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Article

Investigating the Changes in Urban Green-Space Patterns with Urban Land-Use Changes: A Case Study in Hangzhou, China

by
Jiadi Yin
1,2,
Ping Fu
1,*,
Ali Cheshmehzangi
3,
Zhichao Li
2 and
Jinwei Dong
2
1
School of Geographical Sciences and Geospatial Research Group, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China
2
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo 315100, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(21), 5410; https://doi.org/10.3390/rs14215410
Submission received: 14 September 2022 / Revised: 24 October 2022 / Accepted: 25 October 2022 / Published: 28 October 2022
(This article belongs to the Special Issue Geo-Information and Integration for Smart and Friendly Cities)
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Versions Notes

Abstract

:
Urban green space (e.g., parks, farmland, gardens, etc.) design in different urban functional regions (e.g., residential land, commercial land, etc.) depends on different planning purposes. The changes in urban green spaces are highly related to urban land-use changes (e.g., from residential land to commercial land). However, the investigation of urban green space patterns in response to urban land-use changes has been ignored. This research takes Hangzhou city, a typical example in terms of urbanization, population growth, economic development, and land-use changes, as the study site, aiming to explore the landscape patterns of urban green space changes with different urban land-use changes. The results showed that urban green spaces increased from 2017 to 2021, and the growth was mainly concentrated in the urban core area, indicating that the city has made remarkable achievements in the planning of green spaces. Specifically, the increase in urban green spaces in the first ring belt was mainly related to the old town transformation program in the residential land. The change from the residence parcels to the business parcels determined the increase in green spaces in the second and third ring belts, probably because of the attractiveness of customers. In addition, a large number of open-space parcels have changed to business parcels around the urban periphery, which might be due to the transformation from farmland to impervious surfaces. Combined with the urban land-use and green-space policies, the findings highlighted that a reasonable urban land-use layout can promote the optimization and layout of urban green spaces. The private sector (e.g., shopping malls) can also contribute to the increase in green spaces. The understanding of urban green landscapes with different urban land-use changes can provide references for analyzing and optimizing green space in other cities experiencing rapid urban land-use changes.

1. Introduction

According to the UN Department of Economic and Social Affairs (UNDESA), Sustainable Development Goal (SDG) 11.7 aims to provide universal access to accessible urban green spaces [1,2,3]. Upgrading poor-quality urban green-space landscapes, especially for undeveloped regions, is thus needed. The urban green-space planning and management of most regions in China are still at the initial stages and have ample space for further improvement [4,5]. Large and medium-sized cities have an advantage in terms of governmental support, economic investment, and practical research for green-space planning and management [6,7]. For instance, Hangzhou has dedicated itself to protecting urban green space to realize sustainable development [8,9]. Urban land use and greening policies such as “the old town transformation” and “Hangzhou urban green space system plan” are formulated to develop urban green-space landscapes [10]. The understanding of urban green-space patterns in response to urban land-use changes in these cities is valuable for guiding small cities to improve their urban green-space system.
Urban green space refers to all urban land covered by vegetation of any kind, e.g., parks, forests, farmland, and gardens [11,12]. It contains a variety of benefits including physical health benefits, psychological health benefits, socioeconomic benefits, and environmental benefits [12,13,14,15]. For instance, the increasing area of urban green space is highly correlated with the reduction in urban heat islands (UHIs) [16]. Moreover, there is a strong correlation between urban green space quantity and gross domestic product [17]. The rapid urbanization in China significantly affects urban green-space patterns, causing an increase in exposure to the ecosystem and environmental hazards (e.g., urban heat islands, urban flooding, urban heat waves) [18,19,20].
Previous efforts mainly focused on the investigation of urban green-space patterns in response to urban land-cover changes (i.e., physical aspects of urbanization) [21]. However, few studies have examined the urban green-space patterns with urban land-use changes (e.g., socioeconomic aspects of urbanization). In this context, this research first obtained the urban land-use maps in 2017 and 2021 based on a random forest (RF) classifier by integrating the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). Then, we calculated the normalized difference vegetation index (NDVI) index of Gaofen-2 images to extract the urban green space information from 2017 and 2021. Finally, we investigated the landscape patterns of urban green spaces with different urban land-use changes by comprehensive analysis (e.g., landscape analysis, hotspot analysis, and green-space ratio analysis). The integration of approaches focusing on different perspectives can provide a more holistic view of understanding the urban green-space changes and responses to the urban green-space changes.

2. Materials and Methods

2.1. Study Site

Hangzhou is located in the northwestern part of Zhejiang province, which plays a significant role in the Yangtze River Delta Urban Agglomeration in East China. Hangzhou comprises 10 districts, 1 county-level city, and 2 counties. This study only focuses on the most central urban districts, which are Shangcheng District, Gongshu District, and part of Xihu District. This research selects the area surrounded by the third ring (i.e., the Hangzhou belt highway) as the study site (Figure 1). The study area was divided into three units including the 1st ring belt, 2nd ring belt, and 3rd ring belt for further analysis. The population in this region accounts for 45% of the total population in Hangzhou city, which is one of the most representative regions of urban land-use change and socioeconomic development [22,23]. In the past three decades, large amounts of farmland and bareland around the urban periphery changed to construction land, inspired by rapid urbanization [24]. On the other hand, large amounts of green spaces including vertical and horizontal green spaces in internal urban zones are gradually increasing.

2.2. Datasets

2.2.1. Gaofen-2 Images

GF-2 images were produced by the China Academy of Space Technology, with a Ground Sampling Distance of 0.81 m in panchromatic and 3.24 m in multispectral bands on a swath of 45 km [23]. GF-2 imagery is an ideal data source for urban green-space classification. This research uses two scenes of GF-2 high-resolution images in Hangzhou, which were acquired on 10 October 2017 and 27 September 2021, respectively. Both scenes were preprocessed with a quick atmospheric correction method and geometrical rectification in ENVI 5.3.

2.2.2. Sentinel-2A Images

Sentinel-2 is a high-resolution (10 m), multi-spectral, remote-sensing satellite [25]. We selected Sentinel-2A Level 1C images, from 1 January 2017 to 31 December 2017 and from 1 January 2021 to 31 August 2021, for urban land-use classification. The dataset has been processed with radiometric and geometric corrections, which are available from the Google Earth Engine (GEE) cloud computing platform [26].

2.2.3. OpenStreetMap (OSM) Network Data

The OSM road network data has been utilized as a promising dataset for capturing urban functional patterns [27,28,29]. The OSM road network data are in vector format and contain different classes of roads with different road sizes [30]. This research utilized 2021 OSM road network data to delineate the urban parcels in urban land-use mapping. We further manually checked the OSM road network data to guarantee the accuracy of the mapping results.

2.2.4. Points of Interest (POI) Data

POIs are the basis for most of the data supporting location-based applications. POIs contain abundant information, including land-use category geographic locations, and other features (e.g., address, telephone, and postcode) [31,32]. This research utilized POIs in 2017 and 2021 for mapping urban land-use maps in Hangzhou city. We further merged the classification system into four urban land-use types, namely, institution, residence, business, and open space. These are used for analysis for two reasons: (1) they represent the most important socioeconomic activities of Hangzhou city, and (2) the green spaces in these urban land-use types played important social, environmental, and ecological roles for urban residents. In this research, the open-space parcels mainly include bare lands, farmlands, parks, squares, and unused lands. We included farmland within the confines of urban green space because farmland can also provide ecological and environmental benefits to cities. In addition, there is a large amount of farmland at the edge of the study area, and its land-use change has an important impact on green-space research.

2.3. Methods

This research first categorized urban land use into institution, residence, business, and open space in Hangzhou city. Then, we extracted the urban green space information by calculating the NDVI index. Lastly, this research overlaid the urban land-use maps and urban green-space maps to investigate the changes in urban green-space patterns by integrating statistical analysis (e.g., green-space ratio), geospatial analysis (e.g., hotspot analysis), and landscape analysis (e.g., landscape metrics) (Figure 2).

2.3.1. FI-Based Urban Land-Use Mapping

Urban land-use maps were produced by using the feature integration (FI) approach [33,34] (Figure 3). The FI-based method was used to classify urban land-use categories by integrating 18 remote sensing (RS) and geospatial big data (GBD) features based on the random forest (RF) classifier (see the Supplementary Materials). Specifically, this research utilized Sentinel-2A images, OSM road network data, and POIs for urban land-use mapping.
The unit of the urban land-use maps was parcel-based, according to the method of the automated identification and characterization of parcels (AICP) [27]. The OSM road network data were used to delineate the urban land-use parcels. It should be noted that the urban parcels used in 2017 were the same as the urban parcels in 2021 to unify the basic units. The reason for this is that the 2021 OSM road network was developed based on the 2017 OSM road network according to the OSM operating system.
All the operations in the FI-based classification were realized using the GEE platform. We selected the training and testing samples by using the Baidu map, Baidu street view, and a field survey. This research randomly selected 800 samples in 2021 and 800 samples in 2017 for training and testing (Supplementary Materials). The 2017 and 2021 urban land uses were classified relatively well, with an estimated overall accuracy of 0.817 ± 0.032 and 0.802 ± 0.034, respectively (Supplementary Materials). We further checked the 2017 and 2021 urban land-use maps by visual interpretation to obtain high-accuracy products.

2.3.2. Green-Space Mapping

This research maps urban green space by calculating the normalized difference vegetation index (NDVI) of GF-2 high-resolution images (Figure 4).
NDVI has proven to be one of the most effective indices for classifying green space since green spaces absorb most of the red light that hits them while reflecting much of the near-infrared light [35,36]. The NDVI index is calculated using an equation, as follows:
NDVI = ( NIR − RED ) / ( NIR + RED )
where NIR represents the near-infrared channel and RED is the red channel. The result for the calculation of NDVI generally ranges from (−1, 1). A higher number of NIR means a higher possibility of urban green spaces. A careful investigation of the NDVI outputs with GF-2 images on the background revealed the fact that all green-covered areas in the study area had NDVI values above 0.52 in 2017 and 0.48 in 2021. Thus, keeping 0.52 and 0.48 as the thresholds, the urban green spaces were extracted, i.e., all pixels having an NDVI of 0.52 and above were classified as urban green spaces in 2017 and the pixels having an NDVI of 0.48 and above were classified as urban green spaces in 2021. The accuracy assessment was carried out using a random sampling strategy. The test samples were selected by visual interpretation based on Google Earth. Following the approach discussed by Stehman and Foody [37], this research randomly selected 1000 samples in 2017 and 1000 samples in 2021 for testing from the list of points (see the Supplementary Materials). The 2017 and 2021 urban green spaces were classified relatively well, with an estimated overall accuracy of 0.967 ± 0.012 and 0.977 ± 0.010, respectively (Supplementary Materials).

2.3.3. Comprehensive Analysis

(1)
Green-space ratio analysis
The index is defined as the ratio of an area covered by vegetation to the whole area. This research calculates the green-space ratio in different urban land-use types (i.e., institution, residence, business, and open space) to capture the changes in urban green-space patterns with urban land-use changes from 2017 to 2021. The urban green-space ratio for the total area, 1st ring belt, 2nd ring belt, and 3rd ring belt are calculated, respectively.
(2)
Hotspot analysis
This research utilizes the Getis-Ord hotspot of the ArcGIS tool to identify the hotspots and coldspots of urban green spaces in different regions in Hangzhou city. Hotspot analysis utilizes a series of weighted features and identifies statistically significant hotspots and coldspots using Getis-Ord Gi* statistics, which calculates the GiZScore and GiPValue for the selected parameters [38]. Here, this research uses a percentage of confidence level above 90% to identify the hot/cold spots of urban green space.
(3)
Landscape analysis
Landscape metrics are often used to detect and quantify the landscape patterns of urban green spaces [39,40,41]. According to Table 1, this research selects six landscape metrics including four class-level metrics (i.e., patch density (PD), largest patch index (LPI), mean shape index (SHAPE_MN), and patch cohesion index (COHESION)) and two landscape-level metrics (i.e., contagion index (CONTAG) and Shannon’s Evenness Index (SHEI)) by referencing earlier work reported in many studies [42,43]. Among these, PD, LPI, and SHAPE_MN can represent the fragmentation and complexity of urban green-space patches [43,44]; COHESION could reflect the connectivity [45]; CONTAG presents the connectivity of the total landscape [46]; and SHEI presents the distribution of landscapes [47].

3. Results

3.1. Changes in Urban Green-Space Patterns

Figure 5a presents the hotspot analysis of the urban green spaces in Hangzhou city in 2017. The distribution of spatial clustering differed by region in the 2017 hotspots map. Specifically, the clusters of hotspots tended to occur in the urban periphery (i.e., third ring belt), except for the southern part of the city, while the coldspots of low-value regions were mostly found in the city center (i.e., first ring belt and second ring belt). Compared with the 2017 hotspots map, the number of high-value and low-value regions was more likely to undergo a greater change in the 2021 hotspots map (Figure 5b). It was found that the hot-/coldspots of urban green-space coverage are distributed more dispersedly. The clusters of hotspots increased in the second ring belt, and coldspots decreased in the first ring and second ring belt.
Referring to the urban land-use maps in 2017 and 2021, the decrease in urban greenspaces around the urban periphery was mainly due to the decrease in open-space parcels. During 2017 and 2021, a large amount of farmland was transformed into impervious surfaces for construction purposes, while the decrease in the coldspot regions in the city center is probably due to the increase in the institution and residence parcels. The residence and institution parcels have the potential to have a higher ratio of urban green space than other urban land-use categories. In addition, the increase in coldspots within the third ring belt is mainly attributed to the functional transformation of the urban parcels (e.g., the transformation from open space to residence). There will be fewer green spaces in the urban parcels under construction.
It is shown that the overall area of urban green space increased significantly from 67.19 km2 (29%) in 2017 to 86.63 km2 (38%) in 2021 (Table 2). Among different ring belts, the growing ratio of urban green-space patches in the first ring belt was the highest (14%), while the ratio of the increased urban green space in the third ring belt was the lowest (7%).
Fragstats 4.2 was utilized to calculate the class-level and landscape-level metrics of urban green-space patches in Hangzhou city for 2017 and 2021 (Figure 6). Specifically, PD in 2017 was higher than in 2021 (Figure 6a). However, the PD of urban green space in the first ring belt increased by around 9%. This is probably due to the construction of green infrastructures within the old town region, especially in the residence parcels. The LPI in the second ring belt and third ring belt increased, indicating some large parklands that appeared (Figure 6b). The emergence of diversified urban green-space design (e.g., horizontal greenery and vertical greenery) has led to a rise in the complexity of the green-space patches (Figure 6c). The design of green-space layouts in different urban land-use types focuses on different functions. For instance, the green-space patches in the business parcels focus on decoration and entertainment functions rather than ecological values. The COHESION of urban green-space patches increased slightly from 2017 to 2021 (Figure 6d). The CONTAG displays a downward trend, and the SHEI increased, indicating that the urban green-space patches become more evenly dispersed (Figure 6e,f).

3.2. Changes in Urban Green-Space Patterns with Different Urban Land-Use Changes

The green-space ratios were calculated in different urban land-use change types from 2017 to 2021 at different ring levels (Figure 7). The increase in urban green space in the institution parcels is largely dependent on the unchanged institution parcels, indicating well-planned urban green spaces inside the institution parcels (Figure 7a). The change from the residence and business parcels to the institution parcels has also determined the increase in green spaces in the first ring belt, probably because of the urban renewal project within the old town area. Moreover, the increase in the green-space ratio in the residence parcels is significant and is mainly distributed in the unchanged residence parcels from 2017 to 2021 (Figure 7e). It can be seen that the proportion of urban green space in newly built residence parcels is at a high level. The increase in the green-space ratio in the business parcels is mainly transformed from the residence parcels, business parcels, and open-space parcels (Figure 7i). This is probably due to the attractiveness of customers, especially in the second and third ring belts. The green-space ratio in the open-space parcels decreased from 0.16 km2 in 2017 to 0.12 km2 in 2021 in the unchanged open-space parcels, which is mainly related to the decrease in farmland in the third ring belt (Figure 7m). In addition, the change from the institution parcels has also led to a decrease in green spaces, probably due to the relocation of some institutions.
Figure 8 represents the landscape analysis of urban green space in different urban land-use change types in 2017 and 2021. Among them, the changes in the PD and LPI values for all patches varied greatly, while the changes in the SHAPE_MN, COHESION, CONTAG, and SHEI values were less obvious. It can be noted that the PD values for the institution parcels transformed from the four urban land-use types all decreased, suggesting reduced fragmentation in urban parcels. In addition, the SHAPE_MN, COHESION, and SHEI values for all patches increased, indicating more complex, more aggregated, and less dominant green-space patches in the institution parcels in 2021. The CONTAG values for all green-space patches decreased, which means the patches became uneven across the landscape. Specifically, the urban green-space patches in the residence parcels transformed from the open-space parcels, becoming more fragmented, more even, and more dominant. This is probably because the residence parcels transformed from the residence parcels are still in the demolition stage. Unlike the institution and residence parcels in 2021, the urban green-space patches in the business parcels in 2021 show less dominance. In addition, the green-space landscape in the open-space parcels transformed from the residence and business parcels is less fragmented, more complex, less even, and less dominant.

4. Discussion

In this study, we overlapped the urban land-use map (Figure 3) and the urban green-space map (Figure 4) for investigating the changes in urban green-space patterns through comprehensive analysis (e.g., hotspot analysis, green-space ratio analysis, and landscape analysis). The results showed that the integration of approaches focusing on different perspectives can provide a more holistic view of understanding the urban green-space patterns and their changes and thereby may enhance our understanding of the socioeconomic impacts of urban land-use changes.
The implementation of greening and land-use policies drives changes in urban green space and urban land-use patterns in Hangzhou city [49,50]. Among them, the “Hangzhou Urban Green Space System Plan (2002–2020)” aims to optimize the construction of green-space infrastructure in residential areas and improve existing plazas and parks [51].To be more specific, the urban green-space ratio of residential areas in the urban periphery should not be less than 35%, and that in the urban cores should not be less than 30%. As for the institution parcels, the urban green-space ratio should not be less than 35%. The urban green-space ratios of business parcels should not be less than 30%. It can be noted from the urban green-space ratio analysis that the urban green-space area in urban centers increased significantly between 2017 and 2021. The ratios of urban green space for the four urban land-use types (e.g., institution, residence, business, and open space) in 2017 were 31%, 24%, 26%, and 46%, respectively, while in 2021, the ratios increased to 40%, 36%, 41%, and 36%, respectively. In addition, according to the “Hangzhou Urban Master Plan (2001–2020)”, the government has made a reasonable allocation of district parks and community parks around the communities to improve the living quality of urban residents [52]. Furthermore, green corridors have been built to increase the connectivity of urban green-space patches [53]. The aggregation and connectivity of the urban green-space patches, as well as the socioeconomic benefits of urban green spaces, have been significantly improved. The “old town transformation” policy issued in 2017 has also greatly improved the urban green-space landscape in the city center and alleviated the ecological and environmental pressure [54,55]. To achieve economic efficiency and environmentally friendly development, the government championed the demolition of old town regions and built urban green space (e.g., green-space parks and green infrastructures) within residential and commercial land instead [56]. The growing ratio of urban green space in the old town regions was the highest (14%) compared to the other regions in Hangzhou city. The landscape patterns of urban green space in the urban core region were also improved according to the class-level and landscape-level metrics analysis. It also should be noted that the private sector has begun to spontaneously increase the urban green-space areas, although the government has planned the minimum green-space area for various urban land use. For example, the ratio of urban green space for business parcels increased from 26% in 2017 to 41% in 2021. The green-space ratio in business parcels has far exceeded the minimum value set by the government (30%).

5. Conclusions

This research investigates the changes in urban green-space patterns with different urban land-use changes (e.g., from open space to residential) by using a comprehensive analysis. The results demonstrated that the changes in urban land-use patterns are affected by urban land-use changes in Hangzhou city. In general, urban green spaces increased from 2017 to 2021, and the growth was mainly concentrated in the urban core area, indicating that the city has made remarkable achievements in the planning of green spaces. Specifically, the increase in urban green spaces in the first ring belt was mainly related to the old town transformation program in the residential land. The change from the residence parcels to the business parcels determined the increase in green spaces in the second and third ring belts, probably because of the attractiveness of customers. In addition, a large number of open-space parcels have changed to business parcels around the urban periphery, which might be due to the transformation from farmland to impervious surfaces. Lastly, this study verifies that the private sector (e.g., shopping malls) can contribute to the increase in green spaces, an issue that is usually realized by the state. Reasonable urban land-use layouts can promote the optimization and layout of urban green spaces. The understanding of urban green landscapes with different urban land-use changes can provide references for analyzing and optimizing green space in other cities experiencing rapid urban land-use changes.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/rs14215410/s1, Figure S1. Training and testing samples of urban land use mapping: (a) urban land use samples in 2017; (b) urban land use samples in 2021; Figure S2. Training and testing samples of urban green space mapping: (a) urban green space samples in 2017; (b) urban green space samples in 2021; Table S1. RS and GBD features used in FI-based classification; Table S2. Confusion matrix of FI-based classification results in 2017 (I: Institution; R: Residence; B: Business; O: Open Space). UA: users accuracy; PA: producers accuracy; OA: overall accuracy; π_j is the class proportion according to the classified map; Table S3. Confusion matrix of FI-based classification results in 2021 (I: Institution; R: Residence; B: Business; O: Open Space). UA: users accuracy; PA: producers accuracy; OA: overall accuracy; π_j is the class proportion according to the classified map; Table S4. Confusion matrix of the urban green space mapping results in 2017. UA: users accuracy; PA: producers accuracy; OA: overall accuracy; π_j is the class proportion according to the classified map (N: Non-green space; G: Green space); Table S5. Confusion matrix of the urban green space mapping results in 2021. UA: users accuracy; PA: producers accuracy; OA: overall accuracy; π_j is the class proportion according to the classified map (N: Non-green space; G: Green space).

Author Contributions

Conceptualization, J.Y.; methodology, J.Y.; data analysis, J.Y.; writing—original draft preparation, J.Y.; writing—review and editing, P.F., A.C.; supervision, Z.L., J.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

J.Y. acknowledges a Ph.D. scholarship provided by the University of Nottingham Ningbo China and the Institute for Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Study area. (a) Location of the study area; (b) DEM of Hangzhou city; (c) land cover of Hangzhou city; (d) urban parcels of the study area.
Figure 1. Study area. (a) Location of the study area; (b) DEM of Hangzhou city; (c) land cover of Hangzhou city; (d) urban parcels of the study area.
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Figure 2. Methodology workflow.
Figure 2. Methodology workflow.
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Figure 3. Urban land-use maps: (a) 2017 urban land-use map; (b) 2021 urban land-use map.
Figure 3. Urban land-use maps: (a) 2017 urban land-use map; (b) 2021 urban land-use map.
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Figure 4. Urban green-space maps: (a) 2017 urban green-space map; (b) 2021 urban green-space map.
Figure 4. Urban green-space maps: (a) 2017 urban green-space map; (b) 2021 urban green-space map.
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Figure 5. Hotspot analysis of urban green-space patterns at ring levels in 2017 (a) and 2021 (b).
Figure 5. Hotspot analysis of urban green-space patterns at ring levels in 2017 (a) and 2021 (b).
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Figure 6. Landscape analysis of urban green-space patterns at ring levels in 2017 and 2021. (a) PD of urban green spaces from 2017 to 2021; (b) LPI of urban green spaces from 2017 to 2021; (c) SHAPE_MN of urban green spaces from 2017 to 2021; (d) COHESION of urban green spaces from 2017 to 2021; (e) CONTAG of urban green spaces from 2017 to 2021; (f) SHEI of urban green spaces from 2017 to 2021.
Figure 6. Landscape analysis of urban green-space patterns at ring levels in 2017 and 2021. (a) PD of urban green spaces from 2017 to 2021; (b) LPI of urban green spaces from 2017 to 2021; (c) SHAPE_MN of urban green spaces from 2017 to 2021; (d) COHESION of urban green spaces from 2017 to 2021; (e) CONTAG of urban green spaces from 2017 to 2021; (f) SHEI of urban green spaces from 2017 to 2021.
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Figure 7. Green-space ratios in different urban land-use change types. (a–d) Green-space ratios in the institution parcels transformed from the four urban land-use types at different ring levels; (e–h) green-space ratios in the residence parcels transformed from the four urban land-use types at different ring levels; (i–l) green-space ratios in the business parcels transformed from the four urban land-use types at different ring levels; (m–p) green-space ratios in the open-space parcels transformed from the four urban land-use types at different ring levels. (I: institution; R: residence; B: business; O: open space).
Figure 7. Green-space ratios in different urban land-use change types. (a–d) Green-space ratios in the institution parcels transformed from the four urban land-use types at different ring levels; (e–h) green-space ratios in the residence parcels transformed from the four urban land-use types at different ring levels; (i–l) green-space ratios in the business parcels transformed from the four urban land-use types at different ring levels; (m–p) green-space ratios in the open-space parcels transformed from the four urban land-use types at different ring levels. (I: institution; R: residence; B: business; O: open space).
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Figure 8. Landscape analysis of urban green space within different urban land-use change types. (I: institution; R: residence; B: business; O: open space) (a) PD of urban green spaces from 2017 to 2021; (b) LPI of urban green spaces from 2017 to 2021; (c) SHAPE_MN of urban green spaces from 2017 to 2021; (d) COHESION of urban green spaces from 2017 to 2021; (e) CONTAG of urban green spaces from 2017 to 2021; (f) SHEI of urban green spaces from 2017 to 2021.
Figure 8. Landscape analysis of urban green space within different urban land-use change types. (I: institution; R: residence; B: business; O: open space) (a) PD of urban green spaces from 2017 to 2021; (b) LPI of urban green spaces from 2017 to 2021; (c) SHAPE_MN of urban green spaces from 2017 to 2021; (d) COHESION of urban green spaces from 2017 to 2021; (e) CONTAG of urban green spaces from 2017 to 2021; (f) SHEI of urban green spaces from 2017 to 2021.
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Table
Table 1. Description of the landscape metrics used in this study [48].
Table 1. Description of the landscape metrics used in this study [48].
MetricsDescriptionRangeUnit
Class-levelPatch Density (PD)PD measures the density patches for each class.Number of
patches per 100 ha		PD > 0
Largest Patch Index (LPI)LPI quantifies the percentage of the total landscape area comprised by the largest patch.0 < LPI ≤ 100Percent
Mean Shape Index (SHAPE_MN)Average evaluates the complexity of urban green-space patches.AREA_MN ≥ 0None
Patch Cohesion Index (COHESION)COHESION measures the connectedness of urban green-space patches.0 < COHESION < 100None
Landscape-levelContagion Index (CONTAG)CONTAG measures fragmentation in the entire landscape.0 < CONTAG ≤ 100Percent
Shannon’s Evenness Index (SHEI)SHEI measures the dominance of urban green-space patches in the entire landscape.0 ≤ SHEI ≤ 1None
Table
Table 2. Analysis of urban green-space area and the ratio at ring levels in 2017 and 2021.
Table 2. Analysis of urban green-space area and the ratio at ring levels in 2017 and 2021.
Region20172021
Area (km2)RatioArea (km2)Ratio
Total67.190.2986.630.38
First ring0.890.112.120.25
Second ring5.820.219.610.34
Third ring60.460.3274.890.39
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Yin, J.; Fu, P.; Cheshmehzangi, A.; Li, Z.; Dong, J. Investigating the Changes in Urban Green-Space Patterns with Urban Land-Use Changes: A Case Study in Hangzhou, China. Remote Sens. 2022, 14, 5410. https://doi.org/10.3390/rs14215410

AMA Style

Yin J, Fu P, Cheshmehzangi A, Li Z, Dong J. Investigating the Changes in Urban Green-Space Patterns with Urban Land-Use Changes: A Case Study in Hangzhou, China. Remote Sensing. 2022; 14(21):5410. https://doi.org/10.3390/rs14215410

Chicago/Turabian Style

Yin, Jiadi, Ping Fu, Ali Cheshmehzangi, Zhichao Li, and Jinwei Dong. 2022. "Investigating the Changes in Urban Green-Space Patterns with Urban Land-Use Changes: A Case Study in Hangzhou, China" Remote Sensing 14, no. 21: 5410. https://doi.org/10.3390/rs14215410

APA Style

Yin, J., Fu, P., Cheshmehzangi, A., Li, Z., & Dong, J. (2022). Investigating the Changes in Urban Green-Space Patterns with Urban Land-Use Changes: A Case Study in Hangzhou, China. Remote Sensing, 14(21), 5410. https://doi.org/10.3390/rs14215410

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