**1. Introduction**

Since the Anthropocene, human-induced global climate change has had a significant impact on terrestrial ecosystems [1]. As carbon, water, and energy exchangers between land and air, plants can provide people with oxygen, food, fiber, fuel, carbon sinks, and other

**Citation:** Wu, Y.; Yang, J.; Li, S.; Guo, C.; Yang, X.; Xu, Y.; Yue, F.; Peng, H.; Chen, Y.; Gu, L.; et al. NDVI-Based Vegetation Dynamics and Their Responses to Climate Change and Human Activities from 2000 to 2020 in Miaoling Karst Mountain Area, SW China. *Land* **2023**, *12*, 1267. https://doi.org/10.3390/ land12071267

Academic Editor: Eusebio Cano Carmona

Received: 29 March 2023 Revised: 26 May 2023 Accepted: 16 June 2023 Published: 21 June 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

valuable ecosystem services [2–4]. Accordingly, significant changes in the global climate and their impacts on vegetation growth over recent decades have received increasing attention [5]. These include changes in climate and environmental conditions as well as human activities, such as land use change. Environmental factors include a wider range of chemical, physical, and biological elements that can affect ecosystems, including disturbances such as droughts, fires, and floods [6]. The normalized difference vegetation index (NDVI) is becoming a significant indicator in studying the spatial vegetation dynamics of regional ecological changes [7–9]. Thus, the role of climate and human factors in vegetation dynamics is one of the hottest topics in global change science [10,11].

The impact of climate change and human activity on vegetation change has been studied by many scientists. Research shows that climate change mainly impacts vegetation by changing climate, temperature, precipitation, soil moisture, and seasonal variations [7,10,12,13]. Amongst these, temperature controls the growth and distribution of vegetation by preventing the onset, termination, and distribution of the photosynthetic process [14,15], which is considered the main cause of the greening trend in northern latitudinal and elevated regions (Qinghai–Tibetan Plateau) [16–18]. However, in the middle and lower latitudes, temperature does not have an important impact on plant growth. However, precipitation has a clear role in boosting vegetation growth in arid and semi-arid regions [19,20]. For example, in southwest Chinese karsts [21,22] and Xinjiang [23], precipitation seasonality and variability significantly affect vegetation change. Precipitation, rather than temperature, has become the key factor controlling vegetation growth [20,24]. Soil moisture, which is strongly related to temperature and precipitation, has become an important limiting factor in arid and semiarid areas. Furthermore, with high temperatures and heatwaves, the atmospheric vapor pressure deficit (VPD) is becoming an increasingly important driver of plant community function. High VPD can induce plant stomatal closure to prevent high water loss [25,26], which has been identified as a major factor in extreme drought-induced plant death [25]. Clearly, the impacts of climate change on vegetation cover are diverse and complex because of regional variations in climate change and ecological environmental conditions.

At present, vegetation changes and their spatial patterns are strongly affected by human activities. With the rapid development of the world's population and economy, the impact of anthropogenic activities on changes in surface vegetation cover is increasing, affecting the balance of terrestrial ecosystems on a large scale [8]. Among these, land use change is a significant and strongly spatially changing factor in vegetation change. On the one hand, unreasonable agricultural activities, excessive reclamation, grazing, and urban expansion significantly reduce vegetation cover [27,28]. On the other hand, human activities can increase vegetation cover by planting trees, closing hills to reforestation, and improving agricultural technology [29,30]. Besides land use change, many studies have used the night light index (NLI) to characterize regional economic development or urbanization and the intensity of surface human activities [29–31]. Therefore, the NLI was included as one of the factors influencing vegetation in the NDVI.

There are few studies on the connection between vegetation changes and their drivers in important watersheds in the karst region, although there have been many studies on the impact of climate variation and human behaviors on vegetation change [3,7,10,15,19,20]. Miaoling Mountain is an important watershed ecological security barrier in the karst region of southwest China. It is located in the center of the core area of southwest China's karst plateau. The contradiction between man and land is pronounced due to the high ecological sensitivity and vulnerability of karst areas, plus climatic factors. Biomass above ground [32,33] and biomass below ground [34,35] are significantly lower in karstic forests than in non-karstic forests. Therefore, vegetation plays an important role in managing karst desertification and restoring ecosystems [36]. However, to our knowledge, the driving mechanism of human activities on NDVI changes in vegetation in the Miaoling's karst watershed is still unclear; there is no research to quantify how climatic and human factors affect vegetation in the area. Additionally, the impact of each driver on vegetation change has not been quantified.

Based on this deficiency, this article aims to study the vegetation dynamics of important watersheds (Miaoling) in karst areas from the following four perspectives: climate, human activity, topography, and soil. The aims of this article are as follows: (1) to analyze the variation characteristics and trends of the NDVI's spatial distribution in Miaoling from 2000 to 2020; (2) to explore the correlation between climate factors and NDVI; and (3) to explore the key driving factors affecting the NDVI of Miaoling vegetation. The results of this study have far-reaching significance for the sustainable ecological development of karst areas in southwest China and the realization of China's strategic goal of "carbon peaking and carbon neutrality". At the same time, it provides a reference value for the vegetation's dynamic driving factors in the important karst watershed and theoretical guidance for ecological environment management and sustainable development of the watershed.

#### **2. Materials and Methods**

#### *2.1. Study Area*

Mt. Miaoling, the research site, is located in the center of the core region of the Karst Plateau in southwest China. It is an important watershed for the ecological security barrier of the watershed in the karst region of southwest China. Its main range (25.73◦–27.16◦ N, 103.82◦–109.48◦ E) takes the watershed of the Pearl River system and the Yangtze River system as the main axis and then extracts the Miaoling study area from the small watershed in the north and south of the main axis and divides it into east, central, and west sections (Figure 1). From east to west, Miaoling is the watershed of the Yuanjiang River Basin, the Liujiang River Basin, the Wujiang River Basin, the Hongshui River Basin, the Niulan-Jiang-Heng He River Basin, and the Beipanjiang River Basin. The altitude in the area is 146–2877 m, and the peaks are often above 1500–2000 m. The main peak of the eastern section is Leigong Mountain, at up to 2179 m. The middle section of Doupeng Mountain is 1961 m high, and the western section with Laowang Mountain is 2127 m above sea level. The eastern, central, and western parts reach an altitude of 2179 m. Miaoling is a humid, mountainous area with a sub-tropical monsoon climate. The main vegetation in the Miaoling Mountain area consists of mixed evergreen and mixed broadleaf forests as the primary forest type and shrubs, grass, and forbs as the vegetation degradation type, which differs significantly from the zonal non-karst vegetation of evergreen broadleaved forests in subtropical China (ECVC 1980) [37,38]. It is one of the important forest areas in Guizhou Province. Miaoling has no tectonic veins, and the geology and topography of the sections are very different. Additionally, it is formed by a number of north–south anticlines composed of hard rock formations and a combination of uplifted and high regions. The layered landforms composed of planes and large karst basins are the most prominent regional landforms, thus forming unique landscapes such as planes with concentrated arable, basin areas, and terraced fields located high on the hillsides.

#### *2.2. Data*

The NDVI is based on the Google Earth Engine cloud computing platform and selects US remote sensing imagery from the Landsat 5, Landsat 7, and Landsat 8 satellites with 16-day temporal resolution and 30 m spatial resolution. At the same time, the dataset was referenced by the Land Use and Global Change Remote Sensing Team of the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, with a spatial resolution of 30 m and a temporal resolution of each year [39]. Through methods such as series data preprocessing and data smoothing, the maximum NDVI value of each pixel in a year from 2000 to 2020 was obtained. The annual mean NDVI values from 2000 to 2020 were generated using the maximum synthesis method (MSV). When generating long-time series NDVIs, the maximum value synthesis method can decrease the effect of cloud cover, shadows, suspended particles in the atmosphere, etc., so that the error is reduced and the accuracy is improved [39].

**Figure 1.** Location of the Miaoling Mountain area.

The DEM data involved in this study are derived from the geospatial data cloud, which is downloaded using a rectangular frame clipping area and then transformed into raster data through an ArcGIS overlay, which is the basis for the calculation of the total height, slope, and aspect of the research region. The land use change data are sourced from Zenodo (https://zenodo.org/, accessed on 15 June 2020) and published by Huang Xin et al. from Wuhan University in China [40]. The biggest advantage of this dataset is its continuous 30 m land use classification results. The NLI can be used to describe the intensity of human activity and is closely linked to economic development. This study selected it as a factor in human activity and conducted a related driving analysis.

The meteorological data use raster data for precipitation and air temperature with a resolution of 1 km. This dataset introduces the influence of terrain on the climate in the temperature and precipitation data generated by the ANUSPLIN interpolation tool. The interpolation error is the smallest, and the accuracy is greatly improved compared with other interpolation methods [41]. The analysis of meteorological elements is more suitable. The meteorological interpolation software ANUSPLIN is used to interpolate the data into monthly synthetic precipitation multi-band data with a spatial resolution of 1 km. It is highly accurate, has high resolution, is a long-time series, and has better scientific research and application potential [42]. The above-mentioned monthly synthetic temperature and precipitation data are extracted by ArcGIS and then resampled to 30 m.

Soil moisture is an important link between the atmosphere and the terrestrial ecosystem. The soil moisture data included a set of neural nets using a data fusion of up to 11 microwave remote sensing-based soil moisture products. These data were obtained by satellite for the global surface soil moisture for the period 2003–2018 with a spatial resolution of 0.1◦ [43]. The bioclimatic variable data are available at a spatial resolution of 1 km, with historical monthly weather data for 1960–2018 [44]. The range of data downloaded for this study was 2000–2018. The VPD dataset mixes the high spatial resolution climate normal of the Worldclim dataset with the less accurate but time-varying data from CRU Ts4.0 and Japan's 55-year reanalysis (JRA55) with climate-assisted interpolation. Conceptually, the interpolation time variation from CRU Ts4.0/JRA55 is often applied to Worldclim high spatial resolution climatology to create a high spatial resolution dataset covering a wider range of time records. The above data and the data sources involved in the study are as follows (Table 1):


**Table 1.** Data and sources.

### *2.3. Methods*

In this study, in order to explore the characteristics, trends, and driving factors of vegetation change in Miaoling, we used the following methods to analyze the relevant data in sequence and with specific applications: We first conducted trend analysis and a Mann–Kendall (MK) trend test on NDVI changes in Miaoling's vegetation. Additionally, we used the Hurst index to perform a continuous analysis of the change trends of the NDVI to explore the continuity of the Miaoling NDVI trends in the spatial distribution and change trend. The correlation study of the regional distribution and variation trends of NDVI in Miaoling with climatic conditions was completed by using a partial correlation analysis. The association characteristics of the climatic elements, human activities, terrain, and vegetation changes in Miaoling were also studied using a geographic detector.

#### 2.3.1. Theil–Sen Median Trend Analysis

We conducted a trend analysis on the NDVI of Miaoling's vegetation using the Theil–Sen median method, which is referenced in this literature [45] and is expressed in Equation (1) as follows:

$$\beta = \text{median}(\frac{\boldsymbol{\pi}\_{j} - \boldsymbol{\pi}\_{i}}{j - i}), \forall j > i \tag{1}$$

where j and i are the time series data. A value greater than 0 means that the time series shows an upward trend. A value less than 0 means that the time series shows a downward trend, and a value closer to 0 means that the time series changes are not significant. The Theil–Sen median trend analysis and MK test significance test results were superimposed and analyzed. As Table 2 shows, the results are grouped into six categories. Based on the above results, we also conducted breakpoint detection on the dynamic trend of vegetation in Miaoling.


