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荒漠植物含水量的光谱特征分析
引用本文:Zhao Z,Li X,Yin YB,Tang J,Zhou SB. 荒漠植物含水量的光谱特征分析[J]. 光谱学与光谱分析, 2010, 30(9): 2500-2503. DOI: 10.3964/j.issn.1000-0593(2010)09-2500-04
作者姓名:Zhao Z  Li X  Yin YB  Tang J  Zhou SB
作者单位:新疆农业大学草业与环境科学学院,新疆,乌鲁木齐,830052;新疆农业大学林学与园艺学院,新疆,乌鲁木齐,830052;中国科学院新疆生态与地理研究所,新疆,乌鲁木齐,830011
基金项目:国家自然科学基金项目,水利部公益性行业科研项目,阜康北部荒漠过渡带防护林及植被恢复生态科技工程项目,自治区草地资源与生态实验室项目资助 
摘    要:
用美国SVC的HR-768便携光谱仪现地测定了9种荒漠植物的高光谱并在实验室使用烘干法测定相应植物的含水率,对测定的光谱数据使用ENVI软件去除包络线,运用相关系数法分析植物含水率与反射光谱之间的关系,结果表明:978~1 030 nm波段与植物含水率相关性一般,1 133~1 266 nm波段与植物含水率相关性较好,1 374~1 534 nm波段与植物含水率相关性最好,是表达植物含水率大小的特征波段.对1 374~1 534 nm波段光谱数据进行聚类分析,可将测定的植物划分为含水率较高(>70%)、中等(50%~70%)、较低(<50%)3个等级.以上研究揭示了荒漠植物含水量大小和光谱数据之间的关系,为荒漠区生境分析和利用遥感数据进行荒漠植物监测提供了参考依据.

关 键 词:光谱  包络线去除  含水量  荒漠植物

Analysis of spectral features based on water content of desert vegetation
Zhao Zhao,Li Xia,Yin Ye-biao,Tang Jin,Zhou Sheng-bin. Analysis of spectral features based on water content of desert vegetation[J]. Spectroscopy and Spectral Analysis, 2010, 30(9): 2500-2503. DOI: 10.3964/j.issn.1000-0593(2010)09-2500-04
Authors:Zhao Zhao  Li Xia  Yin Ye-biao  Tang Jin  Zhou Sheng-bin
Affiliation:College of Pratacultural and Environmental Science, Xinjiang Agricultural University, Urumqi 830052, China.
Abstract:
By using HR-768 field-portable spectroradiometer made by the Spectra Vista Corporation (SVC) of America, the hyper-spectral data of nine types of desert plants were measured, and the water content of corresponding vegetation was determined by roasting in lab. The continuum of measured hyperspectral data was removed by using ENVI, and the relationship between the water content of vegetation and the reflectance spectrum was analyzed by using correlation coefficient method. The result shows that the correlation between the bands from 978 to 1030 nm and water content of vegetation is weak while it is better for the bands from 1133 to 1266 nm. The bands from 1374 to 1534 nm are the characteristic bands because of the correlation between them and water content is the best. By using cluster analysis and according to the water content, the vegetation could be marked off into three grades: high (>70%), medium (50%-70%) and low (<50%). The research reveals the relationship between water content of desert vegetation and hyperspectral data, and provides basis for the analysis of area in desert and the monitoring of desert vegetation by using remote sensing data.
Keywords:
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