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汶川地震滑坡遙感信息提取及災(zāi)害危險(xiǎn)性評(píng)價(jià)研究
【中文摘要】 地震滑坡是一種常見的地震次生災(zāi)害,因其巨大的致災(zāi)力而廣泛引起人們的關(guān)注。目前,單體滑坡的穩(wěn)定性評(píng)價(jià)和滑坡預(yù)報(bào)研究已取得了較為深入的研究成果,但在向區(qū)域滑坡穩(wěn)定性評(píng)價(jià)推廣時(shí)會(huì)遇到許多困難。隨著遙感和地理信息技術(shù)的發(fā)展,作為一種新的調(diào)查、監(jiān)測途徑和手段,在地震滑坡領(lǐng)域已得到廣泛運(yùn)用。2008年5月12日,四川省汶川縣發(fā)生Ms 8.0強(qiáng)震,直接導(dǎo)致數(shù)萬人死亡,數(shù)十萬人受傷,同時(shí)地震誘發(fā)大量的滑坡、崩塌和泥石流等地質(zhì)災(zāi)害,給當(dāng)?shù)厝嗣駧砹藰O大的影響。為了有效避免次生災(zāi)害對災(zāi)區(qū)造成進(jìn)一步的危害,本文選取重災(zāi)區(qū)——汶川縣作為研究區(qū)域,基于地震滑坡原理,利用遙感與地理信息技術(shù),綜合各種數(shù)字滑坡技術(shù)獲取滑坡信息,全面分析了研究區(qū)內(nèi)滑坡與各影響因子間的相關(guān)性特征,運(yùn)用信息量法與邏輯回歸模型進(jìn)行災(zāi)害危險(xiǎn)性評(píng)價(jià),并對兩種方法進(jìn)行了比較分析,主要研究內(nèi)容如下:(1)基于遙感數(shù)據(jù),論述了一系列數(shù)字圖像處理技術(shù)(影像融合、影像校正),研究獲取影響滑坡災(zāi)害因素信息的方法,并對研究區(qū)地震滑坡特征進(jìn)行分析。(2)按照指標(biāo)體系所確定的指標(biāo)因子進(jìn)行數(shù)據(jù)采集,并按照統(tǒng)一的數(shù)字研究環(huán)境框架的規(guī)范進(jìn)入GIS數(shù)據(jù)庫,建立研究區(qū)域滑...更多坡空間數(shù)據(jù)庫,包括滑坡分布數(shù)據(jù)庫和滑坡因子數(shù)據(jù)庫,為研究區(qū)域地質(zhì)災(zāi)害預(yù)警信息系統(tǒng)建設(shè)提供了豐富的滑坡災(zāi)害基礎(chǔ)數(shù)據(jù)。(3)選取高程、坡度、坡向、巖性、地震斷裂、烈度以及水系因子7個(gè)評(píng)價(jià)因子,采用信息量法與邏輯回歸模型進(jìn)行滑坡災(zāi)害危險(xiǎn)性評(píng)價(jià),將研究區(qū)滑坡危險(xiǎn)性劃分為極輕度、輕度、中度、高度和極高危險(xiǎn)五個(gè)級(jí)別,合理地反映區(qū)內(nèi)滑坡災(zāi)害發(fā)育的總體特征。(4)將信息量模型與邏輯回歸模型進(jìn)行比較分析,發(fā)現(xiàn)邏輯回歸法能較好地避免滑坡危險(xiǎn)度區(qū)劃研究中評(píng)價(jià)因子選擇及權(quán)重賦值的主觀性,具有更高的評(píng)價(jià)精度。在GIS支持下對研究區(qū)遙感影像進(jìn)行解譯和信息提取,獲取其地震滑坡特性,獲得了汶川地區(qū)一些預(yù)測和危險(xiǎn)性分析的結(jié)果,對于該地區(qū)震后恢復(fù)重建、居民地選址、次生災(zāi)害的預(yù)警與防治等工作都有一些建設(shè)性的指導(dǎo)意義
【英文摘要】 As a kind of secondary disaster caused by strong earthquake, the earthquake-induced landslide has drawn much attention in the world due to the severe hazard. Nowadays, researches on single landslides have been carried out more successfully comparing to the ones on regional landslides. To a certain degree, remote sensing and GIS, as new kinds of tools for investigation and monitor, have been used in many researches on landslide susceptibility and hazard mapping.The Ms 8.0 Wenchuan earthquake, occurred on 12 May 2008 in Sichuan Province, made a great many houses collapsed and hundreds thousand people injured. Meanwhile, damage caused by earthquake-induced landslides, collapse and debris flow became the major part of total losses. In order to remove threat from the secondary disasters effectively, this study used information value method and logistic regression, coupled with techniques of remote sensing and GIS, to generate susceptibility maps, taking the case of Wenchuan County. Main con...更多tent and production can be summarized as follows:(1) Based on the remote sensing data, this paper introduces the significance of digital image processing technology (e.g. data fusion and ortho-rectification), illustrates the procedures and techniques of information extraction and analyses the characteristics of earthquake-induced landslides in the research area.(2) To perform the potential analysis, it was necessary to establish spatial database of landslides containing landslides’attributes and variables contributing to landslide in the study area. The database following the unified standard of digital surrounding can provide plenty of basic data for advanced process.(3) Seven factors controlling landslide occurrence have been taken account into the susceptibility assessment, including elevation, slop, aspect, lithology, seismic intensity, distance to faults and rivers. According to the probability that predicts the possibility of landslide occurrence calculated applying information value method and logistic regression separately, the study zone was ultimately categorized into five classes, specifically,“extremely low”,“low”,“moderate”,“high”and“very high”. These results have been proved to reflect closely the spatial distributions of landslides in the study area.(4) It has been found that the predictive capability of logistic regression model appears to be more accurate comparing to information value method. It is mostly because logistic regression could reduce effectively the subjectivity in selection of evaluation factors and weight assignment.In sum, this paper extracts some information from remote sensing data, analyses the characteristic of earthquake-induced landslides under a GIS environment and accordingly generates the susceptibility maps in Wenchuan County, which undoubtedly contributes to recovered construction, location of habitation and prevention of secondary disasters in the study area.
【中文關(guān)鍵詞】 地理信息系統(tǒng); 地震; 滑坡; 危險(xiǎn)性評(píng)價(jià); 信息提取; 遙感
【英文關(guān)鍵詞】 GIS; Earthquake; landslide; susceptibility assessment information extraction; remote sensing
【畢業(yè)論文目錄】
摘要 4-6
ABSTRACT 6-7
圖目錄 11-12
表目錄 12-13
第1章 緒論 13-23
1.1 選題背景及研究意義 13-14
1.2 國內(nèi)外研究進(jìn)展 14-19
1.2.1 遙感數(shù)據(jù)獲取 14-16
1.2.2 滑坡信息提取 16-18
1.2.3 滑坡危險(xiǎn)性評(píng)價(jià) 18-19
1.3 研究內(nèi)容 19
1.4 研究思路及組織結(jié)構(gòu) 19-23
第2章 地震滑坡及遙感數(shù)據(jù)預(yù)處理 23-33
2.1 滑坡概述 23-24
2.1.1 滑坡定義 23-24
2.1.2 滑坡的誘發(fā)因素 24
2.2 地震滑坡特性 24-28
2.2.1 巖體性質(zhì)及結(jié)構(gòu)類型 24-25
2.2.2 地質(zhì)構(gòu)造條件 25-26
2.2.3 地形地貌 26-27
2.2.4 水文地質(zhì) 27
2.2.5 地震作用 27-28
2.2.6 降雨 28
2.3 遙感數(shù)據(jù)預(yù)處理 28-31
2.3.1 遙感圖像選取 28-29
2.3.2 遙感圖像處理 29-31
2.4 本章小結(jié) 31-33
第3章 汶川地震滑坡信息提取與分析 33-49
3.1 研究區(qū)概況 33-35
3.1.1 研究區(qū)地理位置 33-34
3.1.2 地質(zhì)環(huán)境條件 34-35
3.2 滑坡信息提取過程 35-39
3.2.1 數(shù)據(jù) 35-36
3.2.2 圖像處理方法 36-37
3.2.3 基于遙感的滑坡信息提取 37-39
3.3 滑坡分布特征解析 39-40
3.4 滑坡影響因子提取與特征分析 40-48
3.4.1 地形地貌因子提取與分析 40-43
3.4.2 巖性類因子 43-44
3.4.3 地震因子 44-47
3.4.4 水系因子 47-48
3.5 本章小結(jié) 48-49
第4章 汶川地震滑坡危險(xiǎn)性評(píng)價(jià) 49-61
4.1 基于GIS 的滑坡評(píng)價(jià)原理 49-51
4.1.1 信息量模型 49-50
4.1.2 Logistic 回歸模型 50-51
4.2 滑坡危險(xiǎn)性評(píng)價(jià)流程 51-52
4.3 基于信息量的滑坡危險(xiǎn)性評(píng)價(jià)過程 52-57
4.3.1 因子圖層的準(zhǔn)備 52-53
4.3.2 各圖層各類別信息量值計(jì)算 53-54
4.3.3 圖層疊加分析與分類 54-56
4.3.4 評(píng)價(jià)結(jié)果與分析 56-57
4.4 基于LOGISTIC 回歸的滑坡危險(xiǎn)性評(píng)價(jià)過程 57-59
4.4.1 數(shù)據(jù)準(zhǔn)備 57
4.4.2 邏輯回歸系數(shù)計(jì)算 57
4.4.3 圖層計(jì)算與分類 57-58
4.4.4 評(píng)價(jià)結(jié)果與分析 58-59
4.5 兩種評(píng)價(jià)方法對比分析 59-60
4.6 本章小結(jié) 60-61
第5章 結(jié)論與展望 61-63
5.1 主要結(jié)論 61-62
5.2 問題與展望 62-63
參考文獻(xiàn) 63-67
致謝 67-68
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