Spatiotemporal evolution of urban green space and its impact on the urban thermal environment based on remote sensing data: A case study of Fuzhou City, China

Urban Forestry & Urban Greening 41 (2019) 333–343

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Spatiotemporal evolution of urban green space and its impact on the urban thermal environment based on remote sensing data: A case study of Fuzhou City, China Yuanbin Caia,1, Yanhong Chenb,c,d,1, Chuan Tongb,c,

T

⁎

a

College of Environment and Resources, Fuzhou University, Fuzhou 350108, China Key Laboratory of Humid Subtropical Eco-geographical Process of Ministry of Education, Fujian Normal University, Fuzhou 350007, China c School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China d Fuzhou University Zhicheng College, Fuzhou 350002, China b

ARTICLE INFO

ABSTRACT

Handling Editor: Raffaele Lafortezza

Taking the main city of Fuzhou as the study area, the relationship between the spatiotemporal evolution of urban green space (UGS) and the urban thermal environment from 1993 to 2013 was investigated using a set of remote sensing images. The evolution of UGS is obvious in the study area, where UGS loss (42.83 km2) > UGS extension (4.99 km2) > UGS exchange (2.61 km2). UGS loss affects forest/grass > water > wetland. Furthermore, the area defined as high temperature zones increased by 23.11 km2 in 2013, twice as much as that in 1993. However, the influence of UGS on the urban thermal environment differs by type and evolution: water has the greatest cooling effect, followed by wetland and forest/grass, and UGS loss (8.67 ℃) > UGS exchange (4.00 ℃) > UGS extension (2.90 ℃) > UGS unchanged (2.45 ℃). Finally, the vegetation and cooling index classified the mechanism of temperature response induced by different types of UGS evolution. The evolution of UGS loss usually simulated the movement of the corresponding pixel from the low land surface temperature and high vegetation coverage to the opposite situation. Regression analyses demonstrated that the effect of elevated land surface temperature generated from the reduction of water and forest/grass reached 0.81 ℃ and 0.72 ℃, respectively, in 20 years, indicating that the loss of a significant amount of UGS during urbanization was the primary influence on the urban thermal environment. This study may provide more useful information for researchers and decision-makers engaged in urban planning, urban regeneration, and sustainable land development, especially focusing on the issues of climate adaption and the urban heat island (UHI) effect mitigation.

Keywords: Remote sensing Spatiotemporal evolution Thermal environment Urban green space Vegetation and cooling index

1. Introduction Currently, the process of urbanization is occurring at an unprecedented rate across the globe. The expansion of cities has had a significant negative impact on 40% of the surface of the world over the past 100 years (Sterling and Ducharne, 2008). China is, without a doubt, at the stage of rapid urbanization (Li et al., 2009; Jin et al., 2017) and by the end of 2011, the urbanization rate of China was 51.27%, and the urban population was 670 million, which exceeded the rural population for the first time. In 2016, this figure reached 57.35%, which is equivalent to the world average level. By 2030, the Chinese urbanization rate is expected to reach 60% (National Bureau of Statistics of China, 2015). The environmental impact of urbanization

has become a heavily researched subject in the field of resource and environment scientific research (Turner, 2005; Fu et al., 2006; Rana, 2011; Cai et al., 2016a,b; Meerow et al., 2016). Among these issues, the urban heat island (UHI) effect has become a typical thermal environment problem. There is intense correlation between comprehensive urbanization and the UHI effect (Cai et al., 2015; Cai et al., 2016a,b). The UHI effect is associated with many ecological and environmental problems, such as increasing energy consumption, photochemical smog, and the retention of other pollutants. The UHI effect can also trigger respiratory, cerebrovascular, heart, and other related diseases (Buyantuyev and Wu, 2010; Tan et al., 2010; Li and Bou-Zeid, 2013; Li et al., 2015). With the increasing recognition of the hazards caused by the UHI effect, the questions of how to mitigate the UHI effect and

Abbreviations: LULC, land use/land cover; UGS, urban green space; UHI, urban heat island; LST, land surface temperature; RLST, relative land surface temperature ⁎ Corresponding author. E-mail address: [email protected] (C. Tong). 1 These authors contributed to the work equally and should be regarded as co-first authors. https://doi.org/10.1016/j.ufug.2019.04.012 Received 21 June 2018; Received in revised form 20 February 2019; Accepted 20 April 2019 Available online 22 April 2019 1618-8667/ © 2019 Elsevier GmbH. All rights reserved.

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Fig. 1. The location of the study area.

regulate the urban microclimate have become two of the most urgent urban environmental problems. Previous studies have clearly discussed the influencing factors and control measures for urban surface temperature from such aspects as urban land use/land cover (LULC) change, the form of urban space, and urban architecture (Dixon and Mote, 2003; Chen et al., 2006; Takebayashi and Moriyama, 2007; Miao et al., 2009; Cai et al., 2012,2013). Furthermore, many studies show that the urban green space (UGS) (Koc et al., 2018) of the underlying surface in the city, including forests/grass (Ngom et al., 2016), water (Hamada and Ohta, 2010; Cai et al., 2018), and wetlands (Costanza et al., 1997; Cai et al., 2016a,b) play a pivotal role (Kong et al., 2014) in the adjustment of city climate and its response to future climate change (Jaganmohan et al., 2016; Žuvela-Aloise et al., 2016; Garuma et al., 2018; Herath et al., 2018), such as improving the atmospheric conditions for the city population, cooling the air (Tratalos et al., 2007), and relieving the urban heat island effect. The present study of urban green landscapes has found that forested areas, plant community structures, and landscape patterns have different influences on the cooling effect (Bowler et al., 2010; Li et al., 2011; Ren et al., 2016).Compared with land, the high thermal capacity and fluidity of urban water in reservoirs, lakes, and rivers, as well as the low thermal radiation rate, have the effect of cooling and humidifying air. It is found that the cooling effect of the water is affected by its area, shape, location, and surrounding landscape (Saaroni and Ziv, 2003; Sun et al., 2012). Cai et al. (2018) also noted that water bodies have a great impact on the relationship between urban form factors and land surface temperature (LST). Wetlands, as one of the three global ecosystems, perform important ecological functions, such as preserving water sources, purifying water, storing flood water, controlling drought, regulating climate, and maintaining biological diversity, and have a significant impact on global climate

change (Knox, 2001; Cai et al., 2016a,b). Based on this, many scholars have studied the microclimate regulatory function of wetlands, and tried to provide a scientific basis for alleviating the heat island effect (Van et al., 2015; Cai et al., 2016a,b; Zhang et al., 2015). Although these articles have reported that different UGS types mitigate the urban heat island effect, they involve only the cold island effect and its influencing factors for a single urban green space. There is no specific comparative analysis between different types of UGS evolution (Yang et al., 2017) and the urban thermal environment, such that we cannot determine the most critical evolution process which has a great impact on the land surface temperature. Consequently, it is urgent to deeply understand the role of various types of UGS evolution for alleviating the urban heat island effect at present. With rapid urbanization, Fuzhou, as the central city of the Economic Zone on the West Side of the Taiwan Straits, has become one of the “ten big stove cities” in China. The consequence of its outstanding performance is the intensification of the urban heat island effect, with the temperature continuing to increase. Urban green space can effectively alleviate the urban heat island effect and ease the cooling of the surrounding environment. Research on mitigation of the urban heat island in Fuzhou is focused primarily on the cooling impact of UGS evolution and the potential to plan strategic ventilation patterns. To deepen our understanding of the different effects of UGS evolution, the UGS of the main urban area of Fuzhou City was chosen as a case study for analysis under the background of rapid urbanization based on ten remote sensing images from 1993 to 2013 combined with Global Position System, Geographic Information System and Remote Sensing. There are four steps in the research: 1) Analyze the spatial and temporal dynamic change characteristics of UGS and LST over the last 20 years. 2) Compare the cooling effect of different types of UGS and UGS evolution on the urban surface thermal environment. 3) Determine the 334

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mechanism of the thermal environment effect caused by the evolution of UGS through the construction of a vegetation and cooling index (VCX) coordinate system. 4) Quantify the effect on LST resulting from the loss of UGS by regression analysis. Hence, we can investigate the relationship between the spatiotemporal evolution of UGS and the urban thermal environment, which will provide scientific support both for strategies to mitigate the heat island effect and the construction of ecological livable cities in other similar situations.

up, and bare land. Multistep classification can be divided into four steps. First, the optimal separation effect of band combinations was extracted, depending on the target spectral difference. Second, the maximum likelihood was obtained from supervised classification based on the decision tree classification method. Third, the forest/grass information, construction information, and water information were extracted using the normalized difference vegetation index (NDVI), normalized difference build-up index (NDBI), and modified normalized difference water index (MNDWI), respectively, which are defined in Eqs. (1)–(3). Finally, the individual types of land use information were extracted using the mask method. This operation was repeated until all the target classes were extracted. The degree of classification of this study was good, and the result of the classification was reliable. The classification results are shown in Table 2.

2. Research data and methods 2.1. Study area Fuzhou, the capital city of Fujian province, is located in the subtropical temperature zone between latitudes 25° 15′ and 26° 39′ N, and longitudes 118° 08′ and 120° 31′ E (Fig. 1). The city has a well distributed water environment, and a string of wetlands are scattered in Fuzhou estuary basin. This area was chosen as the study area because of the acceleration of urbanization over the last 30 years. With rapid urbanization, the city of Fuzhou is has expanded at an average annual rate of 6 ― 7% between 1996 and 2005 (Wang et al., 2006; Zhang, 2006) leading to excessive building, insufficient and seriously fragmented green areas (Lan et al., 2009), and continuously decreasing wetland areas in the main city (Liu et al., 2014). The average temperature difference between built-up and green areas is as high as 4 ℃ (Huang and Xu, 2005). There is a clear exponential relationship between the presence of impervious surfaces and LST, which causes a serious urban heat island effect (Xu, 2009). Since 2007, the average LST in summer has been up to 35 ― 40℃, and it became the hottest of the “four furnaces” in 2013. The study area consists of four districts: Gulou, Taijiang, Cangshan, and Jinan with a total area of 257.80 km2. As a typical river coastal city, where the Minjiang and the Wulongjiang (the branch of the Minjiang that encircles the south of Nantai Island) flow through the city, it is worthwhile to study the effect of spatial and temporal evolution of urban green space on the urban surface thermal environment in the context of rapid urbanization.

NDVI =

NDBI =

UGDDi =

TM5 TM5 ETM+ ETM+ TM5

07-05-2008 06-06-2009 08-04-2010 07-30-2011 08-04-2013

TM5 TM5 TM5 TM5 OLI

MIR

( (

+

(2)

NIR ) MIR )

GREEN GREEN

+

MIR )

(3)

UGSi UGSi × 100% UGSi × T

(4)

2.3.3. Division of UGS evolution The UGS evolution can be divided into three types: UGS extension (non-UGS is converted to UGS), UGS loss (UGS is converted into nonUGS), and UGS exchange (UGS is changed from once category to another). Hence, the UGS thematic maps of four years were stacked, and the regions in which evolution of UGS occurred in 1993―2000, 2000―2008, 2008―2013, and 1993―2013 were identified. The statistical results are shown in Table 3 and Fig. 2B. 2.3.4. LST retrieval This study primarily adopted the single window algorithm proposed by Qin and Zhang (2001) to retrieve the LST. The ten instances of LST distribution and its standardized classification were obtained using the standard classification formula of surface temperature in Eq. (5). The distribution map of geothermal temperatures in the study area from 1993 to 2013 is shown in Fig. 4, and the transfer matrix statistics table of geothermal temperature is shown in Table 4.

Table 1 Remote sensing data used in the study.

06-26-1993 06-26-1996 06-20-2000 07-05-2002 06-22-2006

NIR )

MIR

where UGSi and UGSi indicate the specific number of two years before and after a certain type of UGS, respectively; T is the time length; and UGDDi is the variation index of the spatial dynamics of certain types of UGS in the corresponding study time.

2.3.1. Classification of remote sensing images According to the standard of the Chinese classification system of land resources, combined with the actual situation of land use, maps, atlases, and administrative boundary maps as auxiliary references, the LULC area is divided into forest/grass, water, wetland, cropland, built-

Sensor

( (

(1)

RED )

2.3.2. Analysis of dynamic changes of UGS The research mainly focused on three types of urban green space: forest/grass, water, and wetland. The dynamic change of UGS was analyzed through the transfer matrix (Gao et al., 2014; Wu et al., 2014). The change degree of UGS was captured by a single UGS change index (UGDDi), which can be derived by Eq. (4). The statistical result is shown in Figs. 2 A and 3 .

2.3. Methodology

Time

NIR

+

where is the reflectance of each band, NIR is the near infrared band, RED is the red band, MIR is the infrared band, and GREEN is the green band.

To estimate changes in UGS and LST, this study is based on the classification and retrieval of LST from a set of remote sensing images (Table 1). Due to the limitation of weather, such as cloud contamination and atmospheric pollution, 10 images were selected to characterize the dynamics of LST. All of the images are derived from the computer network information center (Geospatial Data Cloud) of the Chinese Academy of Sciences. After interpretation and LST retrieval, the spatial statistics analysis of UGS evolution and urban thermal environment in the study areas were conducted using ArcGIS10.2® software.

Sensor

RED )

NIR

MNDWI =

2.2. Data source

Time

( (

TN =

LST LSTmin LSTmax LSTmin

(5)

where TN is the normalized value of surface temperature, in the range of (0,1); LST is the land surface temperature, and min and max represent the minimum and maximum values, respectively. The TN value is divided into six grades (low temperature, sub low temperature, medium 335

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Table 2 Statistical table of the area of LULC of the study area. forest/grass 2

1993 1996 2000 2002 2006 2008 2009 2010 2011 2013

water

wetland 2

cropland 2

area(km )

percentage

area(km )

percentage

area(km )

percentage

area(km )

percentage

area(km2)

percentage

40.99 37.21 36.23 30.28 29.26 24.13 23.43 22.95 21.45 20.11

15.90% 14.43% 14.06% 11.75% 11.35% 9.36% 9.09% 8.90% 8.32% 7.80%

40.04 38.01 35.46 34.02 33.32 27.86 27.65 27.45 27.33 27.32

15.53% 14.74% 13.76% 13.20% 12.92% 10.80% 10.73% 10.65% 10.60% 10.60%

6.02 5.43 4.98 4.78 2.57 2.48 2.43 2.34 2.14 2.02

2.34% 2.11% 1.93% 1.85% 1.00% 0.96% 0.94% 0.91% 0.83% 0.78%

70.55 67.89 62.47 56.14 50.35 42.90 42.45 42.12 42.09 41.90

27.37% 26.33% 24.23% 21.78% 19.53% 16.64% 16.47% 16.34% 16.33% 16.25%

99.46 105.65 115.49 127.89 142.14 159.61 160.14 162.32 163.18 165.25

38.58% 40.98% 44.80% 49.61% 55.14% 61.91% 62.12% 62.96% 63.26% 64.10%

0.74 3.61 3.16 4.69 0.16 0.82 1.70 0.62 1.73 1.19

0.29% 1.40% 1.23% 1.82% 0.06% 0.32% 0.66% 0.24% 0.66% 0.46%

LSTmean

accounting for 55.53% of the total reduction in UGS area, followed by the water (12.72 km2, 33.83%), and the wetlands (4.00 km2, 10.67%). In spite of the minimal loss of wetland, the annual rate of decline was 3.32%, which is 1.30 times and 2.09 times that of forest/grass and water, respectively. The degree of dynamic change of UGS (Fig. 3) fluctuated in general, and its change was closely related to the urban expansion of Fuzhou and the urban planning policies. In the second period (2000 ― 2008), the overall dynamic change of the UGS was ―3.62%, which was the highest in each period, while in the third period (2008 ― 2013), the degree of the dynamic change had dropped to ―1.84%. From the perspective of the UGS types, the average intensity of the wetlands decreased to ―3.32% followed by the forest/grass (―2.55%) and the water (―1.59%). The above results clearly show that the rapid urbanization process has a great influence on the ecological environment of urban suburban areas, and its rapid transformation process has influenced the ecological environment of the city in many aspects.

(6)

where RLSTi is the relative land surface temperature of some certain type of UGS, LSTi is the land surface temperature of one type of UGS, and LSTmean is the average land surface temperature of the study area. 2.3.5. Construction of the vegetation and cooling index (VCX) The study introduced vegetation coverage (Pv) and relative land surface temperature (RLST) as horizontal and vertical coordinates, respectively, to build the VCX spatial model. Quantitative vector fitting was applied to analyze the different types of UGS evolution and their temperature, which in turn will reveal the environmental effects. Among these environmental effects, the vegetation coverage evolved from the normalized vegetation index in Eq. (7). When studying the cooling efficiency of the cold/heat environment, the vector change amplitude of the invariant area is set as the null group, and the error of the UGS evolution area is eliminated. The results of the model construction are shown in Fig. 5. Normalizing the normalized difference vegetation index (NDVI) yielded the following:

NDVI NDVImin 2 ) NDVImax NDVImin

2

bare land

percentage

RLSTi = LSTi

Pv = (

build-up

area(km )

temperature, secondary high temperature, high temperature, and ultrahigh temperature) according to the natural breakpoint classification method as the basis for the division of the level of temperature. Due to the time difference of the remote sensing images, the relative land surface temperature (RLST) index was introduced to investigate the effect of different UGS on the thermal environment of the study area during different periods. The relative surface temperature index can be calculated by Eq. (6).

2

3.1.2. UGS evolution assessment From the statistics table of UGS evolution (Table 3), we know that the total area of UGS in the study area decreased during the period of 1993―2013, and the area difference between the UGS change and UGS unchanged has become larger. In each year, the area of UGS change had the same characteristics: namely, that the proportion of UGS loss was the largest, followed by UGS extension, and UGS exchange. During the study period, the area of UGS loss was 42.83 km2, which was converted from forest/grass, water body, and wetland. According to their contribution, they were ranked as forest/grass (26.82 km2) > water (13.95 km2) > wetland (2.06 km2). The UGS extension area was 4.99 km2, and the contribution of built-up land is the largest followed by the bare land. The total area of UGS exchanged was the smallest, with only 2.61 km2. From the time scale, the annual change rate of the area of UGS evolution in different stages (Table 3) showed that the first period (1993―2000) change was the largest (6.09 km2/yr) followed by the second stage (2000―2008; 4.59 km2/yr), and the third phase (2008―2013; 3.42 km2/yr). The change trend of UGS loss and UGS exchange was similar. The annual change showed little variation from the first stage to the second stage, with an annual decline of 0.10 km2/ yr and 0.05 km2/yr, respectively. However, in the third stage, there was a sharp decline, and the rates were reduced to 58.61% and 50.04% of the first stage, respectively. The annual variation of UGS extension decreased first and later slightly increased to 53.19% of the first stage during the third stage. From the spatial scale, the UGS evolution types in the study area from 1993 to 2013 were superimposed on the administrative division of the research areas and the transformation of the UGS evolution types from administrative region in the study area in the 20 years was calculated (Table 5). The most serious change of UGS evolution in each administrative area was UGS loss, which primarily occurred at the edge

(7)

3. Results and analysis 3.1. Results 3.1.1. UGS assessment From the analysis of LULC changes (Figs. 2–3 and Table 2), we found that the overall characteristics of LULC change in the main urban area of Fuzhou over the 20-year period were as follows: built-up > cropland > forest/grass > water > wetland > bare land. The total area of UGS has undergone considerable changes, showing a continuously decreasing trend at an average annual rate of 2.16%. The annual change rates of built-up land for 1993 ― 2000, 2000 ― 2008 and 2008 ― 2013 were 2.30%, 4.78%, and 0.71%, respectively, with the annual declining rates of UGS being 1.70%, 3.62%, and 1.84%, respectively. By 2013, the total area of UGS was only 49.45 km2, or 19.18% of the study area, while the non-UGS area was up to 80.82%. Changes to the area of UGS showed that different types of UGS have different characteristics. The total area of the forest/grass had been continuously decreasing with the maximum reduction of 20.88 km2, 336

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Fig. 2. UGS evolution in different periods of the study area.

of the central urban area and the suburbs, such as the northern (Wufeng Street) and the western (Hongshan Town) section of Gulou District, the border of Taijiang District and Cangshan District, the Jinan District, Jinshan Street, Cangshan District (Shangdu Street, Chengmen Town, Gaishan Town, and Luozhou Town), Jinan District (Xindian Town and Gushan Town). and other areas. These areas were mainly developed

from forest/grass, wetlands, and water. Among those regions, the largest area of UGS loss occurred in the Cangshan District, which primarily resulted loss or alteration of in forest/grass (10.93 km2), wetland (15.71 km2), and water (1.83 km2), accounting for 35.90%, 51.59%, and 6% of UGS loss, respectively.

337

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Fig. 3. The dynamic variability of UGS of the study area.

3.1.3. Assessment of thermal environment evolution As shown in Fig. 4 and Table 4, the land surface temperature distribution in Fuzhou was basically consistent with the outline of the city proper, its spatiotemporal pattern changed significantly. High temperature zones, that is, areas where the temperature was higher than the medium temperature grade, were identified statistically as covering from 23.53 km2 to 46.64 km2, respectively during the 20 study years. This represents a significant increase of 97.58% in 2013, which was approximately twice as much as that in 1993. Whereas, the areas of low temperature zones, that is those areas with temperatures below the medium temperature grade, decreased by 18.95 km2. This represents a reduction of 27.27%, which was mainly allocated to sub low temperature grade (20.92 km2) and middle temperature grade (7.49 km2). The high temperature and ultra-high temperature areas expanded by 6.66 km2 and 0.39 km2, respectively. Overall, the low temperature area gradually decreased, and the high temperature area gradually increased for the first period (1993―2000). However, the study area showed a slight increase in low temperature zones and a decrease in high temperature zones in the third period (2008―2013) because efforts have been devoted to optimizing the layout of the urban structure and strengthened the greening work during urban renewal and urban expansion in recent years. In 2009, the green area of Fuzhou City reached 54.39 km2, the urban green coverage rate was 39.25%, and the per capita public green area was 10.61 m2. At a spatial scale, the distribution of high temperature zones was consistent with that of the built-up area. The distribution is centered on Gulou District, expanding to the east and south. According to the statistics of the administrative division of the study area (Table 6), the areas of high temperature zones differ between districts. The area of high temperature zones in the Taijiang District decreased, the Gulou District area fluctuated, and the areas of Cangshan and Jinan increased greatly; this distribution mirrors the expansion of urban land. In 2008, approximately one-third of the area in the high temperature zones are obviously distributed in the newly extended Rongqiao investment in the Gulou District, and the in the former Puxiazhou wetlands in the Cangshan District that have been transformed into the Straits International Convention and Exhibition Center since 2000. Furthermore,

there are buildings along flood banks on both sides of the Minjiang River, which exhibit high temperature characteristics. According to the map of LULC change for the 20-year period, the high temperature zones are usually distributed in more built-up areas, while area with a high proportion of green, wetlands, and water shows a lower surface temperature. Hence, we infer that urbanization is the main influencing factor of the formation of urban heat islands, and the main factor causing the disturbance of the UGS temperature in the main urban area of Fuzhou. 3.2. Analysis of the influence of UGS spatiotemporal evolution on the urban thermal environment The LST of UGS (Table 7) in different years can be determined by superimposing the LULC thematic map and the LST map to understand the influence of the spatiotemporal evolution of UGS on the urban thermal environment. Overall, the difference between the surface temperature of UGS and non-UGS is significant, showing that the average surface temperature of water is the lowest in each year, followed by that of wetlands and forest/grass, while the average surface temperature in built-up land and bare land is the highest. The temperature difference between impervious surfaces and water is the greatest, followed by the difference between impervious surfaces and wetland and forest/grass. Hence, when water is converted to other land use types, it will no doubt cause the temperature of these areas to rise substantially, thus aggravating the urban heat island effect in Fuzhou, which can explain why Fuzhou's temperature has remained high for many years. A comparison of the changes in UGS area and surface temperature (Fig. 6) shows that as the area of UGS in the main urban area of Fuzhou gradually decreased the average surface temperature of the study area increased by 13.46 ℃ in 20 years. Compared to the underlying surface of the city, the average temperature and minimum temperature in UGS also showed an obvious upward trend but more moderate, with increases of 8.34 ℃ and 9.55 ℃, respectively. The temperature difference between the minimum temperature and the average temperature of UGS is reducing. Nevertheless, the average temperature difference

Table 3 Statistical table of different types of UGS evolution from 1993 to 2013. Type year

UGS extension 2

1993-2000 2000-2008 2008-2013 1993-2013

UGS loss 2

UGS exchange 2

UGS unchanged

UGS change

area(km )

percentage

area(km )

percentage

area(km )

percentage

area(km )

percentage

area(km2)

percentage

14.63 5.90 5.56 4.99

5.68% 2.29% 2.16% 1.94%

25.28 28.11 10.58 42.83

9.80% 10.90% 4.10% 16.61%

2.69 2.67 0.96 2.61

1.04% 1.04% 0.37% 1.01%

58.99 45.90 42.92 41.84

22.88% 17.80% 16.65% 16.23%

42.60 36.68 17.10 50.43

16.53% 14.23% 6.63% 19.56%

338

2

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Fig. 4. The grade distribution of surface temperature of the study area. Table 4 Statistical table of transfer matrix of different temperature zones from 1993 to 2013 (unit: km2).

T N1 T N2 T N3 T N4 T N5 T N6 2013 Δarea

T N1

T N2

T N3

T N4

T N5

T N6

1993

43.55 13.69 0.04 / / / 57.28 −18.95

20.92 54.63 8.53 3.43 0.44 0.00 87.94 −34.13

7.49 33.33 14.82 9.46 0.87 / 65.97 30.06

3.21 16.01 10.29 6.75 0.77 0.01 37.04 15.97

1.03 4.17 2.16 1.36 0.36 0.03 9.12 6.66

0.02 0.25 0.08 0.07 0.01 0.02 0.45 0.39

76.22 122.08 35.91 21.07 2.46 0.06 257.80 0.00

Table 5 The area of UGS evolution in each administrative region from 1993 to 2013(unit: km2).

UGS UGS UGS UGS

unchanged extension exchange loss

Gulou

Taijiang

Cangshan

Jinan

SUM

4.12 0.97 0.03 4.12

1.71 0.05 0.01 1.12

33.59 3.22 2.54 30.46

2.42 0.75 0.04 7.13

41.84 4.99 2.61 42.83

Table 6 The area of high temperature zones of administrative division in 4 years (unit: km2).

1993 2000 2008 2013

Gulou

Taijiang

Cangshan

Jinan

5.87 9.88 11.41 5.24

8.49 9.95 9.59 4.26

3.69 14.31 39.94 21.30

5.48 7.60 19.01 15.84

environment of the study area (Fig. 7A), we found that the RLST of water, forest/grass, and wetland is lower because of factors such as evaporation, shadow, and vegetation cover; therefore, UGS has a significant effect on the surface thermal environment. The cooling effect ranges from ―9.08 ℃ to ―3.96 ℃, with a magnitude of 5.12 ℃. The contribution of the water is the largest, with the RLST below ―7.15 ℃ followed by wetlands and forest/grass, with the RLST being below ―4.25 ℃ and ―1.20 ℃, respectively. In contrast, the non-UGS has a positive contribution to the surface thermal environment with the total heating range of approximately 3.00 ℃ each year. Due to the impervious surface, intensive human activity and a large amount of energy consumption, the contribution of the built-up land and the bare land is most obvious, showing the highest RLST from 5.03 ℃ to 9.67 ℃, with the lowest RLST above 0.72 ℃. The temperature distribution of the underlying surface shows a strong correlation with the type of land use/

Fig. 5. Change trajectory in VCX space for the period.1993–2013.

between UGS and the underlying surface shows the opposite trend, demonstrating that the advantages of UGS in alleviating the urban heat island phenomenon increase with the increasing intensity of the heat island. 3.2.1. Cooling difference of different types of UGS By comparing the relative surface temperature (RLST) of LULC in each year and observing its contribution to the surface thermal 339

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Table 7 Statistical table of LST in different year of LULC (℃).

1993 1996 2000 2002 2006 2008 2009 2010 2011 2013

forest/grass

water

wetland

cropland

build-up

bare land

UGS

non-UGS

mean

26.70 28.34 31.53 31.43 31.45 30.14 31.39 32.74 34.49 35.55

20.75 22.12 24.52 25.71 26.58 27.78 28.14 28.60 28.95 29.88

23.65 24.17 25.38 26.09 29.42 29.82 30.73 30.95 31.03 31.55

26.98 27.53 33.66 32.99 33.12 33.42 34.01 34.56 36.75 39.82

32.12 34.67 39.86 40.01 40.36 39.61 40.02 42.43 43.18 44.46

28.62 30.62 44.49 43.93 39.98 41.00 41.05 42.17 42.67 43.07

23.94 25.53 28.03 28.13 28.67 28.92 29.07 31.17 31.89 32.29

30.02 32.12 37.78 37.96 38.09 38.28 39.42 41.32 41.25 43.53

27.90 29.54 34.83 35.23 36.15 36.30 37.65 39.43 40.93 41.36

land cover. The cooling effects caused by different land types sorted in order are: water > wetland > forest/grass > cropland > built-up land > bare land. The temperature of the urban construction land with cement and tile structure is higher, while the temperature of the forest medium with high vegetation coverage is the second, and the medium with the lowest temperature is water. Quattrochi et al. (2000) and Liu et al. (2010) have reached similar conclusions when studying the heat island effect in the cities of Atlanta and Wuhan, respectively.

and 2.12 ℃, respectively. The last is areas that evolved into wetland, including water that evolved into wetland that resulted in increases of 2.52 ℃. The UGS extension area reduces LST: the conversion of built-up land into wetland has the most obvious effect (―2.36 ℃), followed by the conversion of bare land into water (―0.19 ℃). The temperature when the area of UGS was unchanged was volatile, and the average RLST change is the following: water (6.55 ℃) > wetland (2.15 ℃) > forest/grass (1.58 ℃). In summary, UGS evolution types have a variety of temperature response patterns. Meanwhile, the cooling contribution of different types of UGS loss also demonstrates the same cooling behavior, which is caused by different types of UGS.

3.2.2. Cooling difference of different types of UGS evolution Thematic maps of the evolution of UGS were overlaid with the RLST between 1993 and 2013 in order to quantify the response of the urban thermal temperature to the changes in UGS. The temperature of different UGS evolution types in the main urban area of Fuzhou generally increased (Fig. 7B), which brings different temperature effects to the thermal environment. The effect of the increase in temperature is as follows: UGS loss (8.67 ℃) > UGS exchange (4.00 ℃) > UGS extension (2.90 ℃) > UGS unchanged (2.45 ℃). From Table 8, it can be seen that UGS loss caused a significant increase in the temperature of the urban thermal environment, in which the loss of the water had most influence, followed by the loss of wetland and forest/grass. When the three UGS types were converted into built-up land or bare land, the average RLST increased significantly by more than 9.03 ℃. The areas where UGS was exchanged also drove the average RLST of the region to change: the influence of evolution into forest/grass is the strongest, with the evolution of wetland into forest/grass leading to a rise of 7.90 ℃ and the evolution of water into forest/grass causing a rise of 5.85 ℃. Secondmost influential are areas that evolved into water, with the evolution of forest/grass (and wetlands into water leading to increases of 3.35 ℃

3.3. Analysis of spatiotemporal evolution based on VCX index By analyzing UGS and LST evolution in the study area during the period of 1993 ― 2013, this research take advantage of VCX index to summarize the mechanisms between the UGS evolution types and the thermal environment effects (Fig. 5). In the VCX coordinate system, the trajectories of image points reveal the direction of evolution of different UGS and their environmental temperature response over a period of time. At the onset of the evolution, UGS loss is located at the lower right corner of the VCX coordinate space, with high vegetation coverage and low relative surface temperature. With the continuous exploitation of the UGS, the pixel of the UGS moves along its vector path to the upper left corner, and the final position of the pixel has the characteristics of low vegetation index and high surface temperature. The change in the UGS extension and the UGS exchange is relatively gentle, but the trend is similar with the pixel moving from low vegetation and high temperature to the opposite direction. The former concentrates mainly on

Fig. 6. Change map of UGS area and LST from 1993 to 2013. 340

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Fig. 7. Change map of RLST of UGS evolution from 1993 to 2013.

creation of warmer local climatic conditions. The reduction of UGS leads to a significant increase in LST and a reduction in surface comfort (Moonen et al., 2012; Viegas et al., 2013; Feyisa et al., 2016; Yang et al., 2017). Therefore, Fuzhou should adopt a strict land use management system to match the infrastructure construction of the local government with function-oriented zoning.

Table 8 Statistical table of RLST difference from1993 to 2013 (unit:℃). 93-13ΔRLST

forest/grass

water

wetland

cropland

build-up

bare land

forest/grass water wetland cropland build-up bare land

2.15 5.85 7.90 2.93 3.29 /

3.35 6.55 2.12 3.02 2.53 −0.19

/ 2.52 1.58 1.16 −2.36 /

5.42 8.78 9.14 6.01 4.37 5.11

9.03 10.30 9.57 9.40 6.04 7.26

9.67 14.80 / 10.16 9.04 13.22

3.4. Analysis by model building between UGS loss and urban thermal environment The above analysis shows that the ratio of UGS has a significant negative correlation with land surface temperature. During 1993―2013, the UGS area reduced by 37.60 km2 (Fig. 2A), which accounted for 14.58% of the main urban area of Fuzhou, where the forest/grass decreased most (20.88 km2, 8.09%) followed by the water (12.72 km2, 4.93%), and the wetland (4.00 km2, 1.55%). Because the forest/grass and water areas were reduced the most, the transfer pattern of urban UGS may also be an important factor (Yu et al., 2018), and this study further uses regression analysis to quantitatively analyze the warming effect caused by the loss of water and forest/grass. The corresponding MNDWI and NDVI of water and forest/grass in the study area in 2013 were normalized to a range of 0―1, equally divided into 10 grades. The average LST of the corresponding grade was calculated, and the linear regression analysis was carried out to determine the following regression relation model (Fig. 8).

the middle part of the VCX coordinate space, while the latter is at the lower left corner, calculated from the vegetation coverage (ΔPV) and relative surface temperature (ΔRLST) in the 20 years of the UGS evolution. UGS loss caused the greatest warming effect (ΔPV = ―0.15, ΔRLST = 6.22 ℃), which was more than decrease in temperature related to UGS extension (ΔPV = 0.10, ΔRLST = ―1.46 ℃) and UGS exchange (ΔPV = 0.07, ΔRLST = ―0.14 ℃). The temperature change caused by UGS loss is 4.26 times the temperature change of the UGS extension despite the change of vegetation cover caused by the UGS loss being approximately 1.5 times that of the UGS extension. Therefore, in the process of urbanization, the expansion of construction land at the expense of ecological land often leads to the decrease of vegetation coverage, which contributes to the overall increase in temperature. Environmentally uninformed city planning typically results in the 341

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Fig. 8. Linear relationship between UGS loss and LST.

Regression analysis shows that the LST will decrease by 1.65 ℃ and 0.89 ℃, respectively, when the ratio of water and forest/grass increases by 10%. To compare the contribution of the loss of water and forest/ grass to the temperature rise in the research area during the 1993 ― 2013 in the UGS loss, the reduction ratio of water and forest/grass in the 20-year period was replaced by the corresponding regression model, and the increasing temperature of the study area calculated was 0.81 ℃ and 0.72 ℃, showing that although the reduction area of forest/grass and water is less than one-tenth of the total area of the study, the warming characteristics caused by the area of forest/grass and water are very obvious. To some extent, the UGS extension can produce a cold island effect, but the decrease of temperature caused by it cannot offset the temperature rise caused by UGS loss.

built-up area, which accounted for 92.68% ― 99.45% of the total area. In contrast, the area covered with a high proportion of forest/grass, wetland, and water showed a lower surface temperature. The VCX coordinate further interpreted different types of UGS evolution had different temperature responses. Regression analysis indicated that the LST would decrease by 1.65 ℃ and 0.89 ℃, respectively, when the proportion of water and forest/grass increased by 10%. Moreover, the increased impervious surface was characterized by the replacement of three sub-categories of natural land (forest/grass, water and wetland). Fourthly, compared with routine in-situ measurement of local meteorological stations which provide locally detailed information but lack of spatial coverage, the satellite-based LST can provide a spatially explicit insight on thermal environment attributed to human activities. Similar to existing studies, the satellite-based LST relies on biophysical features of land cover types, which are subject to human activities, such as changes in patterns and intensity of land development. Therefore, the methods and outcomes of this study can be closely linked with the thermal consequences of city-level decision processes, such as urban planning, urban regeneration, and land conversion, especially focusing on the issues of climate adaption and UHI effect mitigation. Our findings might help to better understand the heterogeneous processes of growth patterns of urban built-up land and the changing UGS area. Therefore, this study provided useful information for researchers and decision-makers engaged in sustainable urban management, land development, and regional developmental inequality.

3.5. Research limitations The major limitations of this study include two facets, including (1) the low temporal resolution of satellite-based LST when compared with routine in-situ measurement of local meteorological stations; (2) the difficulty in linking satellite-based LST and air temperature, the latter is well known to the public. Since there is no instant cause-effect relationship between LST and air temperature for this study area, it is inevitably difficult to deliver the thermal environment information to the citizen if LST information was used. 4. Conclusions

Author contributions

In this study, the primary area of Fuzhou City was used as a case study to explore the relationship between the UGS dynamic evolution and thermal characteristics during China’s rapid urbanization from 1993 to 2013. The mechanism of the urban thermal environment effect was investigated from the view point of UGS evolution. Our findings can be summarized as follows. Firstly, the total area of UGS in the main urban area of Fuzhou has decreased significantly during the 20-year study period. Compared with 1993, the total area of UGS had decreased by 37.60 km2 in 2013. During the process of evolution, most growth in land for urban construction came from UGS loss. Moreover, the area of forest/grass showed the largest decrease, followed by water and wetland, which primarily occurred at the edge of the central urban area and the suburbs. Secondly, the spatiotemporal pattern of urban construction land had changed significantly. The contribution of different types of UGS evolution to the cold island effect was also different. The UGS loss had the largest negative correlation, followed by UGS exchange and UGS extension. In the UGS loss region, water moderated the LST most, followed by wetland and forest/grass. Thirdly, the coupling regression model showed that there was a significant spatial correlation between the evolution of UGS and LST. High temperature zones were typically more-widely distributed in

Chuan Tong and Yuanbin Cai designed the overall ideas for this study. Yuanbin Cai and Yanhong Chen performed data analysis and wrote this manuscript. Yuanbin Cai undertook the major revision work. All the authors got involved in discussion of improving the quality of this manuscript. Chuan Tong was responsible for academic opinion of this manuscript. Conflicts of interest The authors declare no conflict of interest. Acknowledgments This study is support by the Fujian Education Research Project (JT180021) and Social Science Planning Project of Fujian (NO. FJ2016C033). Many thanks to Chinese Academy of Sciences for their data sharing platforms known as Geographic Space Cloud (http://www. gscloud.cn/) and the Open Spatial Data Sharing Project (http://ids. ceode.ac.cn/). 342

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