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Forest Structure - Sentinel-1/2, GEDI - Germany, Yearly

The product shows forest structure information on canopy height, total canopy cover and Above-ground biomass density (AGBD) in Germany as annual products in 10 m spatial resolution. The products were generated using a machine learning modelling approach that combines complementary spaceborne remote sensing sensors, namely GEDI (Global Ecosystem Dynamics Investigation; NASA; full-waveform LiDAR), Sentinel-1 (Synthetic-Aperture-Radar; ESA, C-band) and Sentinel-2 (Multispectral Instrument; ESA; VIS-NIR-SWIR). Sample estimates on forest structure from GEDI were modelled in 10 m spatial resolution as annual products based on spatio-temporal composites from Sentinel-1 and -2. The derived products are the first consistent data sets on canopy height, total canopy cover and AGBD for Germany which enable a quantitative assessment of recent forest structure dynamics, e.g. in the context of repeated drought events since 2018. The full description of the method and results can be found in the publication of Kacic et al. (2023).

Factsheet: Modelling methods for assessing complex and social impacts of policies

The Factsheet results from the research project „Machbarkeitsstudie: Modellierung von Anpassungsmaßnahmen: Akteure, Entscheidungen und Wirksamkeit“, introduces the topic of system modelling approaches and their suitability to assess impacts of policies in the field of climate adaptation and summarizes the main findings of the project (what are complex social systems how are system models helpful? Which approaches are suitable for which questions? How are system modells developed? How can system models support policy-making?).The main insights and information are graphically brought together in form of a decision tree to select suitable modelling methods.

The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990-2017

Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 + UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990-2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g., anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011-2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr-1 (EDGAR v5.0) and 19.0 Tg CH4 yr-1 (GAINS), consistent with the NGHGI estimates of 18.9 +/- 1.7 Tg CH4 yr-1. The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr-1. Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4 yr-1) and urface network (24.4 Tg CH4 yr-1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 Tg CH4 yr-1. For N2O emissions, over the 2011-2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2Oyr-1, respectively, agreeing with the NGHGI data (0.9 +/- 0.6 TgN2Oyr-1). Over the same period, the average of the three total TD global and regional inversions was 1.3 +/- 0.4 and 1.3 +/- 0.1 Tg N2Oyr-1, respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU+UK scale and at the national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4590875 (Petrescu et al., 2020b). © Author(s) 2021.

The consolidated European synthesis of CO2 emissions and removals for the European Union and United Kingdom: 1990-2018

Reliable quantification of the sources and sinks of atmospheric carbon dioxide (CO2), including that of their trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Kyoto Protocol and the Paris Agreement. This study provides a consolidated synthesis of estimates for all anthropogenic and natural sources and sinks of CO2 for the European Union and UK (EU27 + UK), derived from a combination of state-of-the-art bottom-up (BU) and top-down (TD) data sources and models. Given the wide scope of the work and the variety of datasets involved, this study focuses on identifying essential questions which need to be answered to properly understand the differences between various datasets, in particular with regards to the less-well-characterized fluxes from managed ecosystems. The work integrates recent emission inventory data, process-based ecosystem model results, data-driven sector model results and inverse modeling estimates over the period 1990-2018. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported under the UNFCCC in 2019, aiming to assess and understand the differences between approaches. For the uncertainties in NGHGIs, we used the standard deviation obtained by varying parameters of inventory calculations, reported by the member states following the IPCC Guidelines. Variation in estimates produced with other methods, like atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arises from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. In comparing NGHGIs with other approaches, a key source of uncertainty is that related to different system boundaries and emission categories (CO2 fossil) and the use of different land use definitions for reporting emissions from land use, land use change and forestry (LULUCF) activities (CO2 land). At the EU27 + UK level, the NGHGI (2019) fossil CO2 emissions (including cement production) account for 2624 TgCO2 in 2014 while all the other seven bottom-up sources are consistent with the NGHGIs and report a mean of 2588(+/- 463 TgCO2). The inversion reports 2700 TgCO2(+/- 480 TgCO2), which is well in line with the national inventories. Over 2011-2015, the CO2 land sources and sinks from NGHGI estimates report -90 Tg C yr-1 +/- 30 Tg C yr-1 while all other BU approaches report a mean sink of -98 Tg Cyr-1 (+/- 362 Tg of C from dynamic global vegetation models only). For the TD model ensemble results, we observe a much larger spread for regional inversions (i.e., mean of 253 Tg C yr-1 +/- 400 Tg C yr-1). This concludes that (a) current independent approaches are consistent with NGHGIs and (b) their uncertainty is too large to allow a verification because of model differences and probably also because of the definition of "CO2 flux" obtained from different approaches. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4626578 (Petrescu et al., 2020a). © Author(s) 2021

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity-Part II: Geomorphology, Terrain and Surfaces

The 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.

Auftakt für Forschungsprojekt: Auf dem Weg zu einer einheitlichen Bewertung der Wasserqualität im deutsch-niederländischen Wattenmeer

Oldenburg Großer Aufschlag für mikroskopisch kleine Algen: In Anwesenheit von rund 30 Gästen aus Wissenschaft und Wasserwirtschaftsverwaltung der Nachbarstaaten Niederlande und Deutschland wurde in dieser Woche das INTERREG-Projekt „Harmonisierung der Phytoplanktonbewertung im deutsch-niederländischen Wattenmeer“ in Oldenburg einer Fachöffentlichkeit vorgestellt. Von dem grenzüberschreitenden Vorhaben versprechen sich die Forscher ein klareres Bild von der stofflichen Belastung in dem wertvollen gemeinsamen Naturraum. Großer Aufschlag für mikroskopisch kleine Algen: In Anwesenheit von rund 30 Gästen aus Wissenschaft und Wasserwirtschaftsverwaltung der Nachbarstaaten Niederlande und Deutschland wurde in dieser Woche das INTERREG-Projekt „Harmonisierung der Phytoplanktonbewertung im deutsch-niederländischen Wattenmeer“ in Oldenburg einer Fachöffentlichkeit vorgestellt. Von dem grenzüberschreitenden Vorhaben versprechen sich die Forscher ein klareres Bild von der stofflichen Belastung in dem wertvollen gemeinsamen Naturraum. Das interdisziplinäre Forschungsprojekt mit einer Laufzeit von zweieinhalb Jahren befasst sich mit der Bewertung der Wasserqualität. Hierbei stehen mikroskopisch kleine Algen, das Phytoplankton, im Fokus. „Das Vorkommen von Phytoplankton im Wattenmeer wird gegenwärtig im Kontext der Europäischen Wasserrahmenrichtlinie (WRRL) als wichtiger Anzeiger von stofflichen Belastungen zum Beispiel in Folge von Überdüngung (Eutrophierung) ausgewertet“, erklärt Jürgen Knaack vom Niedersächsischen Landesbetrieb für Wasserwirtschaft, Küsten- und Naturschutz (NLWKN) in Oldenburg. Wie aber genau die Bewertung der erhobenen Daten erfolgen soll und welche Grenz- oder Zielwerte gelten sollen, ist zwischen den Nachbarstaaten bislang noch nicht abschließend geklärt. Das Projekt mit dem Kurztitel „Wasserqualität – Waterkwaliteit“ setzt auf mathematische Ökosystem-Modelle und eine bilaterale Kommunikation auch auf wissenschaftlicher Ebene. Unter der Leitung der NLWKN Betriebsstelle Brake-Oldenburg werden die Projektpartner, Rijkswaterstaat (RWS, Niederlande), die Universität Hamburg, die NLWKN Forschungsstelle Küste und das Helmholtz-Institut für Funktionelle Marine Biodiversität an der Universität Oldenburg (AWI-HIFMB) ein gemeinsames Verständnis des Systems Wattenmeer erarbeiten, um zu einer harmonisierten Bewertung des Phytoplanktons zu kommen. Welche Zusammenhänge bestehen im Wattenmeer zwischen Nährstoff-Eintrag und Phytoplanktonentwicklung? Welche Algen zeigen einen guten Zustand an, welche einen schlechten? Wie grün darf das Wasser sein, um noch ökologisch in Ordnung zu sein? Oder fachlich gesprochen: Welche Algenkonzentrationen signalisieren ein gesundes System? „In dem Forschungsansatz sollen verschiedene, das Phytoplankton und die Überdüngung betreffende Parameter in einer grenzübergreifenden Ökosystemmodellierung zusammengeführt und nach wissenschaftlichen Kriterien verknüpft werden“, verdeutlicht Gerrit Niebeek vom niederländischen Projektpartner Rijkswaterstaat. Das Projekt mit einem Gesamtvolumen von knapp 1,4 Millionen Euro wird im Rahmen des INTERREG V A-Programms Deutschland-Nederland durch Mittel aus dem Europäischen Fonds für regionale Entwicklung (EFRE) sowie von den Provinzen Drenthe, Fryslân und Groningen und vom niederländischen Ministerie van Economische Zaken en Klimaat unterstützt. „Wir haben in unserer Grenzregion eine lange Küstenlinie und alleine schon deshalb ein großes Interesse an einer wesentlichen Verbesserung der Wasserqualität. Zudem können wir einen wichtigen Beitrag zur EU-Wasserrahmenrichtlinie leisten. Durch den Wissenstransfer zwischen niederländischen und deutschen Partnern ist außerdem gewährleistet, dass wir auf künftige Veränderungen in den Gewässern schnell reagieren können“, so Armin Gallinat vom INTERREG-Programm-Management der Ems Dollart Region (EDR), welche das Projekt begleitet. Bevor die fünf Projektpartner ihre Beiträge zum Gesamtprojekt detaillierter darstellten, näherten sich die Teilnehmer im Tagungssaal „Hunte“ der Jugendherberge Oldenburg zunächst in vier Fachvorträgen dem Projektthema an: Dr. Justus van Beusekom vom Helmholtz-Zentrum Geesthacht sprach über die Eutrophierung des Wattenmeeres, Dr. Marcel van den Berg von RWS und Dr. Jan Witt vom NLWKN erläuterten daraufhin die Problematik der Bewertung im Zusammenhang mit den Erfordernissen der Wasserrahmenrichtlinie. Dr. Claus-Dieter Dürselen von der Firma AquaEcology zeichnete schließlich zusammen mit seiner Kollegin Dr. Sandra Meier ein Bild der Phytoplanktongemeinschaft in den Küstengewässern.

Landscapes, their exploration and utilisation

A new geological epoch has begun - the Anthropocene. Huge anthropogenic transformations of terrestrial landscapes over the past five decades have forced its declaration. Exploring of interaction of humans with nature in general, and with landscapes in particular, can be characterised properly by the terms "landscape research" and "landscape science". Landscape science has been a traditional scientific discipline of geography. This is the case in Russia, whilst the terms geo-ecology and landscape ecology have become established in the English-speaking scientific community. As landscapes are multifunctional, highly complex systems, landscape research is a platform for disciplinary, interdisciplinary and transdisciplinary research. Landscape research in the Anthropocene must aim to combine landscape sustainability with high quality and productivity. This mission is in accord with the Sustainable Development Goals of the United Nations and the provisions of the Landscape Convention of the European Council. It includes halting landscape degradation, developing cultural landscapes and maintaining semi-natural landscapes. Clean water and air, fertile and healthy soils for food and other ecosystem services and a green and biodiverse environment are attributes of landscapes for the survival and well-being of humans in coexistence with nature. Landscape research must generate knowledge, innovations and responsible decision rules for achieving these aims. Big data gathering and scenario modelling are important for knowledge generation in a globalised world. International long-term experiments, observatories and monitoring systems will deliver data for comprehensive ecosystem models and decision support systems. Technical innovations must be imbedded in cultural solutions for the evolvement of landscapes. Springer International's new book series "Innovations in Landscape Research" aims to support better understanding, monitoring and managing landscapes. It contains a multitude of approaches and data. Some focus is on technical innovations for agri-environmental monitoring, on land and water management and its implications for landscape sustainability. Authors present novel tools for ecosystem modelling and forecasting of landscape processes, and on creating knowledge, rules and approaches for handling the multifunctionality of landscapes. The coming book series may serve as a knowledge, data and communication basis for informed decisions regarding the development of landscapes. It will enlarge our horizon and field of action by building bridges between scientific communities, scientific disciplines, and researchers and citizens. Quelle: https://link.springer.com/

Modelling and mapping of atmospheric nitrogen and sulphur deposition and critical loads for ecosystem specific assessment of threats to biodiversity in Germany – PINETI (Pollutant INput and EcosysTem Impact) Part 1

Biodiversity in Europe is strongly affected by the deposition of nitrogen and sulfur on terrestrial ecosystems. Therefore, the deposition of these atmospheric substances is assessed for the years 2008 and 2009 within the PINETI project. Dry, wet and occult deposition of NHx, NOy, SOx and the base cations Ca2+, Mg2+, K+ und Na+ are calculated and added up to the total deposition. By means of the latter and the Critical Load the Critical Load exceedances for sensitive ecosystems are assessed. Veröffentlicht in Texte | 60/2014.

Detection and attribution of regional CO2 concentration anomalies using surface observations

In this study, observed episodes of CO2 concentrations at eight Northern Hemisphere (NH) sites from 1993 to 2012 were analyzed. Five-day back trajectories were calculated for a potential source contribution function (PSCF) analysis. A normalized weight factor related to the occurrence of the episodes was applied to derive more reasonable CO2 elevations and sequestrations. Weighted elevated (?CO2(W_E)) and sequestered (?CO2(W_S)) CO2 episodes had large spatial discrepancies due to the differentiation of strength and patterns of CO2 emissions/sinks in different regions. The most significant enhancement in CO2 episodes was observed at Asian sites: ?CO2(W_E) increased by approximately 56% at an annual rate of ~4% yr-1 from 1995 to 2010 at Waliguan (WLG) and by approximately 39% (~3% yr-1) from 1997 to 2012 at Yonagunijima (YON). According to the PSCF analysis, these increases are largely attributed to the rapid increase in emissions in China. However, ?CO2(W_S) was also enhanced by 34.4% with a growth rate of 2.3% yr-1 at WLG from 1995 to 2010 and ~26.2% (1.7% yr-1) at YON from 1997 to 2012. Both ?CO2(W_E) and ?CO2(W_S) showed decreasing or relatively flat trends at Monte Cimone and Schauinsland, indicating reductions in emissions and sinks in central Europe. The different intensities/trends in emissions and sinks observed at different sites in the NH show that estimating future CO2 levels is a complex problem. Atmospheric inverse and process-based ecosystem models should use more regional input data at high temporal and spatial resolutions for future carbon flux estimations.<BR>Quelle: www.sciencedirect.com

Modellierung und Kartierung räumlich differenzierter Wirkungen von Stickstoffeinträgen in Ökosysteme im Rahmen der UNECE-Luftreinhaltekonvention

Um bei der Ermittlung von Ursache-Wirkungs-Beziehungen und bei der Bestimmung von Belastbarkeitsgrenzen ökologische Zusammenhänge besser als bisher einzubeziehen, wurde das BERN-Modell auf der Basis empirischer Erhebungen in Deutschland entwickelt. Dazu wurden 14 585 Vegetationsaufnahmen aus ganz Deutschland sowie weitere 2 914 Vegetationsaufnahmen aus Nachbarländern ausgewertet und die entsprechenden gewonnenen Daten aus den Aufnahmen in die BERN-Datenbank integriert. Veröffentlicht in Texte | 08/2010.

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