The prolonged drought of recent years combined with the steadily increasing bark beetle infestation (Ips typographus) is causing enormous damage in Germany’s spruce forests. This preliminary study investigates whether early spruce infestation by the bark beetle (green attack) can be detected using indices based on airborne spatial high-resolution (0.3 m) hyperspectral data and field spectrometer measurements. In particular, a new hyperspectral index based on airborne data has been defined and compared with other common indices for bark beetle detection. It shows a very high overall accuracy (OAA = 98.84%) when validated with field data. Field measurements and a long-term validation in a second study area serve the validation of the robustness and transferability of the index to other areas. In comparison with commonly used indices, the defined index has the ability to detect a larger proportion of infested spruces in the green attack phase (60% against 20% for commonly used indices). This index confirms the high potential of the red-edge domain to distinguish infested spruces at an early stage. Overall, our index has great potential for forest preservation strategies aimed at the detection of infested spruces in order to mitigate the outbreaks.

Dams are crucial for ensuring water and electricity supply, while also providing significant flood protection. Regular monitoring of dam deformations is of vital socio-economic and ecological significance. In Germany, dams must be constructed and operated according to generally accepted rules of engineering. The safety concept for dams based on these rules relies on structural safety, professional operation and maintenance, safety monitoring, and precautionary measures. Rather time-consuming in situ techniques have been employed for these measurements, which permit monitoring deformations with either high spatial or temporal resolution, but not both. As a means of measuring large-scale deformations in the millimeter range, the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique of Persistent Scatterer Interferometry (PSI) is already being applied in various fields. However, when considering the operational monitoring of dams using PSI, specific characteristics need to be considered. For example, the geographical location of the dam in space, as well as its shape, size, and land cover. All these factors can affect the visibility of the structure for the use with PSI and, in certain cases, limit the applicability of SAR data. The visibility of dams for PSI monitoring is often limited, particularly in cases where observation is typically not feasible due to factors such as geographical and structural characteristics. While corner reflectors can improve visibility, their large size often makes them unsuitable for dam infrastructure and may raise concerns with heritage protection for listed dams. Addressing these challenges, electronic corner reflectors (ECRs) offer an effective alternative due to their small and compact size. In this study, we analyzed the strategic placement of ECRs on dam structures. We developed a new CR Index, which identifies areas where PSI alone is insufficient due to unfavorable geometric or land use conditions. This index categorizes visibility potential into three classes, presented in a ‘traffic light’ map, and is instrumental in selecting optimal installation sites. We furthermore investigated the signal stability of ECRs over an extended observation period, considering the Amplitude Dispersion Index (ADI). It showed values between 0.1 and 0.4 for many dam structures, which is comparable to normal corner reflectors (CRs), confirming the reliability of these signals for PSI analysis. This work underscores the feasibility of using ECRs to enhance monitoring capabilities at dam infrastructure.

Canopy height and other vegetation-structure attributes underpin biodiversity and carbon monitoring but mapping them across savannas remains difficult because vegetation changes markedly between wet and dry seasons. Combining Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar with satellite imagery enables wall-to-wall mapping, yet GEDI training samples and satellite-derived predictors are sensitive to phenology. We developed a workflow explicitly incorporating seasonality by selecting leaf-on GEDI observations and extracting SAR and multispectral features when vegetation-structure classes are most distinguishable, and applied it to Kruger National Park, South Africa. Three approaches for identifying leaf-on GEDI observations were compared: an internal metadata flag, a static temporal window, and a new dynamic footprint-specific method using Sentinel-2 kNDVI time series. Random forest models were evaluated by temporal cross-validation, airborne-lidar validation, and pixel-wise comparisons against each other and an independent canopy-height benchmark. Dry-season Sentinel-1 C-band VH backscatter provided strong structural discrimination and dominated the final feature set. All leaf-on strategies achieved near-identical agreement with airborne lidar (RMSE 2.06–2.09 m; benchmark: 2.20 m), but maps differed systematically in calibration and environmental sensitivity. The static-window model exhibited a uniform negative offset, whereas dynamic-model differences varied with slope and land system. On slopes ≥25°, the benchmark reached a +4.5 m median offset relative to a common baseline model, versus -0.15 m for the dynamic model. All models overestimated short and underestimated tall canopies. Our results show that summary accuracy statistics alone cannot distinguish between GEDI-fusion workflows, and that phenology-informed workflows offer a practical, transferable basis for robust savanna structure mapping.