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Spatiotemporal Analysis and Multifactor Driving Mechanisms of Urban Blue Space in Beijing Using Remote Sensing

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14 August 2023

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15 August 2023

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Abstract
With the rapid development in Beijing, there is a critical need to explore the circumstance and reveal the mechanisms of precious urban natural resources. In this context, urban blue space has attracted more and more attention by driving microcirculation, cooling heat islands, and relaxing residential. We extracted the UBS at Beijing using remote sensing, explored the spatial and temporal development in the last two decades via USDA methods, uncovered the full spectrum of landscape patterns from an ecological perspective, and simulated the mechanisms of the UBS area and the landscape quantitatively. We found that: (1) The UBS area in Beijing increased with fluctuation from 2000 to 2020. (2) The spatial clustering has distributed stable with some subtle changes. (3)The ecological circumstance has improved in the last 21 years in Beijing, with the increasing habitat diversity and richness, while the inferior landscape fragmentation has indicated some severe challenges. (4) Natural factors impact urban blue space areas more than social ones, while both similarly influence the UBS landscape. (5) Vegetation circumstances and precipitation are the most important natural factors on both area and landscape of UBS, and population and artificial surface are the most important social factors.
Keywords: 
Subject: Environmental and Earth Sciences  -   Water Science and Technology

1. Introduction

Urban blue space (UBS) refers to spaces of surface water within urban areas, including lakes, channels, and pools [1]. UBS plays a crucial role in various aspects, such as biodiversity conservation [2], climate change mitigation [3], provision of ecosystem services[4], and public health benefits [5]. It also contributes to reducing the heat island effect and regulating the local climate [6,7,8]. Consequently, the management of UBS holds great importance in urban planning and development [9,10]. In addition to its functional benefits, UBS significantly enhances the aesthetics [11] and cultural value [12] of urban environments. It provides a sense of comfort and tranquility, offering respite from the pressures of modern city life [13,14]. Furthermore, UBS helps mitigate flood risks associated with the expansion of artificial surfaces, optimizes land use patterns, improves public satisfaction, and promotes residents' well-being [15,16,17,18,19].In Beijing, as a globally recognized metropolis renowned for its fast-paced lifestyle, serves as an exemplary case study for examining the role of UBS in urban environments.
In terms of the content of existing studies in this field, the focus has primarily been on changes in urban blue spaces (UBS) [20] and patch connectivities [21,22,23]. However, UBS in metropolises serve not only as functional outdoor water bodies but also as unique urban landscapes and spaces with aesthetic and emotional significance. Therefore, it is crucial to pay more attention to the area and landscapes of UBS. Regarding the time scale, most previous research has concentrated on specific years or short-term timelines [24,25,26,27,28]. However, UBS changes occur over long-term and gradual processes, which cannot be adequately captured within a short-term study spanning only three to five years [29]. Additionally, UBS is subject to irregular impacts from extreme weather events such as droughts and floods, which are often overlooked when using equal time interval methods [30]. Hence, conducting long-term studies with more detailed information is essential in this field. In terms of primary data, previous research on UBS landscapes has primarily relied on traditional data sources, such as historical maps and aerial images with large spatial resolutions and limited information [31,32]. These sources only support studies at a patch scale [33,34]. However, with advancements in remote sensing technology, high-quality primary data have become more accessible. Therefore, there is a demand for studies that utilize more detailed information and focus on a smaller scale [35,36]. Considering the potential interference of fragmented and temporal water patches resulting from high-resolution data, as well as the inability of low-resolution data to capture detailed information, this study adopts remote sensing data with a resolution of 30 m × 30 m to accurately extract UBS.
The analysis of component mechanisms, including population, economics, climate, and land use [37], deserves more attention compared to single-factor studies [38] since the spatiotemporal characteristics of UBS are influenced by multiple resource factors. Understanding the interactions and contributions of these factors is crucial in comprehending UBS dynamics. Moreover, qualitative mechanisms hold greater value for policymakers and stakeholders [39,40,41,42,43] involved in urban management and hydrological projects compared to quantitative mechanisms [44,45]. Qualitative insights provide a deeper understanding of the underlying processes and offer more meaningful guidance for decision-making. In many existing studies, the differentiation in vegetation density has been overlooked [46]. However, vegetation density directly affects the water holding capacity and the ability of ecosystems to regulate runoff. Considering that the area of low-density vegetation in Beijing is significantly larger than that of high-density vegetation and that the availability of vegetation density data is limited, the authors have chosen NDVI, which is more sensitive to low-density vegetation surfaces, and EVI, which is more sensitive to high-density vegetation surfaces [47], as proxies to distinguish vegetation density in this study.
Compared to previous studies, this research makes several significant contributions. First, the authors have chosen a sequential 21-year period, which helps avoid information gaps that may occur in previous studies using equal interval methods. By analyzing a longer time span, a more comprehensive understanding of UBS dynamics can be achieved. Second, in terms of spatial and temporal analysis, the remote sensing data utilized in this study offer wider coverage, increased accuracy, and more detailed information compared to traditional data sources such as urban planning drawings and surveying statistics used in previous studies. This enables a more robust and nuanced investigation of urban landscapes. Finally, this research explores the heterogeneity in vegetation density within the mechanism simulation, which has often been overlooked in existing studies. By considering variations in vegetation density, the study provides insights into the impact of different vegetation types and their densities on UBS dynamics. This contributes to a more comprehensive understanding of the mechanisms driving UBS changes.

2. Materials and Methods

2.1. Study Area

The study area of this research is Beijing, which is a prominent political, economic, and cultural center in China. Beijing is situated between 115.7°E - 117.4°E longitude and 39.4°N - 41.6°N latitude [48]. It shares borders with Tianjin in the east and Hebei in the remaining directions (see Figure 1). The city covers an area of 16,410 square kilometers and had a permanent resident population of 21.89 million as of 2021 [49]. Beijing experiences a monsoon-influenced humid continental climate. Summers in Beijing are hot, humid, and prone to rainfall, while winters are cold, dry, and characterized by clear skies. The average annual rainfall in Beijing is approximately 698.4 mm, and the average annual temperature ranges from 9℃ to 19℃ [50].
Beijing serves as a typical case for studying urban blue space (UBS) in a metropolis. The city's UBS plays a crucial role in several aspects. First, due to the frequent intense rainfall and extreme precipitation events that occur during the summer, UBS serves as a vital component of natural reservoirs, helping to absorb and regulate excess water, thereby reducing the risk of flooding. Additionally, UBS in Beijing provides valuable mental and recreational benefits to the public. As a special urban landscape, UBS is a source of mental relaxation and entertainment for the city's residents amidst their fast-paced lives. These blue spaces create a serene and tranquil environment, offering an escape from the hustle and bustle of urban life. The presence of UBS in Beijing contributes to the overall well-being and quality of life for its inhabitants. Considering the dual functions of flood mitigation and mental well-being, studying UBS in Beijing provides valuable insights into the multifaceted role of blue spaces in metropolises.

2.2. Methodology

2.2.1. Spatial Autocorrelation Analysis and Spatial Clustering Analysis

Spatial autocorrelation detects the convergence or dispersion of observations [51,52]. M o r a n ' s I is a widely used classical spatial autocorrelation index. For a series of n variable samples, x i is the observation at location i, and wij is the spatial weight matrix (SWM). Then, M o r a n ' s I is calculated as follows:
I = n ∑ i = 1 n ∑ j = 1 n w i j ( x i − x − ) ( x j − x − ) ∑ i = 1 n ∑ j = 1 n w i j ∑ i = 1 n ( x i − x − ) 2
M o r a n ' s I ranges from -1 to 1. M o r a n ' s   I >0 indicates a positive spatial correlation. The closer it is to 1, the more significant the positive spatial autocorrelation. M o r a n ' s I < 0 indicates a negative spatial correlation. The closer it is to -1, the more significant the negative spatial autocorrelation. M o r a n ' s I =0 means a random distribution [53]. The high/low clustering (Getis-Ord General G*) tool is an effective method for spatial aggregation simulation. The calculation formula is as follows：
G * = ∑ j = 1 n w i j x j − x − ∑ j = 1 n w i j ∑ j = 1 n x j 2 n − 1 x 2 n ∑ j = 1 n w i j w − ∑ j = 1 n w i j 2 n − 1
x − is the average of observations, x 1 ， x 2 , ⋯ , x n w i j is the spatial weight of x i and x j , i , j = 1,2 , ⋯ , n. The higher G* is, the higher the observation clustering, and vice versa. The null hypothesis of General G* assumes that the observations do not cluster spatially [54]. The p value determines whether the null hypothesis should be accepted or not. The z score reflects the dispersion of observations [55].

2.2.2. Principal Components Regression Analysis

Principal component regression analysis (PCR) is used to solve multivariate collinearity problems [56]. Principal component analysis (PCA) converts multiple indexes into several comprehensive indexes by orthogonal rotation transformation, following the premise of minimizing information loss. Generally, the results of PCA are independent variables called principal components [57]. The geometric interpretation and model of PCA are as follows (Figure 2):
The distribution of a series of n binary observations ( x 11 , x 12 , ⋯ , x n 1 , x n 2 ) in the coordinate space composed of X 1 and X 2 is shown in Figure 2. Along the X 1 or X 2 axis, observation points have large discretization indicated by the variance of X 1 or X 2 , respectively. Axes X 1 and X 2 are rotated counterclockwise to axes Y 1 and Y 2 following formula X. The dispersion of n observation points on the Y 1 axis is the largest, indicating that variable Y 1 retains most of the information of the original data.
Y 1 Y 2 = cos ⁡ θ sin ⁡ θ − sin ⁡ θ cos ⁡ θ X 1 X 2 = U X

2.2.3. Grey Relation Analysis

A complex system always involves various elements; the mechanism of each element is hard to simulate quantitively in practice because of the associated interactions. Grey system theory attempts to look for quantitative relationships based on the curve geometry [58-60]. Sequences are closely related when they have tight geometry curves and similar trends, and vice versa. Thus, grey correlation analysis is an effective classical quantitative measure for dynamic series. The formula is as follows:
r x 0 k , x i k = min i ⁡ min k ⁡ x 0 k − x i ( k ) + ξ max i ⁡ max k ⁡ x 0 k − x i ( k ) x 0 k − x i ( k ) + ξ max i ⁡ max k ⁡ x 0 k − x i ( k )
r x 0 , x i = 1 n ∑ k = 1 n r ( x 0 k , x i ( k ) )
where r x 0 k , x i k is the grey correlation coefficient at location k , r x 0 , x i is the data sequence from x 0 t o x i . ξ refers to the resolution coefficient. The value of ξ is inversely proportional to the difference between sequence phases. In classical statistical theory, for a set of observations with a large sample size, the probability of the sample is approximately equal to the frequency. Thus, ξ is equal to 0.5.

2.3. Data and Resources

Remote sensing images from the Google Earth Engine data catalog were used to extract influencing factors. The statistical data were retrieved from the statistical yearbook and official websites (Table 1).
To address the spatial and temporal resolution differences between remote sensing images and statistical data, the influencing factors derived from remote sensing images were aggregated to the district level from the pixel scale. This aggregation process ensures that the data align with the resolution of the statistical data available. Furthermore, to account for the temporal variations within the remote sensing images, the data were further derived to annual averages. This averaging process provides a representative value for each influencing factor, smoothing out short-term fluctuations and capturing the overall trends over time. By aggregating and deriving the data, this study ensures compatibility and consistency between the remote sensing images and the available statistical data, enabling a comprehensive and integrated analysis of the influencing factors at the district level on an annual basis (Figure 3).
Referring to the existing research, considering the actual situation and data availability in Beijing, the authors have selected the following indicators as influencing factors for studying UBS.
Population(POP): Population is a critical factor influencing UBS scope and intensity[61,62]. Domestic water consumption and modifications to surface runoff by human activities significantly impact UBS. Therefore, population is a relevant indicator in this study.
Precipitation(PREP): Urban precipitation plays a vital role in the groundwater recharge and overall water circulation in cities . Extreme weather events associated with global climate change can generate temporary urban blue spaces, such as groundwater puddles. Hence, precipitation is commonly considered an influential indicator in UBS research [63].
Temperature(T): UBS and temperature represent a complex system interactions. UBS helps regulate the local microclimate, mitigating high temperatures and providing substantial cooling effects to the surrounding areas [64]. Higher temperature accelerates waterbody shrinking through increased evaporation. Thus, temperature is a significant factor to consider in UBS studies [65].
Fractional vegetation cover(FVC): Vegetation plays a crucial role in slowing surface runoff and enhancing water conservation capacity[66,67]. Considering the positive influence of vegetation on UBS, FVC is an essential indicator in this study.
Artificial surface proportion(ASP): The proportion of artificial surface in a city significantly affects its surface temperature, leading to either warming or cooling effects. Analyzing ASP helps in understanding urban ecological health and the impact of ASP on UBS [68].
Normalized difference vegetation index: NDVI is closely associated with the cooling effect of urban ecological spaces and precipitation[69]. It is more sensitive than the enhanced vegetation index (EVI) in regions with sparse vegetation, which are often found in metropolises such as Beijing. Thus, NDVI is a suitable indicator for UBS research in Beijing.
Enhanced vegetation index(EVI): EVI is a robust remote sensing index that reflects vegetation density and is especially effective for dense vegetation surfaces. It is closely related to urban microcirculation and blue spaces [70].
Gross domestic product(GDP): GDP measures the gross product of a country and indirectly reflects water consumption, wastewater discharge, and water-use efficiency [71,72]. Considering the implications for water management and efficiency, GDP is a relevant indicator in this study.
Urban environmental management (UEM): UEM encompasses water conservancy, public facilities, and land use planning to ensure that population growth is in alignment with access to natural resources, basic infrastructure, and shelter. UEM, including water management, precipitation collection, and hydrological services, is directly related to UBS.
Educational investment (EDUI): Education investment significantly promotes science and technology, which in turn affects production methods and water consumption efficiency. It is closely related to UBS and its sustainability.
Scientific and technical investment (STI): STI drives the application of technologies such as the Internet of Things (IoT), YunOS IoT, and big data. These technologies optimize water consumption patterns and UBS planning, making STI a relevant indicator in understanding UBS dynamics.

3. Results

3.1. Spatiotemporal Analysis of Blue Space Area

3.1.1. Development Characteristics of the UBS Area in Beijing

The UBS area in Beijing showed a slight increasing trend from 2000 to 2020, with a stable trend from 2004 to 2016. It has been clearly upward since 2016. The area of UBS in Beijing was 124.4 km2 in 2000, reducing to 99.08 km2 in 2004, rising slightly to 121 km2 in 2016, and increasing to 183.4 km2 in the last four years (Figure 4).

3.1.2. Spatial Autocorrelation Analysis of the UBS in Beijing

Considering a confidence level of α=0.05, Moran’s I is always lower than 0.2, which means that the UBS exhibited a random distribution.

3.1.3. Spatial Clustering Pattern of the UBS in Beijing

From 2000 to 2020, the cluster analysis of “high/low” revealed that the agglomeration characteristics of UBS were relatively stable at the county level. However, the significance of clustering in Tai Shitun decreased prominently (Figure 5).

3.2. Spatiotemporal Analysis of the UBS Landscape in Beijing

3.2.1. Analysis of Landscape Indicators

Landscape pattern comprehensively reflect landscape spatial heterogeneity. Patterns reveal the spatial distribution and combination of different patches. These patches are always of various sizes, shapes, and attributes.
In this study, multiple landscape indexes were computed. Specific indicators included LPI, SPLIT, CONTAG, AI, PD, NP, LSI, SHDI, SHEI,and PAFRAC. The detailed descriptions and formulas of these indicators are presented in Table 2 [73].
The elements with upward trends: are LPI, SPLIT, PD, NP, LSI, SHDI, SHEI, and PAFRAC (Figure 6). Their changes show that UBS landscape pattern in Beijing developed stably in the first two decades. The maximum landscape patch area is increasing. Patches are more complex and have a significant change intensity. Diversity and richness are improved. The patches are distributed more evenly. Landscape patch types have become more diverse because of human effects. As a result, the extent of separation, fragmentation, and spatial heterogeneity indexes was more remarkable and higher.
The indicators with downward trends: are AI, CONTAG (Figure 6). In the last 21 years, the landscape connectivity of UBS in Beijing has been shallow, and the downward trend was kept up with the sprawl of urban construction.

3.2.2. Principal Component Analysis of the UBS Spatial Landscape Indices

The cumulative contribution rate of the first two principal components (Z1 and Z2) is 93.9% (Table 3), indicating that the first two principal components contain 93.9% of the information of the 10 original components. Thus, the landscape indexes of the UBS could be significantly extracted to the two component indicators (Formula 1, 2).
Z1=0.925NP+0.925PD+0.384LPI+0.735LSI+0.156PAFRAC-0.913CONTAG+0.456SPLIT+0.784SHDI+0.895SHEI-0.736AI (1)
Z2=0.295NP+0.295NP+0.881LPI+0.66LSI+0.946PAFRAC-0.32CONTAG+0.855SPLIT+0.587SHDI+0.273SHEI-0.659AI (2)
Z1 is highly positively related to NP, PD, SHEI and negatively related to CONTAG, indicating a spatial distribution structure at a landscape scale. The higher the Z1, the greater the NP, PD, and SHEI indexes, and the smaller the CONTAG value, indicating more patches, the higher patch density, the lower agglomeration degree of various patches, and the higher patch diversity. Z2 is positively related to PAFRAC, LPI, and SPLIT, reflecting spatial distribution structure at the patch scale. The higher the Z2, the greater the PAFRAC, LPI, and SPLIT indexes, meaning a more complex patch shape, the more extensive patch area, and greater distance between patches.
Z1 increased steadily from 2000 to 2014 and decreased until 2020, with a downward trend overall. The results revealed that UBS patch number, density, and diversity had increased at fourteen years and then declined in the last six years; the agglomeration weakened and then decreased. Overall the UBS in Beijing has faced severe fragmentation, which is expected to slow in recent years.
In contrast, Z2 decreased in the first decade and increased in the second decade, trending upward. The results showed that the shape complexity, area, and distance decreased first and then increased. From the perspective of the whole period, the UBS in Beijing was disturbed from 2000 to 2020 and has been declining in the last decade.

3.3. Mechanisms Driving the Area of UBS

According to the correlation coefficients, the influencing factors rated from most to least importance are as follows: UEM>EDUI>STI>NDVI>T>GDP>POP>FVC>EVI>PREP>ASP.
The influencing factors were identified as strong factors (UEM, EDUI, STI), medium factors (NDVI, T, GDP, POP), and weak factors (FVC, EVI, PREP, ASP) according to Jenks Natural Breaks Classification.
The results showed that scientific technology factors greatly influenced the UBS area, with correlation coefficients greater than 0.7. The strongest factor is UEM, with the highest coefficient of 0.798, followed by EDUI and STI, with coefficients of 0.759 and 0.758, respectively. The coefficients of NDVI and EVI indicated that the sparse vegetation surface magnified the UBS area more than the dense vegetation surface.

3.4. Mechanisms Influencing the UBS Landscape

From the perspective of UBS landscapes, the influencing factors were rated as follows: PREP> POP> GDP>STI>T>EDUI>UEM>ASP>NDVI>EVI>FVC (Table 4). According to the results of the Jenks Natural Breaks Classification, the strong factors influencing the UBS landscape are PREP, POP, GDP, STI, and T, the medium factors are EDUI, UEM, and ASP, and the weak factors are NDVI, EVI, and FVC. Thus, it is reasonable to conclude that precipitation and human activities influence the UBS landscape more than vegetation factors.

4. Discussion

The UBS area initially decreased from 2000 to 2004 due to significant water consumption resulting from population growth and industrial activities, leading to water shortages [74]. However, a series of measures aimed at improving water resources and protecting urban water bodies subsequently led to a steady expansion of the UBS area. The implementation of the South‒North Water Diversion Project, which began in late 2014, significantly contributed to the increase in UBS area [75]. The year 2016 stands out in particular with a sharp increase in UBS area, which can be explained by the significantly greater annual precipitation since 2015 [76] and the changes in water availability and management practices after the South‒North Water Diversion Project (Figure 7). These findings align with previous research [77].
The landscape pattern of UBS in Beijing showed stability with increasing diversity, richness, and evenness indexes from 2000 to 2020, indicating the generation of more water bodies with diverse properties and purposes. However, the dispersion and spatial heterogeneity indexes were poor, indicating severe fragmentation potentially caused by urban expansion and the erosion of ecological spaces [78]. Water pollution policies implemented since 2015 have helped improve water microcirculation, leading to a reversal of negative trends in 2017, with decreasing density and diversity indexes and increasing connectivity and aggregation indexes.
The spatial distribution of UBS in Beijing appeared random, potentially influenced by artificial water bodies created in heat island-reducing projects over the past two decades [79]. Stable UBS clustering patterns were observed, with unique spots identified, such a shrinking clustering in Taishitun County, extinct clustering in Huairou District, and expanded clustering in Miyun District. Regulation policies, such as vegetable cultivation, reservoir water network development, and reclaimed water usage, are likely related to stable development and clustered expansion. Conversely, decreasing and extinct clusterings could be attributed to disturbances from industrial and agricultural consumption, river channel changes, and artificial water bodies [80].
Among the 11 influencing factors, scientific technology factors have a strong impact on the UBS area, while precipitation and human activities strongly influence UBS landscapes. Scientific technologies enhance production efficiency and water utilization efficiency, reducing water consumption and expanding the UBS area. Precipitation replenishes water storage in urban water bodies and promotes vegetation growth, directly affecting the landscape pattern of the UBS. Human activities, including land use changes and surface modifications, have a direct and significant impact on UBS landscapes. Medium influencing factors, such as sparse vegetation, temperature, economy, and population, have a moderate influence on UBS area and landscapes. Weak factors are associated with land use patterns, particularly in dense vegetation areas, reflecting the limited correlation in these regions [81]. The weak influencing factors in UBS landscapes are closely related to vegetation.

5. Conclusions

The authors of this study utilized remote sensing techniques to extract urban blue space (UBS) in Beijing. They conducted a comprehensive analysis of the spatial and temporal development of UBS in the past two decades using ESDA methods. From an ecological perspective, they examined the full spectrum of landscape patterns and quantitatively simulated the mechanisms of UBS area and landscape changes. The findings revealed that the UBS area in Beijing experienced a decline from 2000 to 2004, followed by a steady increase over the next decade and a significant jump since 2016. The spatial clustering of UBS displayed overall stability with subtle changes. The landscape analysis indicated improvements in ecological circumstances, including increasing habitat diversity and richness, although challenges related to landscape fragmentation were observed. These results underscored the value of ecological projects initiated by the government and public organizations [82].
In terms of mechanisms influencing UBS area, all factors except for artificial surface proportion (ASP) exhibited significant influence, with correlation coefficients greater than 0.6. Scientific technologies emerged as the most influential driver, followed by natural climate factors. Human activities, such as economy and population, showed a weaker influence, while artificial surfaces had the weakest impact. Regarding UBS landscape patterns, natural climate and human activities were identified as the strongest influencing factors, whereas factors related to vegetation showed a weaker influence.
The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), which indicate the conditions of dense and sparse vegetation regions, respectively, exhibited distinct influences on UBS area and similar impacts on UBS landscape patterns.
This study successfully uncovered the spatiotemporal characteristics of the UBS area and landscapes in Beijing from 2000 to 2020 and elucidated the multifactorial mechanisms driving these changes. However, the analysis resolution was limited to counties due to the availability of policy number and statistics data, rather than at the pixel level. Further research is needed to explore UBS at a finer scale.
Additionally, while the study analyzed 11 influencing factors, it is acknowledged that there may be other factors that should have been explored, such as the number and effectiveness of policies. Furthermore, due to data limitations, the study focused on a 21-year period, and longer-term research is warranted in the future.

Author Contributions

D.S. Conceptualization, D.S. W.Z. and Y.C.; Methodology, Y.C., D.S. and W.Z.; Software, Y.L. and N.Z.; Validation, Y.C., D.S., Y.L., and Y.S.; Formal analysis, D.S.; Investigation, W.Z.; Resources, Y.C. and N.Z.;Data curation, Y.C. and D.S.; Writing—original draft preparation, D.S.; Writing—review and editing, Y.C. and Y.S.;Visualization, D.S.; Supervision, D.S.; Project administration, Y.L.; Funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

The research was supported by the China Postdoctoral Science Foundation (Grant No. 2023M733468) and General Program of National Natural Science Foundation of China (Grant No. 41271186).

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Study Area of Beijing.
Figure 1. Study Area of Beijing.
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Figure 2. Illustration of principal component analysis theory.
Figure 2. Illustration of principal component analysis theory.
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Figure 3. Multi-resoure data analysis method.
Figure 3. Multi-resoure data analysis method.
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Figure 4. The area of UBS in Beijing from 2000 to 2020.
Figure 4. The area of UBS in Beijing from 2000 to 2020.
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Figure 5. Spatial clustering pattern of the UBS in Beijing.
Figure 5. Spatial clustering pattern of the UBS in Beijing.
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Figure 6. Landscape indexes development from 2000 to 2020.
Figure 6. Landscape indexes development from 2000 to 2020.
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Figure 7. Development of influencing factors.
Figure 7. Development of influencing factors.
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Table 1. Data and resources.
Table 1. Data and resources.
Number Name Dataset Spatial resolution Temporal resolution
1 POP Gridded Population of the World, Version 4 100 m Yearly
2 PREP ERA5-Land 0.1。*0.1。 Daily
3 T Aqua/Terra MODIS MYD11A2 1000 m Eight days
4 FVC MODIS MCD12Q1 500 m Yearly
5 ASP MODIS MCD12Q1 500 m Yearly
6 NDVI MODIS NDVI MYD13Q1 V6 250 m Sixteen days
7 EVI MODIS NDVI MYD13Q1 V6 250 m Sixteen days
8 GDP Beijing Statistical Yearbook; Beijing Statistical Bulletin of National Economic and Social Development _ Yearly
9 UEM Beijing and each district’s statistical yearbook _ Yearly
10 EDUI Beijing and each district’s statistical yearbook _ Yearly
11 STI Beijing and each district’s statistical yearbook _ Yearly
12 UBS JRC Monthly Water History, v1.3 30 m Monthly
Table 2. Landscape indexes.
Table 2. Landscape indexes.
LPI（Largest patch index） Area percentage of maximum patch
SPLIT (Splitting index) Dispersion among different patches at a landscape scale.
The higher the value of SPLIT, the more separation between studied patch types.
CONTAG (Contagion index) Spatial collection and decentralization. The smaller the value of CONTAG, the more sparse each patch type.
AI（Aggregation index） Connectivity between patches of all patch types. The lower the value is, the more discrete the landscape.
PD (Patch density) Patch density in the landscape reflects the degree and type of landscape fragmentation. Patch density represents the spatial heterogeneity of the landscape per unit area.
NP (Number of patches) Number of all patches distributed in the landscape.
LSI (Landscape shape index) Indicates the change in landscape form. The higher the value, the more complex the shape.
SHDI (Shannon’s diversity index) Reflects how many different quantitative measures are in a dataset.
SHEI (Shannon’s evenness index) Describes the extent of the landscape controlled by minority patch types.
PAFRAC (Perimeter area fractal dimension) The intensity index reflects the disturbance in landscape patterns due to human activities. The higher the value, the greater the landscape's external disturbance.
Table 3. Results of principal component analysis.
Table 3. Results of principal component analysis.
component eigenvalue contribution rate cumulative contribution rate
1 8.162 81.623 81.623
2 1.228 12.280 93.903
3 0.328 3.276 97.179
4 0.222 2.224 99.403
5 0.043 0.427 99.830
6 0.014 0.140 99.970
7 0.003 0.029 99.999
8 0.000 0.001 100.000
9 0.000 0.000 100.000
10 0.000 0.000 100.000
Table 4. Correlation analysis of UBS area and landscapes.
Table 4. Correlation analysis of UBS area and landscapes.
Influencing Factors Correlation Coefficients of UBS Area Correlation Coefficients of UBS Landscapes
UEM 0.798 0.664
EDUI 0.759 0.665
STI 0.758 0.686
NDVI 0.697 0.617
T 0.692 0.685
GDP 0.689 0.691
POP 0.68 0.692
FVC 0.659 0.493
EVI 0.658 0.585
PREP 0.62 0.732
ASP 0.5 0.656
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