*4.2. NDVI Variation and Land Use Change*

Human activity is widely recognized as an important driver of vegetation cover. Land use change, representing human activity, has been shown to be highly correlated with vegetation change [24,26,49,57]. Previous research studies have demonstrated the importance of land use change as a key factor in the spatial distribution of the NDVI, which significantly influences vegetation change, regional ecological security [58], and ecosystem services [59]. Our study, depicted in Figure 6b, reveals that forests in the Miaoling area have the highest NDVI value, followed by grass, shrub areas, arable land, wetland, water areas, and artificial surfaces. The changes in NDVI's distribution across different spatial and temporal scales are driven by both climatic factors and human activities [1,12,15]. The explanatory power of human activities on NDVI changes is much higher than that of climate factors, as shown in Table 4, indicating that NLI, land use change, and human activities play significant roles in the ecosystem. Land use change represent ongoing challenges for vegetation variation and the effects of anthropogenic activity [19].

The land use change characteristics of human activities in Miaoling reveal that vegetation coverage has been significantly impacted by land use change over the past 21 years. Human activities such as returning farmland to forests and grass, traditional farming practices, and the combination of human activities and climate change have played a significant role in improving or degrading the vegetation [53]. Changes in forest, arable land, and artificial land surfaces have had a considerable impact on regional vegetation coverage, primarily due to the tradeoff between forests and arable land. The distribution of land use change and changes significantly influence the NDVI's spatial distribution in the region.

## *4.3. Impact Climatic Factors of NDVI Variation*

Understanding the relationship between regional NDVI changes and climatic factors is critical for predicting regional vegetation changes and for effective ecological restoration management [56]. Climate change affects vegetation growth and change through dynamic changes and interactions between different weather factors. Miaoling's spatial heterogeneity is evident from its correlation distributions between NDVIs and six climate factors. This is due, in part, to the spatial variability of climate change. The strongest correlation among the climatic factors is precipitation seasonality (CV), as shown in Table 3. Compared with

results from other regions, vegetation cover (0.0009/year) in the Miaoling's karst region is more sensitive to changes in climatic factors, including precipitation seasonality (CV) and VPD.

Seasonal changes in precipitation have a significant effect on NDVI, as shown by research [60]. Precipitation is the main driver of vegetation change [61]. Seasonal changes in precipitation can also influence vegetative phenology and cover [20]. In areas of high humidity, the risk of drought is lower, making the growing season of vegetation more sensitive to precipitation seasonality (CV) in order to maximize water benefits [53,55]. The results of this study show that the regularity of the temporal change of NDVI in Miaoling is similar to that of precipitation seasonality (CV) (Figure 10b). Obviously, in the period of precipitation seasonality (CV) and precipitation of the wettest quarter (i.e., plant growth season) (Figure 12a), when precipitation is abundant, with an increase in temperature to a certain extent, the photosynthesis, respiration, and transpiration processes of plants can be increased, and plant growth can be promoted. This is based on the strong precipitation seasonality in Miaoling, in the subtropical monsoon region. The rate of change of precipitation and the precipitation in the wettest season strongly promote the growing season of plants in Miaoling, and the trend of change is consistent, so it has a significant regulating effect on NDVI. NDVI is also highly positively correlated with precipitation seasonality (CV) and precipitation in the wettest season in terms of spatial heterogeneity (Figure 9a,c).

**Figure 12.** Time variation and trend of NDVI and precipitation of wettest quarter (**a**); MAP (**b**); MAT (**c**); soil moisture (**d**).

Furthermore, there is a significant negative correlation between VPD and NDVI, which is second only to precipitation seasonality (CV) in explaining NDVI changes. VPD also plays an important role in the interannual variability of NDVI, as an increase in VPD causes a decrease in NDVI (Figure 10c). However, current Earth system models underestimate VPD's interannual variability and its effect on GPP and NEP [50] by ignoring VPD's indirect influence on NDVI. Due to global warming, VPD is increasing, and vegetation is severely affected [55,62]. In addition, previous research has found that the vegetative landscape is "browning", i.e., plant growth is decreasing [7,63]. Above a certain threshold, plant photosynthesis and growth in most species are limited, which leads to a higher risk of hydraulic failure and a decrease in NDVI.

Temperature and precipitation are the most significant climatic drivers of vegetation growth, as per previous studies [9,11,16,20]. The change in vegetation phenology, structure, and coverage has been observed to be significant from tropical to northern regions and from coastal to inland areas [7,12,19]. However, the karst landscape, which is complex and heterogeneous, has also contributed to vegetation change, including drought and land degradation, due to rocky desertification [64,65]. The results of this study show that NDVI increases with the increase in temperature in the study time sequence (Figure 12c). The increase in temperature can increase the photosynthetic efficiency of vegetation and prolong its growth period, thereby improving the status of NDVI [6]. However, the partial correlation is slightly smaller than that of precipitation (Table 3). Contrary to the results of other studies, "temperature has a stronger impact on NDVI than precipitation" [55,64].

A study conducted on the interaction between NDVI and the climate of karst vegetation in Guizhou revealed that the effect of MAT on NDVI is stronger than that of MAP [53,60]. The study concluded that the explanatory power of MAT on NDVI in the Miaoling area is lower than that of MAP. The binary hydrological structure commonly found in karst regions leads to a substantial loss of precipitation [23], suggesting that water resources are not efficiently utilized for the thriving of vegetation. The study's findings are consistent with the fact that precipitation has a lower positive correlation with NDVI changes. This is particularly relevant in karst areas that are prone to drought, where precipitation changes have a significant impact on vegetation [34,62,66]. It is confirmed that NDVI is more sensitive to precipitation than temperature in Miaoling.

Soil moisture mainly comes from precipitation and affects vegetation growth [8,17]. Therefore, it is also one of the important factors limiting vegetation growth in karst areas. Due to the relatively thin nature of the karst soil layer, soil water is easy to lose, which limits plant growth and ecological recovery in karst areas [50,67]. In this study, the explanatory power of soil moisture is relatively weak, which also proves that the characteristics of soil moisture loss under the dual structure in karst areas are easy and the positive effect on NDVI is not significant. As shown in Figures 9e and 12d, the soil moisture of Miaoling's arable areas (mainly in non-karst areas) is highly positively correlated with NDVI, which accounts for 43.7% of Miaoling.

#### *4.4. Influence of Factor Interaction on NDVI*

Changes in vegetation growth are inextricably linked to climate variation and human activities. Based on the detection of geographical detectors, we further analyzed the interaction of 13 factors on NDVI. We found that NLI between 2000 and 2020 is the strongest driving factor for explaining the changes in vegetation in Miaoling. Additionally, the results showed that the interaction between various factors has significantly higher explanatory power for the changes in the NDVI than itself (Figure 11), which is consistent with other research results [13,16,66].

In the study, NLI has the greatest explanatory power for the trend of vegetation cover change (q = 0.422, Table 4). There are a few studies that combine NLI with NDVI, indicating that urbanization has a negative impact on vegetation coverage or the ecological environment by increasing NLI [30,68,69]. There is a significant negative correlation between vegetation coverage and NLI values. From Figures 5 and 13, it can be seen that there is

a significant overlap between the areas where NDVI continues to degrade and the areas where NLI significantly increases. The areas where NDVI improves mostly correspond to areas with a low or no nighttime light index.

**Figure 13.** Characteristics and spatial distribution of NLI changes from 2000 to 2020.

Studies have shown that the interaction between precipitation seasonality (CV) (q = 0.355), land use change (q = 0.0.397), non-karst areas (q = 0.401), and NLI (q = 0.422) has the most obvious effect on the NDVI of vegetation (Table 4). Unlike other studies [5,7,43], we found that two factors of human activity and their interaction with other factors have a high explanatory power for the changes in NDVI, and it mainly shows a bifactor enhancement; the contribution rate is very large. This also shows that human activity can significantly influence the NDVI in the karst region [37,39,55]. Among them, the interaction between NLI and land use change (q (X13∩X12) = 0.459) has the largest impact on NDVI, and it mainly shows a bifactor enhancement. Therefore, human activities can be identified as the dominant factor in vegetation dynamics, while other factors only serve as constraints in karst basins. Relevant studies on the Loess Plateau have also shown this [15,20,58].

Temperature and precipitation are considered the foremost drivers of vegetation growth with regards to climatic factors, as cited by previous research [8,10,13,19]. Although the influence of mean annual temperature (MAT) (q = 0.155) and mean annual precipitation (MAP) (q = 0.21) on vegetation in Miaoling does not differ significantly, their interactions (q (X4∩X6) = 0.428) exhibit a significantly higher impact on NDVI compared to their individual effects. Furthermore, soil moisture and VPD are strongly linked to temperature and precipitation [69,70]. A few researchers have reported a direct or indirect dependence of VPD's impact on NDVI on the prevailing temperature and soil moisture conditions [25,71,72]. Hence, when the response of plants in an ecosystem decreases the evaporation capacity due to atmospheric drying, the conservation of soil moisture improves, along with some evidence of NDVI growth. This suggests that hydrothermal conditions in subtropical regions significantly influence vegetation growth and change. Moreover, the impact of factors such as altitude, slope, and aspect interaction are relatively minor in driving vegetation trends in Miaoling, but this impact increases significantly under the influence of human activities, reinforcing the crucial role of human activities in vegetation change in the Miaoling (karst area) region. In other words, the varying responses of vegetation NDVI to climate factors and human activities may be explained by the interaction of various factors in terms of temporal and spatial scale differences.

#### *4.5. Limitations of This Study*

In this study, we explored the dynamic change characteristics and trends of the regional vegetation's NDVI in Miaoling and conducted a driving analysis using climate factors, human activities, and topography. However, there are still some limitations. Firstly, we did not analyze the seasonal characteristics of vegetation growth in Miaoling, such as growing season and non-growing season. In future research, we should pay attention to

the differences between these vegetation changes and their comprehensive relationship. Secondly, due to the limitation of spatial resolution differences in NDVI and climate factors, the NDVI variation trend of some pixels may be overestimated or underestimated, as may also be found in correlation analyses. Despite these shortcomings, this work is helpful to comprehensively understand the spatiotemporal characteristics of Miaoling's vegetation and the driving factors of vegetation dynamics. At the same time, it provides a reference value for the dynamic driving factors of vegetation in important karst basins.

#### **5. Conclusions**

Based on NDVI data, the present study examines the alterations in vegetation coverage within a karst basin watershed from 2000 to 2020. Furthermore, the research also investigates the impact exerted by climate factors, topography, and human activities on Miaoling's vegetation and how they interact with each other. The key results of this study are as follows:

(1) Under the pixel scale and spatial distribution in Miaoling, the vegetation coverage gradually increases from west to east. During the study period, the NDVI of Miaoling's vegetation showed an overall upward trend (0.0009/year), with an average value of 0.766, 53.82% of the region continuing to grow, and a distribution pattern of "gradually increasing from west to east". The vegetation in NDVI showing an upward trend is much larger than the area showing a downward trend, and the site with a downward trend is mainly in the western and central parts of Miaoling.

(2) The correlation between the vegetation's NDVI and meteorological factors presents significant spatial heterogeneity. Climate change has a two-sided impact on NDVI changes in vegetation in the study area because there is a positive promoting effect and a relatively inhibitory effect for the NDVI. The NDVI and VPD of the vegetation in the study area show a negative correlation and a positive correlation with the other five climate factors as a whole, with the greatest correlation being with precipitation seasonality (CV).

(3) Compared with climate change and landform factors, human activity factors have a greater driving force on the NDVI of Miaoling's vegetation, and their interaction with other factors is also significantly higher, which also shows that the dynamic change and development trend of the NDVI of Miaoling's vegetation are strongly affected by human activities. Therefore, human activities can be considered the dominant factor driving NDVI changes in the Miaoling area.

**Author Contributions:** Conceptualization, Y.W. and S.L.; methodology, G.L.; software, L.G. and J.Y.; formal analysis, C.G. and F.Y.; investigation, J.Y., L.G., Z.S. and X.Y.; data curation, Y.C., H.P. and X.Y.; writing—original draft preparation, Y.W.; writing—review and editing, C.G. and S.L.; visualization, J.Y. and L.G.; supervision, Y.X.; project administration, G.L.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Guizhou Provincial Science and Technology Projects (QKHJC-ZK [2022] YB334); Guizhou Provincial Science and Technology Projects (QKHZC [2023] YB228) and Doctoral program of Guizhou Education University (X2023024).

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We thank the anonymous reviewers for their valuable comments. We gratefully acknowledge the design of S.L. and the contributions of the co-authors.

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

#### **References**


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