Dead wood such as coarse dead wood debris (CWD) is an important component in natural forests since it increases the diversity of plants, fungi, and animals. It serves as habitat, provides nutrients and is conducive to forest regeneration, ecosystem stabilization and soil protection. In commercially operated forests, dead wood is often unwanted as it can act as an originator of calamities. Accordingly, efficient CWD monitoring approaches are needed. However, due to the small size of CWD objects satellite data-based approaches cannot be used to gather the needed information and conventional ground-based methods are expensive. Unmanned aerial systems (UAS) are becoming increasingly important in the forestry sector since structural and spectral features of forest stands can be extracted from the high geometric resolution data they produce. As such, they have great potential in supporting regular forest monitoring and inventory. Consequently, the potential of UAS imagery to map CWD is investigated in this study. The study area is located in the center of the Hainich National Park (HNP) in the federal state of Thuringia, Germany. The HNP features natural and unmanaged forest comprising deciduous tree species such as Fagus sylvatica (beech), Fraxinus excelsior (ash), Acer pseudoplatanus (sycamore maple), and Carpinus betulus (hornbeam). The flight campaign was controlled from the Hainich eddy covariance flux tower located at the Eastern edge of the test site. Red-green-blue (RGB) image data were captured in March 2019 during leaf-off conditions using off-the-shelf hardware. Agisoft Metashape Pro was used for the delineation of a three-dimensional (3D) point cloud, which formed the basis for creating a canopy-free RGB orthomosaic and mapping CWD. As heavily decomposed CWD hardly stands out from the ground due to its low height, it might not be detectable by means of 3D geometric information. For this reason, solely RGB data were used for the classification of CWD. The mapping task was accomplished using a line extraction approach developed within the object-based image analysis (OBIA) software eCognition. The achieved CWD detection accuracy can compete with results of studies utilizing high-density airborne light detection and ranging (LiDAR)-based point clouds. Out of 180 CWD objects, 135 objects were successfully delineated while 76 false alarms occurred. Although the developed OBIA approach only utilizes spectral information, it is important to understand that the 3D information extracted from our UAS data is a key requirement for successful CWD mapping as it provides the foundation for the canopy-free orthomosaic created in an earlier step. We conclude that UAS imagery is an alternative to laser data in particular if rapid update and quick response is required. We conclude that UAS imagery is an alternative to laser data for CWD mapping, especially when a rapid response and quick reaction, e.g., after a storm event, is required.

Accurate detection and delineation of individual trees and their crowns in dense forest environments are essential for forest management and ecological applications. This study explores the potential of combining leaf-off and leaf-on structure from motion (SfM) data products from unoccupied aerial vehicles (UAVs) equipped with RGB cameras. The main objective was to develop a reliable method for precise tree stem detection and crown delineation in dense deciduous forests, demonstrated at a structurally diverse old-growth forest in the Hainich National Park, Germany. Stem positions were extracted from the leaf-off point cloud by a clustering algorithm. The accuracy of the derived stem co-ordinates and the overall UAV-SfM point cloud were assessed separately, considering different tree types. Extracted tree stems were used as markers for individual tree crown delineation (ITCD) through a region growing algorithm on the leaf-on data. Stem positioning showed high precision values (0.867). Including leaf-off stem positions enhanced the crown delineation, but crown delineations in dense forest canopies remain challenging. Both the number of stems and crowns were underestimated, suggesting that the number of overstory trees in dense forests tends to be higher than commonly estimated in remote sensing approaches. In general, UAV-SfM point clouds prove to be a cost-effective and accurate alternative to LiDAR data for tree stem detection. The combined datasets provide valuable insights into forest structure, enabling a more comprehensive understanding of the canopy, stems, and forest floor, thus facilitating more reliable forest parameter extraction.

Leveraging low-cost drone technology, specifically the DJI Mini 2, this study presents an innovative method for creating accurate, high-resolution digital surface models (DSMs) to enhance topographic mapping with off-the-shelf components. Our research, conducted near Jena, Germany, introduces two novel flight designs, the “spiral” and “loop” flight designs, devised to mitigate common challenges in structure from motion workflows, such as systematic doming and bowling effects. The analysis, based on height difference products with a lidar-based reference, and curvature estimates, revealed that “loop” and “spiral” flight patterns were successful in substantially reducing these systematic errors. It was observed that the novel flight designs resulted in DSMs with lower curvature values compared to the simple nadir or oblique flight patterns, indicating a significant reduction in distortions. The results imply that the adoption of novel flight designs can lead to substantial improvements in DSM quality, while facilitating shorter flight times and lower computational needs. This work underscores the potential of consumer-grade unoccupied aerial vehicle hardware for scientific applications, especially in remote sensing tasks.

Forests’ ecosystems are an essential part of the global carbon cycle with vast carbon storage potential. These systems are currently under external pressures showing increasing change due to climate change. A better understanding of the biophysical properties of forests is, therefore, of paramount importance for research and monitoring purposes. While there are many biophysical properties, the focus of this study is on the in-depth analysis of the connection between the C-band Copernicus Sentinel-1 SAR backscatter and evapotranspiration (ET) estimates based on in situ meteorological data and the FAO-based Penman–Monteith equation as well as the well-established global terrestrial ET product from the Terra and Aqua MODIS sensors. The analysis was performed in the Free State of Thuringia, central Germany, over coniferous forests within an area of 2452 km², considering a 5-year time series (June 2016–July 2021) of 6- to 12-day Sentinel-1 backscatter acquisitions/observations, daily in situ meteorological measurements of four weather stations as well as an 8-day composite of ET products of the MODIS sensors. Correlation analyses of the three datasets were implemented independently for each of the microwave sensor’s acquisition parameters, ascending and descending overpass direction and co- or cross-polarization, investigating different time series seasonality filters. The Sentinel-1 backscatter and both ET time series datasets show a similar multiannual seasonally fluctuating behavior with increasing values in the spring, peaks in the summer, decreases in the autumn and troughs in the winter months. The backscatter difference between summer and winter reaches over 1.5 dB, while the evapotranspiration difference reaches 8 mm/day for the in situ measurements and 300 kg/m²/8-day for the MODIS product. The best correlation between the Sentinel-1 backscatter and both ET products is achieved in the ascending overpass direction, with datasets acquired in the late afternoon, and reaches an R²-value of over 0.8. The correlation for the descending overpass direction reaches values of up to 0.6. These results suggest that the SAR backscatter signal of coniferous forests is sensitive to the biophysical property evapotranspiration under some scenarios.

There is no doubt that unmanned aerial systems (UAS) will play an increasing role in Earth observation in the near future. The field of application is very broad and includes aspects of environmental monitoring, security, humanitarian aid, or engineering. In particular, drones with camera systems are already widely used. The capability to compute ultra-high-resolution orthomosaics and three-dimensional (3D) point clouds from UAS imagery generates a wide interest in such systems, not only in the science community, but also in industry and agencies. In particular, forestry sciences benefit from ultra-high-structural and spectral information as regular tree level-based monitoring becomes feasible. There is a great need for this kind of information as, for example, due to the spring and summer droughts in Europe in the years 2018/2019, large quantities of individual trees were damaged or even died. This study focuses on selective logging at the level of individual trees using repeated drone flights. Using the new generation of UAS, which allows for sub-decimeter-level positioning accuracies, a change detection approach based on bi-temporal UAS acquisitions was implemented. In comparison to conventional UAS, the effort of implementing repeated drone flights in the field was low, because no ground control points needed to be surveyed. As shown in this study, the geometrical offset between the two collected datasets was below 10 cm across the site, which enabled a direct comparison of both datasets without the need for post-processing (e.g., image matching). For the detection of logged trees, we utilized the spectral and height differences between both acquisitions. For their delineation, an object-based approach was employed, which was proven to be highly accurate (precision = 97.5%; recall = 91.6%). Due to the ease of use of such new generation, off-the-shelf consumer drones, their decreasing purchase costs, the quality of available workflows for data processing, and the convincing results presented here, UAS-based data can and should complement conventional forest inventory practices.

Evapotranspiration (ET) is an important variable for analyzing ecosystems, biophysical processes, and drought-related changes in the soil–plant–atmosphere system. In this study, we evaluated freely available ET products from satellite remote sensing (i.e., the Moderate resolution Imaging Spectroradiometer, MODIS; the ESA's Spinning Enhanced Visible and Infrared Imager, SEVIRI; and the Global Land Evaporation Amsterdam model, GLEAM) as well as modeling and reanalysis (i.e., the land component of the Earth system modeling product European Re-Analysis, ERA5-land, and Global Land Data Assimilation System version 2, GLDAS-2) together with in situ observations at eight Integrated Carbon Observation System (ICOS) stations across central Europe between 2017 and 2020. The land cover at the selected ICOS stations ranged from deciduous broad-leaf forests, evergreen needle-leaf forests, and mixed forests to agriculture. Trends in ET were analyzed together with soil moisture (SM) from the Soil Moisture Active Passive (SMAP) mission and the water vapor pressure deficit (VPD) from FLUXNET field measurements over 4 years, including a severe summer drought in 2018 and contrasting wet conditions in 2017. The analyses revealed the increased atmospheric aridity and decreased water supply for plant transpiration under drought conditions, showing that ET was generally lower and VPD higher in 2018 compared to in 2017. Across the study period, results indicate that during moisture-limited drought years, ET strongly decreases due to decreasing SM and increasing VPD. However, during normal or rather-wet years when SM is not limited, ET is mainly controlled by VPD and, hence, the atmospheric demand. The comparison of the different ET products based on time series, statistics, and extended triple collocation (ETC) shows generally good agreement, with ETC correlations between 0.39 and 0.99, as well as root-mean-square errors lower than 1.07 mm d⁻¹. The greatest deviations were found at the agricultural managed sites Selhausen (Germany) and Bilos (France), with the former also showing the highest potential dependencies (error cross-correlation (ECC)) between the ET products (up to 7.6 and outside the acceptable range of −0.5 < ECC < 0.5). Thus, our results indicate that ET products differ most at stations with spatiotemporally varying land cover conditions (a variety of crops over growing periods and between seasons). This is because complex heterogeneity in land cover complicates the estimation of ET, while ET products agree well at evergreen needle-leaf stations with fewer temporal changes throughout the year and between years. The ET products from SEVIRI, ERA5-land, and GLEAM performed best when compared to ICOS observations, with either the lowest errors or the highest correlations.