Spatially Explicit Scenario Analysis of Habitat Quality in a Tropical Semi arid Zone: Case Study of the Sokoto–Rima Basin.
No Thumbnail Available
Raji, Saheed A
Global biodiversity has been steadily dwindling since the turn of the twentieth century. The semi-arid ecological zone has also been decimated to varied degrees due to natural and human-caused reasons. To counteract this trend, a variety of multiscale techniques with differing degrees of spatiotemporal uncertainty have been used. We study landcover-based scenarios related with the condition of biodiversity in the Sokoto-Rima basin (SRB) of northwestern Nigeria to address this gap. The Future Landuse Simulation Software (FLUS) and the spatially explicit InVEST Habitat Quality (HQ) model were used to simulate four alternative HQ scenarios: business as usual (BAUS), accelerated crop expansion (ACES), woodland expansion (WES), and sustainable development scenario (SDS). The data used was ESA CCI landcover (1992–2015), with underlying drivers of landcover prediction including multi-sourced terrain, population, settlements, highways, rivers, waterbodies, rainfall, and temperature. With a kappa coefficient of 0.83, overall accuracy of 93 percent, and a figure of merit of 0.15, the landcover data meets the requirements for multi-scenario modelling. Cropland dominated the SRB between 1992 and 2015, and this tendency is projected to continue in the future. By 2038, it is expected that WES and SDS will improve biodiversity, as represented by a high HQ index of 0.75, whereas BAUS and ACES will degrade the SRB more, as indicated by a low HQ index. As a result, efforts to increase the SRB’s biodiversity necessitate the strict implementation of environmentally friendly laws. This research can be used to examine similar semiarid zones using a scenario-based approach.
Saheed A. Raji, Shakirudeen Odunuga, Mayowa Fasona (2022): Spatially Explicit Scenario Analysis of Habitat Quality in a Tropical Semi arid Zone: Case Study of the Sokoto–Rima Basin. Journal of Geovisualization and Spatial Analysis (2022) 6:11. https://doi.org/10.1007/s41651-022-00106-0. SPRINGER