Validation Samples for SFAC

The 2,072 samples of secondary and 3000 samples of stable forest, respectively were used to assess the accuracy of the results produced by each algorithm and ensemble. Samples for validation were selected randomly from the 7th National Forest Resources Inventory (NFRI) and were compared to the secondary forest maps produced above. The candidate points were visually examined using “Landsat Time Series Explorer”, a shared Application on GEE (https://jstnbraaten.users.earthengine.app/view/landsat-timeseries-explorer). In addition, historical imagery from Google Earth (https://earth.google.com/), GF-6 panchromatic/multispectral (PMS) images (a high-resolution Chinese satellite) (https://data.cresda.cn/#/2dMap) helped to distinguish stable and secondary forest samples. A total of 2,072 validation samples of secondary forest age ranging from 0 to 34 were defined by the re-interpreted approach mentioned above. Over 3,000 candidates of stable forests were systematically sampled from stable forest maps for validation. The classification of these samples of stable forest was ensured by filtering through many public land cover products. As shown in Table 1, these datasets included AGLC-2000-2015, GLC_FCS, FNF, GLC, CLUD, and GFCC. The categorization of the samples as stable forest was ensured by processing using Python, ArcGIS 10.6, and GEE. The 3,000 samples of the stable forest were then completed after manually removing pixels at imperfect sites. The value 1 of class in the data presents the secondary forest, value 0 of class presents the stable forest samples.