A5063873988- Remote Sensing and Land Use
- Geophysics and Gravity Measurements
- Satellite Image Processing and Photogrammetry
- Calibration and Measurement Techniques
- Geological and Geophysical Studies
- Atmospheric and Environmental Gas Dynamics
- Infrared Target Detection Methodologies
- Remote Sensing and LiDAR Applications
- Remote Sensing in Agriculture
- Geochemistry and Geologic Mapping
- Environmental Sustainability and Technology
- Cryospheric studies and observations
- Land Use and Ecosystem Services
- Soil Geostatistics and Mapping
- Forest ecology and management
- Water Resources and Management
- Plant Ecology and Soil Science
- Species Distribution and Climate Change
- Image Processing and 3D Reconstruction
- Indigenous Studies and Ecology
- Hydrocarbon exploration and reservoir analysis
- Fire effects on ecosystems
- Forest Ecology and Biodiversity Studies
- Scientific Research and Discoveries
- Atmospheric Ozone and Climate
Freie Universität Berlin
2022-2025
GeoInformation (United Kingdom)
2024
Karlsruhe Institute of Technology
2014-2023
Universität Ulm
2020
University of Freiburg
2012-2015
Recent technological advances in remote sensing sensors and platforms, such as high-resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of fine-grained earth observation data. Such data reveal vegetation canopies high spatial detail. Efficient methods are needed to fully harness this unpreceded source information for mapping. Deep learning algorithms Convolutional Neural Networks (CNN) currently paving new avenues field image analysis computer vision....
Abstract Remote sensing has developed into an omnipresent technology in the scientific field of forestry and is also increasingly used operational fashion. However, pace level uptake remote technologies forest inventory monitoring programs varies notably by geographic region. Herein, we highlight some key challenges that research can address near future to further increase acceptance, suitability integration remotely sensed data programs. We particularly emphasize three recurrent themes: (1)...
Tree species information is a basic variable for forest inventories. Knowledge on tree relevant biomass estimation, habitat quality assessment, and biodiversity characterization. Hyperspectral data have been proven to high potential the mapping of composition. However, open questions remain concerning robustness existing classification approaches. Here, number approaches were compared classify from airborne hyperspectral across three sites identify single approach which continuously delivers...
Abstract Unmanned Aerial Vehicles ( UAV ) greatly extended our possibilities to acquire high resolution remote sensing data for assessing the spatial distribution of species composition and vegetation characteristics. Yet, current pixel‐ or texture‐based mapping approaches do not fully exploit information content provided by resolution. Here, harness this detail, we apply deep learning techniques, that is, Convolutional Neural Networks CNN s), on regular tiles ‐orthoimagery (here 2 – 5 m)...
Abstract Abiotic ecosystem properties together with plant species interaction create differences in structural and physiological traits among species. Certain cause a spatial temporal variation canopy reflectance that enables the differentiation of functional types, using earth observation data. However, it often remains unclear which drive between since spectral regions electromagnetic radiation is influenced by certain are overlapping. The present study aims to assess relative...
Abstract Plant invasions can result in serious threats for biodiversity and ecosystem functioning. Reliable maps at very‐high spatial resolution are needed to assess dynamics. Field sampling approaches could be replaced by unmanned aerial vehicles ( UAV s) derive such maps. However, pixel‐based species classification high is highly affected within‐canopy variation caused shadows. Here, we studied the effect of shadows on mapping occurrence invasive using ‐based data. MaxEnt one‐class...
Information on tree species composition is crucial in forest management and can be obtained using remote sensing. While the topic has been addressed frequently over last years, sensing-based identification of across wide complex areas still sparse literature. Our study presents a classification large fraction Białowieża Forest Poland covering 62 000 ha being subject to diverse regimes. Key objectives were obtain an accurate map examine if prevalent strategy influences results. Tree was...
Abstract Aim The spectral variability hypothesis (SVH) suggests a link between variation and plant biodiversity. underlying assumptions are that higher in canopy reflectance (depending on scale) is caused by either (1) habitats or linked vegetation types communities with their specific optical community traits (2) the species themselves traits. Methods SVH was examined several empirical remote‐sensing case studies, which often report some correlation biodiversity‐related variables (mostly...
Abstract. Laser scanning from different acquisition platforms enables the collection of 3D point clouds perspectives and with varying resolutions. These allow us to retrieve detailed information on individual tree forest structure. We conducted airborne laser (ALS), uncrewed aerial vehicle (UAV)-borne (ULS) terrestrial (TLS) in two German mixed forests species typical central Europe. provide spatially overlapping, georeferenced for 12 plots. As a result extraction, we furthermore present...
Characterizing the spatial variability of severity wildfires is important to assess ecological and economic consequences coordinate mitigation strategies. Vegetation indices such as differenced Normalized Burn Ratio (dNBR) have become a standard tool burn or fire across larger areas are being used operationally. Despite frequent application dNBR-like vegetation indices, it not yet fully understood which variables exactly drive in dNBR observed by multispectral satellites. One reason for this...