3.2.6. Drought Index (DI)

The balance between moisture inputs, in the form of precipitation, and outputs, in the form of evaporation, is a key determinant of the vegetation type, and the drought index is used here to account for this balance. Figure 7e illustrates marked spatial patterns in the occurrence of drought in Guizhou province, and suggests that the lower values in Qiandongnan are associated with greater forest cover. Statistically, however, the effect of moisture stress is less marked than may have been expected (Table 6).

#### 3.2.7. Slope

In this study, the slope angle was classified into five categories: 0–6◦, 6–15◦,15–25◦, 25–35◦, and >35◦ (Figure 7f). Specifically, the forests are distributed preferentially on slopes of 15–25◦ and occur less frequently on lands with lower slope angles, presumably because these lands are more suited to agriculture and urban development. Table 6 illustrates a very strongly positive correlation between slopes of 15–25◦ and forest area.

From the correlation analysis and multiple general linear models, it can be seen (Table 6) that 15–25◦ slopes play a dominant role in the forest areas, explaining 88.87% of the variation, while accessibility and land-use change account for 2.29% and 2.27%, respectively, followed by population effects (2.23%), GDP (1.88%), mean annual precipitation (0.97%), karstification intensity (0.85%), and drought index (0.64%).

Table 6 further reveals that the mean annual precipitation, GDP, population effects, land-use changes, accessibility, and slopes of 15–25◦ all play a role in forest change. Due to data-availability constraints, we ultimately selected mean annual precipitation, GDP, population, and land-use change (LUC) for further analysis as drivers of forest-cover changes.

#### *3.3. Relative Importance of Drivers Changes over Time*

To determine the relative importance of the drivers for forest change over time, we conducted a correlation analysis and multiple-GLM regression for the different periods (Table 7), taking the mean annual precipitation (MAP), annual mean temperature (MAT), population, GDP, and cropland conversion into forest as key drivers of forest changes over time, although, given the data limitations, the analysis was conducted only from 1990 to 2018, and the data for GDP and population in 1995 were used for 1990.


**Table 7.** Changes in drivers of forest variation over time.

Notes: Annual precipitation (AP, mm), annual mean temperature (AMT, ◦C), population (per person/km2), GDP (RMB 10,000/km2), LCU (km2); \* *p* < 0.05, \*\* *p* < 0.01; SS, proportion of variances explained by the variable.

In Table 8, it can be seen that, prior to 2000, the population exerted the most significant impact on the forests but, after 2000, its influence was reduced to 3.04%. This may be attributed to the type of economic development in Guizhou before 2000 [64], whereby the inhabitants exploited forests for firewood [65] or settled on unused land [46]. Currently, the substitution of gas and hydropower for firewood helps to reduce the pressures of the population on the forests [65]. The negative effect of GDP on forest change diminished over the years, probably due to the transformations associated with economic development, which has reduced dependence on the direct consumption of natural resources, including forests [66]. The impact of the land-use change, mainly the conversion of cropland into forests, increased to 91.81% by 2018, probably as a consequence of the implementation of GFG [67]. While some fluctuations were observed during dry periods, the influence of MAP and MAT on the forest cover did not vary substantially in recent years.

**Table 8.** Comparison of results of this study with other products (areas in km2).


#### **4. Discussion**

#### *4.1. Validation of Forest Change in Guizhou through Comparison with Other Data Sources*

We compared the results of our study with those of other land-use products to verify the trends in the forest changes (Tables 8 and 9). We used ArcGIS spatial analysis to determine the forest change according to MODIS/006/MCD12Q1, GlobeLand30, and GLASS-GLC. It can be seen in Table 6 that all the products show an increasing trend in forest cover during recent decades, albeit with some interannual differences. Table 8 reveals that the data sources, definition/classification criteria, classification technique, and spatial resolution of the land-use data underlie the differences in the estimation of different annual forest areas and forest changes [68,69]. For example, the differences in spatial resolution between GLASS-GLC and CNLUCC may affect the land-cover classification and explain the minor differences in forest-change estimation. Moreover, shrubland is a single class in Global Land 30, and it is not classified as forest land, which may explain why the forest-change increase in Global Land 30 is lower than that in this study. Some grasslands are misclassified as shrublands in MODIS/006/MCD12Q1 and, since shrublands are components of 'forest' [70], the forest change from 2000 to 2018 in that product is much greater than in the results presented by CNLUCC, in which grassland is a single category, independent of forest cover. Moreover, CNLUCC is obtained through detailed field data [71], and it has been widely applied in many major projects, such as the Western Development of China and the second national soil-erosion survey of China, among others (http://www.resdc.cn/data.aspx?DATAID=95 (accessed on 30 January 2020)), which indicates its reliability. It is noteworthy that in CNLUCC, the forest area decreased in 2000, while in GLASS-GLC, it marginally increased. This difference has two possible causes, *viz.* the two products differ in terms of spatial resolution and classification technique. Notably, the spring and summer droughts in 1989 and the serious drought in southwestern China in 2000 may have interrupted the otherwise consistent increase in forest cover over time [72].

**Table 9.** Parameters of data products relating to forest-cover estimation.


Notes: sources of the table are from [67,68].

#### *4.2. The Effects of Ecological Restoration Policy on Forest Change*

Guizhou has the largest karst area in the world [73]. Since the release of the "Decision on Basic Greening of Guizhou in Ten Years" in 1990, China has implemented a batch of key projects to protect and restore the fragile ecological environment. These include the Shelterbelt Program for Upper and Middle Reaches of the Yangtze River (1989), the Natural Forest Protection Project (1998), and the Grain for Green program (1999), all of which were applied in Guizhou and contribute to greening [74,75]. In 2008, China imposed the "Outline of Comprehensive Control Plan for Rocky Desertification in Karst Areas," which included 55 counties in Guizhou Province among China's 100 pilot counties for the comprehensive control of rocky desertification, with the aim of further restoring the ecological environment. In subsequent years, Guizhou vigorously supported the development and protection of forest resources through various initiatives, such as the Afforestation Planning across County and Township and Village in Guizhou Province (2014–2017), the Three Year Action Plan for Green Guizhou Construction (2015–2017), the Guizhou Forestry Industry Three Year Multiplication Plan (2015–2017), the Implementation Plan on Promoting the Development of Forestry Industry in Guizhou Province, and the Ten Forestry Industry Bases Construction Plan of Guizhou Province (2018–2020). Additionally, to strengthen the protection of forest resources, Guizhou strictly implemented a forestcutting quota system to ensure that the total growth of trees was far greater than the total consumption. It also carried out forest-ecological-benefit compensation (2004) and enforced a special law enforcement campaign to protect forest (2014). Guizhou has also reviewed and approved the use of forest land and defined the forestry ecological red line to strictly protect and rationally use forest land resources [76].

In short, China as a whole, and Guizhou in particular, have implemented various targeted policies to hasten vegetation restoration and protect their forests, all of which have contributed to the increase in forest cover (Figure 8). With the implementation of ecologicalrestoration and protection programs, this trend seems set to continue [16]. According to the Guizhou Statistical Yearbook, forest cover is defined as the ratio of the forest area to the total land area, expressed as a percentage. According to national regulations, this also includes shrublands and farmland–forest mosaic areas. Therefore, the level of forest resources and greening is even greater than that recorded in our study.

**Figure 8.** Changes in forest cover (%) in Guizhou province (source: Guizhou Statistical Yearbook).

#### *4.3. Limitations and Prospects*

Although forest changes may be driven by multiple factors, not all of which are addressed in this study, our analysis, based on the conditions of the study area, considers the factors that are most likely to be significant. The findings offer important support for the government in identifying key areas for forest conservation and restoration.

Nevertheless, there are some limitations. For example, the data relating to some of the explanatory variables were not available for the entire period, meaning that in some cases, we used data from the closest suitable year in the analysis. Other relevant driving factors should be the subject of future research and analysis, such as the choice of afforestation species, the method of production of seedlings, and specific planting-environment conditions. Species selection, in particular, is a key challenge in afforestation [77,78], and the

selection of the correct species mixtures can markedly increase the success of the restoration. In addition, the choice of appropriate species for specific environments, which can adapt to current and future environmental conditions, is crucial [79]. In degraded ecosystems, planting species that can withstand particular environmental constraints should be used. Other factors, such as the occurrence of vegetation fires, also need to be considered [80], as these may affect the rate of tree recruitment, forest-age structure, and species composition [81,82]. The effects of soil humidity on forest recovery are complex and may be important in seed germination [83], while soil moisture is a further constraint on successful regeneration [84].

#### **5. Conclusions**

In Guizhou province, forests are a prominent land type due to the favorable hydrothermal conditions, and the results of this study show that the forest cover has increased over the last few decades. In terms of area, Qiandongnan holds the largest share of forest, and experienced the most substantial increase of all the nine municipalities during the study period. On the other hand, Liupanshui, in the west of Guizhou, has the lowest forest cover and exhibited very little change overall. While forest changes are the result of both natural and artificial factors, the relative influence of these factors shifted over time. Prior to 2000, the population exerted a much stronger influence on the forests but, since then, the function of other factors has increased, particularly land-use changes. The nine major municipalities in Guizhou experienced different outcomes as a result, with Qiandongnan exhibiting the highest percentage of farmland converted into forest, at 47%, followed by Zunyi, with 40%, Qiannan, with 39%, and Tongren, with 37%. Bijie has the smallest portion of cropland converted into forest (29%). These results emphasize the dynamic nature of driving forces in determining forest cover and demonstrate the value of geospatial analysis in understanding their emerging influence. The methodology and modeling approach adopted here are used to illustrate the relative roles of natural and management factors and may be applied in other similar regions to reduce forest degradation and increase forest restoration.

**Author Contributions:** Conceptualization: R.C.; methodology: X.G., Q.L. and Z.X.; writing—original draft: X.G. and R.C.; writing—review and editing: X.G., R.C., M.E.M., Q.L., Z.X. and Z.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was conducted with the support of the National Social Science Fund of China (Grant No.20ZDA085), the National Key Research and Development Program of China (Grant No. 2017YFC1503001), and the China Postdoctoral Science Foundation (Grant No. 2022M722055 & 2022TQ0205).

**Data Availability Statement:** The source of relevant data acquisition has been described in the text.

**Conflicts of Interest:** The authors declare no conflict of interest.
