Assessment of post-fire land degradation risk in Lebanon’s 2019 fire affected areas using remote sensing and GIS

George Mitri; Georgy Nasrallah; Karen Gebrael; Joseph Beshara; Maya Nehme

doi:10.1117/12.2571119

26 August 2020 Assessment of post-fire land degradation risk in Lebanon’s 2019 fire affected areas using remote sensing and GIS

George Mitri, Georgy Nasrallah, Karen Gebrael, Joseph Beshara, Maya Nehme

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Proceedings Volume 11524, Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020); 115240T (2020) https://doi.org/10.1117/12.2571119
Event: Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020), 2020, Paphos, Cyprus

Abstract

Assessment of potential risk of land degradation, especially after fire events, is essential for identifying and prioritizing sites for post-fire management. This study aimed to model potential post-fire land degradation risk in Lebanon in order to plan and prioritize post-fire management actions. The specific objectives were to 1) map “burn severity” of the year 2019 using Landsat 8 and Sentinel 2-A imagery and 2) model post-fire land degradation risk using a Geographic ObjectBased Image Analysis (GEOBIA) approach. Initially, mapping burned areas of the 2019 fire season involved the use of a differenced Normalized Burn Ratio (dNBR) of Sentinel 2-A imagery. Another dNBR image was produced with the use of Landsat 8 imagery. Mapping overall burn severity was conducted based on cross-mapping between the two dNBR images which involved data sources of different spatial and spectral characteristics. Potential post-fire degradation risk was modelled in GEOBIA with the combined use of burn severity and topographic data (i.e., slope gradients). In addition, field data was collected to produce a Composite Burn Index (CBI) of fire-affected sites. The correlation between field data (i.e., CBI scores) and imagery analysis (i.e., dNBR data from Sentinel 2-A and Landsat 8) was assessed using a Spearman correlation test. The combined use of satellite remote sensing images (i.e., polygons of postfire degradation risk) and their corresponding ancillary data (i.e., CBI and soil texture) allowed the prioritization of fire affected sites for implementing post-fire restoration measures.

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George Mitri, Georgy Nasrallah, Karen Gebrael, Joseph Beshara, and Maya Nehme "Assessment of post-fire land degradation risk in Lebanon’s 2019 fire affected areas using remote sensing and GIS", Proc. SPIE 11524, Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020), 115240T (26 August 2020); https://doi.org/10.1117/12.2571119

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KEYWORDS

Earth observing sensors

Landsat

Soil science

Data modeling

Vegetation

Image analysis

Image segmentation

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