MSc thesis topic: Combining C- and L-Band radar data for characterizing tropical forest disturbances
Tropical forests experience a vast variety of forest disturbances. The environmental impact of these forest disturbances varies depending on their intensity - which parts of the trees are affected (e.g. tree crown, tree trunk, etc.). Mapping and characterizing these forest disturbances is of great interest for stakeholders and policy makers for limiting their impact and counteracting illegal logging activities.
A detailed characterisations is hampered by cloud coverage for optical sensors. Hereby, radar satellites allow for an opportunity to penetrate clouds and detect large scale forest disturbance also during the night. Nevertheless, freely and commonly used C-band Sentinel-1 radar data is limited due to its sensitivity to changes of tree foliage (wavelength) in the tropics.
Shorter (e.g. C-band) and longer wavelength (L-band) satellite radar data interact differently with various parts of trees. The wavelength of the radar data defines the capability to detect changes , as parts that are penetrated or interacted and backscattered are related to this. Therefore shorter and longer wave length are possible to interact with various parts and detect different forest disturbances.
The combination of dense C- (Senmtinel-1) and L-band (ALOS PALSAR-1/2) satellite time series can be of great help to identify and characterize various tropical forest disturbances in a more detailed way.
For this thesis both C-band (Sentinel-1) and L-band (ALOS PALSAR-2) data are pre-processed and ready to use for the student. The study area is the province Jambi (Indonesia), which shows historically distinct logging activities and despite being illegal ongoing fire usage for logging activities.
Firstly, the student should define key regions of interest (roi) indicating various forest disturbance events (e.g. fire, logging with remaining structure) by visually inspecting optical very high resolution satellite data (e.g. Planet Labs).
Secondly, backscatter and GLCM texture measures responses will be explored and compared. The student assesses the potential of C- and L-band backscatter and GLCM textures for detecting and characterizing fire related and logging forest disturbances with remaining structure.
Finally, the student develops a framework for detecting/characterizing of various forest disturbances utilizing C- and L-band radar data, based on backscatter, GLCM texture and the combination of both.
Software: GEE, R/Python
- Assess the potential to detect and characterize tropical forest disturbances utilizing C- and L-band radar data
- Explore backscatter and GLCM texture disturbance values for C- and L-Band
- Identify similarities or differences for C- and L-Band
- Develop a framework to combine both data streams to characterize various forest disturbances
- CEOS & GFOI, A Layman’s Interpretation Guide to L-band and C-band Synthetic Aperture Radar data. Version 2.0. 2018.
- Manabu Watanabe, Christian N. Koyama, Masato Hayashi, Izumi Nagatani, Takeo Tadono, Masanobu Shimada, Refined algorithm for forest early warning system with ALOS-2/PALSAR-2 ScanSAR data in tropical forest regions, Remote Sensing of Environment, Volume 265, 2021, 112643, ISSN 0034-4257.
- Balling, J.; Verbesselt, J.; De Sy, V.; Herold, M.; Reiche, J. Exploring Archetypes of Tropical Fire-Related Forest Disturbances Based on Dense Optical and Radar Satellite Data and Active Fire Alerts. Forests 2021, 12, 456.
- Johannes Reiche, Eliakim Hamunyela, Jan Verbesselt, Dirk Hoekman, Martin Herold, 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, Volume 204, 2018, Pages 147-161, ISSN 0034-4257.
- Mryka Hall-Beyer (2017) Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales, International Journal of Remote Sensing, 38:5, 1312-1338.
- Hall-Beyer, M., 2007. GLCM Texture: A Tutorial v. 1.0 through 2.7.
- Advanced Earth Observation course
- Geo-scripting course (Good knowledge in scripting is an asset; e.g. Google Earth Engine, R, python, java script)
Theme(s): Sensing & measuring; Modelling & visualisation