Improving Urban Impervious Surface Mapping by Linear Spectral Mixture Analysis and Using Spectral Indices

Authors: Wei Fan*, University of Wisconsin - Milwaukee
Topics: Remote Sensing, Land Use and Land Cover Change, Urban Geography
Keywords: Impervious surface, Linear Spectral Mixture Analysis, NDBI, NDBaI, Albedo
Session Type: Paper
Day: 4/14/2018
Start / End Time: 10:00 AM / 11:40 AM
Room: Galvez, , Marriott, 5th Floor
Presentation File: No File Uploaded

As a key indicator of urban built-up areas, impervious surfaces have been frequently analyzed in the studies of urbanization and environmental impacts. For various science and policy applications, it is necessary to accurately estimate and map urban impervious surface areas. Although linear spectral mixture analysis (LSMA) can provide spatial distribution and quantitative fraction information on urban impervious surfaces, using conventional LSMA alone to accurately extract impervious surfaces remains a great challenge. Misclassifications often occur when urban impervious surfaces are estimated by addition of low-albedo and high-albedo fraction images. To solve these issues, an improved LSMA method was explored in this study by using aided spectral indices to estimate urban impervious surface based on a study of Guangzhou, southern China.
The improved LSMA method included three analytical steps: (1) High-albedo and low-albedo fraction images were extracted from satellite imagery using conventional LSMA; (2) NDBI, NDBaI, and albedo indices were then employed to remove misclassification pixels in the high-albedo and low-albedo fraction images by establishing thresholds for each index; and (3) accuracy of the impervious surface maps was assessed by using root mean square error (RMSE), mean absolute error (MAE), and systematic error (SE). The results indicate that the overall RMSE (0.10) was achieved for the impervious surface map estimated using the improved LSMA compared to 0.15 when conventional LSMA was employed. The improved LSMA method provides a way to enhance urban impervious surface estimation when LSMA is applied, a common approach for median spatial-resolution image mapping.

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