地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (10): 1608-1618.doi: 10.12082/dqxxkx.2019.190102

• 遥感科学与应用技术 • 上一篇    下一篇

Landsat/OLI与夜间灯光数据在提取城市不透水面中的精度差异分析

冯珊珊,樊风雷()   

  1. 华南师范大学地理科学学院,广州 510631
  • 收稿日期:2019-03-06 修回日期:2019-05-15 出版日期:2019-10-25 发布日期:2019-10-29
  • 通讯作者: 樊风雷 E-mail:fanfenglei@gig.ac.cn
  • 作者简介:冯珊珊(1994-),女,广东阳江人,博士生,主要从事城市不透水面研究。E-mail: 2016022046@m.scnu.edu.cn
  • 基金资助:
    国家自然科学基金项目(41201432)

Accuracy Comparison between Landsat/OLI and Nighttime Light Data in Extracting Urban Impervious Surface

FENG Shanshan,FAN Fenglei()   

  1. School of Geography, South China Normal University, Guangzhou 510631, China
  • Received:2019-03-06 Revised:2019-05-15 Online:2019-10-25 Published:2019-10-29
  • Contact: FAN Fenglei E-mail:fanfenglei@gig.ac.cn
  • Supported by:
    National Natural Science Foundation of China(41201432)

摘要:

不透水面作为反映城市发展程度和表征城市生态环境的重要指标,在城市化研究中成为重要的数据源。当前,不透水面信息的获取通常基于遥感数据来开展,包括不同分辨率的遥感数据。这些遥感数据在高精度提取城市不透水面的能力具有较大的差异,会因尺度不同而带来提取精度的偏差。因此,理解不同遥感数据源在不透水面提取上的差异尤为重要。本文利用Landsat/OLI光谱数据和VIIRS/DNB夜间灯光数据分别采用线性光谱混合分析法和大尺度不透水面指数法提取珠江三角洲研究区的不透水面信息,并从不透水面总体精度、不同密度精度对比分析2类数据源提取不透水面的差异。结果表明:① Landsat/OLI和VIIRS/DNB两者提取不透水面的总体精度差异不大,Landsat/OLI提取不透水面的精度总体上略高于VIIRS/DNB。2种数据提取不透水面的均方根误差RMSE分别是0.18和0.21,系统误差SE分别是0.12和0.13,决定系数R 2分别是0.76和0.67。② Landsat/OLI和VIIRS/DNB数据对不同密度不透水面分布区域的提取能力不同:VIIRS/DNB在低密度不透水面区域提取精度高于Landsat/OLI;而Landsat/OLI在中、高密度不透水面区域提取精度均高于VIIRS/DNB。通过2种数据提取精度差异的对比,以期为不同密度的不透水面分布区域提取找到最佳尺度的数据源,提高不透水面提取的效率和精度。

关键词: 不透水面, Landsat/OLI数据, VIIRS/DNB夜间灯光数据, 精度差异, 线性光谱混合分析, 大尺度不透水面指数

Abstract:

Impervious surface is considered as a major indicator of the degree of urbanization and also an important indicator of environmental quality. Currently, impervious surface extraction is usually based on remote sensing data, including different resolution remote sensing data sources. In extracting high-precision impervious surface, there would be great differences in extraction accuracy caused by different spatial scales. Therefore, it is necessary to explore impervious surface extraction characteristics with different remote sensing data sources. This paper used Landsat/OLI spectral data and VIIRS/DNB nighttime data to extract impervious surface and compare their extraction accuracy difference. The primary objective of this study was to determine the optimal data sources for estimating Impervious Surface Percentage (ISP) for regions with different density of impervious surface distribution. Firstly, Linear Spectral Mixture Analysis (LSMA) was used to extract impervious surface with Landsat/OLI data, and Large-scale Impervious Surface Index (LISI) was used to estimate ISP with VIIRS/DNB data. Then, accuracy of the impervious surface extraction results from these two data sources was assessed respectively, based on the Root Mean Square Error (RMSE), Systematic Error (SE), and coefficient of determination (R 2). The accuracy results showed that the overall ISP accuracy based on the Landsat/OLI data was slightly better than that based on the VIIRS/DNB data, with the overall RMSE being 0.18 and 0.21, SE 0.12 and 0.13, and R 2 0.76 and 0.67, respectively. The accuracy assessments from different density results of impervious surface indicated that the extraction capabilities of Landsat/OLI data and VIIRS/DNB data were greatly different for regions with different density of impervious surface distribution. In the region of low-density impervious surface distribution, the extraction accuracy of impervious surface results based on VIIRS/DNB data was better than based on Landsat/OLI data, because the impervious surface information can be effectively distinguished based on light brightness of VIIRS/DNB data. The impervious surface extraction results from Landsat/OLI data had better accuracy in the areas of medium and high-density impervious surface distribution, because the spatial details of high-density urban impervious surface can be extracted more effectively by the spectral differences of Landsat/OLI data. In future studies, more research is needed to explore the impervious surface extraction characteristics with remote sensing data at different spatial scales and to determine the optimal data sources for effectively and accurately estimating impervious surface.

Key words: urban impervious surface, Landsat/OLI data, VIIRS/DNB nighttime light data, accuracy comparison, linear spectral mixture analysis (LSMA), large-scale impervious surface index (LISI)