A geospatial model for mapping desertification risk areas in the Caatinga biome, a semiarid region of Brazil

<p>Desertification is a process characterized by the degradation and drying of soils in arid, semiarid and subhumid regions that results from a combination of climatic factors and human activities. This process influences the productivity potential of the soils, impacting the populations residing in the affected areas, and may cause long-term economic problems and impacts on human health, such as hunger and food insecurity. The aim of this paper is to present a geospatial model for mapping desertification risk areas in northeastern Brazil. The test area for the model was located in the Brazilian semiarid climatic region in the state of Cear&#225;. In this area, the dry season lasts for 7 to 8 months, and the original vegetation belongs to the Caatinga biome. The model was based on algebraic operations between maps of environmental variables, performed in a geographic information system, and based on equations obtained through logistic regression analysis. First, 300 points were mapped in the centroids of desertification polygons (D), and 300 points were mapped in areas where no desertification processes (ND) had occurred. All points were selected by visual interpretation of Sentinel-2A multispectral images. Then, 500 m radius buffers were mapped around the centroids of the D and ND areas, and the mean values of the following environmental variables were extracted within these buffers: the average annual rainfall (RAIN), altitude (ELV), vegetation index dry season (VID), wet season vegetation index (VIM), dry season soil temperature (LTD), and wet season soil temperature (LTM). The mean values &#8203;&#8203;of the RAIN, ELV, VID, VIM, LTM and LTD variables for the D and ND areas were entered in the MedCalc software for logistic regression analysis. The <em>p</em> probability map of desertification occurrence was constructed in ArcGIS Pro using equations for which the parameters were obtained with the logistic regression analysis. The results showed that the variables RAIN, ELV, VID and LTD (p <0.0001) contributed significantly to the occurrence of desertification areas. The value obtained for the area under the ROC curve (AUC) parameter was 0.757, and the percentage of cases correctly classified by the model was 70.17%. In the next step of this research, this model will be tested on a larger area of 72,000 km² that is located in the Jaguaribe River basin, northeastern Brazil.</p>