Characterizing urban redevelopment process by quantifying thermal dynamic and landscape analysis

Habitat International 86 (2019) 61–70

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Characterizing urban redevelopment process by quantifying thermal dynamic and landscape analysis

T

Zhuokun Pana,b, Guangxing Wangb,a, Yueming Hua,c,d,e,∗, Bin Caof a

College of Natural Resources and Environment, South China Agricultural University, Guangzhou, 510642, China Department of Geography and Environmental Resources, Southern Illinois University at Carbondale, IL, 62901, USA c Key Laboratory of Construction Land Transformation, Ministry of Natural Resources of China, Guangzhou, 510642, China d Guangdong Provincial Key Laboratory of Land Use and Consolidation, Guangzhou, 510642, China e Guangdong Provincial Land Information Engineering Research Center, Guangzhou, 510642, China f School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China b

ARTICLE INFO

ABSTRACT

Keywords: Urban redevelopment UHI Mitigation Landscape ecology

Urban redevelopment practices have received substantial attention in urban planning. Remotely sensed thermal infrared monitoring of urban heat island (UHI) is a well-documented topic, however, there is a lack of understanding of the influence on UHI change caused by urban redevelopment process. The objectives of this study was to investigate the urban redevelopment-induced efforts incorporating remote sensing of UHI and land use change. Multi-temporal ASTER thermal infrared images were employed to characterize UHI change; and high resolution Worldview images were employed to perform land use classification. UHI dynamic was quantified with urban heat island ratio index. Analysis of urban redevelopment-induced land use change with response to UHI were carried out. Landscape ecology methods were employed to quantitatively identify land use change with landscape metrics. Result demonstrated that UHI effect had a trend of mitigation during urban redevelopment process in the study area. Urban heat island intensity could be significantly eliminated or weakened by changes of land use composition and spatial configuration. These phenomena were closely related to redevelopment practices such as industrial relocation, buildings demolition, and brownfield transformation. This article presented a case study for characterizing urban redevelopment process with remote sensing monitoring, quantifying the change with urban heat island ratio index and landscape ecology analysis, research findings could be utilized as indicators for urban planning.

1. Introduction Developments of population and social-economy have been leading to dramatic urban sprawling all around the world (Chun & Guldmann, 2014; Li et al., 2011; Long, Tang, Li, & Heilig, 2007; Manakos & Braun, 2014; Rashed & Jürgens, 2010). Tremendous change of urban area can be observed in many cities; but here, growth and decay process occur simultaneously (Banzhaf, Kindler, & Haase, 2007; Bennett & Smith, 2017). In recent years, rapid urbanization has increasingly revealed the need of re-using urban lands once occupied by old factories, villages, and towns (Ferrara, 2008; Xie & Li, 2010). In United State and European countries, the term “brownfield” is regarded as those improvident industrial planning area without adequate pollution control in the past that had become an obstacle to urban sustainability (Chrysochoou et al., 2012; Loures & Vaz, 2018; Rizzo

et al., 2015; Xie & Li, 2010). Take brownfield remediation as an example, the redevelopment of such urban area improves land use efficiency and helps address the problem of land supply scarcity (Xie & Li, 2010). However, evaluating the potentials and benefits of urban redevelopment has not yet received much attention; both government and private decision-makers need to be acquainted with information regarding to redevelopment incentives, public goals, and environmental improvement (Hui, Dong, Jia, & Lam, 2017; Loures & Vaz, 2018; Rizzo et al., 2015). Moreover, technologies such as geographical information system (GIS)-based methods are helpful for an area-wide brownfields evaluation (Chrysochoou et al., 2012; Thomas, 2002). Until recent, using remote sensing methods has not been received much attention in this field, only a few published articles had been reported towards brownfields identification (Atturo et al., 2006; Ferrara, 2008). Besides, thermal infrared monitoring of spatiotemporal dynamic owing

∗ Corresponding author. The College of Natural Resources and Environment, South China Agricultural University, Wushan Road No.483, Tianhe District, Guangzhou, China. E-mail addresses: [email protected] (Z. Pan), [email protected] (Y. Hu).

https://doi.org/10.1016/j.habitatint.2019.03.004 Received 4 May 2018; Received in revised form 20 February 2019; Accepted 11 March 2019 0197-3975/ © 2019 Elsevier Ltd. All rights reserved.

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Fig. 1. Location of Tianhe District in Guangzhou and planned urban redevelopment area.

landscapes configuration; however, characterizing urban redevelopment-induced efforts by using TIRS data together with a landscape ecological purview is still lacking. This study aimed to characterize the urban redevelopment process by quantifying thermal dynamics and landscape analysis. Remotely sensed monitoring as research approaches were to reveal the efforts of urban redevelopment practices. Methods and applications were then examined in current practices of applying thermal infrared and high resolution optical remote sensing data on urban redevelopment process. Results and discussions of this study might inspire new thinking on urban planning.

to urban redevelopment process are rather limited. Land surface temperature (LST) derived from thermal infrared remote sensing (TIRS) has been applied on urban environment to assess urban heat island (UHI) effects for decades. It is well-documented that impervious surface expansion as a result of urban sprawling which forms UHI pattern; it is also clarified that urbanization process leads to land cover changes (e.g., vegetation converted to built-up area) which imposes on urban thermal environment (Chen, Zhao, Li, & Yin, 2006; Corburn, 2009; Deng & Wu, 2013; Heldens, Taubenbock, Esch, Heiden, & Wurm, 2013; Lo & Quattrochi, 2003; Voogt & Oke, 2003; Xiong et al., 2012). Beyond urban land use/land cover (LULC) change, some scholars demonstrated that spatial pattern of UHI can also be affected by spatial configuration of buildings (Coseo & Larsen, 2014; Zhou, Huang, & Cadenasso, 2011), concentrations of “hot spots” sources (Coutts, 2016; Heldens et al., 2013), and influence of building height (Guo, Zhou, Wu, Xiao, & Chen, 2016). It seems to be well-documented in remote sensing literature indicating that most scholars concern the increase of heat island effect, however, few address the mitigation practices on UHI (Heldens et al., 2013; Xue, Fung, & Tsou, 2014; Zhou et al., 2011). Based on literature review, the formation of UHI pattern is clarified to be closely related to land cover, and landscapes configuration. The ideas of quantifying urbanization patterns and evaluating land use planning with landscape metrics had been proposed for years (Leitão & Ahern, 2002; Wu et al., 2012). Landscape metrics had been employed as measures of land cover composition and configuration in evaluating building spatial pattern, patch size, shape, proximity, and connectivity. They can be indicators for the spatial composition and configuration of the map because they measure the characteristics of landscape pattern across space and time (Gustafson, 1998; Leitão & Ahern, 2002; Quattrochi & Luvall, 1999). Having gone through urban redevelopment process, in the sense of landscape ecology, urban land use changes could be revealed by their patch size and morphology, spatial arrangement and combination, homogeneity and heterogeneity. Under this reasoning, urban thermal patterns could be modified by spatial reconfiguration of land use. Therefore, landscape metrics are useful tools for applying landscape ecological concepts to understand urban landscape dynamics as well as thermal pattern change (Li et al., 2011; Xue et al., 2014; Zhao, Wang, & Chen, 2012). Now that satellite-observed TIRS image for urban thermal pattern is clearly related to LULC and

2. Study area and the story behind The City of Guangzhou in Southern China, where lots of scholars have witnessed an astonishing rate of LULC conversion over past three decades due to urbanization. Studies based on multi-temporal Landsat images have revealed that the LULC as well as LST substantially change in Guangzhou due to its rapid urbanization (Chen et al., 2006; Gong, Chen, Liu, & Wang, 2014; Guo et al., 2015; Xiong et al., 2012). However, most of the aforementioned studies were centered on the macroview urban sprawling phenomenon, but micro-scale urban thermal characteristics change were less concerned. Until recently, the perception is still undiscovered that urban redevelopment process might modify thermal pattern due to industrial relocation, brownfields transformation, and buildings demolition. Tianhe District is the geographical center of Guangzhou city where commercial, industrial, cultural and educational activities are taking place. The economic development is characterized by highly developed tertiary industry and rapidly developed high technologies. Tianhe District has witnessed an astonishing rate of land use conversion during its urbanization. With increasing population growth meanwhile loss of urban land resources in Guangzhou, effective land use management and re-planning is urgently needed because there are extensive existence of obsolete factories and unprofitable buildings in Tianhe District (Fig. 1). Besides, those urban villages, obsolete factories and decayed downtowns seems not fit to the development of a modern city (Li, Lin, Li, & Wu, 2014). Therefore, urban renewal has become a major agenda of policy in this region. City planner of Guangzhou has strong determinations and practical measures to carry out urban renewal process 62

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Fig. 2. Photos of redevelopment area in Tianhe District, (1)–(5) were under redeveloped and (6)–(8) were planned (photos taken on Apr 2017).

(Zhang & Li, 2016). Rapid land use changes are taking place owing to the construction activities for land redevelopment in recent years. Several on-site redevelopment phenomena were presented in Fig. 2: (1) It was an urban village with super high density buildings, once crowded with city migrants, the government had ordered to demolish these illegal buildings for years. (2) It used to be a heavy pollution factory and now it has transformed into a cultural and creative industrial park. (3) It used to be a commercial district with old factories surrounded, due to its poor planning and unprofitable management, it was being demolished. (4) It was an thermal power station producing heavy pollution, it had been demolished and new construction raised up; (5) It was an abandoned factory and being rebuilt to a commercial purpose; (6) It is an unprofitable factory but still pending rental; (7) It is a decayed commercial district with fewer people run their business there; (8) It is a cement factory which produces heavy pollution, the government had ordered a shutdown and relocation. Urban redevelopment process indirectly reveals some measures (e.g., re-configuration of land use density, increasing urban greenery, eliminating heavy pollution industry, etc.), based on this reasoning, urban redevelopment activities might influence urban heat island distributions. Consequently, these perceptions might support that redevelopment is helping to mitigate the UHI effect. Hence, thermal infrared and high resolution remote sensing witness these environmental conversions. This opportunity makes Tianhe District a good case of study to investigate the change of urban thermal characteristics. To characterize urban thermal dynamic associated with land use change, this study considered quantifying UHI intensity, land use change detection, and landscape analysis. Methodology and results are presented as follow.

remote sensing satellite. It covers a wide spectral region from visible to thermal infrared with 14 spectral bands (Abrams et al., 2015; Earth Remote Sensing Data Analysis Center, 2007). Thanks to open-access data policy, up-to-date and archive data could be obtained from USGS earth explorer (http://glovis.usgs.gov). ASTER data has 4 thermal infrared band at 90-m resolution which is scarce data source of TIRS data. TIRS image were acquired during urban redevelopment period around 2009. This study selected 4 ASTER images acquired at 2006, 2010, 2013, and 2015 when their dates were close to each other (see Fig. 3 (a)). TIRS images were captured during winter period which spanns November to March in North Hemisphere with free-of-cloud; winter is considered as the optimal period for TIRS data acquisition to study urban heat island effect (Zhao et al., 2012). The preprocessing procedures for TIRS data were summarized as follows. Radiometric calibration is required to convert the original digital number (DN) values into physical units that is at-sensor radiance, described as Eq. (1) (Earth Remote Sensing Data Analysis Center, 2007): Lsensor = (DN - 1) × U

(1)

Where Lsensor is at-sensor radiance, U represented unit conversion coefficient which is equal to 6.882 × 10−3. Thermal infrared band 13 was selected for study. A forward step is to convert the radiance to the at-sensor temperature Tsensor, which retrieved from a simplified Plank's function (Jiménez-Muñoz & Sobrino, 2010) as Eq. (2):

Tsensor =

ln

(

K2 K1 Lsensor

)

+1

(2)

Where coefficient K1 = 865.65, K2 = 1349.82, for band 13. In this study, we did not consider retrieving land surface temperature, because at-sensor temperature can be used to delineate UHI spatial pattern (Chen et al., 2006; Xu, Ding, & Wen, 2009). Secondary, in order to eliminate seasonal variation and in-situ atmospheric effect then making multi-temporal image data comparable, this study proposed two steps to eliminate these possible effects: 1) Histogram matching was applied on TIR images to calibrate the

3. Data and methods 3.1. Thermal infrared data ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) is an advanced multispectral sensor mounted on the Terra 63

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Fig. 3. Remote sensing data of study area: (a) multi-temporal ASTER thermal infrared images; (b) Worldview image.

temperature value then eliminating the multi-temporal “shift”; histogram matching provides a practical adjustment for temperature value among different acquisition time, without altering the UHI spatial pattern in the image scene. 2) Temperature difference normalization was applied on TIRS images in order to obtain a relative UHI, described as Eq. (3):

= (Ti

T¯ )/ T¯

To provide evidences for UHI dynamic related to LULC change, a selection of 6 redevelopment area were used to identify LULC change between 2011 and 2015. Feature extraction (embedded in ENVI 5.1 remote sensing software) provided user-defined scale segmentations and criteria descriptions for spatial, spectral, and texture characteristics (ENVI Tutorials, 2013). Land use classification rules were then established to classify real scene ground objects.

(3)

Where, Π is a relative temperature value range 0–1, Ti is the temperature value of TIR image scene, T¯ is the average temperature value throughout the image scene. Each ASTER data was histogram-matched and difference-normalized to obtain a seasonal-calibrated image. To quantitatively describe UHI change during the urban redevelopment period, we adopted the urban heat island ratio index (URI) to reflect the intensity of urban heat effect (Xu et al., 2009). The URI is calculated by Eq. (4).

URI =

1 n

4. Results 4.1. UHI pattern change Result of UHI pattern change is shown in Fig. 4. The URI index quantitatively describes the UHI intensity changes for the whole scene; the temperature value was reclassified as Severe, High, Medium, Low, Weak and None, representing spatial distribution of UHI level. The URI value marked in the upper left corner of Fig. 4 (a) presented a whole UHI intensity of a specific period. Apparently, spatial pattern of UHI level indicated an intuitive weakening trend throughout the Tianhe District. In addition, a summary of change proportion of UHI levels is shown in Fig. 4 (b); result indicated that the UHI intensity appears to be mitigated during 2006 and 2015, that is weaker UHI area increased, meanwhile stronger UHI decreased. According to Fig. 4, the domain trend of UHI effect in Tianhe District is decreased during the past 10 years. The year before 2009, strong UHI level mainly appeared in the south and southeast of Tianhe District, where used to be widely distributed industrial area with many heavy pollution and energy-intensive factories (see Fig. 1). When the urban redevelopment policy began in 2009, there is a phenomenon indicated UHI intensity had gradually faded away in following years. Attributing to urban renewal practices happened throughout Tianhe District, land use re-configuration and pollution control practices manifested as the increase of green area ratio, decrease of dustfall just

n i pi i=1

(4)

Where n is number of reclassified temperature value levels, usually n is defined as 6 levels; i is the temperature value level range 1–6; w is a weight using the temperature value of corresponding level i; p is the percentage of level i occupying throughout the image scene. 3.2. High spatial resolution data High spatial resolution Worldview images were obtained in 2011 and 2015, respectively (Fig. 3 (b)). They give an intuitive change of LULC owing to urban redevelopment process. The Worldview images have a 2-m pixel resolution for multispectral bands and 0.5-m for panchromatic band. The multispectral bands were pan-sharpened to a pixel resolution of 0.5-m, which provided high quality images for land use classification. 64

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Fig. 4. (a) UHI intensity in Tianhe District in 2006, 2010, 2013, and 2015; (b) Proportional change of UHI level (%) based on pixel statistics throughout image scene; (c) Statistical data indicates urban renewal efforts.

witness these environmental conversions which indicated positive efforts brought by urban redevelopment process. These movements were evident in Fig. 5 (a)-(f) including re-configuration of land use type, and demolishing old factories. A summary of land use change proportion between 2011 and 2015 presented in Fig. 6, giving a description of land conversion. According to bar graph, apparently, land use change between 2011 and 2015 in these 6 sites could tell that UHI mitigation were contributing to increase in vegetation cover; and land use conversion from industrial to modern residential area. A multiple linear regression function had been established based on land use change proportion corresponding to URI, the changing types as independent variable to model the dependent variable URI (Eq. (5)):

indicate these efforts (Fig. 4 (c)). This phenomenon might be closely related to urban renewal policy, especially the so-called “Three Old Transformation” and “Suppress the Second Industry and Develop the Tertiary Industry” movement happened in Guangzhou during the past 10 years. 4.2. Urban LULC change The whole view of Tianhe District presented a decreasing trend of UHI intensity. Now that the relationship between thermal infrared pattern and LULC configuration had been wealthily documented; with regarding to these UHI change, it could be assumed to be the consequences of land conversion due to redevelopment practices. These land use changes took place dispersedly then altered the whole UHI spatial pattern. To provide evidences for UHI dynamic related to LULC change, 6 sites of redevelopment areas were selected based on Worldview image to identify LULC change between 2011 and 2015 (see Fig. 5 (a)-(f)). Feature extraction-based classification had been applied on these 6 sites. Land use classification rules were created and then performed to obtain land use map. Classification included Modern residential area (red), Industrial land (blue), Road (yellow), High density vegetation (dark green), Low density vegetation (light green), Bare soil (brown), and Shade/water/unknown (black). Land use map and their corresponding UHI intensity patterns were presented in Fig. 5. Apparently, LULC changed accompanied with UHI intensity pattern. The URI value marked in thermal infrared image presented a whole UHI intensity. Apparently, the URI value and spatial pattern indicated an intuitive fading of UHI intensity between 2011 and 2015. Urban redevelopment processes indirectly reveal some practices, manifested as re-configuration of impervious land use density, increasing vegetation cover, eliminating energy-intensive industry, etc. Remote sensing intuitively captured these measures, and thermal infrared images

URI = 4.8436 * Low Density vegetation + 4.829 * Industrial land +3.006 * Modern residential area - 0.0717 * High density vegetation + 0.9590 * Road + 1.5683 * Bare soil + 0.7137 * Shade/water/ unknown-1.7155,R-square = 0.95 (5) Statistically, according to Eq. (5), multiple regression equation characterizes the contributors of URI change. The regression coefficient of low vegetation cover, industrial land, and modern residential area are ranking high which means they are mainly contributing to the URI changes, and means they are critical determining factors that influence UHI intensity. This result had an agreement with the major contribution of biophysical components (vegetation cover, impervious surface, etc.) to urban thermal environment (Coseo & Larsen, 2014; Deng & Wu, 2013; Guo et al., 2015). Besides, this finding agreed with the point of view that UHI can be significantly increased or decreased by spatial arrangements of land cover, and also possible adjacent effect from heat sources (Coseo & Larsen, 2014; Zhou et al., 2011). These results suggested that the impact of urbanization on UHI can be mitigated not only 65

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Fig. 5. Selected sites of urban redeveloped area (a)–(f) with change detection 2011(left) and 2015(right), each sub-figure represented a Worldview image scene, land use map and the UHI pattern, correspondingly.

by balancing the relative amounts of land cover, but also by optimizing their spatial configuration.

Characterization of urbanization process would include measurements of urban landscape composition (e.g., land use diversity, abundance), and landscape configuration (e.g., arrangement, shape, and connectivity) (Wu et al., 2012). With regarding to urban redevelopment-induced land use change, linking with these changes to identify their efforts on UHI are still lack of quantitative understanding (Li, Li, & Wu, 2013; Xue et al., 2014). It is necessary to understand the relocation of urban heat islands as well as the causes, revealing a mitigation practice of UHI. Hence research evidences should be provided for recommending municipal planners and decision-makers to formulate specific policies to the problematic land uses, in order to mitigate

4.3. Landscape pattern analysis Having gone through urban redevelopment process, in the sense of landscape ecology, urban land use changes could be revealed by their patch size and morphology, spatial arrangement and combination, homogeneity and heterogeneity. In this sense, landscape metrics are useful and essential tools for applying landscape ecological concepts to understand this process regarding to UHI pattern and land use.

Fig. 6. Summary of land use change proportion in 6 sites (a)–(f) between 2011 and 2015. 66

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Table 1 Selected landscape metrics, descriptions, and calculation methods (McGarigal, 2015). Description and reason for use

Calculation

PD (Patch Density) expresses number of patches on a per unit area basis. It measures spatial heterogeneity of a whole landscape, higher value correlated with high fragmentation and heterogeneity, which means landscape pattern became more complex and irregular. The landscape pattern of urban redevelopment area had been reshaped then appeared change in PD. AI (Aggregation index) shows the frequency with which different pairs of patch types appear side-by-side on the map. AI ranges from 0 to 100 indicates the focal patch type is fully disaggregated or aggregated, respectively. In this sense, land cover feature change together with reconfiguration of land use in the scene will probably alter the AI value. PAFRAC (Perimeter-Area Fractal Dimension) reflects shape complexity across the landscape pattern, a regression relationship is established between perimeter and area over the landscape. In this sense, change of land cover shape, and re-configuration will probably alter the PAFRAC value, a value should be projected to be lower under urban redevelopment processes. LSI (Landscape Shape Index) provides a simple measure of class aggregation or clumpiness, if changes are taking place, area and perimeter of individual landscape patches are modified, LSI quantify these irregular change. It increases as the patch type becomes more irregular. SHDI (Shannon's Diversity Index) is a popular measure of diversity in community ecology. In this sense, urban redevelopment area original transferred to a new scene then landscape heterogeneity is increased, it give an identification of land use re-configuration. MSIDI (Modified Simpson's diversity index) is modified from Simpson's diversity index which is a popular diversity measure borrowed from community ecology. It increases as the number of different patch types increases and the proportional distribution of area among patch types becomes more equitable. It give an identification of land use re-configuration. LPI (Largest Patch Index) quantifies the percentage of total landscape area comprised by the largest patch. As such, it quantifies dominant landscape patterns, richness, then to indicate a direction and intensity of UHI during urban redevelopment process. In this sense, as the decrease of UHI intensity, the change of UHI pattern were projected to have a high LPI value.

PD =

ni (10,000) A

gii

m i=1

AI =

(100) , ni = number of patches in the landscape of patch type (class) i; A = total landscape area.

max

Pi (100) , gii = number of like adjacencies between pixels of patch type i based on the single-

gii

count method; max-gii = maximum number of like adjacencies between pixels of patch type i based on the singlecount method; Pi = proportion of landscape comprised of patch type i.

2 N

PAFRAC =

m i=1

n j = 1 ln pij ln aij N

m i=1

m i=1

n 2 j = 1 ln pij

n j = 1 ln pij m i=1

m i=1

n j = 1 ln pij

n j = 1 ln aij

2

, aij = area of patch ij; pij = perimeter of

patch ij; N = total number of patches in the landscape.

LSI =

ei , ei = total min ei

length of perimeter of class i in terms of number of cell surfaces; includes all landscape

boundary and background edge segments involving class i. min ei = minimum total length of perimeter of class i in terms of number of cell surfaces.

m i=1

SHDI =

MSIDI =

LPI =

m 2 i = 1 Pi ,

ln

max(aij ) A

(Pi ln Pi) , Pi = proportion of the landscape occupied by patch type (class) i.

Pi = proportion of the landscape occupied by patch type i.

(100) , aij = area of patch ij; A = total landscape area.

extreme UHI effect (Li et al., 2011; Weng, Liu, & Lu, 2007). A series of landscape metrics were rationally selected with definitions summarized in Table 1. Landscape metrics of 6 selected sites (a)(f) were calculated based on the Worldview image-derived land use map, as well as the UHI pattern image; the calculations were performed in Fragstats software (McGarigal, 2015). Finally, a quantitative description of landscape changes resulted from urban redevelopment process were obtained, presented in Table 2. The selected landscape metrics described 3 aspects of landscape pattern including shape, aggregation, and diversity. Results were

presented in Table 2, there were several indicators to reflect: 1) decrease of PD and increase of AI suggested that the whole view of landscape had been reshaped and become more aggregative; 2) decrease of PAFRAC and LSI suggested structural change of patches with a less patch edge complexity which means land cover patches distribution became more regular (e.g., re-arranged like a rectangle); 3) increase of MSIDI and SHDI suggested that an increase amount of land cover types in the scene then adding on to landscape diversity. Landscape metrics of UHI pattern between 2010 and 2015 were also calculated to quantitatively describe the changes, results presented in

Table 2 Landscape metrics of land use map between 2011 and 2015. Site

a b c e e f

PD

AI

PAFRAC

LSI

SHDI

MSIDI

2011

2015

2011

2015

2011

2015

2011

2015

2011

2015

2011

2015

10648 11009 8349 6446 7917 14086

8461 10677 8035 4578 7001 8178

95.28 95.05 95.87 96.96 96.81 95.34

96.01 95.49 96.61 97.94 97.01 96.16

1.22 1.23 1.21 1.21 1.22 1.22

1.21 1.21 1.20 1.20 1.21 1.22

51.52 31.72 20.90 19.08 30.70 26.77

43.83 29.08 17.47 13.47 28.15 26.66

1.92 1.84 1.78 1.59 1.67 1.86

1.99 1.89 1.81 1.58 1.72 1.91

1.72 1.65 1.56 1.26 1.37 1.73

1.81 1.76 1.66 1.27 1.39 1.79

67

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Table 3 Landscape metrics of UHI pattern between 2010 and 2015. Site

a b c e e f

PD

AI

LSI

SHDI

MSIDI

LPI

2010

2015

2010

2015

2010

2015

2010

2015

2010

2015

2010

2015

17.97 38.25 41.96 48.43 28.32 46.23

17.96 16.39 27.97 39.35 19.37 39.12

95.02 92.22 92.20 94.57 93.75 92.74

95.00 94.85 94.93 94.99 93.95 93.26

4.19 3.83 3.40 3.03 4.14 3.45

4.13 2.85 2.50 2.85 3.97 3.25

1.43 1.50 1.30 1.48 1.52 1.66

0.86 1.36 1.16 1.38 1.36 1.08

1.33 1.31 1.17 1.34 1.44 1.56

0.66 1.22 1.04 1.28 1.26 1.07

26.63 37.84 25.22 20.21 19.68 25.07

34.42 67.59 35.66 33.86 21.87 38.94

dynamic reflecting the impacts of urban redevelopment. Similar case from Weng et al. (2007), they examined the relationship between LST and urban land cover using landscape metrics; Xue et al. (2014) employed multi-temporal ASTER image and landscape metrics to study the urban process impact on thermal landscape in Hong Kong; but these aforementioned studies were not persuasive enough to manifest a cooling urban design. Besides, the characteristics of urban thermal environment and landscape pattern should be emphasized by the process rather than a single period of image. Wu (2009) stated that “This increasing urban emphasis will provide more opportunities for developing and testing landscape ecological theories and principles, enhance the field's interdisciplinary and transdisciplinary, and make landscape ecology more relevant to society and the world with dynamic landscapes”. Indeed, landscape ecology analysis provides a quantitative description with metrics for characterizing landscape in response to urban redevelopment process. These perspectives also provide an understanding of urban land use and planning that help to mitigate UHI effect (Li et al., 2011; Zhou et al., 2011). Dynamic analysis of landscape characteristics is an important topic had been proposed for decades; numerous metrics have emerged from landscape ecology that are useful for quantitative analysis of land use planning and diagnosis (Leitão & Ahern, 2002; Li et al., 2011; Quattrochi & Luvall, 1999). The relationship between landscape metrics and LST was affirmative that landscape configuration also influences the UHI. But they did not explain when urban land use changes would influence UHI, and how these changes and reconfiguration would reshape the UHI, specifically towards a dynamic urban redevelopment process. This study should be good add-on to landscape planning and monitoring with metrics. To address the problems above, multi-temporal TIRS and high spatial resolution images were adopted to investigate the assumption of UHI mitigation. Selection of 6 redeveloped sites had been performed landscape pattern analysis to better understand how such changes are affecting the UHI spatial pattern. As a whole, these findings had an agreement with the major contribution of land cover (vegetation cover, impervious surface, etc.) to urban thermal environment (Coseo & Larsen, 2014; Deng & Wu, 2013; Guo et al., 2015). Our findings also consented that the impact of urbanization on UHI can be mitigated not only by balancing the relative amounts of land cover features, but also by optimizing their spatial configuration. Likewise, with the reduction and de-concentration of urban hot spots, cooling effect owing to land use conversion and spatial re-configuration might eventually alter the UHI pattern. This study use accepted data sets and techniques to carry out thermal dynamic and land use change regarding to urban redevelopment phenomenon, there are also some implications for readers who are interested in detecting UHI mitigation using TIRS image. The results indicated that 90-m resolution ASTER image is proper for delineating UHI effect for a coverage of 90 km2 Tianhe District. In the future, higher resolution satellite/UAV-based thermal sensors will satisfy spatial and time requirement then advance the understandings in this field (Sobrino, Oltra-Carrió, Sòria, Bianchi, & Paganini, 2012). Although most median resolution satellite data are open access, however, so far

Table 3. From Fig. 5, it presented an intuitive change of UHI intensity. Table 3 presented a numerical change with landscape metrics. Results could be interpreted as: 1) A decrease of PD, LSI, SHDI and MSIDI in 6 sites reflected a decrease in amount of landscape types which indicated a more aggregated landscape pattern; in other words, the UHI pattern became homogeneous with an increase of weaker UHI intensity. 2) An increase of AI and LPI also indicated a homogeneous UHI pattern, this phenomenon could be intuitively identified as weaker UHI levels occupied the most part of scene, and stronger UHI intensity disappeared. Overall, UHI pattern had been reshaped together with a significant decrease of UHI diversity. Landscape metrics quantitatively identified the changes of land cover composition and configuration owing to urban redevelopment practices; additionally, these metrics were applied on landscape pattern of UHI, reflecting the physical change of shape, aggregation, and diversity. These findings suggested that UHI can be mitigated by balancing the relative amounts of land cover and optimizing their spatial configuration. 5. Discussions Many scholars agree that urban redevelopment is a critical issue for social-economy, however, at present very few study, or little attention was focused on remote sensing and GIScience application on this topic; there is still a large gap of research and knowledge on these techniques. This study explored the potentials of multi-temporal thermal infrared and high resolution remote sensing imagery to characterize urban redevelopment process in Tianhe District, Guangzhou city. With respect to UHI monitoring issue, this paper had reviewed the mainstream of current research and tried to bridge the gaps between remote sensing of urban studies and urban redevelopment practices. The research paradigm should play a role in evaluating the environmental impact from redeveloped urban practices, might draw the attention of urban planner, governor, and the public. Research in mapping UHI derived from TIRS images and its relationship with urban sprawling is quite well-documented (Chen et al., 2006; Deng & Wu, 2013; Guo et al., 2015; Lo & Quattrochi, 2003); but the cognition on UHI mitigation owing to urban redevelopment process is rarely found. Dynamic change of urban thermal pattern can reveal specific phenomena behind the development of social-economy, just as land use conversion due to urban construction. With regarding to UHI studies, most of the studies express pessimistic emotions on the formation of UHI due to urban development manifested as sprawling of impervious surface area, but few express the optimistic attitudes towards urban redevelopment practices. The perception of UHI mitigation is still a deductive conclusion without truly validated. Although research on land use composition and configuration had been theoretically demonstrated to reflect UHI formation (Heldens et al., 2013; Li et al., 2011; Zhou et al., 2011), it is still a pressing need to thoroughly understand the causal mechanism identifying the spatio-temporal factors that mitigate the UHI effect. Fortunately, the Tianhe District of Guangzhou City provides a good case of study for remote sensing UHI 68

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high quality TIRS image are usually scarce. In addition, the drawbacks and challenges of thermal infrared image change quantification is attributing to a seasonal variation in UHI spatial pattern, which means optimal period for TIRS change detection is unclear. As a preliminary research in characterizing thermal dynamics towards urban redevelopment-induced phenomenon, there are still knowledge gaps in thermal change definition, characterization, and intrinsic factors of UHI pattern formation. Hopefully this study can inspire new thinking for theoretical and empirical understanding of UHI and landscape change.

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6. Conclusions and perspectives Urban thermal environment and urban redevelopment issues both receive significant attentions. This paper proposed a schematic of using remotely sensed thermal infrared and high resolution image in urban redevelopment monitoring, and presented a case study of using landscape metrics in evaluating urban redevelopment scenarios. UHI intensity dynamic was quantified to identify UHI mitigation during the urban redevelopment period. High resolution optical images were employed to perform land use classification and change analysis. Landscape ecology methods were employed to quantitatively characterize land use change in terms of composition and configuration, and these changes were associated with UHI mitigation. Results suggested that the UHI intensity can be mitigated not only by balancing the relative amounts of land cover features, but also by optimizing their spatial configuration. Hopefully the research outcomes and discussions in this study will fill the knowledge gaps between remote sensing of urban redevelopment practices, and contribute to awareness of urban renewal issues, so as to accelerate the remediation and redevelopment of brownfield sites all over the world. In the future, it is necessary to develop a systematic framework to support the categorization of the needed information, and to support the understandings of urban redevelopment processes. Research outcomes in this field will show an interest for remediation strategies, options and socioeconomic aspects. To this end, it is promising to apply multi-source remote sensing, geospatial Big-data analysis on this issue, to provide research evidences and case-based reasoning for decision support. Acknowledgement This research was supported by China National key research and development program (Grant NO. 2018YFD1100801-01), Guangdong Provincial Science and Technology Project (Grant NO. 2017A050501031, 2017A040406022), Guangzhou Science and Technology Project (Grant NO. 201807010048), and the International Postdoctoral Exchange Fellowship Program 2017 (Grant NO. 20170029). The authors would express appreciation to the colleagues of Guangzhou Urban Renewal Bureau for giving counsels; and appreciation to Prof. Zhu A-xing of Wisconsin University at Madison provide useful suggestion in writing this manuscript; many thanks should be given to reviewers for their inspiring comments. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.habitatint.2019.03.004. References Abrams, M., Tsu, H., KokiIwao, G., Pieri, D., Cudahy, T., & Kargel, J. (2015). The advanced Spaceborne thermal emission and reflection radiometer (ASTER) after fifteen years: Review of global products. International Journal of Applied Earth Observation and Geoinformation, 38, 292–301. Atturo, C., Cianfrone, C., Ferrara, V., Fiumi, L., Fontinovo, G., & Ottavi, C. M. (2006). Remote sensing detection techniques for brownfield identification and monitoring by GIS tools. Transactions on Ecology and the Environment, 1, 241–250.

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