Research Article |
Corresponding author: Guohang Tian ( tgh@henau.edu.cn ) Corresponding author: Yakai Lei ( lykfjyl@henau.edu.cn ) Academic editor: M. Nazre
© 2022 Mengqi Zhao, Yuan Tian, Nalin Dong, Yongge Hu, Guohang Tian, Yakai Lei.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Zhao M, Tian Y, Dong N, Hu Y, Tian G, Lei Y (2022) Spatial and temporal dynamics of habitat quality in response to socioeconomic and landscape patterns in the context of urbanization: A case in Zhengzhou City, China. Nature Conservation 48: 185-212. https://doi.org/10.3897/natureconservation.48.85179
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With the rapid development of urbanization, the habitat quality (HQ) in urban areas has been eroded. This phenomenon is destroying the balance of ecosystems, triggering the reduction of biodiversity and the decay of ecosystem service functions. The study of the relationship between urbanization and HQ in Zhengzhou City is beneficial for the reference of sustainable urban ecological planning and management. Based on landscape classification data and socioeconomic data for three years, this study analyzes the spatial correlations between socioeconomic and landscape pattern factors and HQ, compares the dynamic changes in the explanatory power of different factors, and explores the joint effects between multiple factors. The results show that: (1) The overall value of HQ index in Zhengzhou City decreased by .10 during 2000–2020, mainly occurring in suburban areas, with a small amount of HQ improvement occurring in the core areas of ecological protection, such as mountains and river channels. (2) The spatial autocorrelation of all influencing factors with HQ increased during this period, while the negative impact from socio-economic sources was stronger than the positive impact from landscape patterns. (3) Intensive human activities lead to a single habitat type, which reduces HQ; rich landscape types and complex landscape composition can enhance HQ. Improving the connectivity of blue-green landscapes helps to attenuate the negative effects of urbanization on HQ. (4) Changes of HQ in the study area and the development of multi-factor effects on HQ are driven by the Zhengzhou Metropolitan Area Plan. Urban development policies and management can build idyllic complexes at the edge of urban development, preserving pristine blue-green patches to avoid their homogenized distribution and thus slowing the decline of HQ. The above results provide new ideas for the development of sustainable urban ecology.
Landscape pattern, policy, socioeconomic, urbanization, Zhengzhou Metropolitan Area Plan
Habitat refers to the environment in which organisms live, and habitat quality (HQ) measures the ability of an ecosystem to provide conditions for individuals and populations to survive and reproduce (
Achieving regional ecological sustainability requires exploring the mechanisms by which urbanization affects ecosystem structure and function. Therefore, the responsive relationship between urbanization and HQ has attracted the attention of many scholars. The InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model is commonly used to quantify HQ in recent studies (
In related studies, when analyzing the influence of multiple influencing factors on HQ, SPSS correlation analysis was applied to screen the influencing factors with strong influence on HQ (
As China’s new first-tier city and one of the country’s major transportation hubs, Zhengzhou City is a typical example of urbanization development with its high population flow and rapid urban renewal (
Zhengzhou City is the capital of Henan Province (34°16'N–34°58'N, 112°42'E–114°14'E) and is located in the central-northern part of Henan Province. With a continental monsoon climate and four distinct seasons, it is hot and rainy in summer, but cold and dry in winter. The terrain is high in the west and low in the east, with plains and inclined plains dominating the whole territory, while the western mountainous areas belong to the Funiu Mountains and the rivers in the territory belong to the two major water systems, the Yellow River and the Huaihe River (
The 30 m resolution landscape classification data for 2000, 2010 and 2020 were obtained from GlobleLand30 (http://www.globallandcover.com, accessed on 29 November 2021) released by the Ministry of Natural Resources of China, using the multispectral images without or with few clouds in the vegetation growing season as the information source, and classifying the land use types according to land use attributes and natural attributes. It is divided into 10 primary land use types, and after data merging and clipping, a total of 6 primary land use types are covered in the study area, namely, arable land, forest, grassland, wetland, water, and construction land, with a classification accuracy of more than 83%. The specific classification description is shown in Appendix
The InVEST model assesses the variability and distribution of HQ in the study area based on the sensitivity of different habitat types to stressors and the intensity of external threats to them, and evaluates the biodiversity service function of ecosystem in the study area by the level of the HQ index (
(1)
(2)
where Qxj is the HQ of raster image element x in landscape type j, Hj is the habitat suitability, Dxj denotes the habitat threat level, k is the half-saturation constant, usually taken as half of the maximum value of Dxj, z is the normalization constant, usually taken as 2.5, R denotes the number of threat factors, y is all raster image elements of threat r, Yr indicates the total number of raster image elements occupied by r, ωr is the weight, βx is the is the legal accessibility of raster image element x, Sjr is the sensitivity of land cover j to threat factor r, irxy means the coercive effect of raster image element y on habitat raster image element x.
In landscape classification, the more primitive, complex and large continuous ecosystems have higher suitability and stability, while land types with high intensity of human activities are more likely to threaten the surrounding habitats with strong expansiveness and need to be extracted as threat sources (
Threat factor | dr_max/km | Weight/ωr | Distance-decay function |
---|---|---|---|
Cropland | 4 | 0.5 | exponential |
Construction Land | 8 | 0.9 | exponential |
Landscape code | Habitat type | Habitat suitability | Cropland | Construction Land |
---|---|---|---|---|
10 | Cropland | 0.5 | 0 | 0.5 |
20 | Forest | 1 | 0.6 | 0.4 |
30 | Grassland | 0.8 | 0.8 | 0.6 |
50 | Wetlands | 1 | 0.4 | 0.9 |
60 | Water area | 0.9 | 0.4 | 0.4 |
80 | Construction Land | 0 | 0 | 0 |
The landscape pattern indicators reflect the dynamic changes of the ecosystem under the influence of urbanization as indirect influence factors, and the socio-economic indicators reflect the direct influence of socio-economic development on the ecosystem as direct influence factors. Referring to the relevant literature (
Category | Metrics | Abbreviation | Description |
---|---|---|---|
Landscape pattern | Edge density | ED | Reflects the degree of differentiation or fragmentation of the overall landscape patches.( |
Contagion index | CONTAG | Reflects the degree of agglomeration or extension trend of the plaque. | |
Shannon’s diversity index | SHDI | Reflects landscape heterogeneity.( |
|
Patch density | PD | The number of patches in unit area. | |
Socio-economic | Population density | POP | The number of people per square kilometer. |
Night time light | NTL | Reflects the activity and agglomeration of socio-economic activities. | |
Land urbanization rate | LUR | Proportion of urban land to urban-rural construction land.( |
The application of grid analysis can describe, compare, and analyze regional geographic phenomena in equivalent spatial conditions. 1 km × 1 km grid scale is often applied in articles for studying land use change (
The spatial weight matrix is constructed by GeoDA software to define the spatial relationship between grids, and the queen contiguity is selected to construct the spatial weights with the grid number as the variable, with the following rules:
(3)
where n denotes the number of spatial units, wij denotes the adjacency between region i and j. If they have a common boundary or point, the value is 1, otherwise, the value is 0.
Bivariate spatial autocorrelation analysis can reflect the degree of association between two attribute values of a spatial unit (
(4)
(5)
(6)
where I is the Moran’s I index; n is the number of spatial cells, xi and xj are the observed values of cells i and j, respectively, and wij is the spatial adjacency of cells i and j. S2 is the variance of the observed values. I takes values between [-1,1], and values less than 0 indicate negative spatial correlation, greater than 0 indicate positive spatial correlation, where equal to 0 indicates no correlation and random distribution in space.
The Geodetector can avoid the covariate interference of multiple factors and compare the magnitude of the driving force or explanatory force of multiple influencing factors on the geospatial distribution of something based on spatial heterogeneity (
(7)
where: q is the explanatory power; nk and n are the number of samples within type h of factor A and within the entire study area, respectively; σk2 and σ2 are respectively the discrete variance within type h of factor A and within the entire study area. q takes values between [0,1], and larger values of q indicate greater explanatory power of factor A.
As shown in Fig.
From 2000 to 2020, the area of the low HQ changed greatly, increasing by 1451.68 km2, with a percentage increase of 19.15%; the relatively low HQ zone and high HQ zone showed a decreasing trend, decreasing by 1401.16 km2 and 70.42 km2, with a percentage decrease of 18.45% and 0.92%, respectively, where the high HQ zone showed fluctuating changes. The relatively high HQ zone had the smallest change with an increase of 17.42 km2 and a percentage increase of 0.23% (Table
Classification | Value | 2000 | 2010 | 2020 | |||
---|---|---|---|---|---|---|---|
Area/km2 | Percentage/% | Area/km2 | Percentage/% | Area/km2 | Percentage/% | ||
Low habitat quality | <0.25 | 615.63 | 8.12 | 1237.70 | 16.32 | 2067.30 | 27.27 |
Relatively low habitat quality | 0.25~0.5 | 6021.31 | 79.39 | 5360.43 | 70.68 | 4620.15 | 60.94 |
Relatively high habitat quality | 0.5~0.75 | 52.40 | 0.69 | 54.37 | 0.72 | 69.81 | 0.92 |
High habitat quality | >0.75 | 895.17 | 11.80 | 932.00 | 12.29 | 824.75 | 10.88 |
HQ changes in Zhengzhou City from 2000 to 2020 were calculated by using the ArcGIS software, through the natural break method the results were classified into five categories: significant decrease, slight decrease, no significant change, slight increase, and significant increase (Fig.
Moran’s I indices for seven sets of bivariate variables were obtained using GeoDA software, after 999 random permutations, all of them passed the z-test (p = 0.001), indicating a significant spatial autocorrelation between the bivariate variables at the 99.9% confidence level.
As shown in Table
From Fig.
Among the landscape pattern factors, the distribution and development trend of PD and ED are similar, the H-H cluster is mainly in the western mountainous area, the H-H cluster is surrounded by the H-L outlier in 2000, the H-H cluster gradually expands and the H-L outlier gradually decreases in 2010, and the H-H cluster has been distributed in a continuous pattern in the western part of Zhengzhou City in 2020. There were also many similarities between CONTAG and SHDI. CONTAG and SHDI were dominated by H-L outlier in 2000, which were scattered in the study area, and H-H cluster appeared in the western and northeastern parts of the study area, and then turned out to be dominated by H-H cluster. H-H cluster of CONTAG developing to the southwest and H-H cluster of SHDI clustering steadily in the west, the H-L and L-H outlier scattered at their edges. The L-L cluster of all four landscape pattern indices are increasing in size with the direction of urban expansion and moving to the southeast.
Among the socio-economic factors, the NTL and LUR aggregation area development is more consistent. In 2000, their L-H outlier was mainly distributed in the central part of the study area to the north, and in 2010, they expanded to the south, and in 2020, they were concentrated in the study area in a south-central direction, and a small number of H-H clusters appeared in the suburban areas at the edge of the city. The H-L outlier was distributed around the L-H outlier in 2000, gradually decreasing in size in 2010, then becoming concentrated in the western and northern parts of the study area in 2020. There is less variation in POP, with the L-H outlier mainly in the central part of the study area to the north and the H-L outlier mainly in the western, southwestern and northern parts of the study area, with a significant decrease in the H-H cluster and a small expansion in the other agglomerations over the 20-year period.
The above shows that from 2000 to 2020 the development intensity of the landscape pattern factor, which is positively correlated with HQ, is lower than that of the socioeconomic factor, which is negatively correlated. Besides, the influence of socioeconomic and landscape pattern on HQ has different development direction and magnitude in space and time. The west and the north are the main sites for HQ protection, while the southeast is the key area for urban expansion and intensive development. In the future, metropolitan construction requires zoning plans for the development direction of different areas.
According to the results of the factor detector in the Geodetector, the average deterministic powers (q-value) of the seven driving factors were ranked in descending order: NTL > LUR > PD > POP > ED > SHDI > CONTAG.
In Fig.
Overall, the mutual gap between NTL and LUR is narrowing, and the growth trend of landscape pattern indices is similar. During the 20-year period, the determinants of NTL and LUR respectively increased by 0.21 and 0.20, the determinants of the four landscape pattern indices increased by less than 0.05, and the determinants of POP decreased by 0.06. The overall influence of the socio-economic factors was greater than the landscape pattern factors, denoting that the socio-economic factors have a more prominent influence on HQ.
The results of the ecological detector and interaction detector are shown in Table
Year | ED | CONTAG | SHDI | PD | POP | LUR | NTL | |
---|---|---|---|---|---|---|---|---|
2000 | ED | |||||||
CONTAG | N† | |||||||
SHDI | N | Y† | ||||||
PD | Y | Y† | Y | |||||
POP | Y† | Y† | Y† | N | ||||
LUR | Y† | Y† | Y† | N | N | |||
NTL | Y† | Y† | Y† | Y† | Y | Y | ||
2010 | ED | |||||||
CONTAG | N† | |||||||
SHDI | N | Y† | ||||||
PD | Y | Y† | Y | |||||
POP | Y† | Y† | Y† | N | ||||
LUR | Y† | Y† | Y† | Y | Y | |||
NTL | Y† | Y† | Y† | Y | Y | N | ||
2020 | ED | |||||||
CONTAG | N† | |||||||
SHDI | N | Y† | ||||||
PD | Y | Y† | Y | |||||
POP | N | N | N | N | ||||
LUR | Y | Y† | Y | Y | Y | |||
NTL | Y | Y | Y | Y | Y | N |
In summary, the spatial and temporal distribution of HQ in Zhengzhou City is influenced by a combination of socioeconomic and landscape pattern factors, and the influence of most factors is increasing year by year, but the influence of socioeconomic factors is dominant.
4.1.1.The variation of HQ
HQ in the study area showed a distribution as “high in the northwest and low in the southeast”. With the expansion and construction of Zhengzhou metropolitan area, the urban land gradually evolved from point distribution to continuous distribution in patches, and the agricultural land and forest land at the edge of the city were transformed into construction land. The suburban area is also the main area of reduced HQ, as the flat topography of the central to southeastern part of the study area facilitates the laying and upgrading of traffic routes (
The results showed that the socio-economic factors in the study area had a negative relationship with HQ, and the landscape pattern factors had a positive relationship with HQ. Besides, the deterministic power and spatial aggregation of all influencing factors was increasing year by year, with the strongest explanatory power of NTL, LUR, and PD. The NTL represents the degree of gathering of human activities, and the LUR represents the urbanization ratio per unit area. The higher the NTL and LUR, the more intensive the human activities, the larger the artificial surface area, the more homogeneous the habitat type and the lower the HQ, and vice versa. PD represents the number of patches, the more blue and green patches per unit area indicates the proximity to the natural habitat gathering area, low urban development, high ecological land preservation and good HQ, while the more impervious patches indicate the proximity to the main urban area, high urban development, high ecological land destruction and low HQ, the larger total number of patches the more complex the landscape composition and the higher HQ. CONTAG represents the connectivity of patches, and in the study area CONTAG in combination with either factors showed an effect of increased explanatory power, indicating that blue-green landscape connectivity has an important contribution to HQ.
In a similar study, four landscape pattern indices, including ED and SHDI, also showed significant positive correlations with HQ in the Beijing-Tianjin-Hebei region of China, although the strength of the correlations was weakening year by year (
Changes in socioeconomic indicators and landscape pattern indices mainly originate from policy formulation and implementation, and reasonable policy planning can balance regional development and ecological environment protection (
The response of HQ to urbanization in the study area also corresponds to the content of policy implementation during the same period. After the approval and implementation of the General Land Use Plan of Zhengzhou City (1997~2010), the government has increased the protection of nature reserves, forest parks, wetland parks and water source protection areas based on the existing Songshan Mountain National Forest Park and Yellow River Wetland, and has improved the level of watershed management based on the Yellow River and Huaihe River water system. It has been vigorously promoting the integration process of counties (cities) and districts such as Zhongmou County, Xingyang City, Shangjie District and Xinzheng City with the central city, and accelerating the development of Zhengdongxinqu (it is an independent economic zone) to the east (
Excessive resource exploitation and economic growth will inevitably lead to an ecological crisis, which will in turn lead to the collapse of human society (
Since the choice of research scale affects the development of urban planning schemes (
This paper assesses the change of HQ in Zhengzhou City from 2000 to 2020, analyzes the spatial correlation between HQ and different influencing factors, and compares the magnitude of the explanatory power and the strength of the joint effect of the influencing factors, finally obtaining the following conclusions:
This study provides a clearer picture of the differences in landscape patterns and socioeconomic development on HQ, and denotes that the synergistic construction of construction land and blue-green space driven by policies will contribute to the improvement of HQ, which has important implications for the planning and design of urban regionalization and the sustainable development of ecosystems.
The data presented in this study are available on request from the first author.
The authors would like to thank the support of the International Joint Laboratory of Landscape Architecture, Henan Agricultural University, for their infinite help.
This research was funded by the National Natural Science Foundation of China (grant number: 31600579), Key Technology Program of Henan Province (grant number: 162102310093), 2020 Training Program for Young Backbone Teachers in Higher Education Institutions in Henan Province “Impact of Multi-scale Green Space Planning and Design on Public Health” (grant number: 2020GGJS049), International cooperation research program of Henan province (grant number: HNGD2021035), Research on the whole process of online and offline hybrid international joint training model for graduate students in Landscape Architecture (grant number: 2021SJGLX162Y), and International Joint Laboratory of Landscape Architecture in Henan Province.
Code | Classification | Description |
---|---|---|
10 | Cropland | Land used for growing crops, including paddy fields, irrigated dry land, rain-fed dry land, vegetable land, pasture land, greenhouse land, land with fruit trees and other economic trees between mainly planted crops, as well as tea plantations, coffee plantations and other shrubs for cash crops. |
20 | Forest | Land covered by trees with more than 30% canopy cover, including deciduous broadleaf forest, evergreen broadleaf forest, deciduous coniferous forest, evergreen coniferous forest, mixed forest, and open forest land with a canopy cover of 10–30%. |
30 | Grassland | Land covered by natural herbaceous vegetation with a cover greater than 10%, including grasslands, meadows, savannas, desert grasslands, and urban artificial grasslands, etc. |
50 | Wetlands | Land located in the border zone between land and water, with shallow standing water or excessively wet soil, mostly with boggy or wet plants growing. Includes inland bogs, lake bogs, river floodplain wetlands, forest/shrub wetlands, peat bogs, mangroves, salt marshes, etc. |
60 | Water area | The area covered by liquid water in the land area, including rivers, lakes, reservoirs, ponds, etc. |
80 | Construction land | The surface formed by artificial construction activities, including towns and other types of residential land, industrial and mining, transportation facilities, etc., excluding continuous green areas and water bodies within the construction site. |
Abbreviation | Metrics | Calculation formula | Notes |
---|---|---|---|
ED | Edge density | E is the total edge length of the patches within the landscape; A is the total area of the landscape. Pi is the percentage of area occupied by type i patches; gik is the number of type i patches and type k patches adjacent to each other; m is the total number of landscape patch types. NP is the number of patches. | |
CONTAG | Contagion index | ||
SHDI | Shannon’s diversity index | ||
PD | Patch density | ||
POP | Population density | r is the population size; S is the area. | |
LUR | Land urbanization rate | ul is the scale of urban land use; il is the scale of industrial and mining land use; tl is the scale of transportation land use; rl is the scale of rural settlement land use. |
Interactive Types | Description |
---|---|
q (x1 ∩ x2) > q (x1) + q (x2) | Nonlinearly enhanced |
q (x1 ∩ x2) = q (x1) + q (x2) | Independent |
q (x1 ∩ x2) > Max (q (x1),q (x2)) | Bilinearly enhanced |
Min (q (x1),q (x2)) < q (x1 ∩ x2) < Max (q (x1),q (x2)) | Unique nonlinearly weakened |
q (x1 ∩ x2) < Min (q (x1),q (x2)) | Nonlinearly weakened |
Notes on the data
Data type: pdf. file
Explanation note: This file contains link to download the datas used in the paper and the description of the datas.