發(fā)布時間：2018-04-25 00:12

  本文選題：山區(qū)植被 + 信息提取　； 參考：《南京農(nóng)業(yè)大學(xué)》2013年碩士論文

【摘要】：森林生態(tài)系統(tǒng)是面積最大,且最重要的陸地生態(tài)系統(tǒng),相比其他生態(tài)系統(tǒng)具有最高的生產(chǎn)力和最強的生態(tài)效應(yīng),是生物圈的能量基地,在維持全球生態(tài)平衡和改善生態(tài)環(huán)境方面起著極為重要的作用,同時也是國家經(jīng)濟可持續(xù)發(fā)展的重要物質(zhì)基礎(chǔ)。通過先進的遙感技術(shù),實時、準(zhǔn)確、高效的獲取森林資源信息,監(jiān)測其動態(tài)變化,科學(xué)估算森林的生態(tài)價值,在森林資源快速減少環(huán)境不斷惡化的今天顯得尤為重要。 山區(qū)地形地貌比較復(fù)雜,地面植被覆蓋茂密且光譜信息差異不大,地物受地形影響多以點狀形式分散分布,利用傳統(tǒng)的基于像元的信息提取方法,會造成較嚴重的“椒鹽現(xiàn)象”,且提取的精度不高,難以將提取的結(jié)果用于森林植被的生態(tài)參數(shù)遙感定量估算研究�；诖吮疚脑谇叭搜芯康幕A(chǔ)上圍繞影像中地物特征的構(gòu)建和選取,嘗試構(gòu)建基于知識和特征權(quán)重的信息提取模型,采用面向?qū)ο蠓诸惙椒ń鉀Q山區(qū)植被信息遙感提取困難的問題,探尋中、高空間分辨率的影像用于定量反演的可行性。 本文以安徽省金寨縣為研究區(qū),以單時相的SPOT5影像為數(shù)據(jù)源,基于構(gòu)建的信息提取模型完成了樣區(qū)森林植被信息的提取,同時結(jié)合野外觀測數(shù)據(jù)建立了適合研究區(qū)SPOT5影像反演森林植被LAI的最佳模型。本研究主要研究結(jié)論如下： (1)以影像信息提取的四個步驟為主線,側(cè)重于特征信息的構(gòu)建和選取,通過不同的采樣方法建立地物特征信息樣本庫,采用數(shù)據(jù)挖掘技術(shù)確立特定地物的特征信息,將特征信息用于影像的分割及信息提取規(guī)則的建立。基于此嘗試構(gòu)建了基于知識和特征權(quán)重的信息提取模型。 (2)以研究區(qū)單時相的SPOT5影像為數(shù)據(jù)源,選取了可有效提高SPOT5影像中地物特征信息的波段計算方法：植被指數(shù)(NDVI2=RNIR-RGREEN/RNIR+RGREEN);兩種融合方法：改進型Brovey變換融合及Andorre融合方法。結(jié)合野外觀測的樣點及部分鄉(xiāng)鎮(zhèn)林相圖的矢量化結(jié)果,構(gòu)建了樣本信息庫。采用文中所構(gòu)建的模型完成影像信息的提取。研究區(qū)土地覆蓋分類的總體精度達83%,且除園地的使用精度低于80%外,其他地物類型的精度都能達到80%以上,其中道路及旱地的使用精度高達87%。針葉、闊葉的用戶精度分別為83%、86%。 (3)構(gòu)建了7種植被指數(shù)作為遙感因子,提取DEM上的高程信息作為地理因子。展開因子與葉面積指數(shù)之間的相關(guān)性分析,選取相關(guān)性較高的NDVI、GNDVI、RVI、 SAVI、OSAVI、MSAVI作為自變量,以LAI為因變量構(gòu)建了線性、指數(shù)、對數(shù)、冪函數(shù)四種估算模型,挑選四種一元模型中擬合程度最佳的模型開展預(yù)測精度檢驗工作,同時將所有因子作為自變量,LAI為因變量開展多元逐步回歸分析,對分析后所構(gòu)建的模型開展精度檢驗工作,最終確立RDVI\RVI與葉面積指數(shù)的多元線性模型(LAI=3.4196-0.1241*RDVI+1.0386*RVI)為研究區(qū)SPOT5影像反演LAI的最佳模型,并完成研究區(qū)森林植被的LAI反演制圖。
[Abstract]:Forest ecosystem is the largest and most important terrestrial ecosystem. Compared with other ecosystems, it has the highest productivity and the strongest ecological effect. It is the energy base of the biosphere. It plays an important role in maintaining the global ecological balance and improving the ecological environment. It is also important for the sustainable development of the national economy. Material basis. Through advanced remote sensing technology, real-time, accurate and efficient access to forest resources information, monitoring its dynamic changes, scientific estimation of the ecological value of forest, the rapid decline in the environment of forest resources is becoming more and more important today.
The terrain and geomorphology of the mountain area are complex, the vegetation cover is dense and the spectral information is different, the terrain is influenced by the topography in the form of scattered distribution. Using the traditional information extraction method based on the pixel, it will cause a more serious "salt and pepper phenomenon", and the extraction precision is not high, it is difficult to use the extracted result for the forest vegetation. Based on the previous research, based on the construction and selection of the feature features of the image, this paper tries to construct the information extraction model based on the knowledge and feature weight, and uses the object oriented classification method to solve the problem of the difficulty of Remote Sensing Extraction of vegetation information in mountain areas, and to explore the shadow of high spatial resolution in the exploration. As for the feasibility of quantitative inversion.
This paper takes the Jinzhai County of Anhui Province as the research area, taking the single time phase SPOT5 image as the data source, and based on the constructed information extraction model to complete the extraction of forest vegetation information in the sample area. At the same time, combining the field observation data, the best model for the inversion of the forest vegetation LAI suitable for the SPOT5 image in the study area is established. The main conclusions are as follows:
(1) take the four steps of image information extraction as the main line, focus on the construction and selection of feature information, establish the sample library of feature information by different sampling methods, use the data mining technology to establish the characteristic information of the specific objects, and use the feature information for the segmentation of image and the establishment of information extraction rules. Information extraction model based on knowledge and feature weight.
(2) taking the SPOT5 image of the single time phase in the study area as the data source, the band calculation method which can effectively improve the feature information of the SPOT5 image is selected: the vegetation index (NDVI2=RNIR-RGREEN/RNIR+RGREEN), and the two fusion methods: the improved Brovey transform fusion and the Andorre fusion method. The total precision of the land cover classification of the study area is 83%, and the precision of the other terrain types can reach more than 80%, and the precision of the road and dry land is up to 87%. needles and broadleaved. The accuracy of the user is 83%, respectively, 86%.
(3) the 7 planting index was constructed as a remote sensing factor, and the elevation information on DEM was extracted as a geographical factor. The correlation analysis between the expansion factor and the leaf area index was analyzed, and the higher correlation NDVI, GNDVI, RVI, SAVI, OSAVI, MSAVI were selected as the independent variables, and the four estimation models of the linear, exponential, logarithmic and power functions were constructed with LAI as the variation. The model of the best fitting degree in the four one element model is selected to carry out the prediction accuracy test. At the same time, all the factors are taken as independent variables, and LAI is used to carry out multiple stepwise regression analysis for the dependent variable. The accuracy test of the model after the analysis is carried out, and the multiple linear model of RDVIRVI and leaf area index (LAI=3.4196-0.1241*RD) is finally established. VI+1.0386*RVI) the best model for retrieving LAI from SPOT5 images in the study area, and complete the LAI inversion mapping of forest vegetation in the study area.

【學(xué)位授予單位】：南京農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：Q948;P237

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