A5085283831- Remote Sensing in Agriculture
- Land Use and Ecosystem Services
- Remote Sensing and LiDAR Applications
- Species Distribution and Climate Change
- Geochemistry and Geologic Mapping
- Atmospheric and Environmental Gas Dynamics
- Remote Sensing and Land Use
- Geological Studies and Exploration
- Impact of Light on Environment and Health
- Remote-Sensing Image Classification
- Atmospheric aerosols and clouds
- Cryospheric studies and observations
- Urbanization and City Planning
- Urban Heat Island Mitigation
- Calibration and Measurement Techniques
- Conservation, Biodiversity, and Resource Management
- Rangeland and Wildlife Management
- Climate change and permafrost
- 3D Modeling in Geospatial Applications
- Fire effects on ecosystems
- Rangeland Management and Livestock Ecology
- Advanced Image Fusion Techniques
- Image Processing and 3D Reconstruction
- Plant Ecology and Soil Science
- African Botany and Ecology Studies
Humboldt-Universität zu Berlin
2018-2024
Universität Trier
2014-2024
Lund University
2022
South African Weather Service
2018
Universität Hamburg
2018
Stellenbosch University
2018
Migrations Internationales, Espace Espaces et Sociétés
2011
Ever increasing data volumes of satellite constellations call for multi-sensor analysis ready (ARD) that relieve users from the burden all costly preprocessing steps. This paper describes scientific software FORCE (Framework Operational Radiometric Correction Environmental monitoring), an ‘all-in-one’ solution mass-processing and Landsat Sentinel-2 image archives. is increasingly used to support a wide range operational applications are in need both large area, as well deep dense temporal...
Reliable identification of clouds is necessary for any type optical remote sensing image analysis, especially in operational and fully automatic setups. One the most elaborated widespread algorithms, namely Fmask, was initially developed Landsat suite satellites. Despite their similarity, application to Sentinel-2 imagery currently hampered by unavailability a thermal band, although results can be improved when taking cirrus band into account, cloud detections are unsatisfactory two points....
The Atmospheric Correction Inter-comparison eXercise (ACIX) is an international initiative with the aim to analyse Surface Reflectance (SR) products of various state-of-the-art atmospheric correction (AC) processors. Aerosol Optical Thickness (AOT) and Water Vapour (WV) are also examined in ACIX as additional outputs AC processing. In this paper, general framework discussed; special mention made motivation initiate experiment, inter-comparison protocol, principal results. free open every...
Urban areas and their vertical characteristics have a manifold far-reaching impact on our environment. However, openly accessible information at high spatial resolution is still missing large for complete countries or regions. In this study, we combined Sentinel-1A/B Sentinel-2A/B time series to map building heights entire Germany 10 m grid resolving built-up structures in rural urban contexts. We utilized from the spectral/polarization, temporal dimensions by combining band-wise aggregation...
Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection satellite imagery and number masking algorithms for sensors but very few studies carried out quantitative intercomparison state-of-the-art this domain. This paper summarizes results first Masking Intercomparison eXercise (CMIX) conducted within Committee Earth Observation Satellites (CEOS)...
Spatially explicit knowledge on grassland extent and management is critical to understand monitor the impact of use intensity ecosystem services biodiversity. While regional studies allow detailed insights into land service interactions, information a national scale can aid biodiversity assessments. However, for most European countries this not yet widely available. We used an analysis-ready-data cube that contains dense time series co-registered Sentinel-2 Landsat 8 data, covering Germany....
The dynamics of societal material stocks such as buildings and infrastructures their spatial patterns drive surging resource use emissions. Two main types data are currently used to map stocks, night-time lights (NTL) from Earth-observing (EO) satellites cadastral information. We present an alternative approach for broad-scale stock mapping based on freely available high-resolution EO imagery OpenStreetMap data. Maps built-up surface area, building height, were derived optical Sentinel-2...
We developed a large-area preprocessing framework for multisensor Landsat data, capable of processing large data volumes. Cloud and cloud shadow detection is performed by modified Fmask code. Surface reflectance inferred from Tanré's formulation the radiative transfer, including adjacency effect correction. A precompiled MODIS water vapor database provides daily or climatological fallback estimates. Aerosol optical depth (AOD) estimated over dark objects (DOs) that are identified in combined...
The increasing impact of humans on land and ongoing global population growth requires an improved understanding cover (LC) use (LU) processes related to settlements. heterogeneity built-up areas infrastructures as well the importance not only mapping, but also characterizing anthropogenic structures suggests using a sub-pixel mapping approach for analysing LC from space. We implement regression-based unmixing
Spatially explicit information on cropland use intensity is vital for monitoring land and water resource demands in agricultural systems. Cropping practices underlie substantial spatial temporal variability, which can be captured through the analysis of image time series. Temporal binning helps to overcome limitations concerning operability repeatability mapping large areas improve thematic detail consistency maps We here assessed annual, quarterly, eight-day features five cropping annual...
Gridded population data is widely used to map fine scale patterns and dynamics understand associated human-environmental processes for global change research, disaster risk assessment other domains. This study mapped gridded across Germany using weighting layers from building density, height (both previous studies) type datasets, all created freely available, temporally globally consistent Copernicus Sentinel-1 Sentinel-2 data. We first produced validated a nation-wide dataset of predominant...
Masking of clouds, cloud shadow, water and snow/ice in optical satellite imagery is an important step automated processing chains. We compare the performance masking provided by Fmask (“Function mask” implemented FORCE), ATCOR (“Atmospheric Correction”) Sen2Cor (“Sentinel-2 on a set 20 Sentinel-2 scenes distributed over globe covering wide variety environments climates. All three methods use rules based physical properties (Top Atmosphere Reflectance, TOA) to separate clear pixels from...
The correction of the atmospheric effects on optical satellite images is essential for quantitative and multi-temporal remote sensing applications. In order to study performance state-of-the-art methods in an integrated way, a voluntary open-access benchmark Atmospheric Correction Inter-comparison eXercise (ACIX) was initiated 2016 frame Committee Earth Observation Satellites (CEOS) Working Group Calibration & Validation (WGCV). first exercise extended second edition wherein twelve (AC)...
Built structures increasingly dominate the Earth's landscapes; their surging mass is currently overtaking global biomass. We here assess built in conterminous US by quantifying of 14 stock-building materials eight building types and nine mobility infrastructures. Our high-resolution maps reveal that have become 2.6 times heavier than all plant biomass across country most inhabited areas are mass-dominated buildings or infrastructure. analyze determinants material intensity show densely...
Monitoring the Earth by annually mapping land cover (LC) fractions helps to better understand ongoing processes and changes of use management. At 10 30 m spatial resolution, combination time-series data aggregation, specifically spectral-temporal metrics (STM), regression-based unmixing models has been shown be highly effective in quantifying LC over large areas. However, STM are subject variations densities within between years, which may lead prediction accuracies limit transferability...
We developed a new two-step approach for automated masking of clouds and their shadows in Landsat imagery. The first step consists detecting cloud every image independently by using the Fmask algorithm. modified two features original Fmask: we dropped termination criterion shadow matching, appended darkness filter to counteract false positives bifidly structured dryland areas. second utilizes scene-by-scene detections additional time series probabilities. All clear-sky observations pixel are...
Geometric misalignment between Landsat and Sentinel-2 data sets as well multitemporal inconsistency of Sentinel-2A -2B currently complicate analyses. Operational coregistration imagery is thus required. We present a modification the established Sentinel Registration (LSReg) algorithm. The modifications enabled LSReg to be included in an operational preprocessing workflow automatically coregister large volumes with base images that represent multiannual monthly spectral average values....
The Landsat archive is one of the richest Earth observation datasets available and provides long-term data at fairly high temporal spatial resolution globally. Temporal aggregation frequently used to condense single observations into a more digestible feature space that spatially gap-free fulfill demands many processing strategies rely on homogeneous coverage across large area, e.g., machine learning-based land cover classification. Spectral Metrics (STMs) represent conceptually simple...