Sentinel-1 backscatter time series for characterization of evapotranspiration dynamics over temperate coniferous forests

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

Novel UAV Flight Designs for Accuracy Optimization of Structure from Motion Data Products

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

UNDERCOVEREISAGENTEN - ERSTE EXPEDITION UND AKTUELLER STAND DES ARKTISCHEN PERMAFROSTPROJEKTS

Die Menschen in der Arktis sind seit mehreren Jahrzehnten mit Veränderungen ihres Lebensraums, wie dem Auftauen des Permafrosts durch den globalen Klimawandel, konfrontiert. Ziel dieses Projekts ist es, die Erste Expedition und aktueller Stand des arktischen Permafrostprojekts Auswirkungen des Permafrosttauens durch die Erfassung und Analyse von Bildmaterial von UAVs zusammen mit Schüler*innen (SuS) in Kanada und Deutschland zu untersuchen. Während einer Expedition im September 2022 in Nordkanada durch das AWI, DLR und HeiGIT wurden erste UAV-Daten gemeinsam mit SuS der Moose Kerr School in Aklavik aufgenommen. Neben den rund 30000 Einzelfotos über einer Fläche von ca. 13km² wurden die Grundlagen der Datenerhebung sowie die Projektziele der gemeinschaftlichen Permafrost-Untersuchung vermittelt. Vermittelte Ziele sind die selbstständig fortgeführte Datenaufnahme durch interessierte SuS, sowie die selbstständige Formulierung eigener wissenschaftl. Fragestellungen. Es erfolgte plangemäß die Einarbeitung von lokalem Wissen, um weitere Fragestellungen der lokalen Bevölkerung zu adressieren. Die Daten werden aktuell aufbereitet, um über eine Crowdmapping-Anwendung zur Verfügung gestellt zu werden

Tree Stem Detection and Crown Delineation in a Structurally Diverse Deciduous Forest Combining Leaf-On and Leaf-Off UAV-SfM Data

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

UndercoverEisAgenten - Monitoring Permafrost Thaw in the Arctic using Local Knowledge and UAVs

The Arctic is experiencing severe changes to its landscapes due to the thawing of permafrost influenced by the twofold increase of temperature across the Arctic due to global warming compared to the global average. This process, which affects the livelihoods of indigenous people, is also associated with the further release of greenhouse gases and also connected to ecological impacts on the arctic flora and fauna. These small-scale changes and disturbances to the land surface caused by permafrost thaw have been inadequately documented. To better understand and monitor land surface changes, the project "UndercoverEisAgenten" is using a combination of local knowledge, satellite remote sensing, and data from unmanned aerial vehicles (UAVs) to study permafrost thaw impacts in Northwest Canada. The high-resolution UAV data will serve as a baseline for further analysis of optical and radar remote sensing time series data. The project aims to achieve two main goals: 1) to demonstrate the value of using unmanned aerial vehicle (UAV) data in remote regions of the global north, and 2) to involve young citizen scientists from schools in Canada and Germany in the process. By involving students in the project, the project aims to not only expand the use of remote sensing in these regions, but also provides educational opportunities for the participating students. By using UAVs and satellite imagery, the project aims to develop a comprehensive archive of observable surface features that indicate the degree of permafrost degradation. This will be accomplished through the use of automatic image enhancement techniques, as well as classical image processing approaches and machine learning-based classification methods. The data is being prepared to be shared and analyzed through a web-based crowd mapping application. The project aims to involve the students in independently acquiring data and developing their own scientific questions through the use of this application. In September 2022, a first expedition was conducted in the Northwest Territories, Canada and UAV data was collected with the assistance of students from Moose Kerr School in Aklavik. The data consists of approximately 30,000 individual photos taken over an area of around 13 km². The expedition also provided an opportunity for the students to learn about the basics of data collection and the goals of the collaborative permafrost survey, which included the incorporation of local knowledge to address the questions of the local community. By involving school students in the data acquisition, classification and evaluation process, the project also seeks to transfer knowledge and raise awareness about global warming, permafrost, and related regional and global challenges. Additionally, a connection through the shared research experience between students in Germany and Canada is established to enable the exchange of knowledge. The resulting scientific data will provide new insights into biophysical processes in Arctic regions and contribute to a better understanding of the state and change of permafrost in the Arctic. This project is funded by the German Federal Ministry of Education and Research and was initiated in 2021

UndercoverEisAgenten - The Arctic Permafrost Citizen Science Project

People in the Arctic have been experiencing severe changes to their environments for several decades. In particular the thawing of permafrost affects the livelihoods of indigenous people and has far-reaching ecological impacts including the additional release of greenhouse gases. By fusing local knowledge on landscape changes in Northwest Canada and remote sensing, we seek to better understand and monitor land surface changes attributable to permafrost thaw. The goal is to investigate permafrost thaw impacts through the acquisition and analysis of imagery from Unmanned Aerial Vehicles (UAVs) and satellites together with young Citizen Scientists from schools in Northwest Canada and Germany. For this, we utilize DJI Mini 2 drones in combination with the Litchi for DJI mobile application as the controller software. This combination allows for the easy creation of flight mission with standardized parameters to enable reproducible results. Permafrost landscapes often feature striking polygonal surface structures which change dynamically when thawing processes are in progress. The polygonal landscape structures extend over different spatial scales and can be used to determine the severity of permafrost thaw. While very high-resolution UAV imagery provides detailed insights into the small-scale thermo-hydrological and geomorphological processes, local knowledge and experience is required to identify relevant sites and to set the environmental changes into temporal, cultural, societal, and economic context. These data and background information are urgently needed to improve our prediction on the impacts of permafrost thaw. To this end, school classes in Germany and the Canadian Arctic will collaborate on the analysis of high-resolution remote sensing data. The students will use a mobile application to map striking structures and changes in the land surface on satellites and UAV images. Utilizing feedback from co-creative workshops with German teachers, concepts are being developed to introduce the different topics of this project into school curricula of German high schools. This project will enable the development of better climate adaptation planning tools for local communities and engage Canadian and German students and citizen scientists in Arctic climate research. The project UndercoverEisAgenten, funded by the Federal Ministry of Education and Research in Germany, was initiated in summer 2021

Sentinel-1 Backscatter Time Series for Characterization of Evapotranspiration Dynamics over Temperate Coniferous Forests

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.Mathematical Geodesy and Positionin