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C. Z. Wei; T. Blaschke (2016)
Publisher: Copernicus Publications
Journal: The International Archives of the Photogrammetry
Languages: English
Types: Article
Subjects: TA1-2040, T, TA1501-1820, Applied optics. Photonics, Engineering (General). Civil engineering (General), Technology
With the increasing acceleration of urbanization, the degeneration of the environment and the Urban Heat Island (UHI) has attracted more and more attention. Quantitative delineation of UHI has become crucial for a better understanding of the interregional interaction between urbanization processes and the urban environment system. First of all, our study used medium resolution Chinese satellite data-HJ-1B as the Earth Observation data source to derive parameters, including the percentage of Impervious Surface Areas, Land Surface Temperature, Land Surface Albedo, Normalized Differential Vegetation Index, and object edge detector indicators (Mean of Inner Border, Mean of Outer border) in the city of Guangzhou, China. Secondly, in order to establish a model to delineate the local climate zones of UHI, we used the Principal Component Analysis to explore the correlations between all these parameters, and estimate their contributions to the principal components of UHI zones. Finally, depending on the results of the PCA, we chose the most suitable parameters to classify the urban climate zones based on a Self-Organization Map (SOM). The results show that all six parameters are closely correlated with each other and have a high percentage of cumulative (95%) in the first two principal components. Therefore, the SOM algorithm automatically categorized the city of Guangzhou into five classes of UHI zones using these six spectral, structural and climate parameters as inputs. UHI zones have distinguishable physical characteristics, and could potentially help to provide the basis and decision support for further sustainable urban planning.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Anderson, James Richard. 1976. A Land Use and Land Cover Classification System for Use with Remote Sensor Data. U.S. Government Printing Office.
    • Blaschke, T. 2010. “Object Based Image Analysis for Remote Sensing.” ISPRS Journal of Photogrammetry and Remote Sensing 65 (1): 2-16.
    • Chen, Hsinchun, Chris Schuffels, and Richard E. Orwig. 1996. “Internet Categorization and Search: A SelfOrganizing Approach.” Journal of Visual Communication and Image Representation, Special Issue on Digital Libraries.
    • Chen, Xiao-Ling, Hong-Mei Zhao, Ping-Xiang Li, and ZhiYong Yin. 2006. “Remote Sensing Image-Based Analysis of the Relationship between Urban Heat Island and Land Use/cover Changes.” Remote Sensing of Environment, Thermal Remote Sensing of Urban Areas, 104 (2): 133-46.
    • Drăguţ, L., O. Csillik, C. Eisank, and D. Tiede. 2014. “Automated Parameterisation for Multi-Scale Image Segmentation on Multiple Layers.” ISPRS Journal of Photogrammetry and Remote Sensing 88 (February): 119-27.
    • eCognition Developer, Trimble. 2014. “9.0 User Guide.” Trimble Germany GmbH: Munich, Germany.
    • Grimm, Nancy B, David Foster, Peter Groffman, J Morgan Grove, Charles S Hopkinson, Knute J Nadelhoffer, Diane E Pataki, and Debra PC Peters. 2008. “The Changing Landscape: Ecosystem Responses to Urbanization and Pollution across Climatic and Societal Gradients.” Frontiers in Ecology and the Environment 6 (5): 264-72.
    • Ichii, K., A. Kawabata, and Y. Yamaguchi. 2002. “Global Correlation Analysis for NDVI and Climatic Variables and NDVI Trends: 1982-1990.” International Journal of Remote Sensing 23 (18): 3873-78.
    • Jiménez-Muñoz, Juan C., and José A. Sobrino. 2003. “A Generalized Single-Channel Method for Retrieving Land Surface Temperature from Remote Sensing Data.” Journal of Geophysical Research: Atmospheres 108 (D22): 4688.
    • Kalnay, Eugenia, and Ming Cai. 2003. “Impact of Urbanization and Land-Use Change on Climate.” Nature 423 (6939): 528-31.
    • Karl, Thomas R., Henry F. Diaz, and George Kukla. 1988. “Urbanization: Its Detection and Effect in the United States Climate Record.” Journal of Climate 1 (11): 1099-1123.
    • Kohonen, T. 1990. “The Self-Organizing Map.” Proceedings of the IEEE 78 (9): 1464-80.
    • Liang, Shunlin. 2001. “Narrowband to Broadband Conversions of Land Surface Albedo I: Algorithms.” Remote Sensing of Environment 76 (2): 213-38.
    • Lo, C. P., D. A. Quattrochi, and J. C. Luvall. 1997. “Application of High-Resolution Thermal Infrared Remote Sensing and GIS to Assess the Urban Heat Island Effect.” International Journal of Remote Sensing 18 (2): 287-304.
    • McGranahan, Gordon, Deborah Balk, and Bridget Anderson. 2007. “The Rising Tide: Assessing the Risks of Climate Change and Human Settlements in Low Elevation Coastal Zones.” Environment and Urbanization 19 (1): 17-37.
    • Pettorelli, Nathalie, Jon Olav Vik, Atle Mysterud, Jean-Michel Gaillard, Compton J. Tucker, and Nils Chr. Stenseth. 2005. “Using the Satellite-Derived NDVI to Assess Ecological Responses to Environmental Change.” Trends in Ecology & Evolution 20 (9): 503-10.
    • RIDD, M. K. 1995. “Exploring a V-I-S (vegetation-Impervious Surface-Soil) Model for Urban Ecosystem Analysis through Remote Sensing: Comparative Anatomy for Cities†.” International Journal of Remote Sensing 16 (12): 2165-85.
    • Stewart, I. D., and T. R. Oke. 2012. “Local Climate Zones for Urban Temperature Studies.” Bulletin of the American Meteorological Society 93 (12): 1879- 1900.
    • Streutker, D. R. 2002. “A Remote Sensing Study of the Urban Heat Island of Houston, Texas.” International Journal of Remote Sensing 23 (13): 2595-2608.
    • Taha, Haider. 1997. “Urban Climates and Heat Islands: Albedo, Evapotranspiration, and Anthropogenic Heat.” Energy and Buildings 25 (2): 99-103.
    • Wake, Bronwyn. 2012. “Urban Climate: Defining Local Zones.” Nature Climate Change 2 (7): 487-487.
    • Weng, Qihao, Dengsheng Lu, and Jacquelyn Schubring. 2004. “Estimation of Land Surface Temperature-vegetation Abundance Relationship for Urban Heat Island Studies.” Remote Sensing of Environment 89 (4): 467-83.
    • Wu, Changshan, and Alan T. Murray. 2003. “Estimating Impervious Surface Distribution by Spectral Mixture Analysis.” Remote Sensing of Environment 84 (4): 493-505.
    • Yang, Jun, Hua-Zhang Liu, Chun-Quan Ou, Guo-Zhen Lin, Qin Zhou, Gi-Chuan Shen, Ping-Yan Chen, and Yuming Guo. 2013. “Global Climate Change: Impact of Diurnal Temperature Range on Mortality in Guangzhou, China.” Environmental Pollution 175 (April): 131-36.
    • Yuan, Fei, and Marvin E. Bauer. 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 (3): 375-86.
    • Ziska, Lewis H., Dennis E. Gebhard, David A. Frenz, Shaun Faulkner, Benjamin D. Singer, and James G. Straka. 2003. “Cities as Harbingers of Climate Change: Common Ragweed, Urbanization, and Public Health.” Journal of Allergy and Clinical Immunology 111 (2): 290-95.
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