Four decades of land cover and forest connectivity study in Zambia-An object-based image analysis approach

Land cover dynamics in the tropical dry environments of sub-Saharan Africa are not well understood compared to humid environments, especially on a national scale. Previous studies describing land cover in the dry tropics are spatially or temporally constrained. This study presents the first long-term (1972-2016) nationwide land cover dynamics and forest connectivity analyses for Zambia. We employed 300 Landsat images and an object-based image analysis (OBIA) approach with the Random Forests (RF) classifier to map nine land covers at six time steps (1972, 1984, 1990, 2000, 2008 and 2016). Post-classification change detection was used to understand the dynamics, while landscape metrics were derived to assess the forest structural connectivity. Overall accuracies ranging from 79 to 86% were achieved. In 1972, 48% of Zambia was covered by primary forest and 16% was covered by secondary forest. By 2016, 62.74% of Zambia had undergone changes, with primary forest decreasing by 32% and secondary forest increasing by 23%. Our results showed that forests have been recovering by 0.03 to 1.3% yr(-1) (53, 000-242, 000 ha yr(-1)); however, these rates are markedly lower than deforestation rates (- 0.54 to - 3.05% yr(-1): 83, 000-453, 000 ha yr(-1)). Annual rates of change varied by land cover, with irrigated crops having the largest increase (+ 3.19 yr(-1)) and primary forest having the greatest decrease (-2.48% yr(-1)). Forest connectivity declined by 22%, with primary forest having the greatest decline, while the connectivity for secondary and plantation forest increased. We showed here that land cover in Zambia is highly dynamic with high rates of change, low forest recovery and decline in forest connectivity. These findings will aid in land use planning, reporting for the forthcoming 2020 Global Forest Resources Assessment, and carbon accounting, especially under the reduction in carbon emissions from deforestation and degradation programme (REDD +).