植被特征尺度與尺度優(yōu)化研究
發(fā)布時間：2019-06-26 08:46
【摘要】：尺度是研究事物或事物現(xiàn)象的空間維和時間維的度量大小。地球空間表面復雜,人們在某一尺度上所建立的模型或總結出的規(guī)律,在另一尺度上未必有效或需要修正,不同區(qū)域所需空間觀測尺度不一致,用單一的觀測尺度去衡量整個復雜的地表顯然具有一定的局限性;其次不同的研究方法和研究目的,影像的觀測尺度也各異,因此,最優(yōu)觀測尺度的選擇是有必要的。不同地物有其自身最適宜的觀測尺度,并不是越細微越好,只有在最優(yōu)觀測尺度下,才能進行最全面的數(shù)據(jù)挖掘與探索,數(shù)據(jù)分析也可以得到事半功倍的效果。本文首先深入剖析了遙感影像中尺度選擇的重要性,并簡要介紹了幾種常用的最優(yōu)觀測尺度選擇方法,并以植被冠層像元值和植被遙感反演參量葉面積指數(shù)為研究對象,利用高分辨率Google Earth影像數(shù)據(jù)、1:10萬全國土地利用資料和Landsat TM數(shù)據(jù)為研究數(shù)據(jù),對該三種適宜尺度選擇方法進行了相應的改進,從而分析了不同區(qū)域下各類植被冠層和植被景觀最優(yōu)空間觀測尺度。本文的主要工作和結論有:(1)以亞米級高分辨率森林遙感圖像為研究對象,基于局部方差,進行植被冠層最優(yōu)觀測尺度的選擇。首先基于冠層特征尺度的物理定義,引入局部方差和倒置的指數(shù)擬合模型,建立了冠層特征尺度計算模型,并利用美國北部的洛克研究區(qū)和南部的梅肯研究區(qū)的高分辨率影像進行模型驗證,對論文提出的冠層特征尺度模型進行了定量驗證分析,發(fā)現(xiàn)冠層特征尺度模型值與樹林株行距實測值存在密切聯(lián)系,線性復相關系數(shù)達0.95以上。研究結果表明,冠層特征尺度模型是具有普適性與合理性的,論文建立的冠層特征尺度模型為茂盛植被冠層特征尺度定量計算提供了一種新方法。(2)以亞米級高分辨率森林遙感圖像為研究對象,基于半方差,進行植被冠層最優(yōu)觀測尺度的選擇。首先基于冠層特征尺度的物理定義,引入半方差函數(shù)變程參數(shù),計算了不同影像的半方差變程值,并利用美國北部的洛克研究區(qū)和南部的梅肯研究區(qū)的高分辨率影像進行驗證,發(fā)現(xiàn)半方差函數(shù)計算的變程值與樹林株行距存在密切聯(lián)系,線性復相關系數(shù)達0.91。研究結果表明,論文提出的半方差函數(shù)的變程值為茂盛植被冠層觀測尺度的選擇是可行的,但在普適性上沒有局部方差好。(3)在植被葉面積指數(shù)尺度效應研究基礎上,基于景觀指數(shù)和LAI尺度效應,進行植被景觀最優(yōu)觀測尺度的選擇。首先選用2000年全國1:10萬土地利用資料和Landsat TM數(shù)據(jù),建立了聚合指數(shù)與LAI尺度效應的經(jīng)驗統(tǒng)計模型,設置LAI尺度效應閾值,根據(jù)該統(tǒng)計模型,即確定聚合指數(shù)值,從而計算不同LAI尺度效應條件下中國區(qū)域的最優(yōu)空間觀測尺度,結果表明不同地表區(qū)域所需的空間觀測尺度存在巨大差異,并建立了中國區(qū)域的最優(yōu)空間觀測尺度先驗知識,為智能對地觀測的植被景觀觀測尺度優(yōu)化提供了一種新的方法。
[Abstract]:The dimension is the measure of the dimension of a space or time dimension that studies the phenomenon of things or things. the spatial surface of the earth is complex, the model or the summarized rule of people on a certain scale is not necessarily effective or needs to be corrected on another scale, and the space observation scale required for different regions is not consistent, It is obvious to measure the whole complex surface with a single observation scale; secondly, the different research methods and the research object, the observation scale of the image is also different, and therefore, the choice of the optimal observation scale is necessary. The most suitable observation scale of the different figures is not the finer the better, and the most comprehensive data mining and exploration can be carried out only under the optimal observation scale, and the data analysis can be obtained with less effort. In this paper, the importance of the mesoscale selection of remote sensing images is deeply analyzed, and several common optimal observation scale selection methods are briefly introduced, and the leaf area index of the vegetation canopy image element value and the vegetation remote sensing inversion parameter is the research object. Using the high-resolution Google Earth image data and 1: 100,000 national land-use data and Landsat TM data as the research data, the three suitable scale selection methods were improved, and the optimal spatial observation scale of the vegetation canopy and vegetation landscape under different areas was analyzed. The main work and conclusion of this paper are as follows: (1) The selection of the optimal observation scale of the vegetation canopy is carried out based on the local variance based on the local variance of the sub-rice-level high-resolution forest remote sensing image. based on the physical definition of the scale of the canopy characteristic, the local variance and the inverted index fitting model are introduced, a canopy characteristic scale calculation model is established, and the model verification is carried out by using the high-resolution image of the Locke research area in the north and the Meiken research area in the south, Based on the quantitative analysis of the canopy characteristic scale model proposed in this paper, it was found that the model value of the canopy characteristic was closely related to the measured value of the line spacing of the forest, and the linear complex correlation coefficient was above 0.95. The research results show that the canopy characteristic scale model is universal and reasonable, and the canopy characteristic scale model established by the paper provides a new method for the quantitative calculation of the canopy characteristic scale of the luxuriant vegetation. (2) The optimal observation scale of the vegetation canopy is selected based on the semi-variance based on the semi-variance of the sub-variance. First, based on the physical definition of the scale of the canopy characteristic, the half-variance function variable-range parameter is introduced, the half-variance value of the different images is calculated, and the high-resolution image of the Mekken study area in the northern part of the United States and the southern region of Meken is used for verification, It was found that the value of the half-variance function is closely related to the line spacing of the forest, and the linear complex correlation coefficient is 0.91. The results show that the variation value of the semi-variance function proposed by the paper is feasible for the selection of the canopy observation scale of the luxuriant vegetation, but there is no local variance in universality. (3) On the basis of the scale effect of the vegetation leaf area index, the selection of the optimal observation scale of the vegetation landscape is carried out based on the landscape index and the LAI scale effect. firstly, using the 2000-year national 1: 100,000 land-use data and Landsat TM data, a empirical statistical model of the aggregation index and the LAI scale effect is established, the LAI scale effect threshold is set, and the aggregation index value is determined according to the statistical model, so as to calculate the optimal spatial observation scale of the Chinese region under different LAI scale effect conditions, the results show that the spatial observation scale required for different surface areas is great difference, and the optimal space observation scale prior knowledge of the Chinese region is established, And provides a new method for the intelligent peer-to-earth observation scale optimization of the vegetation landscape.
【學位授予單位】：西安科技大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：Q948;P237
本文編號：2506069
[Abstract]:The dimension is the measure of the dimension of a space or time dimension that studies the phenomenon of things or things. the spatial surface of the earth is complex, the model or the summarized rule of people on a certain scale is not necessarily effective or needs to be corrected on another scale, and the space observation scale required for different regions is not consistent, It is obvious to measure the whole complex surface with a single observation scale; secondly, the different research methods and the research object, the observation scale of the image is also different, and therefore, the choice of the optimal observation scale is necessary. The most suitable observation scale of the different figures is not the finer the better, and the most comprehensive data mining and exploration can be carried out only under the optimal observation scale, and the data analysis can be obtained with less effort. In this paper, the importance of the mesoscale selection of remote sensing images is deeply analyzed, and several common optimal observation scale selection methods are briefly introduced, and the leaf area index of the vegetation canopy image element value and the vegetation remote sensing inversion parameter is the research object. Using the high-resolution Google Earth image data and 1: 100,000 national land-use data and Landsat TM data as the research data, the three suitable scale selection methods were improved, and the optimal spatial observation scale of the vegetation canopy and vegetation landscape under different areas was analyzed. The main work and conclusion of this paper are as follows: (1) The selection of the optimal observation scale of the vegetation canopy is carried out based on the local variance based on the local variance of the sub-rice-level high-resolution forest remote sensing image. based on the physical definition of the scale of the canopy characteristic, the local variance and the inverted index fitting model are introduced, a canopy characteristic scale calculation model is established, and the model verification is carried out by using the high-resolution image of the Locke research area in the north and the Meiken research area in the south, Based on the quantitative analysis of the canopy characteristic scale model proposed in this paper, it was found that the model value of the canopy characteristic was closely related to the measured value of the line spacing of the forest, and the linear complex correlation coefficient was above 0.95. The research results show that the canopy characteristic scale model is universal and reasonable, and the canopy characteristic scale model established by the paper provides a new method for the quantitative calculation of the canopy characteristic scale of the luxuriant vegetation. (2) The optimal observation scale of the vegetation canopy is selected based on the semi-variance based on the semi-variance of the sub-variance. First, based on the physical definition of the scale of the canopy characteristic, the half-variance function variable-range parameter is introduced, the half-variance value of the different images is calculated, and the high-resolution image of the Mekken study area in the northern part of the United States and the southern region of Meken is used for verification, It was found that the value of the half-variance function is closely related to the line spacing of the forest, and the linear complex correlation coefficient is 0.91. The results show that the variation value of the semi-variance function proposed by the paper is feasible for the selection of the canopy observation scale of the luxuriant vegetation, but there is no local variance in universality. (3) On the basis of the scale effect of the vegetation leaf area index, the selection of the optimal observation scale of the vegetation landscape is carried out based on the landscape index and the LAI scale effect. firstly, using the 2000-year national 1: 100,000 land-use data and Landsat TM data, a empirical statistical model of the aggregation index and the LAI scale effect is established, the LAI scale effect threshold is set, and the aggregation index value is determined according to the statistical model, so as to calculate the optimal spatial observation scale of the Chinese region under different LAI scale effect conditions, the results show that the spatial observation scale required for different surface areas is great difference, and the optimal space observation scale prior knowledge of the Chinese region is established, And provides a new method for the intelligent peer-to-earth observation scale optimization of the vegetation landscape.
【學位授予單位】：西安科技大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：Q948;P237
【參考文獻】
相關期刊論文 前1條
1 舒波;;An enhanced landscape aggregation index[J];Journal of Chongqing University(English Edition);2011年04期
，本文編號：2506069
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