Analysis of Land Use and Land Cover Changes in Urban Areas Using Remote Sensing: Case of Blantyre City

Abstract

Blantyre City has experienced a wide range of changes in land use and land cover (LULC). This study used Remote Sensing (RS) to detect and quantify LULC changes that occurred in the city throughout a twenty-year study period, using Landsat 7 Enhanced Thematic Mapper (ETM+) images from 1999 and 2010 and Landsat 8 Operational Land Imager (OLI) images from 2019. A supervised classification method using an Artificial Neural Network (ANN) was used to classify and map LULC types. The kappa coefficient and the overall accuracy were used to ascertain the classification accuracy. Using the classified images, a postclassification comparison approach was used to detect LULC changes between 1999 and 2019. The study revealed that built-up land and agricultural land increased in their respective areas by 28.54 km2 (194.81%) and 35.80 km2 (27.16%) with corresponding annual change rates of 1.43 km·year−1 and 1.79 km·year−1. The area of bare land, forest land, herbaceous land, and waterbody, respectively, decreased by 0.05%, 90.52%, 71.67%, and 6.90%. The LULC changes in the study area were attributed to urbanization, population growth, social-economic growth, and climate change. The findings of this study provide information on the changes in LULC and driving factors, which Blantyre City authorities can utilize to develop sustainable development plans.