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Veröffentlichung In Situ/Remote Sensing Integration to Assess Forest Health̶A Review(2016) Pause, Marion; Rosenthal, Michael; Keuck, Vanessa; Bumberger, Jan; Dietrich, Peter; Heurich, Marco; Jung, András; Lausch, Angela; Schweitzer, ChristianFor mapping, quantifying and monitoring regional and global forest health, satellite remote sensing provides fundamental data for the observation of spatial and temporal forest patterns and processes. While new remote-sensing technologies are able to detect forest data in high quality and large quantity, operational applications are still limited by deficits of in situ verification. In situ sampling data as input is required in order to add value to physical imaging remote sensing observations and possibilities to interlink the forest health assessment with biotic and abiotic factors. Numerous methods on how to link remote sensing and in situ data have been presented in the scientific literature using e.g. empirical and physical-based models. In situ data differs in type, quality and quantity between case studies. The irregular subsets of in situ data availability limit the exploitation of available satellite remote sensing data. To achieve a broad implementation of satellite remote sensing data in forest monitoring and management, a standardization of in situ data, workflows and products is essential and necessary for user acceptance. The key focus of the review is a discussion of concept and is designed to bridge gaps of understanding between forestry and remote sensing science community. Methodological approaches for in situ/remote-sensing implementation are organized and evaluated with respect to qualifying for forest monitoring. Research gaps and recommendations for standardization of remote-sensing based products are discussed. Concluding the importance of outstanding organizational work to provide a legally accepted framework for new information products in forestry are highlighted. Quelle: http://www.mdpi.comVeröffentlichung Understanding forest health with remote sensing, part III(2018) Lausch, Angela; Borg, Erik; Bumberger, Jan; Schweitzer, ChristianForest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support. Quelle: https://www.mdpi.comVeröffentlichung Linking remote sensing and geodiversity and their traits relevant to biodiversity(2019) Lausch, Angela; Baade, Jussi; Bannehr, Lutz; Schweitzer, ChristianIn the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geodiversity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geodiversity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geodiversity characteristics can be recorded. The paper provides an overview of those geotraits. Quelle: https://www.mdpi.comVeröffentlichung Einsatz von Fernerkundungsdaten zur Ableitung aktueller Land- und Waldflächen zur Unterstützung der Berechnung von SDG-Indikatoren(2019) Knöfel, Patrick; Suresh, Gopika; Schweitzer, ChristianZiel der am 25. September 2015 verabschiedeten Agenda 2030 ist es, die globale Entwicklung sozial, ökologisch und wirtschaftlich nachhaltig zu gestalten. In der aktuellen überarbeiteten "Deutschen Nachhaltigkeitsstrategie - Neuauflage 2016" bekennt sich die Bundesregierung auch national zur Agenda 2030. Die damit verbundenen Erwartungen und Anforderungen an die vereinbarten 17 Nachhaltigkeitsziele werden die zukünftige internationale Zusammenarbeit maßgeblich prägen. Eine transparente und nachvollziehbare Ausgestaltung eines Mechanismus zum systematischen Monitoring ist daher erforderlich. Neben den derzeit verwendeten statistischen Informationen wird vermehrt über den Einsatz von raumbezogenen Daten diskutiert, um ein effektives Monitoring der SDGs zu gewährleisten. Am Bundesamt für Kartographie und Geodäsie (BKG) wird derzeit ein Ansatz entwickelt, um aktuelle und konsistente Informationen zu Landbedeckungsänderungen mithilfe von freien Copernicus-Satellitendaten abzuleiten. Diese sollen zur Aktualisierung und Fortführung von BKG-Produkten verwendet werden, wie beispielsweise des digitalen Landbedeckungsmodells für Deutschland, "LBM-DE", welches gleichzeitig auch die Datenbasis des derzeit im Aufbau befindlichen Landschaftsveränderungsdienstes (Laverdi) bildet. Man wäre somit in der Lage, auch zwischen den LBM-DE-Produktionsjahren annähernd kontinuierlich Informationen über die aktuelle Landbedeckung und deren Veränderungen zu erhalten. Die somit theoretisch zu beliebigen Stichtagen verfügbaren, regelmäßig und standardisiert abgeleiteten und statistisch relevanten Geoinformationen können zur quantitativen Beschreibung von ausgewählten UN-SDG-Indikatoren genutzt werden. Neben dem Mehrwert, die die Satellitenfernerkundung hinsichtlich Aktualität und Transparenz der abgeleiteten Indikatoren liefert, ist die skalierbare und dynamische regionale Anpassung der Darstellungsebenen zur besseren Visualisierung hervorzuheben. Am Beispiel des Indikators 15.1.1., "Forest area as a percentage of total land area", wird gezeigt, welchen Beitrag Datensätze aus der Satellitenfernerkundung zum Monitoring von SDG-Indikatoren leisten können. Bei der Berechnung wird auf freie Copernicus-Satellitendaten zurückgegriffen. Die Waldfläche wird mithilfe von Sentinel-2-Fernerkundungsdaten ermittelt, während für die Landfläche Radarinformationen von Sentinel-1 verarbeitet werden. Durch die hohe zeitliche Auflösung der Sentinel-Satelliten ist ein kontinuierliches sowie globales Monitoring des SDG-Indikators gewährleistet. Die entwickelte Methodik wird in didaktisch aufbereiteter Form durch Online-Ressourcen zugänglich gemacht. © Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature 2019Veröffentlichung Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity-Part II: Geomorphology, Terrain and Surfaces(2020) Lausch, Angela; Schaepman, Michael E.; Skidmore, Andrew; Schweitzer, ChristianThe status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring. © 2020 by the authors.