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Analysis of past and future multi-temporal land use and land cover changes in the semi-arid Upper-Mzingwane sub-catchment in the Matabeleland south province of Zimbabwe

ABSTRACT In this study, we analyse past spatio-temporal land use and land cover (LULC) change dynamics in the Upper-Mzingwane sub-catchment (UMS) located in the semi-arid Matabeleland south region of Zimbabwe using Landsat 5 Thematic Mapper (L5-TM) and Landsat 8 Operational Land Imager (L8-OLI) imagery for the periods of 1989, 2004, 2013, and 2018. We also model plausible future LULC scenarios for UMS. We distinguished five LULC classes, i.e. water, bareland, dense woodland, shrubland, and grassland using a hybrid approach entailing image classification in R-software using the Random Forest (RF) and Support Vector Machine (SVM) algorithms. Accuracy assessment and Kappa statistics revealed better performance of the SVM hence its outputs were used in the change analysis (i.e. to quantify LULC transitions and trends statistically). We then utilized the TerrSet Land Change Modeller (LCM) (using the Markov Chain algorithm with Multi-Layer Perceptron) to model future LULC scenarios up to 2038 at 5-year intervals. Results revealed that the grass, shrub, and woody vegetation are predominant land covers covering 48.5%, 31.5%, and 18.8% in 1989 and 54.4%, 28.8%, and 15.8%, respectively, in 2018. Dense woodland cover was projected to experience the greatest net loss of 43.57% while shrubland, grassland, water, and bareland increase by 10.73%, 4.5%, 26.85%, and 15.09%, respectively, between 2023 and 2038. We concluded that the UMS has since 1989, been losing and will continue to lose dense woodland cover into the future possibly due to increased human activities such as small scale and illegal gold mining in the area. As such, immediate remedial action needs to be taken to reverse the observed and possible future negative LULC change trends especially for woodland cover so as to avert likely adverse socio-economic, hydrological, and ecological consequences within and beyond the UMS.