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Forest type cover data of southeast China(2018)

Forest type cover data of southeast China(2018)

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Date: 2020-10-28

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This data set is about the forest cover data of southeast China in 2018. Combining the characteristics and advantages of Landsat and sentinel-2 remote sensing images, spectral, spatial and temporal feature sets reflecting different forest types were studied and established respectively. The characteristics of Landsat long time series and time series harmonic analysis technique were utilized to establish the time feature set of forest type extraction. Based on the spectral-spatial-temporal feature set, the main features in different regions were studied and established by using the Random Forest-Recursive feature elimination algorithm with the support of reference data. According to the spectral-spatial-temporal feature set of different regions, using four machine learning algorithms to establish forest type classification model. Then the forest type maps with 10 spatial resolution in 2018 for southeast China were generated using the determined best fit model for different regions.

Data format
Tif
Subject
Ocean
Data Level
Raw
Data time series
No
temperal
Modern times
spatial
Point
resolution
Low
Country ID
CN
Data Size
MB
Contributor Name
Chengkai,WangJuanle
Contributor Email
ikcest-drr@lreis.ac.cn
Contributor Agency
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
Creation time
Resplnst Name
RespInst Address
A11 Datun Road, Chaoyang District, Beijing
RespInst Postcode
100101
ResPerson Name
Service group of Disaster Risk Reduction Knowledge Service System of IKCEST
ResPerson Email
wangyanjie@lreis.ac.cn
ResPerson Telephone
010-64889048-8006
Update Frequency
Access Link
Version
Data Citation
Forest type cover data of southeast China(2018). Disaster Risk Reduction Knowledge Service of International Knowledge Centre for Engineering Sciences and Technology (IKCEST) under the Auspices of UNESCO, 2020.9.19.
Last Modified
Organization
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences.
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