenes with lower image quality may still be effectively utilized with the application of radiometric correction procedures such as sun glint removal.

The National Ecological Observatory Network (NEON) operates the Airborne Observation Platform (AOP) which collects airborne lidar, imaging spectroscopy and RGB camera information to support the characterization and forecasting of environmental and environmental processes. NEON operates at a continental scale, including observations at sites across the continental Unites States, Alaska, Hawaii and Puerto Rico, and will operate for 30 years. The AOP has been collecting data at sites with NEON for over 10 years, representing a highly valuable resource for conducting ecological change analysis. NEON AOP data highly standardized, and undergoes rigorous quality control and quality assurance processes. Data collected by the AOP is processed into a series of data products that are made freely available for educational and scientific endeavors. This presentation details the current status and future plans for NEON's AOP.

Peatlands are a major component of Canada’s boreal region and play critical roles in carbon storage, biodiversity, hydrology, and climate regulation. Different peatland types respond differently to climate-driven changes in temperature, precipitation, and wildfire risk. Accurate maps of these sub-classes are essential for conservation planning, carbon accounting, and wildfire management. Although national-scale wetland maps for Canada have advanced in recent years, many lack detailed peatland sub-class information and often omit swamps.

This research builds on recent efforts by expanding the spatial extent of peatland sub-class mapping across Canada (excluding the Northern Arctic and Arctic Cordillera) and explicitly incorporating swamps as a separate class. A three-stage hierarchical framework was developed using a combination of optical, radar, and terrain-derived variables. Predictor datasets included Landsat spectral mosaics, NDVI harmonic coefficients, canopy height and closure, ALOS-2/PALSAR-2 L-band backscatter, seasonal Sentinel-1 coherence, and hydrological and geomorphometric derivatives from FABDEM. Reference data were compiled from multiple validated wetland inventories.

A Random Forest classifier was trained and validated at each stage: (1) wetlands vs. uplands and water, (2) peatlands vs. mineral wetlands, and (3) peatland sub-classes and swamps. Accuracy exceeded prior national efforts, with 87% accuracy at Stage 1, 94% at Stage 2, and 72% at Stage 3. Shapley Additive Explanations showed that the SAGA Wetness Index was consistently among the most important predictors, highlighting the central role of topography and moisture distribution. These results demonstrate the value of integrating multi-sensor remote sensing with terrain metrics to improve national-scale wetland classification.

Mangrove forests along Hong Kong’s densely populated subtropical coastline fulfil significant blue carbon storage, shoreline protection and habitat functions but are vulnerable to hydroclimatic and anthropogenic pressures. This study integrates long-term satellite spectral analysis with field-based laser scanning to examine mangrove canopy dynamics and above-ground biomass. Seasonal composites of Landsat-8 imagery (2013 to 2025) were processed in Google Earth Engine, where NDVI, EVI, ATVI and GEMI were calculated. GreenValley DGC-50 SLAM-based backpack laser scanning system to collect plot-scale structural data. We registered, denoised, normalized and segment point clouds to retrieve tree height, crown diameter and diameter at breast height for being further used in species specific allometric equations to estimate biomass. The spectral time series indicates a persistent greening pattern with recurrent seasonal cycles and stronger canopy development after 2018. Comparison with field observations showed that laser-derived tree height was more consistent than DBH and crown diameter, indicating variable parameter accuracy in dense mangrove stands. The LiDAR survey provides valuable detailed structural information and supports biomass estimation for inaccessible areas. The LiDAR survey provides detailed structural information and supports the estimation of biomass, where traditional measurements are hard to obtain. The field survey does not validate long-term spectral trends but rather serves a contemporaneous structural reference frame for interpreting seasonal and interannual spectral variability. The combined framework enables enhanced estimation of mangrove biomass, blue carbon stock monitoring and coastal ecosystem management in Hong Kong.