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 (optical and radar) satellite 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 of 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 solution 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. Openly accessible and timely information on the dynamics and drivers of changes in forests are urgently needed to empower sustainable land management, and law enforcement actions against illegal activities.

We aim to use these unprecedented opportunities of new radar sensors to monitor dynamics in forest landscapes at global scale, high spatial resolution and in near real-time.

Our core research lines are:

  • Fundamental radar methods
  • Near real-time change monitoring
  • Multi-sensor methods combining radar and optical data
  • Characterising forest changes and dynamics

Current PhD and Postdoc projects

  • Dr. Adugna Mullissa (Postdoc): Deep learning for radar-based near-real time change monitoring
  • Dr. Yaqing Gou (Postdoc): Near real-time forest change monitoring
  • Dr. Milutin Milenkovic (Postdoc): Monitoring tropical forest recovery and resilience using big data approaches for optical and radar Sentinel 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 (Junior researcher): Radar-based near real-time change monitoring

Former colleagues

  • Dr. Christelle Braun (Postdoc; 2018 – 04/2020)

Key peer-reviewed references

Fundamental radar methods

  1. Vollrath, A.; Mullissa, A.; Reiche, J. (2020): Angular-Based Radiometric Slope Correction for Sentinel-1 on Google Earth Engine. Remote Sensing. 12, 1867. DOI:10.3390/rs12111867
  2. Mullissa, A. G., Persello, C. and Stein, A. (2019): PolSARNet: A Deep Fully Convolutional Network For Polarimetric SAR Data Classification. Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2, 12, pp. 5300-5309. DOI:10.1109/JSTARS.2019.2956650.
  3. Hoekman, D. H. & Reiche, J. (2015): Multi-model radiometric slope correction of SAR Images of complex terrain using a two-stage semi-empirical approach. Remote Sensing of Environment, 156, 1-10. DOI:10.1016/j.rse.2014.08.037.

Near real-time change monitoring

  1. Brombacher, J., Reiche, J., Dijksma, R. & Teuling A.J. (2020): 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, 111684. DOI: https://doi.org/10.1016/j.rse.2020.111684
  2. Reiche, J., Verhoeven, R.; Verbesselt, J.; Hamunyela, E.; Wielaard, N. & Herold, M. (2018) Characterizing Tropical Forest Cover Loss Using Dense Sentinel-1 Data and Active Fire Alerts. Remote Sensing, 10, 5, 777, doi:10.3390/RS10050777.
  3. Reiche, J., Hamunyela, E., Verbesselt, J., Hoekman, D. & Herold, M. (2018): 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, 147-161. DOI: http://dx.doi.org/10.1016/j.rse.2017.10.034
  4. Reiche, J., Lucas, R., Mitchell, A.L., Verbesselt, J., Hoekman, D.H., Haarpaintner, J., Kellndorfer, J.M., Rosenqvist, A., Lehmann, E.A., Woodcock, C.E., Seifert, F.M. & Herold, M. (2016): Combining satellite data for better tropical forest monitoring. Nature Climate Change, 6, 2, 120-122. DOI:10.1038/nclimate2919.
  5. Reiche, J., de Bruin, S., Hoekman, D. H., Verbesselt, J. & Herold, M. (2015): A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection. Remote Sensing, 7, 4973-4996. DOI:10.3390/rs70504973.
  6. Reiche, J., Verbesselt, J., Hoekman, D. H. & Herold, M. (2015): Fusing Landsat and SAR time series to detect deforestation in the tropics. Remote Sensing of Environment. 156, 276-293. DOI: 10.1016/j.rse.2014.10.001.

Multi-sensor methods combining radar and optical data

  1. Brombacher, J., Reiche, J., Dijksma, R. & Teuling A.J. (2020): 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, 111684. DOI: https://doi.org/10.1016/j.rse.2020.111684
  2. Slagter, B., Tsendbazar, N., Vollrath, A. & Reiche, J. (2020): 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, 102009
  3. Reiche, J., Hamunyela, E., Verbesselt, J., Hoekman, D. & Herold, M. (2018): 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, 147-161. DOI: http://dx.doi.org/10.1016/j.rse.2017.10.034
  4. Reiche, J., Lucas, R., Mitchell, A.L., Verbesselt, J., Hoekman, D.H., Haarpaintner, J., Kellndorfer, J.M., Rosenqvist, A., Lehmann, E.A., Woodcock, C.E., Seifert, F.M. & Herold, M. (2016): Combining satellite data for better tropical forest monitoring. Nature Climate Change, 6, 2, 120-122. DOI:10.1038/nclimate2919.
  5. Reiche, J., de Bruin, S., Hoekman, D. H., Verbesselt, J. & Herold, M. (2015): A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection. Remote Sensing, 7, 4973-4996. DOI:10.3390/rs70504973.
  6. Reiche, J., Verbesselt, J., Hoekman, D. H. & Herold, M. (2015): Fusing Landsat and SAR time series to detect deforestation in the tropics. Remote Sensing of Environment. 156, 276-293. DOI: 10.1016/j.rse.2014.10.001.
  7. Reiche, J., Souza, C., Hoekman, D. H., Verbesselt, J., Haimwant, P. & Herold, M. (2013): Feature level fusion of multi-temporal ALOS PALSAR and Landsat data for mapping and monitoring of tropical deforestation and forest degradation. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 6, 5, 2159–2173. DOI: 10.1109/JSTARS.2013.2245101.

Characterising changes and dynamics

  1. Mulatu, K.A., Decuyper, M., Brede, B., Kooistra, L., Reiche, J., Mora, B. & Herold, M. (2019): Linking Terrestrial LiDAR Scanner and Conventional Forest Structure Measurements with Multi-Modal Satellite Data. Forests, 10, 3. doi: https://doi.org/10.3390/f10030291
  2. Reiche, J., Verhoeven, R.; Verbesselt, J.; Hamunyela, E.; Wielaard, N. & Herold, M. (2018): Characterizing Tropical Forest Cover Loss Using Dense Sentinel-1 Data and Active Fire Alerts. Remote Sensing, 10, 5, 777, doi:10.3390/RS10050777.

Other related publications

  1. Herold, M., Carter, S., Avitabile, V., Espejo, A.B., Jonckheere, I., Lucas, R., McRoberts, R., Næsset, E., Nightingale, J., Petersen, R., Reiche, J., Romijn, E., Rosenqvist, A., Rozendaal, D., Seifert, F-M., Sanz, M.J., De Sy, V (2019): The Role and Need for Space-Based Forest Biomass-Related Measurements in Environmental Management and Policy. Surveys in Geophysics, 40, 4. doi: https://doi.org/10.1007/s10712-019-09507-1
  2. Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Jepsen, M.R., Kuemmerle, T., Meyfroidt, P., Mitchard, E.T.A., Reiche, J., Ryan, C.M. & Waske, B. (2016): A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring. Remote Sensing, 8, 1, 70. D:10.3390/rs8010070.

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.