Skip to main content
SLU publication database (SLUpub)

Research article2019Peer reviewedOpen access

Assessment of vegetation dynamics using remote sensing and GIS: A case of Bosomtwe Range Forest Reserve, Ghana

Mensah, Alex Appiah; Sarfo, Daniel Akoto; Partey, Samuel Tetteh

Abstract

Changing conditions owing to increasing forest fragmentation make land cover and change detection analysis an extremely important consideration for sustainable forest management. This study applied supervised classification using maximum-likelihood algorithm in Quantum GIS to detect land use land cover changes in the Bosomtwe Range Forest Reserve, Ghana from 1991, 2002 and 2017 using Landsat 4 - TM, Landsat 7 - ETM and Sentinel-2 satellite imageries respectively. Based on the results of the study, it is concluded that land use/cover of Bosomtwe Range Forest Reserve have undergone remarkable changes for over the period of 26 years. The current status of forest cover is estimated to be 2995.45 +/- 401.86 ha and 2090.03 +/- 412.78 ha of closed and opened forest canopy respectively. Conversely, built-up areas (1531.68 +/- 487.13 ha) remains virtually high (20%) though it shows a decrease in comparison to the same area in 2002. The land use land cover change map clearly identified probable areas of forest depletion especially in the north eastern and western portions of the reserve. It is recommended that potential spatial drivers of change should be identified to generate suitable image for change modelling of the reserve, coupled with earmarking of degraded areas for reforestation projects to improve upon the forest cover. (C) 2018 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V.

Keywords

Land use/cover change; Change detection; Supervised classification; Bosomtwe range

Published in

The Egyptian journal of remote sensing and space sciences
2019, Volume: 22, number: 2, pages: 145-154
Publisher: ELSEVIER

    Sustainable Development Goals

    Ensure sustainable consumption and production patterns

    UKÄ Subject classification

    Remote Sensing

    Publication identifier

    DOI: https://doi.org/10.1016/j.ejrs.2018.04.004

    Permanent link to this page (URI)

    https://res.slu.se/id/publ/105503