*2.2. Materials*

#### 2.2.1. Land Cover Data

The land cover data used in this study (1995, 2000, 2005, 2010, 2015, and 2020) were downloaded from the land cover classification data set released by the European Space Agency (ESA) climate change initiative [42]. The spatial resolution is 300 m. Using the international Intergovernmental Panel on Climate Change (IPCC) land categories, the land cover types were divided into 10 categories: (i) agriculture, (ii) forest, (iii) grassland, (iv) wetland, (v) settlement, (vi) permanent snow and ice, (vii) shrubland, (viii) sparse vegetation, (ix) bare area, and (x) water (Figure 3). We resampled these land cover data and obtained multi-temporal 1000-m resolution land cover data.

**Figure 3.** Land cover of the GYRR from 1995 to 2020. (**a**) Land cover in 1995; (**b**) land cover in 2000; (**c**) land cover in 2005; (**d**) land cover in 2010; (**e**) land cover in 2015; (**f**) land cover in 2020. Although the size of the pictures was limited, we could still find the subtle differences between them. For example, the settlement areas displayed in red shows an obvious increasing trend.

#### 2.2.2. Spatial Variables Affecting the Land Cover Change

Physical and socioeconomic elements may cause alterations in land cover. For the selection of spatial variables affecting land cover change, we mainly referred to the relevant literature [14,43–49]. After comparison and analysis, we employed a variety of physical and socioeconomic elements (Table 1), including the elevation, topographic relief, slope, annual average temperature, annual average precipitation, river network density, GDP, population, road network density, and city density.

The elevation data (Figure 4a) were downloaded from the EarthEnv website (https:// www.earthenv.org/topography). We found that the landform of the whole GYRR is high in the west region and low in the east region. There are many mountains in the west, and alluvial plains and hills in the east. The highest altitude of the whole area is 6018 m. The

topographic relief was calculated from elevation, and the maximum relief in this region is 1112 m. The high value of relief is mainly distributed in the Taihang Mountains (Figure 4b). The slope data (Figure 4c) were also downloaded from the EarthEnv website (https:// www.earthenv.org/topography). The high value distribution of the slope is similar to the relief high value distribution. The maximum value of the slope is 38.36◦. The temperature and precipitation data were downloaded from the WorldClim website. WorldClim version 2.1 climate data for 1970–2000 was released in January 2020. They provide monthly climate data for minimum, mean, and maximum temperature, precipitation, solar radiation, wind speed, water vapor pressure, and total precipitation at the four spatial resolutions, between 30 s and 10 min. Each download is a "zip" file that contains 12 GeoTiff (.tif) files, one for each month of the year (January is 1; December is 12). We obtained the annual average temperature by averaging the 12-monthly mean temperature data. It was found that the maximum annual average temperature in this area is 16.18°C and the minimum is −13.68 °C (Figure 4d). Due to the influence of monsoons, the temperature in the East is higher, while the influence of ocean in the West is weak, and the temperature is lower. The precipitation data were also taken from the WorldClim website. We summed up the 12-monthly precipitation data to obtain the annual average precipitation. The precipitation in the GYRR decreases from Southeast to Northwest. The annual maximum precipitation can reach 1723 mm (Figure 4e). The river network density was calculated using the river network data (Figure 4f). In addition, data related to human activities mainly include GDP, population, road density, and city density. Due to the accumulation of human beings in the plain area, the four above-mentioned factors show the characteristics of high density in the plain area (Figure 4g−j).

**Table 1.** Data sources.


2.2.3. Mountain Hazards in the GYRR

Mountain hazards generally refer to the hazards that can threaten human beings and their living environment in mountainous areas [57,58]. Tang et al. [59] discussed and defined "mountain hazards" in the 1980s, and considered that landslides, collapses, mudslides, soil erosion, ice avalanches, frozen soil hazards, earthquakes, hail, and other hazards in the mountainous areas could all be classified as mountain hazards. As compared with the above-mentioned broad categories, mountain hazards, in a narrow sense, could be understood as the phenomenon through which the water and soil materials move along the slope under the driving force of gravity and have a certain destructive capacity [60]. Debris flows, landslides, collapses, and mountain torrents are the representatives of common typical mountain hazards. In this study, we collected data on landslides, mountain torrents, and debris flows in the GYRR. For the collection of landslide and debris flow data, the global landslide catalog (GLC) from 2007 to 2017 produced by the National Aeronautics and Space Administration (NASA) of the United States was downloaded to collect rainfall-induced landslide and debris flow events. The data sources of the GLC include media, disaster databases, scientific reports, etc. [61]. On the other hand, the Dartmouth flood Observatory was established in 1993, mainly recording major global flood events from January 1985 [62]. For the mountain torrents, as a special flood occurring in the mountainous areas, the mountainous areas of the GYRR were used to screen the above flood event points and to obtain the mountain torrent points.

**Figure 4.** Impact factors considered in land cover prediction. (**a**) Elevation; (**b**) relief; (**c**) slope; (**d**) annual average temperature; (**e**) annual average precipitation; (**f**) river density; (**g**) GDP; (**h**) population; (**i**) road density; (**j**) city kernel density. We have considered as many natural and socio-economic factors as possible based on the availability of the data.
