Land Use and Land Cover Change Detection in the Bhavani Basin of India through Remote Sensing Approach

Abstract

Using remote sensing data to detect changes in land use and land cover (LULC) is a valuable source of information for various decision support systems. Land use and land cover identification data was used to analyse land conservation, sustainable development, and water resource management. This research aims to determine how the Bhavani basin land use and land cover have changed over the period of time. Land cover changes were detected using Landsat Thematic Mapper (TM) 30 m resolution images in the GIS environment and with image processing techniques for the four years 1999, 2007, 2014, and 2020. The differences in the landuse and land cover classes are described using ERDAS imagine version 2015 and ARC GIS software. The four land cover classes viz. water body, built-up land, barren land, and vegetation were used to classify the region. The accuracy evaluation was assessed separately using the kappa coefficient after carefully examining the image pre-processing and classification. The overall accuracy in the basin was found to be 83.23%, 86.45%, 85.83%, and 88.75 % with a kappa coefficient of 0.79, 0.81, 0.87, 0.85 for the years 1999, 2007, 2014, and 2020 respectively. The Bhavani basin is mostly covered by barren and vegetation. According to the findings, the basin's built-up area has risen by 1.5 percentage to 3.5 percentage in the last 20 years. The increase in the vegetation area and reduction in the barren area may lead to low soil erosion.

Assessing the Impacts of Future Land Use Land Cover and Climate Change on the Water Balance Components and Sediment Yield of The Bhavani Watershed in India

“…Many land prediction models, such as Dinamica, IDRISI's CA_MARKOV, FLUS, SLEUTH, Artificial Neural Network Markov Chain, CA-ANN, CLUE-S, and SERGoM, have been used to predict future LULC. Each model exhibits a specific set of strengths and limitations in effectively addressing the multifaceted issues related to LULC (Abbas et al, 2021;Manikandan & Rangarajan, 2022). Cellular automata models are frequently employed to simulate LULC changes, as they are effective in capturing complex and spatially-random LULC dynamics exhibiting non-linear behavior.…”