Estimation of Solar Energy to be Produced in Changing Land Use of Bina River Basin, Madhya Pradesh

Landuse/landcover (LULC) variations play a significant role in the investigation and monitoring of worldwide alterations. LULC and regular adjustments have generally brought about biodiversity misfortune, deforestation, an unnatural weather change, and increased flood events. With the creation of GIS (Geographical Information System) and remote sensing approaches, LULC planning has specified a valuable and comprehensive approach to enhance the choice of zones intended for industrial, urban, and agricultural regions of an area. Various strategies have been designed for extracting information about the earth’s surface by means of image classification, which are commonly divided as supervised and unsupervised classification, based on the availability of reference data. In this chapter, the supervised method of classification has been used to find the available classes. The land use/land cover classification has been done for satellite images of 2000, 2009, 2014, and 2019 through ERDAS IMAGINE 2015 software. It has been identified that land use has undergone major changes from the year 2000 – 2019. The drastic increase of the builtup area or urban area was from 3.12% to 20.25%, an increase in vegetation area was from 8.81% to 19.60%, an increase in water bodies was from 0.24% to 1.12%, while a reduced land cover in agriculture was from 77.24% to 57.44% and a decrease in barren land was from 10.59% to 1.59% from the year 2000 to 2019. This investigation clearly reveals the critical influence of population and its advancement exercises on LULC change. The current examination outlines that GIS and remote sensing are significant advancements for temporal examination and measurement of spatial occurrence, which is generally impractical to endeavor through ordinary planning strategies. Additionally, in this chapter, an attempt has been made to predict the amount of power generation through solar energy by installing solar panels in the identified barren land, which is obtained by image processing technique. It is predicted that 3 MW of power equivalent to around 4800 MWh of solar energy can be generated.