Direct Disaster Losses: A Case Study of Los Angeles and California Wildfire

Rajani Kant Awasthi

Abstract: Natural disasters, particularly wildfire, floods and cyclones, are biggest threats, causing substantial economic losses. While tangible losses are calculated post-disaster events, indirect losses impacting the broader economy are frequently overlooked. This study aims to compute direct losses for Los Angeles and California wildfire, severely affected, resulting in a direct economic loss of 122 billion dollars. In this study, the complete remote sensing techniques have been employed to assess the direct losses. The Earth Engine version of the Fire Information for Resource Management System (FIRMS) dataset contains the LANCE fire detection product in rasterized form. The near real-time (NRT) active fire locations are processed by LANCE using the standard MODIS MOD14/MYD14 Fire and Thermal Anomalies product. Each active fire location represents the centroid of a 1km pixel that is flagged by the algorithm as containing one or more fires within the pixel. The data are rasterized as follows: for each FIRMS active fire point, a 1km bounding box (BB) is defined; pixels in the MODIS sinusoidal projection that intersect the FIRMS BB are identified; if multiple FIRMS BBs intersect the same pixel, the one with higher confidence is retained; in case of a tie, the brighter one is retained. The image collection ee. Image Collection("FIRMS") has been processed considering bands ? T21? (The brightness temperature of a fire pixel using MODIS channels 21/22) and ?confidence? (A detection confidence intended to help users gauge the quality of individual active fire pixels. The confidence estimate ranges between 0% and 100% for all fire pixels within the fire mask. The confidence field should be used with caution; it is likely that it will vary in meaning in different parts of the world.)

Keywords: Natural disaster, Wildfire, direct economic loss, Remote Sensing and GIS