Area Delineation and Spatial-Temporal Dynamics of Urban Heat Island in Lanzhou City, China Using Remote Sensing Imagery
详细信息    查看全文
  • 作者:Jinghu Pan
  • 关键词:Urban heat island ; Fractal net evolution approach ; Spectral mixture analysis ; Remote sensing ; Lanzhou
  • 刊名:Journal of the Indian Society of Remote Sensing
  • 出版年:2016
  • 出版时间:February 2016
  • 年:2016
  • 卷:44
  • 期:1
  • 页码:111-127
  • 全文大小:1,223 KB
  • 参考文献:An, X. Q., Ma, A. Q., & Liu, D. B. (2008). A GIS-based study for optimizing the total emission control strategy in Lanzhou city. Environmental Modeling and Assessment, 13, 491–501.CrossRef
    Artis, D. A., & Carnahan, W. H. (1982). Survey of emissivity variability in thermography of urban areas. Remote Sensing of Environment, 12, 313–329.CrossRef
    Berk, A., Bernstein, L. S., & Robertson, D. C. (1989). MODTRAN: a moderate resolution model for LOWTRAN 7, technical report GL-TR-89–0122. Lab, Bedford, MA: Geophysics.
    Bevis, M., Businger, S., Herring, T. A., Rocken, C., Anthes, R. A., & Ware, R. H. (1992). GPS meteorology: remote sensing of atmospheric water vapor using the global positioning system. Journal of Geophysical Research, 97, 15787–15801.CrossRef
    Cheng, K. S., Hung, W. C., & Chen, Y. C. (2010). Comparing landcover patterns in Tokyo, Kyoto, and Taipei using ALOS multispectral images. ISPRS Journal of Photogrammetry, 38, 513–516.
    Dash, P., Gottsche, F. M., Olesen, F. S., & Fischer, H. (2002). Land surface temperature and emissivity estimation from passive sensor data: theory and practice–current trends. International Journal of Remote Sensing, 23, 2563–2594.CrossRef
    Deng, C. B., & Wu, C. S. (2013). Examining the impacts of urban biophysical compositions on surface urban heat island: a spectral unmixing and thermal mixing approach. Remote Sensing of Environment, 131, 262–274.CrossRef
    Deng, J. S., Wang, K., Hong, Y., & Qi, J. G. (2009). Spatio–temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization. Landscape and Urban Plan, 92, 187–198.CrossRef
    Devanathan, P., & Devanathan, K. (2011). Heat island effects. In G. M. Sabnis (Ed.), Green building with concrete: sustainable design and construction (pp. 175–226). Boca Raton, FL: CRC Press.CrossRef
    Geoffrey, J. H., Thomas, B., Danielle, J. M., & Andre’, B. (2003). A comparison of three image-object methods for the multiscale analysis of landscape structure. ISPRS Journal of Photogrammetry, 57, 327–345.CrossRef
    Getis, A., & Ord, J. K. (1992). The analysis of spatia1 association by the use of distance statistics. Geographical Analysis, 24, 189–206.CrossRef
    Harlan, S. L., Declet-Barreto, J. H., Stefanov, W. L., & Petitti, D. B. (2013). Neighborhood effects on heat deaths: social and environmental predictors of vulnerability in Maricopa county, Arizona. Environmental Health Perspectives, 121, 197.CrossRef
    Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 395–309.CrossRef
    Irish, R. R. (2000). Landsat 7 science data user’s handbook (pp. 430–15). Report: National Aeronautics and Space Administration.
    Jia, Y. W., Wang, H., & Ni, G. H. (2005). Principle and practice distributed hydrological model of watersheds. Beijing: China Water Public Press (In Chinese).
    Jimenez-Munoz, J. C., & Sobrino, J. A. (2003). A generalized single channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research, 108(D22), 4688. doi:10.​1029/​ 2003JD003480 .CrossRef
    Li, H. F., & Yin, J. Y. (2013). A study on urban thermal field of Shanghai using multi-source remote sensing data. Journal of the Indian Society of Remote Sensing, 41, 1009–1019.CrossRef
    McGarigal, K., Cushmanm, S., Neel, M., & Ene, E. (2002). FRAGSTATS: spatial pattern analysis program for categorical maps. Computer software program produced by the authors at the University of Massachusetts, Amherst, Available at the following web site. www.​umass.​edu/​landeco/​research/​fragstats/​fragstats.​html
    Nichol, J., & Hang, T. P. (2012). Temporal characteristics of thermal satellite images for urban heat stress and heat island mapping. ISPRS Journal of Photogrammetry & Remote Sensing, 74, 153–162.CrossRef
    Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108, 1–24.
    Okwen, R. T., Pu, R., & Cunningham, J. A. (2011). Remote sensing of temperature variations around major power plants as point sources of heat. International Journal of Remote Sensing, 32, 3791–3805.CrossRef
    Owen, T. W., Carlson, T. N., & Gillies, R. R. (1998). An assessment of satellite remotely-sensed land cover parameters in quantitatively describing the climatic effect of urbanization. International Journal of Remote Sensing, 19, 1663–1681.CrossRef
    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. International Journal of Remote Sensing, 22, 3719–3746.CrossRef
    Quan, J. L., Chen, Y. H., Zhan, W. F., Wang, J. F., Voogt, J., & Wang, M. J. (2014). Multi-temporal trajectory of the urban heat island centroid in beijing, china based on a gaussian volume model. Remote Sensing of Environment, 149, 33–46.CrossRef
    Rao, P. K. (1972). Remote sensing of urban heat islands from an environmental satellite. Bulletin of the American Meteorological Society, 53, 647–648.
    Rizwan, A. M., Dennis, L. Y. C., & Liu, C. (2008). A review on the generation, determination and mitigation of urban heat island. Journal of Environmental Sciences, 20, 120–128.CrossRef
    Roberta, A., Stefania, B., & Manuele, P. (2014). Spatial and temporal trends of the surface and air heat island over Milan using MODIS data. Remote Sensing of Environment, 150, 163–171.CrossRef
    Şahin, M., Yildiz, B. Y., Şenkal, O., & Peştemalci, V. (2012). Modelling and remote sensing of land surface temperature in Turkey. Journal of the Indian Society and Remote, 40, 399–409.CrossRef
    Shepherd, J. M. (2006). Evidence of urban-induced precipitation variability in arid climate regimes. Journal of Arid Environments, 67, 607–628.CrossRef
    Sobrino, J. A., Jimenez-Munoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from Landsat TM 5. Remote Sensing of Environment, 90, 434–440.CrossRef
    Sobrino, J. A., Kharraz, J. E., & Li, Z. L. (2003). Surface temperature and water vapor retrieval from MODIS data. International Journal of Remote Sensing, 24, 5161–5182.CrossRef
    Sobrino, J. A., Raissouni, N., & Li, Z. L. (2001). A comparative study of land surface emissivity retrieval from NOAA data. Remote Sensing of Environment, 75, 256–266.CrossRef
    Srivastava, P. K., Majumdar, T. J., & Bhattacharya, A. K. (2010). Study of land surface temperature and spectral emissivity using multi-sensor satellite data. Journal of Earth System Science, 119, 67–74.CrossRef
    Tou, J., & Gonzalez, R. (1974). Pattern recognition principles. Reading, MA: Addison-Wesley.
    Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, 86, 370–384.CrossRef
    Wan, Z., & Dozier, J. A. (1996). Generalized split-window algorithm for retrieving land surface temperature from space. IEEE Transactions on Geoscience and Remote, 34, 892–905.CrossRef
    Weng, Q. (2009). Thermal infrared remote sensing for urban climate and environmental studies: methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64, 335–344.CrossRef
    Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89, 467–483.CrossRef
    Wu, C. (2004). Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sensing of Environment, 93, 480–492.CrossRef
    Wu, C., & Murray, A. T. (2003). Estimating impervious surface distribution by spectral mixture analysis. Remote Sensing of Environment, 84, 493–505.CrossRef
    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 Observations, 32, 67–78.CrossRef
    Xu, H. Q., Ding, F., & Wen, X. L. (2009). Urban expansion and heat island dynamics in the Quanzhou region, China. IEEE J-STARS, 2, 74–79.
    Xu, Y., Qin, Z., & Wan, H. (2010). Spatial and temporal dynamics of urban heat island and their relationship with land cover changes in urbanization process: a case study in Suzhou, China. Journal of the Indian Society and Remote, 38, 654–663.CrossRef
    Yang, J. M., & Qiu, J. H. (1996). The empirical expressions of the relation between precipitable water and ground water vapor pressure for some areas in China. Chinese Journal of Atmospheric Science, 20, 620–626 (In Chinese).
    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 Sensing of Environment, 106, 375–386.CrossRef
    Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24, 583–594.CrossRef
    Zheng, W. W., & Zeng, Y. N. (2013). Retrieval of urban land surface component temperature using multi-source remote-sensing data. Journal of Central South University, 20, 2489–2497.CrossRef
  • 作者单位:Jinghu Pan (1)

    1. College of Geographic and Environmental Science, Northwest Normal University, No.967 Anning East Road, Lanzhou, Gansu Province, People’s Republic of China, 730070
  • 刊物类别:Earth and Environmental Science
  • 刊物主题:Earth sciences
    Geosciences
    Remote Sensing and Photogrammetry
  • 出版者:Springer India
  • ISSN:0974-3006
文摘
One of the key impacts of rapid urbanization on the environment is the effect of urban heat island (UHI). By using the Landsat TM/ETM+ thermal infrared remote sensing data of 1993, 2001 and 2011 to retrieve the land surface temperature (LST) of Lanzhou City, and by adopting object-oriented fractal net evolution approach (FNEA) to make image segmentation of the LST, the UHI elements were extracted. The G* index spatial aggregation analysis was made to calculate the urban heat island ratio index (URI), and the landscape metrics were used to quantify the changes of the spatial pattern of the UHI from the aspects of quantity, shape and structure. The impervious surface distribution and vegetation coverage were extracted by a constrained linear spectral mixture model to explore the relationships of the impervious surface distribution and vegetation coverage with the UHI. The information of urban built-up area was extracted by using UBI (NDBI-NDVI) index, and the effects of urban expansion on city thermal environment were quantitatively analyzed, with the URI and the LST grade maps built. In recent 20 years, the UHI effect in Lanzhou City was strengthened, with the URI increased by 1.4 times. The urban expansion had a spatiotemporal consistency with the UHI expansion. The patch number and density of the UHI landscape were increased, the patch shape and the whole landscape tended to be complex, the landscape became more fragmented, and the landscape connectivity was decreased. The heat island strength had a negative linear correlation with the urban vegetation coverage, and a positive logarithmic correlation with the urban impervious surface coverage.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700