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Spatiotemporal changes in land use and residential satisfaction in the Huai River-Gaoyou Lake Rim area

  • Jianwei Yu , Chenyang Wang , Xiaoting Yang , Hongshen Chen and Limin Liang EMAIL logo
Published/Copyright: December 13, 2023
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Open Geosciences
From the journal Open Geosciences Volume 15 Issue 1

Abstract

This research investigates the dynamic interplay between land use changes and landscape ecological risks in China’s Jiangsu Province, specifically focusing on the Huai River-Gaoyou Lake region. Leveraging multi-temporal remote sensing data from 2000 to 2020, we conducted a comprehensive analysis of shifting land cover patterns and their implications. Employing geostatistical techniques, we developed a landscape ecological risk assessment model to quantify ecological vulnerabilities. Additionally, we conducted a resident satisfaction survey aligned with the “Production–Living–Ecological” framework to gain insights into societal perceptions of land use dynamics. Our findings reveal significant land use shifts driven by urbanization, industrialization, and agricultural modernization. While farmland areas have decreased, water bodies and artificial surfaces have expanded, reflecting societal demands and economic growth. The landscape ecological risk assessment model highlights localized vulnerabilities, emphasizing the intricate relationship between land use changes and ecological risks. The resident satisfaction survey indicates overall contentment with various aspects of land use, especially in terms of residential comfort and transportation convenience. However, ecological concerns underscore the need for a balanced approach to land management that prioritizes ecological sustainability alongside economic development.

Keywords: land use changes; spatial analysis; temporal dynamics; urbanization; sustainable land management; survey

1 Introduction

Land use change is one of the significant factors influencing various geographical processes, regional environmental evolution, and Earth’s surface ecological processes [1,2]. Since the 1990s, land use change has remained a central issue in global environmental change research [3,4]. Terrain factors constitute a fundamental element shaping the landscape pattern of land use [5]. They influence the ways and directions in which land use landscapes change, imposing strong spatial constraints on land resource allocation activities [6]. Therefore, the impact of terrain effects on the landscape pattern of land use has emerged as a focal point in the study of regional land use landscape patterns [7]. In recent years, with the expansion of human activities on the Earth’s surface and the increasing degree of interference with natural ecosystems, a series of changes have occurred in both the original natural environment and landscape patterns [8]. To achieve a harmonious coexistence between humanity and the environment and to effectively mitigate ecological risks, the spatiotemporal evolution of land use and regional ecological risk assessment have become a prominent research domain in the current regional environmental management and ecological restoration efforts.

Ecological risk assessment is a method used to evaluate potential adverse ecological impacts on ecosystems and their components under external pressures [4,9,10]. The term “ecological risk assessment” was first coined by the United States Environmental Protection Agency in 1990 [11]. Over the years, the framework of ecological risk assessment has evolved, leading to increased research and maturation of research paradigms. Recently, ecological risk assessment has been moving toward a multidimensional and multiscale approach. For instance, Zhang and Xu [12] analyzed the temporal changes in the ecological risk index of coastal Chinese cities from 1990 to 2000, followed by a reduction from 2000 to 2015. Similarly, Zou et al. [13] emphasized adaptive strategies based on altitude gradients, demonstrating increasing landscape ecological risk in China’s agricultural-pastoral transition zone. Zhang et al. [14] studied the effects of climate change on ecosystems across different elevations on the Qinghai–Tibet Plateau. They employed ecosystem models at various scales and highlighted the vulnerability and adaptability of the plateau ecosystem. Liu et al. [15] conducted a multiscale ecological risk assessment, exploring the evolution and vulnerability of ecosystems from plant populations to ecosystems. As research progresses, modern ecological risk assessment now considers multidimensional aspects such as societal, economic, and demographic elements, resulting in a more comprehensive analysis. For example, Lin et al. [16] developed a multidimensional ecological risk assessment framework that considers environmental pollution, socioeconomic pressures, and population exposure, providing a comprehensive evaluation of ecosystem health and potential risks. Researchers are increasingly integrating diverse assessment models [17] to obtain more accurate and comprehensive ecological risk assessment results. This trend has made ecological risk assessment a pivotal tool for decision-making and risk management [9]. Governments, enterprises, and social organizations are increasingly using ecological risk assessment outcomes to formulate environmental protection policies [18], plan urban development [10], and manage natural resources [10].

While the field of ecological risk assessment has seen substantial development, regional ecological risk assessment, particularly related to land use dynamic changes, has gained prominence. For instance, Baiocchi et al. [19] demonstrated the relationship between resource development stages and land use changes through land use transition probability matrices and landscape pattern indices. Urban development, marked by the expansion of construction land, the reduction in ecological lands like wetlands and grasslands, and increasing landscape fragmentation, has negative ecological impacts on mining cities [20]. Regional ecological risk problems often result from the interaction of multiple factors [21], making prediction of effects and consequences challenging. Consequently, multidimensional assessment and analysis based on varying regional characteristics are necessary [22].

Over the past 30 years, the Huai River-Gaoyou Lake Basin and the coastal areas of Jiangsu Province have achieved remarkable economic progress, playing significant roles in the Yangtze River Delta and even the entire country [23]. However, this achievement has come at a significant ecological cost, including the loss of high-quality farmland, reduction in rivers and lakes, decline in biodiversity, decrease in natural coastlines, severe decrease in species resources, sustained degradation of ecosystem services, and gradual emergence of soil pollution [24]. The growing contradiction between rapid economic and social development and insufficient resource and environmental carrying capacity [25] underscores the long-term ecological risk in this region.

After a comprehensive consideration, the majority of current ecological risk assessment studies primarily focus on analyzing the impacts of the ecological environment on systems [26]. However, there has been relatively limited attention given to the consideration of social residents’ perceptions and satisfaction with the ecological environment [27]. Concurrently, social satisfaction surveys often confine their scope to factors within the realm of socio-economic aspects, while inadequately addressing ecological risk-related factors [28]. This tendency to emphasize the natural environment over human factors may lead to limitations in the assessment outcomes, thus insufficiently guiding environmental decisions. Nonetheless, with the fusion of social science and environmental science, researchers are increasingly recognizing the pivotal role of social residents’ perceptions and satisfaction in ecological risk assessment [29]. This emphasis stems from a deeper understanding of social justice and principles of sustainable development, underscoring the importance of incorporating social aspects into environmental decision-making [30]. By introducing social residents’ perceptions and satisfaction, researchers can gain more precise insights into the actual societal impacts of ecological risks, thereby facilitating the development of more effective risk management strategies [31].

Therefore, in the exploration of the landscape ecological risk index, treating social residents’ satisfaction as an exploratory supplementary study may potentially address some of the research gaps in this field. By delving into residents’ attitudes, viewpoints, and perceptions of the ecological environment, more comprehensive and accurate data can be obtained, enabling a better analysis of the relationship between ecological risk assessment outcomes and societal perceptions. This aids in revealing the interactive connection between ecological risk assessment results and social satisfaction, thereby offering decision-makers more targeted and comprehensive guidance for balancing ecological protection and social development objectives.

In summary, the objective of this study is to conduct comprehensive research on land use and ecological risk assessment in the Gaoyou Lake area. By collecting geographical spatial data, ecological environmental data, and social survey data, we plan to construct a landscape ecological risk index model for the Huai River-Gaoyou Lake region, taking into account ecological risks of different land use types and societal perceptions of the environment. Through analyzing the relationship between model calculation results and social residents’ satisfaction survey data, we aim to explore the connection between ecological risk and societal perceptions, thereby providing more accurate decision-making foundations for a comprehensive approach. This study not only fills a gap in the current research landscape but also offers comprehensive and practical guidance for real-world applications, providing valuable insights for sustainable development in similar regions. By integrating ecological risk assessment with social residents’ perceptions, we can foster a harmonious coexistence between humans and the environment.

2 Methods

2.1 Study area and the “Red-Line” policy

The study area resides within the Huai River-Gaoyou Lake’s rim region, spanning coordinates from 32°34′42″ to 32°30′54″ north latitude and from 119°34′62″ to 119°45′19″ east longitude. It features diverse landscapes, including lakes, wetlands, farmland, and emerging urban zones, on relatively flat terrain. Gaoyou Lake, a sprawling water body spanning 760.67 km2, is enveloped by extensive wetlands. Urbanization and agriculture have left their marks on the environment. The land use mosaic includes lakes, wetlands, cultivated land, forests, and urban areas, woven intricately into a complex tapestry. Lakes and wetlands support hydrological cycles and biodiversity, while the expansion of urban and agricultural areas reshapes the ecological balance.

Guiding the region is the Ecological Red Line Regional Protection Plan of Jiangsu Province, issued by government, designating a 1 km secondary control zone from Gaoyou Lake’s riverbanks. This policy-driven boundary shapes our research focus. Our study zeroes in on the 1 km ecological risk zone, aligning with the Ecological Red Line Regional Protection Plan of Jiangsu Province policy’s confines. This targeted scope allows us to examine the intricate dynamics of ecological risk within this crucial zone. The period from 2010 to 2020 is chosen, coinciding with the policy’s 2010 inception in Jiangsu Province. This strategic decision allows us to delve into the interplay between policy enforcement and land use transformations. We dissect the relationship between conservation efforts and ecological shifts, thus empowering well-informed conservation and sustainable development strategies (Figure 1).

Figure 1 Geographic location of the study area.
Figure 1

Geographic location of the study area.

2.2 Methodology

2.2.1 Data source and processing

This study obtained LANDSAT TM/ETM +/OLI satellite image data from the official website of the U.S. Geological Survey (https://www.usgs.gov/). Images from the years 2000, 2010, and 2020 were selected, captured between June and September, with an average cloud cover below 10%. The spatial resolution of all images was 30 m.

To effectively handle the data, crucial preprocessing tasks were performed using ERDAS software, including geometric correction, radiometric calibration, atmospheric correction, image fusion, mosaicking, cropping, resampling, and regional cropping. Land use classification was conducted following the Land-Based Classification Standards and adjusted based on the actual conditions of the ACT hydrogeological landscapes (HGL) area, resulting in six categories: cultivated land, forest land, grassland, water bodies, built-up land, and unused land. The Maximum likelihood method was employed for land use classification, yielding a Kappa coefficient of 0.76, indicating a high classification accuracy. Through the integration of Geographic Information System (GIS) and Remote Sensing (RS) techniques, an RS interpretation key was established, considering regional landscape features and spectral characteristics. This approach enabled comprehensive interpretation and analysis of RS images from different time periods, ultimately resulting in the creation of detailed land cover maps for the Huai River Basin and the coastal areas of Jiangsu Province.

It is worth noting that although the temporal span covers the years from 2000 to 2020, our detailed investigation primarily focused on the period from 2010 to 2020. This time frame was chosen due to the availability of higher resolution and more accurate RS images, allowing for a more precise assessment of land use changes and their impacts. Our emphasis on the years 2010–2020 facilitates an understanding of recent land use dynamics, ecological changes, socio-perception, and the interplay of underlying driving factors. Additionally, we conducted field investigations along the Gaoyou Lake shoreline and took into account the actual topography. Considering the 1 km range encircling the lake, we believe this specific terrain holds the highest research significance. Thus, we created visual representations of buffer zones within this 1 km radius encircling Gaoyou Lake to assist in facilitating this study.

2.2.2 Construction of landscape ecological risk assessment model

Landscape indices represent the most widely used quantitative research method in landscape ecology. By employing individual or combined indices, the numerical variations characterize ecological significance that describes not only landscape patterns and changes, but also links these patterns to processes [32]. Landscape patterns encompass types, quantities, and spatial distributions of landscape units, which are expressed using landscape pattern indices. Considering the unique wetland ecosystem in the HGL area and the complexity and irregular distribution of landscape types, this study, based on previous research [33] and the characteristics of landscape patterns in the study area, selected landscape fragmentation, separation, and fractal dimension indices to construct the landscape disturbance index [34]. This index was utilized to derive the landscape vulnerability index by assigning values to different land use types. The landscape loss index was calculated by multiplying the landscape disturbance index and the landscape vulnerability index [35]. Finally, by weighting the proportion of different land use types and the landscape loss index of each ecological risk unit, the Landscape ecological risk index for the study area was established [36]. The ecological significance of each landscape pattern index and their calculation methods are presented in Table 1. Using ArcGIS software, the preprocessed land use data (in raster format, 5 km × 5 km) were processed to obtain numerical values for various landscape indices.

Table 1

Ecological significance and calculation methods of landscape pattern indices

Landscape pattern index Ecological significance Calculation formula
Ecological risk index ( ER I k ) Represents the comprehensive level of ecological environment loss in each ecological risk zone. A higher ER I k indicates potential environmental issues and their impacts ER I k = ∑ i = 1 n A ki A k R i
Landscape loss index (R i ) Represents the extent of natural attribute loss of different landscape types when subjected to natural and anthropogenic disturbances. Constructed using landscape disturbance and vulnerability indices R i = U i × V i
Landscape vulnerability index ( V i ) Represents the resistance of landscapes to external disturbances. A lower V i indicates greater vulnerability and poorer ecological stability. Normalized based on land use types Assigned values to six land use types based on regional characteristics and previous research: Unused land = 6, Water body = 5, Farmland = 4, Grassland = 3, Forest =2, Urban land=1, followed by normalization
Landscape disturbance index ( U i ) Represents the intensity of loss landscapes experience from external disturbances. A higher U i indicates greater ecological risk. Moderate to low disturbances can enhance landscape heterogeneity and benefit biodiversity conservation U i = a C i + b S i + c F i
Landscape fragmentation index ( C i ) Represents the degree of landscape fragmentation due to natural and anthropogenic disturbances. A higher value indicates greater disturbance and poorer stability C i = N i A i
Landscape separation index ( S i ) Represents the degree of dispersion of individual patches of different landscape types in the landscape. A higher value indicates more dispersed patches and more complex internal structure S i = A 2 A i × N i A
Landscape dimension index ( F i ) Represents the influence of patch shape on internal ecological processes. A higher value indicates more complex shapes F i = 2 In ( P i / 4 ) In A i

Note: a , b , c are the weights for landscape fragmentation index, landscape separation index, and landscape dimension index, respectively. They are assigned values of 0.6, 0.3, and 0.1 based on existing research [37–39]. ER I k represents the regional landscape ecological risk index of the k -th zone; n represents the total number of landscape types; A ki represents the area of landscape type i in the k -th zone; A k represents the total area of the k -th zone; N i is the number of patches of landscape type i ; A i is the total area of landscape type i ; P i is the perimeter of landscape type i ; C i is the landscape fragmentation index for the i -th type; S i is the landscape separation index for the i -th type; F i is the landscape dimension index for the i -th type.

2.2.3 Geostatistical analysis

Geostatistics, as a methodology, finds extensive application in analyzing the spatial relationships and patterns of various variables. It encompasses aspects such as detection, simulation, and estimation. Among these, semivariance analysis holds a pivotal role within geostatistics, utilizing statistical techniques to investigate spatial characteristics and correlations. Scholars in the past have extensively discussed the advantages of semivariance analysis, particularly its capabilities in exploring spatial correlations and revealing distribution patterns of geographical phenomena [37]. In our study, we employ geostatistical methods to gain a deeper understanding of the spatial distribution characteristics of ecological risks within the study area. To depict this distribution, we make use of spatial analysis and geostatistical functionality within the ArcGIS software. Initially, we assign the risk indices of 460 risk zones to the center points of their respective sample areas. Subsequently, we apply the theory of semivariogram analysis for fitting, enabling spatial interpolation of risk values at the sample points. Ultimately, through this process, we obtain a spatial distribution map depicting the ecological risk within the study area:

(1) γ ( h ) = 1 2 n ( h ) ∑ i = 1 n ( h ) [ Z ( x i + h ) − Z ( x i ) ] 2 .

In the formula, γ ( h ) represents semivariance, h is the lag distance between samples, Z ( x i ) and Z ( x i + h ) are the risk values at positions x i and x i + h , and n ( h ) is the total number of sample pairs at lag distance h .

Semivariance has three fundamental parameters: nugget ( C 0 ), sill ( C 0 + C ), and range ( A 0 ). When h = 0, γ ( h ) equals the nugget C 0 ; as h increases, γ ( h ) approaches a constant value which is the sill ( C 0 + C ), where C represents the structural variance. The nugget C 0 signifies variability due to random factors, while the structural variance C signifies variability caused by spatial autocorrelation. The sill [ C ( 0 + C ) ] reflects the total variability in the study area; higher values indicate greater spatial heterogeneity. The ratio of nugget to sill [ C 0 / ( C 0 + C ) ] indicates the spatial correlation of variables. If it is less than 25%, strong correlation is indicated; if it is greater than 75%, weak correlation is indicated.

Using GIS spatial analysis models, the semivariance function is fitted. Based on this, Kriging is employed to spatially interpolate the risk values in the HGL area. The interpolated risk values are categorized into five levels: low-risk area (ecological risk index; ERI ≤ 0.25), relatively low-risk area (0.25 < ERI ≤ 0.35), moderate-risk area (0.35 < ERI ≤ 0.45), relatively high-risk area (0.45＜ ERI ≤ 0.55), and high-risk area ( ERI > 0.55).

2.3 Survey of land use satisfaction

In order to assess the satisfaction levels of local residents regarding land use in the Huai River-Gaoyou Lake area, a survey was conducted to gather valuable insights. The selection of satisfaction survey indicators was based on a combination of factors aimed at capturing the understanding of residents’ perceptions and opinions regarding land governance in the coast of Gaoyou Lake. The indicators were carefully chosen to encompass various dimensions of land use, economic value, social benefits, and ecological services.

2.3.1 Survey design

The decision to incorporate the “Production–Living–Ecological” (PLE) framework into our supplementary questionnaire was a strategic choice aimed at enhancing the accuracy and comprehensiveness of local residents’ perspectives on land use within the Huai River-Gaoyou Lake area (Table 2). This approach was carefully chosen for several compelling reasons as proposed by Feng et al. [39]. First, by adopting the PLE framework, we ensure a holistic view that covers the economic, social, and environmental dimensions of land use. This comprehensive perspective goes beyond focusing solely on one aspect and allows us to capture a wide range of factors that influence residents’ lives. Second, the PLE framework helps us navigate the complex trade-offs that often arise in land management. It enables us to identify potential conflicts and synergies between production, livelihood, and ecology, shedding light on areas where interests might clash or align. Additionally, this approach assists in understanding the varying priorities of different segments of the population based on their circumstances and values. By evaluating each dimension individually, we gain insights into which aspect holds the greatest significance for specific groups, providing valuable information for policymakers. The PLE framework also highlights the interconnectedness of these dimensions, enabling us to uncover intricate interactions between economic activities, social benefits, and ecological services. This approach not only allows for qualitative insights, as residents can express their opinions on specific indicators, but also offers a more nuanced understanding of their viewpoints. Ultimately, the integration of the PLE framework into our survey design ensures that the results are not only data-driven but also directly relevant to policy formulation and decision-making, paving the way for responsive and effective land management strategies that cater to the diverse needs and aspirations of the community.

Table 2

Evaluation of functional satisfaction in Huai River-Gaoyou Lake area

Function category Function name Explanation
Production function Agricultural supply Evaluation of residents’ satisfaction with the agricultural supply function in the area, to understand the importance of agriculture in economic development and the demand for agricultural products by residents
Industrial production Evaluation of residents’ satisfaction with the industrial production function in the area, to understand the contribution of the industrial sector to economic development and its impact on residents’ lives
Forest product supply Evaluation of residents’ satisfaction with the forest product supply function in the area, to understand the importance of forestry for the economy and livelihoods
Livelihood function Residential capacity Evaluation of residents’ satisfaction with the residential environment in the area, including comfort and community facilities
Transportation convenience Evaluation of residents’ satisfaction with the level of transportation convenience in the area, including public transportation facilities and road conditions
Recreational services Evaluation of residents’ satisfaction with recreational facilities in the area, including parks and entertainment facilities
Ecological function Water conservation Evaluation of residents’ satisfaction with the water conservation function in the area, to understand the importance of water resources for residents and ecosystems
Soil conservation Evaluation of residents’ satisfaction with the soil conservation function in the area, to understand the impact of soil conservation and quality on agriculture and ecology
Climate regulation Evaluation of residents’ satisfaction with the climate regulation function in the area, to understand the role of ecosystems in climate regulation and temperature reduction
Biodiversity conservation Evaluation of residents’ satisfaction with the biodiversity conservation function in the area, to understand the importance of ecosystems in protecting species diversity and habitats

2.3.2 Data collection and processing

The survey involved trained interviewers conducting face-to-face interviews with respondents, who provided subjective scores. Participants received explanations and instructions, with confidentiality assured for unbiased responses. Ratings were given on a 1–5 scale, reflecting satisfaction levels. To ensure robustness, a representative sample was selected – mirroring demographics and perspectives within the Huai River-Gaoyou Lake area. Stratified random sampling divided the population into groups based on demographics, drawing proportional samples to minimize bias. The participants’ mean age of 40.08 years captures a balanced age distribution within Gaoyou. A near-equal gender split of 49% female and 51% male underscores the survey’s commitment to inclusivity. Additionally, the average household size of 3.50 individuals aligns with the typical composition of local households. This congruence between the surveyed sample and the actual population distribution enhances the survey’s relevance and insights, facilitating informed decisions for land use management and planning in the area. Endorsed by the Ethics Research Committee at the University of Southampton, the survey encompassed 200 participants. Following data collection, a thorough analysis was performed. Respondents’ ratings were collated and statistically analyzed. Descriptive metrics like mean values and standard deviations summarized satisfaction across land use aspects. Inferential techniques such as correlation and regression explored connections between satisfaction and demographics. This aimed to uncover patterns illuminating residents’ perceptions in the Huai River-Gaoyou Lake region. Overall, the survey provided direct local insights. Rigorous methodology ensured findings’ credibility. Analyzing outcomes aids understanding and informs future land use strategies.

3 Results

3.1 Land use types in Gaoyou

The current land use status of the study area was analyzed by geometric correction and image masking of multi-source RS imagery. Based on the spectral and texture features of the images, RS image interpretation markers were established for further image processing. The land use in the Huai River-Gaoyou Lake area of Jiangsu Province was classified into six categories: artificial surfaces, forest, wetland, grassland, farmland, and other land types. The spatial distribution and proportions of each land use type were analyzed. Specific data for the land use status in the Huai River-Gaoyou Lake area are as follows (Figure 2):

Figure 2 Landscape of Gaoyou in 2010–2020. (a) Landscape in 2010. (b) Landscape in 2020.
Figure 2

Landscape of Gaoyou in 2010–2020. (a) Landscape in 2010. (b) Landscape in 2020.

Farmland: The area is 8309.2752 square units, accounting for 71.67% of the total area. Farmland is the main land type used for agricultural cultivation and occupies a large proportion in the Huai River-Gaoyou Lake area of the study.

Forest: The area is 12.2364 square units, accounting for 0.11% of the total area. Forest includes forests, forest farms, and areas with high vegetation coverage. The forest area in the Huai River-Gaoyou Lake area of the study is relatively small.

Grassland: The area is 0.1053 square units, accounting for 0.00% of the total area. Grassland refers to areas mainly covered by grass. The grassland area in the Huai River-Gaoyou Lake area of the study is extremely limited.

Waters: The area is 1757.2635 square units, accounting for 15.16% of the total area. Waters include lakes, rivers, and surrounding wetland areas. The water area in the Huai River-Gaoyou Lake area of the study is relatively large.

Bare ground: The area is 0.0207 square units, accounting for 0.00% of the total area. Bare ground refers to land without vegetation coverage. The bare ground area in the Huai River-Gaoyou Lake area of the study is very limited.

Artificial surfaces: The area is 1514.1258 square units, accounting for 13.06% of the total area. Artificial surfaces refer to urban areas, towns, roads, industrial zones, and other artificially constructed areas. The artificial surface area in the Huai River-Gaoyou Lake area of the study is relatively large.

According to the data analysis for the year 2020, farmland is the dominant land type in the Huai River-Gaoyou Lake area, occupying the largest proportion of the total area. Waters and artificial surfaces also occupy significant proportions, while forest, grassland, and bare ground areas are relatively small. These results may be influenced by various factors such as geography, climate, and human activities (Figure 3).

Figure 3 Land use types in Gaoyou City. (a) 2010. (b) 2020.
Figure 3

Land use types in Gaoyou City. (a) 2010. (b) 2020.

3.2 Dynamics of land use changes

In 2010, the main land use types within the 1 km buffer zone along the Gaoyou Lake are shown in Figure 4a, including farmland, forests, waters, artificial surfaces, bare ground and grassland. Among these, farmland held the predominant share, accounting for 62.10% of the total area. This dominance of farmland highlights the crucial role of agricultural activities in the region during that period. Waters followed next, occupying 30.72%, signifying the significance of the lake’s aquatic environment. Artificial surfaces comprised 6.97%, which can be attributed to urban and built-up areas. In contrast, the proportions of forest, bare ground, and grassland were relatively smaller, at 0.10, 0.00, and 0.10%, respectively, suggesting limited coverage of these land use types within the buffer zone. However, a decade later, by 2020, discernible changes in land utilization had taken place (See Figure 4b), reflecting evolving human and environmental dynamics. The area of farmland slightly decreased from 146.7117 to 146.448 km², indicating a reduction of 0.2637 km². This shift might be linked to changing agricultural practices or land conversion for other purposes. Forest area also experienced a minor decline, diminishing from 0.2313 to 0.1836 km², marking a reduction of 0.0477 km². The decrease in forest area underscores potential human activities such as logging or urban expansion encroaching upon forested areas.

Figure 4 Dynamics of land use changes in coastal zone (2010–2020). (a) 2010. (b) 2020.
Figure 4

Dynamics of land use changes in coastal zone (2010–2020). (a) 2010. (b) 2020.

Notably, grassland no longer appeared in the 2020 data, indicating a complete disappearance with an area decrease of 0.2466 km². The absence of grassland might be indicative of land transformation or reclassification into other categories, highlighting the evolving land use dynamics. Waters also underwent reduction, declining from 72.5787 to 68.1066 km², reflecting a contraction of 4.4721 km². The decrease in water bodies might be attributed to factors such as land reclamation or changes in water levels. Bare ground area slightly decreased as well, from 0.0036 to 0 km², indicating a decrease of 0.0036 km². This could be related to land development, construction, or revegetation efforts. Conversely, artificial surfaces expanded from 16.4682 to 21.2517 km², marking an increase of 4.7835 km². The expansion of artificial surfaces suggests urban growth, infrastructural development, and changes in land use patterns driven by human activities.

Taken together, from 2010 to 2020, the changes in land utilization within the 1 km buffer zone along Gaoyou Lake’s coastline reflect a complex interplay of urbanization, agricultural practices, and environmental changes (Table 3). These transformations underscore the need for sustainable land management practices and effective conservation efforts to maintain a balanced and resilient ecosystem, especially in areas closely linked to vital water bodies like Gaoyou Lake.

Table 3

Land use type changes in the Huai River-Gaoyou Lake study area (2000–2020)

2010 2020 Area change
Land type Area (km²) Proportion (%) Area (km²) Proportion (%) Area change (km2)
Farmland 146.7117 62.10 146.448 62.06 −0.2637
Forest 0.2313 0.10 0.1836 0.08 −0.0477
Grassland 0.2466 0.10 0 0.00 −0.2466
Waters 72.5787 30.72 68.1066 28.86 −4.4721
Bare ground 0.0036 0.00 0 0.00 −0.0036
Artificial surfaces 16.4682 6.97 21.2517 9.01 4.7835

3.3 Changes in landscape index

A comprehensive analysis of the shifting landscape indices within the 1 km ecological protection zone along Gaoyou Lake unveils a tapestry of significant trends and transformative shifts, as gleaned from the data. Spanning a decade from 2010 to 2020, the spatial dynamics of this region underwent a notable metamorphosis (Table 4). The following analysis delves into key facets of these shifts: Foremost, delving into the ebb and flow of patch count and patch density, a distinctive trend emerges, revealing the evolving landscape’s inclination towards fragmentation. In 2010, the patch count stood at 2,695, rising to 3,020 in 2020. This coincided with a commensurate rise in patch density, increasing from 13.6012 patches per square kilometer to 15.2414 patches per square kilometer. Evidently, this underscores a propensity for distinct landscape elements to undergo partition into smaller, potentially distinct units, likely influenced by an interplay of human interventions and natural phenomena.

Table 4

Changes in landscape index of Gaoyou Lake Basin primary reserve

Year Number of patches Patch density (km2) Maximum plaque index Fractal dimension Shannon diversity index Degree of separation Shannon evenness index
2010 2,695 13.6012 16.0987 1.0489 0.8506 0.7216 0.6854
2020 3,020 15.2414 11.3349 1.0489 0.8772 0.7414 0.6981

Further spotlighting this transformation is the conspicuous dip in the maximum patch index. Beginning at 16.0987 in 2010 and descending to 11.3349 in 2020, this nuanced alteration hints at a paradigm shift. Specifically, it reflects a transition wherein larger, dominant landscape fragments are gradually ceding ground to smaller, more intricate counterparts. The emergent outcome is a landscape that not only boasts greater intricacy but also teems with a wider array of diversity. In tandem, the evolutionary trajectory of the Shannon diversity index and Shannon evenness index introduces an intriguing duality. The Shannon diversity index, witnessing an uptick from 0.8506 in 2010 to 0.8772 in 2020, is emblematic of an ecosystem becoming more enriched with diversity during this temporal span. In tandem, the slight uptrend witnessed in the Shannon evenness index alludes to an intriguing trend: a discernible wane in the dominion held by prevalent landscape components over the broader landscape framework. This hints at a transformative journey toward a landscape characterized by heightened diversification and an inherent semblance of uniformity in land utilization practices.

In summation, the landscape indices’ transmutations in the 1 km ecological protection zone along Gaoyou Lake unveil a multifaceted panorama of change that has significantly molded the landscape over the past decade. These evolutions are undoubtedly woven into a complex tapestry driven by a nexus of anthropogenic endeavors, natural fluctuations, and the underpinning strategies of conservation and management. A more exhaustive exploration of these evolving trends will undoubtedly yield deeper insights into the catalyzing factors and potentially offer a compass for crafting cogent strategies for ecological preservation and land-use management in the times ahead.

3.4 Ecological risk assessment

Analyzing the data from Table 5 provides us with valuable insights into the landscape type index of the coastal area along Gaoyou Lake in 2020. Beyond the raw numbers, these indices offer a window into the intricate dynamics of the region’s ecological landscape. As we delve deeper into the numbers and consider their broader implications, a more nuanced understanding of the ecosystem’s condition and potential challenges emerges.

Table 5

Landscape type index in the Huai River-Gaoyou Lake area in 2020

Landscape Type Area (km2) Area ratio (%) Number of plaques Fragmentation Separation degree Dominance Fragile degrees Interference degree
Farmland 146.45 0.62 333 2.27 0.53 0.34 0.67 1.56
Forest 0.18 0.00 38 206.97 44.67 0.02 0.50 137.59
Grassland 0.00 0.00 0 0.00 0.00 0.00 0.33 0.00
Waters 68.11 0.29 1,071 15.73 0.44 0.23 0.83 9.59
Bare ground 0.00 0.00 0 0.00 0.00 0.00 1.00 0.00
Artificial surfaces 21.25 0.09 1,578 74.25 0.64 0.18 0.17 44.76

Beginning with the distribution of areas, the dominance of farmland covering 0.62% of the total area offers a glimpse into the paramount role of agricultural activities in the local economy. This dominance underscores the intricate relationship between human activities and the natural environment. Similarly, the modest proportion of water bodies, at 0.29%, echoes the pivotal status of Gaoyou Lake within the local landscape pattern. The presence of water bodies serves as a testament to the intricate balance of aquatic ecosystems in the region. However, the conspicuous scarcity of grassland and bare ground areas raises questions about the potential drivers behind their near-absence. This scarcity could reflect the influence of both human interventions and natural processes shaping the land use dynamics.

Taking a closer look at the individual indices, we uncover key insights into the ecological connectivity and vulnerability of various landscape types. The fragmentation and separation indices shine light on the degree of continuity within landscapes. Farmland and artificial surfaces, with relatively low indices (2.27 and 0.53, respectively), demonstrate some level of spatial coherence. In contrast, the higher indices for water bodies and forests (15.73 and 206.97, respectively) suggest potential challenges in maintaining continuous habitat corridors. This emphasizes the need to explore strategies that enhance the ecological connectivity of these vital landscape elements. By doing so, we can ensure the resilience and health of the broader ecosystem.

Examining the vulnerability and disturbance indices, we gain insight into the landscape’s susceptibility to disturbances. The higher indices associated with artificial surfaces (0.67 and 44.76) paint a picture of heightened vulnerability to disruptions, likely stemming from anthropogenic activities. Conversely, the lower indices for water bodies and forests (0.23 and 0.02, respectively) signal a greater degree of stability within their natural settings. This emphasizes the importance of safeguarding these less disturbed areas, given their potential to act as ecological refuges and provide essential ecosystem services.

Bringing these indices together, we construct an assessment of ecological risk. The notably elevated indices for artificial surfaces, particularly fragmentation, separation, and disturbance (74.25, 0.64, and 44.76, respectively), underscore potential hotspots of ecological risk. This points to the urgency of targeted ecological restoration efforts in these regions to mitigate potential ecological imbalances and disruptions. Conversely, the relatively favorable aspects demonstrated by water bodies and forests should not lead to complacency. The heightened fragmentation and separation indices for these elements could signify emerging challenges that demand proactive management and conservation strategies.

In summary, the landscape type index for the coastal area along Gaoyou Lake in 2020 provides a lens through which we can perceive potential ecological risks. This analysis not only informs us about the present landscape but also guides us in formulating strategies for responsible land management, ecological restoration, and sustainable development. However, it is important to recognize that a more comprehensive understanding requires continuous research, robust data, and collaboration across various disciplines.

4 Discussion

Spatially, there has been an expansion of water bodies and artificial surfaces, indicating urban area growth and infrastructure construction. The growth of urban and industrial areas has led to a decline in farmland as agricultural land is converted for non-agricultural purposes. Additionally, the fragmentation and reduction in forest and grassland areas suggest habitat fragmentation and potential ecological disruptions. Temporally, the analysis of land use data from 2010 to 2020 reveals a decrease in farmland, forests, grasslands, and bare ground, while water bodies and artificial surfaces have expanded.

The driving factors behind these changes are multifaceted. These changes reflect ongoing processes of urbanization and industrialization, as well as advancements in agricultural modernization. The increase in artificial surfaces can be attributed to the demand for residential, commercial, and industrial spaces driven by population growth and economic development. However, in conjunction with our survey results (Table 6), we have discovered more intriguing aspects:

Table 6

Survey results

Function category Function Average score
Production function Agricultural supply 3.8
Industrial production 3.5
Forest product supply 3.7
Livelihood function Residential capacity 4.5
Transportation convenience 4.3
Recreational services 4.1
Ecological function Water conservation 3.9
Soil conservation 3.8
Climate regulation 3.8
Biodiversity conservation 3.6
Comprehensive score 3.9

Regarding the production function, the survey revealed a satisfaction score of 3.8 for the agricultural supply function. This may be attributed to the decrease in farmland, which has been influenced to some extent by the progress of urbanization. The satisfaction score for the industrial production function was 3.5, indicating the impact of industrialization on the environment, such as pollution and resource utilization. The satisfaction score for the forestry supply function was 3.7, suggesting that despite the reduction in forest area, there is still some supply of forest products, demonstrating a certain contribution of the forestry economy to the residents’ lives in Huai River-Gaoyou area.

In terms of the livelihood function, residents expressed the highest satisfaction with the residential carrying capacity, with a score of 4.5. This is likely due to the relatively comfortable living environment and well-developed community facilities in Huai River-Gaoyou. The satisfaction score for convenient transportation was 4.3, reflecting the improvement in transportation infrastructure and convenience in the area. The satisfaction score for recreational services was 4.1, indicating the availability of recreational places and entertainment facilities in Huai River-Gaoyou, providing certain leisure services.

In the ecological function, the satisfaction score for water conservation function was 3.9. Despite the increase in water bodies, there are still challenges in protecting and managing water resources. The satisfaction score for soil conservation function was 3.8, indicating the ongoing concern for land quality and agricultural production. The satisfaction score for climate regulation function was 3.8, reflecting the residents’ expectations of the ecosystem’s role in climate and temperature regulation. The satisfaction score for biodiversity conservation was 3.6, highlighting the residents’ awareness of the importance of preserving species diversity and habitats despite some pressures on biodiversity.

The overall satisfaction score is 3.9, indicating that residents in Huai River-Gaoyou have a relatively high satisfaction with various functions. However, considering the scores for ecological function, which reveal the expansion of artificial surfaces and certain losses in ecosystem services, it suggests that there is still room for improvement in the attention given to environmental protection and sustainable development during the process of economic growth.

In summary, the ecological economy and residents’ quality of life in Huai River-Gaoyou are influenced by multiple factors such as urbanization, industrialization, and agricultural modernization. While significant progress has been made in certain aspects, such as improvements in living environment and transportation convenience, attention should also be given to the protection and sustainable utilization of ecosystems. To achieve harmony between ecological economy and human-nature coexistence, Huai River-Gaoyou should continue to strengthen efforts in ecological environment protection and sustainable development, promote coordination between economic growth and ecological conservation, and enhance public awareness and participation in environmental protection.

5 Conclusion

This study investigated the spatial and temporal changes in land use in the Huai River-Gaoyou Lake area and analyzed the associated landscape patterns. The analysis revealed significant findings regarding land use dynamics in the region. The results indicated an increase in water bodies and artificial surfaces, accompanied by a decrease in farmland, forests, grasslands, and bare ground. These findings reflect the ongoing processes of rapid urbanization and industrialization in the area, driven by factors such as the demand for urban development, industrial expansion, agricultural modernization, and natural resource exploitation.

Furthermore, the study identified variations in fragmentation, dominance, vulnerability, and disturbance across different land use types, providing insights into the landscape patterns associated with land use changes in the Huai River-Gaoyou Lake area. The survey conducted as part of this study provided additional valuable information. The results showed that residents expressed relatively high satisfaction with various land use functions. However, the satisfaction scores, particularly in ecological aspects, reflected negative consequences brought by the expansion of artificial surfaces and certain losses in ecosystem services.

While this study has made significant contributions to understanding land use dynamics in the Huai River-Gaoyou Lake area, there are several limitations that should be addressed in future research. First, the analysis was based on data from a specific time period, and a longer-term analysis covering a broader temporal scale would provide a more comprehensive understanding of land use changes. Additionally, expanding the research to include a larger region would enhance the generalizability of the findings. Future studies should also consider investigating the socio-economic factors driving land use changes and conducting in-depth assessments of ecosystem services to provide a more holistic perspective. Furthermore, exploring the effectiveness of land use policies and identifying strategies for sustainable land management in the study area would be valuable avenues for future research.

In conclusion, this study makes a significant contribution to the realm of land use research by delving into the spatial and temporal transformations, underlying drivers, and landscape configurations within the Huai River-Gaoyou Lake vicinity. The revelations underscore the urgency of adopting sustainable land management measures in response to the swift urbanization and industrialization unfolding in the area. The survey outcomes yield invaluable insights into the contentment levels of residents concerning diverse land use functions. These findings can be wielded by policymakers and land use strategists to make well-informed choices and devise approaches that harmonize economic advancement with ecological integrity. Nevertheless, to surmount the existing constraints, subsequent and more comprehensive analyses of extensive spatial data from shared memory systems are warranted. Such endeavors will not only address the identified limitations but also facilitate a more exhaustive comprehension of the dynamic land use dynamics within the Huai River-Gaoyou Lake region.

Acknowledgements

We appreciate the valuable comments from the anonymous reviewers.

  1. Funding information: The authors state no funding involved.

  2. Author contributions: J.Y. designed the study and L.L. and C.W. carried them out. H.C. and X. Y. developed the survey and collected data. J.Y. prepared the manuscript with contributions from all co-authors.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The data will be available upon reasonable requests.

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Received: 2023-06-03
Revised: 2023-10-09
Accepted: 2023-10-10
Published Online: 2023-12-13

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  120. Pre-collisional architecture of the European distal margin: Inferences from the high-pressure continental units of central Corsica (France)
  121. The interrelation of natural diversity with tourism in Kosovo
  122. Assessment of geosites as a basis for geotourism development: A case study of the Toplica District, Serbia
  123. IG-YOLOv5-based underwater biological recognition and detection for marine protection
  124. Monitoring drought dynamics using remote sensing-based combined drought index in Ergene Basin, Türkiye
  125. Review Articles
  126. The actual state of the geodetic and cartographic resources and legislation in Poland
  127. Evaluation studies of the new mining projects
  128. Comparison and significance of grain size parameters of the Menyuan loess calculated using different methods
  129. Scientometric analysis of flood forecasting for Asia region and discussion on machine learning methods
  130. Rainfall-induced transportation embankment failure: A review
  131. Rapid Communication
  132. Branch fault discovered in Tangshan fault zone on the Kaiping-Guye boundary, North China
  133. Technical Note
  134. Introducing an intelligent multi-level retrieval method for mineral resource potential evaluation result data
  135. Erratum
  136. Erratum to “Forest cover assessment using remote-sensing techniques in Crete Island, Greece”
  137. Addendum
  138. The relationship between heat flow and seismicity in global tectonically active zones
  139. Commentary
  140. Improved entropy weight methods and their comparisons in evaluating the high-quality development of Qinghai, China
  141. Special Issue: Geoethics 2022 - Part II
  142. Loess and geotourism potential of the Braničevo District (NE Serbia): From overexploitation to paleoclimate interpretation
https://doi.org/10.1515/geo-2022-0566
Keywords for this article
land use changes; spatial analysis; temporal dynamics; urbanization; sustainable land management; survey
Creative Commons
BY 4.0

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  119. Dynamic analysis of MSE wall subjected to surface vibration loading
  120. Pre-collisional architecture of the European distal margin: Inferences from the high-pressure continental units of central Corsica (France)
  121. The interrelation of natural diversity with tourism in Kosovo
  122. Assessment of geosites as a basis for geotourism development: A case study of the Toplica District, Serbia
  123. IG-YOLOv5-based underwater biological recognition and detection for marine protection
  124. Monitoring drought dynamics using remote sensing-based combined drought index in Ergene Basin, Türkiye
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  137. Addendum
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  139. Commentary
  140. Improved entropy weight methods and their comparisons in evaluating the high-quality development of Qinghai, China
  141. Special Issue: Geoethics 2022 - Part II
  142. Loess and geotourism potential of the Braničevo District (NE Serbia): From overexploitation to paleoclimate interpretation
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