Msc thesis subject: Analysing Spatial Patterns of Tropical Forest with Radar Images (Study site: Brazil)

Forest structure is an important parameter for characterising and analysing the state and resilience of tropical forest ecosystem. With Sentinel-1, the European Space Agency is providing free synthetic aperture radar (SAR) images with global coverage, and high spatial- and temporal- resolution. Such data offers the possibility to assess the spatial patterns of tropical forest in details that were not available before.

The aim of this work is to quantify and analyse structure and spatial patterns of tropical forest using segments derived from Sentinel-1 radar images. The study area is located in the Northeast Brazil and covers a fragmented forest around the Amazon river. The input for the segmentation will be annual statistics (mean, median standard deviation, etc.) derived from Sentinel-1 radar time series. Different segmentation algorithms should be tested and evaluated based on their robustness on radar measurement and processing errors. In addition, independent segmentations should be derived using annual data of different years. In the last stage of this work, the segmentation will be validated using airborne LiDAR data, i.e. airborne laser scanning products. Thee performed analysis should contribute to the understanding of radar signals over the vegetation and provide a base for analysing the vegetation structure at the segment level.


  • Derive and analyse multi-annual segments of tropical forest
  • Analyse different segmentation techniques for SAR images
  • Quantify spatial patterns of tropical forest based on derived segments
  • Validate the derived segments with airborne LiDAR data


  • Frison, P.-L.; Fruneau, B.; Kmiha, S.; Soudani, K.; Dufrêne, E.; Le Toan, T.; Koleck, T.; Villard, L.; Mougin, E.; Rudant, J.-P. Potential of Sentinel-1 Data for Monitoring Temperate Mixed Forest Phenology. Remote Sens. 2018, 10, 2049.
  • Rüetschi, M.; Schaepman, M.E.; Small, D. Using Multitemporal Sentinel-1 C-band Backscatter to Monitor Phenology and Classify Deciduous and Coniferous Forests in Northern Switzerland. Remote Sens. 2018, 10, 55.
  • Verhegghen, A.; Eva, H.; Ceccherini, G.; Achard, F.; Gond, V.; Gourlet-Fleury, S.; Cerutti, P.O. The Potential of Sentinel Satellites for Burnt Area Mapping and Monitoring in the Congo Basin Forests. Remote Sens. 2016, 8, 986. • Korosov, Anton & Park, Jeong-Won. (2016). Very High Resolution Classification Of Sentinel-1a Data Using Segmentation And Texture Analysis.
  • R. A. Hill (1999) Image segmentation for humid tropical forest classification in Landsat TM data, International Journal of Remote Sensing, 20:5, 1039-1044, DOI: 10.1080/014311699213082
  • Wan Mohd Jaafar, W.S.; Woodhouse, I.H.; Silva, C.A.; Omar, H.; Abdul Maulud, K.N.; Hudak, A.T.; Klauberg, C.; Cardil, A.; Mohan, M. Improving Individual Tree Crown Delineation and Attributes Estimation of Tropical Forests Using Airborne LiDAR Data. Forests 2018, 9, 759.
  • Franziska Taubert, Rico Fischer, Jürgen Groeneveld, Sebastian Lehmann, Michael S. Müller, Edna Rödig, Thorsten Wiegand & Andreas Huth (2018) Global patterns of tropical forest fragmentation, Nature volume 554, pages 519–522.


  • Advanced Earth Observation
  • Geo-scripting
  • (Geo-)Statistics

Theme(s): Sensing & measuring, Modelling & visualisation