FROM-GLC Plus: toward near real-time and multi-resolution land cover mapping

Global land cover has undergone extensive and rapid changes as a result of human activities and climate change. These changes have had a significant impact on biodiversity, the surface energy balance, and sustainable development. Global land cover data underpins research on the development of earth system models, resource management, and evaluation of the ecological environment. However, there are limitations in the classification detail, spatial resolution, and rapid change monitoring capability of global land cover change data. Building on the earlier Global Land Cover Mapping (Finer Resolution Observation and Monitoring – Global Land Cover, FROM-GLC), we developed the improved Global Land Cover Change Monitoring Platform (FROM-GLC Plus) using methods such as multi-season sample space-time migration, multi-source data time series reconstruction, and machine learning. The FROM-GLC Plus system provides a capacity for producing global land cover change data set from the 1980s with flexibility in spatio–temporal details. The preliminary results show that FROM-GLC Plus provides a framework for near real-time land cover mapping at multi-temporal (annual to daily) and multi-resolution (30 m to sub-meter) levels.

受人类活动与气候变化影响，全球土地覆被发生了广泛且快速的变化。此类变化对生物多样性、地表能量平衡以及可持续发展均产生了显著影响。全球土地覆被数据是地球系统模型研发、资源管理以及生态环境评估等研究的重要支撑。然而，现有全球土地覆被变化数据在分类细节、空间分辨率以及快速变化监测能力方面仍存在局限。本研究在早期全球土地覆被制图产品（高分辨率观测与监测——全球土地覆被，Finer Resolution Observation and Monitoring – Global Land Cover，FROM-GLC）的基础上，通过多季样本时空迁移、多源数据时间序列重构以及机器学习等方法，研发了升级版全球土地覆被变化监测平台（FROM-GLC Plus）。FROM-GLC Plus平台具备生成20世纪80年代以来全球土地覆被变化数据集的能力，可灵活调整时空细节参数。初步结果表明，FROM-GLC Plus可构建一套可实现多时间尺度（从年度到日级）、多分辨率（30米至亚米级）的近实时土地覆被制图框架。