Integrated use of the CA-Markov model and the Trends.Earth module to enhance the assessment of land cover degradation: Application in the Upper Zambezi Basin, southern Africa

Abstract

This study aims to demonstrate the potential of assessing future land cover degradation status by combining the forecasting capabilities of the Cellular-Automata-Markov chain (CA-Markov) models in Idris Selva with the land cover degradation (LCD) model in the Trends.Earth module. The study focuses on the upper Zambezi Basin (UZB) in southern Africa, which is one of the regions with high rates of land degradation globally. Landsat satellite imagery is utilised to generate historical (1993–2023) land cover and land use (LCLU) maps for the UZB, while the European Space Agency Climate Change Initiative (ESA CCI) global LCLU maps are obtained from the Trends.Earth module. The CA-Markov chain model is employed to predict future LCLU changes between 2023 and 2043. The LCD model in the Trends.Earth module in QGIS 3.34 is then used to assess the historical and forecasted land cover degradation status. The findings reveal that land cover degradation maps produced from local LCLU classifications provide more detailed information compared to those produced from the ESA CCI global LCLU product. Between 2023 and 2043, the UZB is predicted to experience a net reduction of approximately 3.2 million hectares of forest cover, with an average annual reduction rate of -0.13%. In terms of land cover degradation, the UZB is forecasted to remain generally stable, with 87% and 96% of the total land cover area expected to be stable during the periods 2023–2033 and 2033–2043, respectively, relative to the base years 2023 and 2033. Reduction in forest cover due to the expansion of grassland, human settlements, and cropland is projected to drive land cover degradation, while improvements in forest cover are anticipated through the conversion of grassland and cropland into forested areas. By leveraging the predictive power of the CA-Markov model and the capabilities of the LCD model, as evidenced in this study, valuable information can be effectively obtained for monitoring land cover degradation. This information can then be used to implement targeted interventions that align with the objective of realising the United Nations' land degradation neutral world target by 2030.