本文選題：空間回歸模型 + 土壤—景觀模型��； 參考：《鄭州大學》2013年碩士論文

【摘要】：常規(guī)數(shù)字土壤制圖技術不能準確揭示土壤在空間上的連續(xù)性和漸變特征,應用計量土壤學新理論和DSM新技術,在區(qū)域尺度上建立空間土壤信息推繹系統(tǒng),完成數(shù)字化土壤制圖對國內土壤地理學及其他相關學科的發(fā)展具有重要推動作用和示范意義。 研究區(qū)位于黃河沖積平原區(qū),受黃河改道、泛濫決口及人類活動影響,地貌類型復雜。本研究首先以模糊c一均值方法進行土壤連續(xù)分類,其次運用空間回歸分析和土壤一景觀定量模型將分類結果和環(huán)境協(xié)變量進行擬合,建立土壤空間預測模型,實現(xiàn)數(shù)字化土壤制圖,并對比分析兩種制圖結果的優(yōu)劣及在研究區(qū)的適用性,主要研究結果如下： (1)運用基于模糊分類算法的OSACA分類系統(tǒng)對土壤樣點進行無監(jiān)督分類,獲取了研究區(qū)五種中心土壤類型以及采樣點與各中心土壤類型間的分類距閔。 (2)空間回歸分析結果表明：運用空間回歸模型進行區(qū)域數(shù)字化土壤制圖具有合理性和必要性；確定性趨勢距離揭示了不同土壤之間自然成土因素的空間差異,非確定性殘差表明了非環(huán)境因素導致的土壤空間變異；研究區(qū)中普通底銹干潤雛形土所占面積最廣,弱鹽灌淤干潤雛形土次之,普通底銹干潤雛形土應舉系所占面積最��；土壤空間變異較大的區(qū)域分布在黃河大堤、大功決口扇以及研究區(qū)東南部。 (3)土壤一景觀模型結果表明：地形因子對平原區(qū)土壤形成影響較小,不適合單獨作為環(huán)境要素組合進行推理制圖。土壤類型圖中普通簡育干潤土潘店系分布面積最大,其次為弱鹽灌淤干潤雛形土,再次為普通人為淤積新成土和普通簡育干潤雛形土應舉系,分布面積最小的是普通底銹干潤雛形土。 (4)對比空間回歸模型和土壤—景觀模型方法的制圖結果,發(fā)現(xiàn)空間回歸模型在進行預測制圖時可以更細致地表達土壤在研究區(qū)的空間分布情況,對環(huán)境因素導致的土壤空間變異敏感度更高,可以很好地反映出區(qū)域尺度上自然成土因素對土壤發(fā)生發(fā)育的影響；土壤—景觀模型方法得到的土壤類型單一且分布集中,不符合土壤發(fā)生學原理；研究區(qū)適合采用空間回歸模型進行土壤預測制圖。
[Abstract]:Conventional digital soil mapping technology can not accurately reveal the spatial continuity and gradual change characteristics of soil. Based on the new theory of soil metrology and the new technology of DSM, the spatial soil information inference system is established on the regional scale.The completion of digital soil mapping plays an important role in promoting and demonstrating the development of soil geography and other related disciplines in China.The study area is located in the alluvial plain area of the Yellow River, which is influenced by the diversion of the Yellow River, the flood crevasse and human activities, and the geomorphologic types are complex.In this study, the fuzzy c-means method was used to classify soil continuously, then spatial regression analysis and soil-landscape quantitative model were used to fit the classification results and environmental covariables to establish the soil spatial prediction model.Digital soil mapping is realized, and the advantages and disadvantages of the two mapping results and their applicability in the study area are analyzed. The main results are as follows:1) using the OSACA classification system based on fuzzy classification algorithm, the unsupervised classification of soil samples was carried out, and the classification distance between the sampling points and the central soil types was obtained.2) the results of spatial regression analysis show that it is reasonable and necessary to use spatial regression model for regional digital soil mapping, and the deterministic trend distance reveals the spatial differences of natural soil-forming factors among different soils.The non-deterministic residuals indicated the spatial variation of soil caused by non-environmental factors, the area of common rust-dry soil was the most extensive, that of weak salt irrigated silt soil was the second, and that of common bottom rust-dry soil was the smallest.The regions with large soil spatial variation are distributed in the Yellow River levee, the Dagong gully fan and the southeastern part of the study area.The results of soil-landscape model show that topographic factors have little influence on soil formation in plain area, so it is not suitable for inferential mapping as a combination of environmental factors alone.In the map of soil types, the distribution area of Pandian system was the largest, followed by weak salt irrigation and silt dry moistening soil, and again for the ordinary people to silt up newly formed soil and the ordinary simple breeding dry moisturizing soil should be used.The smallest distributed area is the rust-dry rust-dry embryonic soil.4) comparing the mapping results of spatial regression model and soil-landscape model, it is found that spatial regression model can more accurately express the spatial distribution of soil in the study area.The sensitivity of soil spatial variation caused by environmental factors is higher, which can well reflect the influence of natural soil-forming factors on the development of soil on a regional scale, and the soil types obtained by soil-landscape model method are single and concentrated.It does not accord with the principle of soil phylogeny, and the spatial regression model is suitable for soil prediction mapping in the study area.
【學位授予單位】：鄭州大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：P285.1

【參考文獻】

相關期刊論文 前10條

1 胡青峰;張子平;何榮;牛遂旺;;基于Geoda 095i區(qū)域經(jīng)濟增長率的空間統(tǒng)計分析研究[J];測繪與空間地理信息;2007年02期

2 于洋;劉吉平;徐艷艷;;東北典型黑土區(qū)土壤養(yǎng)分空間分異影響因素分析[J];水土保持研究;2009年05期

3 孫福軍;雷秋良;劉穎;李華蕾;王秋兵;;數(shù)字土壤制圖技術研究進展與展望[J];土壤通報;2011年06期

4 朱阿興,李寶林,楊琳,裴韜,秦承志,張甘霖,蔡強國,周成虎;基于GIS、模糊邏輯和專家知識的土壤制圖及其在中國應用前景[J];土壤學報;2005年05期

5 史舟;R M Lark;;土壤學的新分支——計量土壤學(Pedometrics)的形成與發(fā)展[J];土壤學報;2007年05期

6 連綱;郭旭東;傅伯杰;虎陳霞;;黃土高原縣域土壤養(yǎng)分空間變異特征及預測——以陜西省橫山縣為例[J];土壤學報;2008年04期

7 檀滿枝;陳杰;;模糊邏輯在土壤連續(xù)分類和制圖表達中的應用及展望[J];土壤學報;2009年01期

8 楊琳;Fahmy Sherif;Hann Sheldon;朱阿興;秦承志;徐志剛;;基于土壤-環(huán)境關系的更新傳統(tǒng)土壤圖研究[J];土壤學報;2010年06期

9 檀滿枝;密術曉;李開麗;史學正;陳杰;;模糊集理論在土壤剖面質地構型中的應用——以河南省封丘沖積平原區(qū)土壤為例[J];土壤學報;2011年02期

10 王改粉;趙玉國;楊金玲;張甘霖;趙其國;;流域尺度土壤厚度的模糊聚類與預測制圖研究[J];土壤;2011年05期

相關碩士學位論文 前3條

1 劉鵬飛;基于分類距離的數(shù)字土壤制圖技術的研究[D];鄭州大學;2011年

2 郭澎濤;丘陵山地預測性土壤制圖研究[D];西南大學;2009年

3 于洋;模糊聚類分析中模糊c均值聚類計算方法研究[D];沈陽工業(yè)大學;2009年

，

本文編號：1749151