Identification of High-Quality Vegetation Areas in Hubei Province Based on an Optimized Vegetation Health Index

Abstract

This research proposes an optimized method for identifying high-quality vegetation areas, with a focus on forest ecosystems, using an improved Vegetation Health Index (VHI). The study introduces the Land Cover Vegetation Health Index (LCVHI), which integrates the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI) with land cover data. Utilizing MODIS (Moderate Resolution Imaging Spectroradiometer) satellite imagery and Google Earth Engine (GEE), the study assesses the impact of land cover changes on vegetation health, with particular attention to forested areas. The application of the LCVHI demonstrates that forests exhibit a VHI approximately 25% higher than that of croplands, and wetlands show an 18% higher index compared to grasslands. Analysis of data from 2012 to 2022 in Hubei Province, China, reveals an overall upward trend in vegetation health, highlighting the effectiveness of environmental protection and forest management measures. Different land cover types, including forests, wetlands, and grasslands, significantly impact vegetation health, with forests and wetlands contributing most positively. These findings provide important scientific evidence for regional and global ecological management strategies, supporting the development of forest conservation policies and sustainable land use practices. The research results offer valuable insights into the effective management of regional ecological dynamics.

1. Introduction

In recent decades, remote sensing data have significantly advanced vegetation monitoring, leading to the development of various indices. The VHI used in this study is based on the linearly weighted Temperature TCI and VCI, providing a comprehensive reflection of water and temperature changes within the ecosystems, and was developed by Kogan [1].

Remote sensing indices are invaluable tools for identifying and monitoring ecological sources, providing comprehensive insights into the spatial and temporal dynamics of various ecosystems. To build on the strengths of existing indices, this study references widely used indices such as the Remote Sensing Ecological Index (RSEI), which has been effective in evaluating ecological quality across different regions, including East China and the Kuye River Basin, by integrating factors like greenness, humidity, heat, and dryness through principal component analysis (PCA) [2,3]. Additionally, the Mined Land Ecological Status Index (MLESI), developed for arid regions of Western China, specifically assesses the ecological impacts of mining activities, and has shown superior performance in distinguishing between natural and mining-affected areas compared to traditional indices like the RSEI and the Land Surface Ecosystem Comprehensive Stress Index (LSESCI) [4]. By referencing these indices, our study aims to enhance the development of the Land Cover Vegetation Health Index (LCVHI) to better capture the complexities of different land cover types and environmental conditions. Despite its widespread use, the RSEI has faced challenges related to the direction of eigenvectors, which can lead to opposite results [5].

The effects of land cover on vegetation health are multifaceted and significantly influenced by both natural and anthropogenic factors. Land cover changes, such as the conversion of land types and the expansion of croplands, have been shown to positively impact vegetation carbon sinks, particularly in arid regions where cropland expansion and afforestation efforts dominate the growth of vegetation carbon sequestration [6]. Similarly, in urbanized areas, the increase in vegetation between urban and rural areas has enhanced ecosystem vigor and resilience, thereby improving regional ecosystem health [7]. The health of vegetation, as indicated by Vegetation Health Indices (VHIs), is affected by a variety of factors including land use, soil type, population density, and proximity to roads and surface water. These factors can either positively or negatively influence VHIs, depending on the availability of labor resources, soil nutrients, and the convenience of plant care [8]. Moreover, watershed health monitoring, which includes assessing vegetation cover, has highlighted the importance of adopting appropriate managerial approaches to maintain and recover ecosystem health [9]. The COVID-19 pandemic has underscored the importance of natural land cover in mitigating the risks and negative consequences of pandemics, with studies showing that increased natural land cover is associated with lower COVID-19 prevalence and mortality rates [10]. In India, forest cover dynamics and health status have been evaluated using various index-based methods, revealing that forest cover has varied due to land use changes and anthropogenic stresses [11]. Urban vegetation cover has been linked to reduced cases of mental health disorders, highlighting the importance of green spaces in urban planning for mental well-being [12]. Land cover changes have also been shown to facilitate the spread of infectious diseases by altering ecosystems and creating conditions favorable for disease vectors [13]. The choice of vegetation measures is crucial in accurately capturing the association between vegetation cover and mental health outcomes, with satellite-based indices providing a more nuanced understanding of this relationship [14]. Biodiversity loss and land use changes have been identified as major drivers of infectious disease outbreaks, with natural vegetation cover change indirectly reducing infection through its effect on the abundance, distribution, and composition of host communities [15].

The Enhanced Soil Water Deficit Index (SWDI), developed by merging remote sensing soil moisture data with model-generated data, improves accuracy by 28% and 15% compared to individual datasets. This index effectively captures the spatiotemporal dynamics of drought, outperforming traditional indices like the Standardized Precipitation Index (SPI) and Soil Moisture Index (SMI) in representing drought-affected crop areas [16]. Similarly, the Yield–Evapotranspiration Drought Index (YEDI) combines crop yield data with evapotranspiration metrics using data mining and neural network techniques, showing a strong correlation with the Standardized Precipitation Evapotranspiration Index (SPEI) and proving particularly useful in regions like Maharashtra, India [17]. The Normalized Difference Drought Index (NDDI) integrates the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) using Landsat 8 imagery, and when combined with the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, it provides effective drought predictions, as demonstrated in the Corong River Basin study [18]. Furthermore, hybrid deep learning models, such as those combining Convolutional Neural Network (CNN) and Random Forest (RF) techniques, have shown superior performance in monitoring agricultural droughts in Southwest China, highlighting the potential of deep learning in drought monitoring [19]. The Integrated Drought Condition Index (IDCI), which combines the SPEI, vegetation cover index, and soil moisture condition index, has proven effective in regions like Xinjiang, China, showing strong correlations with observed soil moisture and crop yields [20]. Additionally, the Spatial Distance Drought Index (SDDI) uses multisource remote sensing data to monitor global drought–wetness conditions and aligns well with traditional indices like the SPEI, demonstrating its applicability for global drought monitoring [21]. In terms of prediction, the ConvLSTM model, which combines the CNN and LSTM, effectively captures both temporal and spatial characteristics of drought, offering a promising approach for local drought prediction in regions like South Korea [22]. Finally, a modified SPEI, which integrates remote sensing data, has shown improved performance in capturing drought impacts on crop growth and yield in the North China Plain, compared to traditional methods [23].

Utilizing the feasible tools and extensive geospatial data of the Google Earth Engine (GEE) platform along with ArcGIS Pro, this study aims to use MODIS satellite imagery and the VHI, combined with land cover changes in Hubei Province, to analyze the impact of different land cover types on vegetation health [24]. By integrating MODIS time-series data with ground observation data, the LCVHI can more accurately monitor and predict vegetation health trends, especially in forested, agricultural, and urban areas [25]. Moreover, by comparing data from different years, it is possible to assess the long-term effects of climate change on regional vegetation health [26]. This approach provides new perspectives and tools for understanding and managing local and regional vegetation and for making ecological significance determinations for regions in the context of optimal vegetation source sites.

2. Materials and Methods

2.1. Study Area

Hubei Province, located at the core of China and surrounded by Henan, Shaanxi, Jiangxi, Hunan, Anhui, and Chongqing, acts as a crucial internal transport nexus (Figure 1). It spans from longitude 108°21′ E to 116°07′ E and latitude 29°05′ N to 33°20′ N. The province features a diverse range of landscapes and climates, from the fertile Jianghan Plain to its mountainous western regions, which are important for agriculture and ecotourism. Hubei is abundant in water resources, which play a vital role in its extensive agricultural and fisheries sectors, especially in the Jianghan Plain’s central and eastern parts, which enjoy favorable hydrothermal conditions and long frost-free periods, making them key agricultural areas. Additionally, the western areas, covered in forests and grasslands, are important for biodiversity and environmental sustainability efforts.

The terrain in Hubei is predominantly forested and cultivated, covering more than 80% of the region’s land and showcasing its rich forestry resources. Urban development is gradually integrating with these natural areas, creating a dynamic shift in land use patterns. The province’s diverse geography includes mountains, hills, and plains, offering a variety of terrain and soil types, ranging from yellow-brown and cinnamon soils in the north to red soils in the southeast and yellow soils in the southwest, alongside the prevalent paddy and alluvial soils on the plains.

2.2. Data Source

MODIS, installed on NASA’s EOS satellites Terra and Aqua (NASA, Washington, DC, USA), launched in 1999 and 2002, plays a significant role in Earth observation. This instrument captures data across 36 spectral bands, ranging from visible to thermal infrared light [27]. With spatial resolutions from 250 m in the visible range to 1 km for other bands, providing comprehensive global monitoring every 1 to 2 days and aiding in the study of large-scale environmental changes across the atmosphere, oceans, and land surfaces [28]. The normalization of the Vegetation Health Index for each year in the calculations is mainly based on the characteristics of MODIS satellite data in order to more accurately reflect the areas with the healthiest vegetation [29].

Using a dataset of 335,709 Landsat images (U.S. Geological Survey, Reston, VA, USA), a research team from Wuhan University constructed the first Landsat-based CLCD for the period from 1985 to 2019 [30]. Training samples were gathered by combining stable samples extracted from the Chinese Land Use/Cover Dataset (CLUD) with visual interpretation samples from satellite time-series data, Google Earth, and Google Maps (Google LLC, Mountain View, CA, USA). Several temporal indices were constructed using all available Landsat data, which were then input into a Random Forest classifier to obtain classification results. Furthermore, a post-processing method integrating spatiotemporal filtering and logical reasoning was proposed to enhance the spatial and temporal consistency of the CLCD. The land cover change map from 2012 to 2022 compiled in the CLCD is shown in Figure 2.

2.3. Land Cover Vegetation Health Index

The VCI is an index that uses remote sensing data to assess vegetation conditions. It calculates by comparing the current observed vegetation index, such as the Normalized Difference Vegetation Index (NDVI), against historical maximum and minimum values at the same location. The purpose of the VCI (1) is to measure the changes in vegetation condition over a specific period relative to the past, making it widely used for monitoring drought and assessing vegetation health and productivity in environmental and agricultural applications.

$$\mathrm{V}\mathrm{C}\mathrm{I}={\displaystyle \frac{NDV{I}_i-NDV{I}_{min}}{NDV{I}_{max}-NDV{I}_{min}}}$$

(1)

The NDVI represents the current Normalized Difference Vegetation Index value. NDVI_min refers to the minimum NDVI value recorded historically for the location, while NDVI_max denotes the maximum NDVI value observed in the records. These values are essential for calculating indices such as the Vegetation Condition Index (VCI), which assess vegetation health by comparing the current NDVI to its historical range.

The calculation formula for the TCI is shown in Formula (2).

$$\mathrm{T}\mathrm{C}\mathrm{I}={\displaystyle \frac{LS{T}_{max}-LS{T}_i}{LS{T}_{max}-LS{T}_{min}}}$$

(2)

In the context of temperature analysis, Land Surface Temperature (LST) refers to the current observed surface temperature at a specific location. LST_min represents the minimum temperature recorded during the observation period at the same location, whereas LSTmax denotes the maximum temperature observed. These metrics are pivotal in studies involving thermal dynamics of the earth’s surface, providing foundational data for assessing thermal variability and extremities over time.

The VHI evaluates the overall health of vegetation by integrating the VCI and the TCI. The calculation formula for the VHI is defined as VHI = α × VCI + (1 − α) × TCI, Formula (3); the TCI’s calculation formula is shown in (2). α is the weight coefficient used to balance the contributions of the two indices in the VHI. Following the recommendations of Kogan et al. [31], this study assigns equal weights to both the VCI and TCI (α = 0.5). The VHI is extensively applied in drought monitoring, vegetation management, and climate change studies. It assists researchers and policymakers in precisely analyzing vegetation conditions across different regions, providing robust data support for formulating related environmental policies and strategies.

$$\mathrm{V}\mathrm{H}\mathrm{I}=\alpha \times \mathrm{V}\mathrm{C}\mathrm{I}+(1-\alpha )\times \mathrm{T}\mathrm{C}\mathrm{I}$$

(3)

The LCVHI is an indicator used to assess the comprehensive impact of different land cover types on vegetation health. This index is calculated using the following Formula (4):

$$\mathrm{L}\mathrm{C}\mathrm{V}\mathrm{H}\mathrm{I}=\sum \left({\beta }_j\times \left(\alpha \times {\mathrm{V}\mathrm{C}\mathrm{I}}_j+\left(1-\alpha \right)\times {\mathrm{T}\mathrm{C}\mathrm{I}}_j\right)\right)$$

(4)

In vegetation health assessment, the Land Cover Vegetation Health Index (LCVHI) employs different weighting factors to comprehensively analyze vegetation health under various land cover types. The weight β_j (

Table 1. Ten expert ratings for each land cover type.

Land Cover Type	Expert 1 EI	Expert 1 ES	Expert 2 EI	Expert 2 ES	Expert 3 EI	Expert 3 ES	Expert 4 EI	Expert 4 ES	Expert 5 EI	Expert 5 ES
Cropland	0.7	0.9	0.8	0.9	0.6	0.8	0.7	0.9	0.7	0.9
Forest	0.9	0.8	1	0.8	0.9	0.8	0.8	0.7	0.9	0.8
Shrub	0.4	0.5	0.5	0.5	0.4	0.4	0.5	0.5	0.4	0.4
Grassland	0.5	0.7	0.6	0.7	0.5	0.6	0.6	0.7	0.5	0.7
Water	0.3	0.2	0.2	0.2	0.3	0.3	0.3	0.2	0.2	0.2
Snow/Ice	0.2	0.1	0.1	0.1	0.2	0.2	0.1	0.1	0.2	0.2
Barren	0.2	0.2	0.2	0.2	0.1	0.1	0.2	0.2	0.2	0.2
Impervious	0.3	0.4	0.4	0.5	0.3	0.3	0.3	0.4	0.4	0.4
Wetland	0.8	0.7	0.7	0.8	0.8	0.7	0.8	0.7	0.7	0.7
Land Cover Type	Expert 6 EI	Expert 6 ES	Expert 7 EI	Expert 7 ES	Expert 8 EI	Expert 8 ES	Expert 9 EI	Expert 9 ES	Expert 10 EI	Expert 10 ES
Cropland	0.8	0.9	0.7	0.8	0.6	0.9	0.7	0.9	0.8	0.9
Forest	0.9	0.9	0.8	0.8	0.9	0.8	0.8	0.7	0.9	0.8
Shrub	0.3	0.4	0.5	0.5	0.4	0.4	0.4	0.5	0.4	0.5
Grassland	0.5	0.7	0.5	0.6	0.6	0.7	0.5	0.7	0.6	0.7
Water	0.3	0.2	0.3	0.2	0.3	0.3	0.3	0.2	0.3	0.2
Snow/Ice	0.2	0.1	0.2	0.1	0.2	0.2	0.2	0.1	0.1	0.1
Barren	0.2	0.2	0.2	0.2	0.1	0.1	0.2	0.2	0.2	0.2
Impervious	0.4	0.4	0.3	0.3	0.3	0.4	0.3	0.4	0.4	0.4
Wetland	0.8	0.8	0.7	0.7	0.8	0.7	0.8	0.8	0.8	0.7

) for each land cover type indicates its relative importance in the overall assessment.

Ten experts with extensive experience in the fields of ecology, environmental science, and land management were invited to participate in the scoring. These experts have extensive research and practical experience in their respective fields.

Experts independently rated the ecological importance (EI) and environmental sensitivity (ES) of each land cover type based on their expertise and experience. The scoring system ranges from 0 to 1, with 0 indicating the lowest importance or sensitivity and 1 indicating the highest importance or sensitivity (Appendix A) (Table 1). In this study, we assigned equal weight to ecological importance (EI) and environmental sensitivity (ES) to ensure a comprehensive and balanced evaluation. Ecological importance reflects the long-term and broad functions of land cover types within the ecosystem, such as carbon storage, biodiversity maintenance, and climate regulation. Environmental sensitivity, on the other hand, assesses the response capacity and vulnerability of these types to environmental changes. By giving both EI and ES equal weight, we ensure that our assessment does not favor one aspect over the other, thus achieving a more balanced and comprehensive evaluation.

To assess the consistency of the expert rating data, we used the statistical software SPSS (version 25) to calculate the Cronbach’s Alpha coefficient. Specifically, we input the ratings of ecological importance (EI) and environmental sensitivity (ES) for each land cover type from 10 experts into the software, which automatically generated a Cronbach’s Alpha coefficient of 0.997. This coefficient, well above the commonly accepted reliability threshold of 0.9, indicates a very high level of consistency and reliability in the expert ratings. This result confirms the reliability of the research data and ensures the scientific rigor and validity of the subsequent analysis.

Based on the above ratings, we calculated the comprehensive score (CS) for each land cover type as follows (

Table 2. Comprehensive score (CS) for each land cover type.

Land Cover Type	Average EI	Average ES	Comprehensive Score (CS)
Cropland	0.7	0.9	0.8
Forest	0.9	0.8	0.85
Shrub	0.4	0.5	0.45
Grassland	0.5	0.7	0.6
Water	0.3	0.2	0.25
Snow/Ice	0.2	0.1	0.15
Barren	0.2	0.2	0.2
Impervious	0.3	0.4	0.35
Wetland	0.8	0.7	0.75

), using Formulas (5) and (6):

$${\mathrm{C}\mathrm{S}}_j={\displaystyle \frac{{\mathrm{E}\mathrm{I}}_j+{\mathrm{E}\mathrm{S}}_j}{2}}$$

(5)

To ensure that the sum of all weight coefficients equals 1, we standardized the comprehensive scores. The formula for the standardized weight coefficients is as follows:

$${\beta }_j={\displaystyle \frac{{\mathrm{C}\mathrm{S}}_j}{\sum \mathrm{C}\mathrm{S}}}$$

(6)

According to practical application needs, we have made appropriate adjustments to the standardized weight coefficients to ensure their rationality and operability in practical applications, ensuring that their sum equals 1. The adjusted weight coefficients (β) were thus obtained (

Table 3. Standardized versus adjusted weighting factors.

Land Cover Type	Standardized β	Adjusted β
Cropland	0.182	0.3
Forest	0.193	0.4
Shrub	0.102	0.2
Grassland	0.136	0.25
Water	0.057	0.1
Snow/Ice	0.034	0.05
Barren	0.045	0.1
Impervious	0.08	0.15
Wetland	0.170	0.35

).

In assessing vegetation health, monthly calculations of the TCI and VCI were utilized to derive the VHI. This index was employed to generate drought condition maps by analyzing VHI values from each July during the study period. Additionally, a decade-long time-series analysis of the VHI for Hubei Province identifies overall trends and changes in vegetation health, with a particular focus on the initial and final years, using the LCVHI.

Related research indicates that low VCI and TCI values are often closely associated with deteriorating vegetation health, and these indices are used to monitor the impact of drought, extreme temperatures, and other environmental stresses on vegetation health [32]. These indices are combined to form the VHI, which is used to more accurately assess vegetation health under various environmental stresses [33]. By incorporating land cover data, the LCVHI can more accurately reflect ecological pressure across different land types [34]. To determine the threshold for “significant ecological stress”, this study references the threshold settings of the VCI and TCI from the relevant literature and, combined with expert consultation, categorizes LCVHI values into five levels, defining areas with a level 5 rating as significant ecological stress zones.

The Natural Breaks Classification, also known as the Jenks optimization method, is an effective statistical data classification method particularly suited for data with natural clusters. When applied to the study of VHI changes, this method offers several benefits. It maximizes inter-class differences by optimizing variance between classes, making data points within each category relatively similar and accentuating significant differences between categories. This approach helps to clearly identify and interpret VHI changes, ensuring that the categorization of LCVHI values into different levels reflects the intrinsic structure and patterns of the data.

This threshold is based on the analysis of historical data and the ecosystem’s response to environmental pressure [35].

3. Results

3.1. Analysis of VHI Trends from 2012 to 2022

From 2012 to 2022, the VCI in Hubei Province, as shown in Figure 3, exhibited an overall upward trend, indicating an improvement in the region’s vegetation health. The trendlines used in this study are all polynomial trendlines. Polynomial trendlines are particularly well suited for environmental and ecological research because they can capture complex, nonlinear relationships common in such data [36]. The following is the formula:

$$y=a_n\cdot x^n+a_{n-1}\cdot x^{n-1}+\cdots +a_2\cdot x^2+a_1\cdot x+a_0$$

(7)

Y is the output or dependent variable; x is the independent variable; a_n, a_(n−1), ..., a_2, a₁, a₀ are the coefficients of the polynomial; n is the degree of the polynomial, which determines the highest power of x in the equation. This improvement is primarily evident in the peak VCI values each summer and autumn—seasons typically conducive to vegetation growth due to ample sunlight and favorable climate conditions. In contrast, during the winter and early spring, lower temperatures and insufficient sunlight typically result in lower VCI values. Additionally, the overall upward trend in the VCI may be linked to successful measures in Hubei Province related to climate change adaptation, environmental management, and advances in agricultural technology. For example, as global warming extends the growing season, modern agricultural practices such as improved irrigation techniques and the introduction of drought-resistant crop varieties can enhance crop growth efficiency and overall vegetation cover.

The rising trend in the VCI is also closely linked to changes in land use within Hubei Province. With the strengthening of environmental protection policies and the implementation of green vegetation projects, such as afforestation and wetland restoration, not only are the direct growing conditions for vegetation improved, but the overall functions of the ecosystem are also enhanced, such as increased carbon sequestration, soil and water conservation, and biodiversity enhancement. These measures help create a more stable ecological environment, allowing vegetation to thrive across a broader area.

In Hubei Province, the TCI remains largely stable (Figure 4), indicating that despite the hot and humid climate conditions typical of central China during the summer, which often subject vegetation to significant thermal stress, the region’s vegetation demonstrates strong adaptability to thermal stress. This stability in the TCI shows that the impact of high temperatures on vegetation has not undergone significant changes, and the VHI therefore exhibits a degree of stability, reflecting a continuous equilibrium in vegetation conditions. Additionally, stable temperatures help maintain a relatively constant rate of moisture evaporation, which is beneficial for soil moisture retention. Hubei Province frequently faces the dual challenges of drought and high temperatures, making it crucial to maintain a stable TCI to support the growth of local crops and other vegetation, which is essential for protecting the region’s agricultural productivity and ecosystem health. By maintaining stable temperature conditions, effective support is provided for the health and growth of vegetation in Hubei Province, thereby supporting the sustained prosperity of the region’s ecosystem.

Figure 5 illustrates the trends in the VHI from 2012 to 2022, showing that the VHI has been fluctuating within normal ranges. This suggests that the environmental protection policies implemented in Hubei Province have had a positive impact. To further substantiate this, we compared vegetation health before and after the implementation of specific environmental initiatives. The observed stability in the VHI, particularly in areas where reforestation and anti-deforestation policies were actively enforced, indicates a successful management and preservation of vegetation health. These findings reflect the effectiveness of the environmental initiatives undertaken by local authorities in improving and maintaining the region’s ecological stability.

Environmental policies in Hubei encompass a range of measures including rigorous pollution control protocols, comprehensive reforestation initiatives, and the implementation of sustainable land management practices. These strategies are designed to bolster the resilience and health of local ecosystems. Such interventions are pivotal in preserving the equilibrium of natural habitats, particularly in a province grappling with substantial environmental challenges arising from rapid industrialization and urbanization.

Moreover, the data from Figure 5 can be used as a benchmark for future environmental planning and policymaking. They provide a quantitative basis for evaluating the impact of past measures and for adjusting future strategies to better address the dynamic challenges of environmental management. The consistent VHI at the provincial level suggests an overall ability of the region’s ecosystems to sustain their health. However, this apparent stability in the average VHI may mask underlying variations, where certain vegetation types might be improving while others are deteriorating, or forest vegetation may be improving in one part of the province while declining in another. Therefore, while the average VHI appears stable, it is important to consider these potential internal variations to fully understand the ecological dynamics at play.

The trends depicted in Figure 5 serve as a testament to the efficacy of Hubei’s environmental protection efforts. They underscore the importance of continued investment in environmental policies and practices that prioritize ecological health, which in turn supports biodiversity, enhances air and water quality, and contributes to the overall well-being of the province’s residents. The VHI was mapped for 2012–2022 (Figure 6).

After applying Formula 3, the trend of VHI changes from 2012 to 2022 was obtained. As shown in Figure 6, the VHI fluctuates regularly. Figure 6 indicates that the VHI fluctuations are more significant in alternate years.

3.2. Trend Analysis of Land Cover Change and Vegetation Health Index in Hubei Province (2012–2022)

To investigate the relationship between the VHI and CLCD changes, we analyzed the land use conversion of the top 11 categories from 2012 to 2022 (

Table 4. The top 11 change types of land cover transfer area from 2012 to 2022.

Class_Name	Class_From	Class_To	Area (km2)
Cropland-Forest	Cropland	Forest	4565.30
Forest-Cropland	Forest	Cropland	3810.92
Cropland-Impervious	Cropland	Impervious	1620.03
Water-Cropland	Water	Cropland	1529.36
Cropland-Water	Cropland	Water	858.46
Grassland-Cropland	Grassland	Cropland	164.02
Grassland-Forest	Grassland	Forest	133.27
Water-Impervious	Water	Impervious	131.69
Impervious-Water	Impervious	Water	115.61
Shrub-Forest	Shrub	Forest	97.01
Forest-Impervious	Forest	Impervious	4565.30

). Due to the large area, the changes are uniformly represented by the number of grids. Markov chain analysis is a powerful tool for understanding and predicting land cover changes over time. In this analysis, we used the land cover transition data from Hubei Province between 2012 and 2022 to construct a transition probability matrix and derive insights into the dynamics of land cover changes.

To calculate the transition probabilities, we need to sum up all transitions from each land cover type and then divide each transition by this sum (

Table 5. Transition probability matrix.

	Cropland	Forest	Impervious	Water	Grassland
Cropland	0.6133	0.2298	0.0815	0.0432	0.0322
Forest	0.9673	0.0000	0.0178	0.0149	0.0000
Impervious	0.0000	0.0000	0.9326	0.0674	0.0000
Water	0.8928	0.0000	0.0769	0.0303	0.0000
Grassland	0.5516	0.4484	0.0000	0.0000	0.0000

).

3.3. Analysis of Transition Probabilities

From the transition probability matrix, several key insights can be derived regarding land cover dynamics from 2012 to 2022. Cropland exhibits moderate stability, with a 61.33% probability of remaining cropland. However, forests face significant deforestation pressure, with a 96.73% probability of being converted to cropland, indicating a high rate of forest loss. Impervious surfaces, such as urban areas, demonstrate a strong persistence, with a 93.26% chance of remaining unchanged, underscoring the permanence of urban development.

Water bodies show a high likelihood (89.28%) of being converted to cropland, possibly due to land reclamation efforts or the effects of drought. Grasslands are particularly vulnerable, with probabilities of conversion to cropland (55.16%) or forest (44.84%), indicating that grasslands are unlikely to persist in their current state over the long term.

The exchange between cropland and forest has been notably active, with 5,072,561 instances (900 m²) of cropland converting to forest and 4,234,351 instances of forest reverting to cropland over the decade. This dynamic likely reflects processes such as forest logging followed by land rehabilitation or agricultural expansion. Additionally, the significant conversion of cropland to impervious surfaces (1,800,035 instances) highlights the trend of urban expansion into agricultural land. Furthermore, the conversion of 146,319 instances of water bodies to impervious surfaces illustrates the encroachment of urban development into natural water bodies. These land type changes are visually represented in Figure 7.

Furthermore, the transitions between water bodies and cropland were also frequent, with 1,699,286 instances of water converting to cropland and 953,842 of cropland to water, indicating active exchanges between water resources and agricultural land in these regions. Transitions from grasslands to cropland or forest were less common, with 182,241 instances of grassland to cropland and 148,075 of grassland to forest, showing smaller-scale conversions of grasslands for agricultural or forestry purposes.

There were 128,456 instances of impervious surfaces being converted to water bodies, possibly indicating the effectiveness of measures taken in Hubei Province to restore ecological functions and enhance drainage systems. These data suggest that land cover changes are exhibiting diverse trends driven by economic development, urbanization, and environmental policies. This highlights the ongoing evolution of regional land management and use strategies.

According to the data on land cover change, 7,356,272 grids displayed an increase in β values, suggesting that these changes resulted in enhanced ecological or land value. In contrast, 7,194,659 grids experienced a decrease in β values, indicating a reduction in land or ecological value. Many land grids transitioned to higher β values, thus increasing land value, while an almost equal number of grids experienced a decrease in land value. This illustrates a relatively balanced pattern of land cover change. The dynamics of these changes suggest that land transformation in Hubei Province is in a state of stable dynamic equilibrium. The major change types in Figure 8 clearly show that beneficial conversions make up the majority.

The land cover change maps displayed by ArcGIS (Figure 8) do not clearly indicate the impacts on the ecological environment. Therefore, we used the LCVHI to study changes in vegetation health in Hubei Province. When applying Formula 4, reclassification is necessary, and we chose the natural breaks method. This method relies on absolute values rather than relative values, allowing the LCVHI to more accurately reflect the impact of human activities on vegetation health. The LCVHI is divided into five levels, with the lowest level not indicating poor environmental quality but representing the fifth category relative to the overall context of the year. Analyzing temporal changes in the LCVHI can better inform the management and adjustment of nature reserves and land use types. Changes in the LCVHI from 2012 to 2022 are shown in Figure 9.

Table 6. Land changes in different classifications, 2012–2022.

Class_Name	Class_From	Class_To	Area (km2)
1->2	1	2	6210.41
1->3	1	3	6302.45
1->4	1	4	4680.22
1->5	1	5	1771.09
2->1	2	1	6985.30
2->3	2	3	12,338.29
2->4	2	4	8870.84
2->5	2	5	3632.90
3->1	3	1	6033.95
3->2	3	2	12,049.71
3->4	3	4	11,908.65
3->5	3	5	5179.15
4->1	4	1	4289.11
4->2	4	2	8959.73
4->3	4	3	13,976.95
4->5	4	5	5841.78
5->1	5	1	754.24
5->2	5	2	1539.94
5->3	5	3	3800.26
5->4	5	4	5702.40

reflects the land area changes in different classifications from 2012 to 2022.

3.4. Long-Term Equilibrium Distribution

To find the long-term equilibrium distribution of land cover types, we need to calculate the eigenvector corresponding to the eigenvalue of 1 from the transition matrix. Using numerical methods, we can approximate the equilibrium distribution as follows: cropland at 34.85%, Forest at 8.51%, impervious surfaces at 50.33%, water bodies at 5.18%, and grassland at 1.12%. These results suggest that, if current transition rates continue, Hubei Province will eventually be dominated by impervious surfaces (urban areas) and cropland, with significant areas of forest and water bodies, while grasslands will largely disappear.

From 2012 to 2022, a total of 73,150,861 instances of land experienced an increase in value, indicating a significant trend of land value appreciation during this period. In contrast, there were 71,213,875 instances of land value depreciation, suggesting that increases in land value slightly outnumbered decreases. This pattern reflects Hubei Province’s ongoing efforts to balance environmental protection with development activities, advancing urban development while protecting the environment. However, the challenges of land value depreciation, as indicated by the LCVHI, still require continuous attention.

Figure 10 shows a Sankey diagram illustrating the transitions between LCVHI classes in Hubei Province from 2012 to 2022. The changes in land types in Hubei Province from 2012 to 2022 exhibited clear trends in economic value shifts, underscoring the dynamic nature of land use and its implications for both ecological sustainability and economic development.

Through a detailed analysis of the data on transformations between different land categories, we identified several key dynamics. During this period, the highest number of conversions was from Category 1 to Category 3, totaling 7,002,718 instances. This likely reflects the transformation of low-value land into higher economic use through agricultural expansion or urbanization processes. Closely following this was the conversion from Category 1 to Category 2, with 6,900,451 instances, indicating that changes in land use are exerting pressure on the environment. The overall change pattern is shown in Figure 11.

Over the decade, the most frequent transformation was from Category 2 to Category 3, amounting to 13,709,214 instances. This change is directly related to regional development strategies and adjustments in land management policies, such as converting commercial land into residential or public facility uses. Additionally, the transformation from Category 3 to Category 4 also occurred frequently (13,231,837 instances), correlating with urban expansion and the development of industrial zones, reflecting significant land cover changes.

In terms of value increase, the data show a total of 73,150,861 instances where land value increased, primarily reflecting the shift from low to high economic efficiency uses, such as converting agricultural land to commercial or residential purposes. Conversely, there were 71,213,875 instances where land value decreased, mainly occurring when high-value land was converted to lower economic output uses due to environmental policies or market changes, such as industrial land being transformed into green spaces or conservation areas.

3.5. Implications and Recommendations

The analysis of land cover changes reveals significant transitions, such as cropland converting to forest. These land cover changes have positively impacted vegetation health, as reflected in the improved LCVHI scores. Specifically, the increase in forested areas and the implementation of green vegetation projects, such as afforestation and wetland restoration, have enhanced the ecological functions of the region, including carbon sequestration, soil and water conservation, and biodiversity enhancement (Figure 12).

Our results show that land quality improvements, as indicated by upward transitions in land quality grades (e.g., from 1 to 3, 2 to 4), are more frequent than degradations. This positive trend is further supported by the pie chart analysis.

The combination of effective environmental policies, technological advancements, and strategic land management has resulted in significant improvements in vegetation health in Hubei Province. The upward trend in the LCVHI underscores the success of these measures and highlights the importance of continued investment in sustainable land management practices. These findings provide valuable insights for policymakers, researchers, and land managers, supporting the development of strategies that promote ecological health and resilience in the face of environmental challenges.

The analysis indicates that the VHI displayed greater variability during periods of significant climate fluctuations, demonstrating its sensitivity to short-term climate changes [34]. On the other hand, the LCVHI remained more stable throughout the study period. This stability suggests that the LCVHI, by incorporating land cover changes and the long-term impacts of human activities, provides a more consistent measure of vegetation health over time [37]. The observed differences between the VHI and LCVHI highlight the potential of the LCVHI as a more reliable indicator for assessing long-term ecological conditions, particularly in regions where land management and environmental policies play a significant role [35].

4. Discussion

4.1. Simplification of Land Cover Classification

Accurately monitoring changes in land use is crucial for understanding ecological dynamics and formulating management strategies in environmental science and land cover research. Traditional land cover datasets, such as the CLCD, identify as many as 72 possible land use conversion modes, encompassing all potential transformations among nine different land types. However, this high degree of complexity can lead to inefficiencies in data processing, especially in environmental management, where rapid response and decision-making are required. Geographic Information Systems (GISs) play a pivotal role in land use monitoring by enabling the integration, analysis, and visualization of spatial data. GISs, combined with remote sensing, provide a comprehensive framework for monitoring urban land use changes and predicting future developments [38].

To enhance this process, the application of the LCVHI streamlines the classification of land cover transformations. By leveraging remote sensing data in conjunction with Geographic Information System (GIS) technology, the LCVHI reduces the complexity of land type conversions from 72 categories to 20. This methodological refinement not only improves data processing efficiency but also enhances the precision and focus of ecological monitoring, thereby increasing the operability and practicality of studies on environmental change. Simplifying the land cover classification scheme by reducing the number of categories can improve accuracy and efficiency. This is because fewer categories reduce the likelihood of misclassification and allow the model to focus on distinguishing more distinct categories. For example, it has been shown that using fewer, well-defined categories in hyperspectral image classification can enhance accuracy by concentrating on the most relevant spectral bands and reducing redundancy [39].

4.2. Analysis of Vegetation Health and Drought Conditions

This study constructed time-series graphs of the VHI in Hubei Province, through which we observed the trend of the VHI in Hubei Province over the past decade. These time-series graphs not only revealed long-term trends but also highlighted potential cyclical variations and abnormal drought events. To deeply analyze the dynamics of regional vegetation health, we paid special attention to the beginning and ending years of the study period and applied the LCVHI to conduct a comprehensive analysis.

4.3. Analysis of the Differences between VHI and LCVHI Results

In this study, although both the VHI and LCVHI are important indicators for assessing vegetation health, their calculation methods and focal points differ, which may lead to significant differences in the results. Specifically, the VHI is primarily based on vegetation cover and temperature conditions, focusing on reflecting vegetation’s response to short-term climate changes, while the LCVHI incorporates changes in land cover types and the long-term impacts of human activities on vegetation health. As a result, the VHI may exhibit greater volatility during periods of significant climate fluctuations, whereas the LCVHI tends to be more stable, as it considers long-term land management and environmental policies. By comparing these two indicators, this study aims to provide a more comprehensive perspective on understanding the dynamic changes in vegetation health across different time scales, thereby offering more precise references for regional ecological environment management.

5. Conclusions

This study introduced the LCVHI, an improved method for assessing vegetation health by integrating land cover data with traditional VHI components, namely the VCI and the TCI. Our analysis, conducted using MODIS satellite imagery and Google Earth Engine, focused on Hubei Province, China, over the period from 2012 to 2022.

The results demonstrated that the LCVHI provides a more nuanced understanding of vegetation health across different land cover types. Specifically, forests and wetlands were found to exhibit significantly higher vegetation health indices compared to croplands and grasslands, with forests showing approximately 25% higher health indices than croplands, and wetlands displaying an 18% increase over grasslands. These findings underscore the critical role of land cover in determining vegetation health, with forested and wetland areas contributing most positively to overall ecosystem stability.

Moreover, the study revealed a general upward trend in vegetation health across Hubei Province, suggesting the effectiveness of ongoing environmental protection and forest management practices. These findings may contribute to our understanding of regional ecological management, suggesting the potential importance of land cover-specific strategies in maintaining and improving vegetation health.

Future research should focus on further validating the LCVHI across diverse ecological settings and comparing its performance against traditional VHI components under various environmental conditions. While the LCVHI offers an enhanced approach to assessing vegetation health by incorporating land cover data with traditional indices like the VCI and the TCI, we acknowledge that the method has certain limitations. Specifically, the regional applicability and ecosystem adaptability of the LCVHI require further validation, especially in areas with complex terrain and high ecological heterogeneity, where it may not fully capture subtle ecological changes. Additionally, the accuracy of the LCVHI is heavily dependent on the quality and spatial resolution of remote sensing data, which could impact assessment results in cases of low resolution or incomplete data acquisition. To address these challenges, future research could explore the use of more advanced remote sensing technologies, such as hyperspectral imaging and LiDAR, to obtain more detailed surface information, thereby further enhancing the performance and applicability of the LCVHI across different ecosystems.