热带地理 ›› 2016, Vol. 36 ›› Issue (6): 969-975.doi: 10.13284/j.cnki.rddl.002894

• 论文 • 上一篇    下一篇

基于改进线性光谱解混和 SCS-CN 模型的广州主城区降雨产流模拟

许剑辉1,赵 怡1,2,3,钟凯文1,刘旭拢1   

  1. (1.a.广州地理研究所;b.广东省遥感与地理信息系统应用重点实验室;c.广东省地理空间信息技术与应用公共实验室;广州 510070;2.中国科学院 广州地球化学研究所,广州 510640;3.中国科学院大学,北京 100049)
  • 收稿日期:2016-06-23 出版日期:2016-11-05 发布日期:2016-11-05
  • 作者简介:许剑辉(1984―),男,广东人,助理研究员,博士,主要从事城市遥感与数据同化,(E-mail)xujianhui306@163.com
  • 基金资助:

    广州地理研究所优秀青年创新人才基金资助项目;广东省水利科技创新项目(2015-13);广东省自然科学基金(2014A030313747);广东省科学院平台环境与能力建设专项资金项目(2016GDASPT-0103)

Runoff Simulation of Main Urban Area in Guangzhou City Based on the Improved Linear Spectral Mixture Analysis and SCS-CN Models

XU Jianhui1,ZHAO Yi1,2,3,ZHONG Kaiwen1,LIU Xulong1   

  1. (1.a.Guangzhou Institute of Geography;b.Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System;c.Guangdong Open Laboratory of Geospatial Information Technology and Application,Guangzhou 510070,China;2.Guangzhou Institute of Geochemistry,China Academy of Sciences,Guangzhou 510640,China;3.University of Chinese Academy of Sciences,Beijing 100049,China)
  • Received:2016-06-23 Online:2016-11-05 Published:2016-11-05

摘要:

城市降雨产流特征的定量研究对于城市雨水资源开发利用、科学规划和管理具有十分重要的意义。以广州市主城区为研究对象,提出一种改进的线性光谱解混方法提取遥感影像的不透水面、植被和土壤盖度,并结合SCS-CN 产流模型进行城区降雨径流模拟。首先利用线性光谱解混方法提取 2015-10-18 Landsat 8 OLI 遥感影像的不透水面、植被和土壤盖度;然后利用归一化建筑物指数和归一化植被指数进一步优化解混结果;最后结合优化的不透水面、植被和土壤盖度计算合成的 CN 值,并应用 SCS-CN 产流模型分析研究区在不同降雨重现期的降雨产流分布特征。结果表明:改进的线性光谱解混方法能较好地提高不透水面、植被和土壤盖度的提取精度;不透水面盖度越高的区域,CN 值越高;CN 值对地表径流深模拟有显著的影响;在不同重现期降雨条件下,研究区地表径流深空间分布格局差异显著;总体上,降雨量越大,不透水面盖度越高,形成的地表径流深越深,内涝发生的可能性越大。

关键词: NDVI, NDBI, 线性光谱解混, SCS-CN, 径流深

Abstract:

Quantitative research of the urban rainfall-runoff process is of great importance for exploiting, utilizing, planning, and managing the urban rainwater resources. In this study, an improved linear spectral mixture analysis (LSMA) method is developed by integrating the normalized difference built-up index (NDBI) and normalized difference vegetation index (NDVI). The improved LSMA method needs two steps: (1) the representative endmembers are first selected by combining a high-resolution image from Google Earth; (2) the results of LSMA are post-processed with NDBI and NDVI. During the post-process, if the NDBI value of a pixel is greater than -0.15, that pixel in the low-albedo fraction image is classified into low-albedo impervious surface; otherwise, pixels in the low-albedo fraction image are kept and classified as low-albedo pervious surface. In the low-albedo pervious surface fraction image, pixels with NDVI value less than 0.2 are classified as low-albedo soil fraction, other pixels are classified as low-albedo vegetation fraction. Finally, the impervious surface fraction is equal to the summation of high-albedo and low-albedo impervious surface fractions. The vegetation fraction can be estimated by the addition of original vegetation and low-albedo vegetation fractions. The soil fraction can be calculated by the summation of original soil and low-albedo soil fractions. Combining Landsat 8 OLI image on 18 th October, 2015 in the main urban area of Guangzhou, this improved LSMA method is then implemented to extract the fraction maps of impervious surface, vegetation, and soil. The results show that the improved LSMA with higher accurate results outperforms the conventional LSMA. This method can reduce errors in the low-albedo image. On the whole, the improved LSMA can significantly reduce the bias and root-mean-square error (RMSE). A soil conservation service curve number (SCS-CN) method is applied for simulating and analyzing the surface runoff under different return periods of precipitation. The key parameter CN in the SCS-CN is estimated with the fractions of impervious surface, vegetation, and soil and their corresponding initial CN values. The results show that high-quality impervious surface, vegetation, and soil fractions may be used to calculate the real CN. Higher CNs are located in these regions with higher impervious surface fraction except water. High CN indicates a low infiltration rate. The cumulative infiltration increases with the decreasing of CN. CN has a significant impact on the surface runoff simulation. A distinct spatial difference can be found in the surface runoff simulation. In general, if the precipitation intensity and impervious surface fraction are high, and the surface runoff is deep, the region will be easy to form waterlogging.

Key words: Normalized Difference Vegetation Index, Normalized Difference Built-up Index, Linear spectral mixture analysis, SCS-CN, runoff