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  本文選題：干旱半干旱灌區(qū)　切入點：土壤次生鹽漬化　出處：《中國農業(yè)大學》2016年博士論文

【摘要】：土壤鹽漬化是干旱、半干旱地區(qū)最主要和極易發(fā)生的土地退化現(xiàn)象,嚴重影響生態(tài)環(huán)境質量,制約著人類社會和經濟的發(fā)展。灌區(qū)土壤次生鹽漬化已成為限制我國生態(tài)和經濟發(fā)展的主要因素,更對我國的糧食生產造成嚴重威脅。平羅縣作為我國傳統(tǒng)的農業(yè)灌溉區(qū)和產糧大縣,土壤次生鹽漬化現(xiàn)象十分嚴重。亟需對該區(qū)的土壤鹽漬化程度和分布情況進行快速、全面和深入的了解。本文利用遙感和地理信息系統(tǒng)技術等手段對研究區(qū)的土壤鹽漬化進行有效監(jiān)測,并深入分析了影響土壤鹽漬化形成與發(fā)展的自然與人為因素,在此基礎上實現(xiàn)了該區(qū)土壤鹽漬化的模擬和預測。研究成果如下：(1)研究區(qū)的土壤鹽漬化有明顯的表聚現(xiàn)象,土壤表層的含鹽量有較強的空間變異。利用冗余分析得到K+、Na+、SO42-、Cl-與土壤全鹽量的相關性很強,HC03-和Ca2+與pH值相關性更強,而且S042-和Na+是對鹽漬化程度貢獻最強的陰陽離子。各層土壤含鹽量都具有中等強度的空間相關性,其半方差函數(shù)模型均可以用指數(shù)模型進行擬合。表層與深層土壤鹽分的空間分布格局存在一定的差異。(2)對環(huán)境小衛(wèi)星的高光譜數(shù)據(jù)進行線性光譜混合分解。利用純凈像元指數(shù)和最小噪聲分離法提取了水體、鹽分、植被和暗色物質等端元。對不同條件下的線性光譜混合分解方法進行對比分析,得到全約束條件下的線性光譜混合分解的效果最好且物理意義更明確�；谠摲椒ǖ柠}分豐度結果探討了環(huán)境小衛(wèi)星高光譜數(shù)據(jù)在土壤鹽漬化等級分類與制圖中的應用。(3)充分利用環(huán)境小衛(wèi)星多光譜數(shù)據(jù)的優(yōu)勢,基于研究區(qū)內的農業(yè)種植模式和物候信息,建立了適合研究區(qū)的土地利用／覆被分類系統(tǒng)。構建能夠反映地表植被信息變化的NDVI時間序列,提取了表示該區(qū)物候信息且對各地類有較強分異性的時間維特征參數(shù)。結合光譜特征參數(shù)構建了基于專家知識的決策樹,實現(xiàn)了研究區(qū)高精度的土地利用/覆被分類。(4)以采樣點的實際控制面積為土壤鹽漬化研究尺度,利用環(huán)境小衛(wèi)星遙感數(shù)據(jù)、DEM等地理數(shù)據(jù)以及土地利用數(shù)據(jù)等,提取了影響土壤鹽漬化的自然因素和人為因素。構建了樣方尺度中既能間接反映不同作物對鹽分的響應,又能直接反映不同土地利用方式對土壤鹽漬化影響的以作物面積為權重的冠層響應鹽分指數(shù)這一綜合指標。利用BP神經網絡建立各指標因子對EC的預測模型。研究區(qū)土壤鹽漬化程度受到自然因素和人為因素的共同影響,且不同因素之間存在著相互作用和不同的尺度效應,對鹽漬化的預測精度有一定影響。
[Abstract]:Soil salinization is the most important and easily occurring phenomenon of land degradation in arid and semi-arid areas, which seriously affects the quality of ecological environment. The secondary salinization of soil in irrigated area has become the main factor that restricts the ecological and economic development of our country. Pingluo County, as a traditional agricultural irrigation area and a big grain-producing county in China, has a very serious secondary salinization phenomenon. It is urgent to carry out rapid soil salinization and distribution in this area. In this paper, we use remote sensing and GIS technology to monitor soil salinization effectively, and analyze the natural and human factors that affect the formation and development of soil salinization. On this basis, the simulation and prediction of soil salinization in this area have been realized. The research results are as follows: (1) the phenomenon of soil salinization in the study area is obvious. There is strong spatial variation in the salt content in the surface layer of soil. By using redundant analysis, the correlation between K ~ (2 +) Na ~ (2 +) so _ (42) ~ (-) Cl- and soil total salt content is very strong. HC03- and Ca2 are more closely correlated with pH value. Moreover, S042- and Na are the most important ions that contribute to the salinization degree. The semi-variance function model can be fitted by exponential model, and the spatial distribution pattern of salt in surface and deep soil is different. The hyperspectral data of environmental small satellite are decomposed by linear spectral mixing. The pure pixel index and the minimum noise separation method were used to extract the water body. Salt, vegetation and dark matter, etc. The linear spectral mixing decomposition methods under different conditions are compared and analyzed. The results show that the linear spectral mixing decomposition under the condition of full constraint is the best and the physical significance is more clear. Based on the salt abundance results of this method, the classification and mapping of soil salinization by environmental small satellite hyperspectral data are discussed. To make full use of the advantages of environmental small satellite multispectral data, Based on the agricultural planting pattern and phenological information in the study area, a land use / cover classification system suitable for the study area was established, and a NDVI time series which could reflect the change of surface vegetation information was constructed. The time dimension characteristic parameters which represent phenological information in this area and are different from each other are extracted, and the decision tree based on expert knowledge is constructed by combining the spectral characteristic parameters. The high precision land use / cover classification of the study area is realized. The actual control area of the sampling point is taken as the scale of soil salinization research, and the geographic data such as Dem and land use data are used. The natural and human factors affecting soil salinization were extracted. It can also directly reflect the effect of different land use patterns on soil salinization, which is a comprehensive index of canopy response salt index with crop area as weight. BP neural network is used to establish the prediction model of EC by each index factor. The degree of soil salinization is affected by both natural and human factors. The interaction between different factors and the different scale effect have certain influence on the prediction accuracy of salinization.
【學位授予單位】：中國農業(yè)大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：S156.41

【參考文獻】

相關期刊論文 前1條

1 ;A Spectral Index for Estimating Soil Salinity in the Yellow River Delta Region of China Using EO-1 Hyperion Data[J];Pedosphere;2010年03期

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本文編號：1699905