Key Lab of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China.
Institute of Future Cities, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR.
Sci Data. 2022 Mar 28;9(1):110. doi: 10.1038/s41597-022-01204-w.
In the past decades, China has undergone dramatic land use/land cover (LULC) changes. Such changes are expected to continue and profoundly affect our environment. To navigate future uncertainties toward sustainability, increasing efforts have been invested in projecting China's future LULC following the Shared Socioeconomic Pathways (SSPs) and/or Representative Concentration Pathways (RCPs). To supplements existing datasets with a high spatial resolution, comprehensive pathway coverage, and delicate account for urban land change, here we present a 1-km gridded LULC dataset for China under 24 comprehensive SSP-RCP scenarios covering 2020-2100 at 10-year intervals. Our approach is to integrate the Global Change Analysis Model (GCAM) and Future Land Use Simulation (FLUS) model. This dataset shows good performance compared to remotely sensed CCI-LC data and is generally spatio-temporally consistent with the Land Use Harmonization version-2 dataset. This new dataset (available at https://doi.org/10.6084/m9.figshare.14776128.v1 ) provides a valuable alternative for multi-scenario-based research with high spatial resolution, such as earth system modeling, ecosystem services, and carbon neutrality.
在过去几十年中，中国经历了剧烈的土地利用/土地覆盖（LULC）变化。预计这种变化将持续下去，并深刻影响我们的环境。为了应对未来的不确定性，实现可持续发展，人们加大了力度，根据共享社会经济路径（SSP）和/或代表性浓度路径（RCP），对中国未来的土地利用/土地覆盖进行预测。为了用高空间分辨率、全面的路径覆盖和精细的城市土地变化来补充现有数据集，我们在这里提供了一个 1 公里格网的中国土地利用/土地覆盖数据集，涵盖了 2020 年至 2100 年的 24 个综合 SSP-RCP 情景，时间间隔为 10 年。我们的方法是整合全球变化分析模型（GCAM）和未来土地利用模拟（FLUS）模型。与遥感 CCI-LC 数据相比，该数据集表现良好，并且与土地利用协调版本 2 数据集在时空上基本一致。这个新数据集（可在 https://doi.org/10.6084/m9.figshare.14776128.v1 获得）为基于多情景的研究提供了有价值的替代方案，如地球系统建模、生态系统服务和碳中和等，这些研究都需要高空间分辨率。
