本文關(guān)鍵詞： 戈壁 決策樹(shù) 礫石粒徑 主成分分析 遙感 哈密　出處：《中國(guó)林業(yè)科學(xué)研究院》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：戈壁是我國(guó)西北干旱地區(qū)的一種主要地表景觀類型，其在我國(guó)西北地區(qū)廣泛分布。戈壁中蘊(yùn)藏著豐富的自然資源，具有重大的經(jīng)濟(jì)價(jià)值，同時(shí)也擁有國(guó)防、政治和社會(huì)等方面的重要意義。但該區(qū)域自然環(huán)境惡劣，傳統(tǒng)調(diào)查方式費(fèi)時(shí)費(fèi)力，不利于進(jìn)行研究，因此目前關(guān)于戈壁的研究資料較少。隨著現(xiàn)代科學(xué)技術(shù)的發(fā)展，尤其是遙感技術(shù)及空間信息技術(shù)的進(jìn)步，為戈壁分布和面積的確定，以及戈壁地面物質(zhì)組成劃分與識(shí)別提供很好的技術(shù)支持。由于研究資料較少，目前戈壁分布及面積不清，而精確弄清戈壁的分布及面積是開(kāi)展戈壁研究的基礎(chǔ)與前提，，因此，需要首先較精確地提取出戈壁區(qū)。在戈壁各特征中，地面物質(zhì)組成特征不僅直接影響其他特征的性質(zhì)，并且很大程度上決定改造利用的難易。戈壁表面礫石粒徑尺寸反映戈壁形成過(guò)程信息，研究它可幫助了解戈壁特征，分析戈壁造成自然災(zāi)害的原因，認(rèn)識(shí)沙粒遷移、沙漠?dāng)U展以及指導(dǎo)防沙工程。 該研究以戈壁典型分布區(qū)新疆天山山脈東段的哈密地區(qū)為研究區(qū)，以2010年Landsat5TM遙感影像及空間分辨率為30m*30m的DEM為基本數(shù)據(jù)源，首先在分析不同地類遙感影像的光譜特征基礎(chǔ)上，建立了基于專家知識(shí)的決策樹(shù)信息提取模型，對(duì)影像土地利用類型進(jìn)行分類，較好地提取出戈壁區(qū)；戈壁地表礫石粒徑和遙感多光譜數(shù)據(jù)、植被指數(shù)及地學(xué)因子存在相關(guān)關(guān)系，但這些因子間可能存在著多重相關(guān)性，如利用這些因子直接建模估測(cè)戈壁地表礫石粒徑，則可能出現(xiàn)病態(tài)模型。利用主成分分析法篩選因子，既可保留多個(gè)相關(guān)因子的主要信息，又可避免因子間共線性的問(wèn)題，達(dá)到降維，簡(jiǎn)化模型的作用。因此,基于ENVI4.8軟件的主成分分析模塊，從研究選擇的43個(gè)遙感及地學(xué)因子（主要包括影像各波段信息、DEM、NDVI、 GEMI，影像經(jīng)K-T變換得到SBI、GVI、WVI三個(gè)分量，通過(guò)紋理分析得到的各個(gè)波段的均值、方差、信息熵、相關(guān)性及對(duì)比度等紋理因子，以及利用DEM提取的粗糙度等）中，篩選提取其主成分。以主成分作為自變量，野外調(diào)查得到的戈壁礫石粒徑為因變量，借助SPSS18軟件中的多元回歸分析功能，建立戈壁地表礫石粒徑的回歸模型，模型經(jīng)方差分析及相關(guān)性檢驗(yàn)，達(dá)到顯著相關(guān)水平�；诮⒌念A(yù)估模型，進(jìn)行了戈壁地表礫石粒徑估測(cè)，并驗(yàn)證了其模型估算精度。本研究可幫助我們精確提取戈壁區(qū)，并能了解戈壁的特征，為戈壁區(qū)改造利用、區(qū)域減災(zāi)、西部經(jīng)濟(jì)建設(shè)服務(wù)。主要研究結(jié)果如下： （1）利用決策樹(shù)分類法，較為準(zhǔn)確地將未利用地與其它土地類型分開(kāi)，將戈壁較為精確地提取出來(lái)，研究區(qū)總體分類精度達(dá)到90%以上，Kappa系數(shù)為0.919，戈壁的提取精度到達(dá)了95%以上，實(shí)現(xiàn)了對(duì)戈壁精確提取。 （2）基于主成分分析法，篩選與研究區(qū)戈壁地表礫石粒徑相關(guān)的遙感及地學(xué)因子，提取主成分，前5個(gè)主成分因子貢獻(xiàn)率達(dá)到98%，反映了樣本的主要信息，以前5個(gè)主成分為自變量，相應(yīng)的礫石粒徑為因變量，建立戈壁地表礫石粒徑的預(yù)估模型，經(jīng)驗(yàn)證，達(dá)到顯著相關(guān)水平，顯著水平α=0.01，相關(guān)系數(shù)R=0.825，利用該模型進(jìn)行戈壁表面礫石粒徑定量遙感反演，經(jīng)驗(yàn)證，估測(cè)值與實(shí)測(cè)值緊密相關(guān)，相關(guān)系數(shù)R=0.778，模型預(yù)估效果較好，為戈壁區(qū)域研究提供了技術(shù)支持。
[Abstract]:Gobi is one of the main landscape types in the arid area of Northwest China, which is widely distributed in the northwest region of China. Gobi is rich in natural resources, has great economic value, but also has important significance for national defense, political and social aspects. But the harsh natural environment, the traditional time-consuming investigation laborious, not conducive to study, so there are fewer studies on Gobi. With the development of modern science and technology, especially remote sensing technology and spatial information technology, in order to determine the distribution and area of Gobi, and the Gobi ground material composition classification and recognition provide good technical support. Because of less information, at present Gobi distribution and area is not clear, and the distribution and area of accurate understand Gobi is the foundation and premise, so the research carried out in Gobi, we first need to accurately extract the region in Gobi. The characteristics of Gobi, the nature of the ground material composition not only directly affect other characteristics, and largely determine the transformation and utilization difficult. Gobi surface gravel particle size reflects the Gobi formation process of information, it can help us understand the characteristics of Gobi, Gobi made a analysis of the causes of natural disasters, to understand sand desert expansion and migration the guidance of sand control engineering.
The study on Gobi typical area of Xinjiang Tianshan Mountains of Eastern Hami area as the study area, using remote sensing and spatial resolution of 30m*30m DEM Landsat5TM in 2010 as the basic data source, first in the analysis of spectral characteristics of different types of remote sensing images, a decision tree model to extract information based on expert knowledge, the image of land the classification, extract the Gobi District of Gobi; the surface grain size of gravel and multispectral remote sensing data, vegetation index and the relationship between geological factors, but these factors may have multiple correlations, such as the use of these factors directly modeling estimation Gobi surface gravel size, it may be ill conditioned model analysis method. Screening factor by using principal component can not only keep the main information of multiple factors, but also can avoid the factor of multicollinearity, achieve dimension reduction, the simplified model Role. Therefore, principal component analysis module based on ENVI4.8 software, from the selection of the 43 remote sensing and geographical factors (including the image information of each band, DEM, NDVI, GEMI, K-T transform of image by SBI, GVI, WVI three components, through the analysis of the texture by each band mean and variance. Information entropy, correlation and contrast of the texture factor, and use the DEM to extract roughness etc.), the selected principal components. Using principal components as independent variables, field investigation by the Gobi gravel size as the dependent variable, using multiple regression analysis in SPSS18 software, establish the regression model of Gobi surface gravel size the model, by analysis of variance and correlation test, reached significant level. The prediction model based on the surface of Gobi gravel size estimation, and verify the estimation accuracy of the model. This research can help us to accurately extract Gobi District, and can understand the characteristics of Gobi, for the transformation and utilization of Gobi District, regional disaster reduction, the western economic construction service. The main research results are as follows:
(1) the decision tree classification method, more accurately the unused land and other land types separately, Gobi will be accurately extracted from the study area, the overall classification accuracy of more than 90%, Kappa coefficient was 0.919. The extraction accuracy of Gobi reached more than 95%, to realize the accurate extraction of Gobi.
(2) based on principal component analysis method, screening and study area of Gobi surface gravel size related to remote sensing and geographical factors, principal component extraction, the first 5 principal components factor contribution rate reached 98%, reflects the main information of samples, the previous 5 principal components as independent variables, the corresponding size of rock fragments as the dependent variable the establishment of Gobi, the surface gravel size prediction model, after verification, reached significant level, a significant level of alpha =0.01, correlation coefficient R=0.825, the model of Gobi surface size of rock fragments in quantitative remote sensing inversion, verified, estimated value is closely related with the measured value, the correlation coefficient R=0.778 model to forecast the effect, provide technical support for the Gobi region.

【學(xué)位授予單位】：中國(guó)林業(yè)科學(xué)研究院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：P237;P90

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