Development of an urban landcover classification scheme suitable for representing climatic conditions in a densely built-up Asian megacity

Abstract The objective of this paper is to acquire a better insight into a landcover classification scheme which is suitable for representing the climatic properties of Asian megacities. Seoul, Republic of Korea, was used as a testbed. First, landcover classification was performed using Landsat 7 ETM+ using eight landcover types suitable for representing the climatic conditions of Seoul. As a next step, 10 land surface and thermal parameters were estimated for individual landcover classes. Finally, the suitability of the climatologically classified urban landcover scheme was analyzed in two ways. Firstly, the spatial distribution of land surface temperature (LST) was retrieved using the thermal band of Landsat 7 ETM+ and the retrieved LST values over eight landcover classes were statistically analyzed. Secondly, 24-h temperature changes based on both eight landcover classes and four classes were simulated using the meso-scale model MetPhoMod. These were compared with measurement data from 24 automatic weather stations in Seoul. This study showed that the eight landcover classification scheme is suitable for representing the climatic properties of Seoul, which is one of the Asian megacities. Moreover, the eight classes represent the thermal conditions better than the four conventional classes during nighttime, which implies that this eight landcover classification scheme can be useful to analyze the nocturnal urban heat island effect.

[1]  D. Lu,et al.  Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies , 2004 .

[2]  S. Perego Metphomod – a Numerical Mesoscale Model for Simulation of Regional Photosmog in Complex Terrain: Model Description and Application During Pollumet 1993 (Switzerland) , 1999 .

[3]  Qihao Weng Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends , 2009 .

[4]  Jingfen Sheng,et al.  Watershed urbanization and changing flood behavior across the Los Angeles metropolitan region , 2009 .

[5]  J. Fenger,et al.  Urban air quality , 1999 .

[6]  Marcel Bottema,et al.  Urban roughness modelling in relation to pollutant dispersion , 1997 .

[7]  Jeffrey G. Masek,et al.  Sensitivity of surface climate to land surface parameters: A case study using the simple biosphere model SiB2 , 2006 .

[8]  Mathias W. Rotach,et al.  On the influence of the urban roughness sublayer on turbulence and dispersion , 1999 .

[9]  Dieter Scherer,et al.  Improved concepts and methods in analysis and evaluation of the urban climate for optimizing urban planning processes , 1999 .

[10]  Ulrich Corsmeier,et al.  Effects of Urban Land Use on Surface Temperature in Berlin: Case Study , 2007 .

[11]  Jung-Hun Woo,et al.  The contribution of megacities to regional sulfur pollution in Asia , 2003 .

[12]  Jong-Jin Baik,et al.  Spatial and Temporal Structure of the Urban Heat Island in Seoul , 2005 .

[13]  E. Ng Policies and technical guidelines for urban planning of high-density cities – air ventilation assessment (AVA) of Hong Kong , 2008, Building and Environment.

[14]  S. Nolf,et al.  A sensitivity assessment of terrestrial identification in remote sensing , 2002 .

[15]  Sietse O. Los,et al.  Implications of land-cover misclassification for parameter estimates in global land-surface models: An example from the simple biosphere model (SiB2) , 1999 .

[16]  Jochen Mülder,et al.  Regional climatic mapping as a tool for sustainable development. , 2008, Journal of environmental management.

[17]  G. Valenti,et al.  A hyperbolic model for the effects of urbanization on air pollution , 2010 .

[18]  Timothy R. Oke,et al.  Aerodynamic Properties of Urban Areas Derived from Analysis of Surface Form , 1999 .

[19]  J. Monteith,et al.  Boundary Layer Climates. , 1979 .

[20]  R. Lazar,et al.  An urban climate analysis of Graz and its significance for urban planning in the tributary valleys east of Graz (Austria) , 1999 .

[21]  H. Regan,et al.  Relationships between Human Disturbance and Wildlife Land Use in Urban Habitat Fragments , 2008, Conservation biology : the journal of the Society for Conservation Biology.

[22]  Dieter Scherer,et al.  Automated classification of planning objectives for the consideration of climate and air quality in urban and regional planning for the example of the region of Basel/Switzerland , 2001 .

[23]  M. Geyer,et al.  A Kappa Opioid Model of Atypical Altered Consciousness and Psychosis: U50488, DOI, AC90179 Effects on Prepulse Inhibition and Locomotion in Mice. , 2009, Journal of young investigators.

[24]  C. Woodcock,et al.  Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? , 2001 .

[25]  Z. Y. Wang,et al.  Proceedings of the Second International Symposium on Environmental Hydraulics , 1991 .

[26]  Keisuke Hanaki,et al.  Impact of anthropogenic heat on urban climate in Tokyo , 1999 .

[27]  Xiaoling Chen,et al.  Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes , 2006 .

[28]  Hüseyin Toros,et al.  Effects of urbanization on climate of İstanbul and Ankara , 1995 .

[29]  Qihao Weng A remote sensing?GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China , 2001 .

[30]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[31]  R. Betts,et al.  Climate and more sustainable cities: climate information for improved planning and management of cities (producers/capabilities perspective) , 2010 .

[32]  Valéry Masson,et al.  ECOCLIMAP: a global database of land surface parameters at 1 km resolution , 2005 .

[33]  E. P. Evans,et al.  Land use, water management and future flood risk. , 2009 .

[34]  Y. Yasuoka,et al.  Assessment with satellite data of the urban heat island effects in Asian mega cities , 2006 .