發(fā)布時間：2018-04-30 20:02

  本文選題：植被覆蓋度 + 像元二分模型��； 參考：《新疆師范大學(xué)》2017年碩士論文

【摘要】：植被覆蓋度在一定程度上反映了陸地生態(tài)系統(tǒng)中植被類型、數(shù)量和質(zhì)量上的變化,是反映植被狀況的一個重要指標(biāo)。以博斯騰湖西岸湖濱綠洲作為研究區(qū),以1990、2000、2015年三個不同時期的遙感影像為基本數(shù)據(jù)源,結(jié)合實(shí)地調(diào)研數(shù)據(jù)利用像元二分模型對研究區(qū)1990、2000、2015年植被覆蓋度進(jìn)行估算,并結(jié)合研究區(qū)實(shí)際植被覆蓋情況對植被覆蓋度等級進(jìn)行劃分。運(yùn)用矩陣分析明確1990~2015年所有植被覆蓋類型面積變化特征。利用差值法求對3期的植被覆蓋度進(jìn)行計(jì)算,分析1990~2015年間植被覆蓋度空間變化特征。對比BP神經(jīng)網(wǎng)絡(luò)模型和LMBP神經(jīng)網(wǎng)絡(luò)模型。選取精度較高的模型對植被覆蓋度進(jìn)行預(yù)測。從自然因素和人文因素兩個方面選取影響植被覆蓋度變化的驅(qū)動因子,通過相關(guān)性分析,探討極低植被覆蓋度、低植被覆蓋度、中等植被覆蓋度以及高植被覆蓋度等四種覆蓋度類型變化主要驅(qū)動因素。主要研究結(jié)論如下:1.1990~2015年,極低植被覆蓋度、低植被覆蓋度面積減少,中等植被覆蓋度、高植被覆蓋度面積增多。近25a,高植被覆蓋區(qū)面積變化明顯,由71.93km2上升至361.17km2,增長近5倍。極低植被覆蓋度面積則消減323.94km2,整體下降81.28%。2.近25a間,低植被覆蓋度朝中等植被覆蓋度轉(zhuǎn)移率最大,轉(zhuǎn)移率為59.01%,轉(zhuǎn)移面積156.41km2。其次是極低植被覆蓋度朝中植被覆蓋度轉(zhuǎn)移,轉(zhuǎn)移面積227.12km2,轉(zhuǎn)移率為45.51%,再次是極低植被覆蓋度朝中植被覆蓋度轉(zhuǎn)移,轉(zhuǎn)移面積達(dá)到142.10km2,轉(zhuǎn)移率達(dá)到28.50%。最后是高植被度蓋度朝極低植被覆蓋區(qū)轉(zhuǎn)移,轉(zhuǎn)移率只有0.048%。3.1990~2015年,研究區(qū)植被覆蓋度改善面積占了總面積的65.95%,面積達(dá)到558.47km2。明顯改善面積為243.94km2,占總面積28.81%。改善區(qū)域主要表現(xiàn)在研究區(qū)的中部及東部,退化區(qū)域表現(xiàn)在研究區(qū)南部和西部小部分區(qū)域。近25a植被覆蓋度總體呈現(xiàn)改善趨勢。4.構(gòu)建BP神經(jīng)網(wǎng)絡(luò)模型和LMBP神經(jīng)網(wǎng)絡(luò)模型,并進(jìn)行對比分析。結(jié)果表明LMBP神經(jīng)網(wǎng)絡(luò)模型精度高于BP神經(jīng)網(wǎng)絡(luò)模型,LMBP神經(jīng)網(wǎng)絡(luò)模型預(yù)測出真實(shí)值與預(yù)測值的相關(guān)系數(shù)達(dá)到0.9400,相關(guān)性較高,說明運(yùn)用LMBP神經(jīng)網(wǎng)絡(luò)預(yù)測研究區(qū)植被覆蓋度是可行的。對研究區(qū)2020年植被覆蓋度預(yù)測,結(jié)果表明植被覆蓋度呈現(xiàn)退化趨勢。5.植被覆蓋度變化受自然因素和人文因素的共同影響。極低植被覆蓋度與年均降水量呈現(xiàn)顯著負(fù)相關(guān)(通過p0.05檢驗(yàn)),相關(guān)系數(shù)為-0.969。低植被覆蓋度與中等植被覆蓋度區(qū)域面積變化與人口總數(shù)相關(guān),相關(guān)系數(shù)分別為-0.972和0.888。高植被覆蓋度區(qū)域面積與農(nóng)業(yè)生產(chǎn)總值相關(guān),相關(guān)系數(shù)為0.988.
[Abstract]:Vegetation coverage to some extent reflects the change of vegetation type, quantity and quality in terrestrial ecosystem, and it is an important index to reflect vegetation status. Taking the oasis on the west coast of Bosten Lake as the research area, taking the remote sensing images of three different periods in 1990 and 2015 as the basic data sources, combining with the field survey data, using the pixel dichotomy model to estimate the vegetation coverage in the study area of 1990 ~ 2000 and 2015. Combined with the actual vegetation coverage in the study area, the classification of vegetation coverage was carried out. The area variation characteristics of all vegetation cover types from 1990 to 2015 were determined by matrix analysis. The difference method is used to calculate the vegetation coverage in three periods, and the spatial variation characteristics of vegetation coverage from 1990 to 2015 are analyzed. The BP neural network model and the LMBP neural network model are compared. High precision models are selected to predict vegetation coverage. In this paper, we select the driving factors which influence the change of vegetation coverage from two aspects of natural factors and human factors. Through the correlation analysis, we discuss the extremely low vegetation coverage, the low vegetation coverage, the low vegetation coverage, the low vegetation coverage and the low vegetation coverage. The main driving factors of the change of the four types of coverage are middle vegetation coverage and high vegetation coverage. The main conclusions are as follows: 1. From 1990 to 2015, very low vegetation cover degree, low vegetation coverage area decreased, middle vegetation coverage degree and high vegetation coverage area increased. In the past 25 years, the area of high vegetation cover area changed obviously, from 71.93km2 to 361.17km2, the increase was nearly 5 times. The area of extremely low vegetation cover was reduced by 323.94 km2, and the whole area decreased by 81.28. 2. In the past 25 years, the transfer rate of low vegetation coverage to middle vegetation coverage was the highest, with a transfer rate of 59.01 and a transfer area of 156.41 km2. Secondly, the very low vegetation cover was transferred to the middle vegetation cover, with a transfer area of 227.12 km2, and the transfer rate was 45.51.The second was the very low vegetation coverage to the middle vegetation coverage, the transfer area was 142.10 km2, and the transfer rate was 28.50. Finally, the high vegetation coverage was transferred to the very low vegetation cover area, the transfer rate was only 0.048. 3.1990-2015. The improved vegetation coverage area accounted for 65.95km2 of the total area in the study area, and the area reached 558.47km2. The obvious improved area was 243.94 km2, accounting for 28.81% of the total area. The improvement area is mainly in the middle and east of the research area, and the degraded area is in the south and west of the study area. The vegetation coverage showed a trend of improvement in the past 25 years. BP neural network model and LMBP neural network model are constructed and compared. The results show that the accuracy of LMBP neural network model is higher than that of BP neural network model. The correlation coefficient between the real value and the predicted value is 0.9400, which indicates that it is feasible to use LMBP neural network to predict vegetation coverage in the study area. The vegetation coverage in the study area in 2020 was predicted, and the results showed that the vegetation coverage showed a degradation trend of .5. The change of vegetation coverage is influenced by both natural and human factors. There was a significant negative correlation between very low vegetation coverage and average annual precipitation (the correlation coefficient was -0.969 through p0.05 test). The regional area changes of low and moderate vegetation cover were related to the total population, and the correlation coefficients were -0.972 and 0.888, respectively. The area of high vegetation coverage is related to the agricultural gross domestic product, the correlation coefficient is 0. 988.
【學(xué)位授予單位】：新疆師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：Q948

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