TY - JOUR A1 - Sogno, Patrick A1 - Klein, Igor A1 - Kuenzer, Claudia T1 - Remote sensing of surface water dynamics in the context of global change — a review JF - Remote Sensing N2 - Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource — if not overexploited — sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution. KW - remote sensing KW - surface water KW - dynamics KW - global change KW - earth observation KW - hydrology KW - biosphere KW - anthroposphere KW - review Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-275274 SN - 2072-4292 VL - 14 IS - 10 ER - TY - JOUR A1 - Reiners, Philipp A1 - Sobrino, José A1 - Kuenzer, Claudia T1 - Satellite-derived land surface temperature dynamics in the context of global change — a review JF - Remote Sensing N2 - Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the study periods: A majority of the studies started around the year 2000 and thus had a study period shorter than 25 years. The following suggestions were made to increase the utilization of LST time series in climate research: The prolongation of the time series by, e.g., using AVHRR LST, the better representation of LST under clouds, the comparison of LST to traditional climate change measures, such as air temperature and reanalysis variables, and the extension of the validation to heterogenous sites. KW - remote sensing KW - land surface temperature KW - temperature KW - dynamics KW - global change KW - climate change KW - global warming KW - earth observation KW - review Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-311120 SN - 2072-4292 VL - 15 IS - 7 ER - TY - JOUR A1 - Reinermann, Sophie A1 - Gessner, Ursula A1 - Asam, Sarah A1 - Ullmann, Tobias A1 - Schucknecht, Anne A1 - Kuenzer, Claudia T1 - Detection of grassland mowing events for Germany by combining Sentinel-1 and Sentinel-2 time series JF - Remote Sensing N2 - Grasslands cover one-third of the agricultural area in Germany and play an important economic role by providing fodder for livestock. In addition, they fulfill important ecosystem services, such as carbon storage, water purification, and the provision of habitats. These ecosystem services usually depend on the grassland management. In central Europe, grasslands are grazed and/or mown, whereby the management type and intensity vary in space and time. Spatial information on the mowing timing and frequency on larger scales are usually not available but would be required in order to assess the ecosystem services, species composition, and grassland yields. Time series of high-resolution satellite remote sensing data can be used to analyze the temporal and spatial dynamics of grasslands. Within this study, we aim to overcome the drawbacks identified by previous studies, such as optical data availability and the lack of comprehensive reference data, by testing the time series of various Sentinel-2 (S2) and Sentinal-1 (S1) parameters and combinations of them in order to detect mowing events in Germany in 2019. We developed a threshold-based algorithm by using information from a comprehensive reference dataset of heterogeneously managed grassland parcels in Germany, obtained by RGB cameras. The developed approach using the enhanced vegetation index (EVI) derived from S2 led to a successful mowing event detection in Germany (60.3% of mowing events detected, F1-Score = 0.64). However, events shortly before, during, or shortly after cloud gaps were missed and in regions with lower S2 orbit coverage fewer mowing events were detected. Therefore, S1-based backscatter, InSAR, and PolSAR features were investigated during S2 data gaps. From these, the PolSAR entropy detected mowing events most reliably. For a focus region, we tested an integrated approach by combining S2 and S1 parameters. This approach detected additional mowing events, but also led to many false positive events, resulting in a reduction in the F1-Score (from 0.65 of S2 to 0.61 of S2 + S1 for the focus region). According to our analysis, a majority of grasslands in Germany are only mown zero to two times (around 84%) and are probably additionally used for grazing. A small proportion is mown more often than four times (3%). Regions with a generally higher grassland mowing frequency are located in southern, south-eastern, and northern Germany. KW - earth observation KW - remote sensing KW - harvests KW - cutting events KW - grazing KW - pasture KW - meadow KW - optical KW - SAR KW - PolSAR KW - InSAR Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-267164 SN - 2072-4292 VL - 14 IS - 7 ER - TY - JOUR A1 - Mayr, Stefan A1 - Klein, Igor A1 - Rutzinger, Martin A1 - Kuenzer, Claudia T1 - Systematic water fraction estimation for a global and daily surface water time-series JF - Remote Sensing N2 - Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product's performance regarding mixed water/non-water pixels by an average of 11.6% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series. KW - earth observation KW - landsat KW - MODIS KW - remote sensing KW - probability KW - Sentinel-2 KW - subpixel KW - water Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-242586 SN - 2072-4292 VL - 13 IS - 14 ER - TY - THES A1 - Höser, Thorsten T1 - Global Dynamics of the Offshore Wind Energy Sector Derived from Earth Observation Data - Deep Learning Based Object Detection Optimised with Synthetic Training Data for Offshore Wind Energy Infrastructure Extraction from Sentinel-1 Imagery T1 - Globale Dynamik des Offshore-Windenergiesektors abgeleitet aus Erdbeobachtungsdaten - Deep Learning-basierte Objekterkennung, optimiert mit synthetischen Trainingsdaten für die Extraktion von Offshore-Windenergieinfrastrukturen aus Sentinel-1 Bildern N2 - The expansion of renewable energies is being driven by the gradual phaseout of fossil fuels in order to reduce greenhouse gas emissions, the steadily increasing demand for energy and, more recently, by geopolitical events. The offshore wind energy sector is on the verge of a massive expansion in Europe, the United Kingdom, China, but also in the USA, South Korea and Vietnam. Accordingly, the largest marine infrastructure projects to date will be carried out in the upcoming decades, with thousands of offshore wind turbines being installed. In order to accompany this process globally and to provide a database for research, development and monitoring, this dissertation presents a deep learning-based approach for object detection that enables the derivation of spatiotemporal developments of offshore wind energy infrastructures from satellite-based radar data of the Sentinel-1 mission. For training the deep learning models for offshore wind energy infrastructure detection, an approach is presented that makes it possible to synthetically generate remote sensing data and the necessary annotation for the supervised deep learning process. In this synthetic data generation process, expert knowledge about image content and sensor acquisition techniques is made machine-readable. Finally, extensive and highly variable training data sets are generated from this knowledge representation, with which deep learning models can learn to detect objects in real-world satellite data. The method for the synthetic generation of training data based on expert knowledge offers great potential for deep learning in Earth observation. Applications of deep learning based methods can be developed and tested faster with this procedure. Furthermore, the synthetically generated and thus controllable training data offer the possibility to interpret the learning process of the optimised deep learning models. The method developed in this dissertation to create synthetic remote sensing training data was finally used to optimise deep learning models for the global detection of offshore wind energy infrastructure. For this purpose, images of the entire global coastline from ESA's Sentinel-1 radar mission were evaluated. The derived data set includes over 9,941 objects, which distinguish offshore wind turbines, transformer stations and offshore wind energy infrastructures under construction from each other. In addition to this spatial detection, a quarterly time series from July 2016 to June 2021 was derived for all objects. This time series reveals the start of construction, the construction phase and the time of completion with subsequent operation for each object. The derived offshore wind energy infrastructure data set provides the basis for an analysis of the development of the offshore wind energy sector from July 2016 to June 2021. For this analysis, further attributes of the detected offshore wind turbines were derived. The most important of these are the height and installed capacity of a turbine. The turbine height was calculated by a radargrammetric analysis of the previously detected Sentinel-1 signal and then used to statistically model the installed capacity. The results show that in June 2021, 8,885 offshore wind turbines with a total capacity of 40.6 GW were installed worldwide. The largest installed capacities are in the EU (15.2 GW), China (14.1 GW) and the United Kingdom (10.7 GW). From July 2016 to June 2021, China has expanded 13 GW of offshore wind energy infrastructure. The EU has installed 8 GW and the UK 5.8 GW of offshore wind energy infrastructure in the same period. This temporal analysis shows that China was the main driver of the expansion of the offshore wind energy sector in the period under investigation. The derived data set for the description of the offshore wind energy sector was made publicly available. It is thus freely accessible to all decision-makers and stakeholders involved in the development of offshore wind energy projects. Especially in the scientific context, it serves as a database that enables a wide range of investigations. Research questions regarding offshore wind turbines themselves as well as the influence of the expansion in the coming decades can be investigated. This supports the imminent and urgently needed expansion of offshore wind energy in order to promote sustainable expansion in addition to the expansion targets that have been set. N2 - Der Ausbau erneuerbarer Energien wird durch den sukzessiven Verzicht auf fossile Energieträger zur Reduktion der Treibhausgasemissionen, dem stetig steigenden Energiebedarf sowie, in jüngster Zeit, von geopolitischen Ereignissen stark vorangetrieben. Der offshore Windenergiesektor steht in Europa, dem Vereinigten Königreich, China, aber auch den USA, Süd-Korea und Vietnam vor einer massiven Expansion. In den nächsten Dekaden werden die bislang größten marinen Infrastrukturprojekte mit tausenden neu installierten offshore Windturbinen realisiert. Um diesen Prozess global zu begleiten und eine Datengrundlage für die Forschung, für Entscheidungsträger und für ein kontinuierliches Monitoring bereit zu stellen, präsentiert diese Dissertation einen Deep Learning basierten Ansatz zur Detektion von offshore Windkraftanalagen aus satellitengestützten Radardaten der Sentinel-1 Mission. Für das überwachte Training der verwendeten Deep Learning Modelle zur Objektdetektion wird ein Ansatz vorgestellt, der es ermöglicht, Fernerkundungsdaten und die notwendigen Label synthetisch zu generieren. Hierbei wird Expertenwissen über die Bildinhalte, wie offshore Windkraftanlagen aber auch ihre natürliche Umgebung, wie Küsten oder andere Infrastruktur, gemeinsam mit Informationen über den Sensor strukturiert und maschinenlesbar gemacht. Aus dieser Wissensrepräsentation werden schließlich umfangreiche und höchst variable Trainingsdaten erzeugt, womit Deep Learning Modelle die Detektion von Objekten in Satellitendaten erlernen können. Das Verfahren zur synthetischen Erzeugung von Trainingsdaten basierend auf Expertenwissen bietet großes Potential für Deep Learning in der Erdbeobachtung. Deep Learning Ansätze können hierdurch schneller entwickelt und getestet werden. Darüber hinaus bieten die synthetisch generierten und somit kontrollierbaren Trainingsdaten die Möglichkeit, den Lernprozess der optimierten Deep Learning Modelle zu interpretieren. Das in dieser Dissertation für Fernerkundungsdaten entwickelte Verfahren zur Erstellung synthetischer Trainingsdaten wurde schließlich zur Optimierung von Deep Learning Modellen für die globale Detektion von offshore Windenergieanlagen eingesetzt. Hierfür wurden Aufnahmen der gesamten globalen Küstenlinie der Sentinel-1 Mission der ESA ausgewertet. Der abgeleitete Datensatz, welcher 9.941 Objekte umfasst, unterscheidet offshore Windturbinen, Trafostationen und im Bau befindliche offshore Windenergieinfrastrukturen voneinander. Zusätzlich zu dieser räumlichen Detektion wurde eine vierteljährliche Zeitreihe von Juli 2016 bis Juni 2021 für alle Objekte generiert. Diese Zeitreihe zeigt den Start des Baubeginns, die Bauphase und den Zeitpunkt der Fertigstellung mit anschließendem Betrieb für jedes Objekt. Der gewonnene Datensatz dient weiterhin als Grundlage für eine Analyse der Entwicklung des offshore Windenergiesektors von Juli 2016 bis Juni 2021. Für diese Analyse wurden weitere Attribute der Turbinen abgeleitet. In einem radargrammetrischen Verfahren wurde die Turbinenhöhe berechnet und anschließend verwendet, um die installierte Leistung statistisch zu modellieren. Die Ergebnisse hierzu zeigen, dass im Juni 2021 weltweit 8.885 offshore Windturbinen mit insgesamt 40,6 GW Leistung installiert waren. Die größten installierten Leistungen stellen dabei die EU (15,2 GW), China (14,1 GW) und das Vereinigte Königreich (10,7 GW). Von Juli 2016 bis Juni 2021 hat China 13 GW installierte Leistung ausgebaut. Die EU hat im selben Zeitraum 8 GW und das Vereinigte Königreich 5,8 GW offshore Windenergieinfrastruktur installiert. Diese zeitliche Analyse verdeutlicht, dass China der maßgebliche Treiber in der Expansion des offshore Windenergiesektors im untersuchten Zeitraum war. Der abgeleitete Datensatz zur Beschreibung des offshore Windenergiesektors wurde öffentlich zugänglich gemacht. Somit steht er allen Entscheidungsträgern und Stakeholdern, die am Ausbau von offshore Windenergieanlagen beteiligt sind, frei zur Verfügung. Vor allem im wissenschaftlichen Kontext dient er als Datenbasis, welche unterschiedlichste Untersuchungen ermöglicht. Hierbei können sowohl Forschungsfragen bezüglich der offshore Windenergieanlagen selbst, als auch der Einfluss des Ausbaus der kommenden Dekaden untersucht werden. Somit wird der bevorstehende und dringend notwendige Ausbau der offshore Windenergie unterstützt, um neben den gesteckten Zielen auch einen nachhaltigen Ausbau zu fördern. KW - deep learning KW - offshore wind energy KW - artificial intelligence KW - earth observation KW - remote sensing Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-292857 ER - TY - JOUR A1 - Holzwarth, Stefanie A1 - Thonfeld, Frank A1 - Abdullahi, Sahra A1 - Asam, Sarah A1 - Da Ponte Canova, Emmanuel A1 - Gessner, Ursula A1 - Huth, Juliane A1 - Kraus, Tanja A1 - Leutner, Benjamin A1 - Kuenzer, Claudia T1 - Earth Observation based monitoring of forests in Germany: a review JF - Remote Sensing N2 - Forests in Germany cover around 11.4 million hectares and, thus, a share of 32% of Germany's surface area. Therefore, forests shape the character of the country's cultural landscape. Germany's forests fulfil a variety of functions for nature and society, and also play an important role in the context of climate levelling. Climate change, manifested via rising temperatures and current weather extremes, has a negative impact on the health and development of forests. Within the last five years, severe storms, extreme drought, and heat waves, and the subsequent mass reproduction of bark beetles have all seriously affected Germany’s forests. Facing the current dramatic extent of forest damage and the emerging long-term consequences, the effort to preserve forests in Germany, along with their diversity and productivity, is an indispensable task for the government. Several German ministries have and plan to initiate measures supporting forest health. Quantitative data is one means for sound decision-making to ensure the monitoring of the forest and to improve the monitoring of forest damage. In addition to existing forest monitoring systems, such as the federal forest inventory, the national crown condition survey, and the national forest soil inventory, systematic surveys of forest condition and vulnerability at the national scale can be expanded with the help of a satellite-based earth observation. In this review, we analysed and categorized all research studies published in the last 20 years that focus on the remote sensing of forests in Germany. For this study, 166 citation indexed research publications have been thoroughly analysed with respect to publication frequency, location of studies undertaken, spatial and temporal scale, coverage of the studies, satellite sensors employed, thematic foci of the studies, and overall outcomes, allowing us to identify major research and geoinformation product gaps. KW - remote sensing KW - earth observation KW - forest KW - forest monitoring KW - forest disturbances KW - Germany KW - review Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-216334 SN - 2072-4292 VL - 12 IS - 21 ER - TY - JOUR A1 - Hoeser, Thorsten A1 - Bachofer, Felix A1 - Kuenzer, Claudia T1 - Object detection and image segmentation with deep learning on Earth Observation data: a review — part II: applications JF - Remote Sensing N2 - In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I. KW - artificial intelligence KW - AI KW - machine learning KW - deep learning KW - neural networks KW - convolutional neural networks KW - CNN KW - image segmentation KW - object detection KW - earth observation Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-213152 SN - 2072-4292 VL - 12 IS - 18 ER - TY - JOUR A1 - Ha, Tuyen V. A1 - Huth, Juliane A1 - Bachofer, Felix A1 - Kuenzer, Claudia T1 - A review of Earth observation-based drought studies in Southeast Asia JF - Remote Sensing N2 - Drought is a recurring natural climatic hazard event over terrestrial land; it poses devastating threats to human health, the economy, and the environment. Given the increasing climate crisis, it is likely that extreme drought phenomena will become more frequent, and their impacts will probably be more devastating. Drought observations from space, therefore, play a key role in dissimilating timely and accurate information to support early warning drought management and mitigation planning, particularly in sparse in-situ data regions. In this paper, we reviewed drought-related studies based on Earth observation (EO) products in Southeast Asia between 2000 and 2021. The results of this review indicated that drought publications in the region are on the increase, with a majority (70%) of the studies being undertaken in Vietnam, Thailand, Malaysia and Indonesia. These countries also accounted for nearly 97% of the economic losses due to drought extremes. Vegetation indices from multispectral optical remote sensing sensors remained a primary source of data for drought monitoring in the region. Many studies (~21%) did not provide accuracy assessment on drought mapping products, while precipitation was the main data source for validation. We observed a positive association between spatial extent and spatial resolution, suggesting that nearly 81% of the articles focused on the local and national scales. Although there was an increase in drought research interest in the region, challenges remain regarding large-area and long time-series drought measurements, the combined drought approach, machine learning-based drought prediction, and the integration of multi-sensor remote sensing products (e.g., Landsat and Sentinel-2). Satellite EO data could be a substantial part of the future efforts that are necessary for mitigating drought-related challenges, ensuring food security, establishing a more sustainable economy, and the preservation of the natural environment in the region. KW - drought KW - drought impact KW - agricultural drought KW - hydrological drought KW - meteorological drought KW - earth observation KW - remote sensing KW - Southeast Asia KW - Mekong Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-286258 SN - 2072-4292 VL - 14 IS - 15 ER - TY - JOUR A1 - Bell, Alexandra A1 - Klein, Doris A1 - Rieser, Jakob A1 - Kraus, Tanja A1 - Thiel, Michael A1 - Dech, Stefan T1 - Scientific evidence from space — a review of spaceborne remote sensing applications at the science–policy interface JF - Remote Sensing N2 - On a daily basis, political decisions are made, often with their full extent of impact being unclear. Not seldom, the decisions and policy measures implemented result in direct or indirect unintended negative impacts, such as on the natural environment, which can vary in time, space, nature, and severity. To achieve a more sustainable world with equitable societies requires fundamental rethinking of our policymaking. It calls for informed decision making and a monitoring of political impact for which evidence-based knowledge is necessary. The most powerful tool to derive objective and systematic spatial information and, thus, add to transparent decisions is remote sensing (RS). This review analyses how spaceborne RS is used by the scientific community to provide evidence for the policymaking process. We reviewed 194 scientific publications from 2015 to 2020 and analysed them based on general insights (e.g., study area) and RS application-related information (e.g., RS data and products). Further, we classified the studies according to their degree of science–policy integration by determining their engagement with the political field and their potential contribution towards four stages of the policy cycle: problem identification/knowledge building, policy formulation, policy implementation, and policy monitoring and evaluation. Except for four studies, we found that studies had not directly involved or informed the policy field or policymaking process. Most studies contributed to the stage problem identification/knowledge building, followed by ex post policy impact assessment. To strengthen the use of RS for policy-relevant studies, the concept of the policy cycle is used to showcase opportunities of RS application for the policymaking process. Topics gaining importance and future requirements of RS at the science–policy interface are identified. If tackled, RS can be a powerful complement to provide policy-relevant evidence to shed light on the impact of political decisions and thus help promote sustainable development from the core. KW - earth observation KW - evidence-based policy KW - policy cycle KW - decision-making KW - sustainable development KW - science–policy interface Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-303925 SN - 2072-4292 VL - 15 IS - 4 ER -