Prof. Dr. Patrick Hostert
Profil
Forschungsthemen39
A Surveillance System for Assessing and Monitoring of Desertification
Quelle ↗Zeitraum: 03/2005 - 10/2010 Projektleitung: Prof. Dr. Patrick Hostert
Copernicus Data for Mapping Shifting Cultivation Dynamics in Conservation Areas of Mozambique
Quelle ↗Förderer: Sonstige Internationale Organisationen Zeitraum: 01/2025 - 03/2026 Projektleitung: Prof. Dr. Patrick Hostert
Deutsch-Russischer Workshop zum Thema "Studying the effects of land change on biodiversity in Russian Biosphere Reserves"
Quelle ↗Förderer: DFG sonstige Programme Zeitraum: 01/2007 - 02/2007 Projektleitung: Prof. Dr. Patrick Hostert
Die Rolle räumlicher Muster von Materialbeständen im Hinblick auf die Nachhaltigkeitstransformation unserer Gesellschaft (MAT_STOCKS)
Quelle ↗Förderer: Horizon 2020: ERC Advanced Grant Zeitraum: 03/2018 - 08/2024 Projektleitung: Prof. Dr. Patrick Hostert
EnMAP
Quelle ↗Förderer: Helmholtz-Gemeinschaft Zeitraum: 12/2007 - 10/2008 Projektleitung: Prof. Dr. Patrick Hostert
EnMAP-Box II - Weiterentwicklung der EnMAP-Box, einer Softwareumgebung zur Verarbeitung und Analyse von Bilddaten des EnMAP Hyperspectral Imager
Quelle ↗Zeitraum: 01/2013 - 12/2015 Projektleitung: Prof. Dr. Patrick Hostert
EnMAP-Box I - Weiterentwicklung einer Softwareumgebung zur Prozessierung und Analyse von Bilddaten des Satelliten EnMAP
Quelle ↗Zeitraum: 01/2010 - 12/2012 Projektleitung: Prof. Dr. Patrick Hostert
EnMAP Core Science Team - Monitoring von Ökosystemübergängen
Quelle ↗Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 01/2010 - 12/2012 Projektleitung: Prof. Dr. Patrick Hostert
EnMAP Core Science Team - Natürliche Ökosysteme und Ökosystemübergänge
Quelle ↗Förderer: Bundesministerium für Wirtschaft und Energie Zeitraum: 06/2013 - 12/2016 Projektleitung: Prof. Dr. Patrick Hostert
EnMAP Nutzungsvorbereitung - HyMap Befliegung 2009
Quelle ↗Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 08/2009 - 03/2010 Projektleitung: Prof. Dr. Patrick Hostert
EnMAP Science Advisory Group - Monitoring von Vegetation in Zeiten des globalen Wandels
Quelle ↗Förderer: Bundesministerium für Wirtschaft und Energie Zeitraum: 01/2017 - 11/2020 Projektleitung: Prof. Dr. Patrick Hostert
ESF-Workshop: Europe's Green Backbone - Post-Socialist Land Use Change in the Carpathian Region (EuCaRe) (Veranstaltung 08.10.-10.10.08, Berlin)
Quelle ↗Zeitraum: 10/2008 - 10/2008 Projektleitung: Prof. Dr. Patrick Hostert
EXIST SEED: Gründerstipendium: Geo-Atelier
Quelle ↗Förderer: Bundesministerium für Wirtschaft und Energie Zeitraum: 10/2007 - 10/2008 Projektleitung: Prof. Dr. Patrick Hostert
Geofernerkundung der Landnutzung in städtischen und ländlichen Räumen
Quelle ↗Förderer: DFG Sachbeihilfe Zeitraum: 05/2008 - 10/2008 Projektleitung: Prof. Dr. Patrick Hostert
GeoMultiSens – Skalierbare multisensorale Analyse von Geofernerkundungsdaten
Quelle ↗Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 09/2014 - 12/2017 Projektleitung: Prof. Dr. Patrick Hostert
GreenGrass 2: Innovative Nutzung des Grünlands für eine nachhaltige Intensivierung der Landwirtschaft im Landschaftsmaßstab
Quelle ↗Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 01/2025 - 12/2028 Projektleitung: Prof. Dr. Patrick Hostert
GreenGrass – Innovative Nutzung des Grünlands für eine nachhaltige Intensivierung der Landwirtschaft im Landschaftsmaßstab
Quelle ↗Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 03/2019 - 08/2024 Projektleitung: Prof. Dr. Patrick Hostert
Informal settlements, economic and environmental change, and public health - Strategies to improve the quality of life in Dhaka
Quelle ↗Förderer: DFG Sachbeihilfe Zeitraum: 05/2011 - 12/2013 Projektleitung: Prof. Dr. Patrick Hostert, Prof. Dr. rer. nat. Wilfried Endlicher, Prof. Dr. Elmar Kulke
Informal Settlements, economic and environmental change, and public health - strategies to improve the quality of life in Dhaka
Quelle ↗Förderer: DFG Sachbeihilfe Zeitraum: 01/2009 - 12/2012 Projektleitung: Prof. Dr. Patrick Hostert
Innovative multitemporale Entmischungsansätze für das Monitoring von Feuerökosystemen mittels EnMAP
Quelle ↗Förderer: Bundesministerium für Wirtschaft und Energie Zeitraum: 09/2022 - 12/2026 Projektleitung: Prof. Dr. Patrick Hostert
I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks
Quelle ↗Zeitraum: 01/2011 - 12/2014 Projektleitung: Prof. Dr. Patrick Hostert
Klima und Wasser im Wandel
Quelle ↗Förderer: ESB: Berlin University Alliance Zeitraum: 01/2022 - 12/2024 Projektleitung: Prof. Dr. Patrick Hostert
Kohlenstoffspeicherung, Biodiveristät und Sozialstrukturen im südlichen Amazonien: Modellierung und Implementierung eines Kohlenstoff-optimierten Landmanagements
Quelle ↗Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 06/2011 - 08/2016 Projektleitung: Prof. Dr. Patrick Hostert, Prof. Dr. Tobia Lakes
Land-use and land-cover change effects on biodiversity in European Russia
Quelle ↗Förderer: DFG Sachbeihilfe Zeitraum: 06/2009 - 01/2013 Projektleitung: Prof. Dr. Patrick Hostert
Megastädte: Informelle Dynamik des Globalen Wandels
Quelle ↗Förderer: DFG Sachbeihilfe Zeitraum: 11/2006 - 03/2009 Projektleitung: Prof. Dr. Patrick Hostert
Near-Real Time Derivation of Land Surface Phenology using Sentinel Data: the FORCE-NRT approach
Quelle ↗Förderer: Helmholtz-Gemeinschaft Zeitraum: 11/2017 - 10/2019 Projektleitung: Prof. Dr. Patrick Hostert
Nutzung von Sentinel Daten zu Kohlenwasserstoffquantifizierung und REDD+ Monitoring
Quelle ↗Förderer: Bundesministerium für Wirtschaft und Energie Zeitraum: 05/2013 - 12/2016 Projektleitung: Prof. Dr. Patrick Hostert
Preparation of the CAE Castro Verde 2011
Quelle ↗Zeitraum: 02/2011 - 07/2011 Projektleitung: Prof. Dr. Patrick Hostert
Rekonstruktion der Störungsdynamiken Mitteleuropäischer Wälder anhand von Fernerkundungsdaten (P.R.I.M.E. DAAD Programme)
Quelle ↗Förderer: DAAD Zeitraum: 11/2016 - 04/2018 Projektleitung: Prof. Dr. Patrick Hostert
Remote sensing of the forest transition and its ecosystem impacts in mountain environments.
Quelle ↗Zeitraum: 01/2010 - 06/2014 Projektleitung: Prof. Dr. Patrick Hostert
Satellitengestützte Information zur Grünlandbewirtschaftung
Quelle ↗Förderer: Bundesministerium für Landwirtschaft, Ernährung und Heimat Zeitraum: 12/2017 - 02/2021 Projektleitung: Prof. Dr. Patrick Hostert
SFB 1404/1: Adaptive, verteilte und skalierbare Analyse massiver Satellitendaten (TP B05)
Quelle ↗Förderer: DFG Sonderforschungsbereich Zeitraum: 07/2020 - 06/2024 Projektleitung: Prof. Dr. Ulf Leser, Prof. Dr. Patrick Hostert
SFB 1404/2: Effiziente Ausführung von DAWs zur Vorhersage von Waldsterblichkeit unter Verwendung inkrementeller Daten (TP B07)
Quelle ↗Förderer: DFG Sonderforschungsbereich Zeitraum: 07/2024 - 06/2028 Projektleitung: Prof. Dr. Patrick Hostert, Prof. Dr. Martin Herold
SFB 1404/2: Transparente Multi-Center Datenanalyseworkflows für die Erdbeobachtung (TP B05)
Quelle ↗Förderer: DFG Sonderforschungsbereich Zeitraum: 07/2024 - 06/2028 Projektleitung: Prof. Dr. Patrick Hostert, Prof. Dr. Ulf Leser
Städtisches Umweltmonitoring mit spektral und geometrisch hoch auflösenden Fernerkundungsdaten (I)
Quelle ↗Förderer: DFG Sachbeihilfe Zeitraum: 02/2004 - 06/2007 Projektleitung: Prof. Dr. Patrick Hostert
Städtisches Umweltmonitoring mit spektral und geometrisch hoch auflösenden Fernerkundungsdaten (II)
Quelle ↗Förderer: DFG Sachbeihilfe Zeitraum: 10/2007 - 12/2014 Projektleitung: Prof. Dr. Patrick Hostert
The Living Planet Fellowship - ISLAND2VAP
Quelle ↗Förderer: Sonstige Internationale Organisationen Zeitraum: 03/2015 - 03/2017 Projektleitung: Prof. Dr. Patrick Hostert
VOLANTE
Quelle ↗Zeitraum: 11/2010 - 11/2013 Projektleitung: Prof. Dr. Patrick Hostert
Weiterentwicklung der EnMAP-Box im Rahmen der Nutzungsvorbereitungen zur EnMAP-Mission
Quelle ↗Zeitraum: 01/2016 - 12/2018 Projektleitung: Prof. Dr. Patrick Hostert
Mögliche Industrie-Partner10
Stand: 26.4.2026, 19:48:44 (Top-K=20, Min-Cosine=0.4)
- 53 Treffer85.0%
- I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon StocksK85.0%
- I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks
- 120 Treffer85.0%
- Satellitengestützte Information zur GrünlandbewirtschaftungK85.0%
- Satellitengestützte Information zur Grünlandbewirtschaftung
- 65 Treffer85.0%
- Copernicus Data for Mapping Shifting Cultivation Dynamics in Conservation Areas of MozambiqueK85.0%
- Copernicus Data for Mapping Shifting Cultivation Dynamics in Conservation Areas of Mozambique
- 120 Treffer85.0%
- Satellitengestützte Information zur GrünlandbewirtschaftungK85.0%
- Satellitengestützte Information zur Grünlandbewirtschaftung
- 119 Treffer85.0%
- Satellitengestützte Information zur GrünlandbewirtschaftungK85.0%
- Satellitengestützte Information zur Grünlandbewirtschaftung
- 60 Treffer85.0%
- GreenGrass 2: Innovative Nutzung des Grünlands für eine nachhaltige Intensivierung der Landwirtschaft im LandschaftsmaßstabK85.0%
- GreenGrass 2: Innovative Nutzung des Grünlands für eine nachhaltige Intensivierung der Landwirtschaft im Landschaftsmaßstab
Horizont group GmbH
KPT60 Treffer85.0%- GreenGrass 2: Innovative Nutzung des Grünlands für eine nachhaltige Intensivierung der Landwirtschaft im LandschaftsmaßstabK85.0%
- GreenGrass 2: Innovative Nutzung des Grünlands für eine nachhaltige Intensivierung der Landwirtschaft im Landschaftsmaßstab
- 52 Treffer85.0%
- I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon StocksK85.0%
- I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks
- 37 Treffer85.0%
- GreenGrass – Innovative Nutzung des Grünlands für eine nachhaltige Intensivierung der Landwirtschaft im LandschaftsmaßstabK85.0%
- GreenGrass – Innovative Nutzung des Grünlands für eine nachhaltige Intensivierung der Landwirtschaft im Landschaftsmaßstab
- 8 Treffer62.3%
- Urbane Biodiversität und ÖkosystemdienstleistungenP62.3%
- Urbane Biodiversität und Ökosystemdienstleistungen
Publikationen25
Top 25 nach Zitationen — Quelle: OpenAlex (BAAI/bge-m3 embedded für Matching).
Remote Sensing of Environment · 2493 Zitationen · DOI
Landsat 8, a NASA and USGS collaboration, acquires global moderate-resolution measurements of the Earth's terrestrial and polar regions in the visible, near-infrared, short wave, and thermal infrared. Landsat 8 extends the remarkable 40 year Landsat record and has enhanced capabilities including new spectral bands in the blue and cirrus cloud-detection portion of the spectrum, two thermal bands, improved sensor signal-to-noise performance and associated improvements in radiometric resolution, and an improved duty cycle that allows collection of a significantly greater number of images per day. This paper introduces the current (2012–2017) Landsat Science Team's efforts to establish an initial understanding of Landsat 8 capabilities and the steps ahead in support of priorities identified by the team. Preliminary evaluation of Landsat 8 capabilities and identification of new science and applications opportunities are described with respect to calibration and radiometric characterization; surface reflectance; surface albedo; surface temperature, evapotranspiration and drought; agriculture; land cover, condition, disturbance and change; fresh and coastal water; and snow and ice. Insights into the development of derived ‘higher-level’ Landsat products are provided in recognition of the growing need for consistently processed, moderate spatial resolution, large area, long-term terrestrial data records for resource management and for climate and global change studies. The paper concludes with future prospects, emphasizing the opportunities for land imaging constellations by combining Landsat data with data collected from other international sensing systems, and consideration of successor Landsat mission requirements.
Remote Sensing of Environment · 1064 Zitationen · DOI
Formal planning and development of what became the first Landsat satellite commenced over 50 years ago in 1967. Now, having collected earth observation data for well over four decades since the 1972 launch of Landsat-1, the Landsat program is increasingly complex and vibrant. Critical programmatic elements are ensuring the continuity of high quality measurements for scientific and operational investigations, including ground systems, acquisition planning, data archiving and management, and provision of analysis ready data products. Free and open access to archival and new imagery has resulted in a myriad of innovative applications and novel scientific insights. The planning of future compatible satellites in the Landsat series, which maintain continuity while
Remote Sensing · 778 Zitationen · DOI
Imaging spectroscopy, also known as hyperspectral remote sensing, is based on the characterization of Earth surface materials and processes through spectrally-resolved measurements of the light interacting with matter. The potential of imaging spectroscopy for Earth remote sensing has been demonstrated since the 1980s. However, most of the developments and applications in imaging spectroscopy have largely relied on airborne spectrometers, as the amount and quality of space-based imaging spectroscopy data remain relatively low to date. The upcoming Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill this gap. An overview of the main characteristics and current status of the mission is provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible and near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km-wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to four days at the Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.
Remote Sensing of Environment · 659 Zitationen · DOI
Since 1972, the Landsat program has been continually monitoring the Earth, to now provide 50 years of digital, multispectral, medium spatial resolution observations. Over this time, Landsat data were crucial for many scientific and technical advances. Prior to the Landsat program, detailed, synoptic depictions of the Earth's surface were rare, and the ability to acquire and work with large datasets was limited. The early years of the Landsat program delivered a series of technological breakthroughs, pioneering new methods, and demonstrating the ability and capacity of digital satellite imagery, creating a template for other global Earth observation missions and programs. Innovations driven by the Landsat program have paved the way for subsequent science, application, and policy support activities. The economic and scientific value of the knowledge gained through the Landsat program has been long recognized, and despite periods of funding uncertainty, has resulted in the program's 50 years of continuity, as well as substantive and ongoing improvements to payload and mission performance. Free and open access to Landsat data, enacted in 2008, was unprecedented for medium spatial resolution Earth observation data and substantially increased usage and led to a proliferation of science and application opportunities. Here, we highlight key developments over the past 50 years of the Landsat program that have influenced and changed our scientific understanding of the Earth system. Major scientific and programmatic impacts have been realized in the areas of agricultural crop mapping and water use, climate change drivers and impacts, ecosystems and land cover monitoring, and mapping the changing human footprint. The introduction of Landsat collection processing, coupled with the free and open data policy, facilitated a transition in Landsat data usage away from single images and towards time series analyses over large areas and has fostered the widespread use of science-grade data. The launch of Landsat-9 on September 27, 2021, and the advanced planning of its successor mission, Landsat-Next, underscore the sustained institutional support for the program. Such support and commitment to continuity is recognition of both the historic impact the program, and the future potential to build upon Landsat's remarkable 50-year legacy.
Remote Sensing · 646 Zitationen · DOI
The wealth of complementary data available from remote sensing missions can hugely aid efforts towards accurately determining land use and quantifying subtle changes in land use management or intensity. This study reviewed 112 studies on fusing optical and radar data, which offer unique spectral and structural information, for land cover and use assessments. Contrary to our expectations, only 50 studies specifically addressed land use, and five assessed land use changes, while the majority addressed land cover. The advantages of fusion for land use analysis were assessed in 32 studies, and a large majority (28 studies) concluded that fusion improved results compared to using single data sources. Study sites were small, frequently 300–3000 km 2 or individual plots, with a lack of comparison of results and accuracies across sites. Although a variety of fusion techniques were used, pre-classification fusion followed by pixel-level inputs in traditional classification algorithms (e.g., Gaussian maximum likelihood classification) was common, but often without a concrete rationale on the applicability of the method to the land use theme being studied. Progress in this field of research requires the development of robust techniques of fusion to map the intricacies of land uses and changes therein and systematic procedures to assess the benefits of fusion over larger spatial scales.
Anthropocene · 578 Zitationen · DOI
Land systems are the result of human interactions with the natural environment. Understanding the drivers, state, trends and impacts of different land systems on social and natural processes helps to reveal how changes in the land system affect the functioning of the socio-ecological system as a whole and the tradeoff these changes may represent. The Global Land Project has led advances by synthesizing land systems research across different scales and providing concepts to further understand the feedbacks between social-and environmental systems, between urban and rural environments and between distant world regions. Land system science has moved from a focus on observation of change and understanding the drivers of these changes to a focus on using this understanding to design sustainable transformations through stakeholder engagement and through the concept of land governance. As land use can be seen as the largest geo-engineering project in which mankind has engaged, land system science can act as a platform for integration of insights from different disciplines and for translation of knowledge into action.
Remote Sensing of Environment · 532 Zitationen · DOI
Remote Sensing of Environment · 526 Zitationen · DOI
Many applications that target dynamic land surface processes require a temporal observation frequency that is not easily satisfied using data from a single optical sensor. Sentinel-2 and Landsat provide observations of similar nature and offer the opportunity to combine both data sources to increase time-series temporal frequency at high spatial resolution. Multi-sensor image compositing is one way for performing pixel-level data integration and has many advantages for processing frameworks, especially if analyses over larger areas are targeted. Our compositing approach is optimized for narrow temporal-intervals and allows the derivation of time-series of consistent reflectance composites that capture field level phenologies. We processed more than a year's worth of imagery acquired by Sentinel-2A MSI and Landsat-8 OLI as available from the NASA Harmonized Landsat-Sentinel dataset. We used all data acquired over Germany and integrated observations into composites for three defined temporal intervals (10-day, monthly and seasonal). Our processing approach includes generation of proxy values for OLI in the MSI red edge bands and temporal gap filling on the 10-day time-series. We then derive a national scale crop type and land cover map and compare our results to spatially explicit agricultural reference data available for three federal states and to the results of a recent agricultural census for the entire country. The resulting map successfully captures the crop type distribution across Germany at 30 m resolution and achieves 81% overall accuracy for 12 classes in three states for which reference data was available. The mapping performance for most classes was highest for the 10-day composites and many classes are discriminated with class specific accuracies >80%. For several crops, such as cereals, maize and rapeseed our mapped acreages compare very well with the official census data with average differences between mapped and census area of 11%, 2% and 3%, respectively. Other classes (grapevine and forest classes) perform slightly less well, likely, because the available reference data does not fully capture the variability of these classes across Germany. The inclusion of the red edge bands slightly improved overall accuracies in all cases and improved class specific accuracies for most crop classes. Similarly, our gap filling procedure led to improved mapping accuracies when compared to nongap-filled 10-day features. Overall, our results demonstrate the valuable potential of approaches that utilize data from Sentinel-2 and Landsat which allows for detailed assessments of agricultural and other land-uses over large areas.
Remote Sensing of Environment · 460 Zitationen · DOI
The United States (U.S.) federal government provides imagery obtained by federally funded Earth Observation satellites typically at no cost. For many years Landsat was an exception to this trend, until 2008 when the United States Geological Survey (USGS) made Landsat data accessible via the internet for free. Substantial increases in downloads of Landsat imagery ensued and led to a rapid
Canadian Journal of Remote Sensing · 439 Zitationen · DOI
Free and open access to the more than 40 years of data captured in the Landsat archive, combined with improvements in standardized image products and increasing computer processing and storage capabilities, have enabled the production of large-area, cloud-free, surface reflectance pixel-based image composites. Best-available-pixel (BAP) composites represent a new paradigm in remote sensing that is no longer reliant on scene-based analysis. A time series of these BAP image composites affords novel opportunities to generate information products characterizing land cover, land cover change, and forest structural attributes in a manner that is dynamic, transparent, systematic, repeatable, and spatially exhaustive. Herein, we articulate the information needs associated with forest ecosystem science and monitoring in a Canadian context, and indicate how these new image compositing approaches and subsequent derived products can enable us to address these needs. We highlight some of the issues and opportunities associated with an image compositing approach and demonstrate annual composite products at a national-scale for a single year, with more detailed analyses for two prototype areas using 15 years of Landsat data. Recommendations concerning how to best link compositing decisions to the desired use of the composite (and the information need) are presented, along with future research directions.
Land Use Policy · 435 Zitationen · DOI
Current Opinion in Environmental Sustainability · 368 Zitationen · DOI
Future increases in land-based production will need to focus more on sustainably intensifying existing production systems. Unfortunately, our understanding of the global patterns of land use intensity is weak, partly because land use intensity is a complex, multidimensional term, and partly because we lack appropriate datasets to assess land use intensity across broad geographic extents. Here, we review the state of the art regarding approaches for mapping land use intensity and provide a comprehensive overview of available global-scale datasets on land use intensity. We also outline major challenges and opportunities for mapping land use intensity for cropland, grazing, and forestry systems, and identify key issues for future research.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 326 Zitationen · DOI
Information on the changing land surface is required at high spatial resolutions as many processes cannot be resolved using coarse resolution data. Deriving such information over large areas for Landsat data, however, still faces numerous challenges. Image compositing offers great potential to circumvent such shortcomings. We here present a compositing algorithm that facilitates creating cloud free, seasonally and radiometrically consistent datasets from the Landsat archive. A parametric weighting scheme allows for flexibly utilizing different pixel characteristics for optimized compositing. We describe in detail the development of three parameter decision functions: acquisition year, day of year and distance to clouds. Our test site covers 42 Landsat footprints in Eastern Europe and we produced three annual composites. We evaluated seasonal and annual consistency and compared our composites to BRDF normalized MODIS reflectance products. Finally, we also evaluated how well the composites work for land cover mapping. Results prove that our algorithm allows for creating seasonally consistent large area composites. Radiometric correspondence to MODIS was high (up to R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> > 0.8), but varied with land cover configuration and selected image acquisition dates. Land cover mapping yielded promising results (overall accuracy 72%). Class delineations were regionally consistent with minimal effort for training data. Class specific accuracies increased considerably (~10%) when spectral metrics were incorporated. Our study highlights the value of compositing in general and for Landsat data in particular, allowing for regional to global LULCC mapping at high spatial resolutions.
IEEE Transactions on Geoscience and Remote Sensing · 318 Zitationen · DOI
The accuracy of supervised land cover classifications depends on factors such as the chosen classification algorithm, adequate training data, the input data characteristics, and the selection of features. Hyperspectral imaging provides more detailed spectral and spatial information on the land cover than other remote sensing resources. Over the past ten years, traditional and formerly widely accepted statistical classification methods have been superseded by more recent machine learning algorithms, e.g., support vector machines (SVMs), or by multiple classifier systems (MCS). This can be explained by limitations of statistical approaches with regard to high-dimensional data, multimodal classes, and often limited availability of training data. In the presented study, MCSs based on SVM and random feature selection (RFS) are applied to explore the potential of a synergetic use of the two concepts. We investigated how the number of selected features and the size of the MCS influence classification accuracy using two hyperspectral data sets, from different environmental settings. In addition, experiments were conducted with a varying number of training samples. Accuracies are compared with regular SVM and random forests. Experimental results clearly demonstrate that the generation of an SVM-based classifier system with RFS significantly improves overall classification accuracy as well as producer's and user's accuracies. In addition, the ensemble strategy results in smoother, i.e., more realistic, classification maps than those from stand-alone SVM. Findings from the experiments were successfully transferred onto an additional hyperspectral data set.
Ecosystems · 317 Zitationen · DOI
Remote Sensing of Environment · 298 Zitationen · DOI
Global Change Biology · 295 Zitationen · DOI
Abstract Terrestrial gross primary production (GPP) is an important parameter to explore and quantify carbon fixation by plant ecosystems at various scales. Remote sensing (RS) offers a unique possibility to investigate GPP in a spatially explicit fashion; however, budgeting of terrestrial carbon cycles based on this approach still remains uncertain. To improve calculations, spatio‐temporal variability of GPP must be investigated in more detail on local and regional scales. The overarching goal of this study is to enhance our knowledge on how environmentally induced changes of photosynthetic light‐use efficiency (LUE) are linked with optical RS parameters. Diurnal courses of sun‐induced fluorescence yield ( F Syield ) and the photochemical reflectance index of corn were derived from high‐resolution spectrometric measurements and their potential as proxies for LUE was investigated. GPP was modeled using Monteith's LUE‐concept and optical‐based GPP and LUE values were compared with synoptically acquired eddy covariance data. It is shown that the diurnal response of complex physiological regulation of photosynthesis can be tracked reliably with the sun‐induced fluorescence. Considering structural and physiological effects, this research shows for the first time that including sun‐induced fluorescence into modeling approaches improves their results in predicting diurnal courses of GPP. Our results support the hypothesis that air‐ or spaceborne quantification of sun‐induced fluorescence yield may become a powerful tool to better understand spatio‐temporal variations of fluorescence yield, photosynthetic efficiency and plant stress on a global scale.
Environmental Research Letters · 268 Zitationen · DOI
The demand for agricultural products continues to grow rapidly, but further agricultural expansion entails substantial environmental costs, making recultivating currently unused farmland an interesting alternative. The collapse of the Soviet Union in 1991 led to widespread abandonment of agricultural lands, but the extent and spatial patterns of abandonment are unclear. We quantified the extent of abandoned farmland, both croplands and pastures, across the region using MODIS NDVI satellite image time series from 2004 to 2006 and support vector machine classifications. Abandoned farmland was widespread, totaling 52.5 Mha, particularly in temperate European Russia (32 Mha), northern and western Ukraine, and Belarus. Differences in abandonment rates among countries were striking, suggesting that institutional and socio-economic factors were more important in determining the amount of abandonment than biophysical conditions. Indeed, much abandoned farmland occurred in areas without major constraints for agriculture. Our map provides a basis for assessing the potential of Central and Eastern Europe’s abandoned agricultural lands to contribute to food or bioenergy production, or carbon storage, as well as the environmental trade-offs and social constraints of recultivation.
International Journal of Applied Earth Observation and Geoinformation · 259 Zitationen · DOI
Land cover mapping of large areas using chain classification of neighboring Landsat satellite images
2009Remote Sensing of Environment · 252 Zitationen · DOI
Remote Sensing of Environment · 235 Zitationen · DOI
Remote Sensing · 234 Zitationen · DOI
The EnMAP-Box is a toolbox that is developed for the processing and analysis of data acquired by the German spaceborne imaging spectrometer EnMAP (Environmental Mapping and Analysis Program). It is developed with two aims in mind in order to guarantee full usage of future EnMAP data, i.e., (1) extending the EnMAP user community and (2) providing access to recent approaches for imaging spectroscopy data processing. The software is freely available and offers a range of tools and applications for the processing of spectral imagery, including classical processing tools for imaging spectroscopy data as well as powerful machine learning approaches or interfaces for the integration of methods available in scripting languages. A special developer version includes the full open source code, an application programming interface and an application wizard for easy integration and documentation of new developments. This paper gives an overview of the EnMAP-Box for users and developers, explains typical workflows along an application example and exemplifies the concept for making it a frequently used and constantly extended platform for imaging spectroscopy applications.
Remote Sensing · 230 Zitationen · DOI
Geospatial co-registration is a mandatory prerequisite when dealing with remote sensing data. Inter- or intra-sensoral misregistration will negatively affect any subsequent image analysis, specifically when processing multi-sensoral or multi-temporal data. In recent decades, many algorithms have been developed to enable manual, semi- or fully automatic displacement correction. Especially in the context of big data processing and the development of automated processing chains that aim to be applicable to different remote sensing systems, there is a strong need for efficient, accurate and generally usable co-registration. Here, we present AROSICS (Automated and Robust Open-Source Image Co-Registration Software), a Python-based open-source software including an easy-to-use user interface for automatic detection and correction of sub-pixel misalignments between various remote sensing datasets. It is independent of spatial or spectral characteristics and robust against high degrees of cloud coverage and spectral and temporal land cover dynamics. The co-registration is based on phase correlation for sub-pixel shift estimation in the frequency domain utilizing the Fourier shift theorem in a moving-window manner. A dense grid of spatial shift vectors can be created and automatically filtered by combining various validation and quality estimation metrics. Additionally, the software supports the masking of, e.g., clouds and cloud shadows to exclude such areas from spatial shift detection. The software has been tested on more than 9000 satellite images acquired by different sensors. The results are evaluated exemplarily for two inter-sensoral and two intra-sensoral use cases and show registration results in the sub-pixel range with root mean square error fits around 0.3 pixels and better.
Remote Sensing of Environment · 227 Zitationen · DOI
Urban areas and their vertical characteristics have a manifold and far-reaching impact on our environment. However, openly accessible information at high spatial resolution is still missing at large for complete countries or regions. In this study, we combined Sentinel-1A/B and Sentinel-2A/B time series to map building heights for entire Germany on a 10 m grid resolving built-up structures in rural and urban contexts. We utilized information from the spectral/polarization, temporal and spatial dimensions by combining band-wise temporal aggregation statistics with morphological metrics. We trained machine learning regression models with highly accurate building height information from several 3D building models. The novelty of this method lies in the very fine resolution yet large spatial extent to which it can be applied, as well as in the use of building shadows in optical imagery. Results indicate that both radar-only and optical-only models can be used to predict building height, but the synergistic combination of both data sources leads to superior results. When testing the model against independent datasets, very consistent performance was achieved (frequency-weighted RMSE of 2.9 m to 3.5 m), which suggests that the prediction of the most frequently occurring buildings was robust. The average building height varies considerably across Germany with lower buildings in Eastern and South-Eastern Germany and taller ones along the highly urbanized areas in Western Germany. We emphasize the straightforward applicability of this approach on the national scale. It mostly relies on freely available satellite imagery and open source software, which potentially permit frequent update cycles and cost-effective mapping that may be relevant for a plethora of different applications, e.g. physical analysis of structural features or mapping society's resource usage.
Remote Sensing of Environment · 217 Zitationen · DOI
Kooperationen36
Bestätigte Forscher↔Partner-Paare aus HU-FIS — Gold-Standard-Positive für das Matching.
GreenGrass 2: Innovative Nutzung des Grünlands für eine nachhaltige Intensivierung der Landwirtschaft im Landschaftsmaßstab
university
I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks
other
Satellitengestützte Information zur Grünlandbewirtschaftung
other
Satellitengestützte Information zur Grünlandbewirtschaftung
company
Nutzung von Sentinel Daten zu Kohlenwasserstoffquantifizierung und REDD+ Monitoring
university
GreenGrass 2: Innovative Nutzung des Grünlands für eine nachhaltige Intensivierung der Landwirtschaft im Landschaftsmaßstab
university
GreenGrass – Innovative Nutzung des Grünlands für eine nachhaltige Intensivierung der Landwirtschaft im Landschaftsmaßstab
other
Satellitengestützte Information zur Grünlandbewirtschaftung
other
SFB 1404/2: Effiziente Ausführung von DAWs zur Vorhersage von Waldsterblichkeit unter Verwendung inkrementeller Daten (TP B07)
other
Horizont group GmbH
GreenGrass 2: Innovative Nutzung des Grünlands für eine nachhaltige Intensivierung der Landwirtschaft im Landschaftsmaßstab
company
I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks
other
International Centre for Research in Agroforestry
I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks
research_institute
Satellitengestützte Information zur Grünlandbewirtschaftung
other
GreenGrass 2: Innovative Nutzung des Grünlands für eine nachhaltige Intensivierung der Landwirtschaft im Landschaftsmaßstab
university
I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks
university
I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks
other
Satellitengestützte Information zur Grünlandbewirtschaftung
other
I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks
university
I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks
foundation
SFB 1404/2: Effiziente Ausführung von DAWs zur Vorhersage von Waldsterblichkeit unter Verwendung inkrementeller Daten (TP B07)
university
GreenGrass 2: Innovative Nutzung des Grünlands für eine nachhaltige Intensivierung der Landwirtschaft im Landschaftsmaßstab
company
Copernicus Data for Mapping Shifting Cultivation Dynamics in Conservation Areas of Mozambique
other
I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks
university
Informal settlements, economic and environmental change, and public health - Strategies to improve the quality of life in Dhaka
university
I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks
university
Die Rolle räumlicher Muster von Materialbeständen im Hinblick auf die Nachhaltigkeitstransformation unserer Gesellschaft (MAT_STOCKS)
university
GreenGrass 2: Innovative Nutzung des Grünlands für eine nachhaltige Intensivierung der Landwirtschaft im Landschaftsmaßstab
university
GreenGrass – Innovative Nutzung des Grünlands für eine nachhaltige Intensivierung der Landwirtschaft im Landschaftsmaßstab
university
I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks
university
GreenGrass 2: Innovative Nutzung des Grünlands für eine nachhaltige Intensivierung der Landwirtschaft im Landschaftsmaßstab
university
Remote sensing of the forest transition and its ecosystem impacts in mountain environments.
university
I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks
university
Vietnam National University of Agriculture
I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks
university
Satellitengestützte Information zur Grünlandbewirtschaftung
company
I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks
other
GeoMultiSens – Skalierbare multisensorale Analyse von Geofernerkundungsdaten
research_institute
Stammdaten
Identität, Organisation und Kontakt aus HU-FIS.
- Name
- Prof. Dr. Patrick Hostert
- Titel
- Prof. Dr.
- Fakultät
- Mathematisch-Naturwissenschaftliche Fakultät
- Institut
- Geographisches Institut
- Arbeitsgruppe
- Geofernerkundung
- Telefon
- +49 30 2093-45832
- HU-FIS-Profil
- Quelle ↗
- Zuletzt gescrapt
- 26.4.2026, 01:06:28