A5035304253- Remote Sensing in Agriculture
- Remote Sensing and LiDAR Applications
- Land Use and Ecosystem Services
- Remote Sensing and Land Use
- Rangeland Management and Livestock Ecology
- Flood Risk Assessment and Management
- Archaeological Research and Protection
- 3D Surveying and Cultural Heritage
- Species Distribution and Climate Change
- Hydrology and Watershed Management Studies
- Maritime and Coastal Archaeology
- Plant Water Relations and Carbon Dynamics
- Forest ecology and management
- African Botany and Ecology Studies
- Hydrology and Drought Analysis
- Climate variability and models
- Climate change impacts on agriculture
- Forest Ecology and Biodiversity Studies
- Geophysics and Gravity Measurements
- Remote-Sensing Image Classification
- Environmental Changes in China
- Botany and Plant Ecology Studies
- Oceanographic and Atmospheric Processes
- Plant Ecology and Soil Science
- Climate change and permafrost
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
2016-2025
University of Würzburg
2007-2012
RWTH Aachen University
1988
Westfälische Hochschule
1988
University of Geneva
1986
Central Europe was hit by several unusually strong periods of drought and heat between 2018 2020. These droughts affected forest ecosystems. Cascading effects with bark beetle infestations in spruce stands were fatal to vast areas Germany. We present the first assessment canopy cover loss Germany for period January 2018–April 2021. Our approach makes use dense Sentinel-2 Landsat-8 time-series data. computed disturbance index (DI) from tasseled cap components brightness, greenness, wetness....
Droughts are amongst the most destructive natural disasters in world. In large regions of Africa, where water is a limiting factor and people strongly rely on rain-fed agriculture, droughts have frequently led to crop failure, food shortages even humanitarian crises. eastern southern major drought episodes been linked El Niño-Southern Oscillation (ENSO) events. this context with limited in-situ data available, remote sensing provides valuable opportunities for continent-wide assessment high...
Crop type classification using Earth Observation (EO) data is challenging, particularly for crop types with similar phenological growth stages. In this regard, the synergy of optical and Synthetic-Aperture Radar (SAR) enables a broad representation biophysical structural information on target objects, enhancing mapping. However, fusion multi-sensor dense time-series often comes challenge high dimensional feature space. study, we (1) evaluate how usage only optical, SAR, their affect...
To reduce the risks of climate change, governments agreed in Paris Agreement to limit global temperature rise less than 2.0 °C above pre-industrial levels, with ambition keep warming 1.5 °C. Charting appropriate mitigation responses requires information on costs mitigating versus associated damages for two levels warming. In this assessment, a critical consideration is impact crop yields and yield variability regions currently challenged by food insecurity. The current study assessed impacts...
Nationwide and consistent information on agricultural land use forms an important basis for sustainable management maintaining food security, (agro)biodiversity, soil fertility, especially as German agriculture has shown high vulnerability to climate change. Sentinel-1 Sentinel-2 satellite data of the Copernicus program offer time series with temporal, spatial, radiometric, spectral characteristics that have great potential mapping monitoring crops. This paper presents approach which...
Remote sensing multi-decadal time-series provide important information for analysing long-term environmental change. The Advanced Very High Resolution Radiometer (AVHRR) has been providing data since the early 1980s. Normalised Difference Vegetation Index (NDVI) derived thereof can be used monitoring vegetation conditions. This study presents novel TIMELINE NDVI product, which provides a consistent set of daily, 10-day, and monthly composites at 1 km spatial resolution based on AVHRR Europe...
Cropland monitoring is important for ensuring food security in the context of global climate change and population growth. Freely available satellite data allow large areas, while cloud-processing platforms enable a wide user community to apply remote sensing techniques. Remote sensing-based estimates cropped area crop types can thus assist sustainable land management developing countries such as Ethiopia. In this study, we developed method cropland type classification based on Sentinel-1...
Monitoring the spatio-temporal development of vegetation is a challenging task in heterogeneous and cloud-prone landscapes. No single satellite sensor has thus far been able to provide consistent time series high temporal spatial resolution for such areas. In order overcome this problem, data fusion algorithms as Enhanced Spatial Temporal Adaptive Reflectance Fusion Model (ESTARFM) have established frequently used recent years generate high-resolution series. make it applicable larger scales...
Burkina Faso ranges amongst the fastest growing countries in world with an annual population growth rate of more than three percent. This trend has consequences for food security since agricultural productivity is still on a comparatively low level Faso. In order to compensate productivity, areas are expanding quickly. The mapping and monitoring this expansion difficult, even basis remote sensing imagery, extensive farming practices frequent cloud coverage area make delineation cultivated...
Central Europe experienced several droughts in the recent past, such as year 2018, which was characterized by extremely low rainfall rates and high temperatures, resulting substantial agricultural yield losses. Time series of satellite earth observation data enable characterization past drought events over large temporal spatial scales. Within this study, Moderate Resolution Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) 250 m time were investigated for vegetation...
Large-scale crop type mapping often requires prediction beyond the environmental settings of training sites. Shifts in phenology, field characteristics, or ecological site conditions previously unseen area, may reduce classification performance machine learning classifiers that overfit to This study aims assess spatial transferability Random Forest models for across Germany. The effects different input datasets, i.e., only optical, Synthetic Aperture Radar (SAR), and optical-SAR data...
Monitoring forest conditions is an essential task in the context of global climate change to preserve biodiversity, protect carbon sinks and foster future resilience. Severe impacts heatwaves droughts triggering cascading effects such as insect infestation are challenging semi-natural forests Germany. As a consequence repeated drought years since 2018, large-scale canopy cover loss has occurred calling for improved disturbance monitoring assessment structure conditions. The present study...
The characterization and evaluation of the recent status biodiversity in Southern Africa’s Savannas is a major prerequisite for suitable sustainable land management conservation purposes. This paper presents an integrated concept vegetation type mapping dry savanna ecosystem based on local scale in-situ botanical survey data with high resolution (Landsat) coarse (MODIS) satellite time series. In this context, semi-automated training database generation procedure using object-oriented image...
The understanding and assessment of surface water variability inland bodies, for example, due to climate human impact, requires steady continuous information about its inter- intra-annual dynamics. In this letter, we present an approach using dynamic threshold techniques utilizing time series generate a data set containing detected bodies on global scale with daily temporal resolution. Exemplary results the year 2013 that were based moderate resolution imaging spectroradiometer products are...