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

Abstract

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 runoff, 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.

Comparing Urban Impervious Surface Identification Using Landsat and High Resolution Aerial Photography

“…Only one of these studies evaluates the effectiveness of a multi-scale approach (from a finer scale to increasingly larger scales-see [23]). Very few have compared applications of alternative techniques within the same milieu (see [49][50][51] Therefore, the objective of this paper is to evaluate accuracies of selected image classification strategies, as applied to Landsat imagery. It will assess the effectiveness of Landsat data for mapping urban impervious surfaces by using a reference data set manually delineated from high-resolution aerial photos.…”

Spatial and temporal variations of urban heat island effect and the effect of percentage impervious surface area and elevation on land surface temperature: Study of Chandigarh city, India

“…The contribution of cropland and forest towards increase in LST varies distinctly between day time and night time due to differences in their thermal inertias (Qiao et al, 2013). The spatial pattern of impervious surfaces has been related to the urban development and it has consequently been related to the regional eco-environmental changes (Li et al, 2011).…”

Using long time series of Landsat data to monitor impervious surface dynamics: a case study in the Zhoushan Islands

“…The high-resolution data may better detect the imperviousness of the ring belts, from the central to the urban-rural peripheral, than the Landsat data. 26 In this study, the 2011 SPOT5/ Worldview-2 data were only used to validate the extraction of impervious surfaces. Highresolution satellites might be used in the near future when they become economically affordable for large-scale mappings.…”

“…This is particularly true for applying low-and medium-resolution remote sensing data. 26 The construction of the NDISI needs thermal data, and therefore the remotely sensed data without thermal bands are useless in the indicator. Visual interpretation or multispectral classification is mainly applied to high-resolution imagery.…”