ENDSI增强型雪指数提取积雪研究

doi:10.6046/gtzyyg.2018.01.09

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摘要

快速、准确、客观地提取积雪覆盖信息,获得积雪覆盖时空分布资料,是资源生态环境变化研究中的基本问题,卫星遥感技术为有效解决这个问题提供了技术支持。归一化差值雪指数(normalized difference snow index,NDSI)法利用积雪在绿光波段(0.53~0.59 μm)高反射和短波红外波段(1.57~1.65 μm)强吸收特征,可实现遥感自动提取积雪区。以Landsat8 OLI影像为数据源根据积雪的光谱特征,在加入波段B1(0.433~0.453 μm)和B2(0.450~0.515 μm)特征的基础上,运用提出的增强型雪指数(enhanced normalized difference snow index,ENDSI),从OLI影像上进行积雪自动提取。研究结果表明,对积雪厚度变化ENDSI敏感度强于NDSI; 在裸土、薄雪及厚雪区,随着积雪厚度的增加,ENDSI值变化幅度强于NDSI,能有效增大雪与非雪的差异; 当ENDSI阈值取0.3时,可以有效区分雪与非雪,提高积雪提取精度。

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	庞海洋
	孔祥生
	汪丽丽
	钱永刚

关键词 ： ENDSI, NDSI, 积雪, Landsat8 OLI

Abstract：

Detecting snow cover information and snow space-time distribution quickly and accurately is a basic problem of ecological environment changes in the resources. Remote sensing technology effectively provides technical support for solving this problem. Normalized difference snow index (NDSI) is an important method for automatic extracting snow cover information using spectral features of snow, which have high reflection in the green band (0.53~0.59 μm) and strong absorption characteristics in short wave infrared band (1.57~1.65 μm). By using Landsat8 OLI images as the data source and according to the spectral characteristics of snow, the authors propose the enhanced normalized difference snow index (ENDSI) based on adding emissivity characteristics of snow in first band B1 (0.433~0.453 μm) and second band B2 (0.450~0.515 μm), and the utilization of this index to extract snow from OLI images. Simulation and case study results show the following characteristics: the sensitivity of ENDSI is stronger than that of NDSI for the snow thickness; with the increase of the thickness of snow, the change of ENDSI value is stronger than that of NDSI; ENDSI can effectively increase the difference between snow and non-snow; it is easy to extract snow from the image with 0.3 as ENDSI threshold and, in this way, snow extraction accuracy is improved.

Key words： ENDSI NDSI snow Landsat8 OLI

收稿日期: 2016-08-27 出版日期: 2018-02-08

TP79

基金资助:国家自然科学基金项目“高分热像数据和光谱数据的植被气孔导度反演机理研究”(编号: 41271342)和山东省高等学校科技计划项目“‘土法炼焦’等多种高温污染点源遥感自动提取方法及应用”(编号: J12LH01)共同资助

作者简介:

第一作者: 庞海洋(1991-),男,硕士研究生,主要从事遥感科学与技术的科研工作。Email:fuyunmeili@163.com。

引用本文:

庞海洋, 孔祥生, 汪丽丽, 钱永刚. ENDSI增强型雪指数提取积雪研究[J]. 国土资源遥感, 2018, 30(1): 63-71.
Haiyang PANG, Xiangsheng KONG, Lili WANG, Yonggang QIAN. A study of the extraction of snow cover using nonlinear ENDSI model. Remote Sensing for Land & Resources, 2018, 30(1): 63-71.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2018.01.09 或 https://www.gtzyyg.com/CN/Y2018/V30/I1/63

Tab.1 研究区遥感数据源

Fig.1 研究区位置

Fig.2 研究区主要地物光谱曲线

Fig.3 ENDSI和 NDSI提取积雪像元总量及变化数量

Fig.4 研究区5中积雪和裸土光谱曲线

描述	公式	编号
积雪在短波红外波段反射率	ρ_snowswir= $∫ 1.57 1.65 ρ (λ) Γ (λ) dλ ∫ 1.57 1.65 Γ (λ) dλ$ =0.15	(7)
裸土在短波红外波段反射率	ρ_soilswir= $∫ 1.57 1.65 ρ (λ) Γ (λ) dλ ∫ 1.57 1.65 Γ (λ) dλ$ =0.25	(8)
积雪在可见光波段反射率	ρ_snow=ρ_{blue violet}+ρ_blue+ρ_green=3ρ_green-0.1	(9)
裸土在可见光波段反射率	ρ_soil=ρ_{blue violet}+ρ_blue+ρ_green=3ρ_green-0.06	(10)
积雪原始NDSI	NDSI_snow= $ρ green - 0.15 ρ green + 0.15$ (0.1≤ρ_green≤1)^①	(11)
裸土原始NDSI	NDSI_soil= $ρ green - 0.25 ρ green + 0.25$ (0≤ρ_green≤0.15)	(12)
积雪变换后的ENDSI	ENDSI_snow= $3 ρ green - 0.1 - 3.7 × 0.15 3 ρ green - 0.1 + 0.15$ (0.1≤ρ_green≤1)	(13)
裸土变换后的ENDSI	ENDSI_soil= $3 ρ green - 0.06 - 3.7 × 0.25 3 ρ green - 0.14 + 0.25$ (0≤ρ_green≤0.15)	(14)

Tab.2 ENDSI线性模拟公式

Fig.5 NDSI 和 NDSI 在绿光波段反射率的变化趋势(由特定值画出)

Fig.6 验证区ENDSI和NDSI积雪提取结果

Tab.3 ENDSI和NDSI提取积雪像元统计

Tab.4 验证区监督分类与NDSI分类结果混淆矩阵

Tab.5 验证区监督分类与ENDSI分类结果混淆矩阵

Fig.7 目视解译验证实例

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