Conference Agenda

Outbreaks of the European spruce bark beetle (Ips typographus) have intensified across Central and Northern Europe due to droughts, storms, and other extreme climatic events. Resulting Norway spruce mortality has reduced growing stock and weakened forest carbon uptake, creating an urgent need for rapid, operational tools for early detection. Pre-emergence detection, i.e. identifying infested trees before brood emergence, is particularly valuable, yet field surveys remain too slow and costly at large scales. UAV-based optical remote sensing offers high-resolution, flexible, and timely mapping at the single-tree level, allowing detailed observation of spectral changes soon after attack.

Despite many recent UAV studies, the reliability and transferability of pre-emergence detection remain unclear. Differences in sensor types (multispectral vs. hyperspectral), band configurations—especially in the red-edge and green-shoulder regions—and analytical approaches have produced inconsistent results. Many models are developed within single sites and often lack standardized accuracy metrics or cross-site validation, limiting insights into robustness under varying ecological and climatic conditions.

To address this, we compiled six UAV datasets from four major outbreak regions—southern Sweden, southern Finland, the southeast Alps in Italy, and Czechia—covering multispectral and hyperspectral campaigns at the single-tree level. Using these harmonized data, we compare machine-learning models for classifying tree health based on spectral features and vegetation indices. A central focus is transferability. We test models across regions using cross-regional, joint, and leave-one-region-out schemes to quantify generalization across contrasting climates, outbreak phases, and stand structures. The results reveal consistently informative spectral regions and modelling strategies, offering practical guidance for operational early-warning systems.

Natural and anthropogenic disturbances are impacting the health of forests worldwide. Monitoring forest disturbances at scale is important to inform conservation efforts. Here, we present a scalable approach for country-wide mapping of forest greenness anomalies at the 10 m resolution of Sentinel-2. Using relevant ecological and topographical context and an established representation of the vegetation cycle, we learn a predictive quantile model of the normalised differential vegetation index (NDVI) derived from Sentinel-2 data. The resulting expected seasonal cycles are used to detect NDVI anomalies across Switzerland between April 2017 and August 2025. Goodness-of-fit evaluations show that the conditional model explains 65% of the observed variations in the median seasonal cycle. The model benefits most from the local context information during the green-up period. The approach produces coherent spatial anomaly patterns and enables country-wide quantification of forest browning. Case studies with independent reference data from known events illustrate that the model reliably detects different types of disturbances.

The exponential spread of the bark beetle (Ips typographus L.) outbreaks across Europe in recent years has led to heightened interest in remote sensing-based detection. This increase is closely linked with ongoing climate change, which has led to rising temperatures, prolonged dry periods, and increasing frequency and intensity of both biotic and abiotic disturbances. These conditions created a favourable environment for bark beetle proliferation, resulting in larger and more widespread infestations. Effective detection and management of these outbreaks is crucial for forest officals, necessitating the implementation of monitoring systems that complement traditional ground-based efforts. At present, remote sensing approaches for bark beetle detection mainly rely on optical data to identify changes in spectral reflectance of vegetation. In this study, we utilised annual Sentinel-1 synthetic aperture radar (SAR) composites from 2021 to 2023 for infestation detection. A Random Forest classification model was applied to distinguish between healthy and infested forest areas. Additionally, vegetation indices were incorporated to evaluate and compare the results. A reference dataset was used to validate model performance. Our results show that the Sentinel-1 based approach achieved lower accuracies (max. overall accuracy: 0.78), compared to Sentinel-2 (max. overall accuracy: 0.87). Despite this, the Sentinel-1 data proved valuable as a tool for bark beetle infestations detection, especially in scenarios where optical data may be unavailable or limited. These results underscore the importance of integrating SAR data into remote sensing workflows to improve the detection of bark beetle outbreaks.

Forest ecosystems are increasingly threatened by biotic and abiotic disturbances that are intensifying under a changing climate. Accurate detection of tree stress is essential for effective forest management, as stress strongly increases vulnerability to damaging agents such as pests, pathogens, and fire. Tree water functioning is a key indicator of physiological status, yet traditional field-based methods for monitoring water transport – such as sap flow measurements – require costly instrumentation and can only be applied to a limited number of trees.

Hyperspectral remote sensing offers a powerful means to upscale forest health monitoring, but its effectiveness depends on robust spectral indicators that reliably reflect physiological change. Green-Shoulder Indices (GSI), which leverage reflectance features in the 490–560 nm region linked to carotenoid dynamics, have been previously used to monitor tree health. Because carotenoids are closely tied to photosynthetic regulation, stress responses, and canopy vitality, GSI have emerged as promising indicators of health decline. Notably, they have shown strong performance in detecting Norway spruce trees in the early stages of bark beetle infestation.

This study investigates how GSI can be further strengthened as indicators of forest hydraulic functioning by integrating hyperspectral drone imagery with continuous sap flow monitoring. By linking canopy spectral responses to internal water transport dynamics, we aim to advance GSI as operational tools for large-scale forest health surveillance and disturbance detection.

Carotenoids regulate photoprotection and respond early to stress, but their retrieval from canopy reflectance is often unstable because green-band signals are confounded by canopy structure, illumination/view geometry, and covariance with chlorophyll. This study proposes and evaluates the sensitivity of green-shoulder indices (derived from 490–550 nm bands) to carotenoid content in vegetation. We use FluSAIL simulations to generate canopy reflectance under wide-ranging biochemical and structural conditions and benchmark multiple green-region indices (490–560 nm, including PRI-type formulations) for their sensitivity and stability to carotenoids. We then transfer the best-performing index–carotenoid relationship to independent real-world datasets with pigment measurements at both leaf and canopy scales (ANGERS, LOTUS, CABO) to test generalization beyond the simulation domain. Results showed that a curvature-based green-shoulder index provided the most consistent carotenoid sensitivity, with the strongest and most stable VI–Car relationships across varying chlorophyll–carotenoid coupling, LAI, and sun–sensor conditions. Validation on measured spectra confirms that green-shoulder indices can predict carotenoid content with high accuracy and improved transferability compared with conventional green indices.

Non-stand replacing (NSR) disturbances—low- to moderate-severity events causing single-tree mortality or canopy thinning—are driven by agents such as drought, insects, pathogens, low-intensity fire, wind, and snow. Their variable duration, frequency, and extent challenge detection using medium-resolution optical imagery because changes are spectrally subtle and spatially complex. We developed a framework to detect NSR disturbances in boreal forests on a sub-annual basis using Sentinel-2 (S2) time series. Key methods include the spectral normalisation of monthly cloud-free composites via weighted multidimensional medians (medoid and geometric median), as well as improvements to the sensitivity and robustness of the HILANDYN algorithm. Observation weights are based on spectral distance measures (Euclidean distance and Spectral Angle Mapper), normalised using an adaptive sigmoid function. Normalisation reduced seasonality patterns by 41.4%, leaving only 13.7% of the tested time series with a significant seasonal pattern. Validated on more than 10,000 points, the best F1 and F2 scores were 0.75 and 0.72, respectively, when using seven S2 variables. These metrics increased to 0.80 and 0.81, respectively, when including detections in the subsequent vegetative season. The geometric median outperformed the medoid, and the optimal spectral indices varied by agent, e.g., NBR for canopy removal, red-edge indices for wind and snow damage. While the framework effectively maps natural and anthropogenic NSR events, reducing detection lag at high latitudes remains a priority.