Revealing the proximate drivers behind global tree cover loss using multisourced remote sensing products during 2000–2020
Authors/Creators
- 1. Aerospace Information Research Institute, Chinese Academy of Sciences
Description
Revealing the proximate drivers behind global tree cover loss using multisourced remote sensing products during 2000–2020
Description:
Understanding the proximate drivers of tree cover loss is crucial for guiding forest management policies, while significant challenges remain in accurately and comprehensively identifying these drivers on a global scale. Here, we developed a decision tree framework using multisourced remote sensing products, including land-cover, human footprint, fire, forest management practices, and standardized-precipitation evapotranspiration index, to attribute the global tree cover loss to five human drivers and three natural drivers.
Essential Information
1) Filename: StartYear_EndYear_Latitude[num]Longitude[num]
2) Data type: uint8
3) Spatial Resolution: 30 m
4) Temporal Resolution: 2000–2005, 2005–2010, 2010–2015, and 2015–2020.
5) Coordinate System: GCS_WGS_1984
6) Value: The values 1-8 correspond to Impervious Surface Expansion, Agricultural Encroachment, Forestry Activity, Human-Induced Fire, Other Human drivers, Wildfire, Persistent Drought, and Other Natural drivers, respectively.