Suchergebnisse

Durch das europäische Erdbeobachtungsprogramm Copernicus entsteht seit 2014 eine moderne und leistungsfähige Infrastruktur für Erdbeobachtung und Geoinformation. Es beinhaltet eine Weltraumkomponente, eine Dienste Komponente sowie eine In-Situ Komponente. Die Weltraumkomponente umfasst die Sentinel Erdbeobachtungssatelliten und die zugehörige Infrastruktur, z. B. das Bodensegment. Die Dienste Komponente deckt die sechs thematischen Bereiche Land, Klimawandel, Atmosphäre, Meere, Krisen- und Katastrophenmanagement und Sicherheit ab. Die Kerndienste sind damit beauftragt, fertige Datenprodukte bereitzustellen. Die Produkte integrieren u. a. Daten der Sentinel Missionen, Daten aus beitragenden Missionen, Insitu- und Modelldaten. Sowohl auf europäischer Ebene als auch auf nationaler Ebene laufen derzeit Überlegungen, wie Copernicus-Daten und Dienste auch für die Wasserwirtschaft besser genutzt und weiterentwickelt werden können. In diesem Sachverständigengutachten war zu untersuchen, in welchem Maße Copernicus Daten und Dienste herangezogen werden können, um einen Beitrag für das Hochwasserrisikomanagement in Deutschland zu leisten sowie um ggf. bundesweit vergleichbare Daten für die Berichterstattung zur Umsetzung der Hochwasserrisikomanagementrichtlinie bereitzustellen. Nach einem einführenden Überblick über Copernicus erfolgt eine Darstellung des Wissens- und Kenntnisstandes bei der Anwendung von Copernicus-Daten und Diensten im Hochwasserrisikomanagement. Die Untersuchungen hierzu erfolgten durch Expertengespräche, eine Literaturrecherche und eine Untersuchung von bisher durchgeführten Aktivierungen des Copernicus Katastrophen- und Krisenmanagementdienstes in Deutschland. In einem zweiten Schritt wurde eine Zuordnung von Copernicus Daten und Diensten zu den einzelnen Umsetzungsschritten der Hochwasserrisikomanagement-Richtlinie durchgeführt. Ein weiterer Fokus liegt auf der Untersuchung von Einsatzmöglichkeiten von Copernicus für eine bundeseinheitliche Schadenspotentialermittlung. Im Weiteren werden anhand von vier Fallbeispielen die Potentiale eines satellitenbasierten Monitorings demonstriert bzw. der Mehrwert einer Integration von Copernicus Datenprodukten aufgezeigt. Aus Sicht der Autoren besteht ein hohes Potential für den Einsatz von Copernicus-Daten und Diensten für das Hochwasserrisikomanagement. Dieses wird von den Fachexperten v. a. in den Behörden der Bundesländer bisher noch nicht ausgeschöpft. Eine umfangreichere Nutzung wird empfohlen. Um die Akzeptanz hierfür zu erhöhen sind jedoch weitere Maßnahmen des Bundes und von Bundesländerseite notwendig. Weitere Schulungen in den Behörden zur Nutzung der bestehenden Dienste und Daten, eine Copernicus Arbeitsgruppe Wasserwirtschaft, eine Plattform zum Wissensmanagement und Pilotprojekte zum Test von Arbeitsweisen und Produkten sollten zeitnah umgesetzt werden, um das große Potential auszuschöpfen, welches Copernicus schon heute bietet.

Die Bewertung des Zustands von Ökosystemen und das Monitoring von Indikatoren der Deutschen Anpassungsstrategie an den Klimawandel sind wichtige Aufgaben des Umweltbundesamtes. Dafür sind detaillierte Datengrundlagen, u. a. über das Vorkommen verschiedener Baumarten notwendig. Im Rahmen der vorliegenden Machbarkeitsstudie wurden anhand repräsentativer Testgebiete die Möglichkeiten einer satellitenbasierten Erkennung von Baumarten untersucht. Die Studie hatte auch das Ziel, die Möglichkeiten einer bundesweiten Umsetzung zu bewerten. Im Fokus stand dabei die Nutzung entgeltfreier Daten des Europäischen Copernicus Programmes. Die Studie gibt einen Überblick über vorhandene Forstkarten unterschiedlicher Maßstäbe in Deutschland und Europa und erläutert vorhandene Ansätze zur Klassifikation von Baumarten mittels Satellitenfernerkundung. Dies beinhaltet einen Vergleich unterschiedlicher Satellitenbilddaten, Klassifizierungsalgorithmen, sowie Validierungsdaten. Anhand von fünf Fallstudien wurde untersucht, inwieweit sich die elf Hauptbaumarten erfolgreich aus den Satellitenbilddaten ableiten lassen. Die erzielten Ergebnisse zeigen, dass sich eine bundesweite Baumartenklassifikation mit dem vorgestellten Ansatz erzielen lässt. Von großer Bedeutung sind jedoch die Anzahl zur Verfügung stehender wolkenfreier Satellitenszenen und das Vorhandensein von Referenz-daten zum Training der Klassifikationsalgorithmen und zur Validierung der Ergebnisse.

Forest 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. ; Peer Reviewed

Forest 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.

Forest 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.

The European Green Deal, published as one major work stream of the new European Commission at the end of 2019, represents a new and ambitious approach to put environment and sustainability more at the heart of European policy. The announcements there must now be brought to life through various strategies and implementation measures. This study analyses the European Green Deal from the perspective of the German Environment Agency and places it in the context of the global challenge of achieving the United Nations' sustainable development goals. To this end, the individual thematic areas of the European Green Deal and their measures are analysed and we discuss what further measures may be necessary to achieve the self-imposed EU goals, but also the United Nations goals and other long-term goals such as the Paris Climate Agreement. In addition to necessary measures in the fields of human well-being, sustainable economic activity, sustainable food systems, climate and energy, urban development and digitalization, the paper also clearly states that structural adjustments are needed as key levers to achieve the desired goals. New approaches are needed in the governance of sustainability policy, in the economic and financial sector, in civil society involvement, and in science and innovation. The study concludes that the European Green Deal is an important step forward, but that further efforts beyond those described there are still needed in order to achieve a sustainable Europe.