Land use and land cover change detection and prediction based on CA-Markov chain in the savannah ecological zone of Ghana

Abstract

Environmental problems have accompanied the accelerated land use and land cover change (LULCC), yet few local level studies make an attempt to assess the dynamics of LULCC. This work employed GIS and remote sensing to quantify the past and predict future dynamics of LULCC based on the synergy Cellular Automata (CA) - Markov Chain Model (MCM). The results revealed that agricultural land in the Bongo district witnessed the greatest expan sion from 10.03% to 27.17% of total area from 1990 to 2019, while wooded savannah area witnessed the greatest decline from a share of 42.26% to 15.51% of total area from 1990 to 2019. In the Kassena-Nankana West (KNW) district, shrub and tree savannah and agricultural land expanded from 32.91% to 54.2% and 9.44% to 18.16% of the total area, respectively, at the expense of wooded savannah area (-32.9% of total area) between 1990 and 2019. Future predictions based on prevailing socio-economic development demonstrate that the observed trend would continue till the 2050 period. In the Bongo district, the settlement area will witness the highest proportion of net increase in total area (5.63 km2) at the expense of wooded savannah (-11.26 km2) between 2019 and 2050. Conversely, in the KNW district, the shrub and tree savannah area will experience the highest proportion of net gain in total area (156.02 km2) at the expense of wooded savannah area (-111.49 km2) between 2019 and 2050. This result is an indication that the synergy CA-MCM have effectively captured the spatiotemporal trend in LULCC in this study.