*2.2. Data*

## 2.2.1. Land-Use Data

Land use (1-km-resolution raster) data, based on visual interpretation of Landsat TM/ETM imagery, were obtained for the years 1980, 1990, 2000, 2010, and 2018 from the Resource and Environment Data Cloud Platform (China's multi-period land-use–landcover remote-sensing data-monitoring set (CNLUCC); Resource and Environment Data Registration and Publishing System) [48]. The dataset is the most freely available dataset in China and has been widely used for detecting land-use change and analyzing ecosystem services from local to national scale. Its accuracy in identifying cropland and built-up areas is over 85%; its average accuracy for other land-use types exceeds 75%. Primary land-use categories identified were cropland, forest, grassland, water, built-up, and 'others' (Table 1); secondary categories included 25 sub-types of land use.

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**Table 1.** Land-use–land-cover classification in Guizhou, China.


#### 2.2.2. Forest-Change Drivers

In addition to the mapping of land-use changes, several other drivers were considered in developing the model. Given the vulnerability of the region to rocky desertification, karstification intensity (KI) was included as a potentially important factor. The KI was obtained from the Guizhou Institute of Mountain Resources. Slopes were also considered important, as these influence the spread of forests that affect forest growth [49]. We derived the slope factor from a digital elevation model (DEM) (2009) at a 30-m resolution from

Geospatial Data Cloud (https://www.gscloud.cn/ (accessed on 30 January 2020)). Climate characteristics, particularly drought frequency and intensity, also have a significant impact on vegetation cover [50], and Guizhou is frequently affected by drought, which restricts forest growth. As a result, both the drought index (the ratio of annual evaporation capacity to annual precipitation) and mean annual precipitation were included as potential drivers. The drought index was provided by the Guizhou Institute of Mountainous Climate and Environment. Other factors relating to human activities, including urbanization, are known to play significant roles in forest change [51]. Land-use change and ecological restoration projects are considered direct human-activity factors [52,53] and, given that Guizhou has been at the forefront of China's economic growth since 2000, with the highest growth rate in the country for the last three consecutive years, balancing economic development with environmental protection is highly challenging [54]. Accordingly, we also included factors associated the anthropogenic influence: GDP, population, and accessibility for analyzing forest dynamics. The mean annual temperature/precipitation, accessibility, population (people/km2), and GDP of nine municipalities in Guizhou were obtained from the Resource and Environment Data Cloud Platform (REDCP) [48].
