Response of Ecosystem Service Value to Landscape Pattern Changes under Low-Carbon Scenario: A Case Study of Fujian Coastal Areas

Cai, Guo; Lin, Yuying; Zhang, Fazi; Zhang, Shihe; Wen, Linsheng; Li, Baoyin

doi:10.3390/land11122333

Cited by 13 publications

(11 citation statements)

References 52 publications

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“…Based on the equivalent scale of ecosystem service value per unit area of the Chinese terrestrial ecosystem established by Xie Gaodi et al [33]. Based on the type of land use in the study area, the built-up land service value equivalent was set to 0 [2,5,53]. Based on the equivalence coefficient method, the ecosystem service value of Heilongjiang Province was assessed (Equations ( 1) and ( 2)).…”

Section: Esv Evaluationmentioning

confidence: 99%

Section: Esv Evaluationmentioning

confidence: 99%

“…Ecosystems, as the unity of life and environment on Earth, are not only the basis for human survival and development, but also provide the material basis for the formation and maintenance of ecosystem services and functions [1]. Ecosystem services are life support products and services that humans obtain directly or indirectly through the structures, processes and functions of ecosystems [2][3][4][5]. The concept of Ecological Services Value (ESV) was derived by assigning quantifiable value attributes to ecosystem functions.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Scenario Land Use Change Simulation and Spatial Response of Ecosystem Service Value in Black Soil Region of Northeast China

Jiang

Teng

et al. 2023

Land

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Simulating the spatial response of ecosystem service value (ESV) caused by land use change in Heilongjiang Province under different scenarios in the future is of great significance for ensuring ecological security and sustainable development in the region. Heilongjiang Province, an important grain-producing region in China, is taken as the research area in this study. Five land use maps (2000, 2005, 2010, 2015, and 2020) were used to evaluate the change of ecosystem service value in Heilongjiang Province in the past 20 years. In addition, the area of each land use type under different future scenarios was predicted by Markov model and MOP model, the future land use pattern was simulated based on PLUS model, the ESV under different scenarios was calculated, and the spatial distribution and the degree of ESV clustering were further explored. The results show that: (1) During 2000–2020, the built-up land in Heilongjiang Province continued to grow, with a total increase of 5076 km2 during the 20-year period, the area of water continued to grow, the area of unused land gradually decreased, and the area of farmland and forest land changed less. (2) During the study period, the ESV in 2000, 2005, 2010, 2015, and 2020 were 1320.8 billion yuan, 1334.5 billion yuan, 1342.1 billion yuan, 1327.6 billion yuan, and 1328.4 billion yuan, respectively. Generally, it shows a fluctuating trend. (3) The ESV of natural development scenario (NDS), economic priority scenario (ERS) and ecological protection scenario (EPS) are 1334.3 billion yuan, 1254.8 billion yuan and 1455.6 billion yuan, respectively. The ESV of different scenarios is quite different. (4) The spatial distribution of ESV was higher in the northwest, central, and southeast, and lower in the east and west. The hot and cold spots of ESV are widely distributed and the degree of polymerization is high. The methods and conclusions of this study can provide scientific reference for the optimization of national spatial pattern and the formulation of sustainable development policy.

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Section: Esv Evaluationmentioning

confidence: 99%

Section: Esv Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Scenario Land Use Change Simulation and Spatial Response of Ecosystem Service Value in Black Soil Region of Northeast China

Jiang

Teng

et al. 2023

Land

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“…Dynamic changes in landscape patterns are closely related to ESV [15,16]. In recent years, research on the connections between ecosystem services and landscape patterns has become increasingly popular for large-scale areas such as cities [17][18][19][20], river basins [21][22][23], wetlands [24][25][26], and coastal zones [27]. Additionally, researchers have focused on small-scale areas, such as central urban areas [28], ecological protection areas [29,30], forests [31,32], farmlands [33,34], citrus orchards [35], and other areas.…”

Section: Introductionmentioning

confidence: 99%

Identifying the Relationships between Landscape Pattern and Ecosystem Service Value from a Spatiotemporal Variation Perspective in a Mountain–Hill–Plain Region

Han,

Li,

Wei

et al. 2023

Forests

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Identifying the changes in landscape pattern and ecosystem service value (ESV) and clarifying their relationship in temporal changes and spatial variations can provide insight into regional landscape features and scientific support for regional landscape planning. Leveraging land use data from the Yihe River Basin, we quantitatively assessed the landscape pattern and ESV shifts spanning from 2000 to 2018 using the landscape pattern indexes and the equivalence factor method. We employed Pearson correlation metrics and the geographically weighted regression model to explore the interrelation of their spatiotemporal variations. Our results show the following: (1) Forestland represents the most expansive land cover category. Apart from construction land, all other types experienced a decline in area. The most notable change occurred in the area of construction land. (2) The aggregation of the overall landscape shows a downward trend. The levels of fragmentation, landscape diversity, and richness increased. (3) Throughout the entire study period, the overall ESV gradually decreased, and the land cover type with the greatest contribution to the ESV was forestland. (4) In terms of temporal changes, the patch density and edge density of the overall area are significantly negatively correlated with total ESVs. The largest values for the patch index, perimeter–area fractal dimension (PAFRAC), and aggregation are significantly positively correlated with total ESVs. (5) In terms of spatial variation, the contagion index (CONTAG), PAFRAC, and the Shannon diversity index (SHDI) were noticeably correlated with ESVs. The CONTAG is positively correlated with ESVs upstream, but negatively midstream and downstream. The SHDI is negatively correlated with ESVs upstream, but positively midstream and downstream. The PAFRAC exhibits a positive correlation with ESVs for the most part. The association between the landscape pattern indexes and ESVs exhibits temporal and spatial inconsistencies in most instances, suggesting a spatiotemporal scale effect in their relationship. This study recommends that the local government devises a long-term strategy for urban development and exercises stringent control over the unregulated expansion of construction land. Through reasonable territorial spatial planning, government departments could enhance the connectivity of the overall landscape pattern of the Yihe River Basin to achieve the reasonable allocation and sustainable development of regional resources.

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“…The relevant studies [40][41][42][43][44] have mostly concentrated on multi-scenario simulations for areas of South China and North China, such as Shenzhen City [45], Chongqing City [46], Tibet [47], and the middle and lower reaches of the Yangtze River [48][49][50], with less research on green space in Northeast China. Cai et al [50] analyzed the changes in landscape pattern and ESV in coastal areas of Fujian over the past 20 years using a patch generation land use simulation model, landscape pattern index, and ecological service value estimation method. Yang et al [51] forecasted the land use change and landscape pattern of Zhangjiajie in 2030 using GeoSOS-FLUS software and proposed an optimization strategy for the future development of Zhangjiajie City.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scenario Simulation of Green Space Landscape Pattern in Harbin City Based on FLUS Model

Gao

2023

IJERPH

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In this study, the change in green space in different scenarios and the index characteristics of landscape patterns were analyzed and were conducive to providing the decision basis for future green space planning in Harbin, a city in Northeast China. The FLUS model was used to predict the layout of green space, and the prediction results were analyzed and evaluated using the landscape index method. Combined with the MOP model and LINGO12.0, the objective function of economic benefit and ecological benefit was established to maximize the comprehensive benefit. As revealed by the outcome, from 2010 to 2020, the fragmentation degree of cultivated land, forest, and grassland decreased, and the overall landscape level tended to be diversified and uniform. In the status quo scenario, the cultivated land and the forest land were increased, whereas the water area and the wetland changed little, and its overall benefit was the lowest. The forest was increased by 137.46 km² in the ecological protection scenario, the largest among the three scenarios, and the overall water quality improved. In the economic development scenario, the cultivated land tended to expand rapidly, the connectivity was increased, and the area of forest was decreased by 69.19 km², and its comprehensive benefit is lower than that under the scenario of ecological protection. The sustainable development scenario achieved the most significant economic and ecological benefits, with a total income of CNY 435,860.88 million. Therefore, the future green space pattern should limit the expansion of cultivated land, maintain the spatial pattern of woodland and wetland, and enhance the protection of water area. In this study, Harbin green space was studied from different scenario perspectives, combined with landscape pattern index and multi-objective planning, which is of great significance for Harbin green space planning decisions in the future and improving comprehensive benefits.

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Response of Ecosystem Service Value to Landscape Pattern Changes under Low-Carbon Scenario: A Case Study of Fujian Coastal Areas

Cited by 13 publications

References 52 publications

Multi-Scenario Land Use Change Simulation and Spatial Response of Ecosystem Service Value in Black Soil Region of Northeast China

Multi-Scenario Land Use Change Simulation and Spatial Response of Ecosystem Service Value in Black Soil Region of Northeast China

Identifying the Relationships between Landscape Pattern and Ecosystem Service Value from a Spatiotemporal Variation Perspective in a Mountain–Hill–Plain Region

Multi-Scenario Simulation of Green Space Landscape Pattern in Harbin City Based on FLUS Model

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