植被调节下的干旱区不透水面覆盖率遥感估算方法

doi:10.13474/j.cnki.11-2246.2018.0048

测绘通报 ›› 2018, Vol. 0 ›› Issue (2): 78-82.doi: 10.13474/j.cnki.11-2246.2018.0048

植被调节下的干旱区不透水面覆盖率遥感估算方法

沈谦¹, 朱长明¹, 张新², 黄巧华¹, 杨程子¹, 赵南¹

1. 江苏师范大学地理测绘与城乡规划学院, 江苏徐州 221116;
2. 中国科学院遥感与数字地球研究所, 北京 100101

收稿日期:2017-05-09 修回日期:2017-10-17 出版日期:2018-02-25 发布日期:2018-03-06
通讯作者: 朱长明。E-mail:zhuchangming@jsnu.edu.cn E-mail:zhuchangming@jsnu.edu.cn
作者简介:沈谦(1992-),男,硕士生,主要从事遥感信息智能提取及城市遥感等方面的研究。E-mail:shenqian_gis@outlook.com
基金资助:
国家自然科学基金（41201460；61473286）；国家科技支撑计划（2015BAJ02B01）；水利部科研专项（201201092）

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

SHEN Qian¹, ZHU Changming¹, ZHANG Xin², HUANG Qiaohua¹, YANG Chengzi¹, ZHAO Nan¹

1. School of Geography, Geomatics & Planning, Jiangsu Normal University, Xuzhou 221116, China;
2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Received:2017-05-09 Revised:2017-10-17 Online:2018-02-25 Published:2018-03-06

摘要/Abstract

摘要：

基于DMSP/OLS夜间灯光数据的居住区指数模型（HSI）广泛应用于区域尺度城市不透水面扩张监测。但是，在干旱区由于受到裸岩、沙漠、戈壁等低植被覆盖区干扰，HSI算法的精度和适应性受到了一定的影响。为解决这一问题，本文利用植被覆盖度作为调节系数，对灯光数据与植被指数进行动态调整，构建了适用于干旱区的城市植被调节不透水指数（VAISI）；然后采用SVR模型，通过机器学习的方法构建了城市不透率参考数据与VAISI之间的非线性关系模型，实现对干旱区区域尺度不透水面覆盖率估算；最后，对模型估算结果进行了精度验证和比较分析。试验结果表明：在干旱区，VAISI解决了由于灯光溢出问题及城市周边裸土等低植被覆盖等因素导致的城市周边裸土像元不透率估算过高问题，一定程度上提高了城市内部不透水面空间分布信息的表达能力，有效克服了非灯光区估算结果高于背景值的现象。平均相关系数R由0.69提升到0.79，RMSE由0.17降至0.14。

关键词: 不透水面, 干旱区, DMSP/OLS, 遥感监测

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

中图分类号:

沈谦, 朱长明, 张新, 黄巧华, 杨程子, 赵南. 植被调节下的干旱区不透水面覆盖率遥感估算方法[J]. 测绘通报, 2018, 0(2): 78-82.

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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植被调节下的干旱区不透水面覆盖率遥感估算方法

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

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