摘要
快速准确地获取一个地区的土地覆盖变化信息,可为该地区的社会经济发展、生态环境建设、国土空间规划等提供重要支撑。以广东省为研究区,以Google Earth Engine(GEE)云平台为支撑,以Sentinel-1/2和Landsat7/8数据为遥感数据源,结合多源时序影像和DSM影像,利用机器学习分类方法进行了土地覆盖类型快速监测。
Rapid and accurate acquisition of land cover change information in a region, can provide important support for the socio-economic development, ecoenvironment construction and territorial space planning of the region. In this study, taking Guangdong Province as the study area, supported by Google Earth Engine(GEE) cloud platform, taking Sentinel-1/2 and Landsat 7/8 data as remote sensing data sources, combining multi-source temporal images with DSM images, we used machine learning algorithm to carry out land cover type monitoring.
出处
《地理空间信息》
2021年第9期73-78,82,I0002,共8页
Geospatial Information
基金
国家自然科学基金资助项目(41871318)
广东省自然资源厅2020年科技资助项目(GDZRZYKJ2020007)。
