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Article

Responses of Climatic Drought to Vegetation Cover Dynamics: A Case Study in Yunnan, China

by
Yangtao Wan
1,
Han Han
1,
Yao Mao
2 and
Bao-Jie He
3,4,5,*
1
School of Architecture and Urban Planning, Yunnan University, Kunming 650550, China
2
School of Geographical Sciences, Southwest University, Chongqing 400715, China
3
Centre for Climate–Resilient and Low–Carbon Cities, School of Architecture and Urban Planning, Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing University, Chongqing 400045, China
4
School of Architecture, Design and Planning, The University of Queensland, Brisbane, QLD 4072, Australia
5
CMA Key Open Laboratory of Transforming Climate Resources to Economy, Chongqing 401147, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(10), 1689; https://doi.org/10.3390/f15101689
Submission received: 9 August 2024 / Revised: 30 August 2024 / Accepted: 20 September 2024 / Published: 25 September 2024
(This article belongs to the Topic Climate Change and Environmental Sustainability, 3rd Volume)
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Abstract

:
Vegetation cover can regulate regional climate and associated dry–wet variations. However, the effects of the quantitative structure and landscape pattern of vegetation cover on climatic drought remain unclear. Yunnan Province in China, with its abundant vegetation resources, provides a good setting for addressing this research gap. Our objective is to provide guiding recommendations for climate-warming mitigation through the study of the topic. This study adopted four periods of vegetation cover data, from 1992 to 2020, and explored their dynamics. Monthly average precipitation and temperature data from 125 meteorological stations in Yunnan were used to calculate standardized precipitation–evapotranspiration index (SPEI) for 1992–2020 to understand the responses of climatic drought to vegetation cover dynamics. The correlations between quantitative structure, landscape pattern, and climatic drought were investigated by Pearson’s correlation coefficient in 10 km, 20 km, 30 km, and 40 km grid cells, respectively. The results indicate that changes in the quantitative structure of vegetation could influence regional climates, with the contributions to climatic drought mitigation ranked in the following order: broad-leaved forest > shrubland > needle-leaved forest > cropland > grassland. Landscape patterns significantly affected local climates, where broad-leaved and needle-leaved forests had the strongest and most stable correlations with climatic drought, whereas shrubland and grassland showed weaker correlations. The correlations between landscape patterns and climatic drought were stronger during the dry season than the rainy season. Factors such as the landscape dominance index, fragmentation index, and aggregation index had a significant impact on climatic drought. The dominant and aggregated-distribution broad-leaved forests were conducive to climatic drought mitigation, while needle-leaved forests, croplands, and grasslands might exacerbate climatic drought.

1. Introduction

Atmospheric and land-use changes resulting from human activities have been shown to significantly enhance global drought disasters [1,2]. Drought, which is one of the most prevalent and severe natural disasters [3,4], has become a prominent research focus in meteorology, ecology, and geography [5,6,7]. The mechanisms of drought formation and occurrence are intricate and characterized by multiple factors, including precipitation, geographic attributes, and human activities [8]. Typically, drought can be categorized into four types: climatic, hydrological, agricultural, and socio-economic droughts [9]. Climatic drought associated with precipitation and temperature is recognized as the dominant [4]. The climatic drought arises from a water deficit caused by an imbalance between evapotranspiration and precipitation, where water expenditure exceeds water revenue [10], due to a combination of water cycling, water redistribution and water balance in a continuous system of “soil-plant-atmosphere” [11]. In particular, precipitation is an important link in the atmospheric water cycle because vegetation transpiration directly affects its occurrence [12].
Studies have explored the relationship between vegetation transpiration, precipitation, and other climatic factors, revealing that vegetation contributes to atmospheric humidity and potential precipitation through transpiration [13]. In addition, forest transpiration results in a higher volume of water circulating in the dry season than in the wet season [14]. Reforestation projects carried out in China over the past decades have been found to have some impact on the improvement of surface temperatures in arid regions, such as the Loess Plateau [15,16], and the alleviation of drought in the Yunnan region [17]. Many studies have investigated how vegetation transpiration affects climatic conditions, including temperature, precipitation, and other factors [18,19], and their interactions [20]. These studies indicate that changes in rainfall and heat directly impact the dry–wet state of the climate, with the latter influenced by the quantitative structure and landscape pattern of various vegetation cover types in the region. Therefore, alterations in the quantitative structure and landscape pattern of different vegetation cover types can regulate the occurrence of droughts. Exploring this potential relationship is crucial because it can offer a solid foundation for managing dry–wet environments and mitigating drought risks through the optimization of vegetation cover types.
Changes in the quantitative structure of vegetation cover types can impact drought dynamics given variations in water transpiration and retention capacities. Previous studies have examined the response to changes in vegetation cover rate [21,22], with linear regression models correlating vegetation cover with humidity, air temperature, and precipitation [23,24]. Most of the data samples of studies on the response of climatic drought to vegetation are based on vegetation coverage rate and normalized-difference vegetation index (NDVI) [25]. However, it is difficult to accurately reflect the relationship between the quantitative structure of different vegetation cover types and climatic drought, making the spatial response characteristics of drought to changes in different vegetation cover types a research challenge. Second, the study by Han et al. [26] highlights the impact of vegetation cover landscape patterns on the dynamics of dry–wet climate conditions. The different characteristics of vegetation in terms of spatial distribution, such as aggregation, connectivity, and diversity, contribute to dry–wet climate changes. However, it is not clear how regional landscape patterns of different vegetation cover types affect the dry–wet state, the landscape pattern indicators that influence dry–wet dynamics, and the specific influence mechanisms. This study delves into the potential causes and impacts of drought on various unit scales. The objectives were to (1) quantify spatiotemporal drought changes and analyze alterations in the quantitative structure and landscape pattern of vegetation cover types, (2) identify key indicators of vegetation cover types and landscape patterns that stably affect climate and drought, and (3) uncover the influence mechanism through which changes in the quantitative structure and landscape patterns of different vegetation cover types impact climatic drought, to propose strategies to regulate the dry–wet environments and reduce drought risk. Overall, this study not only provides insights into the relationships among quantitative structure, landscape pattern of vegetation cover, and drought dynamics, but also lays a foundation for adjusting the type of structure of vegetation cover to mitigate drought effects.

2. Materials and Methods

2.1. Study Area

Yunnan Province straddles the tropical, subtropical, and temperate regions of the Northern Hemisphere and exhibits a diverse climate characterized by distinct dry and wet seasons, which is a crucial area for global biodiversity conservation [27] and an essential ecological barrier and ecologically fragile area in China [28]. Yunnan has an average annual precipitation exceeding 900 mm (range 547–2300 mm) in most areas (Yunnan Provincial People’s Government Website). It has a complex and varied topography and is located in the southwestern part of the Yunnan-Guizhou Plateau, with an average altitude of around 2000 m. Population size of 46.73 million as of 2024. It possesses rich vegetation resources due to its diverse ecological and geographical conditions, which serves as an excellent case study for examining the relationship between vegetation cover type structure and climatic drought.
The complex topography and geomorphology lead to significant spatial and temporal variations in light, heat, water, and air distributions. According to Yunnan’s 14th Five-Year Plan for Regional Coordinated Development (Yunnan Provincial People’s Government Website), Yunnan Province is divided into five regions: northwest, northeast, central, southwest, and southeast Yunnan (Figure 1). These regions are research units that investigate the impact of quantitative structures and landscape patterns relevant to vegetation cover types on drought changes, in terms of influencing factors and mechanisms.
Yunnan is undergoing an increasing drought trend that threatens its biodiversity and agricultural economy and may lead to the deterioration of the inhabitant environment. Influenced by human intervention in land use, vegetation cover types [29], and factors such as topography and climate [30], Yunnan is increasingly prone to drought disasters [31,32]. Over the past 30 years, extensive deforestation in the predominantly subtropical climate of Yunnan has led to the conversion of primary forest land into cropland or economic forest land, causing changes in the vegetation cover structure. This transformation has been linked to a warm-drying trend in many areas of the province [33]. Moreover, this trend, along with the frequency of extreme droughts, is likely to increase [34]. Therefore, it is essential to investigate the controllable factors influencing climatic drought in Yunnan and to implement practical measures to mitigate its impact.

2.2. Data Sources

The research framework of this study is shown in Figure 2. The land cover (LC) data were obtained from the CCI/C3S LC map data product provided by the European Space Agency (http://maps.elie.ucl.ac.be/CCI/viewer/download.php, accessed on 10 August 2023). The LC map data are a land-cover type product, demonstrating the distribution of land-cover types on a global scale at a resolution of 300 m for each year from 1992 to 2020. These LC types are integrated standards from the FAO Land Cover Classification System, the UNEP-GEMS Global Land Cover Classification, and ISO standards.
In this study, the original classification was derived from the European Space Agency’s Legend for LC Map v2.0.7. The original 29 LC types were grouped into five vegetation cover types: broad-leaved forests, needle-leaved forests, shrublands, grasslands, and croplands (Table 1). The remaining LC types were combined into the label as ‘others’. County-level monthly surface-observed average precipitation and temperature data from 1992 to 2020 were sourced from the National Meteorological Information Center of China (http://data.cma.cn/, accessed on 12 August 2023). The dataset included information from 125 national stations located in Yunnan Province.

2.3. Climatic Drought Indicators

Climatic drought indicators serve as a fundamental basis for drought research and provide quantitative expressions of drought conditions. Commonly utilized indicators include the Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), Composite Meteorological Drought Index (CI), Z Index, Aridity Index, and standardized precipitation–evapotranspiration index (SPEI), among others [35,36,37,38]. Although these indicators can reflect the drought characteristics of a region to a certain extent, many have limitations in their applicability across regions [39]. For example, the SPI and Z Index focus solely on precipitation factors [40], PDSI is complex for the accurate calculation and assessment of short-term drought events accurately [41], and CI is overly sensitive to precipitation events with challenging determinations of weighting factors and thresholds [42]. However, the SPEI, building upon the SPI, effectively considers temperature fluctuations alongside precipitation factors and can provide a comprehensive assessment of regional drought intensity [43]. Accordingly, the SPEI has been widely used in drought monitoring across various regions [44,45,46], with studies highlighting its efficacy in Yunnan as well [34,47]. The SPEI calculation method was based on existing literature [48].
The distinct rainy and dry seasons in Yunnan exhibit significant changes in precipitation patterns, with high rainfall in spring and summer, and low rainfall in fall and winter. There was a noticeable uneven distribution of precipitation throughout the year and prominent spatial heterogeneity. Therefore, this study categorized precipitation into the rainy season (May–October, covering summer and fall) and the dry season (November–April of the following year, covering winter and spring) for the analysis of the climatic drought distribution in Yunnan (Figure 2) [49,50].

2.4. Landscape Pattern Index Analysis of Vegetation Cover

Landscape patterns are a synthesis of multiple drivers, such as geographic processes, hydro-meteorological effects, human activities, and social and economic development [51,52]. The landscape pattern index is a widely used quantitative indicator that focuses on analyzing changes in the structural composition and spatial configuration of vegetation landscapes [53]. As we focused on the spatial distribution patterns of the vegetation landscape, including fragmentation, connectivity, diversity, etc., the general indicators [54,55] selected at the class and landscape levels were as follows: (1) The landscape pattern index at the class level was divided into four categories, including LDI, LSI, LFI, and LAI, with a total of 18 indicators. The LDI quantifies landscape dominance by certain land-cover types. The LSI measures patch shape complexity. The LFI assesses landscape fragmentation into smaller, isolated patches. The LAI evaluates the clustering of similar land cover types. (2) The landscape pattern index at the landscape level was divided into two categories, including SDI and LEI, with a total of four indicators. The SDI quantifies landscape diversity by assessing the variety and abundance of land cover types. The LEI measures the evenness of their distribution across the landscape. The specific meanings and the acronyms of 22 representative indicators are listed in Table 2 (the calculation method is given in Appendix A).
These secondary indicators for the five categories were weighted using normalization and entropy weighting. The analysis returned three composite indicators: LDI, LCDI (Landscape Clustering–Dispersal Index, a composite index of LAI and LFI), and LDEI (Landscape Diversity–Evenness Index, a composite index of SDI and LEI). The LDI assessed the occupancy rates of different vegetation cover types in five regions of Yunnan. LCDI was used to examine fragmentation and aggregation characteristics. The LDEI evaluated the overall abundance and uniformity of vegetation. LSI here does not exhibit a significantly change over time; this step is not analyzed.
This paper adopted the gridding method to divide spatial cells for correlation studies. In order to select appropriate spatial-scale grid cells, this paper calculated the landscape pattern index of vegetation cover in batches of four windows of 10 km × 10 km, 20 km × 20 km, 30 km × 30 km, and 40 km × 40 km in magnitude (Figure 2), thereby comparing and analyzing the results. The window size with the most significant correlation among different scale scenarios was selected as the grid for subsequent studies.

2.5. Spatial and Statistical Analysis

ArcGIS 10.8 was utilized for grid clipping and spatial analysis, Fragstats 4.2.1 was used for the calculation of landscape pattern indices of vegetation cover, and SPSS Statistics 27 software was used for Pearson correlation analysis (Figure 2). The specific steps are as follows: (1) in ArcGIS 10.8, the vegetation cover image and spatial interpolation image of the drought index in two seasonal phases were divided into small pane cells according to different grids; (2) 22 representative indicators at the class and landscape levels were analyzed using Fragstats 4.2.1; (3) the quantitative structure, landscape pattern index, and SPEI mean values of different vegetation cover types were obtained, after which their variations over the past 30 years were analyzed, and the Pearson correlation analysis in SPSS 27 was conducted; (4) the results based on the most suitable grid were selected for analysis after comparing the correlation with the significance; and (5) the number of vegetation transfers and landscape pattern index were analyzed by Pearson correlation with SPEI change values, respectively. MATLAB R2023a was used to plot and analyze the correlation heat map and to obtain the final conclusion.

3. Results

3.1. Spatiotemporal Dynamics of Climatic Drought

Figure 3 presents the average values of the SPEI in the five major regions from 1992 to 2020, the SPEI values in the rainy and dry seasons were significantly different. In the dry seasons, there was a clear aridification trend, with SPEI in northeast Yunnan and northwest Yunnan showing a monotonically decreasing trend over the past 30 years. In contrast, central Yunnan, southwest Yunnan, and southeast Yunnan exhibited some fluctuations, transitioning from aridification to wetting around 2010, and then aridification after 2015. The overall trend in the province indicates an aggravation of climatic drought. During the rainy season, each region experienced substantial fluctuations in SPEI values over 30 years. Although, based on the linear fitting trend, southwest Yunnan, northeast Yunnan, and southeast Yunnan showed a slight humidification trend; northwest Yunnan remained relatively stable; but Central Yunnan displayed an aridification trend. In recent years, the SPEI in the rainy season has fluctuated around the average value with no clear changes.
Kriging Interpolation [56] of the SPEI values from 125 Meteorological Stations, Figure 4 maps the spatial distribution of monthly SPEI variation in 1992–2020 to reflect climatic drought changes. In dry seasons, the value of the SPEI change generally exhibited a gradual transition from positive in the southern part to negative in the northern part, displaying a negative correlation with latitude. There was a transition from wetting to aridification conditions, where southeast Yunnan and southwest Yunnan mostly experienced a trend towards humidification, while the northern regions from Central Yunnan were progressively arid. Owing to the wet climate and topography, local areas in northeast Yunnan bordering Sichuan and Guizhou to the east exhibited a humidification trend. In comparison, during rainy seasons, there was a notable aridification trend in most parts of central Yunnan, southeastern northwest Yunnan, and western southeast Yunnan. Central Yunnan served as the epicenter of aridification, spreading outward in all directions before gradually slowing and transitioning to humidification. Overall, the climatic drought patterns during the rainy and dry seasons in Yunnan exhibited significant spatiotemporal heterogeneity. Spatial differences have been attributed to factors such as topography, elevation, atmospheric circulation, and human activities [57].

3.2. Spatiotemporal Dynamics of Vegetation Cover Quantitative Structure

The vegetation cover type transfer matrix (Table 3) and Sankey diagram (Figure 5) were obtained by processing the data of five vegetation cover types from 1992 to 2020 in Yunnan. The total area of the five vegetation cover types changed from 381,836 km2 in 1992 to 379,463 km2 in 2020, remaining largely stable. The three types of vegetation cover experienced a decrease in area, with shrublands decreasing the most, followed by croplands and grasslands. Two types of vegetation showed an increasing trend, with needle-leaved forests experiencing the largest increase, followed by broad-leaved forests. Overall, the area dominated by shrublands witnessed the fastest decline, with a notable shift towards broad-leaved forests, needle-leaved forests, and other vegetation cover types.
Figure 6 presents the distribution of the five vegetation cover types in Yunnan in 1992 and 2020. Broad-leaved forests were mainly found in the subtropical regions of western and southern Yunnan, and some were also concentrated in northeast Yunnan. Needle-leaved forests were most densely distributed in central and northwest Yunnan, increasing in number from southwest Yunnan to the north. Shrublands were widely and uniformly distributed in most areas of the province, but more in the south and less so in the north. Grasslands were concentrated in northwest, southeast, and eastern Yunnan, with a scattered presence elsewhere. Croplands were predominantly located around cities in northwest, northeast, central, and southeast Yunnan. Needle-leaved forests and shrublands were evenly distributed across most areas, while broad-leaved forests, grasslands, and croplands had typical clustering characteristics. The different vegetation types had different trends in localities in 2020 relative to 1992, and the overall distribution pattern was stable.
Figure 6 and Figure 7 show the spatial transfer information of different vegetation cover types in Yunnan from 1992 to 2020. The analysis revealed that the highest area of transfer was from shrublands to needle-leaved forests, with an area of 9875.1 km2, which occurred widely in most areas of Yunnan. Following this, the transfer from shrublands to broad-leaved forests was also prominent, with an area of 8929.1 km2. The transfer between these two scenarios was primarily in tropical and subtropical areas such as southwest Yunnan, southeast Yunnan, and southern northwest Yunnan. Three other vegetation cover transformations exceeded 1000 km2. The first involved the conversion of broad-leaved forests to shrublands (1890.3 km2), mainly in tropical and subtropical regions like the western part of northwest Yunnan and southwest Yunnan. The second was the shift from grasslands to needle-leaved forests (1068.7 km2), predominantly in northwest Yunnan. The third was the change from shrublands to croplands (1007.9 km2), in the southern regions of northwest Yunnan, southwest, and southeast Yunnan. In addition to these, there are still some relatively atypical vegetation type shifts occurring throughout the province.

3.3. Spatiotemporal Dynamics of Vegetation Landscape Patterns

The vegetation LDI reflects the relative importance of a specific vegetation cover type in a region, with a higher index indicating a higher proportion. As shown in Figure 8, shrubland decreased and needle-leaved forests increased in dominance. Broad-leaved forests notably increased in southwest and northwest Yunnan, while remaining stable in other regions. Cropland dominance decreased in southeast and northeast Yunnan but remained stable elsewhere, and grassland showed a generally stable trend across the province.
The vegetation LCDI assesses the spatial concentration of landscapes with the same vegetation cover type within a specific region. A higher index value indicates a greater concentration of landscapes with vegetation cover types, while a lower value indicates a more fragmented distribution. Figure 9 shows the fragmentation trends in different vegetation cover types in the five major regions of Yunnan over 30 years. Aggregation of needle-leaved forests and shrublands showed a monotonically decreasing trend in all regions. Broad-leaved forests initially experienced a slight increase followed by a significant decrease, resulting in an overall downward trend. Grassland exhibited a small decline in the first two decades but saw a sharp increase from 2010 to 2020, showing an overall upward trend. The cropland concentration displayed a consistent increase in southwest Yunnan and northwest Yunnan, a fluctuating pattern in southeast Yunnan, northeast Yunnan, and central Yunnan, and remained relatively stable.
The vegetation LDEI assesses the diversity and abundance of landscapes with the same vegetation cover type within a specific region. A higher vegetation LDEI suggests a greater number of landscapes with the same vegetation cover type and a relatively even spatial distribution. Figure 10 shows that central and southwest Yunnan experienced a slightly fluctuating upward trend in vegetation species richness in the past 30 years, with an increase in the vegetation landscape abundance and a tendency towards spatially uniform distribution. In comparison, northeast Yunnan, northwest Yunnan, and southeast Yunnan exhibited a general downward trend in vegetation landscape diversity, but the index increased in recent years after a decreasing trend in 1992–2010.
Further analysis of the landscape indices in Yunnan revealed that shrubland dominance decreased significantly in all five regions, while needle-leaved forests underwent a sharp rise, broad-leaved forests increased in southwest and southeast Yunnan, croplands slightly decreased in these regions, and other types remained stable across all regions. Grassland aggregation and distribution increased greatly in all regions, while needle-leaved forests, broad-leaved forests, and shrublands gradually fragmented. Croplands experienced a slight agglomeration in northwest, southwest, and central Yunnan and showed a small fragmentation trend in southeast and northeast Yunnan. Vegetation landscape distribution in southeast and northeast Yunnan was increasingly diversified in species and spatially uniform, whereas in southwest, northwest, and central Yunnan, there was a trend towards landscape diversity reduction and evenness decreased.

3.4. Scale Effects of Vegetation Landscape Pattern Indexes

The study area was divided into four gridding scenarios, based on which the Pearson correlation coefficients between landscape pattern index and SPEI change were calculated at the class level. The correlations of 18 class-level indicators and four landscape-level indicators with SPEI were analyzed during the rainy and dry seasons, as shown in Figure 11. The results indicate that all correlation coefficients were significant at the 0.01 level. In the plot, each column represents one coefficient under four scales as a research unit, and the size of the coefficients within a column is sorted into two parts, with positive correlations shown in the red system and negative correlations in the blue system. The intensity of the color indicates the strength of the correlation, with darker colors representing greater absolute values. Green indicates correlations close to 0, considered invalid. The correlation level gradually increased when expanding from 10 km × 10 km to 30 km × 30 km in the four gridding scenarios. However, the correlations of the SPEI with most indicators weakened when the grid was expanded to 40 km × 40 km, although some indicators showed stronger correlations. Therefore, the most suitable research grid range identified was 30 km × 30 km. Subsequently, all other analyses in this study adopted 30 km × 30 km as the basic research unit.

3.5. Correlation Analysis between Vegetation Cover Type Structure and SPEI Change

3.5.1. Correlation of Vegetation Cover Quantitative Structure with SPEI Change

Pearson correlation coefficients between the shifted area of vegetation cover types and SPEI during the rainy and dry seasons were calculated (Figure 12). Samples of the shifted areas of different vegetation cover types in 1992 and 2020 were used to determine their area shift values, while the change in SPEI over the past 30 years was calculated using a 15-year time unit. In the analysis, grid cells with vegetation transfers of 50 km2 or more were selected for analysis because transfers with smaller sample sizes were not statistically representative. The correlation coefficients were categorized into two types and three levels, as shown in the legend to Figure 12.
There was a consistent trend that the conversion of shrublands to broad-leaved forests was strongly positively correlated with SPEI change. However, the conversion of broad-leaved forests to shrublands showed diverse positive or negative correlations in Yunnan. This variability might be attributed to diverse types of shrublands in Yunnan, ranging from Eucalyptus forests to rocky thickets in plateau mountains, each with unique physiological characteristics and responses to climatic drought. The conversion of shrubland to needle-leaved forest and the conversion of needle-leaved forest to shrubland showed consistently negative and positive correlations with SPEI changes, implying that shrubland had a stronger drought mitigation capacity than needle-leaved vegetation. The conversion of cropland to broad-leaved forest and the conversion of broad-leaved forest to cropland showed highly positive and comparatively negative correlations with SPEI change, respectively. This indicates that broad-leaved forests had a stronger drought mitigation capacity than cropland. The inter-conversion between shrublands and croplands showed that shrublands had a greater drought mitigation capacity than croplands.
Moreover, the shift from croplands to needle-leaved forests in northeast Yunnan showed a relatively positive correlation with SPEI change, highlighting the superior drought mitigation capacity of needle-leaved forests over cropland. The inter-conversion between needle-leaved forests and grasslands, as well as that between shrublands and grasslands, exhibited a complex pattern of influence, encompassing both positive and negative effects. This complexity may stem from differences in the natural and biological characteristics of specific vegetation cover types across different areas, such as albedo, leaf area index, surface roughness, and rooting depth. Alternatively, these patterns could be attributed to the limited impact of changes in vegetation cover types alone on the natural elements of the region, such as topography, elevation, and atmospheric circulation, resulting in divergent impact trends. Due to the abundance of vegetation types in localized ecosystems, the vertical distribution structure of vegetation on small-scale plots might regulate the correlation between the main vegetation cover types and drought condition changes. Furthermore, the variable scales of vegetation cover type transfer in certain regions, coupled with lower sample sizes, could introduce instability into the correlations.
Ultimately, our study ranked the quantitative structure of different vegetation cover types based on their efficacy in mitigating drought from highest to lowest as follows: broad-leaved forests, shrublands, needle-leaved forests, croplands, and grasslands. It should be emphasized that although the results may not be entirely consistent in all regions of Yunnan, a comparison with previous research [58] and adjustments for unclear impact relationships supported the overall determination of this ranking.

3.5.2. Correlation of Vegetation Cover Landscape Patterns with SPEI Change

Yunnan was divided into 433 vector units with a grid resolution of 30 km × 30 km, and vegetation landscape pattern indices were calculated at 18 class levels and four landscape levels. A Pearson correlation analysis was performed on the vegetation landscape pattern index and SPEI change during the rainy and dry seasons. As shown in Figure 13, all the correlation coefficients were significant, at a 0.01 level. The landscape pattern indices of the five vegetation cover types exhibited different degrees of correlation with SPEI change. Needle-leaved forests, shrublands, and grasslands showed higher correlation with SPEI change in dry seasons, whereas cropland vegetation showed higher correlations in rainy seasons. Broad-leaved forests exhibited high correlations during both the rainy and dry seasons. Eighteen class-level landscape pattern indicators, except for IJI and PAFRAC, showed significant correlations with SPEI changes for most vegetation cover types. Four landscape-level indicators were significantly and negatively correlated with SPEI changes only in the rainy seasons, indicating distinct seasonal variations in their impacts.
At the class level, seven indicators (CA, PLAND, NP, PD, LPI, TE, and ED) in the vegetation LDI were significantly correlated with SPEI change in more than three vegetation cover types. The landscape dominance indicators of needle-leaved forest, cropland, and grassland vegetation were mostly negatively correlated with the SPEI change. This indicates a positive association between the area share of needle-leaved forests and grassland vegetation and climatic aridification in the region. Indicators for broad-leaved forests mostly showed a strong positive correlation with SPEI changes, suggesting a positive impact on drought mitigation. Shrubland vegetation landscape dominance was negatively correlated with SPEI in the rainy season and positively correlated in the dry season, highlighting its diverse influence during different seasons. Moreover, the landscape dominance of needle-leaved forests, broad-leaved forests, and grasslands consistently showed negative correlations with SPEI change during both the rainy and dry seasons, indicating a stable impact on aridification. However, the seasonal correlation of landscape dominance and SPEI change in croplands and shrublands exhibited considerable variation, indicating unstable relationships.
The vegetation LSI, consisting of PAFRAC and SSI, assesses the spatial complexity of vegetation landscapes. The indices of needle-leaved forests, broad-leaved forests, and croplands were positively correlated with SPEI change, suggesting that the spatial complexity of these vegetation types positively affected drought mitigation. Croplands showed high correlations with SPEI changes in both the rainy and dry seasons. Shrublands and grasslands exhibited negative correlations with SPEI change, indicating that the increasing trend in the landscape morphological complexity of these two types of vegetation could contribute to aridification. Among the vegetation types, only croplands consistently showed a stable and strong correlation with SPEI change in both rainy and dry seasons, highlighting the stable positive impact of its vegetation landscape shape complexity on drought mitigation.
The vegetation LFI, measured by sub-indicators DIVISION, SPLIT, and NLSI, showed significant correlations with SPEI change. The three sub-indicators of needle-leaved forests, croplands, and grasslands exhibited strong positive correlations with the SPEI, suggesting that the fragmentation of these vegetation types helps alleviate drought. Cropland and grassland fragmentation showed distinct correlations with SPEI changes during the dry and rainy seasons, respectively. However, needle-leaved forest fragmentation consistently mitigated climatic drought in both seasons. The fragmentation index for broad-leaved forests and shrublands was significantly negatively correlated with the SPEI change. Overall, the result indicates that the fragmentation of broad-leaved forest contributed more to aridity development.
Among the six sub-indicators for the vegetation LAI, only IJI showed a low correlation with the SPEI change. The other five sub-indicators, CLUMPY, PLADJ, MESH, AI, and COHESION, exhibited high correlations with SPEI change. These five sub-indicators of needle-leaved forests, croplands, and grasslands showed significant negative correlations with SPEI change, indicating that the aggregation and distribution of these three vegetation types contributed to climate aridification. Notably, needle-leaved forests and croplands maintained stable correlations with SPEI change across both rainy and dry seasons, whereas grasslands showed a correlation only in dry seasons. This implies that the aggregation of needle-leaved forests and croplands helped stabilize climate aridification. Broad-leaved forests and shrublands displayed significantly positive correlations with SPEI change, indicating a mitigating effect on aridity. Four sub-indicators (ED, LPI, PLAND, and CA) of shrubland showed relatively positive correlations with SPEI change only in dry seasons, while the five sub-indicators (ED, TE, LPI, PLAND, and CA) of broad-leaved forest were highly positively correlated in both the rainy and dry seasons, indicating that the aggregation of broad-leaved forests had a stabilizing effect on aridity mitigation.
At the landscape level, the combined ecological significance of the four sub-indicators SHDI, SIDI, SHEI, and SIEI, included in the vegetation SDI and LEI, was the diversity and uniformity of the distribution of vegetation landscapes in a given area. Both the vegetation SDI and LEI in Yunnan were significantly negatively correlated with the SPEI change in the rainy season, while the correlations were weaker in the dry season. This suggests that the abundance and even distribution of vegetation cover types in Yunnan had a strong positive influence on the formation of climatic drought.
Overall, the correlations between the landscape pattern index and SPEI change for different vegetation cover types in Yunnan in both the rainy and dry seasons showed different characteristics. Most of the 18 class-level indicators of needle-leaved forests and broad-leaved forests exhibited consistent correlation trends with SPEI change in rainy and dry seasons, but the correlation of needle-leaved forests was notably stronger in the dry season. Grasslands displayed stronger correlations with SPEI change in the dry season than those in the rainy season for 11 out of 18 class-level indicators. Most indicators of shrubland showed stronger correlations with SPEI changes in the dry season. The correlation between LDI and SPEI change showed opposite trends in the rainy and dry seasons. Artificial vegetation of the croplands exhibited different trends from other vegetation cover types, with 11 out of 18 landscape pattern indicators having stronger correlations with SPEI changes in the rainy season. Regarding the stability of the correlation between landscape pattern indicators and SPEI change, needle-leaved forests and broad-leaved forests demonstrated more stable and consistent correlations with SPEI change in both seasons, suggesting a more stable impact on climatic drought.

4. Discussions

Regional climatic drought is influenced mainly by the effect of surface evapotranspiration on the water cycle, in which vegetation evapotranspiration plays a decisive role [12]. The quantitative structure and landscape pattern characteristics of vegetation cover exhibited different mechanisms for regional climatic drought. In terms of population structure, a high to low distribution of broad-leaved forests, shrublands, needle-leaved forests, croplands and grasslands would be conducive to drought mitigation. In terms of landscape pattern, a clustered distribution of broad-leaved forests, lower fragmentation and a clustered distribution of various vegetation cover types could be effective in mitigating climatic drought.

4.1. Impacts of Vegetation Quantitative Structure on Climatic Drought

Previous studies demonstrated that albedo was the primary factor determining vegetation’s impact on precipitation [59], with Hoffmann et al. [60] also linking the decrease in precipitation to changes in albedo and roughness. Charney [61] and Lean [62] also argued that increased albedo could inhibit tropical precipitation by reducing heat flux and convection, while the reduction in roughness negatively affected precipitation by lowering the surface latent heat flux. In addition, Schulze et al. [63] suggested that vegetation rooting depth influenced rainfall variability, and Randal et al. [64,65] analyzed how LAI affected the atmospheric water cycle and precipitation through transpiration. In terms of synthesizing these research findings, functional differences in transpiration and water-holding capacity among the various vegetation cover types were primarily attributed to their distinct natural and biological characteristics, such as albedo, leaf area index, surface roughness, and root depth (Table 4).
Higher vegetation albedo means that more solar radiation is reflected back into the atmosphere, thereby reducing surface energy absorption [69], lowering temperatures, and decreasing water loss through evapotranspiration. Grasslands, croplands, and needle-leaved forests typically exhibited higher albedos compared to broad-leaved forests due to their small, sharp leaves and simpler vegetation structure. The leaf area index, an index measuring the leaf surface area per unit ground area, influenced water transfer to the atmosphere through transpiration. Higher values generally indicate higher evaporation rates owing to the increasing surface area available for gas exchange [70,71]. This contributes to regional hydrological cycle enhancement, near-surface microclimate improvement, and soil moisture improvement [72,73,74,75]. Broad-leaved forests generally have a higher leaf area index than needle-leaved forests, grasslands, and croplands. Areas with low surface roughness generally have higher wind speeds, which are associated with lower precipitation [76]. Woody plants generally exhibit greater surface roughness than other natural land covers, facilitating faster energy transfer between the surface and atmosphere, and typically resulting in higher precipitation. The depth of a plant’s root system determines the soil depth at which plants can draw water. Broad-leaved forests, with their deep root system, can access moisture from deeper soil layers in comparison to grasslands, croplands, and needle-leaved forests [77,78]. Broad-leaved forests also have a higher potential to maintain extensive leaf cover and sustained transpiration, which can reduce surface temperatures, retain surface moisture, and sustain water fluxes during periods (i.e., dry seasons) of reduced surface soil moisture [79], thereby lowering the risk of extreme drought. The values of these four biological characteristics for shrublands were generally intermediate between those of broad-leaved forests and grasslands.
Overall, broad-leaved forests are more effective in mitigating climatic droughts due to their low albedo, high leaf area index, high aerodynamic roughness, and deep root systems. Accordingly, this provides greater transpiration, water storage and moisture retention capacity, thereby conducive to the regional hydrological cycle. The results are in line with existing conclusions that evapotranspiration is greater in woodlands than that in other land uses, thus activating the hydrological cycle and increasing soil moisture [72,74,80]. In general, an increase in woodland area can enhance precipitation [81]. Simulation experiments of large-scale regional deforestation [82,83,84] also consistently showed a reduction in average precipitation across all regions. However, these studies overlooked the differential impacts of broadleaf and needle-leaved forests on climatic aridity. Our study showed that, unlike broadleaf forests, needle-leaved forests—despite having rich root systems and tall trunks—possessed needle-like leaves with lower light-trapping capacity and weaker transpiration, leading to a distinct influence on the water vapor cycle. Moreover, existing studies suggested a strong correlation between shrubland and drought, with shrubland being significantly negatively correlated with drought and flood intensity compared to woodland. However, our findings indicated that the effects of shrubs and needle-leaved forests were not stable, varying with local shrubland characteristics. Finally, compared to broadleaf forests, grassland and arable vegetation exhibited biophysical traits that could reduce surface temperature, retain less moisture, and have weaker transpiration, contributing to a stronger association with climatic aridity, consistent with previous studies as well [85].

4.2. Impacts of Vegetation Landscape Patterns on Climatic Drought

Vegetation cover, which is the core LC element, can regulate wet–dry climates. The regulatory effects depend on the vegetation landscape pattern in terms of composition, distribution, morphology, and configuration. In this study, we verified that the vegetation landscape pattern influenced climatic drought at the class and landscape levels.
This effect can be explained in three ways. First, alterations in vegetation landscape patterns, meaning changes in surface physical properties [1], could significantly alter vegetation evapotranspiration [86], surface runoff [87], and hydrological processes [88], as well as near-surface meteorological factors, such as surface albedo, temperature, and humidity [89,90,91]. Second, surface radiation and biogeochemical fluxes could be disturbed by changes in vegetation landscape patterns [92,93,94,95], and geophysical characteristics could be affected by regional circulation. Accordingly, there was a reverse influence on the surface temperature and precipitation, or a feedback to the regional circulation changes in regulating the regional wet–dry balance [96]. Third, changes in vegetation landscape patterns would lead to modifications in ecosystem functions and services [97], affecting the transfer and exchange of matter, energy, and information, thereby influencing the regional hydrological cycle and resulting in climatic drought.
At the class level, landscape patterns of vegetation cover types had diverse impacts and contributions to regional wet–dry climates [26]. Our study showed that enhanced broad-leaved forest landscape dominance, aggregation, and fragmentation were likely to reduce climatic drought intensity. Statistical analysis revealed that 13 of the correlation coefficients were highly significant, indicating that broad-leaved forest patches were closely associated with drought. The high dominance of broad-leaved forests meant higher landscape abundance, and their abundance increased the probability of precipitation to a certain extent [81], resulting in a reduction in the risk of extreme drought. The LAI and LFI showed opposite effects on dry and wet climate changes, where the increase in landscape aggregation of broad-leaved forests could ameliorate regional climatic drought. However, their fragmentation tended to exacerbate regional aridity and might have a complex impact on forest climate interactions, with implications for atmospheric circulation, the water cycle, and precipitation [98]. These effects might be related to the increase in the connectivity of broad-leaved forests, which could further facilitate water vapor circulation, increase the photosynthetic and transpiration efficiency of vegetation, reduce heat accumulation, and improve climatic conditions and land surface wet–dry environments. Similarly, existing studies showed that the increase in forest landscape richness, dominance, and connectivity reduced drought intensity and risk, effectively balancing wet–dry conditions across regions [26].
However, the impact varied significantly for croplands, grasslands, and needle-leaved forests. The variations were mainly related to differences in photosynthesis and surface energy exchange processes, which are associated with differences in the natural and biological characteristics of different vegetation types. Compared with broad-leaved forests, croplands and grasslands have inferior properties (e.g., height, leaf area index, surface roughness, and root depth) which are less conducive to maintaining a moist near-surface environment. Consequently, landscape dominance and aggregation increase in cropland and grassland exacerbated climatic drought, whereas the fragmentation increase generated an opposite result. Previous studies [26] suggested that the increase in spatial connectivity and morphological complexity in grassland landscapes tended to exacerbate drought risk which was consistent with our findings. However, the drought mitigation from arable landscapes may arise from differences in the dominant crop species adapted to specific regions, leading to varying climatic impacts, which contrasts with our findings.
Previous studies regarded woodland or forest as a unified whole, ignoring the distinct roles of broad-leaved forests and needle-leaved forests in influencing wet–dry environments. Our study revealed that while needle-leaved forests and broad-leaved forests had similarities in vegetation height and surface roughness, their roles in shaping climatic droughts were completely different. The LDI and LAI enhancement in needle-leaved forests played a contributing role in climatic drought, probably due to their high albedo, low leaf area index, and root depth, compared with broad-leaved forests. Different from other vegetation cover types, shrublands had a diverse effect on wet–dry environments, with their LDI showing opposite correlations during the rainy and dry seasons. Shrublands contributed to climatic drought during the rainy season but mitigated aridity in the dry season. These effects might be due to the distinct biological characteristics of different shrubland species, such as differences in albedo, height, and leaf area index for evergreen broad-leaved shrublands in southern Yunnan and alpine savanna shrublands in northern regions. Ultimately, different shrublands exerted diverse effects on climatic conditions and wet–dry surface environments in different seasons.
In general, the landscape pattern indices with the highest and most stable contributions to climatic drought were the LDI, LFI, and LAI. Among all five types of vegetation cover (broad-leaved forest, cropland, grassland, needle-leaved forest, and shrubland), these three indices demonstrated more significant influences. Whereas needle-leaved forests, broad-leaved forests, shrublands, and grasslands reflected the lack of stability of the impact, croplands reflected a more stable mitigation effect on climatic drought. This suggests that the complexity of cropland landscape patches is inversely associated with climatic droughts. This should be tailored to the local context in which cropland reclamation in Yunnan is generally along the periphery of towns and cities, and croplands are characterized by high spatial shape complexity. Low vegetation cover in densely populated urban areas is often linked to aridification, leading to a negative association between the spatial shape of croplands and climatic drought.
At the landscape level, the vegetation SDI and LEI in Yunnan were significantly and negatively correlated with the SPEI change in the rainy seasons, suggesting that increasing vegetation diversity and evenness might promote droughts. This can be explained by the fact that at a certain scale, similar vegetation clusters could be interconnected through the root system and subsurface moisture, forming a relatively stable moisture network [99]. This synergistic effect increased the efficiency of water acquisition and utilization by vegetation, as well as the flow and circulation of water vapor, ultimately alleviating climatic drought. Conversely, the mixed distribution of multiple vegetation cover types could reduce the connectivity of similar vegetation patches, potentially hindering the aggregation of water vapor, thus aggravating drought formation. Existing research [26] was aligned with our findings that at the holistic landscape level, extreme drought conditions tended to improve with the enhancement of the largest patch percentage, patch density, and spatial connectivity, while they worsened as landscape fragmentation and separation increased. The finding [73] that high patch number and even spatial distribution had a negative relationship with flooding also corroborated our conclusions.
Overall, our study identified that the vegetation cover types with the highest and most stable contributions to climatic drought were broad-leaved and needle-leaved forests in Yunnan. Regarding seasonal differences, all landscape pattern indicators of broad-leaved and needle-leaved forests showed significant and strong correlations with the SPEI in both the rainy and dry seasons. However, the correlations were much stronger in the dry season. The same trend was observed for shrublands and grasslands, with most landscape pattern indicators showing strong correlations with SPEI in dry seasons and weak correlations in rainy seasons. Cropland displayed an opposite impact pattern, with its landscape pattern indices showing stronger correlations with SPEI in the rainy season than the dry season. A possible explanation for the reasons behind the different patterns is related to the influence of regional circulation. Yunnan’s precipitation is mainly influenced by Indian and East Asian summer winds, making its regional climate sensitive to circulation currents associated with global climate change [100,101]. In rainy seasons, Yunnan is strongly influenced by the monsoon, leading vegetation transpiration to have a weaker effect on wet and dry climate changes, so that vegetation effects are only better exhibited in the dry season. Moreover, the contribution of vegetation evapotranspiration to precipitation varies between rainy and dry seasons. A study reported that during the rainy months, water surface evaporation played a more significant role in precipitation formation [102], while the predominance of vegetation transpiration efficiency in dry months suggests that surface evaporation factors, which have a stronger impact on the hydrological cycle during the rainy season, dominate precipitation generation and thus weaken the impact of vegetation transpiration.

4.3. Implications for Optimizing the Quantitative Structure and Landscape Pattern

This study offers insights into optimizing wet–dry environments and mitigating drought risk. First, given the interventions of human activities to the quantitative structure of vegetation cover, it is crucial to prioritize the rational maintenance of vegetation cover to overcome escalating drought and warming. In the past several decades, Yunnan Province has pursued economic-oriented development but has not launched effective guidance for vegetation protection beyond increasing the greening ratio. Many areas have sought development by cutting old growth jungles and replacing them with cash crops. Therefore, in terms of policy and regulation formulation, ecological compensation and restoration measures should be adopted by replacing economically important forests and cash crops with broad-leaved forests. In particular, it is tangible to combine payment for ecosystem services with strict conservation programs. Second, the findings on the impact of vegetation type on climatic drought can provide valuable guidance for optimizing vegetation landscape patterns at the class level. Broad-leaved forests have a stronger capacity for water retention and transpiration than needle-leaved forests, croplands, and grasslands. Therefore, the landscape concentration, dominance, and spatial connectivity of broad-leaved forests should be improved as much as possible. In comparison, the proportion of grassland should be appropriately weakened to guarantee basic ecological functions and needs. Third, man-made damage to the natural environment such as the disorderly expansion of construction land, deforestation to create farmland, and mining reclamation, have severely fragmented the vegetation landscape at the regional scale and reduced the connectivity of vegetation patches. Accordingly, in vegetation landscape planning and land management practices, it is important to constrain fragmented vegetation landscape patches and integrate small and irregular patches with the surrounding large-scale vegetation landscapes to enhance connectivity and reduce separation among vegetation patches. In areas with undeveloped bare land, converting them into vegetation cover can enhance vegetation landscape connectivity, thereby improving ecosystem functioning and stability to mitigate climatic droughts.

5. Conclusions

This study examined the effects of quantitative structure and landscape patterns of vegetation cover types on SPEI changes in Yunnan, China. Existing studies suggest that LC changes can potentially trigger climate change (e.g., temperature, humidity, and precipitation) [2], by regulating the exchange of energy, moisture, and aerosols between the land and the atmosphere [103]. This study is the first close investigation into the relationships between quantitative structure, landscape patterns of vegetation cover, and climatic drought. The results verified that the quantitative structure of vegetation cover could affect regional drought. Broad-leaved forests had a stabilizing and significant mitigating effect on drought, while needle-leaved forests, grasslands, and croplands could exacerbate drought to different degrees. Given the diverse impacts (i.e., either positive or negative), shrublands should be analyzed with aspect to the phytophysiological characteristics of different species. Moreover, this study revealed that the landscape pattern of vegetation cover affected the local climate and drought in different ways. Broad-leaved and needle-leaved forests made the highest and the most stable contribution to climatic drought, as demonstrated by the high correlations throughout the year. Shrublands and grasslands showed stronger correlations with climatic drought in the dry season and weaker correlations in the rainy season. Three indices, vegetation LDI, LFI, and LAI, which are associated with the physiological characteristics of vegetation covers had significant impacts on climatic drought. The dominant distribution and aggregation of broad-leaved forests was conducive to climatic drought mitigation, while needle-leaved forests, croplands, and grasslands had the potential to exacerbate climatic drought.
This study has several limitations. Multicollinearity among landscape pattern indices was not analyzed, restricting conclusions to secondary indicators type. The synergistic interaction between climate and vegetation complicates the assessment of net driving forces. Additionally, due to limited access to detailed vegetation data, the impact of finer vegetation structures on climate-induced drought was not fully explored. Future studies should focus on three aspects. First, beyond vegetation types, regional drought characteristics are analyzed in conjunction with geographic and biological features. Second, the effect of vegetation cover on climatic drought will be explored with finer and higher resolutions. Third, the impact of the vertical distribution structure of different vegetation types will be revealed, particularly in mountainous regions with diverse vertical ecosystems, such as Yunnan.
Overall, this study is novel in explaining the reasons behind the different impacts of vegetation cover on climatic drought in Yunnan during both the rainy and dry seasons. Yunnan Province, which has a humid subtropical climate, has expanded beyond traditional arid areas. This study addresses the complex relationship between vegetation and climatic drought, providing a basic understanding of the prominent impacts of vegetation cover quantitative structure of on regional climatic drought. These findings can serve as valuable references for non-traditional arid regions. In addition, with the rich vegetation landscape resources in Yunnan, this study enhances the understanding of vegetation landscape regulation for regional drought mitigation.

Author Contributions

Y.W.: conceptualization; data curation; formal analysis; investigation; methodology; software; visualization; writing—original draft. H.H.: investigation; methodology; project administration; review and editing. Y.M.: investigation; resources; software; writing—review and editing. B.-J.H.: conceptualization; data curation; project administration; writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This project is supported by the National Natural Science Foundation of China (No. 42301339), the Fundamental Research Funds for the Central Universities (Grant No. 2024CDJXY014), the China Meteorological Administration “Research on value realization of climate ecological products” Youth Innovation Team Project (No. CMA2024QN15), and the CMA Key Open Laboratory of Transforming Climate Resources to Economy (No. 2023018).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We appreciate the constructive comments from three reviewers and the editors.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A

Table A1. Landscape Pattern Index Formula Details.
Table A1. Landscape Pattern Index Formula Details.
Landscape Pattern
Indexes		Calculation FormulaInterpretation
CA
Total (Class) Area		 CA = ∑ j = 1 n a ij ( 1 10000 ) aij = area (m2) of patch ij.
PLAND
(Percentage of Landscape)		 PLAND = P i = ∑ j = 1 n a ij A ( 100 ) Pi = proportion of the landscape occupied by patch type (class) i.
aij = area (m2) of patch ij.
A = total landscape area (m2).
LPI
(Largest Patch Index)		 LPI =   max ( a ij ) j = 1 n A ( 100 ) aij = area (m2) of patch ij.A = total landscape area (m2).
TE
(Total Edge)		 TE = ∑ k = 1 m e ik eik = total length (m) of edge in landscape involving patch type (class) i; includes landscape boundary and background segments involving patch type i.
ED
(Edge Density)		 ED = ∑ k = 1 m e ik A ( 10000 ) eik = total length (m) of edge in landscape involving patch type (class) i; includes landscape boundary and background segments involving patch type i.
A = total landscape area (m2).
NP
(Number of Patches)		 NP = n i ni = number of patches in the landscape of patch type (class) i.
PD
(Patch Density)		 PD = n i A ( 10000 ) ( 100 ) ni = number of patches in the landscape of patch type (class) i.
A = total landscape area (m2).
PAFRAC
(Perimeter-Area Fractal Dimension)		 PAFRAC = 2 [ n i ∑ j = 1 n ( lnp ij ⋅ lna ij ) ] − [ ( ∑ j = 1 n lnp ij ) ( ∑ j = 1 n lna ij ) ] ( n i ∑ j = 1 n lnp ij 2 ) − ( ∑ j = 1 n lnp ij ) 2 aij = area (m2) of patch ij.
pij = perimeter (m) of patch ij.
ni = number of patches in the landscape of patch type (class) i.
LSI
(Landscape Shape Index)		 LSI = 0 . 25 ∑ k = 1 m e * ik A e * ik = total length (m) of edge in landscape between patch types (classes) i and k; includes the entire landscape boundary and some or all background edge segments involving class i.
A = total landscape area (m2).
DIVISION
(Landscape Division Index)		 DIVISION = [ 1 − ∑ j = 1 n ( a ij A ) 2 ] aij = area (m2) of patch ij.
A = total landscape area (m2).
SPLIT
(Splitting Index)		 SPLIT = A 2 ∑ j = 1 n a ij 2 aij = area (m2) of patch ij.
A = total landscape area (m2).
NLSI
(normalized Landscape Shape Index)		 nLSI = e i − min e i max e i − min e i ei = total length of edge (or perimeter) of class i in terms of number of cell surfaces; includes all landscape boundary and background edge segments involving class i.
min ei = minimum total length of edge (or perimeter) of class i in terms of number of cell surfaces (see below).
max ei = maximum total length of edge (or perimeter) of class i in terms of number of cell surfaces (see below).
MESH
(Effective Mesh Size)		 MESH = ∑ j = 1 n a ij 2 A ( 1 10000 ) aij = area (m2) of patch ij.
A = total landscape area (m2).
CLUMPY
(Clumpiness Index)		 CLUMPY = [ G i − P i 1 − P i   for   G i ≥ P i g G i − P i 1 − P i   for   G i < P i ; P i ≥ 5 P i − G i − P i   for   G i < P i ; P i < 5 ] Gi = number of like adjacencies (joins) between pixels of patch type (class) i based on the double-count method.
Pi = proportion of the landscape occupied by patch type (class) i.
IJI
(Interspersion and Juxtaposition Index)		 IJI = − ∑ k = 1 m [ ( e ik ∑ k = 1 m e ik ) ln ( e ik ∑ k = 1 m e ik ) ] ln ( m − 1 ) ( 100 ) eik = total length (m) of edge in landscape between patch types (classes) i and k.
m = number of patch types (classes) present in the landscape, including the landscape border, if present.
PLADJ
(Percentage of Like Adjacencies)		 PLADJ = ( g ii ∑ k = 1 m g ik ) ( 100 ) gii = number of like adjacencies (joins) between pixels of patch type (class) i based on the double-count method.
gik = number of adjacencies (joins) between pixels of patch types (classes) i and k based on the double-count method.
AI
(Aggregation Index)		 AI = [ g ii max → g ii ] ( 100 ) gii = number of like adjacencies (joins) between pixels of patch type (class) i based on the single-count method.
max-gii = maximum number of like adjacencies (joins) between pixels of patch type (class) i (see below) based on the single-count method.
COHESION
(Patch Cohesion Index)		 COHESION = [ 1 − ∑ j = 1 n p ij * ∑ j = 1 n p ij * a ij * ] ⋅ [ 1 − 1 Z ] − 1 ⋅ ( 100 ) pij * = perimeter of patch ij in terms of number of cell surfaces.
aij * = area of patch ij in terms of number of cells.Z = total number of cells in the landscape.
SHDI
(Shannon’s Diversity Index)		 SHDI = − ∑ i = 1 m ( P i * lnP i ) Pi =proportion of the landscape occupied by patch type (class) i.
SIDI
(Simpson’s Diversity Index)		 SIDI = 1 − ∑ i = 1 m P i 2 Pi = proportion of the landscape occupied by patch type (class) i.
SHEI
(Shannon’s Evenness Index)		 SHEI = − ∑ i = 1 m ( P i * lnP i ) lnm Pi = proportion of the landscape occupied by patch type (class) i.
m = number of patch types (classes) present in the landscape, excluding the landscape border if present.
SIEI
(Simpson’s Evenness Index)		 SIEI = 1 − ∑ i = 1 m P i 2 1 − ( 1 m ) Pi = proportion of the landscape occupied by patch type (class) i.
m = number of patch types (classes) present in the landscape, excluding the landscape border if present.

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Figure 1. Location of Yunnan Province and its digital elevation model.
Figure 1. Location of Yunnan Province and its digital elevation model.
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Figure 2. Flowchart of the methodology used for data processing and analysis.
Figure 2. Flowchart of the methodology used for data processing and analysis.
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Figure 3. Average SPEI values in dry and rainy seasons in five regions of Yunnan in 1992–2020.
Figure 3. Average SPEI values in dry and rainy seasons in five regions of Yunnan in 1992–2020.
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Figure 4. Spatial distribution of monthly SPEI variations in dry and rainy seasons in five regions of Yunnan in 1992–2020.
Figure 4. Spatial distribution of monthly SPEI variations in dry and rainy seasons in five regions of Yunnan in 1992–2020.
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Figure 5. Sankey diagram of vegetation cover type transfer in Yunnan between 1992 and 2020.
Figure 5. Sankey diagram of vegetation cover type transfer in Yunnan between 1992 and 2020.
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Figure 6. Spatial distribution of vegetation cover in Yunnan in 1992 and 2020.
Figure 6. Spatial distribution of vegetation cover in Yunnan in 1992 and 2020.
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Figure 7. Transfer of nine types of vegetation cover in Yunnan from 1992 to 2020.
Figure 7. Transfer of nine types of vegetation cover in Yunnan from 1992 to 2020.
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Figure 8. Changes in vegetation LDI in Yunnan from 1992 to 2020.
Figure 8. Changes in vegetation LDI in Yunnan from 1992 to 2020.
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Figure 9. Changes in vegetation LCDI in Yunnan Province from 1992 to 2020.
Figure 9. Changes in vegetation LCDI in Yunnan Province from 1992 to 2020.
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Figure 10. Changes in vegetation LDEI in Yunnan in 1992–2020.
Figure 10. Changes in vegetation LDEI in Yunnan in 1992–2020.
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Figure 11. Scale–effect correlation heat map of vegetation landscape pattern index.
Figure 11. Scale–effect correlation heat map of vegetation landscape pattern index.
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Figure 12. Correlation between the number of vegetation cover transfers and SPEI change.
Figure 12. Correlation between the number of vegetation cover transfers and SPEI change.
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Figure 13. Correlation between vegetation landscape pattern index and SPEI change.
Figure 13. Correlation between vegetation landscape pattern index and SPEI change.
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Table 1. Classification of vegetation cover types.
Table 1. Classification of vegetation cover types.
Classification CodeOriginal CategoryReclassification
150, 60, 90 × 0.5Broad-leaved forest
270, 80, 90 × 0.5Needle-leaved forest
340, 100, 120Shrubland
4110, 130Grassland
510, 20, 30Cropland
6140, 150, 160, 170, 180, 190, 200, 210, 220Others
Table 2. Description of landscape pattern indicators of vegetation cover.
Table 2. Description of landscape pattern indicators of vegetation cover.
Landscape Pattern
Indexes Category		Landscape Pattern IndicatorsInterpretation
Class levelLandscape Dominance Index
(LDI)		Total Class Area (CA)The sum of the area of a specific type of landscape
Percentage of Landscape (PLAND)The percentage of the area of a specific type of landscape
Largest Patch Index (LPI)The area of the largest patch in proportion to the total landscape area
Total Edge (TE)The sum of the lengths of side segments related to the vegetation patch type
Edge Density (ED)Boundary length per unit area
Number of Patches (NP)Number of patches in the type or landscape mosaic (higher values indicating more patches)
Patch Density (PD)Number of vegetation patches per unit area (indicating finer segmentation)
Landscape Shape Index (LSI)Perimeter–Area Fractal Dimension (PAFRAC)Reflection of landscape fractal character
Landscape Shape Index (SSI)Measurement of landscape shape complexity
Landscape Fragmentation Index
(LFI)		Landscape Division Index (DIVISION)Proportion of vegetation patch area to total landscape area
Splitting Index (SPLIT)Degree of dispersion or fragmentation of vegetation patches of the same type
Normalized Landscape Shape Index (NLSI)Measurement of landscape shape complexity and concentration of patches
Landscape Aggregation Index
(LAI)		Effective Mesh Size (MESH)Size of patch when landscape is subdivided into segments
Clumpiness Index (CLUMPY)Representation of aggregation and dispersion of vegetation patches
Interspersion and Juxtaposition Index (IJI)Representation of dispersion and juxtaposition of vegetation patches and is a measure of connectivity and distribution patterns among vegetation patches
Percentage of like Adjacencies (PLADJ)Proximity between similar landscape types (higher values indicating more concentration)
Aggregation Index (AI)Degree of aggregation of landscape patches (better polymerization with larger values)
Patch Cohesion Index (COHESION)The spatial distribution and organization of vegetation patches in the landscape reflect the connectivity and aggregation within units
Landscape levelLandscape Diversity Index
(SDI)		Shannon’s Diversity Index (SHDI)The diversity of the landscape is indicated by larger values, which signify a greater abundance of patch types and distribution. A value of 0 indicates that the landscape contains only one patch
Simpson’s Diversity Index (SIDI)
Landscape Evenness Index
(LEI)		Shannon’s Evenness Index (SHEI)The heterogeneity of the landscape is reflected in the Shannon’s Evenness Index, with smaller values suggesting a stronger dominance of a few types and larger values indicating a more homogeneous distribution of landscape types
Simpson’s Evenness Index (SIEI)
Table 3. Transfer matrix of vegetation cover types in Yunnan Province, 1992–2020.
Table 3. Transfer matrix of vegetation cover types in Yunnan Province, 1992–2020.
2020 (km2)Broad-Leaved ForestCroplandGrasslandNeedle-Leaved ForestShrublandOthersSum
1992 (km2)
Broad-leaved forest58,750.02289.2397.6246.141890.2917.0661,090.36
Cropland834.8358,529.47107.36412.29824.281559.2562,267.47
Grassland90.69149.5528,811.011068.73626.22462.6931,208.89
Needle-leaved forest18.1855.20392.0698,354.42879.9637.3099,737.12
Shrubland8929.101007.95479.019875.07106,898.94342.12127,532.19
Others8.9711.775.677.4411.562540.362585.76
Sum68,631.7860,043.1729,892.73109,764.10111,131.254958.78384,421.79
Table 4. Characteristics of physiological indicators for different vegetation [66,67,68].
Table 4. Characteristics of physiological indicators for different vegetation [66,67,68].
Physiological IndicatorsBroad-Leaved ForestNeedle-Leaved ForestShrublandGrasslandCropland
Albedolowlowerlow/highhighhigh
Leaf area indexhighlowlow/highlowlow
Surface roughnesshighhighlow/highlowlow
Root depthhighlowlow/highlowlow
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Wan, Y.; Han, H.; Mao, Y.; He, B.-J. Responses of Climatic Drought to Vegetation Cover Dynamics: A Case Study in Yunnan, China. Forests 2024, 15, 1689. https://doi.org/10.3390/f15101689

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Wan Y, Han H, Mao Y, He B-J. Responses of Climatic Drought to Vegetation Cover Dynamics: A Case Study in Yunnan, China. Forests. 2024; 15(10):1689. https://doi.org/10.3390/f15101689

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Wan, Yangtao, Han Han, Yao Mao, and Bao-Jie He. 2024. "Responses of Climatic Drought to Vegetation Cover Dynamics: A Case Study in Yunnan, China" Forests 15, no. 10: 1689. https://doi.org/10.3390/f15101689

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