リモートセンシングとGISのジャーナル

概要

Monitoring Forest Cover Change in Kalomo Hills Local Forest Using Remote Sensing and GIS: 1984-2018

Teddy Mbanga Mbanga*, Mark C. Mulenga, Garikai Membele

The purpose of the study was to use remote sensing and GIS to assess forest cover change in Kalomo hills local forest. It sought to determine land cover change, quantify forest cover change, and identify the drivers of forest cover change over this period. This was a case study which employed mixed methods. Primary data and secondary data sources were used. Landsat satellite imagery of the study area were used. Image processing, classification and analysis of remote sensing satellite imagery data was conducted using Arc GIS 10.3 to produce change maps, and land cover change statistics. The study employed supervised image classification using the maximum-likelihood classifier algorithm on Landsat images for 1984, 2004, and 2018. Change detection was performed using the post-classification comparison from which change matrices were generated. Thematic analysis was performed in Statistical Package for Social Scientists version 22 and Microsoft Excel 2013. Land cover change during the period under study was mainly from forest to cropland and grassland. The forest reserve covers 162, 200 hectares. It lost about 82, 975 hectares of forest since 1984 at an average rate of approximately 2, 514 hectares per year, equivalent to 2 percent per annum, leaving only 24.2 percent of the original forest unchanged. By 2018 forest cover as a percentage of the total forest reserve area was approximately 23.1 percent. The main proximate driver of forest cover change in Kalomo hills local forest was found to be agricultural expansion for cropland. The others were wood extraction and infrastructure extension. The main underlying driver of forest cover change was found to be population growth. The findings provide a basis for appropriate policy intervention to ensure sustainable utilisation of the forest and forest resources.