Authors: Xiaojun Yang*, Florida State University, Di Shi, Department of Geography and Atmospheric Science, University of Kansas, Shijun Jiang, Institute of Groundwater and Earth Sciences, Jinan University, Guangzhou, China , Hongyu Yan, Institute of Groundwater and Earth Sciences, Jinan University, Guangzhou, China
Topics: Remote Sensing, Land Use and Land Cover Change, Environmental Science
Keywords: Dianchi Watershed, land cover classification, remote sensing, machine learning, thematic mapping accuracy
Session Type: Poster
Start / End Time: 9:55 AM / 11:35 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: No File Uploaded
The Dianchi Watershed in Yunnan Province, China extends across an area of about 2707 km2, among which Lake Dian has a surface area of nearly 298 km2. With a population of more than 4 million in 2018, this area has experienced significant urban and agricultural development since the late 1980s, which has brought on various environmental problems such as water pollution. In this study, we developed a remote sensing approach to map land cover types for the Dianchi watershed using satellite imagery and machine-learning methods. To support the remote sensing work, we conducted two field trips to the study site in Summer 2017 and Winter 2018. Some preliminary results are reported here.