Impact of the Accuracy of Land Cover Data sets on the Accuracy of Land Cover Change Scenarios in the Mono River Basin, Togo, West Africa

Abstract

Knowledge about land use and land cover (LULC) dynamics is of high importance for a number of environmental studies including the development of water resources, land degradation and food security. Often, available global or regional data sets are used for impact studies, although they have not been validated for the area of interest. Validation is especially required if data are used to set up a land change model predicting future changes for management purposes. Therefore, three different LULC maps of the Mono River Basin in Togo were evaluated in this study. The analyzed maps were obtained from three sources: CILSS (2 km resolution), ESA (300 m), and Globeland (30m) datasets. Validation was performed using 1,000 reference points in the watershed derived from satellite images. The results reveal CILSS as the most accurate data set with a Kappa coefficient of 68% and an overall accuracy of 83%. CILSS data shows a decrease of savanna and forest whereas an increase of cropland over the period 1975 to 2013. The increase of cropland area of 30.97% from 1975 to 2013 can be related to the increase in population and their food demand, while the losses of forest area and the decrease of savanna are further amplified by using wood as energy sources and the lack of forest management. The three datasets were used to simulate future LULC changes using the Terrset Land Change Modeler. The validation of the model using CILSS data for 2013 showed a quality of 50.94%, it is only 40.04% for ESA and 20.13% for Globeland30. CILSS data was utilized to simulate the LULC distribution for the years 2020 and 2027 because of its satisfactory performances. The results show that a high spatial resolution is not a guarantee of high quality. The results of this study can be used for impact studies and to develop management strategies for mitigating negative effects of land use and land cover change.