Monitoring Land Cover changes in the tropical high forests using multi-temporal remote sensing and spatial analysis techniques

Abstract

Abstract The study applied multi-temporal optical remote sensing and spatial analytical techniques to assess the status of the eight (8) forest reserves in the tropical high forests of Ghana under the management of the Goaso Forest District. Landsat data of 1990 (Thematic mapper [TM]), 2013 (Enhanced Thematic Mapper [ETM+]) and 2017 Operational Land Imager/Thermal Infrared Sensor (OLI_TIRS) were classified into closed canopy forest, open canopy forest, bare land/built-up, farmland, and degraded land using the Maximum Likelihood algorithm. Post classification change detection techniques were then used to analyse the changes that had occurred over the study period. It was observed that about 37.63% of the closed canopy forests have been converted to the other covers types. The findings showed that the forest estates in the District, which was one of the richest in the High forests in terms of species diversity and richness, had been severely degraded. This could have dire implications on biodiversity, carbon sequestration and livelihood of fringe communities. Concurrent restoration of degraded forest cover and integration of remote sensing and spatial analysis techniques into routine forestry management practices at both District and National levels would provide an effective mechanism to curb the high rate of forests depletion in Ghana.