Assessment of Land Use-land Cover Change in Irga River Catchment Using Object-based Image Classification Technique

Abstract

Land use-land cover (LULC) change analysis is essential for understanding the spatial and temporal change of landscape during a known long period for sustainable management of natural resources. The main objective of this study was to assess land use-land cover change using an object-based image classification technique which is a recent image classification technique with better accuracy than traditional pixel based image classification. The study was conducted in the catchment area of the Irga River, a tributary of the Barakar River, which falls in the Giridih district of Jharkhand (India). The catchment of the study area was delineated using SRTM DEM data (30 m spatial resolution). LANDSAT images (TM and OLI-TIRS) were used to develop the land use- land cover maps of 1997, 2007, and 2017 using object-based image analysis (OBIA). The images were classified and analyzed using ArcGIS and eCognition Developer 64 software. The accuracy of the classified images for each year was assessed by preparing the error matrix and calculating the Kappa coefficient. The overall accuracies of classified images were computed to be 88%, 83% and 91% while Kappa coefficients were found to be 0.8455, 0.7706 and 0.8796 for year 1997, 2007 and 2017 respectively. Over the 20 years (1997-2017), agricultural land increased by 12.23%, settlement increased by 76.62%, wasteland decreased by 39.59%, vegetation increased by 14.83%, water-bodies increased by 26.29%, and river area decreased by 16.66%. The analysis indicated an increasing trend in agricultural land, settlement, and vegetation while decreasing trends in wasteland and river areas. However, no definite trend was observed in the extent of the water-bodies.The results indicated that waste land greatly reduced and converted into settlement and agricultural land in the catchment.