Damaged Area Assessment of Cultivated Agricultural Lands Affected by Cyclone Bulbul in Coastal Region of Bangladesh Using Landsat 8 OLI and TIRS Datasets

Abstract Tropical cyclones cause tremendous damage to agricultural lands in global south countries almost every year. It is significantly important to identify the damage assessment for rice growing agricultural land areas in the south and south-east Asian countries. Therefore, the objective of this research was to develop a new damaged area assessment (DAA) method to measure the area of each damage type class (DTC) for cyclone-prone agricultural lands using Landsat 8 OLI and TIRS datasets. The weighted overlay method was incorporated with a pix-code sum (plus+) operation based on conditional pseudocode algorithm using NDVI, SAVI and SMI change detection (CD) and change type classes (CTC). The assessment of the damaged area classes was reported as (1) Not damaged (7.71 km2, 2.5%); (2) slightly damaged (32.96 km2, 10.66%); (3) moderately damaged (79 km2, 5.56%); (4) very damaged (131.56 km2, 42.56%); and (5) extremely damaged (57.85 km2, 18.72%) at cyclone Bulbul affected Kalapara sub-district in the coastal region of Bangladesh. This cyclone-prone hotspot was adjacent with the Bay of Bengal and total 420 ground reference points were selected by their farmers’ location to validate our research. In the study, it was randomly observed that totals of 4 (0.95%); 13 (3.10%); 52 (12.38%); 205 (48.81%); and 146 ground reference points (34.76%) were accurately distributed into the said damaged type classes (1-5) respectively. The DAA method with DTCs could be helpful for researchers creating new yield loss assessment policies to help affected farming communities to prepare an effective recovery plan for their needs fulfilment in the cyclone-prone countries.