Effects of spatial resolution of remotely sensed data on estimating urban impervious surfaces

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• 作者：Weifeng Li^a ; ^{rceelwf@yahoo.com.cn"" rel=""nofollow} ; Zhiyun Ouyang^a ; ^{zyouyang@rcees.ac.cn"" rel=""nofollow} ; Weiqi Zhou^b ; Qiuwen Chen^a
• 关键词：remote sensing ; impervious surface ; landscape pattern ; spatial resolution ; object-based image analysis ; urban landscape
• 刊名：Journal of Environmental Sciences
• 出版年：2011
• 出版时间：August 2011
• 年：2011
• 卷：23
• 期：8
• 页码：1375-1383
• 全文大小：1595 K

文摘

Impervious surfaces are the result of urbanization that can be explicitly quantified, managed and controlled at each stage of land development. It is a very useful environmental indicator that can be used to measure the impacts of urbanization on surface runoif, water quality, air quality, biodiversity and microclimate. Therefore, accurate estimation of impervious surfaces is critical for urban environmental monitoring, land management, decision-making and urban planning. Many approaches have been developed to estimate surface imperviousness, using remotely sensed data with various spatial resolutions. However, few studies, have investigated the effects of spatial resolution on estimating surface imperviousness. We compare medium-resolution Landsat data with high-resolution SPOT images to quantify the imperviousness in Beijing, China. The results indicated that the overall 91 % accuracy of estimates of imperviousness based on TM data was considerably higher than the 81 % accuracy of the SPOT data. The higher resolution SPOT data did not always predict the imperviousness of the land better than the TM data. At the whole city level, the TM data better predicts the percentage cover of impervious surfaces. At the sub-city level, however, the ring belts from the central core to the urban-rural peripheral, the SPOT data may better predict the imperviousness. These results highlighted the need to combine multiple resolution data to quantify the percentage of imperviousness, as higher resolution data do not necessarily lead to more accurate estimates. The methodology and results in this study can be utilized to identify the most suitable remote sensing data to quickly and efficiently extract the pattern of the impervious land, which could provide the base for further study on many related urban environmental problems.