CA-Markov Approach in Dynamic Modelling of LULCC Using ESA CCI Products over Zambia

Abstract

The Markov, Cell Atom and CA-Markov modules in TerrSet v19.0 have been applied to predict LULC maps for 2030 over Zambia. The European Space Agency Climate Change Initiative (ESA CCI) classified LULC maps for 2000, 2010 and 2020 were used in this study. The ESA-CCI LULC maps were reclassified using QGIS 3.20 into 10 classes. The 2000 and 2010 LULC maps were used to predict the 2020 LULC maps. The Kappa statistics between the 2020 reference and predicted LULC maps was kappa (0.9918). The probability and transition matrix between the 2010 and 2020 LULC maps were used as inputs into the CA-Markov module to generate the 2030 LULC map. The LULCC from 2020-2030 shows an expansion and contraction of different classes. However, Built-up (42.38% [481.82 km2]) constitutes major changes among the LULC classes. However, Cropland, Dense forest, Grassland, Wetland and Bare land will reduce by 376.00, 1087.65, 70.60, 26.67 and 0.36 km2, respectively. Other LULC changes from 2020-2030 are in seasonally flooded grassland (94.66 km2), Sparse forest (497.05 km2), Shrub land (410.11 km2) and Water body (77.63 km2). The prediction of future LULC from historical LULC using CA-Markov model plays a significant role in policy making and land use planning.