**3. Results**

#### *3.1. Spatial Distribution and Trend Change of NDVI*

This article uses a combination of the Theil–Sen test and the MK method, as well as breakpoint detection, to count pixels of vegetation coverage from 2000 to 2020. Research results show that Miaoling has a mean NDVI of 0.766 (Figure 2c). In addition, the average NDVI variation range of Miaoling over the years is 0.659–0.827, with a rising trend with an increase of 0.9 × <sup>10</sup>−3/year (R2 = 0.053, *<sup>p</sup>* > 0.05) (Figure 2a). In addition, the breakpoint detection results indicate that there is a breakpoint in the Miaoling study time series (Figure 2b). As shown in Figure 2c, the change trend before and after 2011 was opposite. Before 2011, the NDVI in Miaoling showed an increasing trend, while it tended to decline after 2011. For the period 2000–2020, the overall NDVI in the Miaoling region slowly increased. However, there are significant fluctuations, mainly reflected in the sharp decline after 2010 and 2017 and the sharp growth in 2013. Especially after 2011 and 2018, the NDVI values tended to decrease, with a decrease rate of over 10% compared to the previous year's NDVI value in 2019.

The characteristics of the spatial distribution of the NDVI in Miaoling are shown in Figure 3. The eastern section is mainly characterized by a significant growth distribution of NDVI, accounting for 40.6% of the area. The NDVI in the middle segment mainly shows moderate growth, with an area ratio of 30.5%. The northern and western sections of the middle section are mainly characterized by a decrease and no change, with area proportions of 12.9% and 16%, respectively.

**Figure 2.** The variation characteristics of NDVI values in Miaoling from 2000 to 2020. (**a**) The interannual variation and trend fitting of NDVI values in Miaoling; (**b**) Breakpoint detection results; (**c**) Trend fitting of NDVI before and after breakpoints.

**Figure 3.** Distribution of the inter-annual NDVI in the Miaoling region from 2000 to 2020.

The annual average NDVI change can reflect the dynamic characteristics of vegetation and serve as an important indicator of vegetation health and ecosystem stability. Thus, the Theil–Sen median trend analysis was used to analyze the NDVI trend over the last 21 years at the pixel scale. We found that the spatial distribution of NDVI is highly heterogeneous (Figure 4a). The NDVI in Miaoling increased in most areas and decreased in some local areas. The change trend of NDVI in most areas is an increasing trend (accounting for 68.73%). Among them, the highest growth is concentrated in the east, accounting for about half of the total growth. In addition, the slightly degraded and significantly degraded areas account for 32.61%. The significantly degraded areas are mainly distributed in the western part of the study area. The remaining 8.66% did not show a significant change.

**Figure 4.** The trend of NDVI distribution (**a**) and MK test results (**b**) in the Miaoling area from 2000 to 2020. The results divide the change trend into five grades: significant degradation, *p* < 0.01; mild degradation, *p* < 0.05; no significant change, *p* > 0.05; mild improvement, *p* < 0.01; significant improvement, *p* < 0.05.

The MK test results indicate that from 2000–2020 (Figure 4b), the change in the NDVI in most areas of Miaoling is relatively strong, with a mainly strong distribution in the eastern, central–southern, and a small part of the western areas, while the area with an insignificant change is mainly distributed across the majority of the western Miaoling area.

We also tested the persistence of NDVI changes in Miaoling through the Hurst index, and the mean Hurst index of NDVI in Miaoling was found to be 0.56 (Figure 5). Additionally, the region with continuous growth (0.5 < H < 1) has the widest distribution (53.82%), mainly in the eastern and central parts of the study area. However, most of the west area is still trending down. As can be seen, the Hurst index of the NDVI has similar spatial heterogeneity to the distribution of the NDVI in the study area.

#### *3.2. Impact Factor of Land Use Change on the NDVI*

The karst area is mainly concentrated in the eastern and middle sections of the Miaoling area (Figure 6a), dominated by woodland and followed by grass. The non-karst area is mainly distributed in the western section and is mostly arable land, followed by grass (Figure 6b). The NDVI of the karst area is higher than that of the non-karst area in the Miaoling area.

**Figure 5.** The Hurst index distribution of NDVI in Miaoling. 0<H< 0.5; continuous decline; H = 0.5, no significant change; 0.5 < H < 1, continuous growth.

**Figure 6.** Karst distribution in Miaoling area of Guizhou Province (**a**) and spatial distribution characteristics of land use types (**b**) in 2000 to 2020.

The overall average proportion of land use types in the Miaoling area (Figure 7) is as follows: wood land (50%) > arable land (31.60%) > grass (14%) > artificial surface (1.93%) > shrub (1.45%) > waters (0.93%) > wetlands (0.09%). The total proportion of forest and arable land fluctuates, and the proportion of artificial surface continues to increase; grass, water, wetlands, and shrubs fluctuate slightly. Comparing the spatial distribution of NDVI in Miaoling (Figure 3) with land use types, it can be roughly divided into the following characteristics: The land use types corresponding to NDVI reduction areas are mainly concentrated in areas such as artificial surfaces, cultivated land, and wetlands. The NDVI of forested areas mainly manifests as growth.

The most immediate effect of land use change on NDVI is change. The most notable changes in NDVI are specifically in arable land and woods. Figure 8 shows that the Miaoling region's degraded regions of wood land, arable land, and grass are primarily located in the center region. As a result, it is clear from Figure 6b that the primary land use categories (wood land, grass, and arable land) in the Miaoling region are spread in regions with high concentrations of human activity (artificial surface areas).

**Figure 7.** Total area ratio of each land use type in Miaoling, Guizhou, from 2000 to 2020.

**Figure 8.** Woodland (**a**), grass (**b**), and arable land (**c**) change distribution characteristics in Miaoling.

#### *3.3. Impact Climate Factors of the NDVI*

Climatic factors are as important as human activities (land use change) in influencing vegetation dynamics. Our analysis of the partial correlation between NDVI and climate factors was calculated at the image metric scale for the studied region from the year 2000 to 2020. The findings of the partial correlation study make it clear that the NDVI and precipitation seasonality (CV) have the strongest association, whose mean correlation coefficient was 0.32. As shown in Table 3, their correlation strength is in order of precipitation seasonality (CV) (0.32) > VPD (−0.26) > precipitation in the wettest quarter (0.23) > MAP (0.21) > soil moisture (0.17) > MAT (0.15). The other five factors, with the exception of VPD, have an overall positive correlation with the NDVI.

The results showed significant spatial heterogeneity in the NDVI vegetation–climate correlation in the Miaoling region. In combination with Figure 9 and Table 3, 77.8% of the precipitation seasonality (CV) area was positively correlated with the NDVI (Figure 9a), which was widely distributed in the study area. A total of 71.9% of the VPD was significantly negatively correlated with the NDVI, and only 18.9% of the area was positively correlated with the NDVI, which was mainly distributed in the western and central parts of the study area (Figure 9b). A total of 58.2% of Precipitation of wettest quarter was significantly positively correlated with NDVI over the total area, and the areas with a significant negative correlation were mainly concentrated in the western part of the study area (Figure 9c). Precipitation was significantly positively correlated with NDVI in 54.2% of the spatial area and distributed in the westernmost, easternmost, and northernmost parts of the study area (Figure 9d). A total of 53.7% of the soil moisture area was significantly positively correlated with NDVI, while the easternmost area was significantly negatively correlated (Figure 9e). The least correlated climatic factor with NDVI in Miaoling was temperature, which was significantly positively correlated with NDVI in 47.5% of the study area, while MAT was significantly negatively correlated with NDVI in the far west and north (Figure 9f).

**Figure 9.** *Cont*.

**Figure 9.** Spatial distribution characteristics of partial correlation between NDVI in Miaoling and precipitation seasonality (CV) (**a**), VPD (**b**), precipitation of wettest quarter (**c**), MAP (**d**), soil moisture (**e**), and MAT (**f**).

In summary, precipitation seasonality (CV) and VPD in climate factors showed positive and negative related relationships with NDVI distribution and were all widely distributed across the study area, but the spatial heterogeneity of other climatic factors with NDVI was significantly complicated with them.

On the time scale, the average annual value of NDVI shows significant fluctuations in the study time series, but overall, it shows a slow growth trend (Figure 2a). The linear fitting trend of precipitation seasonality (CV) and the NDVI is upward (Figure 10a). Figure 10b shows that the NDVI value and precipitation seasonality (CV) have basically similar dynamic changes, and the NDVI increases and decreases with the increase and decrease in precipitation seasonality (CV). This result shows that precipitation seasonality (CV) has a positive effect on the increase and decrease in NDVI. On the contrary, the fitting trend of VPD and NDVI is decreasing (Figure 10c) because the general feature of VPD change is a significant upward trend, while the change of NDVI is contrary to the fluctuation of VPD. Therefore, it can also be concluded that there is a negative correlation between VPD and NDVI.

**Figure 10.** Variation trends and correlations of precipitation seasonality (CV), VPD, and NDVI. (**a**) Linear fitting of NDVI and precipitation seasonality (CV) trend; (**b**) Annual average change of NDVI and precipitation seasonality (CV); (**c**) Linear fitting of NDVI and VPD trend; (**d**) Annual average change of NDVI and VPD.

#### *3.4. Detection of the Impact of Key Factors on NDVI*

#### 3.4.1. Detection Factor Influence

In order to further explore the driving characteristics of various influencing factors on the dynamic changes of Miaoling's vegetation, this section used 13 factors (Table 2) covering climate, soil, terrain, geomorphology, and human activities to conduct factor driving force and factor interaction detection on the NDVI of Miaoling's vegetation. To explore the contribution rate and interaction of climate factors and human activities to vegetation change in the Miao Mountains and further verify the differences in the main impact factors of NDVI in the Miao Mountains. The q value of the factor detection results reflects the influence of each factor on the NDVI of Miaoling's vegetation (explanatory power). The factor detection results show that the explanatory power of each factor on the NDVI is in the following order: NLI > non-karst > land use change > precipitation seasonality (CV) > karst > VPD > elevation > precipitation of the wettest quarter > MAP > slope > soil moisture > MAT > aspect. Specifically, NLI (X13) has the strongest explanatory power for NDVI (q = 0.422), and aspect (X9) has the smallest explanatory power for NDVI (q = 0.125).

The explanatory power of the above factors passed the 0.05 test with a confidence level of 95%. Overall, the explanatory power of human activities on vegetation NDVI is greater than the climactic factors in Miaoling. Additionally, the order of partial correlation and explanatory power of each factor for NDVI is consistent.

#### 3.4.2. Detection Factor Interaction Analysis

The change in vegetation and the existence of spatial heterogeneity are driven by many factors. The interaction detection results of various influencing factors in Miaoling (Figure 11) show that their interaction is manifested as dual factor enhancement and nonlinear enhancement, and all interaction factors have obvious enhancement characteristics on the driving force of NDVI compared to a single influencing factor. Among them, the interaction between land use change and NLI [q (X12∩X13) = 0.459] has the strongest explanatory power for the spatial distribution of NDVI, showing a driving feature of dual factor enhancement. On the contrary, the interaction between soil moisture and aspect orientation [q (X5∩X9) = 0.112] has the least explanatory power on NDVI. In addition, the research results also indicate that the interaction between human activity factors and other factors is significantly greater than that between other factors. It can be seen that the explanatory power of the interaction between human activities and other factors in the Miaoling area is dominant and that it is a key driving factor affecting the vegetation change in the Miaoling area.

**Figure 11.** Explanatory power of interaction between key factors. X1, precipitation seasonality (CV); X2, VPD; X3, precipitation of wettest quarter; X4, MAP; X5, soil moisture; X6, MAT; X7, elevation; X8, slope; X9, aspect; X10, karst; X11, non-karst; X12, land use change; X13, NLI.

#### **4. Discussion**

#### *4.1. Analysis of the Spatial Distribution Trend of NDVI*

Previous research has confirmed that the ecological environment of karst regions has greatly improved [32–34,38,51–53]. Our research findings indicate that the Miaoling region has exhibited sluggish growth in NDVI over time compared to non-karst areas, indicating an improvement in their ecological environment [53,54]. This positive ecological change is attributed to the residents' strong awareness of environmental protection as well as the preservation of subtropical evergreen broad-leaved forests, evergreen and deciduous broadleaved mixed forests, and evergreen shrubs in the area [34,38]. Furthermore, the vegetation coverage exhibited a distribution pattern of "gradually increasing from west to east", as confirmed by an average Hurst index of 0.56. The slow and incremental increase in NDVI observed in our study area is consistent with the continuous improvement in vegetation coverage seen in other study areas across China [20–22,55]. The spatial heterogeneity of the impact of all climate factors on vegetation cover change is evident. Nevertheless, as demonstrated by Figures 6 and 7, the utilization of arable land and the increase in artificial surfaces have resulted in a consistent decline in NDVI values in the related areas.

As mentioned above, the NDVI variation in Miaoling not only shows a slow upward trend over the whole period, but there are also strong downward processes. As shown in Figure 2, NDVI declined considerably after 2010 and 2018. Notably, a considerable decrease occurred in 2012, and this phenomenon can be primarily attributed to the backdrop of global climate change, paralleling the NDVI changes seen in other karst regions of China. Investigations of this phenomenon reveal that the most severe drought and extreme weather in 50 years occurred in the karst region of southwest China in 2010 [56], leading to a sharp decline in Miaoling's NDVI that continued through 2012 and culminated in the lowest NDVI value of the entire research period. Furthermore, it is likely that the natural disasters that occurred in 2017 have had a negative impact on vegetation dynamics, thereby comprehensively influencing the overall NDVI trend in the Miaoling Mountains during the past 21 years.
