甘肃河西地区荒漠化土地光谱特征研究

引用本文

魏怀东, 李亚, 张勃, 李菁菁, 丁峰, 陈芳. 甘肃河西地区荒漠化土地光谱特征研究[J]. 光谱学与光谱分析, 2019,39(11): 3508-3513.
WEI Huai-dong, LI Ya, ZHANG Bo, LI Jing-jing, DING Feng, CHEN Fang. Spectral Characteristics of Desertification Land in Gansu Hexi Corridor[J]. Spectroscopy and Spectral Analysis, 2019,39(11): 3508-3513.
Doi:10.3964/j.issn.1000-0593(2019)11-3508-06 复制到剪切板

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甘肃河西地区荒漠化土地光谱特征研究

魏怀东^1,², 李亚^2,^*, 张勃¹, 李菁菁², 丁峰², 陈芳²

1. 西北师范大学地理与环境科学学院, 甘肃兰州 730070

2. 甘肃省治沙研究所, 甘肃兰州 730070

*通讯联系人 e-mail: lygdci@126.com

作者简介: 魏怀东, 1972年生, 西北师范大学地理与环境科学学院研究员 e-mail: weihdgs@126.com

收稿日期: 2018-10-10

基金: 国家自然科学基金项目(31360204, 41661008), 甘肃省基础研究创新群体项目(145RJIA335), 甘肃省自然科学基金项目(1506RJZA137), 甘肃省科技计划项目(17JR3TA007, 18JR2TA015)资助

摘要

甘肃河西走廊土地荒漠化严重影响了当地居民的生产生活环境, 高光谱遥感技术是荒漠化土地退化程度、土地类型识别、遥感反演等的重要研究手段, 以河西地区荒漠化土地为研究对象, 分析其光谱特征与植被退化程度、植物类型、季节变化等的关系, 探讨河西地区荒漠化土地的光谱特征。主要结果有: (1)当植被覆盖度小于20%, 同一类型不同退化阶段的植被光谱对沙地光谱的影响很小, 沙地光谱反射率与裸地接近, 尤其当植被盖度小于10%时, 沙地与裸地的光谱曲线几乎重合, 仅从植被景观很难反映出土地的沙化程度。 (2)不同的植被类型对沙地光谱的反射率有一定的影响, 以白刺为建群种的沙地光谱反射率较高, 其次为梭梭沙地, 多枝柽柳沙地相对较低, 植被不同演替阶段下指示性植物的沙地光谱可以反映土地的沙化过程。 (3)在植物生长季, 沙地光谱反射率受土壤、植物含水量及植物物候期的影响, 8月—10月高于其他月份, 7月最低, 沙地光谱波形曲线的季节变化规律可以反映出沙地土壤含水量的变化。研究结果对荒漠化土地遥感监测中土地沙化程度判定、季节信息提取、植被覆盖度估算等提供研究基础。

关键词: 河西地区; 荒漠化土地; 光谱特征

中图分类号:S181 文献标志码:A

Spectral Characteristics of Desertification Land in Gansu Hexi Corridor

WEI Huai-dong^1,², LI Ya^2,^*, ZHANG Bo¹, LI Jing-jing², DING Feng², CHEN Fang²

1. College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China

2. Gansu Desert Control Research Institute, Lanzhou 730070, China

*Corresponding author

Abstract

The desertification of Gansu Hexi Corridor is serious, which seriously affects the environment of living and production activities of local residents. Hyperspectral remote sensing technology is an important research method for desertification land degradation degree, land type identification and remote sensing inversion. This paper takes desertification land in Hexi area as the study object. The object was analyzed for the relationship between its spectral characteristics and vegetation degradation degree, plant type, season and soil type, and the spectral characteristics of desertified land in Hexi region were discussed. The main results are as follows: (1) When the vegetation coverage is less than 20%, the vegetation degradation has little effect on the sand spectrum in different degradation stages of the same vegetation type, and the spectral reflectance of the sand is close to the bare land, especially when the vegetation coverage is less than 10%, the spectral curves of sand and bare land almost coincide, and it is difficult to reflect the degree of desertification of the land only from the vegetation landscape. (2) Different vegetation types have a certain influence on the reflectivity of sandy land spectrum. The spectral reflectance of sandy land with Nitraria tangutorum Bobr as constructive species is higher, followed by Haloxylon ammodendron sandy land, and the Tamarix ramosissim sandy land are relatively low. The sandy spectrum of the indicator plants under different succession stages of vegetation can reflect the desertification process of the land. (3) During the plant growing season, the spectral reflectance of sandy land is affected by soil, plant water content and plant phenology, which is higher than other months from August to October and lowest in July. The seasonal variation of the spectral curve of sandy land can reflect the changes in soil water content in sandy land. The research results provide a research basis for the determination of land desertification degree, seasonal information extraction and vegetation coverage estimation in remote sensing monitoring of desertification land.

Keyword: Hexi area; Desertification land; Spectral characteristics

文章图片

引言

土地荒漠化己经成为当今全球最严重的环境与社会经济问题之一, 是全球变化研究中的一个重要内容。它不仅威胁到整个人类的生存环境, 而且是制约全球经济发展及影响社会稳定的重要因素。土壤性状和植被覆盖呈现出的不同景观特征, 是指示土地荒漠化程度的重要指标。近年来, 随着高光谱遥感技术的发展与应用, 遥感影像的空间分辨率和光谱分辨率得到很大提高, 高光谱遥感技术以其波段多、波长范围窄、信息量大、针对性强, 同时对土地破坏小等特点, 成为荒漠化土地退化程度、土地类型识别、遥感反演等的重要研究手段, 尤其在植被资源匮乏、生态环境恶劣的西北干旱区更具现实意义^{[1, 2]}。

土地光谱特征受时间、地点和环境背景等各种因素的相互作用, 是土壤光谱和植被光谱的综合反映。近年来, 一些学者针对不同程度、不同类型沙化土地, 不同物候期植被和沙漠腹地冬季地面光谱的测定与分析开展了荒漠化土壤光谱特征研究, 认为沙化土地的光谱特征与沙化程度、植被覆盖度、季节、天气等各种因素有关^{[3, 4, 5]}。甘肃河西走廊占甘肃省总面积的60%以上, 由于降水稀少, 蒸发强烈, 生态环境脆弱, 该地区土地荒漠化严重, 河西地区荒漠化土地占全省总土地面积的42.5%, 荒漠化严重影响了当地居民生活生产活动的正常开展, 目前尚没有针对该地区荒漠化土地光谱特征的研究。本研究以河西地区荒漠化土地为研究对象, 分析其光谱与植被退化程度、植物类型、季节变化等的相关性, 探讨河西地区荒漠化土地的光谱特征, 对荒漠化土地遥感监测中土地沙化程度、植被退化程度判定, 季节信息提取, 植被覆盖度估算等奠定研究基础。

1 研究区概况

河西走廊东起乌鞘岭, 西至古玉门关, 南北介于南山和北山之间, 东西长约1 200 km, 南北宽约100~200 km, 为西北— 东南走向的狭长平地。属大陆性干旱气候, 气候干燥、冷热变化剧烈, 风大沙多; 自东而西年降水量逐渐减少, 降水年际变化大; 年均气温5.8~9.3 ℃, 昼夜温差大; 日照充足, 光照资源丰富。主要有盐渍化、沙漠、戈壁等不同立地类型, 地表生长稀疏的旱生、超旱生植被, 分布较常见的有唐古特白刺(Nitraria tangutorum)、多枝柽柳(Tamarix ramosissima)、沙蒿(Artemisia arenaria)、梭梭(Haloxylon ammodendron)、沙拐枣(Calligonum rubicundum)等。

2 实验部分

2.1 样地选择与设置

2017年, 在河西地区分别选择以典型荒漠灌木梭梭、唐古特白刺、多枝柽柳为优势种的群落, 要求植被分布、土壤基质相对均一, 地貌地形起伏较小。每种群落选择生长正常、衰退、严重衰退3类样地, 共9个样地, 每个样地面积50 m× 50 m。

2.2 光谱测定

测定方法: 选择晴朗、无云、光照条件好, 风力较小, 满足光谱测定分析(analytical spectral devices, ASD)野外测量标准的天气, 时间在10:00— 14:00之间, 利用全光谱便携式地物光谱仪Fieldspec^® 4 Standard-Res(FS4S)进行植物和土壤光谱的测定, 光谱范围为350~2 500 nm, 光谱采样间隔约1.5 nm, 光谱分辨率3.5 nm, 探头视场角度为25° , 测量高度为20 cm。每个样地内, 对每种植物随机选取10株进行测定, 每株植物选取5个不同的部位测定5组, 每组5个重复; 同时随机选择10处裸地(裸地按照沙地、结皮、丘间地等不同类型分类测定)测定光谱, 每处5个重复。不同样地不同种植物测量前利用白板校正1次。测量时光谱仪探头垂直向下对准被测对象, 垂直距离随被测对象大小变化, 保证视场覆盖整个被测对象。

表1 样地信息 Table 1 Site information

测定时间: 5月— 10月份, 每月月初在植被生长正常的样地测定分析土壤光谱的季节特征, 共计6次; 7月份在所有样地测定分析不同植被覆盖度下土壤、植被的光谱。

2.3 样地调查

测定植物光谱的同时, 测量植株的高度、冠幅, 估算每一株植物和每一类裸地的分盖度, 记录样地内每种植物的总株数, 并记录样地土壤、地形地貌、植被生长状况等因子。

2.4 数据处理与分析

由于野外光谱仪测量时受环境影响较大, 存在较多干扰信息, 因此对测得的原始光谱数据进行筛选, 通过检查被测物体和标准板的光谱数据和光谱曲线, 去除了其中无效的数据, 保证光谱数据的有效性。用ViewSpecPro软件取均值作为该植被和土壤的反射光谱。

植物的盖度^[5]采用式(1)来计算

$植被覆盖度 = \frac{平均冠幅 \times 平均分盖度 \times 总株数}{样地面积} \times 100 % (1)$

裸地的盖度采用式(2)来计算

$裸地盖度 = 1 - 所有物种盖度之和 (2)$

沙地光谱反射率采用植被和土壤的复合地物光谱反射率, 采用式(3)来计算

$\begin{array}{l} 沙地光谱反射率 = 植物光谱反射率 \times 植被覆盖度 + \\ 土壤光谱反射率 \times 土壤所占比例 (3) \end{array}$

3 结果与讨论

3.1 不同地表覆盖类型沙地的光谱特征

3.1.1 植被不同退化程度

植被不同退化程度, 沙地的光谱反射率均高于植物冠层的反射率, 尤其是从570 nm开始, 沙地的光谱反射率显著高于植物冠层光谱反射率, 光谱曲线基本与沙、丘间地等裸地光谱曲线重合(图1)。沙地光谱曲线总体表现为: 基本都呈波浪形^[6], 在可见光和近红外的350~900 nm波段都存在一个明显的弓形突起区^[7], 这是荒漠化土地退化土壤的一个典型光谱特征; 在350~600 nm可见光区段持续增加, 且增速较快, 600~690 nm之间比较平缓, 690 nm之后持续波动缓慢增加, 2 130 nm之后波动缓慢降低。

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	图1 植被不同退化程度沙地光谱特征Fig.1 Spectral characteristics of sandy land with different degrees of degradation of vegetation

同一建群种不同退化程度, 植被的覆盖度随着退化程度的加剧明显降低, 白刺沙地依次为: 9.16%, 6.35%和4.30%, 梭梭沙地依次为: 15.71%, 9.62%和2.69%, 多枝柽柳沙地依次为: 18.63%, 15.28和8.94%, 且植物冠层的光谱反射率也随着植被退化程度的加剧逐渐降低(图2), 但沙地的光谱反射率受植被退化程度的影响很小, 光谱反射率大小没有明显的差异, 尤其是在衰退和严重衰退的梭梭沙地、严重衰退的白刺沙地, 裸地的比例高达90.38%, 97.42%和95.7%, 沙地的光谱曲线和裸地的基本重合。

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	图2 不同退化程度植物冠层光谱反射率曲线Fig.2 Spectral curves of plant canopy with different degrees of degradation

3.1.2 不同植被类型

不同植被类型沙地光谱的曲线变化规律基本一致, 但反射率大小有一定差异, 以白刺为建群种沙地的总体光谱反射率相对较高, 其次为梭梭沙地, 多枝柽柳沙地的光谱反射率相对较低(图3)。

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	图3 不同植被类型沙地光谱曲线Fig.3 Sand spectral curves of different vegetation types

3.2 土壤光谱的季相变化特征

分析白刺结皮、梭梭结皮、多枝柽柳结皮及梭梭沙地的光谱在5月— 10月的动态变化(图4)。白刺结皮和多枝柽柳结皮, 5月— 10月之间光谱曲线波形及反射率大小基本相同, 350~1 830 nm之间基本重合, 缓慢增加; 1 830~1 915 nm之间, 8月和9月反射率明显增加, 尤其是8月份; 之后不同月份间基本一样, 缓慢降低; 1 830~1 915 nm之间有一个弱的水吸收带。

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	图4 土壤光谱的季相变化规律Fig.4 Seasonal variation rule of soil spectrum

梭梭沙地和梭梭结皮光谱曲线波形变化在不同季节间基本一致, 但反射率大小不同月份间有差异, 9月和10月基本一样, 反射率最高, 其次为8月、 6月、 5月, 7月份最低; 在350~600 nm之间增加相对较快, 600 nm之后增幅相对平缓, 从2 130 nm开始波动降低。在1 380~1 410 nm和1 830~1 915 nm之间有两个弱水分吸收带。

3.3 讨论

已有研究认为, 沙地的光谱反射率随着植被覆盖度的减小而增加, 沙化土地植被覆盖度可以反映出沙化程度的大小, 同时沙化土地反射率受植被和土壤水分的影响, 在中红外波段1 300~2 500 nm低于近红外波段750~1 300 nm, 在900~1 000 nm植被的特征波段曲线差异较大^{[7, 8, 9]}。但在本研究中, 尽管不同植被覆盖度下沙地的光谱反射率大小不同(图3), 植被覆盖度与沙地光谱的反射率为负相关(图5), 同时同一建群种在不同退化程度, 植物冠层的光谱反射率随着退化程度的加剧逐渐减小, 但沙地的光谱特征并没有表现出随着植被覆盖度的减小而增强的规律; 此外, 尽管不同种类不同退化程度植被的反射率在中红外波段1 300~2 500 nm之间均低于近红外波段750~1 300 nm(图2), 但沙地的光谱反射率同样没有这样的规律, 且在900~1 000 nm等植被特征波段曲线没有明显变化。主要是因为本研究区植被覆盖度均较低(均小于20%), 植被对整个沙地的光谱影响较小, 沙地的光谱特征更接近于裸地, 即在植被盖度小于20%的荒漠化土地, 对植被分布为同一建群种的沙地, 仅通过植被的不同退化阶段, 很难识别出土地的沙化程度, 需进一步探讨新的土壤光谱指数来提取可识别的信息, 建立地物光谱特征与土地沙化之间的数量关系, 进而研究沙化土地的变化机制。不同植被覆盖度下沙地光谱反射率大小的差异主要是由于不同类型植物冠层光谱的差异引起的, 在研究区常见的白刺、梭梭、多枝柽柳沙地, 白刺沙地的光谱反射率高于其他2种沙地, 因此, 根据植物的演替规律, 选择不同演替阶段不同类型的指示性植物^[10], 综合分析沙地的光谱特征, 可以更有效地探讨沙化土地的变化机制。

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	图5 植被覆盖度与沙地光谱反射率的相关性曲线Fig.5 Correlation curve between vegetation coverage and spectral reflectance of sandy land

在沙区, 土壤、植物的含水量及植物生长量等都随着季节变化呈周期性变化, 这种变化对沙地光谱反射率也有一定影响。在项目区, 年降雨量非常稀少(110 mm左右), 且季节差异大, 主要集中在8月— 10月, 同时该时间段为荒漠植被的主要营养生长期, 白刺、梭梭、多枝柽柳沙地及梭梭结皮的光谱反射率在8月— 10月均高于其他月份; 7月为当地较干旱的时间段, 该时间段的光谱反射率也相对较低。不同季节沙地光谱反射率大小在水分吸收带1 380~1 410和1 830~1 915 nm之间有一定的差异, 尤其是在沙地土壤含水量相对较高的8月— 10月差异明显, 即沙地光谱的波形曲线可以反映出土壤含水量的变化, 特定波段的波谱特征分析是深入探讨土地沙化机制的有效手段之一。

4 结论

(1)当植被覆盖度小于20%, 同一植被类型下不同退化程度植被对沙地光谱的影响很小, 沙地光谱反射率介于植被与沙、丘间地等裸地之间, 但更接近裸地, 尤其当植被盖度小于10%时, 沙地与裸地的光谱曲线几乎重合, 仅从植被景观很难反映出土地的沙化程度。

(2)不同的植被类型对沙地光谱的反射率有一定的影响, 以白刺为建群种的沙地光谱反射率较高, 其次为梭梭沙地, 多枝柽柳沙地相对较低, 植被不同演替阶段下指示性植物的沙地光谱可以反映土地的沙化过程。

(3)在植物生长季, 沙地光谱反射率受土壤、植物含水量及植物物候期的影响, 8月— 10月高于其他月份, 7月最低, 沙地光谱波形曲线的季节变化规律可以反映出沙地土壤含水量的变化。

参考文献

文献列表

[1]	TONG Qing-xi, ZHANG Bing, ZHANG Li-fu(童庆禧, 张兵, 张立福). Journal of Remote Sensing(遥感学报), 2016, 20(5): 689. [本文引用:1]
[2]	NIU Ya-long, LIU Ting-xi, DUAN Li-min, et al(牛亚龙, 刘廷玺, 段利民, 等). Pratacultural Science(草业科学), 2017, 34(7): 1365. [本文引用:1]
[3]	ZHENG Guo-xiong, LI Xiao-song, ZHANG Kai-xuan, et al(郑国雄, 李晓松, 张凯选, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(4): 1063. [本文引用:1]
[4]	FENG Xiu-rong, BU Chong-feng, HAO Hong-ke, et al(冯秀绒, 卜崇峰, 郝红科, 等). Journal of Natural Rescources(自然资源学报), 2015, 30(6): 1024. [本文引用:1]
[5]	CUI Xiang-hui, LU Qi, GUO Hao(崔向慧, 卢琦, 郭浩). Journal of Desert Research(中国沙漠), 2017, 37(6): 1121. [本文引用:2]
[6]	PENG Zheng-lin, MAO Xian-cheng, LIU Wen-yi, et al(彭正林, 毛先成, 刘文毅, 等). Remote Sensing for Land and Resources(国土资源遥感), 2011, 89(2): 19. [本文引用:1]
[7]	GAO Zhi-hai, BAI Li-na, WANG Feng-yu, et al(高志海, 白黎娜, 王琫瑜, 等). Scientia Silvae Sinicae(林业科学), 2011, 47(6): 9. [本文引用:2]
[8]	DUAN Rui-lu, LIU Ting-xi, ZHANG Sheng-wei, et al(段瑞鲁, 刘廷玺, 张圣微, 等). Arid Zone Research(干旱区研究), 2014, 31(4): 750. [本文引用:1]
[9]	ZUO Lu, WANG Huan-jiong, LIU Rong-gao, et al(左璐, 王焕炯, 刘荣高, 等). Chinese Journal of Applied Ecology(应用生态学报), 2018, (2): 599. [本文引用:1]
[10]	ZHAO Zhen-liang, Tashpolat Tiyip, SUN Qian, et al(赵振亮, 塔西甫拉提·特依拜, 孙倩, 等). Progress in Geography(地理科学进展), 2014, 33(2): 280. [本文引用:1]

2016

0.0

... 近年来, 随着高光谱遥感技术的发展与应用, 遥感影像的空间分辨率和光谱分辨率得到很大提高, 高光谱遥感技术以其波段多、波长范围窄、信息量大、针对性强, 同时对土地破坏小等特点, 成为荒漠化土地退化程度、土地类型识别、遥感反演等的重要研究手段, 尤其在植被资源匮乏、生态环境恶劣的西北干旱区更具现实意义^[1,2] ...

2017

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2016

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... 近年来, 一些学者针对不同程度、不同类型沙化土地, 不同物候期植被和沙漠腹地冬季地面光谱的测定与分析开展了荒漠化土壤光谱特征研究, 认为沙化土地的光谱特征与沙化程度、植被覆盖度、季节、天气等各种因素有关^[3,4,5] ...

2015

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2017

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... 植物的盖度^[5]采用式(1)来计算 ...

2011

0.0

... 沙地光谱曲线总体表现为: 基本都呈波浪形^[6], 在可见光和近红外的350~900 nm波段都存在一个明显的弓形突起区^[7], 这是荒漠化土地退化土壤的一个典型光谱特征 ...

2011

0.0

GAO

Zhi-hai

, BAI

Li-na

, WANG

Feng-yu

, et al(高志海, 白黎娜, 王琫瑜, 等). Scientia Silvae Sinicae(林业科学), 2011, 47(6): 9.

The topsoil spectral characteristics and spectral segments sensitivity to soil organic matter (SOM) were studied on the basis of laboratory analysis and spectral measurement of soil samples collected from 2 typical areas of desertification, and then models for estimating SOM of topsoil layer were established. The results showed that the soils of desertified lands had the unique wave-shaped spectral curves, with an obvious bow-shaped protuberance area in 500-900 nm wavelength range, which could be used to extract SOM information from soil spectrum. It was found by the correlation analysis that there were two spectral segments sensitive to SOM, centered at 600 and 830 nm wavelengths respectively. The result of model validation indicated that the models, taking the logarithm reflectance and reciprocal reflectance at 588 nm (lg R 588 , 1/ R 588 ) and the first derivative reflectance of reciprocal reflectance and logarithm reflectance at 835 nm (1/ R 835 )', (lg R 835 )' as independent variable respectively, could be used to accurately estimate the SOM content in desertified lands.

1. Key Laboratory of Forestry Remote Sensing and Information Techniques of State Forestry Administration Institute of Forest Resource Information Techniques, Chinese Academy of Forestry Beijing 100091;2. Institute of Remote Sensing Applications, Chinese Academy of Sciences Beijing 100101;3. Water & Soil Conservation College, Beijing Forestry University Beijing 100083;4. Paulownia Research and Development Center of State Forestry Administration Zhengzhou 450003

在对2个荒漠化典型区土壤采样、化验分析和光谱测量的基础上,分析荒漠化土地土壤的反射光谱特征及土壤有机质的光谱敏感范围,构建多种土壤有机质含量高光谱估测模型。结果表明: 荒漠化土地土壤具独特的波浪型光谱曲线,其主要特点是在可见光和近红外的500~900 nm光谱范围存在一个明显的弓形突起区,其对提取土壤有机质信息有实际意义; 相关分析发现,在中心波长分别为600和830 nm的可见光和近红外光光谱范围分别存在1个有机质光谱敏感区; 土壤有机质含量高光谱估测模型验证结果表明,利用波长588 nm处的反射光谱对数lg R 588 和反射光谱倒数1/ R 588 以及波长835 nm处的反射光谱倒数的导数(1/ R 835 )'和反射光谱对数的导数(lg R 835 )'分别建立的模型,可以较好地估测荒漠化土地土壤有机质含量。

... 3 讨论已有研究认为, 沙地的光谱反射率随着植被覆盖度的减小而增加, 沙化土地植被覆盖度可以反映出沙化程度的大小, 同时沙化土地反射率受植被和土壤水分的影响, 在中红外波段1 300~2 500 nm低于近红外波段750~1 300 nm, 在900~1 000 nm植被的特征波段曲线差异较大^[7,8,9] ...

2014

0.0

DUAN

Rui-lu

, LIU

Ting-xi

, ZHANG

Sheng-wei

, et al(段瑞鲁, 刘廷玺, 张圣微, 等). Arid Zone Research(干旱区研究), 2014, 31(4): 750.

Spectral data are the joint affecting results of many factors, and there may be the “same objects with different spectra” or “different objects with the same spectra”. In this study, four common spectral matching algorithms, [WTBX]i.e.[WTBZ] the Minimum Distance (MD), Spectral Similarity Angle (SSA), Spectral Correlation Coefficient (SCC) and Spectral Information Divergence (SID), were used to carry out the matching analysis on the denoised spectral data of plants growing on sand dune in the Horqin Sandy Land. It was found that the minimum distance method could be used to distinguish the typical plant species on sand dune in the study area. On which a matching analysis of spectral derivative was carried out, it was found that the distinguishing effects of spectral similar angle and spectral correlation coefficient were significantly increased, and even a negative value of the matching degree of second derivative spectrum occurred. The band TM1, TM2, TM3 and TM4 of vegetation spectral data were selected to process, and it was verified that the band TM2 and TM4 could be used to improve the distingishing accuracy. After analyzing the basic information of single band with the statistically based analysis, it was found that the amount of information contained in a single band was in an order of TM1>TM4>TM2>TM3, and it was considered that the band TM4 should be the first choice to distinguish the plant species in the study area based on a comprehensive consideration of the highly overlapped spectral reflectance curve and the spectral matching results of single band of plant species.

地物光谱数据是多因素共同作用的结果，可能会出现“同物异谱”或“异物同谱”现象。以科尔沁沙地典型沙丘—草甸相间区域为研究区，以野外实测沙丘植被光谱数据为基础，利用最小距离法、光谱相似角度、光谱相关系数和光谱信息散度4种光谱匹配算法，对去噪后植物光谱特征数据进行匹配分析，发现最小距离法可以区分出本区沙丘典型植被。在此基础上，对光谱导数进行匹配分析，发现光谱相似角度和光谱相关系数识别效果增强明显，二阶导数光谱特征匹配度甚至出现了多个负值。最后选取TM1、TM2、TM3和TM4波段植被光谱数据做同样处理，证实TM2、TM4波段能够提高识别精度；同时，基于统计学基础分析单波段基础信息，发现单波段所包含信息量依次为TM1> TM4>TM2>TM3，综合考虑TM1波段内各植被光谱反射率特征曲线重叠严重和单波段植被光谱特征匹配度结果，认为TM4波段为该地区植被物种识别的首选波段。

2018

0.0

2014

0.0

ZHAO

Zhen-liang

, Tashpolat

Tiyip

, SUN

Qian

, et al(赵振亮, 塔西甫拉提·特依拜, 孙倩, 等). Progress in Geography(地理科学进展), 2014, 33(2): 280.

Soil salinization is a process of global land degradation, which hazards the environment. It is caused by inefficient irrigation and the excessive use of water. It reduces the productivity of land. Xinjiang is the typical area of arid and semi-arid region. The monitoring of soil salinization timely and effectively is not only beneficial to the production of agriculture, but also in favor of sustainable development of agricultural land. The purpose of this study is how to improve the classification accuracy. In this paper, the author uses Spectral Angle Mapper method extracting the information of soil salinaztion. In order to improve the accuracy of classification, the author determines the appropriate soil and vegetation spectral library. The appropriate soil and vegetation spectral library decide the accuracy. The use of field measurements of soil spectral reflectance is used to study the soil spectral characteristics. It is combined with hyperspectral data of the Chinese environmental and disaster monitoring and forecasting of small satellites and classified soil salinization based on hyperspectral image. In the first part of this paper, according to the definition of degree of soil salinaztion, four classed of soil are classified, namely non-salined soil, slight-salined soil, moderate-salined soil and heavy-salined soil. The data from four classes of soil can be converted to fourteen transforms of soil spectral reflectance. There are fifteen transforms of soil spectral reflectance. They are the original and the converted fourteen transforms of soil spectral reflectance. According to the findings of the correlation analysis of fifteen transforms of soil spectral reflectance with soil salt content, regression analysis are done. The equations are chosen to estimate soil salt content. And root mean square error ( RMSE ) is employed to verify the accuracy of the equations. The best equations of estimating soil salt content are decided. In the second part of this paper, the author decides the soil spectral library according to the result of spectral characteristics. The vegetation spectral library is based on field measurements and actual survey. In the final part of this paper, the author uses the SAM method to classify the hyperspectral image based on the soil spectral library and vegetation spectral library. Such a classification is proved good, laying the foundation for the region's hyperspectral applications, giving warning to regional farmers for appropriate farming methods, provided the data for the region's sustainable development. This article identifies the soil and vegetation spectral library of the study area. This helps to further study on spectral characteristics in this region. Environmental and disaster monitoring and forecasting of small satellite is designed and developed by our country. China centre for resources satellite data and application provided for our researchers free. HSI hyperspectral data is also China's the only hyperspectral remote sensing images. This study attempts to contribute to our development of hyperspectral remote sensing images. To speed up the satellite for the applications of environmental monitoring. It is better for the country's environmental monitoring services. The development of hyperspectral remote sensing images will bring a new opportunity and challenge to remote sensing technology. Our researchers should strive for the development of hyperspectral remote sensing technology. The development of hyperspectral remote sensing will promise a better future.

1. College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China; 2. Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China

土壤盐渍化严重制约土地生产力，实时监测土壤盐渍化有利于农业正常生产。选择新疆渭干河—库车河绿洲的光谱反射率数据，研究不同程度盐渍化土壤的光谱特征；并对绿洲所在的库车县的环境与灾害监测预报小卫星的高光谱数据进行盐渍化信息提取。提取步骤为：首先对土壤光谱反射率数据进行14种形式的变换，再与土壤含盐量进行相关分析、逐步回归分析，建立估算不同盐渍化程度的土壤含盐量方程，用均方根误差验证方程的精度；其次，建立植被和土壤波谱库；最后，在波谱库的数据基础上，使用波谱角分类法（SAM）对环境与灾害监测预报小卫星的高光谱数据进行分类。用同步实测数据对分类效果进行精度评价，效果较好，这一结果为今后该区域的高光谱应用奠定了基础，对区域农民耕作方式提出了警示，为区域可持续发展实践提供了参考。

... 不同植被覆盖度下沙地光谱反射率大小的差异主要是由于不同类型植物冠层光谱的差异引起的, 在研究区常见的白刺、梭梭、多枝柽柳沙地, 白刺沙地的光谱反射率高于其他2种沙地, 因此, 根据植物的演替规律, 选择不同演替阶段不同类型的指示性植物^[10], 综合分析沙地的光谱特征, 可以更有效地探讨沙化土地的变化机制 ...