Learning-Based Methods for Detection and Monitoring of Shallow Flood-Affected Areas: Impact of Shallow-Flood Spreading on Vegetation Density

Abstract

Abstract This study aims to investigate the impacts of shallow flood spreading on vegetation density using a time-series collection of Landsat images spanning 2012–2020. To do this, Support Vector Machine (SVM), Random Forest (RF), Classification and Regression tree (CART) and Deep Learning Convolutional Neural Network (DL-CNN) algorithms were employed for flood-affected areas mapping and monitoring. The models were trained by using 214, 235, 230, and 219 ground truth data for years 2012, 2014, 2017 and 2020 respectively. Our accuracy assessment via the area under curve (AUC) method reveals that the DL-CNN outperforms the SVM, the RF and the CART models for detecting and mapping shallow-flood-affected areas. The findings of this study further revealed significant changes in the NDVI values within a period before and after flood occurrence. While the mean values of the NDVI were estimated 0.232, 0.221, 0.213, and 0.232 for years 2012, 2014, 2017, and 2020, respectively, prior to flood spreading, these values increased up to 0.464, 0.476, 0.355 and 0.444, respectively following flood occurrence. Furthermore, physical-chemical soil properties (e.g., clay, EC, Na, and MgHCO3), have grown considerably in the study region following the flood spreading.