A5079074963- Remote Sensing in Agriculture
- Species Distribution and Climate Change
- Remote Sensing and Land Use
- Remote Sensing and LiDAR Applications
- Land Use and Ecosystem Services
- Plant Water Relations and Carbon Dynamics
- Ecology and Vegetation Dynamics Studies
- Satellite Image Processing and Photogrammetry
- Tree-ring climate responses
- Geochemistry and Geologic Mapping
- Remote-Sensing Image Classification
- Calibration and Measurement Techniques
- Urban Heat Island Mitigation
- Leaf Properties and Growth Measurement
- Soil Geostatistics and Mapping
- Spectroscopy and Chemometric Analyses
- Hydrology and Watershed Management Studies
- Climate variability and models
- Environmental and Cultural Studies in Latin America and Beyond
- Animal Ecology and Behavior Studies
- Smart Agriculture and AI
- Advanced Image Fusion Techniques
- Fire effects on ecosystems
- Forest ecology and management
- Soil Moisture and Remote Sensing
German Centre for Integrative Biodiversity Research
2022-2025
Helmholtz Centre for Environmental Research
2015-2024
Remote Sensing Solutions (Germany)
2024
University of Reading
2023
Leipzig University
2020-2022
Imperial College London
2005-2009
Potsdam Institute for Climate Impact Research
2004-2005
Summary Climate change effects on seasonal activity in terrestrial ecosystems are significant and well documented, especially the middle higher latitudes. Temperature is a main driver of many plant developmental processes, cases temperatures have been shown to speed up development lead earlier switching next ontogenetic stage. Qualitatively consistent advancement vegetation spring has documented using three independent methods, based ground observations, remote sensing, analysis atmospheric...
Abstract Biodiversity includes multiscalar and multitemporal structures processes, with different levels of functional organization, from genetic to ecosystemic levels. One the mostly used methods infer biodiversity is based on taxonomic approaches community ecology theories. However, gathering extensive data in field difficult due logistic problems, especially when aiming at modelling changes space time, which assumes statistically sound sampling schemes. In this context, airborne or...
Abstract Climate extremes are on the rise. Impacts of extreme climate and weather events ecosystem services ultimately human well‐being can be partially attenuated by organismic, structural, functional diversity affected land surface. However, ongoing transformation terrestrial ecosystems through intensified exploitation management may put this buffering capacity at risk. Here, we summarize evidence that reductions in biodiversity destabilize functioning facing extremes. We then explore if...
Quantifying the accuracy of remote sensing products is a timely endeavor given rapid increase in Earth observation missions. A validation site for Sentinel-2 was hence established central Germany. Automatic multispectral and hyperspectral sensor systems were installed parallel with an existing eddy covariance flux tower, providing spectral information vegetation present at high temporal resolution. Normalized Difference Vegetation Index (NDVI) values from ground-based sensors compared NDVI...
The machine learning method, random forest (RF), is applied in order to derive biophysical and structural vegetation parameters from hyperspectral signatures. Hyperspectral data are, among other things, characterized by their high dimensionality autocorrelation. Common multivariate regression approaches, which usually include only a limited number of spectral indices as predictors, do not make full use the available information. In contrast, methods, such RF, are supposed be better suited...
The Normalized Difference Vegetation Index (NDVI), has been increasingly used to capture spatiotemporal variations in cover factor (C) determination for erosion prediction on a larger landscape scale. However, NDVI-based C (Cndvi) estimation per se is sensitive various biophysical variables, such as soil condition, topographic features, and vegetation phenology. As result, Cndvi often results incorrect values that affect the quality of prediction. aim this study multi-temporally estimate...
Abstract Questions Can we map both discrete Natura 2000 habitat types and their floristic variability using multispectral remote sensing data? How do these data perform compared to full range imaging spectroscopy Which spectral spatial characteristics of are important for accurate mapping habitats variability? Location A mire complex in B avaria, southern G ermany. Methods To compare the performance data, airborne ( AISA Dual) were spectrally spatially resampled two state‐of‐the‐art sensors...
The use of Pseudoinvariant Areas (PIA) makes it possible to carry out a reasonably robust and automatic radiometric correction for long time series remote sensing imagery, as shown in previous studies large data sets Landsat MSS, TM, ETM+ imagery. In addition, they can be employed obtain more coherence among from different sensors. present work validates the PIA pairs images acquired almost simultaneously (Landsat-7 (ETM+) or Landsat-8 (OLI) Sentinel-2A (MSI)). Four region SW Spain,...
Phenological metrics extracted from satellite data (phenometrics) have been increasingly used to access timely, spatially explicit information on crop phenology, but rarely calibrated and validated with field observations. In this study, we developed a calibration procedure make phenometrics more comparable ground-based phenological stages by optimising the settings of Best Index Slope Extraction (BISE) smoothing algorithms together thresholds. We six-year daily Moderate Resolution Imaging...
Abstract Mapping vegetation as hard classes based on remote sensing data is a frequently applied approach, even though this crisp, categorical representation not in line with nature's fuzziness. Gradual transitions plant species composition ecotones and faint compositional differences across different patches are thus poorly described the resulting maps. Several concepts promise to provide better These include (1) fuzzy classification (a.k.a. soft classification) that takes probability of an...
The monitoring of ecosystems alterations has become a crucial task in order to develop valuable habitats for rare and threatened species. information extracted from hyperspectral remote sensing data enables the generation highly spatially resolved analyses such species’ habitats. In our study we combine species ordination with reflectance signatures predict occurrence probabilities Natura 2000 habitat types their conservation status. We examine how accurate threat, expressed by pressure...
The identification of spatial and temporal patterns soil properties moisture structures is an important challenge in environmental monitoring as well for landscape model approaches. This work examines the use hyperspectral remote sensing techniques quantifying geophysical parameters from reflectance vegetation canopy. These can be used proxies underlying water conditions. Different spectral index derivatives, single band reflectance, indices airborne sensor AISA were quantified tested...
The analysis of hyperspectral images is an important task in Remote Sensing. Foregoing radiometric calibration results the assignment incident electromagnetic radiation to digital numbers and reduces striping caused by slightly different responses pixel detectors. However, due uncertainties some remains. This publication presents a new reduction framework that efficiently linear nonlinear miscalibrations image-driven, recalibration rescaling. proposed framework—Reduction Of Miscalibration...