Exploring the future of Kafue National Park, Zambia: Scenario-based land use and land cover modelling to understand drivers and impacts of deforestation. (March 2023) - Dietz, Julia; Treydte, Anna Christina; Lippe, Melvin
Abstract: Land use and land cover (LULC) dynamics in tropical forests of sub-Saharan Africa are often difficult to quantify and predict, despite rapid forest losses and increasing human population pressure. As deforestation threatens the biodiversity of both flora and fauna, we used LULC change assessment and scenario modelling to analyse whether policy measures can safeguard the multi-functionality of tropical dry forests in western Zambia from 2010 to 2040. Our data comprised information on deforestation and human encroachment due to i.e., agricultural expansion, charcoal production, infrastructure development in the Kafue National Park (NP) and adjacent Game Management Areas (GMAs) (total area: 7, 102, 147 ha), which is part of the first Reducing Emissions from Deforestation and Forest Degradation (REDD+) focus areas in Zambia. We modelled a business-as-usual scenario (BAU) and four REDD+ policy-screening scenarios with varying levels of protection enforcement and future annual deforestation rates. We quantified scenario impacts on forest cover using three indicators: cropland and forest-related LULC trajectory, forest connectivity, and long-term carbon stock changes in 2040. Scenario results suggested that only under strong enforcement and low demand for agricultural areas, deforestation in Kafue NP and GMAs could be avoided by 93% (40, 457 ha) and 1% in carbon stocks could be gained by 2040 in comparison to BAU. Spatial analyses revealed that cropland expansion will continue to encroach protected areas. We highlight that variations in carbon stocks and forest fragmentation were small across scenarios which has implications for land use management and the expected future benefits of REDD+ projects. The combination of GIS, scenario development and LULC modelling helped to identify and locate potential future deforestation and LULC changes. This can support appropriate management pathways of REDD+ induced local and national leakage effects and related decision making. Highlights: LULC prediction for 2040 using a multi-layer perceptron neural network algorithm to locate potential future deforestation. Development of a BAU and four policy-screening scenarios with varying protection enforcement and deforestation demands. REDD+ scenarios reveal that encroachment inside protection zones can be reduced compared to BAU. REDD+ scenarios reveal that agricultural areas in the northeast and south of the leakage area continue to expand.