Forest change (including forest loss and gain) is a complex ecological process influenced by natural and human activities, and has important impacts on global material cycles and energy flows. Based on long-term tree-canopy cover (TCC) data, the Bi-temporal class-probabilities model was used to detect forest changes, and a dataset of forest change of the Natural Forest Conversion Program area in northeast China from 1986 to 2018 was obtained (spatial resolution 30 meters with a temporal resolution of 1 year). The method of stratified random sampling was used to select 1000 points in the reserve and visual interpretation was carried out to evaluate the accuracy of forest change. The results show that the accuracy of forest loss (producer's accuracy = 85.21%; user's accuracy = 84.26%) and forest restoration (producer's accuracy = 87.74%; user's accuracy = 88.31%) are both high, which can effectively reflect the forest change status of the protected area.

File naming and required software

The data is stored in GeoTIFF format and can be read by ArcGIS, QGIS and other software. Where, loss TIF is forest loss data, gain is forest restoration data

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Cite as:

Wang, J., He, Z., Wang, C., Feng, M., Pang, Y., Yu, T., Li, X. (2022). Forest change dataset during the 1986–2018 in the Natural Forest Conversion Program, northeast China.. A Big Earth Data Platform for Three Poles, DOI: 10.11888/Terre.tpdc.272579. CSTR: 18406.11.Terre.tpdc.272579. (Download the reference： RIS | Bibtex )

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