Applications of Remote Sensing in Land Use Land Cover Mapping by Using Support Vector Machine

The primary aim of this chapter is to identify the land use and land cover (LULC) types of Malwa-specific region that lies in Madhya Pradesh (India). The concept of the digital change detection method performed through remote sensing data can aid toward identifying the changes that are in accordance with LULC information. The land use and land cover approach exploited satellite imagery. This study undertaking takes advantage of the support vector machines (SVM) for the pixel-by-pixel supervised classification of a Landsat satellite image in 2003 and 2023 using the ArcGIS tool for the 20 years of analysis in two parts. Covering general trends, different types of land use and land cover are taken into consideration, which are the lands with water bodies, agricultural lands, forests, and arid areas. To achieve this, it was possible to identify the changes based on the remote sensing using Landsat 5 image in year 2003 and Landsat 8 image in year 2023. In the study, support vector machine algorithm identifies land use and land cover categorization within the Malwa area of Madhya Pradesh, India. The kappa coefficient of 0.844 in the validation results of the supervised classification on support vector machines (SVM) is quite high. It demonstrates the ability to produce reliable coverage outcome, and SVM procedures are likely to be of particular value in land cover classification. In order to know the environmental status of the Malwa region in the state of Madhya Pradesh in India, this chapter explains the relevance of digital change detection.