EGU24-19533, updated on 11 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Detection of Forest disturbances using multi source Remote sensing data. 

Basil Tufail, Emilio Dorigatti, Michele Claus, Alexander Jacob, and Peter James
Basil Tufail et al.
  • Eurac , Earth Observation, Bolzano, Italy (

With recent impacts due to the climate crisis, the number of extreme events has increased globally. Floods, droughts, windthrows, and landslides are affecting the environment around us, increasing the difficulty of mapping and monitoring its conditions.  Forests are particularly suffering from such events and the ones affected by repeated damage usually don’t have enough time to recover and become more vulnerable to other threats. The 2018 Vaia storm, the subsequent snow breaks, and the spread of bark beetles in the forests of Trentino-South Tyrol are prominent examples that caused large-scale disturbances in the region. In this research, we focus on the detection of forest disturbances caused by such extreme events. Approaches like Breaks for Additive Season and Trend (Bfast) have been implemented to detect breaks and vegetation response patterns [1] whereas Continuous Change detection and Classification (CCDC) also performs well with long-term optical time series data like Landsat and Sentinel-2 to monitor vegetation phenology [2]. However, only a few studies have focused on advances in Synthetic Aperture Radar (SAR) data for detecting changes in radar datasets [3]. SAR data can provide timely information on disturbances in areas where frequent cloud cover makes it impossible to map the changes with optical data for long periods. Thus, the ability to acquire imagery regardless of clouds and severe weather conditions makes SAR data a viable solution to map such disturbances, though this still requires further testing. This study aims at benchmarking methodologies applied using open-source software's to create change detection maps with freely available data including both optical and SAR. Using various, already established change detection methods implemented in FAIR (Findability, Accessibility, Interoperability, and Reusability) manner to evaluate the added benefit of fusing data from different kinds of sensors. 

[1] Watts, Laura M., and Shawn W. Laffan. "Effectiveness of the BFAST algorithm for detecting vegetation response patterns in a semi-arid region." Remote Sensing of Environment 154 (2014): 234-245. 

[2] Zhou, Qiang, Jennifer Rover, Jesslyn Brown, Bruce Worstell, Danny Howard, Zhuoting Wu, Alisa L. Gallant, Bradley Rundquist, and Morgen Burke. "Monitoring landscape dynamics in central us grasslands with harmonized Landsat-8 and Sentinel-2 time series data." Remote Sensing 11, no. 3 (2019): 328. 

[3] Hirschmugl, Manuela, Janik Deutscher, Carina Sobe, Alexandre Bouvet, Stéphane Mermoz, and Mathias Schardt. "Use of SAR and optical time series for tropical forest disturbance mapping." Remote Sensing 12, no. 4 (2020): 727. 

How to cite: Tufail, B., Dorigatti, E., Claus, M., Jacob, A., and James, P.: Detection of Forest disturbances using multi source Remote sensing data. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19533,, 2024.