TY - THES A1 - Noellie Ahou RUETH, geb. YAO T1 - Mapping Bushfire Distribution and Burn Severity in West Africa Using Remote Sensing Observations T1 - Satellitengestützte Kartierung der Verteilung von Buschfeuern und ihrer Auswirkung auf die Vegetation in Westafrika N2 - Fire has long been considered to be the main ecological factor explaining the origin and maintenance of West African savannas. It has a very high occurrence in these savannas due to high human pressure caused by strong demographic growth and, concomitantly, is used to transform natural savannas into farmland and is also used as a provider of energy. This study was carried out with the support of the BIOTA project funded by the German ministry for Research and Education. The objective of this study is to establish the spatial and temporal distribution of bushfires during a long observation period from 2000 to 2009 as well as to assess fire impact on vegetation through mapping of the burn severity; based on remote sensing and field data collections. Remote sensing was used for this study because of the advantages that it offers in collecting data for long time periods and on different scales. In this case, the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument at 1km resolution is used to assess active fires, and understand the seasonality of fire, its occurrence and its frequency within the vegetation types on a regional scale. Landsat ETM+ imagery at 30 m and field data collections were used to define the characteristics of burn severity related to the biomass loss on a local scale. At a regional scale, the occurrence of fires and rainfall per month correlated very well (R2 = 0.951, r = -0.878, P < 0.01), which shows that the lower the amount of rainfall, the higher the fire occurrence and vice versa. In the dry season, four fire seasons were determined on a regional scale, namely very early fires, which announce the beginning of the fires, early and late fires making up the peak of fire in December/January and very late fires showing the end of the fire season and the beginning of the rainy season. Considerable fire activity was shown to take place in the vegetation zones between the Forest and the Sahel areas. Within these zones, parts of the Sudano-Guinean and the Guinean zones showed a high pixel frequency, i.e. fires occurred in the same place in many years. This high pixel frequency was also found in most protected areas in these zones. As to the kinds of land cover affected by fire, the highest fire occurrence is observed within the Deciduous woodlands and Deciduous shrublands. Concerning the burn severity, which was observed at a local scale, field data correlated closely with the ΔNBR derived from Landsat scenes of Pendjari National Park (R2 = 0.76). The correlation coefficient according to Pearson is r = 0.84 and according to Spearman-Rho, the correlation coefficient is r = 0.86. Very low and low burn severity (with ΔNBR value from 0 to 0.40) affected the vegetation weakly (0-35 percent of biomass loss) whereas moderate and high burn severity greatly affected the vegetation, leading to up to 100 percent of biomass loss, with the ΔNBR value ranging from 0.41 to 0.99. It can be seen from these results that remotely sensed images offer a tool to determine the fire distribution over large regions in savannas and that the Normalised Burn Ratio index can be applied to West Africa savannas. The outcomes of this thesis will hopefully contribute to understanding and, eventually, improving fire regimes in West Africa and their response to climate change and changes in vegetation diversity. N2 - Feuer ist ein wichtiger ökologischer Faktor für die Biokomplexität und den Fortbestand der westafrikanischen Savannen. Feuer kommt in westafrikanischen Savannen - insbesondere wegen des hohen Bevölkerungsdrucks infolge des starken Bevölkerungswachstums – immer häufiger vor. Es wird sowohl zur Umwandlung natürlicher Savannen in landwirtschaftliche Flächen als auch als Energielieferant verwendet. Diese Studie wurde mit der Unterstützung des BIOTA-Projekts durchgeführt, das vom Bundesministerium für Bildung und Forschung, BMBF, gefördert wird. Ziel dieser Dissertation ist die Bestimmung der räumlichen und zeitlichen Verteilung von Buschfeuern während eines langen Beobachtungszeitraumes (2000-2009) sowie die Kartierung von Brandschäden zum Verständnis der Auswirkung von Feuer auf die lokale Vegetationsstruktur. Fernerkundungs- sowie Felddaten werden in dieser Arbeit verwertet. Fernerkundungsdaten wurden für diese Studie verwendet, da sie größere Flächen abdecken und die Daten über längere Zeiträume in verschiedenen Maßstäben zur Verfügung stehen. Daten des Moderate Resolution Imaging Spectroradiometer (MODIS)-Satelliten mit einer Auflösung von 1 km wurden verwendet, um in regelmäßigen Abständen aktive Feuer zu kartieren und die saisonale Verteilung von Feuern, ihr Vorkommen und ihre Häufigkeit nach Vegetationstyp zu bestimmen. Mit Landsat ETM+ Satellitendaten (Auflösung von 30 Metern) und Felddaten wurden auf einem lokalen Maßstab Brandschadensklassen definiert. Mittels der Biomasse-Felddaten wurde dann der Biomasse-Verlust abgeschätzt. Die Feuerhäufigkeit, mittels Satellitendaten ermittelt, und der Niederschlag pro Monat zeigen eine sehr gute Korrelation (R2 = 0.951, r = -0.878, P < 0.01). Daraus lässt sich schließen, dass Feuer bevorzugt in den Monaten vorkommen, in denen es wenig Niederschlag gibt und umgekehrt. Auf regionaler Ebene wurden innerhalb der Trockenzeit vier Feuer-Perioden identifiziert; ‚sehr frühe Feuer’, die am Anfang der Feuersaison entstehen, ‚frühe bis späte Feuer’, die den Höhepunkt der Feueraktivität im Dezember/Januar bilden, und ‚sehr späte Feuer’ am Ende der Feuersaison bis zum Beginn der Regenzeit. Es zeigte sich, dass eine beträchtliche Feueraktivität in den Vegetationszonen zwischen dem Regenwald und der Sahelzone vorherrscht. Besonders wiesen Teile der ‚Sudan-Guinea’- und der ‚Guinea’-Savanne eine hohe Feuerpixel-Frequenz auf, d.h. hier traten Feuer in vielen Jahren am selben Ort auf. Diese hohe Pixelfrequenz wurde auch in den meisten Schutzgebieten in diesen Savannen-Gebieten gefunden. Was die Landbedeckung betrifft, so ergaben die Ergebnisse, dass das höchste Feueraufkommen in den laubabwerfenden Waldgebieten und im laubabwerfenden Buschland vorkam. Die Brandschäden, bzw. der Einwirkungsgrad, wurden im lokalen Maßstab untersucht. Hier korrelierten die Felddaten zur Brandeinwirkung signifikant mit den sogenannten ΔNBR-Index-Werten, die von Landsat-Aufnahmen des Pendjari-Nationalparks hergeleitet wurden (R2 = 0,76). Der Korrelationskoeffizient nach Pearson ist r = 0,84, und nach Spearman-Rho ist der Koeffizient r = 0,86. Bei sehr niedrigen Brandschäden (mit ΔNBR-Werten zwischen 0 und 0,40) war die Vegetation schwach beeinträchtigt (0-35 % Biomasseverlust), während die Vegetation bei mäßigen und hohen Brandschäden sehr beeinträchtigt war und bis zu 100 % der Biomasse verbrannt war; hier lag der ΔNBR zwischen 0,41 und 0,99. Diese Ergebnisse zeigen, dass durch Satellitenbilder die Verteilung von Feuern über größere Flächen in der afrikanischen Savanne effektiv bestimmt werden kann, und dass der Normalized Burn Ratio-Index auf Westafrika zur Berechnung von Brandschadens-Klassen anwendbar ist. Die Ergebnisse liefern einen entscheidenden Beitrag zum Verständnis von Feuer-Ausprägungen in Westafrika, um letztlich den Einsatz von Feuer, auch im Hinblick auf den Klimawandel und die Veränderungen in der Vegetationsdiversität, verbessern zu können. KW - Westafrika KW - Fernerkundung KW - Feuerökologie KW - Vegetation KW - Kartierung KW - Nationalparks KW - Landsat ETM+ KW - burn severity KW - MODIS KW - remote sensing KW - fire ecology KW - fire mapping KW - national parks KW - vegetation Y1 - 2010 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-54244 ER - 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 - Ziegler, Alice A1 - Meyer, Hanna A1 - Otte, Insa A1 - Peters, Marcell K. A1 - Appelhans, Tim A1 - Behler, Christina A1 - Böhning-Gaese, Katrin A1 - Classen, Alice A1 - Detsch, Florian A1 - Deckert, Jürgen A1 - Eardley, Connal D. A1 - Ferger, Stefan W. A1 - Fischer, Markus A1 - Gebert, Friederike A1 - Haas, Michael A1 - Helbig-Bonitz, Maria A1 - Hemp, Andreas A1 - Hemp, Claudia A1 - Kakengi, Victor A1 - Mayr, Antonia V. A1 - Ngereza, Christine A1 - Reudenbach, Christoph A1 - Röder, Juliane A1 - Rutten, Gemma A1 - Schellenberger Costa, David A1 - Schleuning, Matthias A1 - Ssymank, Axel A1 - Steffan-Dewenter, Ingolf A1 - Tardanico, Joseph A1 - Tschapka, Marco A1 - Vollstädt, Maximilian G. R. A1 - Wöllauer, Stephan A1 - Zhang, Jie A1 - Brandl, Roland A1 - Nauss, Thomas T1 - Potential of airborne LiDAR derived vegetation structure for the prediction of animal species richness at Mount Kilimanjaro JF - Remote Sensing N2 - The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results. KW - biodiversity KW - species richness KW - LiDAR KW - elevation KW - partial least square regression KW - arthropods KW - birds KW - bats KW - predictive modeling Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-262251 SN - 2072-4292 VL - 14 IS - 3 ER - TY - JOUR A1 - Redlich, Sarah A1 - Zhang, Jie A1 - Benjamin, Caryl A1 - Dhillon, Maninder Singh A1 - Englmeier, Jana A1 - Ewald, Jörg A1 - Fricke, Ute A1 - Ganuza, Cristina A1 - Haensel, Maria A1 - Hovestadt, Thomas A1 - Kollmann, Johannes A1 - Koellner, Thomas A1 - Kübert‐Flock, Carina A1 - Kunstmann, Harald A1 - Menzel, Annette A1 - Moning, Christoph A1 - Peters, Wibke A1 - Riebl, Rebekka A1 - Rummler, Thomas A1 - Rojas‐Botero, Sandra A1 - Tobisch, Cynthia A1 - Uhler, Johannes A1 - Uphus, Lars A1 - Müller, Jörg A1 - Steffan‐Dewenter, Ingolf T1 - Disentangling effects of climate and land use on biodiversity and ecosystem services—A multi‐scale experimental design JF - Methods in Ecology and Evolution N2 - Climate and land-use change are key drivers of environmental degradation in the Anthropocene, but too little is known about their interactive effects on biodiversity and ecosystem services. Long-term data on biodiversity trends are currently lacking. Furthermore, previous ecological studies have rarely considered climate and land use in a joint design, did not achieve variable independence or lost statistical power by not covering the full range of environmental gradients. Here, we introduce a multi-scale space-for-time study design to disentangle effects of climate and land use on biodiversity and ecosystem services. The site selection approach coupled extensive GIS-based exploration (i.e. using a Geographic information system) and correlation heatmaps with a crossed and nested design covering regional, landscape and local scales. Its implementation in Bavaria (Germany) resulted in a set of study plots that maximise the potential range and independence of environmental variables at different spatial scales. Stratifying the state of Bavaria into five climate zones (reference period 1981–2010) and three prevailing land-use types, that is, near-natural, agriculture and urban, resulted in 60 study regions (5.8 × 5.8 km quadrants) covering a mean annual temperature gradient of 5.6–9.8°C and a spatial extent of ~310 × 310 km. Within these regions, we nested 180 study plots located in contrasting local land-use types, that is, forests, grasslands, arable land or settlement (local climate gradient 4.5–10°C). This approach achieved low correlations between climate and land use (proportional cover) at the regional and landscape scale with |r ≤ 0.33| and |r ≤ 0.29| respectively. Furthermore, using correlation heatmaps for local plot selection reduced potentially confounding relationships between landscape composition and configuration for plots located in forests, arable land and settlements. The suggested design expands upon previous research in covering a significant range of environmental gradients and including a diversity of dominant land-use types at different scales within different climatic contexts. It allows independent assessment of the relative contribution of multi-scale climate and land use on biodiversity and ecosystem services. Understanding potential interdependencies among global change drivers is essential to develop effective restoration and mitigation strategies against biodiversity decline, especially in expectation of future climatic changes. Importantly, this study also provides a baseline for long-term ecological monitoring programs. KW - study design KW - biodiversity KW - climate change KW - ecosystem functioning KW - insect monitoring KW - land use KW - space-for-time approach KW - spatial scales Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-258270 VL - 13 IS - 2 ER - TY - JOUR A1 - Dhillon, Maninder Singh A1 - Dahms, Thorsten A1 - Kuebert-Flock, Carina A1 - Rummler, Thomas A1 - Arnault, Joel A1 - Steffan-Dewenter, Ingolf A1 - Ullmann, Tobias T1 - Integrating random forest and crop modeling improves the crop yield prediction of winter wheat and oil seed rape JF - Frontiers in Remote Sensing N2 - The fast and accurate yield estimates with the increasing availability and variety of global satellite products and the rapid development of new algorithms remain a goal for precision agriculture and food security. However, the consistency and reliability of suitable methodologies that provide accurate crop yield outcomes still need to be explored. The study investigates the coupling of crop modeling and machine learning (ML) to improve the yield prediction of winter wheat (WW) and oil seed rape (OSR) and provides examples for the Free State of Bavaria (70,550 km2), Germany, in 2019. The main objectives are to find whether a coupling approach [Light Use Efficiency (LUE) + Random Forest (RF)] would result in better and more accurate yield predictions compared to results provided with other models not using the LUE. Four different RF models [RF1 (input: Normalized Difference Vegetation Index (NDVI)), RF2 (input: climate variables), RF3 (input: NDVI + climate variables), RF4 (input: LUE generated biomass + climate variables)], and one semi-empiric LUE model were designed with different input requirements to find the best predictors of crop monitoring. The results indicate that the individual use of the NDVI (in RF1) and the climate variables (in RF2) could not be the most accurate, reliable, and precise solution for crop monitoring; however, their combined use (in RF3) resulted in higher accuracies. Notably, the study suggested the coupling of the LUE model variables to the RF4 model can reduce the relative root mean square error (RRMSE) from −8% (WW) and −1.6% (OSR) and increase the R 2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. Moreover, the research compares models yield outputs by inputting three different spatial inputs: Sentinel-2(S)-MOD13Q1 (10 m), Landsat (L)-MOD13Q1 (30 m), and MOD13Q1 (MODIS) (250 m). The S-MOD13Q1 data has relatively improved the performance of models with higher mean R 2 [0.80 (WW), 0.69 (OSR)], and lower RRMSE (%) (9.18, 10.21) compared to L-MOD13Q1 (30 m) and MOD13Q1 (250 m). Satellite-based crop biomass, solar radiation, and temperature are found to be the most influential variables in the yield prediction of both crops. KW - crop modeling KW - random forest KW - machine learning KW - NDVI KW - satellite KW - landsat KW - sentinel-2 KW - winter wheat Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-301462 SN - 2673-6187 VL - 3 ER - TY - JOUR A1 - Latifi, Hooman A1 - Holzwarth, Stefanie A1 - Skidmore, Andrew A1 - Brůna, Josef A1 - Červenka, Jaroslav A1 - Darvishzadeh, Roshanak A1 - Hais, Martin A1 - Heiden, Uta A1 - Homolová, Lucie A1 - Krzystek, Peter A1 - Schneider, Thomas A1 - Starý, Martin A1 - Wang, Tiejun A1 - Müller, Jörg A1 - Heurich, Marco T1 - A laboratory for conceiving Essential Biodiversity Variables (EBVs)—The ‘Data pool initiative for the Bohemian Forest Ecosystem’ JF - Methods in Ecology and Evolution N2 - Effects of climate change‐induced events on forest ecosystem dynamics of composition, function and structure call for increased long‐term, interdisciplinary and integrated research on biodiversity indicators, in particular within strictly protected areas with extensive non‐intervention zones. The long‐established concept of forest supersites generally relies on long‐term funds from national agencies and goes beyond the logistic and financial capabilities of state‐ or region‐wide protected area administrations, universities and research institutes. We introduce the concept of data pools as a smaller‐scale, user‐driven and reasonable alternative to co‐develop remote sensing and forest ecosystem science to validated products, biodiversity indicators and management plans. We demonstrate this concept with the Bohemian Forest Ecosystem Data Pool, which has been established as an interdisciplinary, international data pool within the strictly protected Bavarian Forest and Šumava National Parks and currently comprises 10 active partners. We demonstrate how the structure and impact of the data pool differs from comparable cases. We assessed the international influence and visibility of the data pool with the help of a systematic literature search and a brief analysis of the results. Results primarily suggest an increase in the impact and visibility of published material during the life span of the data pool, with highest visibilities achieved by research conducted on leaf traits, vegetation phenology and 3D‐based forest inventory. We conclude that the data pool results in an efficient contribution to the concept of global biodiversity observatory by evolving towards a training platform, functioning as a pool of data and algorithms, directly communicating with management for implementation and providing test fields for feasibility studies on earth observation missions. KW - bohemian forest ecosystem KW - data pool KW - forest ecosystem science KW - remote sensing KW - remote sensing‐enabled essential biodiversity variables Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-262743 VL - 12 IS - 11 ER - TY - JOUR A1 - Dhillon, Maninder Singh A1 - Dahms, Thorsten A1 - Kübert-Flock, Carina A1 - Steffan-Dewenter, Ingolf A1 - Zhang, Jie A1 - Ullmann, Tobias T1 - Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria JF - Remote Sensing N2 - The increasing availability and variety of global satellite products provide a new level of data with different spatial, temporal, and spectral resolutions; however, identifying the most suited resolution for a specific application consumes increasingly more time and computation effort. The region’s cloud coverage additionally influences the choice of the best trade-off between spatial and temporal resolution, and different pixel sizes of remote sensing (RS) data may hinder the accurate monitoring of different land cover (LC) classes such as agriculture, forest, grassland, water, urban, and natural-seminatural. To investigate the importance of RS data for these LC classes, the present study fuses NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16 days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16 days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, eight day)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions’ cloud or shadow gaps without losing spatial information. These eight synthetic NDVI STARFM products (2: high pair multiply 4: low pair) offer a spatial resolution of 10 or 30 m and temporal resolution of 1, 8, or 16 days for the entire state of Bavaria (Germany) in 2019. Due to their higher revisit frequency and more cloud and shadow-free scenes (S = 13, L = 9), Sentinel-2 (overall R\(^2\) = 0.71, and RMSE = 0.11) synthetic NDVI products provide more accurate results than Landsat (overall R\(^2\) = 0.61, and RMSE = 0.13). Likewise, for the agriculture class, synthetic products obtained using Sentinel-2 resulted in higher accuracy than Landsat except for L-MOD13Q1 (R\(^2\) = 0.62, RMSE = 0.11), resulting in similar accuracy preciseness as S-MOD13Q1 (R\(^2\) = 0.68, RMSE = 0.13). Similarly, comparing L-MOD13Q1 (R\(^2\) = 0.60, RMSE = 0.05) and S-MOD13Q1 (R\(^2\) = 0.52, RMSE = 0.09) for the forest class, the former resulted in higher accuracy and precision than the latter. Conclusively, both L-MOD13Q1 and S-MOD13Q1 are suitable for agricultural and forest monitoring; however, the spatial resolution of 30 m and low storage capacity makes L-MOD13Q1 more prominent and faster than that of S-MOD13Q1 with the 10-m spatial resolution. KW - Landsat KW - Sentinel-2 KW - NDVI KW - fusion KW - agriculture KW - grassland KW - forest KW - urban KW - water Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-323471 SN - 2072-4292 VL - 14 IS - 3 ER - TY - JOUR A1 - Dhillon, Maninder Singh A1 - Kübert-Flock, Carina A1 - Dahms, Thorsten A1 - Rummler, Thomas A1 - Arnault, Joel A1 - Steffan-Dewenter, Ingolf A1 - Ullmann, Tobias T1 - Evaluation of MODIS, Landsat 8 and Sentinel-2 data for accurate crop yield predictions: a case study using STARFM NDVI in Bavaria, Germany JF - Remote Sensing N2 - The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km\(^2\)) for the year 2019. For WW and OSR, the synthetic products’ high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R\(^2\) = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt/ha for WW, R\(^2\) = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R\(^2\) = 0.77 and relative RMSE (RRMSE) = 8.17%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R\(^2\) = 0.66 and RRMSE = 11.35%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world. KW - MODIS KW - Sentinel-2 KW - Landsat 8 KW - sustainable agriculture KW - decision-making KW - winter wheat KW - oil seed rape KW - resolution Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-311132 SN - 2072-4292 VL - 15 IS - 7 ER - TY - JOUR A1 - Dhillon, Maninder Singh A1 - Dahms, Thorsten A1 - Kübert-Flock, Carina A1 - Liepa, Adomas A1 - Rummler, Thomas A1 - Arnault, Joel A1 - Steffan-Dewenter, Ingolf A1 - Ullmann, Tobias T1 - Impact of STARFM on crop yield predictions: fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany JF - Remote Sensing N2 - Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km\(^2\)), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R\(^2\) of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R\(^2\) of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R\(^2\) of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R\(^2\) = 0.88) and OSR (R\(^2\) = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R\(^2\) of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively. KW - MOD13Q1 KW - precision agriculture KW - fusion KW - sustainable agriculture KW - decision making KW - winter wheat KW - oil-seed rape KW - crop models Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-311092 SN - 2072-4292 VL - 15 IS - 6 ER -