SyntEO: Synthetic Dataset Generation for Earth Observation with Deep Learning -- Demonstrated for Offshore Wind Farm Detection

With the emergence of deep learning in the last years, new opportunities arose in Earth observation research. Nevertheless, they also brought with them new challenges. The data-hungry training processes of deep learning models demand large, resource expensive, annotated datasets and partly replaced knowledge-driven approaches, so that model behaviour and the final prediction process became a black box. The proposed SyntEO approach enables Earth observation researchers to automatically generate large deep learning ready datasets and thus free up otherwise occupied resources. SyntEO does this by including expert knowledge in the data generation process in a highly structured manner. In this way, fully controllable experiment environments are set up, which support insights in the model training. Thus, SyntEO makes the learning process approachable and model behaviour interpretable, an important cornerstone for explainable machine learning. We demonstrate the SyntEO approach by predicting offshore wind farms in Sentinel-1 images on two of the worlds largest offshore wind energy production sites. The largest generated dataset has 90,000 training examples. A basic convolutional neural network for object detection, that is only trained on this synthetic data, confidently detects offshore wind farms by minimising false detections in challenging environments. In addition, four sequential datasets are generated, demonstrating how the SyntEO approach can precisely define the dataset structure and influence the training process. SyntEO is thus a hybrid approach that creates an interface between expert knowledge and data-driven image analysis.Comment: 25 pages, 12 figure

Application of Remote Sensing Data for Locust Research and Management-A Review

Recently, locust outbreaks around the world have destroyed agricultural and natural vegetation and caused massive damage endangering food security. Unusual heavy rainfalls in habitats of the desert locust (Schistocerca gregaria) and lack of monitoring due to political conflicts or inaccessibility of those habitats lead to massive desert locust outbreaks and swarms migrating over the Arabian Peninsula, East Africa, India and Pakistan. At the same time, swarms of the Moroccan locust (Dociostaurus maroccanus) in some Central Asian countries and swarms of the Italian locust (Calliptamus italicus) in Russia and China destroyed crops despite developed and ongoing monitoring and control measurements. These recent events underline that the risk and damage caused by locust pests is as present as ever and affects 100 million of human lives despite technical progress in locust monitoring, prediction and control approaches. Remote sensing has become one of the most important data sources in locust management. Since the 1980s, remote sensing data and applications have accompanied many locust management activities and contributed to an improved and more effective control of locust outbreaks and plagues. Recently, open-access remote sensing data archives as well as progress in cloud computing provide unprecedented opportunity for remote sensing-based locust management and research. Additionally, unmanned aerial vehicle (UAV) systems bring up new prospects for a more effective and faster locust control. Nevertheless, the full capacity of available remote sensing applications and possibilities have not been exploited yet. This review paper provides a comprehensive and quantitative overview of international research articles focusing on remote sensing application for locust management and research. We reviewed 110 articles published over the last four decades, and categorized them into different aspects and main research topics to summarize achievements and gaps for further research and application development. The results reveal a strong focus on three species-the desert locust, the migratory locust (Locusta migratoria), and the Australian plague locust (Chortoicetes terminifera)-and corresponding regions of interest. There is still a lack of international studies for other pest species such as the Italian locust, the Moroccan locust, the Central American locust (Schistocerca piceifrons), the South American locust (Schistocerca cancellata), the brown locust (Locustana pardalina) and the red locust (Nomadacris septemfasciata). In terms of applied sensors, most studies utilized Advanced Very-High-Resolution Radiometer (AVHRR), Satellite Pour l'Observation de la Terre VEGETATION (SPOT-VGT), Moderate-Resolution Imaging Spectroradiometer (MODIS) as well as Landsat data focusing mainly on vegetation monitoring or land cover mapping. Application of geomorphological metrics as well as radar-based soil moisture data is comparably rare despite previous acknowledgement of their importance for locust outbreaks. Despite great advance and usage of available remote sensing resources, we identify several gaps and potential for future research to further improve the understanding and capacities of the use of remote sensing in supporting locust outbreak- research and management

Earth observation for exposome mapping of Germany: analyzing environmental factors relevant to non-communicable diseases

Non-communicable diseases - NCDs - (e.g., asthma, cancer, or diabetes) are a major concern for society and medicine. According to the World Health Organization, NCDs are responsible for > 70 % of global premature deaths. Apart from increasing mortality, these diseases strain one’s immune system which leads to higher susceptibility to transmittable diseases. NCD-susceptibility depends on the genome (genetic predisposition), behavior (lifestyle), and exposome of a person. The exposome is a composition of environmental parameters such as exposure to air pollution, noise, extreme temperatures, or surrounding greenness. Using Earth Observation data, the majority of factors making up the exposome can be monitored over long periods of time at high resolution and with nearly global coverage. Still, exposome maps and products communicating NCD risk are not widely available. In this study, we utilize eight land surface datasets (distance to green spaces, distance to blue spaces, temperature, noise from industry, as well as road, rail, and air traffic, and light pollution) as well as two air pollution datasets (PM2.5 and NO2) to map health-relevant environmental exposure. We use an established cumulative approach and incorporate exposure-response relationships from scientific literature to map environments that impact public health for the complete area of Germany. We present results communicating exposure relevant to myocardial infarction risk. The methodology is transferable to other NCDs and other areas of interest. In the context of the global health burden from NCDs and ongoing global change, this approach supplies findings for communicating health-relevant exposure

Multi-annual grassland mowing dynamics in Germany: spatio-temporal patterns and the influence of climate, topographic and socio-political conditions

Introduction: Grasslands cover one third of the agricultural area in Germany and are mainly used for fodder production. However, grasslands fulfill many other ecosystem functions, like carbon storage, water filtration and the provision of habitats. In Germany, grasslands are mown and/or grazed multiple times during the year. The type and timing of management activities and the use intensity vary strongly, however co-determine grassland functions. Large-scale spatial information on grassland activities and use intensity in Germany is limited and not openly provided. In addition, the cause for patterns of varying mowing intensity are usually not known on a spatial scale as data on the incentives of farmers behind grassland management decisions is not available.Methods: We applied an algorithm based on a thresholding approach utilizing Sentinel-2 time series to detect grassland mowing events to investigate mowing dynamics in Germany in 2018–2021. The detected mowing events were validated with an independent dataset based on the examination of public webcam images. We analyzed spatial and temporal patterns of the mowing dynamics and relationships to climatic, topographic, soil or socio-political conditions.Results: We found that most intensively used grasslands can be found in southern/south-eastern Germany, followed by areas in northern Germany. This pattern stays the same among the investigated years, but we found variations on smaller scales. The mowing event detection shows higher accuracies in 2019 and 2020 (F1 = 0.64 and 0.63) compared to 2018 and 2021 (F1 = 0.52 and 0.50). We found a significant but weak (R2 of 0–0.13) relationship for a spatial correlation of mowing frequency and climate as well as topographic variables for the grassland areas in Germany. Further results indicate a clear value range of topographic and climatic conditions, characteristic for intensive grassland use. Extensive grassland use takes place everywhere in Germany and on the entire spectrum of topographic and climatic conditions in Germany. Natura 2000 grasslands are used less intensive but this pattern is not consistent among all sites.Discussion: Our findings on mowing dynamics and relationships to abiotic and socio-political conditions in Germany reveal important aspects of grassland management, including incentives of farmers

SyntEO: Synthetic dataset generation for Earth observation and deep learning - Demonstrated for offshore wind farm detection

With the emergence of deep learning in the last years, new opportunities arose in Earth observation research. Nevertheless, they also brought with them new challenges. The data-hungry training processes of deep learning models demand large, resource expensive, annotated datasets and partly replaced knowledge-driven approaches so that model behaviour and the final prediction process became a black box. The proposed SyntEO approach enables Earth observation researchers to automatically generate large deep learning ready datasets by merging existing and procedural data. SyntEO does this by including expert knowledge in the data generation process in a highly structured manner to control the automatic image and label generation by employing an ontology. In this way, fully controllable experiment environments are set up, which support insights in the model training on the synthetic datasets. Thus, SyntEO makes the learning process approachable, which is an important cornerstone for explainable machine learning. We demonstrate the SyntEO approach by predicting offshore wind farms in Sentinel-1 images on two of the worlds largest offshore wind energy production sites. The largest generated dataset has 90,000 training examples. A basic convolutional neural network for object detection, that is only trained on this synthetic data, confidently detects offshore wind farms by minimising false detections in challenging environments. In addition, four sequential datasets are generated, demonstrating how the SyntEO approach can precisely define the dataset structure and influence the training process. SyntEO is thus a hybrid approach that creates an interface between expert knowledge and data-driven image analysis

Global dynamics of the offshore wind energy sector monitored with Sentinel-1: Turbine count, installed capacity and site specifications

With the promotion of renewable energy production and a planned phaseout of fossil fuels until 2040, the offshore wind energy sector has expanded and will continue to increase its capacity in the upcoming decades. This study presents a novel approach to deriving the installed capacity of offshore wind turbines on a global scale from spaceborne radar imagery provided by the Sentinel-1 mission. This new method contributes significantly to the information depth of freely available data sets, which provide the spatiotemporal patterns of offshore wind turbines. Furthermore, by combining freely available Earth observation and GIS data, commonly reported attributes of the offshore wind energy sector are compiled to provide a first impression of how this data can be used. All attributes are investigated to provide an in-depth overview of the developments in the offshore wind energy sector over the last five years. Between July 2016 and June 2021, the installed capacity worldwide grew from 13.5 GW to 40.6 GW. This corresponds to an increase of 27.1 GW or 200%. In total 8885 OWTs were installed until June 2021 with an additional 852 under construction. The European Union (15.2 GW), China (14.1 GW) and the United Kingdom (10.7 GW) are the three major contributors to the offshore wind energy sector. China has seen the largest growth in the last five years of 13 GW, followed by the EU with 8 GW and the UK with 5.8 GW. The provided analysis at the end of this study describes the offshore wind energy sector in a transition phase between decades of maturity and massive growth at a time when carbon-neutral energy production is massively supported. Overall the proposed approaches for independent offshore wind turbine capacity estimation and spatiotemporal investigation of the offshore wind energy sector can be used by all stakeholders involved in the upcoming challenge of integrated planning and implementation of offshore wind energy projects

Forest Biodiversity Monitoring Based on Remotely Sensed Spectral Diversity - A Review

Forests are essential for global environmental well-being because of their rich provision of ecosystem services and regulating factors. Global forests are under increasing pressure from climate change, resource extraction, and anthropologically-driven disturbances. The results are dramatic losses of habitats accompanied with the reduction of species diversity. There is the urgent need for forest biodiversity monitoring comprising analysis on alpha, beta, and gamma scale to identify hotspots of biodiversity. Remote sensing enables large-scale monitoring at multiple spatial and temporal resolutions. Concepts of remotely sensed spectral diversity have been identified as promising methodologies for the consistent and multi-temporal analysis of forest biodiversity. This review provides a first time focus on the three spectral diversity concepts vegetation indices, spectral information content, and spectral species for forest biodiversity monitoring based on airborne and spaceborne remote sensing. In addition, the reviewed articles are analyzed regarding the spatiotemporal distribution, remote sensing sensors, temporal scales and thematic foci. We identify multispectral sensors as primary data source which underlines the focus on optical diversity as a proxy for forest biodiversity. Moreover, there is a general conceptual focus on the analysis of spectral information content. In recent years, the spectral species concept has raised attention and has been applied to Sentinel-2 and MODIS data for the analysis from local spectral species to global spectral communities. Novel remote sensing processing capacities and the provision of complementary remote sensing data sets offer great potentials for large-scale biodiversity monitoring in the future

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments