Radar Remote Sensing

Our research utilizes radar remote sensing for unravelling human activities in and dynamics of forest ecosystems at regional to global scales, 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, their drivers and associated greenhouse gas emissions 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, law enforcement actions against illegal activities, and climate change law implementations.

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
  • 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
  • Anne-Juul Welsink (PhD student): Forest monitoring using advanced earth observation datasets
  • Sietse van der Woude (PhD student): Advancing European forest monitoring by combining satellite and ground-based data streams

Former colleagues

  • Dr. Christelle Braun (Postdoc 2018 - 2020) - continued at UNEP, Senegal
  • Dr. Wanda de Keersmaecker (Postdoc 2019 – 2021) - continued at VITO, Belgium
  • Dr. Ovidiu Csillik (Postdoc 2019-2021) - continued at JPL, USA

Key publications

Fundamental radar methods

Near real-time change monitoring

Characterising changes and dynamics

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.

Code 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.

Code for deSpeckNet (Mullissa et al, 2020): Generalizing Deep Learning Based SAR Image Despeckling: https://github.com/adugnag/deSpeckNet (original Matlab code) | https://github.com/adugnag/deSpeckNet-TF-GEE (python implementation to seamlessly integrate Sentinel-1 SAR image preparation in GEE with deep learning in Tensorflow for SAR image despeckling)

Code for CV-deSpeckNet (Mullissa et al, 2021) - Despeckling Polarimetric SAR Data Using a Multistream Complex-Valued Fully Convolutional Network: https://github.com/adugnag/CV-deSpeckNet

Code for Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine (Mullissa et al, 2021): https://github.com/adugnag/gee_s1_ard