Spatiotemporal Dynamics and Prediction of Habitat Quality Based on Land Use and Cover Change in Jiangsu, China

Abstract

Analyzing the spatiotemporal evolution characteristics of urban land use and habitat quality is crucial for the sustainable development of urban ecological environments. This study utilizes the land use data of Jiangsu Province for the years 2000, 2010, and 2020, applying the FLUS model to investigate the driving force behind land expansion and to simulate a prediction for the land use of 2030. By integrating the InVEST model and landscape pattern indices, this study analyzes the spatiotemporal evolution characteristics of habitat quality in Jiangsu Province and uses geographical detector analysis to examine the synergistic effects of influencing factors. The results indicate that, from 2000 to 2020, habitat degradation in Jiangsu Province progressively increased, with the spatial distribution of degradation levels showing a gradual change. Under the ecological protection scenario in 2030, habitat fragmentation was alleviated. Conversely, under the economic development scenario, habitat quality further deteriorated, resulting in the largest area of low-quality regions. Minimal changes occurred under the natural development scenario. (2) The landscape indices in Jiangsu Province experienced significant changes from 2000 to 2020. The continuous expansion of urban land into other land use types led to a trend of fragmentation, with a clear increasing trend in dispersion, sprawl, and Shannon’s diversity index, accompanied by a decrease in cohesion. (3) The dominant interacting factors affecting habitat quality were combinations of socioeconomic factors with other factors, indicating that the economy largely determines the spatial distribution pattern of habitat quality. The findings of this study provide optimization strategies for future spatial planning of land use types in Jiangsu Province and offer references for habitat quality restoration efforts in the region.

1. Introduction

Since the industrial revolution, with the development of productivity, the process of urbanization has accelerated, and human activities have had a profound impact on the Earth’s ecology. Human activities, such as those associated with urban sprawl, agricultural development, and resource extraction, can lead to habitat fragmentation, destroying the habitats necessary for the survival of species and limiting their ability to migrate in response to climate change [1]. As the world’s second-largest economy, China has seen rapid urbanization in recent years, significantly altering land use patterns and having a notable impact on habitat quality [2]. Habitat quality refers to the ability of an ecosystem to provide conditions suitable for the continuous development and survival of individuals and populations. It is used as an indicator to measure the health of ecosystems and can, to a certain extent, reflect the biodiversity of a region [3]. Numerous studies have shown that land use change is a primary driving force in the evolution of habitat quality and is crucial for its development [4,5,6,7,8,9].

Monitoring habitat quality requires a comprehensive consideration of the characteristics of ecosystem elements in different regions, as well as the natural environment and the differences in economic and social development. Traditional sampling-based ground monitoring methods struggles to to meet this demand. Currently, research on habitat quality often employs indicator systems or modeling methods. The indicator method is often used for the habitat quality evaluation of individual species, based on the survival environment requirements of the species, to determine the distribution range and characteristics of its suitable living environment. On this basis, an evaluation indicator system is constructed to conduct a comprehensive evaluation of habitat quality [10]. For example, Zhang Meng et al. conducted a comprehensive survey of bird resources in different habitats, seasons, and distribution altitudes on the southern slope of the Qilian Mountains [11]. Liang evaluated the habitat suitability of Hainan Pterocarpus tonkinensis and analyzed the main environmental factors affecting the distribution of its suitable living area [12]. This method is highly accurate and targeted, but data collection and tracking costs are expensive, making it unsuitable for large-scale comprehensive evaluation studies, though it is suitable for small-scale nature reserves or small watershed areas [13]. Compared with the indicator method, the modeling method has the advantages of easy data acquisition, low cost, and strong visibility, and is often used for multi-scale quantitative habitat quality assessment [14]. Currently, the models used include the habitat suitability index (HSI) model, the maximum entropy (MaxEnt) model, and the InVEST model. Of these, the InVEST model is the most mature and has clear advantages in terms of application cost and evaluation accuracy. It has been widely used as part of existing research in fields such as regional ecosystem services and watershed management, natural capital conservation, ecosystem service payments, and changes in ecosystem productivity [15,16,17,18,19,20]. In their study of the spatiotemporal changes in habitat quality, Xu et al. used the InVEST model to study the spatiotemporal changes in land use, habitat quality, and habitat degradation in the middle and lower reaches of the Yangtze River from 1980 to 2018 [21]. Li et al. used the InVEST model to analyze the spatiotemporal evolution of habitat quality in Yulin City based on three periods of land use data in 1995, 2005, and 2015 [22]. Mélanie et al. used the InVEST habitat quality model to explore the changes in habitat quality in the upper Paraguay River basin in Brazil from 1989 to 2050 [23]. There are many research results based on the InVEST model, but in terms of the timescale of the research, current scholars’ research has focused mostly on exploring the spatiotemporal differentiation characteristics and driving factors of habitat quality, and less research has been carried out on the simulation and prediction of future periods of habitat quality or on their driving factors [24].

Jiangsu Province is a vital part of the Yangtze River Delta, serving as a crucial hub in the “Belt and Road” region and connecting the Beijing–Tianjin–Hebei area with the Yangtze River Delta. The province is rich in water resources and wetlands and has a relatively comprehensive set of ecological protection policies and measures [25]. However, in recent years, economic development and urban expansion in Jiangsu Province have led to changes in land use types, continuously increasing the degree of habitat fragmentation and leading to the severe degradation of regional habitats. Research on regional habitat quality is therefore of significant theoretical and practical importance.

This study uses Jiangsu Province as a case study. Based on historical land use data, the FLUS model is employed to predict land use distribution in Jiangsu Province under different scenarios in 2030. On this basis, the InVEST model is applied to assess habitat quality. Geographical detector is used to explore the key natural and socio-economic drivers affecting the spatial and temporal differentiation of habitat quality. The results are expected to provide long-term data support for the current protection of habitat quality in Jiangsu Province and to provide a scientific basis for habitat quality management.

2. Materials and Methods

2.1. Study Area

Jiangsu Province is located in the central coastal region of China, at the lower reaches of the Yangtze River and the Huai River, in the eastern coastal area of China, bordering the Yellow Sea to the east and the Yangtze River estuary to the south. It spans from 116°18′E to 121°57′E longitude and 30°45′N to 35°20′N latitude, with a total area of approximately 106,000 square kilometers (Figure 1). Jiangsu Province is an area of low elevation in China, with the vast majority of the region below 50 m above sea level. The terrain is diverse, characterized by plains, hills, and an interlacing network of rivers, with low mountains and hills mostly concentrated in the southwestern part. Jiangsu Province is one of the most economically developed areas in China, with early urban development, rapid growth, rapid population increase, and a high overall level of urbanization. It is one of the regions in China with a good living environment, high degree of development and utilization, economic prosperity, and a dense population. By the end of 2023, the permanent population of Jiangsu Province was 85.26 million people, and the regional gross domestic product was CNY 12,822.22 billion, making it the second province in the country to exceed a GDP of CNY 10 trillion [25,26]. Jiangsu Province places a high emphasis on the demarcation of ecological red lines and habitat protection. With an urgent need for habitat protection, it is important that China promotes integrated and high-quality development. To ensure that ecological space adapts to current economic and social development planning and to ensure the actual protection of the ecological environment, analysis of the changes in habitat quality in Jiangsu Province over the past few decades, as well as their influencing factors, is of great significance in the promotion of the sustainable and coordinated development of Jiangsu Province.

2.2. Data

2.2.1. Land Use and Cover Change Dataset

This study employs land use datasets of Jiangsu Province at a scale of 1:100,000 for the years 1995, 2000, 2005, 2010, 2015, and 2020. The datasets are derived from the Yangtze River Delta Science Data Center, National Science and Technology Infrastructure of China, and National Earth System Science Data Sharing Infrastructure (http://nnu.geodata.cn (accessed on 1 January 2022)), with a spatial resolution of 30 m by 30 m. The datasets have achieved a comprehensive evaluation accuracy of 95%, compliant with the cartographic accuracy standards for a 1:100,000 scale [27]. The data are formatted in ESRI shapefile and utilize the Krasovsky_1940_Albers spatial projection. The dataset comprises 6 primary categories and 25 secondary categories. In accordance with research requirements, land use types in Jiangsu Province are classified into arable land, forest land, grassland, water area, construction land, and unutilized land based on the primary categories.

2.2.2. Basic Data on the City

The foundational data on the city characteristics, including the district boundaries, city names, capital cities, road networks, urban centers, etc. were provided by the Yangtze River Delta Science Data Center, National Science and Technology Infrastructure of China, and National Earth System Science Data Sharing Infrastructure (http://nnu.geodata.cn, accessed on 1 January 2022) [27]. These data were stored in shapefile format for subsequent spatial analysis.

2.2.3. Natural and Socio-Economic Factors Data

Land use change is closely related to the natural environment, socio-economic development, and location factors. In this study, the driving factors used to simulate future land use evolution include topographical factors (elevation, slope, aspect), climate change (annual precipitation, annual temperature), socio-economic factors (population density, GDP), and location distribution (administrative divisions). The DEM data are sourced from the ASTER GDEM data product, and the slope and aspect data are derived from the analysis of DEM data using ArcGIS 10.3 software, with a spatial resolution of 30 m × 30 m. Annual precipitation and temperature data are statistically organized according to administrative divisions and are in xlsx format. Population density and GDP data are in TIFF format, with a spatial resolution of 1 km × 1 km, and were obtained from the National Earth System Science Data Sharing Service Platform. To ensure the uniformity of the research data, the coordinate system is re-projected to WGS1984_UTM_ZONE_51N [26,27].

2.3. Method

This study is structured into three main components: land use simulation, analysis of the spatiotemporal distribution characteristics of habitat quality, and analysis of factors influencing habitat quality. The research utilizes land use data from Jiangsu Province, covering the period from 2000 to 2020, and while considering the historical evolution of land use and relevant policies. Scenarios for natural development, economic priority, and ecological protection are designed to simulate the land use patterns in Jiangsu Province by 2030. The Markov chain method is employed to calculate the land use requirements for different scenarios in 2030, followed by the application of the FLUS model for forecasting. Subsequently, the InVEST model is applied to assess habitat quality. An analysis of factors influencing habitat quality is conducted in two parts. Firstly, the overall changes in habitat quality are examined through variations in landscape pattern indices. Secondly, ecosystem services, slope, aspect, soil, population distribution, GDP, and topography are selected as influencing factors. Geographical detector analysis is then used to assess the interactive effects of these factors on changes in habitat quality (Figure 2).

2.3.1. CA–Markov Model

The future land use simulation (FLUS) model effectively addresses the competitive relationships between various types of land use by incorporating an adaptive inertial competition mechanism based on the roulette wheel selection from the traditional cellular automata (CA) model and by introducing an artificial neural network (ANN) to obtain suitability probabilities. This approach enhances the accuracy of simulations and offers the advantages of high computational efficiency and a large simulation range, making it widely used in land use change simulation [28,29,30]. The main computational formulas are as follows:

$$P\left(g,k,t\right)=\sum {jW}_{j,k}\times sigmoid({net}_j\left(g,t\right))$$

(1)

$$sigmoid({net}_j\left(g,t\right))={\displaystyle \frac{1}{{1+e}^{-{net}_j\left(g,t\right)}}}$$

(2)

$${net}_j\left(g,t\right)=\sum {jW}_{j,k}\times x_i(g,t)$$

(3)

where P(g,k,t) represents the probability of grid cell g being occupied by land use type k at time t; w_j,k is the adaptive weight between the hidden layer and the output layer; sigmoid(net,(g,t)) is the activation function from the hidden layer to the output layer; net_j(g,t) denotes the signal received by neuron j in the hidden layer from all input neurons on grid cell g at time t; w_i,j is the adaptive weight between the input layer and the hidden layer; and x_i(g,t) is the i^th variable associated with input neuron i on grid cell g at time t.

The Markov chain transition matrix is applied to study the evolution characteristics of land use between different periods and generates a transition probability matrix that can predict future development trends. The formula is as follows:

$$S_{ij}=\left(\begin{array}{cccc}{a}_{11}& a_{12}& \cdots & a_{1n}\\ a_{21}& a_{22}& \cdots & a_{2n}\\ ⋮& ⋮& \dots & ⋮\\ a_{n1}& a_{n2}& \dots & a_{nn}\end{array}\right)$$

(4)

where n represents the type of land use and S_ij represents the area of the land use type i at the beginning of the study period that is converted to the land use type j at the end of the study period. When i = j, this indicates that the land use type has not changed.

Based on the historical land use change situation and current government policy, three development scenarios were designed to predict the land use situation in 2030 [31]. The land use conversion regulation is as follows:

• Natural development scenario: This scenario continues the development trend of land use in Jiangsu Province before 2020, predicting the total demand area for land use types in 2030 by following natural development patterns.
• Ecological protection scenario: This scenario restricts urbanization to direct land use towards ecological protection. The “Jiangsu Province ‘14th Five-Year’ Forestry and Grassland Protection and Development Plan Outline” proposes a target of 24.1% forest coverage by 2025 and 26% by 2030 [31]. Therefore, based on the total land demand under scenario S1 in 2030, and while considering the structure of ecological, agricultural, and urban land use, the conversion probability for each type of land use is set, with a 20% decrease in the probability of arable land, forest land, and grassland becoming construction land; a 60% increase in the probability of arable land becoming forest land or grassland; a prohibition of water bodies becoming construction land; and an ecological red line area within the region as a restricted expansion area.
• Economic development scenario: Jiangsu Province has always been at the forefront of urbanization development in China, and it is expected that the possibility of various types of land use becoming construction land will increase. Based on the total demand area for each land use and cover type under the natural development scenario in 2030, the proportion of forest land, grassland, and water bodies being converted to construction land increases by 15%, 10%, and 10%, respectively, with a 60% decrease in the possibility of construction land being converted to other types, and with free transfer between the other types of land use [31,32].

The land use type conversion cost matrix, as a condition for implementing various scenarios, quantitatively represents the feasibility of converting between two land use types. Territorial spatial planning determines future land use status by adjusting both land use types and their spatial distribution. The land use transition cost matrix for the different scenarios is shown in

Table 1. Land use transfer cost matrix under different development scenarios.

Development Scenario	Land Use Type	Arable Land	Forest	Grassland	Water Area	Construction Land	Unutilized Land
Natural development scenario	Arable land	1	1	1	1	1	0
	Forest	1	1	1	1	1	0
	Grassland	1	1	1	1	1	0
	Water area	1	1	1	1	1	0
	Construction land	1	1	1	1	1	0
	Unutilized land	1	1	1	1	1	0
Ecological protection scenario	Arable land	1	1	1	1	1	0
	Forest	0	1	1	0	0	0
	Grassland	0	1	1	0	0	0
	Water area	1	1	1	1	1	0
	Construction land	1	1	1	1	1	0
	Unutilized land	1	1	1	1	1	0
Economic development scenario	Arable land	1	1	1	1	1	0
	Forest	1	1	1	1	1	0
	Grassland	1	1	1	1	1	0
	Water area	1	1	1	1	1	0
	Construction land	0	0	0	0	1	0
	Unutilized land	1	1	1	1	1	0

.

2.3.2. Habitat Quality Model

This study employs the habitat quality module within the integrated valuation of ecosystem services and tradeoffs (InVEST) model to assess the habitat quality in Jiangsu Province. The InVEST model evaluates habitat quality by assessing the intensity of stress factors in the ecological environment and the sensitivity of different land use types to these stress factors. It calculates the degree of habitat degradation based on the maximum stress distance, weight, and spatial decay type of various stress sources, and further calculates the habitat quality [32,33].

Habitat degradation degree refers to the extent of degradation caused to the habitat by stress factors, with a higher degree indicating greater degradation. The calculation formula is as follows:

$$D_{xj}={\sum }_1^r{\sum }_1^y\left({\displaystyle \frac{{\omega }_r}{\sum _{r=1}^n{\omega }_r}}\right)\times r_y\times i_{rxy}\times {\beta }_x\times S_{jr}$$

(5)

$$i_{rxy}=\left\{\begin{array}{l}1-\left({\displaystyle \frac{d_{xy}}{d_{rmax}}}\right)\\ exp\left[-\left({\displaystyle \frac{2.99}{d_{rmax}}}\right)\times d_{xy}\right]\end{array}\right.$$

(6)

where ω_r represents the weight of different stress factors; r_y is the intensity of the stress factor; β_x denotes the resistance level of the habitat to disturbances; S_jr is the relative sensitivity of different habitats to different stress factors; i_rxy is the impact of stress factor r on grid y on grid x; r stands for habitat stress factors; y represents the grids within the influence of stress factor r; d_xy is the distance between grid x and grid y; and d_rmax is the range of influence of stress factor r.

The calculation formula for habitat quality is as follows:

$$Q_{xj}=H_{xj}\times \left[1-\left({\displaystyle \frac{D_{xj}^2}{D_{xj}^2+k^2}}\right)\right]$$

(7)

where Q_xj represents the habitat quality of grid x in land use type j; D_xj is the degree of habitat degradation, indicating the level of habitat degradation in grid x of land use type j; H_xj is the habitat adaptability of grid x in land use type j; and k is the half-saturation constant. The habitat quality value ranges from 0 to 1, with higher Q_xj values indicating better habitat quality.

This study refers to the relevant literature and selects paddy fields, dry land, urban land, rural residential points, other buildings, and unused land as stress factors [32,33]. It also refers to the InVEST model user manual and the research results of scholars, and sets the maximum impact distance and weight of different stress factors, as well as habitat suitability and the sensitivity of different habitats to stress factors, based on actual conditions (

Table 2. Threat factors and stress intensity.

Land Use Type	Habitat Suitability	Paddy Field	Dry Farmland	Urban Land	Rural Settlements	Other Construction	Unutilized Land
Arable land	0.4	0	0	0.8	0.7	0.8	0.4
Forest	1	0.6	0.7	0.8	0.7	0.8	0.4
Grassland	0.6	0.7	0.6	0.8	0.7	0.7	0.6
Water area	0.8	0.5	0.3	0.4	0.3	0.2	0.1
Construction area	0	0	0	0.6	0.5	0	0
Unutilized land	0.4	0.4	0	0	0.1	0.1	0.2

and

Table 3. The sensitivity of different land use types to threat factors.

Threat Factors	Weight	Maximum Influence Distance	Decay Type
Paddy field	0.8	5	Linear
Dry farmland	0.6	9	Linear
Urban land	0.8	6	Exponential
Rural settlement	0.7	2	Exponential
Other construction	0.7	1	Exponential
Unutilized land	0.5	6	Linear

).

2.3.3. Geographic Detector Model

The geographic detector (GD) model can be employed to explore the impact intensity and direction of various geographic factors and their interactive effects on the research subject. This method integrates spatial analysis from geography with the concept of disease detectors from epidemiology, aiming to reveal how geographical environmental factors influence social, economic, and ecological phenomena [34]. By calculating and comparing the q values of individual factors and the q values after the overlay of two factors, the GD can determine whether there is an interaction between the two factors, as well as the strength, the direction, and whether the interaction is linear or nonlinear. Considering the characteristics of the geographic detector and the features of the study area in this research, seven explanatory factors were selected, including ecosystem services, slope, soil, population, GDP, aspect, and topography. The formula is as follows:

$$q=1-{\displaystyle \frac{{\sum }_{h=1}^L{N}_h{\sigma }_h^2}{{N\sigma }^2}}=1-{\displaystyle \frac{SSW}{SST}}$$

(8)

where h = 1, …, L represents the stratification of variable Y or factor X, that is, classification or partitioning; N_h and N are the number of units in layer h and the entire area, respectively; ${\sigma }_h^2$ and ${\sigma }^2$ are the variances of Y values in layer h and the entire area, respectively; and SSW and SST represent the sum of within-layer variances and the total sum of squares for the entire area, respectively.

2.3.4. Landscape Pattern Index Analysis

Landscape pattern indices are statistical indicators used to describe and quantify the characteristics of surface landscapes. In this study, four typical landscape pattern indices were selected: discreteness, contagion, aggregation, and Shannon’s diversity index [35,36].

Contagion (CONTAG) refers to the degree of aggregation or the tendency to spread of different patch types in the landscape pattern. The calculation formula is as follows:

$$CONTAG=\left[1+{\displaystyle \frac{{\sum }_{i=1}^m{\sum }_{k=1}^m\left[\left(p_i\right)\left(\frac{g_{ik}}{{\sum }_{k=1}^m{g}_{ik}}\right)\times \left[ln\left(p_i\right)\left(\frac{g_{ik}}{{\sum }_{k=1}^m{g}_{ik}}\right)\right]\right]}{2ln\left(m\right)}}\right]\times 100$$

(9)

where p_i is the proportion of the total area that patch type i occupies; g_ik is the connectivity coefficient between patch types i and k; and m is the number of different patch types in the landscape. A higher value of CONTAG indicates a more aggregated distribution of patches, while a lower value suggests a more dispersed distribution. CONTAG ranges from 0 to 1, with 0 indicating no edge.

The Shannon diversity index (SHDI) is a measure used to reflect the complexity of landscape patterns. The calculation formula is as follows:

$$SHDI=-{\sum }_{i=1}^n\left(p_i\right)\times ln\left(p_i\right)$$

(10)

where n is the number of landscape patches and $p_i$ is the proportion of the area of landscape patch type i relative to the total area of all landscape patches.

The division (DIVISION) index measures the dispersion of patches within a certain landscape type. The calculation formula is as follows:

$$DIVISION=Di/Ai$$

(11)

where D_i is the distance of landscape type I and A_i is the area of landscape type i.

The cohesion index (COHESION) measures the physical connectivity between different patch types. The calculation formula is as follows:

$$COHESION=\left[1-{\displaystyle \frac{{\sum }_{i=1}^m{\sum }_{j=1}^n{p}_{ij}}{{\sum }_{i=1}^m{\sum }_{j=1}^n{p}_{ij}\sqrt{a_{ij}}}}\right]\times {\left[1-{\displaystyle \frac{1}{\sqrt{A}}}\right]}^{-1}\times 100$$

(12)

where p_ij is the proportion of the ij-th patch type; a_ij is the number of pixels in the ij-th patch; and A is the total number of pixels.

3. Results

3.1. Land Use and Cover Change Situation from 2000 to 2020

The total land area of Jiangsu Province is approximately 101,866 km². The distribution and changes in land use/cover in Jiangsu Province from 2000 to 2020 are shown in

Table 4. The 2000–2020 land use and cover situation (unit: km²).

Land Use Type	Arable Land	Forest	Grassland	Water Area	Construction Land	Unutilized Land	Sum
2000	70,021	3382	1079	12,971	14,356	57	101,866
2010	67,319	3364	964	13,278	16,895	46	101,866
2020	62,422	3042	726	14,183	21,342	151	101,866

and Figure 3. The dominant land use type is arable land, accounting for more than 60%. Following that is construction land, ranging from 13.32% to 20.95%, and then water bodies, which account for between 12.16% and 13.92%. Forests and grasslands cover relatively smaller areas, with a combined percentage of less than 5% (Table 4). In terms of spatial distribution, arable land is widespread and forms clusters in the central and eastern parts of Jiangsu Province. Construction land exhibits distinct agglomeration characteristics in the northern part and in the areas south of the Yangtze River, with a particularly notable expansion trend in the southern part of Jiangsu. Over the time period from 2000 to 2020, the most significant change was a 12.43% reduction in arable land, in contrast, construction land showed the most prominent expansion, with a 57.34% increase. The areas of expansion are primarily concentrated in the cities south of the Yangtze River and central node cities such as Xuzhou and Yangzhou in the north, reflecting the results of Jiangsu’s rapid economic growth and urbanization development.

According to the land use transition matrix, the transfer of land use in Jiangsu Province from 2000 to 2020 is quite complex (

Table 5. The 2000–2020 land use transition situation (unit: km²).

Land Use Type	Arable Land	Forest	Grassland	Water Area	Construction Land	Unutilized Land	Sum
Arable land	54,471	640	102	2191	12,582	35	70,021
Forest	771	2118	33	66	359	35	3382
Grassland	232	25	380	331	107	4	1079
Water area	1669	76	174	10,305	688	59	12,971
Construction land	5239	177	37	1285	7605	13	14,356
Unutilized land	40	6	0	5	1	5	57
Sum	62,422	3042	726	14,183	21,342	151	101,866

). A significant amount of arable land has been converted to construction land, accounting for 91% of the sources of construction land expansion, making it the primary source of construction land growth. Water bodies have increased due to the conversion of arable and construction land, with respective proportions of 57% and 33%. This is attributed to the vigorous development of aquaculture in Jiangsu Province, which has led to the transformation of arable and construction land into fishponds and aquaculture facilities in coastal areas. The areas of forest and grassland have both decreased, with more than one-third being converted into arable and construction land.

3.2. Evolution of Landscape Pattern Indices

During the study period, landscape pattern indices were calculated and visualized for Jiangsu Province (Figure 4). From 2000 to 2020, the landscape pattern in Jiangsu Province exhibited significant changes, with notable increases in the indices of dispersion, sprawl, and Shannon’s diversity. The mean value of dispersion rose from 0.30 to 0.34, indicating an increased fragmentation of landscape elements and a decrease in the uniform distribution of various land use types, such as arable land, grasslands, and water bodies. The expansion of construction land and the reduction of green spaces have contributed to a rise in surface temperature. The sprawl index increased from 21.64 to 22.54 on average, suggesting a more intensive and multi-element landscape pattern, with heightened landscape fragmentation and exacerbated urban heat island effects. The average value of Shannon’s diversity index increased from 0.46 to 0.50, indicating a richer yet more fragmented land use pattern in Jiangsu Province, with a higher content of uncertain information and a greater SHDI value, reflecting a reduced contribution of rare land uses like forests and grasslands to surface temperature. The cohesion index showed a declining trend, with the average value dropping from 82.48 to 80.90, implying a more dispersed landscape structure, exacerbated issues of habitat fragmentation, species loss, and destruction of green spaces, and a more pronounced urban heat island effect.

Over the two decades, Jiangsu Province’s rapid economic development has led to distinct changes in land use patterns. Human factors have impacted habitat quality, with the expansion of construction land, development of arable land, and reduction of forest land leading to more complex land use scenarios. Anthropogenic disturbances have transformed the landscape from a singular, homogeneous, and continuous entity to one that is complex, heterogeneous, and discontinuous. The continuous expansion of construction land into other land uses has resulted in a trend toward fragmentation, increasing the overall landscape’s dispersion and sprawl. Concurrently, a balanced distribution of different land use types has emerged, creating a mixed land use pattern, thus stabilizing the Shannon diversity index at a relatively low level. To specifically analyze the impact of land use changes on the ecological environment from 2000 to 2020, habitat quality analysis is necessary.

3.3. Habitat Quality Situation from 2000 to 2020

Using the InVEST model, we calculated the habitat quality in Jiangsu Province from 2000 to 2020 and divided it into five equal intervals: low (0.0–0.2), lower (0.2–0.4), medium (0.4–0.6), higher (0.6–0.8), and high (0.8–1.0). Landscape pattern indices were then used to characterize the evolution of habitat quality.

As shown in Figure 5, there was a general downward trend in habitat quality in Jiangsu Province between 2000 and 2020, with varying rates of decline at different timescales. From 2000 to 2010, the province’s economic growth was relatively slow, resulting in minor changes in land use and only a slight decline in habitat quality. In contrast, from 2010 to 2020, the rapid economic development and significant acceleration of urbanization led to substantial land development activities, which had a more pronounced negative impact on the ecological environment, leading to a more noticeable decline in habitat quality.

Spatially, the habitat quality generally shows a trend of decreasing from the coastal zone to the inland areas. In the early stages, low-grade habitat quality areas were mostly concentrated along the Yangtze River and its southern areas, exhibiting an expansion trend of “point-surface-belt” centered around urban cities. With further economic development, the urbanization process in the northern part of Jiangsu accelerated, land development activities became more frequent, the degree of ecological environment destruction intensified, and the areas with deteriorating habitat quality gradually expanded inland. At the same time, higher and high-quality habitat quality areas are found to be mainly distributed in coastal cities, water bodies, and their adjacent areas. These areas have retained better ecology due to their undergoing relatively less development. Medium and lower habitat quality areas are highly correlated with the extensively distributed arable land in Jiangsu Province. The impact of agricultural activities and human disturbances makes it difficult for these areas to achieve higher levels of habitat quality.

The InVEST model was applied to calculate the habitat quality in Jiangsu Province from 2000 to 2020 (Figure 6). The habitat quality was categorized into five equal intervals: low (0.0–0.2), lower (0.2–0.4), medium (0.4–0.6), higher (0.6–0.8), and high (0.8–1.0). Landscape pattern indices were utilized to characterize the evolution of habitat quality. The analysis revealed that, between 2000 and 2020, the overall trend of habitat quality in Jiangsu Province showed a decline, with varying rates of decrease at different timescales. From 2000 to 2010, the economic development in Jiangsu Province was relatively slow, leading to minor changes in land use and only a slight decrease in habitat quality. In contrast, from 2010 to 2020, the rapid economic growth and significant acceleration of urbanization led to substantial land development activities, causing more noticeable negative impacts on the ecological environment and a more pronounced decline in habitat quality.

To explore the degradation of habitats, a visual study of the distribution of habitat quality in Jiangsu Province over the years was conducted (Figure 7). Between 2000 and 2020, the phenomenon of habitat quality degradation in Jiangsu Province was quite evident. Areas of high degradation are primarily located along the banks of water bodies and on the fringes of major cities, such as along the Yangtze River, around Lake Tai, and in the built-up areas of southern cities. As coastal areas serve as transitional zones between land and water, they are susceptible to natural factors such as soil erosion and rising sea levels. Moreover, these areas are habitats and breeding grounds for many biological populations; thus, any disruption to these regions can significantly impact the entire ecosystem, leading to persistently high levels of habitat degradation. Additionally, developed cities often undergo extensive changes in land use. The rapid development and urbanization processes have led to the destruction and alteration of substantial natural habitats, such as forests being cleared for construction, wetlands being filled in, farmland being converted to urban land, and resource extraction activities [37]. These processes result in the disruption of the original natural ecosystems, causing a decline in their quality. In developed cities like Nanjing, Suzhou, and Nantong, the degree of habitat degradation is significantly higher than in the northern part of Jiangsu Province. This indicates that urban expansion and environmental degradation are significant factors leading to the deterioration of habitat quality.

In 2000, areas of high degradation were mainly concentrated in the southern part of Jiangsu; however, over time, the situation in the northern part of Jiangsu has become increasingly severe, especially after 2010. The urbanization process in the northern region has accelerated, intensifying land use and consequently leading to further degradation of habitat quality. In contrast, the degree of habitat degradation in coastal areas has remained relatively low. This may be due to the stricter ecological protection policies in the coastal areas of Jiangsu Province, which have reduced the destructive impact of human activities on habitats. The coastal zone of Jiangsu Province is an essential component of the provincial territorial spatial planning system and a crucial basis for various marine spatial planning. It plays a significant role in building a unified system of territorial spatial planning and in supporting the construction of a strong maritime province. Therefore, Jiangsu Province has taken into account both short-term and long-term protection and development needs, scientifically determined the marine protection and utilization objectives for 2025 and 2035, comprehensively planned its marine functional zoning and control requirements, and systematically constructed a new pattern for the protection and development of the marine areas in the province. This has resulted in the coastal areas of Jiangsu Province being regions with higher habitat quality within the province.

3.4. Habitat Quality Prediction in 2030 Under Three Scenarios

To forecast the distribution of habitat quality in Jiangsu Province by 2030, a scenario prediction of land use conditions was first conducted. The FLUS model was utilized to predict the changes in land use in Jiangsu Province by 2030 under three development scenarios (Figure 8). According to the land use transfer matrix, there are significant differences in land use and transfer under different scenarios in Jiangsu Province for the year 2030. Under the natural development scenario (

Table 6. The 2020–2030 land use transition situation under the natural development scenario (unit: km²).

Land Use Type	Arable Land	Forest	Grassland	Water Area	Construction Land	Unutilized Land	Sum
Arable land	55,223	134	99	1516	5450	0	62,422
Forest	224	2768	13	2	35	0	3042
Grassland	37	3	487	138	61	0	726
Water area	970	9	6	13,048	150	0	14,183
Construction land	3562	23	4	82	17,671	0	21,342
Unutilized land	0	0	0	0	0	151	151
Sum	60,016	2937	609	14,786	23,367	151	101,866

), the distribution and trends of land use change are similar to those of historical years: arable land remains the largest proportion of land use, accounting for 58.9%, a reduction of 4.1% compared with 2020, with the reduced arable land mostly converted to construction land and accounting for 75.7% of the total conversion proportion. Therefore, the expansion of construction land is mainly sourced from arable land, with the area of construction land increasing by 9.5% compared with 2020. Due to the expansion of construction land, the areas of forest land and grassland have slightly decreased compared with 2020. Under the ecological protection scenario (

Table 7. The 2020–2030 land use transition situation under the ecological protection scenario (unit: km²).

Land Use Type	Arable Land	Forest	Grassland	Water Area	Construction Land	Unutilized Land	Sum
Arable land	55,402	324	395	1584	4717	0	62,422
Forest	0	2988	54	0	0	0	3042
Grassland	0	6	720	0	0	0	726
Water area	982	11	4	13,053	133	0	14,183
Construction land	3700	27	3	88	17,524	0	21,342
Unutilized land	0	0	0	0	0	151	151
Sum	60,084	3356	1176	14,725	22,374	151	101,866

), due to the strengthened protection of forest land and grassland, the areas of these two types of land have been restored, increasing by 10.3% and 62.0%, respectively, when compared with 2020. This indicates that, under the ecological protection scenario (

Table 8. The 2020–2030 land use transition situation under the economic development scenario (unit: km²).

Land Use Type	Arable Land	Forest	Grassland	Water Area	Construction Land	Unutilized Land	Sum
Arable land	57,779	97	90	1127	3329	0	62,422
Forest	233	2767	11	4	27	0	3042
Grassland	44	3	536	114	29	0	726
Water area	1004	4	6	13,042	127	0	14,183
Construction land	0	0	0	0	21,342	0	21,342
Unutilized land	0	0	0	0	0	151	151
Sum	59,060	2871	643	14,287	24,854	151	101,866

), the ecological environment has improved, and the trend of construction land expansion has been curbed, with an increase of only 4.8% compared with 2020, which is only half the growth degree of the natural development scenario. Under the economic development scenario, due to the emphasis on economic development and urbanization, and the prohibition of the conversion of construction land, the growth rate of construction land is the highest among the three scenarios, reaching 16.5%. At the same time, the reduction of arable land is the greatest, with a reduction of 5.7% compared with 2020, and the forest land and grassland have decreased by 5.6% and 11.4%, respectively.

Using the FLUS–InVEST model to predict habitat quality in Jiangsu Province under different scenarios by 2030, the results indicate (Figure 9) that, under the economic development scenario, the decline in habitat quality is the most pronounced. This is primarily due to the significant increase in the demand for construction land during the economic development process, especially in the northern part of Jiangsu, where the acceleration of urbanization and the expansion of construction land area led jointly to the deterioration of habitat quality. Under this scenario, the decline in habitat quality is highly consistent with the expansion of construction land. In contrast, under the ecological protection scenario, due to the need for environmental protection, the conversion of forest land and grassland is restricted, which results in a rebound in the area of high-quality habitat regions. Furthermore, the ecological protection scenario has, to some extent, curbed the conversion of high-quality areas to low-quality areas. The newly added high-quality habitat areas are mainly distributed in the central region of Jiangsu, reflecting the positive impact of environmental protection policies on habitat quality under this scenario.

3.5. Influencing Factors for Habitat Quality Changes

This study employs the geographic detector (GeoD) to analyze the interactive effects of various influencing factors. It examines the factors affecting habitat quality from both natural and socio-economic perspectives, selecting ecosystem services, slope, aspect, soil, population distribution, GDP, and topography as influencing factors. Based on the GeoD analysis, the study assesses the interactive effects of different factors (

Table 9. Result of synergistic influencing factors of habitat quality.

Synergistic Influencing Factors	Result
Ecosystem services∩slope	Bifactorial enhancement
Ecosystem services∩soil	Bifactorial enhancement
Ecosystem services∩population	Bifactorial enhancement
Ecosystem services∩GDP	Bifactorial enhancement
Ecosystem services∩slope	Bifactorial enhancement
Ecosystem services∩topography	Nonlinear enhancement
Slope∩soil	Bifactorial enhancement
Slope∩population	Bifactorial enhancement
Slope∩GDP	Bifactorial enhancement
Slope∩aspect	Bifactorial enhancement
Slope∩topography	Bifactorial enhancement
Soil∩population	Nonlinear enhancement
Soil∩GDP	Bifactorial enhancement
Soil∩aspect	Bifactorial enhancement
Soil∩topography	Nonlinear enhancement
Population∩GDP	Bifactorial enhancement
Population∩aspect	Bifactorial enhancement
Population∩topography	Nonlinear enhancement
GDP∩aspect	Nonlinear enhancement
GDP∩topography	Bifactorial enhancement
Aspect∩topography	Bifactorial enhancement

).

This study employs geographic detector analysis to explore the interactive effects of various influencing factors on habitat quality in Jiangsu Province (

Table 10. Analysis of the interactive effects of factors influencing habitat quality.

	Ecosystem Services	Slope	Soil	Population	GDP	Aspect	Topography
Ecosystem services	0.662377
Slope	0.663463	0.093827
Soil	0.700596	0.097915	0.095353
Population	0.700596	0.097915	0.097517	0.095353
GDP	0.994496	0.991437	0.994494	0.994494	0.991417
Aspect	0.6632	0.004028	0.098883	0.098883	0.991437	0.001586
Topography	0.667008	0.003575	0.104837	0.104837	0.991437	0.005313	0.001154

). The factors considered include ecosystem services, slope, aspect, soil, population distribution, GDP, and topography, which are analyzed from both natural and socio-economic perspectives. The findings are as follows:

(1)

Ecosystem services and factors such as slope, soil, population, and GDP: There is a significant synergistic effect between ecosystem services and these factors, indicating a spatial synergy in their influence on habitat quality. For instance, the synergistic effect between ecosystem services and slope may reflect the contribution of natural topographical variations to the enhancement of ecological service functions. Meanwhile, the synergistic effect between ecosystem services and GDP suggests the potential driving role of economic development in ecological protection and improvement.

(2)

The interaction of GDP with other factors: The interaction effect of GDP with other factors is generally strong, especially with slope, soil, and population distribution, with q values close to 1. This indicates that GDP plays a leading role in the improvement of habitat quality. Economic development often accompanies the construction of infrastructure and the strengthening of environmental management, which may explain the positive impact of GDP growth on habitat quality. However, this impact is not linear but rather the result of a combination of natural conditions and economic activities.

(3)

Interaction effects among natural factors: The interactive effects between ecosystem services and natural factors, such as aspect and topography, are also significant, particularly the nonlinear enhancing effect between ecosystem services and topography. This suggests that the influence of natural conditions on habitat quality has complex nonlinear characteristics. It may imply that the provision efficiency and scope of ecological services may be limited under different topographical conditions, especially in areas with complex or extreme terrain, where ecosystem functions may be more constrained.

(4)

Interaction between slope, soil, and population distribution: There is also a significant synergistic effect between factors such as slope, soil, and population distribution. These results indicate that there is a complex interplay between natural geographical conditions and social activities, both of which jointly affect the spatial pattern of habitat quality. For example, in areas with dense population, the improvement of soil quality contributes more significantly to habitat quality, reflecting the enhancing effect of the combination of population distribution and soil.

Overall, the analysis results from the geographic detector provide important evidence for understanding the driving forces behind habitat quality, further demonstrating the complex relationship between natural conditions and socio-economic development in shaping habitat quality.

4. Discussion

4.1. Distribution and Influencing of Habitat Quality

Habitat quality is a crucial indicator for measuring the ecological environment of a region. Predicting the future trends of habitat quality can provide a scientific basis for governments to formulate policies related to the protection of the ecological environment, prevention of ecological quality deterioration, and rational allocation of natural resource management. The degradation of habitat quality is a complex process involving multiple factors and aspects that interact with each other. In this process, urbanization and economic development in Jiangsu Province are considered the most important driving factors.

In this study, it was observed that, between 2000 and 2020, habitat degradation in Jiangsu Province intensified progressively, while habitat quality exhibited a secular decline. High-value areas were mainly distributed in water bodies and the coastal regions of central Jiangsu, while low-value areas were mostly located in built-up areas. The urbanization development in Jiangsu started early and developed rapidly, with the level and quality generally showing a steady upward trend. After 1978, Jiangsu achieved a significant transformation from a planned economic system to a market economic system, fully stimulating the regional economic vitality and accelerating economic growth. The rapid economic development led to a large population aggregation within the region. By 2020, both the permanent resident urbanization rate and the household registration urbanization rate in Jiangsu became the highest in China, with the gap between the two continuing to narrow, and the level of urbanization development continued to lead among provinces in the nation. At the same time, the continuous expansion of urban built-up areas and the construction of various infrastructure facilities have encroached upon habitats, causing their increasing fragmentation. Secondly, the highly developed economy has led to a large influx of foreign people to towns and cities. The demand for construction land in developed cities and their counties and districts in Jiangsu Province continues to expand. This not only occupies a large amount of arable land but also causes severe disturbances and threats to the surrounding ecological environment, resulting in a decline in habitat quality [38,39,40]. High habitat quality areas are mainly distributed near water bodies. Taihu Lake, Hongze Lake, Gaoyou Lake, and the Yangtze River are the main contributors to the high habitat quality in Jiangsu Province. Due to geographical location and other factors, they are still relatively less affected by human activities, and habitat quality remains at a high level. However, with the continuous acceleration of urban development and construction, the issue of development and protection cannot be ignored and the contradiction between people and land will become more prominent, land use types will transfer more rapidly, and habitat quality may continue to degrade.

Under the ecological protection scenario in 2030, habitat fragmentation is somewhat mitigated, while under the economic development scenario, habitat quality further degrades, with the largest area of low-quality regions, and the natural development scenario shows little change. This is because the ecological protection scenario strictly implements ecological protection policies, strengthens the protection of high-quality land such as forests and grasslands, and further reduces the expansion speed of urban land, which can improve the ecological environment in Jiangsu Province and thereby enhance habitat quality. In contrast, under the land use model dominated by the economic development scenario, habitat quality will further decline. This is mainly because the economic development scenario focuses on the protection of construction land, and urban economies will still adopt an extensive development form with high land consumption, leading to an increase in the demand for urban land [41,42]. The expansion of urban land is highly consistent with the expansion of low-value habitat quality areas. Therefore, the trend of declining habitat quality in Jiangsu Province has not changed.

Moreover, the continued decline in habitat quality may lead to a reduction in biodiversity, adversely affecting the stability and resilience of ecosystems. Such changes can diminish the capacity of ecosystems to withstand external shocks, such as climate change and human activities, thereby impacting the provision of essential ecosystem services, including water purification, soil retention, and carbon storage. This degradation poses a significant threat not only to the regional ecological environment but also to the sustainable development of human society [43,44,45]. For instance, habitat degradation may reduce agricultural productivity, jeopardizing food security and increasing the economic costs associated with ecosystem services.

Therefore, future urban planning and economic development policies must emphasize the importance of ecological protection, integrating ecological, economic, and social dimensions to achieve true sustainability. Governments should promote the construction of green infrastructure, encourage the adoption of environmentally friendly land use practices, and mitigate negative impacts on habitats. Additionally, enhancing public awareness of the significance of ecological protection through education and community engagement is vital for fostering sustainable practices. Only through the collective efforts of society can we effectively reverse the trend of declining habitat quality, ensuring the health and stability of ecosystems and laying a solid foundation for future development.

4.2. Recommendation for Future Policy

By analyzing the trends in habitat quality and influencing factors in Jiangsu Province, this study reveals the consistency between changes in habitat quality and the evolution trends of land use types in the region. As the pace of urban development and construction continues to accelerate, land use types are expected to shift more rapidly, leading to the continuous degradation of habitat quality. To counteract this trend, the following strategies and recommendations for territorial spatial planning are proposed:

Enhance ecological protection measures. Under the ecological protection scenario, the recovery of habitat quality indicates that implementing stringent ecological protection policies and strengthening environmental conservation efforts can promote the improvement of urban habitat quality [46]. Land use planning should focus on enhancing the ecological value of forests, grasslands, and other lands by optimizing the spatial layout of habitats such as forests, grasslands, and water bodies. This approach aims to improve the level of habitat quality in Jiangsu Province. At the policy level, measures should include the conversion of farmland to forests, the protection of biodiversity, and the implementation of ecological protection red lines, permanent basic farmland and urban development boundaries, and various marine protection lines.

Improve the ecological protection compensation mechanism. Firstly, the government should allocate special funds to support ecological protection projects and provide financial incentives to encourage enterprises and individuals to engage in ecological restoration activities. Secondly, Jiangsu Province should develop and refine its ecological compensation mechanism by exploring diversified implementation pathways. This involves formulating flexible compensation standards tailored to local conditions to meet the needs of different ecological function zones. Such measures include reward policies for ecological service providers, ensuring that environmental protection efforts align with local economic development and improvements in residents’ living standards [20,47]. By fostering collaborative relationships among local governments, enterprises, and communities, a shared interest can be created to encourage widespread participation in ecological protection efforts, thereby establishing a positive feedback loop.

Establish a natural protected area system and promote regional coordinated coupling relationships. It is essential to scientifically demarcate the protection scope and functional zoning of natural protected areas, accelerate the integration and optimization of various types of natural protected areas, and strictly control non-ecological activities within these areas. Additionally, special protected areas should be established for rare and endangered species. These efforts aim to achieve positive ecosystems succession, enhance ecosystem stability and biodiversity levels, and improve ecosystem service functions.

4.3. Limitations

This study has the following limitations. Socioeconomic policies regarding the development and protection of different land use types will directly affect the prediction of future land use, which in turn influences the prediction of habitat quality. For instance, policies aimed at restoring habitat quality, such as converting farmland back to forest, habitat restoration, and the establishment of nature reserves, can repair impaired habitat quality and reduce the rate of habitat degradation. In contrast, government policies aimed at achieving full employment and rapid economic growth, such as promoting industrialization and urbanization, may lead to further damage to habitat quality. Although this study takes into account the recent habitat quality restoration policies issued by the Jiangsu Provincial Department of Natural Resources, there may still be errors in predictions for long time spans. Additionally, the use of land use type as a single stressor in the estimation of habitat quality in this study has certain limitations. Future research should expand the selection of stressors, such as the impacts of extreme weather events like heavy rain and heatwaves, and the deterioration of water quality, to more comprehensively assess habitat quality.

5. Conclusions

This study utilizes land use data from 2000 to 2020 and employs the FLUS–InVEST model to forecast land use conditions in 2030, assessing the spatial pattern of habitat quality in Jiangsu Province from 2000 to 2030 and analyzing the evolution and influencing factors of its spatiotemporal pattern. The following conclusions were drawn:

1. From 2000 to 2020, the degree of habitat degradation in Jiangsu Province increased progressively, with the spatial distribution of habitat degradation levels showing a gradient distribution pattern. Under the ecological protection scenario in 2030, habitat fragmentation is expected to be somewhat mitigated, while under the economic development scenario, habitat quality is predicted to further deteriorate and show the largest area of low-quality regions. The natural development scenario shows little change.

2. Landscape indices in Jiangsu Province underwent significant changes from 2000 to 2020, with dispersion, contagion, and Shannon’s diversity indices showing a clear increasing trend, while cohesion showed a declining trend. The overall landscape presented an intensive configuration with multiple elements, resulting in increased dispersion and contagion. At the same time, different types of land use showed a balanced distribution trend in the landscape, forming a mixed distribution of various land uses, thus Shannon’s diversity index gradually stabilized at a relatively low level.

3. There is a synergistic enhancement relationship between different influencing factors, thus the comprehensive effect of different factors enhances the explanatory power for habitat quality. The interaction between GDP and other factors has a high degree of explanation, indicating that the dominant interactive factors affecting habitat quality are combinations of socioeconomic factors with other factors. In future scenarios of territorial spatial planning, economic development shows the greatest impact on habitat quality.