The current study implements a cellular automata-based model for the development of land use/land cover (LULC) future scenarios using a Remote Sensing (RS) Imagery series (1985 to 2018) as data input and focusing on human activities drivers in a 6700-km² watershed vital for the water security of Paraiba state, Brazil. The methodology has three stages: the first stage is the pre-processing of images and preparing them as data input for the cellular automata land use model built in the R software environment (SIMLANDER); the stage of calibration establishes the variables and verifies the influence of each one on the LULC of the region; the last step corresponds to the validation procedures. After model calibration, land use maps for future scenarios (2019 to 2045) were simulated. The results estimate a reduction of 737 km² of natural land cover between the years 2019 and 2045. The spatial distribution of anthropogenic interference predicted a more significant degradation in the central region of the basin. This fact can be potentially attributed by the water availability increasing from the São Francisco River diversion. It is possible to identify an ascending trend of anthropogenic actions in the semi-arid region, which host the exclusively Brazilian biome—Caatinga—and contains biodiversity that cannot be found anywhere else on the Earth. The model helps large-scale LULC modelling based on RS products and expands the possibilities of hydrological, urban and social modelling in the Brazilian context.

当前的研究使用遥感（RS）影像系列（1985年至2018年）作为数据输入并关注6700年人类活动驱动因素，实现了一种基于元胞自动机的模型，用于开发土地利用/土地覆盖（LULC）未来情景。 -公里²分水岭对于巴西帕拉伊巴州的水安全至关重要。该方法包括三个阶段：第一阶段是图像的预处理，并将其准备为在R软件环境（SIMLANDER）中建立的细胞自动机土地利用模型的数据输入。校准阶段确定变量并验证每个变量对区域的LULC的影响；最后一步对应于验证过程。在模型校准后，模拟了未来情景（2019年至2045年）的土地利用图。结果估计减少了737 km ²在2019年至2045年期间的自然土地覆盖面积为100％。人为干扰的空间分布预测流域中部地区的退化将更为严重。这一事实可能归因于圣弗朗西斯科河分流带来的可用水量增加。可以确定半干旱地区人为活动的上升趋势，该地区仅拥有巴西生物群系-凯廷加（Caatinga），并且拥有地球上其他任何地方都找不到的生物多样性。该模型有助于基于RS产品的大规模LULC建模，并扩展了在巴西范围内进行水文，城市和社会建模的可能性。