Dr. Jun Ma received a PhD in ecology from the Shenyang
Institute of Applied Ecology, Chinese Academy of Sciences in 2015. From 2015 to
2017, he engaged in postdoctoral research at the Institute of Biodiversity
Science, Fudan University. Since 2017, he has served as a young associate
researcher and associate researcher at Fudan University. His research interests
are ecological remote sensing and landscape ecology. His research uses methods
such as remote sensing, geographic information systems, and ecological models
to explore the relationship between landscape pattern dynamics and ecosystem
processes at multiple scales. His main focus is on (1) landscape patterns such
as forests, cities, farmland, and wetlands under the background of global
change., and the relationship between dynamics and ecosystem processes; (2) land use
remote sensing classification mapping and the ecological effects of land use
changes based on the remote sensing big data platform (GEE); (3)
satellite remote sensing and UAV remote sensing (LiDAR, multispectral,
Hyperspectral, etc.) estimation of vegetation carbon cycle parameters (biomass,
productivity, phenological dynamics).
Research Keywords & Expertise
Land Use
Landscape Ecology
ecological remote sens...
Geographic information...
Short Biography
Dr. Jun Ma received a PhD in ecology from the Shenyang
Institute of Applied Ecology, Chinese Academy of Sciences in 2015. From 2015 to
2017, he engaged in postdoctoral research at the Institute of Biodiversity
Science, Fudan University. Since 2017, he has served as a young associate
researcher and associate researcher at Fudan University. His research interests
are ecological remote sensing and landscape ecology. His research uses methods
such as remote sensing, geographic information systems, and ecological models
to explore the relationship between landscape pattern dynamics and ecosystem
processes at multiple scales. His main focus is on (1) landscape patterns such
as forests, cities, farmland, and wetlands under the background of global
change., and the relationship between dynamics and ecosystem processes; (2) land use
remote sensing classification mapping and the ecological effects of land use
changes based on the remote sensing big data platform (GEE); (3)
satellite remote sensing and UAV remote sensing (LiDAR, multispectral,
Hyperspectral, etc.) estimation of vegetation carbon cycle parameters (biomass,
productivity, phenological dynamics).