本文選題：成都平原　切入點：土地覆被/變化模擬及預測　出處：《四川師范大學》2017年碩士論文　論文類型：學位論文

【摘要】：土地覆被/變化模擬及預測,能夠更好地了解土地轉(zhuǎn)移機制和合理規(guī)劃未來土地的利用。論文依托“教育部重點實驗室”和“省國土資源協(xié)同創(chuàng)新中心”科研平臺和國家自然科學基金(面上項目)等完成。研究利用RS和GIS技術,使用決策樹和面向?qū)ο笙嘟Y(jié)合的分類方法分別對2005a和2015a影像數(shù)據(jù)提取了成都平原土地利用數(shù)據(jù),利用該數(shù)據(jù)分析了10a間研究區(qū)內(nèi)各土地利用類型之間動態(tài)轉(zhuǎn)移過程。利用相關性系數(shù)選出量化表達LUCC程度的最佳變量。分析了矢量化鄉(xiāng)鎮(zhèn)邊界和13級漁網(wǎng)尺度下空間影響因子(土地利用面積、空間自相關性、離散度)與LUCC的相關性并利用主成分綜合得分計算出最佳網(wǎng)格化尺度。利用主成分和線性回歸構建了空間影響因子、地形因子(海拔、坡度、坡向)、距離因子(距道路距離、距水體距離、距林地距離、距耕地距離、距建設用地距離)、核密度因子(道路核密度、水體核密度、林地核密度、耕地核密度、建設用地核密度)與LUCC的回歸方程,從而利用回歸方程制作了各影響因子對LUCC影響的概率圖。利用馬爾可夫模型預測了2015a和2025a LUCC的數(shù)量,結(jié)合概率圖預測2015a LUCC的空間格局,并檢驗結(jié)果精度,最后預測2025a LUCC的空間格局。研究表明:(1)所有表達研究區(qū)LUCC程度的變量中,剩余空間變化率最能表達LUCC的程度。鄉(xiāng)鎮(zhèn)邊界和13級漁網(wǎng)尺度中270m×270m尺度的綜合得分最高,因此定量化表達空間影響因子與LUCC關系的最佳網(wǎng)格尺度為270m×270m。在主成分和線性回歸方程表達中,水體和耕地的主成分回歸方程擬合度較高,分別為0.462和0.344,林地、草地和耕地的線性回歸擬合度較高,分別為0.418、0.615和0.591。因此空間影響因子定量化表達LUCC的概率效果較好。(2)海拔、坡度與LUCC所構建的回歸方程中6次項方程的擬合程度最高。海拔對水體回歸方程擬合度為0.847,對林地0.703,對耕地0.448,對建設用地0.574;坡度對水體回歸方程擬合度為0.947,對林地0.996,對耕地0.871,對建設用地0.866,因此海拔、坡度能夠很好的定量化表達LUCC的概率。坡向擬合度較低。距道路距離中冪函數(shù)回歸方程最高,均達到0.7以上,而距土地利用類型中擬合程度較高的回歸方程類型不相同,主要為6次項和冪函數(shù)兩種方程。對于整個研究區(qū),構建核密度與LUCC的回歸方程擬合度都較低,但分聚集對象單獨構建方程的擬合度很高,因此土地利用類型在空間上聚集對象不同,其LUCC的程度也不同。(3)經(jīng)預測,2025a研究區(qū)水體、未利用地和建設用地面積將持續(xù)增加,而林地、耕地和草地面積將持續(xù)減少�；赗S和GIS成都平原2015a LUCC的預測得到建設用地精度最高達75.11%,其次耕地為71.23%,林地69.97%,水體最低為43.5%,因此該土地利用模型對于在空間上聚集程度較高的土地利用類型預測精度較高,而離散型預測精度較低。
[Abstract]:Land cover / change modelling and forecasting, The paper relies on the key Laboratory of Ministry of Education and the Cooperative Innovation Center of Provincial Land and Resources, and the National Natural Science Foundation (NSF). Research and use of RS and GIS technology, The land use data of Chengdu Plain were extracted from 2005 and 2015a image data by using decision tree and object oriented classification method, respectively. The data were used to analyze the dynamic transfer process between different land use types in the study area for 10 years. The best variable for quantifying the degree of LUCC was selected by using the correlation coefficient. The vectorized boundary of villages and towns and the scale of 13 grade fishing nets were analyzed. Spatial impact factors (land use area, The correlation between spatial autocorrelation, dispersion and LUCC and the calculation of optimal gridding scale using principal component synthesis score. The spatial influence factors, topographic factors (elevation, slope, slope) were constructed by principal component and linear regression. Slope direction, distance factor (distance from road, distance from water body, distance from forest land, distance from cultivated land, distance from construction land), nuclear density factor (road nuclear density, water body nuclear density, forest core density, cultivated land nuclear density), The regression equation between the kernel density of construction land and LUCC is used to make the probability map of the influence factors on LUCC. The numbers of 2015a and 2025a LUCC are predicted by Markov model, and the spatial pattern of 2015a LUCC is predicted by combining the probability map. Finally, the spatial pattern of 2025a LUCC was predicted. The change rate of residual space can best express the degree of LUCC. The comprehensive score of 270m 脳 270m scale in the scale of township boundary and 13th grade fishing net is the highest. Therefore, the best mesh scale of quantitative expression spatial influence factor and LUCC is 270m 脳 270m.The principal component regression equation of water body and cultivated land has higher fitting degree in the expression of principal component and linear regression equation, which are 0.462 and 0.344, respectively. The fitting degree of linear regression of grassland and cultivated land was 0.418 ~ 0.615 and 0.591, respectively. Therefore, the probability of quantifying expression of LUCC by spatial influential factors was better. The fitting degree of the regression equation of slope and LUCC is the highest. The fitting degree of regression equation of elevation to water body is 0.847, to forest land is 0.703, to cultivated land is 0.448, to construction land is 0.574.The fitting degree of slope to regression equation of water body is 0.947, and to forest. Land 0.996, to cultivated land 0.871, to construction land 0.866, so altitude, The slope can quantify the probability of LUCC. The slope fitting degree is low. The power function regression equation is the highest in the distance from the road, and the regression equation is above 0.7, but the regression equation with higher fitting degree from the land use type is not the same. For the whole study area, the fitting degree of the regression equation of constructing kernel density and LUCC is low, but the fitting degree of constructing equation by dividing aggregation object alone is very high. Therefore, land use types are different in spatial aggregation, and their LUCC levels are also different. 3) it is predicted that the area of unused land and construction land will continue to increase, while the forest land will continue to increase, after predicting the water body in the study area of 2025a. The area of cultivated land and grassland will continue to decrease. Based on the prediction of LUCC in Chengdu plain of GIS and RS in 2015a, the accuracy of construction land is up to 75.1111, followed by cultivated land is 71.23cm, woodland is 69.97, and water is the lowest 43.5. therefore, the land use model can converge in space. The prediction accuracy of land use types with higher concentration is higher than that of other land use types. The accuracy of discrete prediction is low.
【學位授予單位】：四川師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：P237;P208;F301.2

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