Improving Fractional Impervious Surface Mapping Performance through Combination of DMSP-OLS and MODIS NDVI Data

Wei Guo,Wenhui Kuang,Dengsheng Lu

doi:10.3390/rs9040375

Abstract

Impervious surface area (ISA) is an important parameter for many studies such as urban climate, urban environmental change, and air pollution; however, mapping ISA at the regional or global scale is still challenging due to the complexity of impervious surface features. The Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data have been used for ISA mapping, but high uncertainty existed due to mixed-pixel and data-saturation problems. This paper presents a new index called normalized impervious surface index (NISI), which is an integration of DMSP-OLS and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, in order to reduce these problems. Meanwhile, this newly developed index is compared with previously used indices—Human Settlement Index (HSI) and Vegetation Adjusted Nighttime light Urban Index (VANUI)—in ISA mapping performance. We selected China as an example to map fractional ISA distribution through a support vector regression approach based on the relationship between the index and Landsat-derived ISA data. The results indicate that the proposed NISI provided better ISA estimation accuracy than HSI and VANUI, especially when the fractional ISA in a pixel is relatively large (i.e., >0.6) or very small (i.e., <0.2). This approach can be used to rapidly update ISA datasets at regional and global scales.

Highlights

Rapid population increase and economic conditions have resulted in unprecedented urbanization in China in the past four decades [1,2,3]
The results indicate that the proposed normalized impervious surface index (NISI) provided better Impervious surface area (ISA) estimation accuracy than Human Settlement Index (HSI) and Vegetation Adjusted Nighttime light Urban Index (VANUI), especially when the fractional ISA in a pixel is relatively large (i.e., >0.6) or very small (i.e.,
It indicates that the data-saturation problem in urban regions and blooming effects in the urban-rural interfaces are obvious in OLSnor data (Figure 3a,b), but not for NDVImax and NISI (Figure 3c,d)

Summary

Introduction

Rapid population increase and economic conditions have resulted in unprecedented urbanization in China in the past four decades [1,2,3]. Due to its unique characteristics in data collection and presentation, has been extensively applied for mapping urban distribution and dynamic change [3,8,12,13,14,15,16,17]. 2017, 9, 375 and confusion of different land cover types make it difficult to directly use remote sensing data for mapping urban land cover distribution and detecting its change. Impervious surface area (ISA) has been regarded as an important attribute to represent urban distribution and expansion [18]. Much research has been conducted to accurately map ISA distribution using different remote sensing data, especially Landsat imagery, due to its long-term data availability at no cost and suitable spectral and spatial resolutions [19,20,21,22]

Results

Discussion

Conclusion