本文選題：土壤分類 + 光譜特征參數(shù)��； 參考：《遙感學(xué)報(bào)》2017年01期

【摘要】：提出了一種無(wú)損、快速、成本低的土壤分類方法,選取松嫩平原4種典型土壤(黑土、黑鈣土、風(fēng)砂土和草甸土)耕層(0—20 cm)土樣的實(shí)驗(yàn)室反射光譜數(shù)據(jù)作為研究對(duì)象,采用重采樣、包絡(luò)線消除法處理光譜數(shù)據(jù),提取反映反射光譜特征的光譜特征參數(shù),利用K均值聚類(K-means clustering)和決策樹(shù)(decision tree)分別進(jìn)行聚類分析和分類模型構(gòu)建,實(shí)現(xiàn)土壤的快速分類。結(jié)果表明,利用表層土壤反射光譜特征參數(shù)構(gòu)建的決策樹(shù)分類模型可以對(duì)研究區(qū)土壤進(jìn)行分類。研究成果有望加快土壤制圖,為土壤理化性質(zhì)的時(shí)空變化研究提供技術(shù)支持。
[Abstract]:A fast and low cost soil classification method was proposed. The laboratory reflectance spectrum data of four typical soils (black soil, calcareous soil, aeolian sandy soil and meadow soil) were studied.Resampling and envelope elimination are used to process spectral data, and spectral characteristic parameters reflecting the characteristics of reflection spectrum are extracted. Cluster analysis and classification model are constructed using K-means clustering of K-means and decision tree tree, respectively.The rapid classification of soil is realized.The results show that the decision tree classification model based on the characteristic parameters of surface soil reflectance spectrum can be used to classify the soil in the study area.The research results are expected to accelerate soil mapping and provide technical support for the spatiotemporal variation of soil physical and chemical properties.
【作者單位】： 東北農(nóng)業(yè)大學(xué)資源與環(huán)境學(xué)院;黑龍江省農(nóng)墾科學(xué)院科技情報(bào)研究所;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(編號(hào):41501357) 黑龍江省普通高等學(xué)校新世紀(jì)優(yōu)秀人才培養(yǎng)計(jì)劃 黑龍江省博士后啟動(dòng)基金(編號(hào):LBH-Q13026)~~
【分類號(hào)】：S155

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本文編號(hào)：1735365