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Estimation of large-scale impervious surface percentage by fusion of multi-source time series remote sensing data

  • role： First author第一作者
  • Affiliation：

    School of Geomatics & Planning, Jiangsu Normal University, Xuzhou 221116, China

  • Email：jsnulifanggang@163.com
  • Introduction：李方刚，1992年生，男，硕士研究生，研究方向为遥感图像信息处理。E-mail:jsnulifanggang@163.com
LI Fanggang1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geomatics & Planning, Jiangsu Normal University, Xuzhou 221116, China

  • Email：lierzhu2008@126.com
  • Introduction：李二珠，1988年生，男，讲师，研究方向为遥感图像处理与模式识别。E-mail:lierzhu2008@126.com
LI Erzhu1*,  
  • Affiliation：

    State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences, Urumqi 830011, China

SAMAT Alim2,  
  • Affiliation：

    School of Geomatics & Planning, Jiangsu Normal University, Xuzhou 221116, China

ZHANG Lianpeng1,  
  • Affiliation：

    School of Geomatics & Planning, Jiangsu Normal University, Xuzhou 221116, China

LIU Wei1,  
  • Affiliation：

    School of Geomatics & Planning, Jiangsu Normal University, Xuzhou 221116, China

HU Jinshan1

Resümee

Impervious surface information is an important indicator for monitoring urban expansion and regional ecological environment change. Extracting impervious surface information based on remote sensing technology is of great importance. The traditional large-scale impervious surface percentage estimation model is estimated by a single-factor regression model based on the correlation between the specific remote sensing information and the impervious surface ratio. Given the influence of the amount of information and universality of specific remote sensing information, these methods have considerable limitations in large-scale impervious surface extraction, and their regional adaptability of estimation results is slightly different. To address this problem, this study proposes a method for estimating the impervious surface based on multiple features remote sensing information to compensate for the uncertainty of single feature in large-scale impervious surface extraction. First, MOD13Q1, MOD09A1 products, nighttime lighting data (NPP-VIIRS), and Landsat 8 OLI are used as remote sensing data sources to construct multiple index features of impervious surface information from different angles. On this basis, a multi-regression model is used to establish a multifactor impervious surface percentage estimation model to realize remote sensing estimation of large-scale impervious surface percentage. This study selects 13 typical cities across the country as the main research area to verify the proposed model. Results show that the method can adapt to the estimation of impervious surface percentage in different regions and that it is better than traditional methods in complex urban areas. The effect has significantly improved the accuracy of the estimation of impervious surface percentage within the city.

Schlüsselwort

remote sensing;impervious surface;multi-features;night lights;Landsat 8 OLI;MODIS

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