Cropland Mapping and Change Detection: Toward Zimbabwean Cropland Inventory

Accurate and spatially explicit cropland maps are crucial for many applications, which include sustainable crop monitoring, food security, and land and agriculture planning and management. Zimbabwe lacks reliable data on cropland extent of the old and new re-allocated areas for inventory purposes. Objectives of this paper are to map cropland utilizing: 1) automatic classification; 2) multi-classifier system (MCS); and 3) normalized difference vegetation index and bare-soil index (NDVI-BSI) thresholding and determine the spatiotemporal cropland changes. Change detection is implemented through a post-classification statistical method. The classified results are compared with SADC and ESA land cover products, GFSAD30AFCE cropland layer, and Google Earth imagery. Results reveal that MCS and NDVI-BSI performed the best and achieved overall accuracies of 80.54% and 79.32% for 2013, and for 2018, they attained accuracies of 87.90% and 88.56%, respectively. Automated classification, MCS, and NDVI-BSI thresholding produced average cropland areas of 3416396, 10346778, and 9788833 Ha, respectively. Visual assessment observations show that NDVI-BSI thresholding outperformed the other two techniques. Comparing further the MCS and NDVI-BSI thresholding approaches’ results of total cropland areas of Zimbabwe’s ten provinces for the years 2013 and 2018, coefficients of determination of 0.8404 and 0.9619, respectively, are achieved. Change detection shows a general increase in the cropland area due to human activities despite the prolonged drought. However, we recommend further exploration of NDVI-BSI threshold values to derive cropland layers since the method is robust and can be automated easily and faster without inputting training data. We also recommend simulation of the changes in cropland areas using cellular automata and/or agent-based modeling.