Estimating Urban Impervious Surface Percentage with ERS-1/2 InSAR Data
利用雷达干涉数据进行城市不透水层百分比估算

Impervious surfaces are usually defined as anthropogenic features through which water cannot infiltrate the soil,typically including buildings,roads,parking lots,sidewalks,and other built surfaces.Due to the close correlation with the spatial extent and intensity of urban development,impervious surface has been recently recognized as a key environmental indicator in assessing urban ecological condition and utilized to investigate urban hydrology,urban climate,land use planning and resource management.Over the past decades,extensive researches have been carried out to map impervious surfaces cost-effectively with satellite remote sensing data,especially multi-spectral optical images(e.g.Landsat TM/ETM and SPOT imagery).However,an accurate representation of impervious surface is still a challenge using these middle-resolution optical remote sensing data,because of the complexity of urban/suburban landscapes and the spectral confusions among different land-use/cover types(such as between barren land and parking lots).The spectral confusion as well as the presence of mixed pixel may result in an overestimation of impervious surface distribution in the less-developed areas,but underestimation in the well-developed areas.Unlike optical images that represent the spectral reflectivity of the targets illuminated by sun light,synthetic aperture radar(SAR) images are very sensitive to the surface roughness,shape,structure,dielectric properties of the illuminated ground objects and can provide information complementary to optical data.And recent advantages have shown that the use of SAR interferometric coherence products(e.g.coherence,ratio intensity and average intensity) can improve the capability to distinguish natural land covers and built-up objects in urban area.These SAR feature images generally are derived from SAR interferometry(InSAR) pairs acquired with relatively small perpendicular baseline and long interval.The main aim of this paper is to explore potentials of the use of InSAR data in mapping impervious surface cover.In this study,a CART-based approach is developed to quantify urban impervious surfaces as a continuous variable(impervious surface percentage,ISP) by using multi-source remote sensing datasets in which aerial photograph with high spatial resolution was used as training/test data of ISP estimation and medium-resolution imagery(e.g.InSAR feature images) to extrapolate imperviousness over large spatial areas.This approach produces a rule-based model for prediction of ISP based on training data,and can allow impervious surface cover to be mapped at sub-pixel level of medium remote sensing data.It involves the following steps:(1) development of training/test data using 33cm-resolution digital aerial CIR photography;(2) design of predictive variables,establishment and assessment of final regression tree modeling;(3) spatial extendibility of ISP prediction modeling with InSAR feature images,and(4) accuracy assessment of ISP mapping.A cas