極端干旱區(qū)荒漠稀疏河岸林遙感分類研究

摘要： 研究以位于極端干旱區(qū)的塔里木河干流中下游地區(qū)為例,基于Landsat TM影像,結(jié)合決策樹分類、幾何光學(xué)模型與光譜角匹配,解決混合像元信息分解,實(shí)現(xiàn)干旱區(qū)稀疏荒漠河岸林類別識(shí)別.首先從遙感視角的角度,將地物分解為目標(biāo)和背景,提出塔里木河干流荒漠河岸林植被分類系統(tǒng);其次以多變量決策樹法將非荒漠植被信息剔除,采用幾何光學(xué)模型模擬各類荒漠植被的像元光譜,最后以光譜角匹配的方法將荒漠植被進(jìn)一步進(jìn)行分解,得到塔里木河干流中下游地區(qū)典型研究區(qū)的植被分類專題圖,分類精度結(jié)果表明:基于混合像元分解與幾何光學(xué)模型的分類方法總精度達(dá)到了79.43%,Kappa系數(shù)為0.718,表明分類質(zhì)量良好. Abstract： Taking the desert riparian forest belts along both riversides of the middle and lower reaches at the Tarim River Basin as the research object and making use of Landsat TM data, a new classify method of combining decision tree, Geometric Optical models and Spectral Angle Mapper is introduced to identify the desert riparian forest sort in extreme arid region. Firstly, a new classification system of desert riparian forest was brought forward, dividing the target into object and background from view of remote sensing. Secondly, the non-desert vegetation information was masked off by using the method of decision tree;the spectrum of the desert riparian forest pixels were simulated with the pure Geometric Optical and Geometric Optical-Radiative Transfer model, then to map the vegetation of the study area using Spectral Angle Mapper based on the pixel spectrum simulated. The results indicate that the quality of classification is good, with the accuracy coefficient to 79.43% and the Kappa coefficient to 0.718. 作 者： 古麗·加帕爾    陳曦    馬忠國    常存    Guli·Jiapaer    CHEN Xi    MA Zhong-guo    CHANG Cun   作者單位： 中國科學(xué)院,新疆生態(tài)與地理研究所,新疆,烏魯木齊,830011;中國科學(xué)院,綠洲生態(tài)與荒漠環(huán)境重點(diǎn)實(shí)驗(yàn)室,新疆,烏魯木齊,830011  期 刊： 中國沙漠  ISTICPKU   Journal： JOURNAL OF DESERT RESEARCH  年，卷(期)： 2009, 29(6)  分類號(hào)： X87  關(guān)鍵詞： 極端干旱區(qū)    荒漠稀疏河岸林    決策樹    幾何光學(xué)模型    光譜角填圖    Keywords： extreme arid region    sparse desert riparian forest    decision tree    Geometric Optical model    Spectral Angle Mapper   

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