Global 30-m impervious-surface dynamic dataset in 2000-2020 (GISD30_2000-2020)

How to accurately capture the impervious surface dynamics from time series remote sensing imagery has always been the focus and hotspot of urban and environmental remote sensing research. To tackle this issue, a high confidence training dataset at the global scale was created by utilizing prior land cover data according to a series of extraction rules. The spectral generalization approach and a sample migration strategy were developed to achieve long-term dynamic monitoring of impervious surfaces. The GISD30 dataset was ultimately produced by classifying the time-series Landsat reflectance imagery by integrating the local adaptive random forest model and the spectral generalization approach.

如何从时间序列遥感影像中精准捕捉不透水面（impervious surface）的动态变化，始终是城市与环境遥感研究的核心焦点与热点议题。为破解这一难题，研究团队依据一系列提取规则，结合先验土地覆盖数据构建了全球尺度的高置信度训练数据集。为实现不透水面的长期动态监测，研究人员研发了光谱泛化方法与样本迁移策略。最终，通过整合局部自适应随机森林模型与光谱泛化方法，对时间序列陆地卫星（Landsat）反射率影像开展分类，由此生成了GISD30数据集。