本文選題：光譜分析 + 植被重金屬污染�。� 參考：《江西農(nóng)業(yè)大學(xué)學(xué)報(bào)》2017年06期

【摘要】：植被重金屬污染的植物體內(nèi)重金屬元素含量反演方法一直是高光譜遙感研究熱點(diǎn)之一。設(shè)置不同濃度銅離子(Cu~(2+))脅迫梯度下的玉米盆栽實(shí)驗(yàn),并測(cè)量不同濃度Cu~(2+)脅迫下玉米葉片的光譜數(shù)據(jù)及其葉片中富集的Cu~(2+)含量。由于健康的與受Cu~(2+)脅迫污染的玉米葉片光譜在曲線(xiàn)形態(tài)上相似度仍很高,且傳統(tǒng)的光譜相似性測(cè)度方法難以區(qū)分污染光譜的變異性弱差信息,因而采用離散小波變換多層分解、奇異值分解、光譜角度量等理論方法對(duì)光譜形態(tài)及變異信息進(jìn)行轉(zhuǎn)換處理,再通過(guò)正切與對(duì)數(shù)函數(shù)擴(kuò)大光譜轉(zhuǎn)換后的變異信息的方式,構(gòu)建了正切奇異向量角(tangent singular vector angle,TSVA)和對(duì)數(shù)奇異向量角(logarithmic singular vector angle,LSVA)的玉米葉片中Cu~(2+)含量遙感預(yù)測(cè)模型。結(jié)果表明,TSVA和LSVA模型預(yù)測(cè)玉米葉片中的Cu~(2+)含量較為理想,也能很好地區(qū)分不同濃度Cu~(2+)脅迫梯度下的光譜變異信息。通過(guò)預(yù)測(cè)值與實(shí)測(cè)值的結(jié)果比較與相關(guān)性分析(其相關(guān)系數(shù)均大于0.91),驗(yàn)證了TSVA和LSVA模型預(yù)測(cè)玉米葉片中Cu~(2+)含量的有效性和可行性。
[Abstract]:The inversion method of heavy metal content in vegetation contaminated by heavy metals has been one of the hotspots in hyperspectral remote sensing. The pot experiment of maize under different concentration of Cu ~ (2) stress was carried out. The spectral data of maize leaves and the contents of Cu ~ (2) enriched in maize leaves were measured under different concentrations of Cu ~ (2) stress. Because the spectral similarity between healthy maize leaves and maize leaves contaminated by Cu2 stress is still very high, and the traditional spectral similarity measurement method is difficult to distinguish the weak difference information of pollution spectra. Therefore, the discrete wavelet transform multilayer decomposition, singular value decomposition, spectral angle quantity and other theoretical methods are used to transform and process the spectral morphology and variation information, and then the variation information after spectral transformation is expanded by tangent and logarithmic functions. A remote sensing prediction model of Cu2 content in maize leaves was established based on tangent singular vector angle (tangent singular vector anglev (tangent singular vector) and logarithmic singular vector angle (logarithmic singular vector anglev LSVA). The results showed that TSVA and LSVA models were ideal for predicting Cu2 content in maize leaves, and could also distinguish the spectral variation information under different Cu ~ (2) stress gradients. The validity and feasibility of TSVA and LSVA models in predicting Cu2 content in maize leaves were verified by comparing the predicted values with the measured values and the correlation analysis (the correlation coefficients were all greater than 0.91).
【作者單位】： 中國(guó)礦業(yè)大學(xué)(北京)地球科學(xué)與測(cè)繪工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(41271436) 中央高校基本科研業(yè)務(wù)費(fèi)專(zhuān)項(xiàng)資金(2009QD02)~~
【分類(lèi)號(hào)】：S127;S513;X87
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本文編號(hào)：2053097