Published August 16, 2022
| Version v1
Dataset
Open
Annual carbon density and annual forest probability maps
Creators
- 1. University of Copenhagen
- 2. Key Laboratory for Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China Huanjiang Observation and Research Station for Karst Ecosystem, Chinese Academy of Sciences, Huanjiang, China
- 3. Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, CE Orme des Merisiers, Gif sur Yvette, France
- 4. Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
- 5. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain CREAF, Cerdanyola del Vallès, Spain
- 6. ISPA, UMR 1391, INRA Nouvelle-Aquitaine, Bordeaux Villenave d'Ornon, France
- 7. Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, USA
- 8. Department of Geosciences, Texas Tech University, Lubbock, TX, 79409, USA
- 9. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
- 10. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
- 11. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Description
The annual carbon density (file name is GBRF+year) shows aboveground woody biomass and uses boosted regression trees, which were trained with a static global benchmark map of carbon density of woody vegetation for 2018, using MODIS (MCD43A4 7 bands; NDII, EV12, MCD43A3 shortwave albedo) and STRM data. The carbon density was mapped at 1-ha spatial grid cells and the unit is MgC/ha.
The forest probability maps (file name is LT_nbr_paramset01_fitted.+year) were created from annual MODIS data at the resolution of 500m x 500m. The forest maps take values ranging from 0 to1, and the probability shows the likeliness if a pixel belongs to the forest (1) or non-forest (0) class. The higher the value, the higher the probability that the pixel is forest land.
Notes
Citation: Tong, X., Brandt, M., Yue, Y. et al. Forest management in southern China generates short term extensive carbon sequestration. Nat Commun 11, 129 (2020). https://doi.org/10.1038/s41467-019-13798-8
Files
GBRf_2002.tif
Files
(1.7 GB)
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