基于遥感影像的黄河内蒙古段冰-水分类研究

doi:10.12118/j.issn.1000-6060.2021.347

摘要/Abstract

摘要：

及时获取凌汛期河冰和水体的空间分布特征,对于预测冰凌灾害、提高防凌信息化管理水平有重要意义。遥感技术是当前获取河冰和水体空间分布的最主要手段之一。但是,黄河水体有大量悬浮泥沙,这给基于遥感技术的高精度冰-水分类带来了挑战。以黄河内蒙古段为例,基于Landsat 8 OLI遥感影像数据,在利用归一化积雪指数（NDSI）及河道矢量数据排除无关地物的基础上,对比了近红外波段反射率值、归一化差异水体指数（NDWI）、归一化积雪指数（NDSI）、改进的归一化积雪指数（MNDSI）以及归一化差异未封冻水体指数（NDUWI）在黄河内蒙古段典型河道河冰、水体分类中的表现,计算各指标总体分类精度及Kappa系数并进行阈值稳定性分析。结果表明：在利用NDSI和高清历史影像排除河道外无关地物后,NDUWI在各子段影像中的总体分类精度和Kappa系数均达到90.00%及0.90以上,其河冰、水体最优区分阈值大体分布于阈值中值附近。研究结果可为凌汛期黄河冰凌监测方法的选取以及冰上爆破位置的拟定提供依据。

关键词: 河冰, 遥感指数, 阈值稳定性, 不确定性, Landsat 8, 黄河内蒙古段

Abstract:

Timely delivery of detailed information on the spatial distribution of river ice during ice-flood season is highly valuable for predicting, and improving communication on, ice disaster. Remote sensing technology provides a key method for obtaining the spatial distribution of river ice. However, the large amount of suspended sediment in the Yellow River represents a challenge to high-precision discrimination between ice and water based on remote sensing technology. Taking the Inner Mongolia reach of the Yellow River as an example, this study compares and evaluates the performance of five indices in the classification of river ice and water: near-infrared reflectance; normalized difference water index (NDWI); normalized difference snow index (NDSI); improved normalized snow index (MNDSI); and normalized difference unfrozen water index (NDUWI). The overall classification accuracy and Kappa coefficient (a measure of reliability) were calculated for each index, and the threshold stability of each index was analyzed. The results show that NDUWI achieves the highest accuracy and reliability (Kappa coefficient) in each studied subregion. The overall classification accuracy and Kappa coefficient of NDUWI are more than 90.0% and 0.90, respectively, and the optimal discrimination threshold between river ice and ice-free water is close to the median value. These results can provide a basis for the selection of ice monitoring methods and optimization of ice-blasting locations on the Yellow River during ice-flood season.

Key words: river ice, remote sensing index, threshold stability, uncertainty, Landsat 8, Inner Mongolia reach of the Yellow River

翟涌光,张鑫,冀鸿兰,牟献友,张宝森. 基于遥感影像的黄河内蒙古段冰-水分类研究[J]. 干旱区地理, 2022, 45(3): 763-773.

ZHAI Yongguang,ZHANG Xin,JI Honglan,MOU Xianyou,ZHANG Baosen. Ice-water classification in Inner Mongolia reach of the Yellow River based on remote sensing images[J]. Arid Land Geography, 2022, 45(3): 763-773.

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Landsat 8 OLI 影像条带号	日期（年-月-日）	平均大气柱水汽量/g·cm^-2	混合层高度/km	Landsat 8 OLI 影像条带号	日期（年-月-日）	平均大气柱水汽量/g·cm^-2	混合层高度/km
129.032	2014-01-17	0.29	1.42	129.032	2017-02-26	0.22	2.19
128.032	2016-02-01	0.19	1.69	127.032	2018-02-15	0.12	2.23
128.032	2018-02-22	0.10	1.20	128.032	2018-02-06	0.19	2.20
130.032	2020-02-10	0.13	1.43	129.032	2018-02-13	0.17	2.23
128.032	2020-02-28	0.22	0.96	127.032	2019-01-17	0.10	2.19
127.032	2015-02-23	0.17	2.10	128.032	2019-01-24	0.26	2.13
128.032	2015-02-14	0.12	2.12	129.032	2019-02-16	0.28	2.22
129.032	2015-02-21	0.23	2.13	127.032	2020-02-21	0.22	2.13
127.032	2016-02-10	0.21	2.22	128.032	2020-02-12	0.14	2.13
128.032	2016-02-01	0.22	2.13	129.032	2020-02-19	0.22	2.22
129.032	2016-02-08	0.15	2.19	127.032	2021-01-22	0.29	2.22
127.032	2017-02-28	0.11	2.14	128.032	2021-01-29	0.29	2.19
128.032	2017-02-19	0.13	3.65	129.032	2021-02-21	0.23	2.13

	河冰真实值	水体真实值
河冰预测值	a₁₁	a₁₂
水体预测值	a₂₁	a₂₂

阈值范围	近红外波段反射率值	NDWI	NDUWI	NDSI	MNDSI
最小	0~0.37	0~0.37	0~0.22	0~0.86	-0.85~-0.05
最大	0~0.62	0~0.99	0~0.78	0~1.00	-0.98~0

分类精度	近红外波段反射率值	NDWI	NDUWI	NDSI	MNDSI
p₀/%	99.12	98.25	99.89	98.34	99.21
κ	0.98	0.98	0.99	0.9	0.98