Method for Arid Land Impervious Surface Percentage Estimation by Vegetation Index Adjustment

doi:10.13474/j.cnki.11-2246.2018.0048

Abstract

Abstract:

DMSP/OLS nighttime light data has been widely used in reginal impervious surface percentage estimation and monitoring. But, in the arid area, because of bare soil, desert and other the low rate of vegetation covered area effect, the accuracy and robustness of the existed algorithm was decreased seriously. In order to solve this issue, this paper used the vegetation coverage as the adjustment coefficient to adjust the light data and NDVI dynamically and build vegetation adjusted impervious surface index (VAISI). Impervious surface reference data were extracted from Landsat image, which was used as the sample data and validation data of model. And then, the non-linear relationship was built between impervious surface reference data and VAISI by SVR model to estimate the impervious surface percentage. The result indicated that the VAISI model solved the problem of higher estimated in arid land because of the bare soil around the city and the low rate of vegetation covered area, improved the impervious surface's space distribution information inside city, and overcame the obstacle of that the non-light area is higher than background values. The average correlation coefficient between the estimated result by the VAISI and the reference data were increased from 0.68 to 0.79 and RMSE was decreased from 0.17 to 0.13.

Key words: impervious surface, arid land, DMSP/OLS, remote sensing monitoring

CLC Number:

SHEN Qian, ZHU Changming, ZHANG Xin, HUANG Qiaohua, YANG Chengzi, ZHAO Nan. Method for Arid Land Impervious Surface Percentage Estimation by Vegetation Index Adjustment[J]. 测绘通报, 2018, 0(2): 78-82.

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