Conference Agenda

Urban trees play a crucial role in mitigating urban heat islands through shading and transpiration, processes directly linked to Leaf Area Index (LAI). However, estimating LAI for individual urban trees remains challenging due to their geometric and temporal heterogeneity.

This study evaluates the PATH (Path length distribution) method, a terrestrial laser scanning (TLS) based approach, to estimate LAI for three urban tree species in Strasbourg, France. The PATH method models foliage area volume density from point clouds, accounting for non-random foliage arrangements and woody structure contributions, unlike traditional optical methods.

TLS campaigns were conducted in three streets at three phenological. The sensitivity of PATH to geometric reconstruction parameters was assessed to optimize LAI estimation. Results show that envelope geometry significantly influences PAI estimates, with concave shapes (of at least 3000 facets) yielding more accurate values, while leaf angle distribution has minimal impact.

The obtained LAI estimates varied by species, reflecting species-specific crown densities. PATH-derived PAI was compared to LAI-2000 optical sensor measurements and direct LAI obtained by leaf collection. PATH estimates aligned more closely with true LAI than LAI-2000, especially during early leaf expansion, though discrepancies arose due to branch pruning and polycyclic flushing. The study highlights the importance of adapting scanning protocols and PATH parameters to species-specific morphology.

In conclusion, this work highlights the potential of TLS-based methods for providing robust PAI estimates for urban trees. Future research will link these species-specific estimates to urban microclimate benefits.

South Africa is the leading soybean producer in Africa, contributing approximately 35% of the continent’s total production. Soybean is important for national food security and agricultural sustainability–– serving as a key nitrogen-fixing crop that support soil fertility and economic growth. Whilst monitoring biochemical parameters such as leaf chlorophyll content (LCC) is essential for assessing the soya bean health, cultivar-level variability can complicate the use of remote sensing–based approaches. This study evaluates the performance of four machine-learning algorithms, XGBoost, Random Forest, Partial Least Squares Regression, and Artificial Neural Network, using unmanned Aerial Vehicle based data across 15 soybean cultivars during the early reproductive phase. Results show that model performance is strongly cultivar dependent. Tree-based models achieved the highest accuracy, with XGBoost and Random Forest reaching RMSE values as low as 2.9 µmol m⁻² for PHIP62T16R and R² values up to 0.96 for RA655R, while ANN and PLSR performed substantially worse for cultivars with more complex spectral responses, such as PAN1555R. Residual results from generalised models revealed systematic over- and under-prediction in several cultivars, indicating that models developed using pooled data are unable to fully account for cultivar-specific spectral differences. Variable-importance analyses identified red-edge, NIR, and greenness-enhancing indices as the most influential predictors of LCC, highlighting their strong sensitivity to canopy structure and chlorophyll variation. Overall, the study shows that cultivar-specific, ensemble-based modelling delivers stronger predictions of chlorophyll in soybean. Incorporating cultivar information and using stratified model calibration improves the reliability of UAV-based chlorophyll monitoring in heterogeneous soybean canopies.

The monitoring of crop traits on a landscape scale is of key interest in the context of precision farming and food production. Many studies use moderate-resolution satellite data like Sentinel-2, Landsat for crop monitoring. However, enhanced spatial resolution is improving monitoring quality significantly. In this context, commercial but expensive very high resolution (VHR) satellite data from Ikonos, Quickbird, Formosat-2, and WorldView-2 have been successfully applied for crop monitoring over the last two decades.

The focus is on the research question “Can Pléiades Neo data quantify plot-scale variation in dry biomass and N uptake?” and on developing an analysis workflow which could support precision farming on a landscape scale using VHR satellite data.

In this contribution, we propose the application of pansharpened Pléiades Neo satellite data for the monitoring of crop traits like dry biomass and N uptake - in our study for winter wheat. The very high spatial resolution of 0.3 m even allows to investigate field experiments with plot sizes of several m2 and therefore, would be suitable for crop phenotyping.

Smallholder farmers play a critical role in the growing of underutilized crops, such as sweet potato. Obtaining accurate maps of sweet potato biophysical variables is essential for farmers to assess and monitor crop health at different growth stages. Integrating radiative transfer model (RTM) data with vegetation indices (VIs) based on unmanned aerial vehicle (UAV) data, may have the potential for accurately estimating leaf chlorophyll concentration (LCC) across multiple crop varieties. Firstly, in this paper we developed and tested varying hybrid retrieval models by combining PROSAIL RTMs with broadband, narrowband and leaf-pigment VIs applied to 2-cm resolution UAV imagery, to retrieve LCC over 20 sweet potato varieties at 120 days i.e. matured growth stage. Secondly, the best hybrid retrieval model was transferred to a different site which contain similar sweet potato varieties at matured growth stage for the estimation of sweet potato LCC. Results show that the most accurate retrievals of LCC were achieved by integrating a larger database containing 11000 PROSAIL simulated reflectance samples with broadband indices, particularly the enhanced vegetation index (EVI) with coefficient of determination (R2) of 0.85, root mean squared error (RMSE) of 5.93 µg/cm2, and relative RMSE (RRMSE) of 9.87%. Furthermore, when transferred to a different site containing similar sweet potato varieties at matured growth stage, this model achieved 60% agreement with field LCC measurements and responded fairly well by capturing LCC variability. These findings have significant implications in sweet potato breeding programmes for developing new cultivars.

Remote sensing and laboratory assays can improve field-scale crop assessment and management. This exploratory pilot study analyses relationships between leaf tissue nutrients and UAV-derived normalised difference vegetation index (NDVI) using seventeen paired samples collected across a mixed crop trial. Tissue measures for nitrogen, phosphorus and potassium were standardised and entered into principal component analysis to reduce pairwise correlation and extract orthogonal nutrient axes. The first principal component explained 54.79% of variance, the second explained 34.10%, together accounting for 88.9%. Principal component scores for the first two axes were used in linear and polynomial regression models to predict NDVI. Model skill was assessed on training data and with leave-one-out cross-validation, and bootstrap resampling produced empirical confidence intervals for component loadings. Linear models built on principal components provided the most stable cross-validated performance, while polynomial expansions improved training fit but generalised poorly. These findings indicate that a low-dimensional nutrient representation can predict NDVI with reasonable stability and that combining spectral and biochemical data supports spatially explicit nutrient assessment. The study recommends expanded and stratified sampling, reflectance calibration and targeted spectral bands for follow-up studies, and external validation before wider applications.

This study integrates multispectral (UAV) and hyperspectral (ground-based) remote sensing data to estimate coffee (Coffea arabica) yield using machine learning algorithms. Forty field plots were analyzed with multispectral Mavic 3M imagery and hyperspectral Blue Wave spectroradiometer data. Spectral indices such as NDVI, NDRE, GNDVI, CIRE, and PRI were correlated with yield, revealing distinct responses across spectral domains. Neural networks achieved the best predictive performance (R = 0.93; RMSE = 7.9%), followed by SVM models (R = 0.90). The Red Edge and Green bands were most sensitive to productivity variations in multispectral data, while hyperspectral narrowband indices provided superior correlations with canopy physiological traits. The integration of both datasets highlights the complementary strengths of spatially extensive multispectral imagery and the spectral precision of hyperspectral sensing. This multiscale approach enables more accurate and operational yield estimation for perennial crops and supports the development of precision agriculture protocols for coffee production systems.