Land use land cover change detection using gis and remote sensing: case study of soro district, southern Ethiopia.

Abstract

Land use/ land cover change has become a central component in current strategies for managing natural resources and monitoring environmental changes. Hence, information about it is essential for the selection, planning and implementation of land use schemes. This project examines the use of GIS and Remote Sensing in mapping Land Use/Land Cover in Soro District between 1987 and 2017 so as to detect and analyze the changes that has taken place in their status between these periods. In order to achieve these, satellite data of Landsat MSS for 1987, TM for 2002 and ETM for 2017 have been obtained and preprocessed using ERDAS Imagine. The Maximum Likelihood Algorithm of Supervised Classification has been used to generate land use and land cover maps. For the accuracy of classified Land Use/Land Cover maps, a confusion matrix was used to derive overall accuracy and results were above the minimum and acceptable threshold level. Post classification comparison change detection method was employed to identify gains and losses between Land Use/Land Cover classes. The satellite image results show that cultivated land decreased in the first period but increased in the second and the entire study periods. Grassland increased in the first period and decreased in the second period. Wetland is the most converted cover type during the entire study period and forest increased in first study period and decreased in second study period. The impact of this LULC change is more significant on the socioeconomic condition and status of the study area.