結(jié)合地形因子的酉陽縣針葉林實際地表生物量遙感估算
發(fā)布時間：2018-07-29 20:56
【摘要】：森林生物量是衡量生態(tài)系統(tǒng)生產(chǎn)力的重要指標(biāo),也是研究森林生態(tài)系統(tǒng)物質(zhì)循環(huán)的重要基礎(chǔ)。隨著遙感技術(shù)的快速發(fā)展,影像數(shù)據(jù)參與森林生物量反演的方法與手段日益推廣和完善。山地地區(qū)地形復(fù)雜,運用傳統(tǒng)生態(tài)學(xué)方法進(jìn)行大區(qū)域的生物量測量耗時長、受限較大,在該區(qū)運用遙感數(shù)據(jù)反演生物量具有重要的現(xiàn)實意義。在遙感反演過程中,地形起伏對像元生物量影響顯著,而目前生物量反演研究中的地形校正主要為地表輻射校正,較少關(guān)注地形因素引起的像元實際地表面積和投影面積差異,而這種差異將直接導(dǎo)致植被生物量估算結(jié)果的偏差。本研究以重慶市酉陽縣為例,以GF-1 WFV數(shù)據(jù)和DEM數(shù)據(jù)為基礎(chǔ),結(jié)合影像因子和地形因子建立酉陽縣針葉林生物量反演模型。在此基礎(chǔ)上,結(jié)合地表面積計算方法和物質(zhì)守恒定律,得到地形輻射校正基礎(chǔ)上的針葉林實際地表生物量,并且定量地分析和討論了地形起伏對酉陽縣針葉林生物量估算的影響,旨在探討GF-1WFV數(shù)據(jù)在反演針葉林生物量信息等方面的潛力,同時也為研究區(qū)內(nèi)的林業(yè)資源管理、生態(tài)系統(tǒng)研究工作以及林業(yè)生態(tài)工程實施提供理論依據(jù)與參考。主要研究結(jié)果包括:(1)結(jié)合GIS空間分析,得到研究區(qū)的實際地表面積。影像地形校正后,單位像元面積最高值為965平方米,校正前后像元面積變化量最高達(dá)709平方米,約為影像像元單位面積的2.7倍。高值區(qū)域主要為酉陽縣兩大高蓋地及南部山區(qū),低值主要集中在地形較平坦、坡度較小的東部平壩區(qū),與研究區(qū)地形起伏特點大體一致。(2)在SPSS軟件平臺上分析了41種影像因子、5種地形因子與針葉林生物量的相關(guān)性。結(jié)果表明,3*3窗口下,GF-1 WFV影像紅光波段生成的紋理信息與生物量信息相關(guān)性總體較低,而影像近紅外波段、近紅外波段生成的均值紋理、差值植被指數(shù)和修正型植被指數(shù)與生物量信息的相關(guān)性顯著。(3)對遙感影像進(jìn)行分類實驗,包括最大似然法監(jiān)督分類和BP人工神經(jīng)網(wǎng)絡(luò)分類兩種。分類結(jié)果顯示,與基于統(tǒng)計理論的傳統(tǒng)監(jiān)督分類方法相比,人工神經(jīng)網(wǎng)絡(luò)在數(shù)據(jù)信息不完備的情況下,依然能夠在模式識別、知識處理等方面獲得較理想的效果。加入近紅外波段correlation紋理的三層BP人工神經(jīng)網(wǎng)絡(luò)總體分類精度為86.62%,比傳統(tǒng)監(jiān)督分類的精度增加了9.38%。與基于統(tǒng)計理論的傳統(tǒng)監(jiān)督分類方法相比,人工神經(jīng)網(wǎng)絡(luò)方法是獲取GF影像地物空間分布信息的更佳途徑。(4)利用多元線性逐步回歸分析得到基于影像和地形因子的地面生物量反演模型,模型的相關(guān)系數(shù)為0.853。利用模型分別反演三維尺度與二維尺度上的像元生物量,研究結(jié)果表明,地形校正前后,單位像元面積上的生物量變化最大值為258.5t/hm2,研究區(qū)針葉林地表實際生物量總值比校正前增加了431423.98t/hm2,生物量變化比率為8.54%,地形對生物量遙感反演精度的影響不可忽視。
[Abstract]:The forest biomass is an important index to measure the productivity of the ecosystem, and it is also an important basis for the study of the material circulation of the forest ecosystem. With the rapid development of remote sensing technology, the methods and means to participate in the inversion of forest biomass are increasingly popularized and perfected. The terrain of the mountain area is complex and the traditional ecological methods are used in the large area. The measurement of biomass is very time-consuming and limited. It is of great practical significance to use remote sensing data to retrieve biomass in this area. In the process of remote sensing inversion, the terrain undulation has a significant influence on the biomass, while the topographic correction in the study of biomass inversion is mainly the surface radiating correction, less attention to the pixel reality caused by the terrain factors. The difference between the surface area and the projected area will lead to the deviation of the estimated results of vegetation biomass. This study takes Youyang County, Chongqing, as an example, based on GF-1 WFV data and DEM data, and combines image and topographic factors to establish a coniferous forest biomass back model in Youyang county. On the basis of this, the surface area is calculated. The law of conservation of material and the law of conservation of matter, the actual surface biomass of coniferous forests on the basis of Topographic Radiation Correction, and the quantitative analysis and discussion of the effects of topographic fluctuations on the estimation of the biomass of coniferous forests in Youyang county are analyzed and discussed. The purpose is to explore the potential of GF-1WFV data in the inversion of the biomass information of coniferous forests and to the forestry capital in the study area. Source management, ecosystem research work and forestry ecological engineering implementation provide theoretical basis and reference. The main research results include: (1) the actual surface area of the study area is obtained with the GIS spatial analysis. After the image terrain correction, the maximum area of unit pixel area is 965 square meters, and the area change of the corrected pixel area is up to 709 square meters before and after the correction. It is about 2.7 times the unit area of the image pixel. The high value area is mainly two high cover and southern mountainous area of Youyang county. The low value is mainly concentrated in the eastern flat dam area with relatively flat terrain and smaller slope. (2) 41 kinds of image factors, 5 terrain factors and coniferous forests are analyzed on the SPSS software platform. The results show that the correlation between the texture information generated by the red band of the GF-1 WFV image and the biomass information under the 3*3 window is low, but the correlation of the mean texture, the difference vegetation index and the modified vegetation index in the near infrared band of the image is significant. (3) the remote sensing image is divided into two parts. Class experiments, including two kinds of maximum likelihood supervised classification and BP artificial neural network classification, show that, compared with the traditional supervised classification based on statistical theory, artificial neural networks can still achieve better results in pattern recognition and knowledge management in the case of incomplete data information. The overall classification precision of the three layer BP artificial neural network with band correlation texture is 86.62%. Compared with the traditional supervised classification, the accuracy of the artificial neural network is increased by 9.38%. and the traditional supervised classification based on statistical theory. The artificial neural network method is a better way to obtain the spatial distribution information of the GF image objects. (4) multivariable linear stepwise regression is used. An inversion model of ground biomass based on images and terrain factors is obtained. The correlation coefficient of the model is 0.853. using the model to invert the pixel biomass on the three-dimensional scale and the two-dimensional scale. The results show that the maximum value of the biomass on the unit pixel area is 258.5t/hm2 before and after the topographic correction, and the coniferous forest land in the study area is real. The total biomass ratio increased by 431423.98t/hm2 and the biomass change ratio was 8.54% before the correction. The influence of topography on the accuracy of biomass remote sensing inversion can not be ignored.
【學(xué)位授予單位】：西南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S718.5;S771.8
本文編號：2153993
[Abstract]:The forest biomass is an important index to measure the productivity of the ecosystem, and it is also an important basis for the study of the material circulation of the forest ecosystem. With the rapid development of remote sensing technology, the methods and means to participate in the inversion of forest biomass are increasingly popularized and perfected. The terrain of the mountain area is complex and the traditional ecological methods are used in the large area. The measurement of biomass is very time-consuming and limited. It is of great practical significance to use remote sensing data to retrieve biomass in this area. In the process of remote sensing inversion, the terrain undulation has a significant influence on the biomass, while the topographic correction in the study of biomass inversion is mainly the surface radiating correction, less attention to the pixel reality caused by the terrain factors. The difference between the surface area and the projected area will lead to the deviation of the estimated results of vegetation biomass. This study takes Youyang County, Chongqing, as an example, based on GF-1 WFV data and DEM data, and combines image and topographic factors to establish a coniferous forest biomass back model in Youyang county. On the basis of this, the surface area is calculated. The law of conservation of material and the law of conservation of matter, the actual surface biomass of coniferous forests on the basis of Topographic Radiation Correction, and the quantitative analysis and discussion of the effects of topographic fluctuations on the estimation of the biomass of coniferous forests in Youyang county are analyzed and discussed. The purpose is to explore the potential of GF-1WFV data in the inversion of the biomass information of coniferous forests and to the forestry capital in the study area. Source management, ecosystem research work and forestry ecological engineering implementation provide theoretical basis and reference. The main research results include: (1) the actual surface area of the study area is obtained with the GIS spatial analysis. After the image terrain correction, the maximum area of unit pixel area is 965 square meters, and the area change of the corrected pixel area is up to 709 square meters before and after the correction. It is about 2.7 times the unit area of the image pixel. The high value area is mainly two high cover and southern mountainous area of Youyang county. The low value is mainly concentrated in the eastern flat dam area with relatively flat terrain and smaller slope. (2) 41 kinds of image factors, 5 terrain factors and coniferous forests are analyzed on the SPSS software platform. The results show that the correlation between the texture information generated by the red band of the GF-1 WFV image and the biomass information under the 3*3 window is low, but the correlation of the mean texture, the difference vegetation index and the modified vegetation index in the near infrared band of the image is significant. (3) the remote sensing image is divided into two parts. Class experiments, including two kinds of maximum likelihood supervised classification and BP artificial neural network classification, show that, compared with the traditional supervised classification based on statistical theory, artificial neural networks can still achieve better results in pattern recognition and knowledge management in the case of incomplete data information. The overall classification precision of the three layer BP artificial neural network with band correlation texture is 86.62%. Compared with the traditional supervised classification, the accuracy of the artificial neural network is increased by 9.38%. and the traditional supervised classification based on statistical theory. The artificial neural network method is a better way to obtain the spatial distribution information of the GF image objects. (4) multivariable linear stepwise regression is used. An inversion model of ground biomass based on images and terrain factors is obtained. The correlation coefficient of the model is 0.853. using the model to invert the pixel biomass on the three-dimensional scale and the two-dimensional scale. The results show that the maximum value of the biomass on the unit pixel area is 258.5t/hm2 before and after the topographic correction, and the coniferous forest land in the study area is real. The total biomass ratio increased by 431423.98t/hm2 and the biomass change ratio was 8.54% before the correction. The influence of topography on the accuracy of biomass remote sensing inversion can not be ignored.
【學(xué)位授予單位】：西南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S718.5;S771.8
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 程傳錄;蔣光偉;田曉靜;馬新瑩;王文利;;一種顧及坡度因子與極值的地表面積計算法[J];測繪通報;2017年01期
2 張超;金虹杉;劉哲;李智曉;寧明宇;孫海艷;;基于GF遙感數(shù)據(jù)紋理分析識別制種玉米[J];農(nóng)業(yè)工程學(xué)報;2016年21期
3 李微;牟蒙;陳官濱;劉偉男;劉遠(yuǎn);劉長發(fā);;基于TSAVI的OLI模擬數(shù)據(jù)翅堿蓬生物量反演研究[J];光譜學(xué)與光譜分析;2016年05期
4 楊偉志;趙鵬祥;薛大慶;侯逸晨;張曉莉;王志濤;;基于Landsat-8影像的西寧市南北山森林生物量估測模型研究[J];西北林學(xué)院學(xué)報;2016年02期
5 張偉;陳蜀蓉;侯平;;基于Landsat5 TM遙感影像估算江山市公益林生物量[J];西部林業(yè)科學(xué);2016年01期
6 黃健熙;賈世靈;武洪峰;蘇偉;;基于GF-1 WFV影像的作物面積提取方法研究[J];農(nóng)業(yè)機械學(xué)報;2015年S1期
7 楊閆君;占玉林;田慶久;顧行發(fā);余濤;王磊;;基于GF-1/WFVNDVI時間序列數(shù)據(jù)的作物分類[J];農(nóng)業(yè)工程學(xué)報;2015年24期
8 安海波;李斐;趙萌莉;劉亞俊;;基于優(yōu)化光譜指數(shù)的牧草生物量估算[J];光譜學(xué)與光譜分析;2015年11期
9 王新云;郭藝歌;何杰;;基于HJ1B和ALOS/PALSAR數(shù)據(jù)的森林地上生物量遙感估算[J];生態(tài)學(xué)報;2016年13期
10 孫斌;李增元;郭中;高志海;王t+瑜;;高分一號與Landsat TM數(shù)據(jù)估算稀疏植被信息對比[J];遙感信息;2015年05期
相關(guān)博士學(xué)位論文 前2條
1 劉俊;基于ALOS遙感影像紋理信息的懷柔區(qū)針、闊葉林蓄積量反演模型研究[D];北京林業(yè)大學(xué);2014年
2 王光華;北京森林植被固碳能力研究[D];北京林業(yè)大學(xué);2012年
相關(guān)碩士學(xué)位論文 前7條
1 張穎;基于高分辨率遙感和極化雷達(dá)數(shù)據(jù)的大興安嶺地區(qū)森林地上生物量估測[D];北京林業(yè)大學(xué);2016年
2 楊偉志;基于OLI數(shù)據(jù)的西寧市森林生物量估測研究[D];西北農(nóng)林科技大學(xué);2015年
3 郭云;基于多源數(shù)據(jù)的山區(qū)森林地上生物量反演及動態(tài)模擬[D];福州大學(xué);2014年
4 張若嵐;基于ETM+數(shù)據(jù)的遙感影像地形輻射校正研究[D];長安大學(xué);2012年
5 趙靜;基于BP人工神經(jīng)網(wǎng)絡(luò)的遙感影像土地覆蓋分類研究[D];中國地質(zhì)大學(xué);2010年
6 李娜;川西亞高山森林植被生物量及碳儲量遙感估算研究[D];四川農(nóng)業(yè)大學(xué);2008年
7 馮險峰;GIS支持下的中國陸地生物量遙感動態(tài)監(jiān)測研究[D];陜西師范大學(xué);2000年
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