Agricultural damage assessment for paddy fields from land use land cover and flood extent maps of five unions of Sunamganj using remote sensing–based analysis

Abstract

Flooding is a major issue in Bangladesh, and the severity of floods grows each year, resulting in fatalities, crop damage, and economic loss. A wave of high-intensity flash floods from the neighbouring country's mountains hit Bangladesh's Sylhet division in 2022. The research aimed to generate a map to identify the extent of floods during peak flood days in Sunamganj unions. Inundation images were gathered via the Google Earth Engine (GEE) platform, with threshold techniques used to detect flooded areas. During the primary data collection field visit, areas of four different categories—rivers, haors, paddy fields, and trees—were observed and pinpointed in Google Maps. They were then used to train classifiers in GEE to identify and classify each category to produce the land use/land cover (LULC) maps of pre- and post-flood events. Ground truthing was performed to determine the accuracy of the flood extension, duration, and agricultural loss. The flood extension map of the study area was found to be a close match to the map collected from the Flood Forecasting and Warning Centre (FFWC). A damage and economic loss assessment for affected vegetation areas was done using the flood extent map and the pre- and post-flood LULC maps. There was a significant change in LULC during flood events, particularly in paddy fields and haors. The study's findings estimate significant economic loss due to agricultural land damage. Maps of flooded areas of the kind produced in the study can help decision-makers take prompt action.

JBIP, Vol. 16, 2023, pp. 1-18