TY - RPRT A1 - Müller, Jörg A1 - Scherer-Lorenzen, Michael A1 - Ammer, Christian A1 - Eisenhauer, Nico A1 - Seidel, Dominik A1 - Schuldt, Bernhard A1 - Biedermann, Peter A1 - Schmitt, Thomas A1 - Künzer, Claudia A1 - Wegmann, Martin A1 - Cesarz, Simone A1 - Peters, Marcell A1 - Feldhaar, Heike A1 - Steffan-Dewenter, Ingolf A1 - Claßen, Alice A1 - Bässler, Claus A1 - von Oheimb, Goddert A1 - Fichtner, Andreas A1 - Thorn, Simon A1 - Weisser, Wolfgang T1 - BETA-FOR: Erhöhung der strukturellen Diversität zwischen Waldbeständen zur Erhöhung der Multidiversität und Multifunktionalität in Produktionswäldern. Antragstext für die DFG Forschungsgruppe FOR 5375 T1 - BETA-FOR: Enhancing the structural diversity between patches for improving multidiversity and multifunctionality in production forests. Proposal for DFG Research Unit FOR 5375 BT - β\(_4\) : Proposal for the 1st phase (2022-2026) of the DFG Research Unit FOR 5375/1 (DFG Forschergruppe FOR 5375/1 – BETA-FOR), Fabrikschleichach, October 2021 N2 - Der in jüngster Zeit beobachtete kontinuierliche Verlust der β-Diversität in Ökosystemen deutet auf homogene Gemeinschaften auf Landschaftsebene hin, was hauptsächlich auf die steigende Landnutzungsintensität zurückgeführt wird. Biologische Vielfalt ist mit zahlreichen Funktionen und der Stabilität von Ökosystemen verknüpft. Es ist daher zu erwarten, dass eine abnehmende β-Diversität auch die Multifunktionalität verringert. Wir kombinieren hier Fachwissen aus der Forstwissenschaft, der Ökologie, der Fernerkundung, der chemischen Ökologie und der Statistik in einem gemeinschaftlichen und experimentellen β-Diversitätsdesign, um einerseits die Auswirkungen der Homogenisierung zu bewerten und andererseits Konzepte zu entwickeln, um negative Auswirkungen durch Homogenisierung in Wäldern rückgängig zu machen. Konkret werden wir uns mit der Frage beschäftigen, ob die Verbesserung der strukturellen β-Komplexität (ESBC) in Wäldern durch Waldbau oder natürliche Störungen die Biodiversität und Multifunktionalität in ehemals homogenen Produktionswäldern erhöhen kann. Unser Ansatz wird mögliche Mechanismen hinter den beobachteten Homogenisierungs-Diversitäts-Beziehungen identifizieren und zeigen, wie sich diese auf die Multifunktionalität auswirken. An elf Standorten in ganz Deutschland haben wir dazu zwei Waldbestände als zwei kleine "Waldlandschaften" ausgewählt. In einem dieser beiden Bestände haben wir ESBC (Enhancement of Structural Beta Complexity)-Behandlungen durchgeführt. Im zweiten, dem Kontrollbestand, werden wir die gleich Anzahl 50x50m Parzellen ohne ESBC einrichten. Auf allen Parzellen werden wir 18 taxonomische Artengruppen aller trophischer Ebenen und 21 Ökosystemfunktionen, einschließlich der wichtigsten Funktionen in Wäldern der gemäßigten Zonen, messen. Der statistische Rahmen wird eine umfassende Analyse der Biodiversität ermöglichen, indem verschiedenen Aspekte (taxonomische, funktionelle und phylogenetische Vielfalt) auf verschiedenen Skalenebenen (α-, β-, γ-Diversität) quantifiziert werden. Um die Gesamtdiversität zu kombinieren, werden wir das Konzept der Multidiversität auf die 18 Taxa anwenden. Wir werden neue Ansätze zur Quantifizierung und Aufteilung der Multifunktionalität auf α- und β-Skalen verwenden und entwickeln. Durch die experimentelle Beschreibung des Zusammenhangs zwischen β-Diversität und Multifunktionalität in einer Reallandschaft wird unsere Forschung einen neuen Weg einschlagen. Darüber hinaus werden wir dazu beitragen, verbesserte Leitlinien für waldbauliche Konzepte und für das Management natürlicher Störungen zu entwickeln, um Homogenisierungseffekte der Vergangenheit umzukehren. N2 - The recently observed consistent loss of β-diversity across ecosystems indicates increasingly homogeneous communities in patches of landscapes, mainly caused by increasing land-use intensity. Biodiversity is related to numerous ecosystem functions and stability. Therefore, decreasing β-diversity is also expected to reduce multifunctionality. To assess the impact of homogenization and to develop guidelines to reverse its potentially negative effects, we combine expertise from forest science, ecology, remote sensing, chemical ecology and statistics in a collaborative and experimental β-diversity approach. Specifically, we will address the question whether the Enhancement of Structural Beta Complexity (ESBC) in forests by silviculture or natural disturbances will increase biodiversity and multifunctionality in formerly homogeneously structured production forests. Our approach will identify potential mechanisms behind observed homogenization-diversity-relationships and show how these translate into effects on multifunctionality. At eleven forest sites throughout Germany, we selected two districts as two types of small ‘forest landscapes’. In one of these two districts, we established ESBC treatments (nine differently treated 50x50 m patches with a focus on canopy cover and deadwood features). In the second, the control district, we will establish nine patches without ESBC. By a comprehensive sampling, we will monitor 18 taxonomic groups and measure 21 ecosystem functions, including key functions in temperate forests, on all patches. The statistical framework will allow a comprehensive biodiversity assessment by quantifying the different aspects of multitrophic biodiversity (taxonomical, functional and phylogenetic diversity) on different levels of biodiversity (α-, β-, γ-diversity). To combine overall diversity, we will apply the concept of multidiversity across the 18 taxa. We will use and develop new approaches for quantification and partitioning of multifunctionality at α- and β- scales. Overall, our study will herald a new research avenue, namely by experimentally describing the link between β-diversity and multifunctionality. Furthermore, we will help to develop guidelines for improved silvicultural concepts and concepts for management of natural disturbances in temperate forests reversing past homogenization effects. KW - Waldökosystem KW - Biodiversität KW - BETA-Multifunktionalität KW - beta-multifunctionality KW - BETA-Diversität KW - beta diversity KW - Forschungsstation Fabrikschleichach Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-290849 ER - TY - JOUR A1 - Fisser, Henrik A1 - Khorsandi, Ehsan A1 - Wegmann, Martin A1 - Baier, Frank T1 - Detecting moving trucks on roads using Sentinel-2 data JF - Remote Sensing N2 - In most countries, freight is predominantly transported by road cargo trucks. We present a new satellite remote sensing method for detecting moving trucks on roads using Sentinel-2 data. The method exploits a temporal sensing offset of the Sentinel-2 multispectral instrument, causing spatially and spectrally distorted signatures of moving objects. A random forest classifier was trained (overall accuracy: 84%) on visual-near-infrared-spectra of 2500 globally labelled targets. Based on the classification, the target objects were extracted using a developed recursive neighbourhood search. The speed and the heading of the objects were approximated. Detections were validated by employing 350 globally labelled target boxes (mean F\(_1\) score: 0.74). The lowest F\(_1\) score was achieved in Kenya (0.36), the highest in Poland (0.88). Furthermore, validated at 26 traffic count stations in Germany on in sum 390 dates, the truck detections correlate spatio-temporally with station figures (Pearson r-value: 0.82, RMSE: 43.7). Absolute counts were underestimated on 81% of the dates. The detection performance may differ by season and road condition. Hence, the method is only suitable for approximating the relative truck traffic abundance rather than providing accurate absolute counts. However, existing road cargo monitoring methods that rely on traffic count stations or very high resolution remote sensing data have limited global availability. The proposed moving truck detection method could fill this gap, particularly where other information on road cargo traffic are sparse by employing globally and freely available Sentinel-2 data. It is inferior to the accuracy and the temporal detail of station counts, but superior in terms of spatial coverage. KW - Sentinel-2 KW - truck detection KW - road traffic KW - machine learning Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-267174 SN - 2072-4292 VL - 14 IS - 7 ER - TY - JOUR A1 - Halbgewachs, Magdalena A1 - Wegmann, Martin A1 - da Ponte, Emmanuel T1 - A spectral mixture analysis and landscape metrics based framework for monitoring spatiotemporal forest cover changes: a case study in Mato Grosso, Brazil JF - Remote Sensing N2 - An increasing amount of Brazilian rainforest is being lost or degraded for various reasons, both anthropogenic and natural, leading to a loss of biodiversity and further global consequences. Especially in the Brazilian state of Mato Grosso, soy production and large-scale cattle farms led to extensive losses of rainforest in recent years. We used a spectral mixture approach followed by a decision tree classification based on more than 30 years of Landsat data to quantify these losses. Research has shown that current methods for assessing forest degradation are lacking accuracy. Therefore, we generated classifications to determine land cover changes for each year, focusing on both cleared and degraded forest land. The analyses showed a decrease in forest area in Mato Grosso by 28.8% between 1986 and 2020. In order to measure changed forest structures for the selected period, fragmentation analyses based on diverse landscape metrics were carried out for the municipality of Colniza in Mato Grosso. It was found that forest areas experienced also a high degree of fragmentation over the study period, with an increase of 83.3% of the number of patches and a decrease of the mean patch area of 86.1% for the selected time period, resulting in altered habitats for flora and fauna. KW - Landsat KW - Google Earth Engine KW - spectral mixture analysis KW - deforestation KW - forest degradation KW - landscape metrics KW - forest fragmentaion KW - Mato Grosso Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-270644 SN - 2072-4292 VL - 14 IS - 8 ER -