Radar Remote Sensing
Our research utilizes radar remote sensing for unravelling human activities in and dynamics of forest ecosystems, and has a strong focus on near real-time change monitoring. We develop machine learning methods using radar and multi-sensor data to monitor change dynamics and their drivers more accurately and timely.
Satellite radar remote sensing uses long-wavelength energy that penetrates through clouds and is sensitive to changes in vegetation physical structure. These characteristics are major advantages for monitoring forest dynamics, in particular in tropical regions where cloud cover is persistent. New and near-future radar satellites provide spatially detailed information almost daily and cloud computing solutions now enable large-scale applications.
An alarming rate of human-induced forest loss combined with more frequent extreme climate events strongly increases the pressure on the Earth’s forest ecosystems. At the same time, countries and major commodity sectors commit to zero-deforestation pledges. We work on openly accessible and timely information on the dynamics and drivers of changes in forests urgently needed to empower sustainable land management, and law enforcement actions against illegal activities.
Our core research lines are:
- Fundamental radar methods
- Near real-time change monitoring
- Characterising forest changes and dynamics
Current PhD and Postdoc projects
- Dr. Adugna Mullissa (Postdoc): Radar and deep learning methods for near-real time forest change monitoring
- Dr. Yaqing Gou (Postdoc): Assessing large-scale tropical forest change dynamics
- Dr. Milutin Milenkovic (Postdoc): Monitoring tropical forest recovery and resilience using big data approaches for radar and lidar satellite data
- Dr. Wanda de Keersmaecker (Postdoc): Monitoring tropical forest recovery capacity using Sentinel-1
- Johannes Balling (PhD student): Characterizing fire-related tropical forest change dynamics utilizing multi-sensor remote sensing data
- Bart Slagter (PhD Student): Detecting, characterizing and predicting small-scale forest disturbance using satellite data and artificial intelligence
Former colleagues
- Dr. Christelle Braun (Postdoc; 2018 – 04/2020)
Key publications
Fundamental radar methods
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deSpeckNet: Generalizing Deep Learning-Based SAR Image Despeckling
IEEE Transactions on Geoscience and Remote Sensing (2020). - ISSN 0196-2892 - p. 1 - 15. -
Angular-Based Radiometric Slope Correction for Sentinel-1 on Google Earth Engine
Remote Sensing 12 (2020)11. - ISSN 2072-4292 - 14 p. -
Polsarnet: A deep fully convolutional network for polarimetric sar image classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (2019)12. - ISSN 1939-1404 - p. 5300 - 5309. -
Multi-model radiometric slope correction of SAR images of complex terrain using a two-stage semi-empirical approach
Remote Sensing of Environment 156 (2015). - ISSN 0034-4257 - p. 1 - 10.
Near real-time change monitoring
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Forest disturbance alerts for the Congo Basin using Sentinel-1
Environmental Research Letters 16 (2021)2. - ISSN 1748-9318 -
Near-daily discharge estimation in high latitudes from Sentinel-1 and 2: A case study for the Icelandic Þjórsá river
Remote Sensing of Environment 241 (2020). - ISSN 0034-4257 -
Characterizing Tropical Forest Cover Loss Using Dense Sentinel-1 Data and Active Fire Alerts
Remote Sensing 10 (2018)5. - ISSN 2072-4292 - 18 p. -
Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2
Remote Sensing of Environment 204 (2018). - ISSN 0034-4257 - p. 147 - 161. -
Combining satellite data for better tropical forest monitoring
Nature Climate Change 6 (2016)2. - ISSN 1758-678X - p. 120 - 122. -
A Bayesian approach to combine Landsat and ALOS PALSAR time series for near real-time deforestation detection
Remote Sensing 7 (2015). - ISSN 2072-4292 - p. 4973 - 4996. -
Fusing Landsat and SAR time series to detect deforestation in the tropics
Remote Sensing of Environment 156 (2015). - ISSN 0034-4257 - p. 276 - 293.
Characterising changes and dynamics
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Linking Terrestrial LiDAR Scanner and Conventional Forest Structure Measurements with Multi-Modal Satellite Data
Forests 10 (2019)3. - ISSN 1999-4907 - 19 p. -
Characterizing Tropical Forest Cover Loss Using Dense Sentinel-1 Data and Active Fire Alerts
Remote Sensing 10 (2018)5. - ISSN 2072-4292 - 18 p.
Other related publications
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Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands, South Africa
International Journal of applied Earth Observation and Geoinformation 86 (2020). - ISSN 0303-2434 -
The Role and Need for Space-Based Forest Biomass-Related Measurements in Environmental Management and Policy
Surveys in Geophysics 40 (2019)4. - ISSN 0169-3298 - p. 757 - 778. -
A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring
Remote Sensing 8 (2016)1. - ISSN 2072-4292 - 23 p.
Available open-source software
‘Bayts’ R-package: https://github.com/jreiche/bayts.
Set of tools to apply the probabilistic machine learning approach of Reiche et al. (2015, 2018) to combine multiple Radar and/or optical satellite time series and to detect deforestation/forest cover loss in near real-time.
Scripts for Angular-based radiometric slope correction of Sentinel-1 on Google Earth Engine (Vollrath et al 2020): https://github.com/ESA-PhiLab/radiometric-slope-correction.
Scripts for deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling: https://github.com/adugnag/deSpeckNet