Machine learning system to assess rice crop change detection from satellite-derived RGVI due to tropical cyclones using remote sensing dataset

Abstract

Tropical cyclones strike massive devastation of agricultural crops in South Asian countries practically every year. For assessing impacts on primary land use and land cover (LULC) classes of Patuakhali district in Bangladesh, this study utilized the Landsat-8 image dataset to compute change detection indices of a consistent cyclone landfall period in the late rainy season (October and November). Different stage of cyclones based on their wind speed (km/h) were also analyzed to explore agricultural crop change detection due to the tropical cyclone (TC) Bulbul (made landfall on November 9th, 2019). Three years later, cyclone Sitrang made landfall on October 24th, 2022, and was employed for the test results that the Bulbul's rice growth vegetation index (RGVI)-CD trained in the study's machine learning (ML) system. Therefore, the objective of this study was to determine rice crop changes from RGVI due to a cyclone (Bulbul) to evaluate growth change status by another cyclone (Sitrang) using machine learning system. In Patuakhali district, the rice crops were damaged mainly and affected in classification as moderately changed detected (MCD) 26.07%, very changed detected (VCD) 48.83%, and extremely changed detected (ECD) 17.73% in total agricultural lands area (1548.93 km2). It was also significantly monitored for the rice crops to change as rice growth vegetation index (RGVI)-CD related to cyclone wind speed (km/h), DEM, distance from the rivers, and shorelines. From the result, the symmetric regression of coefficient (R2) of (i) linear regression, (ii) fine tree, (iii) support vector machine, (iv) ensemble boosted trees, and (v) trilayered neural network were obtained 0.735, 0.734, 0.737, 0.756 and 0.736 as good-fitted test outcomes respectively. Applying the five machine learning models that were created from cyclone Sitrang, it was discovered that the test set in this system was accurate to the observed forecast with acceptable RMSE and MAE values. Therefore, study can be helpful for researchers creating new damaged and yield loss area assessment policies to help TC-affected farming communities, especially rice growers, prepare an effective emergency response plan for their socioeconomic needs fulfillment in the changing climate perspectives for attaining sustainable development.