Spatiotemporal influences of land use/cover changes on the heat island effect in rapid urbanization area
Ying XIONG, Fen PENG, Bin ZOU
Spatiotemporal influences of land use/cover changes on the heat island effect in rapid urbanization area
Rapid urban sprawl and growth led to substantial urban thermal environment changes and influenced the local climate, environment, and quality of life of residents. Taking the Chang-Zhu-Tan urban agglomeration in China as a case, this study firstly identified the spatiotemporal patterns of surface urban heat island intensity (SUHII) and the land use/cover changes (LUCC) based on multi-temporal Landsat TM satellite data over 21 years, and then investigated the relationship between LUCC and SUHII by methods of logistic regression model and centroid shift analysis. The results showed that green spaces (e.g., cropland, forestland) of 899.13 km2 had been converted to built-up land during the 1994–2015 period, which caused significant urban expansion. The SUHII was the highest for built-up land, high for unused land, low for cropland and grassland, and the lowest for forestland and open water. Many areas experienced extensive rapid urbanization because of the emergence of the urban agglomeration, which resulted in the loss of green spaces and increased SUHI effects over the 21-year study period. In addition, the results of centroid shift analysis found that the growth of SUHII and the expansion of high SUHII areas are closely related to the expansion of an existing urban area in Xiangtan, while the increases of building density and height in Changsha resulted in the decrease of SUHII and spatiotemporal change of high SUHII areas. The analysis of the effects of land use/cover types on the SUHII in this study will contribute to future urban land use allocation for the mitigation of SUHI formation.
land use/cover change / urbanization / remote sensing / surface urban heat island intensity / centroid shift analysis
[1] |
Arnfield A J (2003). Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. Int J Climatol, 23(1): 1–26
CrossRef
Google scholar
|
[2] |
Azevedo I, Leal V M S (2017). Methodologies for the evaluation of local climate change mitigation actions: a review. Renew Sustain Energy Rev, 79: 681–690
CrossRef
Google scholar
|
[3] |
Cai Y, Zhang H, Zheng P, Pan W (2016). Quantifying the impact of land use/land cover changes on the urban heat island: a case study of the natural wetlands distribution area of Fuzhou City, China. Wetlands, 36(2): 285–298
CrossRef
Google scholar
|
[4] |
Connors J P, Galletti C S, Chow W T L (2013). Landscape configuration and urban heat island effects: assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona. Landsc Ecol, 28(2): 271–283
CrossRef
Google scholar
|
[5] |
Du H, Wang D, Wang Y, Zhao X, Qin F, Jiang H, Cai Y (2016). Influences of land cover types, meteorological conditions, anthropogenic heat and urban area on surface urban heat island in the Yangtze River Delta Urban Agglomeration. Sci Total Environ, 571: 461–470
CrossRef
Google scholar
|
[6] |
Emmanuel R, Fernando H J S (2007). Urban heat islands in humid and arid climates: role of urban form and thermal properties in Colombo, Sri Lanka and Phoenix, USA. Clim Res, 34(3): 241–251
CrossRef
Google scholar
|
[7] |
Fang X, Zou B, Liu X, Sternberg T, Zhai L (2016). Satellite-based ground PM2.5 estimation using timely structure adaptive modeling. Remote Sens Environ, 186: 152–163
CrossRef
Google scholar
|
[8] |
Gluch R, Quattrochi D A, Luvall J C (2006). A multi-scale approach to urban thermal analysis. Remote Sens Environ, 104(2): 123–132
CrossRef
Google scholar
|
[9] |
Guo G, Wu Z, Xiao R, Chen Y, Liu X, Zhang X (2015). Impacts of urban biophysical composition on land surface temperature in urban heat island clusters. Landsc Urban Plan, 135: 1–10
CrossRef
Google scholar
|
[10] |
Hilbe J M (2009). Logistic Regression Models. Chapman and Hall/CRC Press, 220–225
|
[11] |
Ichinose T, Lei L, Lin Y (2017). Impacts of shading effect from nearby buildings on heating and cooling energy consumption in hot summer and cold winter zone of China. Energy Build, 136(1): 199–210
CrossRef
Google scholar
|
[12] |
Janssen L L F, Vanderwel F J M (1994). Accuracy assessment of satellite derived land-cover data: a review. Photogramm Eng Remote Sensing, 60(4): 419–426
|
[13] |
Jiang X, Zou B, Feng H, Tang J, Tu Y, Zhao X (2019). Spatial distribution mapping of Hg contamination in subclass agricultural soils using GIS enhanced multiple linear regression. J Geochem Explor, 196: 1–7
CrossRef
Google scholar
|
[14] |
Kalnay E, Cai M (2003). Impact of urbanization and land-use change on climate. Nature, 423(6939): 528–531
CrossRef
Google scholar
|
[15] |
Kang H, Zhu B, Zhu T, Sun J, Ou J (2014). Impact of megacity Shanghai on the urban heat-island effects over the downstream city Kunshan. Boundary-Layer Meteorol, 152(3): 411–426
CrossRef
Google scholar
|
[16] |
Kayet N, Pathak K, Chakrabarty A, Sahoo S (2016). Spatial impact of land use/land cover change on surface temperature distribution in Saranda Forest, Jharkhand. Model Earth Syst Environ, 2(3): 127
CrossRef
Google scholar
|
[17] |
Kim Y H, Baik J J (2005). Spatial and temporal structure of the urban heat island in Seoul. J Appl Meteorol, 44(5): 591–605
CrossRef
Google scholar
|
[18] |
Kneizys F X, Abreu L W, Anderson G P, Chetwynd J H, Shettle E P, Berk A, Bernstein L S, Robertson D C, Acharya P K, Rothman L A, Selby J E A, Gallery W O, Clough S A (1996). The MODTRAN 2/3 report & LOWTRAN 7 model, F19628-91-C-0132. Phillips Laboratory Hanscom AFB, Bedford
|
[19] |
Lamarca C, Qüense J, Henríquez C (2018). Thermal comfort and urban canyons morphology in coastal temperate climate, Concepción, Chile. Urban Climate, 23: 159–172
CrossRef
Google scholar
|
[20] |
Lan C, Ming L I (2018). Spatial-temporal feature of urban heat island in Changzhutan main urban area and the relationship with land use change. Geomatics & Spatial Information Technology, 41(4): 84–89 (in Chinese)
|
[21] |
Li H, Sun D, Yu Y, Wang H, Liu Y, Liu Q, Du Y, Wang H, Cao B (2014). Evaluation of the VIIRS and MODIS LST products in an arid area of Northwest China. Remote Sens Environ, 142(1): 111–121
CrossRef
Google scholar
|
[22] |
Liu L, Zhang Y (2011). Urban heat island analysis using the Landsat TM data and ASTER data: a case study in Hong Kong. Remote Sens, 3(7): 1535–1552
CrossRef
Google scholar
|
[23] |
Liu X, Hu G, Chen Y, Li X, Xu X, Li S, Pei F, Wang S (2018). High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sens Environ, 209: 227–239
CrossRef
Google scholar
|
[24] |
Liu X, Li X, Shi X, Zhang X, Chen Y (2010). Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata. Int J Geogr Inf Sci, 24(5): 783–802
CrossRef
Google scholar
|
[25] |
Liu X, Liang X, Li X, Xu X, Ou J, Chen Y, Li S, Wang S, Pei F (2017). A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landsc Urban Plan, 168: 94–116
CrossRef
Google scholar
|
[26] |
Magee N, Curtis J, Wendler G (1999). The urban heat island effect at Fairbanks, Alaska. Theor Appl Climatol, 64(1–2): 39–47
CrossRef
Google scholar
|
[27] |
Mohan M, Kandya A (2015). Impact of urbanization and land-use/land-cover change on diurnal temperature range: a case study of tropical urban airshed of India using remote sensing data. Sci Total Environ, 506– 507: 453–465
CrossRef
Google scholar
|
[28] |
Oke T R (1973). City size and the urban heat island. Atmos Environ, 7(8): 769–779
CrossRef
Google scholar
|
[29] |
Peng F, Wong M S, Ho H C, Nichol J, Chan P W (2017). Reconstruction of historical datasets for analyzing spatiotemporal influence of built environment on urban microclimates across a compact city. Build Environ, 123: 649–660
CrossRef
Google scholar
|
[30] |
Qin Z, Karnieli A, Berliner P (2001). A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. Int J Remote Sens, 22(18): 3719–3746
CrossRef
Google scholar
|
[31] |
Rozenstein O, Qin Z, Derimian Y, Karnieli A (2014). Derivation of land surface temperature for Landsat-8 TIRS using a split window algorithm. Sensors (Basel), 14(4): 5768–5780
CrossRef
Google scholar
|
[32] |
Santamouris M (2013). Using cool pavements as a mitigation strategy to fight urban heat island—a review of the actual developments. Renew Sustain Energy Rev, 26(10): 224–240
CrossRef
Google scholar
|
[33] |
Savić S, Unger J, Gál T, Milošević D, Popov Z (2013). Urban heat island research of Novi Sad (Serbia): a review. Geogr Pannon, 17(1): 32–36
CrossRef
Google scholar
|
[34] |
Schwarz N, Lautenbach S, Seppelt R (2011). Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures. Remote Sens Environ, 115(12): 3175–3186
CrossRef
Google scholar
|
[35] |
Scott L M, Janikas M V (2010). Spatial Statistics in ArcGIS. Handbook of Applied Spatial Analysis, 27–41
|
[36] |
Small C (2001). Estimation of urban vegetation abundance by spectral mixture analysis. Int J Remote Sens, 22(7): 1305–1334
CrossRef
Google scholar
|
[37] |
Stone B, Hess J J, Frumkin H (2010). Urban form and extreme heat events: are sprawling cities more vulnerable to climate change than compact cities? Environ Health Perspect, 118(10): 1425–1428
CrossRef
Google scholar
|
[38] |
Wang X, Sun X, Tang J, Yang X (2015). Urbanization-induced regional warming in Yangtze River Delta: potential role of anthropogenic heat release. Int J Climatol, 35(15): 4417–4430
CrossRef
Google scholar
|
[39] |
Wong M S, Peng F, Zou B, Shi W Z, Wilson G J (2016). Spatially analyzing the inequity of the Hong Kong urban heat island by socio-demographic characteristics. Int J Env Res Pub He, 13(3): 317
CrossRef
Google scholar
|
[40] |
World Health Organization (2017). Global Health Observatory (GHO) data. World Health Organization, Geneva
|
[41] |
Wu H, Ye L P, Shi W Z, Clarke K C (2014). Assessing the effects of land use spatial structure on urban heat islands using HJ-1B remote sensing imagery in Wuhan, China. International Journal of Applied Earth Observation and Geoinformation, 32(1): 67–78
CrossRef
Google scholar
|
[42] |
Wu J (2014). Urban ecology and sustainability: the state-of-the-science and future directions. Landsc Urban Plan, 125: 209–221
CrossRef
Google scholar
|
[43] |
Xu S, Zou B, Shafi S, Sternberg T (2018). A hybrid Grey-Markov/ LUR model for PM10 concentration prediction under future urban scenarios. Atmos Environ, 187: 401–409
CrossRef
Google scholar
|
[44] |
Yang C, He X, Yan F, Yu L, Bu K, Yang J, Chang L, Zhang S (2017). Mapping the influence of land use/land cover changes on the urban heat island effect—a case study of Changchun, China. Sustainability, 9(2): 312
CrossRef
Google scholar
|
[45] |
Yuan F, Bauer M E (2007). Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens Environ, 106(3): 375–386
CrossRef
Google scholar
|
[46] |
Zeng Y, Huang W, Zhan F B, Zhang H, Liu H (2010). Study on the urban heat island effects and its relationship with surface biophysical characteristics using MODIS imageries. Geo Spat Inf Sci, 13(1): 1–7
CrossRef
Google scholar
|
[47] |
Zhai L, Li S, Zou B, Sang H, Fang X, Xu S (2018). An improved geographically weighted regression model for PM2.5 concentration estimation in large areas. Atmos Environ, 181: 145–154
CrossRef
Google scholar
|
[48] |
Zhai L, Zou B, Fang X, Luo Y, Wan N, Li S (2016). Land use regression modeling of PM2.5 concentrations at optimized spatial scales. Atmos, 8(1): 1
CrossRef
Google scholar
|
[49] |
Zou B, Peng F, Wan N, Wilson J G, Xiong Y (2014). Sulfur dioxide exposure and environmental justice: a multi-scale and source-specific perspective. Atmos Pollut Res, 5(3): 491–499
CrossRef
Google scholar
|
[50] |
Zou B, Pu Q, Bilal M, Weng Q, Zhai L, Nichol J E (2016a). High-resolution satellite mapping of fine particulates based on geographically weighted regression. IEEE Geosci Remote Sens Lett, 13(4): 495–499
CrossRef
Google scholar
|
[51] |
Zou B, Xu S, Sternberg T, Fang X (2016b). Effect of land use and cover change on air quality in urban sprawl. Sustainability, 8(7): 677
CrossRef
Google scholar
|
[52] |
Zou B, You J, Lin Y, Duan X, Zhao X, Xin F, Campen M J, Li S (2019). Air pollution intervention and life-saving effect in China. Environ Int, doi: 10.1016/j.envint.2018.10.045
|
/
〈 | 〉 |