Assessment of spatiotemporal dynamics of land and vegetation cover change detection in Maze National Park, Southwest Ethiopia

Environ Monit Assess. 2022 May 27;194(7):460. doi: 10.1007/s10661-022-10039-2.

Abstract

Biodiversity conservation areas include National Parks which are integral parts of protected areas. In parks, the dynamics of land and vegetation covers are significantly affected by anthropogenic activities and natural factors. These resources are exposed to challenges due to unwise practices. Assessing and monitoring the status of park's natural resources enables us to understand the extent of land and vegetation resources. Few studies have been conducted in Maze National Park (MzNP) but none of them quantified the land and vegetation cover dynamics. The purpose of this study is, therefore, to quantify the extents spatiotemporal dynamics of land and vegetation cover of MzNP using GIS and RS technology considering three decades Landsat images acquired from USGS. GPS and GCPs were collected for the analysis of land cover classification and accuracy assessment, respectively. The trend analysis result revealed that scattered tree and grass land cover area have increased from 19.8% in 1987 to 51.6% in 2019. The area of sparse vegetation cover increased from 19.7% in 2000 to 78.4% in 2019. Highly dense and dense vegetation cover decreased remarkably. Accuracy assessment was 98% and computed Kappa coefficient was 0.97. The results suggested that the extent of MzNP's vegetation cover change was basically due to anthropogenic pressure. This study could be used to alarm responsible bodies to restructure and implement better environmental protection strategies for restoration of the rapidly deteriorating vegetation resource of MzNP.

Keywords: Accuracy assessment; Change detection; Error matrix; Kappa coefficient; Land use land cover; Maze National Park; NDVI; Spatiotemporal dynamics.

MeSH terms

  • Conservation of Natural Resources
  • Environmental Monitoring* / methods
  • Ethiopia
  • Parks, Recreational*
  • Trees