State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, PR China.
National Tibetan Plateau Data Center, Beijing, 100101, PR China.
Sci Data. 2023 Oct 28;10(1):748. doi: 10.1038/s41597-023-02637-7.
A fine global future land use/land cover (LULC) is critical for demonstrating the geographic heterogeneity of earth system dynamics and human-earth interaction. In this study, we produced a 1 km global future LULC dataset that takes into account future climate and socio-economic changes as well as the impact of simulated results of the former year on temporally adjacent periods. By incorporating the variations in climatic and socio-economic factors, we differentiated LULC suitability probabilities for historical and future periods across representative SSP-RCP scenarios. Then, by using an improved cellular automata model-PLUS to simulate the patch-level changes of various land classes, we iteratively downscaled water-basin-level LULC demands in various future scenarios to a spatial resolution of 1 km. Our dataset achieves a high degree of simulation accuracy (Kappa = 0.94, OA = 0.97, FoM = 0.10) and precisely captures the spatial-temporal heterogeneity of global LULC changes under the combined effects of climate change and socio-economic development. This robust and fine-scale LULC dataset provides valuable spatially-explicit information essential for earth system modeling and intricate dynamics between anthropogenic activities and the environment.
精细的全球未来土地利用/土地覆被（LULC）对于展示地球系统动态和人类-地球相互作用的地理异质性至关重要。在本研究中，我们生成了一个 1km 全球未来 LULC 数据集，该数据集考虑了未来气候和社会经济变化，以及前一年模拟结果对相邻时段的影响。通过纳入气候和社会经济因素的变化，我们区分了代表 SSP-RCP 情景下历史和未来时期的 LULC 适宜性概率。然后，我们使用改进的细胞自动机模型-PLUS 来模拟各种土地类别在斑块层面的变化，我们将各种未来情景下的流域水平 LULC 需求迭代下推至 1km 的空间分辨率。我们的数据集具有很高的模拟精度（Kappa=0.94，OA=0.97，FoM=0.10），并精确捕捉了气候变化和社会经济发展综合影响下全球 LULC 变化的时空异质性。这个稳健的、细粒度的 LULC 数据集提供了对地球系统建模和人为活动与环境之间复杂动态至关重要的有价值的空间显式信息。
