This study explores the application of unoccupied aerial vehicles (UAVs) and structure from motion (SfM) techniques for digital forest inventories, addressing key challenges in sustainable and cost-effective forest monitoring. UAV-SfM data products such as 3D point clouds and orthomosaics were generated from a study site in the Hainich National Park, Germany, to create a comprehensive digital representation of the forest structure, encompassing canopy, stems and ground components. Subsequently, this data was leveraged for the automated derivation of forest parameters such as tree stem position, individual tree crown delineation (ITCD), diameter at breast height (DBH), and coarse wood debris (CWD). The employed algorithms involve deep learning models (U-Nets), clustering techniques, and object-based methods.

Dams are traditionally monitored by in-situ geodetic measurements, such as pendulum, trigonometry, and GNSS. However, due to factors like topography and practicability, many dams are only monitored by trigonometric measurements, which are carried out only once or twice a year, due to high cost and time investment. Persistent Scatterer Interferometry (PSI) offers the possibility of monitoring such infrastructures at more regular intervals. In particular, existing ground motion services relying on the PSI technique provide deformation time series for long time intervals, allowing for trend and anomaly detection in the deformation patterns. This study presents a first assessment of the German Ground Motion Service (BBD) for the monitoring of a gravity dam in the Free State of Thuringia, Germany. Despite the suboptimal dam orientation for the PSI technique, BBD data exhibits good agreement with in-situ measurements (achieving R2values above 0.5 and up to 0.8 for the descending direction). These results confirm the utility of such services for infrastructure monitoring. However, it is imperative to account for dam orientation and the update frequency of these services in operational practices.

Traditional methods for monitoring embankment dams and gravity dams typically rely on on-site geodetic measurements, such as pendulum devices, trigonometry, and Global Navigation Satellite System (GNSS) technology. However, the feasibility of these instruments is often constrained by factors such as cost and accessibility. In such cases, satellite remote sensing, particularly Persistent Scatterer Interferometry (PSI), can offer a valuable alternative. Despite its cost-effectiveness in measuring deformations, PSI is not universally applicable to all dams due to various geometric factors. To address this limitation, the CR-Index provides insights into the suitability of PSI for monitoring specific structures. This study focuses on the development and application of the CR-Index for embankment dams and gravity dams in Western Germany. Based on the results, well-suited dams were equipped with innovative electronic corner reflectors to enhance their visibility in radar imagery and enable a more effective PSI analysis.

This study investigates the integration of low-cost unoccupied aerial vehicles (UAVs) and community engagement in permafrost monitoring. Utilizing novel UAV flight patterns and crowdsourced data analysis, including a Convolutional Neural Network (CNN), the study enhances digital surface models (DSMs) for identifying ice-wedge polygons. Conducted in rapidly changing Arctic regions, it demonstrates an overall accuracy of 74.56% in feature detection. This approach offers improved resolution in environmental monitoring and suggests potential for broader application and rapid disaster response through community-sourced scientific analysis and consumer UAVs.

Storage of interception water in the canopy (Sc) heavily affects measurements of vegetation optical depth (VOD) from rain, dew and fog, impeding the direct retrieval of tree physiological parameters such as biomass and plant water content. This study presents a time series decomposition of VOD from Global Navigation Satellite System-Transmissometry (GNSS-T) into biomass, plant moisture content (Mg) and Sc. The experiment was conducted at eddy covariance (EC) towers in two temperate forest types in Germany, over the entire vegetation period of 2023 and under fairly wet conditions. Sc-values were 1.5 times (needleleaf) to two times (broadleaf) higher than the average diurnal Mgcycle, allowing partitioning of interception water storage from plant water. Furthermore, we found indications that Scmaxima did not linearly increase with precipitation, suggesting sensitivity of VOD to saturation effects when canopy interception storage reaches a maximum during strong precipitation events. Results indicate the sensitivity of VOD from GNSS-T to canopy wetness. This allows partitioning of canopy water storage from other VOD components and improves the usefulness of VOD as a remote sensing metric for forest canopy water relations. Moreover, it opens pathways to quantify Scand evaporation fluxes independently from EC measurements and field experiments.

Deformation monitoring is a crucial task for dam operators, particularly given the rise in extreme weather events associated with climate change. Further, quantifying the expected deformations of a dam is a central part of this endeavor. Current methods rely on in situ data (i.e., water level and temperature) to predict the expected deformations of a dam (typically represented by plumb or trigonometric measurements). However, not all dams are equipped with extensive measurement techniques, resulting in infrequent monitoring. Persistent Scatterer Interferometry (PSI) can overcome this limitation, enabling an alternative monitoring scheme for such infrastructures. This study introduces a novel monitoring approach to quantify expected deformations of gravity dams in Germany by integrating the PSI technique with in situ data. Further, it proposes a methodology to find proper statistical representations in a data-driven manner, which extends established statistical approaches. The approach demonstrates plausible deformation patterns as well as accurate predictions for validation data (mean absolute error=1.81 mm), confirming the benefits of the proposed method.

The Sentinel-1 (S-1) microwave measurements pose a unique opportunity for estimating forest parameters from satellite time series with increased data availability from overlapping orbits. Leveraging data from different orbits comes with varying viewing geometries and acquisition schedules, which can be considered in artificial neural networks.We adapt a transformer architecture for mapping the full S-1 data against median forest height values. By doing so, we propose per-orbit temporal encoders to handle different acquisition times by position, missing data by attention masking, and the addition of viewing geometry context.We show that our adjustments improve performance, with a greater impact of the additional data and a slight improvement in the masking. By optimizing the hyperparameters, our proposed method achieves a preliminary RMSE of 5.9m and an rRMSE of 35% in predicting the per-pixel vertical median forest height.

In this work, an attempt was made to determine trends in backscatter variation due to row orientation for four crop types: winter wheat, spring barley, rapeseed, and corn. An averaged backscatter from the fields of three study areas with different geographic locations was considered over 5 years (2017-2021) by considering ascending/descending orbits and different phenological groups.The results of this study indicate that VV polarization is higher when the SAR signal is directed perpendicular or parallel to the rows of winter wheat, barley, and canola (at a 0°, 90° aspect angle) and lower at 45° (90° scale). For a 10° difference in aspect angle, variation in VV was observed at one standardized unit for spring barley and winter wheat.The trend has been observed for all considered study areas, thus validating our observations.

This study presents a comparison between satellite-based vegetation optical depth (VOD) from multi-frequency radiometry (X-, C- and L-band), VOD-derived relative water content (RWC) and auxiliary data (e.g., evapotranspiration and soil moisture), which are investigated for their sensitivity to water status of tree canopies under dry and wet conditions for a temperate forest in Thuringia, Central Germany. For this, we estimated RWC directly from VOD normalization assuming no major changes in vegetation biomass or plant structure during the study period (2015-2019).Our results show that RWC seasonalities are aligned for all investigated frequencies showing its maximum in early summer when leaves and twigs of the top and low canopy are particularly wet and photosynthetically active. Investigating drought versus non-drought years, we observed that X-band RWC is the one better capturing drought status by exhibiting low values in the extreme drought year 2018 compared to the wet year 2017 while L-band RWC reflects the ecological memory from the extreme drought conditions in 2018 in year 2019 estimates.

Tracking seasonal dynamics of evapotranspiration (ET) across global biomes and along seasonal time periods using remote sensing is vital for monitoring ecosystem health and indicating early signals of drought. In this study, we assess the potential of adding weather and illumination-independent signals from active and passive microwave remote sensing (SAR backscatter & vegetation optical depth, VOD) to the established set of ET products, like from optical/thermal remote sensing (MODIS, SEVIRI) and reanalysis (ERA-5 land, GLDAS) data.Our study covers a four-year period (2017-2020), including dry (2018 & 2019) and wet (2017) years. The study was conducted over eight ICOS sites across Europe. These sites are predominantly forested with a low biomass dynamic over the observation period.We find that the ET products from in situ Eddy Covariance (EC), MODIS, and GLDAS deviate relatively minor along the seasons (< 1 [mm/day]), but differ between years. Here, the years (2017-2020) indicate a slightly different ET rate between in situ measurements (EC) and derived products (MODIS & GLDAS), which is currently being investigated. The microwave-based indicators (backscatter & VOD) are proxies by their nature and serve as first-order indicators of relative dynamics allowing the identification of seasonal patterns of ET as well as their spatio-temporal anomalies along both dry and wet years.

Microwave scattering from forests generates pixel geolocation shifts in Synthetic aperture radar (SAR) data that require an adequate representation within digital elevation models (DEM) for preprocessing. We analyze the impact of DEM properties on the radiometry and geolocation of radiometric terrain corrected (RTC) Copernicus Sentinel-1 imagery of forests to improve consistency in backscatter intensities for time series analyses. To account for the penetration depth of the C-Band sensor, we approximate the structure of stands in a temperate deciduous forest using height percentiles from Aerial Laser Scanning (ALS) point clouds in the Hainich National Park, Germany. Comparing the RTC results obtained using DEMs of SRTM, Copernicus, and ALS DEMs, the latter reduces topographically induced errors, resulting in visibly smaller effects from topography and spatially shifted information. Based on the P50 ALS vegetation elevation, results show homogeneous intensities within the same orbit and reduce variance from 2.4 dB2to 1.2 dB2in the difference of mid-range data from ascending and descending azimuth directions. Over forest, we observe lower intensities on sensor-facing and increased intensities on away-facing slopes and correlations with the illuminated pixel area (IPA) and local incidence angle. We reduce this bias with linear regressions of intensity on IPA. ALS DEMs in RTC and the proposed regression correction increase the consistency of images across orbits, measured by the inter-orbit range, throughout the selected year at our study site. We suggest the proposed method applies to other areas, requiring further testing under different forest types and topography.

In mixed-initiative systems where teams of humans and automated agents collaborate to perform decision-making tasks, determining factors of joint performance include human cognitive workload and the level of trust placed by the operators in the automation. Both workload and trust are dynamic variables that change over time based on current task allocation and on the result of past interactions. In this paper, we propose a methodology leveraging quantitative models of trust and workload to automatically and dynamically suggest efficient task allocations in mixed human-machine systems. Our approach is based on a Markov decision process framework and is presented for concreteness in the context of a human-machine team performing repeated binary decision-making tasks. Simulation results show the emergence of interesting automation behaviors such as seeking trust, attempting to repair trust after an error and adjusting human workload for optimal performance. Overall, the human-aware dynamic task allocation strategy shows the potential of significant team performance improvement compared to a static task distribution, even in the presence of significant errors in the trust and workload models used.

A method of physical integration of electromagnetic (EM) interaction models is presented here to estimate the backscattering coefficient (BSC) for L-band, brightness temperature (TB) for L- and C-Band and the reflectance for visible (VIS) and near-infrared (NIR) region for dynamic vegetated terrain. The SPIN (Spectrum Invariant Interaction) model is obtained by solving vector radiative transfer (VRT) equations kernel-based and therefore for different wave interaction mechanisms. To demonstrate its application for the microwave region, the measurements during the growing cycle of corn from the Eleventh Microwave, Water, and Energy Balance Experiment (MicroWEX-11) have been used. For the optical part the results are compared with the PROSAIL model. By applying the SPIN model in the radar regime, it could be shown that the modeled backscattering coefficients (BSC) correlate strongly with the vertical polarization measurements (Pearson 0.83, R20.69) and are less correlated with the horizontal measurements (Pearson 0.45, R20.20). In addition, the modeled brightness temperatures (L- and C-band) in both polarization states are also correlated with the MicroWEX-11 measurements (L-band: Pearson 0.755, R20.57; C-band: Pearson 0.73, R20.53). Finally, the optical results are consistent with the results of other standard optical models (Pearson 0.99, R20.98), like PROSAIL.

The advantages of SAR sensors for remote sensing applications have been proven many times already. Especially due to the high resolutions that the new generation of SAR sensors (e.g. TerraSAR-X, COSMO-Skymed) can achieve, those are becoming very interesting for the analysis of urban areas. Up to now, mainly interferometric data are used for retrieving the 3D information of building, due to their precise phase information. However, such methodology suffers from the relatively long time span that is required to obtain the data (e.g. TerraSAR-X repeat-pass: 11 days). SAR radargrammetry, on the contrary, has the advantage that the required acquisitions are obtained in shorter time spans, what can be helpful in cases where rapid response is on demand. By matching corresponding points of a stereo image pair, surface height can be retrieved. Our approach propose to determine 3D building parameters, especially height information, relying on the radargrammetric analysis of building layover areas. In this paper, we newly combined a hierarchical matching approach with a matching criterion based on the coefficient of variation. Our method shows improvement if compared to previous works.

The high-resolution space borne sensor TerraSAR-X shows good ability for extracting height information by use of repeat-pass interferometry. Indeed, the interferometric phase is still noisy due to temporal decorrelation. The new TanDEM-X mission offers for the first time the possibility of performing high resolution single-pass space borne interferometry, providing good coherence, and thus allowing a better mapping of the 3D shape of objects. These data are still affected by noise though and a filtering is prerequisite in order to enhance object recognition. In previous work, we presented several filtering methods in order to reduce the interferometric noise at building location. We particularly showed the benefits of introducing GIS information such as building footprints into the filtering. In this paper, we present a new filtering method, consisting of combining GIS-supported and area filters for better consideration of large building shape. Results of the GIS supported filter and of the new combined filter are presented on TanDEM-X data.