發(fā)布時(shí)間：2018-05-09 03:07

  本文選題：森林類(lèi)型 + 精細(xì)分類(lèi)　； 參考：《中國(guó)林業(yè)科學(xué)研究院》2016年博士論文

【摘要】：近年來(lái),隨著遙感技術(shù)的發(fā)展和遙感影像林業(yè)應(yīng)用的深入,應(yīng)用中高空間分辨率遙感影像開(kāi)展森林類(lèi)型精細(xì)識(shí)別和森林資源動(dòng)態(tài)變化監(jiān)測(cè)成為目前研究的熱點(diǎn)之一。雖然遙感影像分類(lèi)技術(shù)取得了長(zhǎng)足的發(fā)展,但是已有研究表明,森林類(lèi)型信息獲取中仍存在精度不高、詳細(xì)程度和可信度差等技術(shù)難點(diǎn),以及森林資源動(dòng)態(tài)變化監(jiān)測(cè)尚未完全克服信息獲取周期長(zhǎng)、變化信息提取困難、新方法應(yīng)用少、自動(dòng)化程度低、成果質(zhì)量和精度欠佳等突出問(wèn)題。本文面向國(guó)家森林資源監(jiān)測(cè)調(diào)查的行業(yè)應(yīng)用需求,重點(diǎn)研究高精度森林類(lèi)型精細(xì)識(shí)別方法和森林資源動(dòng)態(tài)變化監(jiān)測(cè)技術(shù),為及時(shí)、準(zhǔn)確掌握森林資源現(xiàn)狀和動(dòng)態(tài)變化趨勢(shì)提供可靠支撐,為森林資源空間配置、優(yōu)化調(diào)整與輔助決策提供技術(shù)支持。本文主要內(nèi)容和結(jié)論如下:(1)以嘉陵江上游甘肅省小隴山林業(yè)實(shí)驗(yàn)局百花林場(chǎng)為例,探討復(fù)雜中山區(qū)域、多源數(shù)據(jù)支持下,高空間分辨率遙感影像森林類(lèi)型層次化精細(xì)分類(lèi)方法。以SPOT5和高分一號(hào)(GF-1)遙感影像為主要數(shù)據(jù)源,綜合利用影像光譜特征、植被指數(shù)特征、紋理特征與時(shí)相特征、地形特征、森林資源“二類(lèi)調(diào)查”成果數(shù)據(jù)與林相圖等輔助信息,及典型地類(lèi)與主要森林類(lèi)型外業(yè)調(diào)查樣本數(shù)據(jù),發(fā)展了針對(duì)暖溫帶典型天然次生林區(qū)、復(fù)雜山區(qū)地形條件下高空間分辨率遙感影像森林類(lèi)型多層次信息提取與森林類(lèi)型精細(xì)識(shí)別的有效方法。采用分層隨機(jī)抽樣的獨(dú)立檢驗(yàn)樣本對(duì)分類(lèi)結(jié)果中7類(lèi)林地與森林類(lèi)型進(jìn)行精度驗(yàn)證,并對(duì)5類(lèi)主要森林類(lèi)型精細(xì)識(shí)別結(jié)果進(jìn)行面積統(tǒng)計(jì),與“二類(lèi)調(diào)查”及影像解譯結(jié)果各類(lèi)型面積統(tǒng)計(jì)值進(jìn)行對(duì)比分析。研究結(jié)果表明,本文所發(fā)展的分類(lèi)方法對(duì)森林類(lèi)型信息提取精度較高,有林地、其他林地、苗圃地等7類(lèi)林地和森林類(lèi)型總體分類(lèi)精度達(dá)92.28%,總Kappa系數(shù)為0.899 6;油松林、華山松林、日本落葉松林、櫟類(lèi)落葉闊葉林、其他落葉闊葉混交林等5類(lèi)主要森林類(lèi)型面積統(tǒng)計(jì)結(jié)果的平均相對(duì)精度為92.4%。本文發(fā)展的多源數(shù)據(jù)支持下的多層次森林類(lèi)型精細(xì)分類(lèi)方法是一種有效的森林類(lèi)型信息精準(zhǔn)監(jiān)測(cè)方法,具有精度高和可信度高的優(yōu)勢(shì),且森林類(lèi)型精細(xì)識(shí)別詳細(xì)程度達(dá)到優(yōu)勢(shì)樹(shù)種(組)級(jí)別,是解決復(fù)雜山區(qū)森林類(lèi)型信息提取與精細(xì)識(shí)別的一種有效手段,可滿(mǎn)足森林資源調(diào)查、變化監(jiān)測(cè)、數(shù)字更新等林業(yè)應(yīng)用需求。(2)以甘肅省天水市為例,以1990年~2015年五期冬夏時(shí)相l(xiāng)andsattm/oli遙感影像為主要數(shù)據(jù)源,結(jié)合輔助數(shù)據(jù)和外業(yè)實(shí)地樣本點(diǎn),在對(duì)光譜特征、指數(shù)特征、時(shí)相特征等分類(lèi)特征綜合分析的基礎(chǔ)上,選取ndvi、ndwi、ndi和mtvi等四個(gè)指數(shù)作為特征變量,發(fā)展了基于兩種非參數(shù)分類(lèi)器(隨機(jī)森林(rf)和參數(shù)優(yōu)化支持向量機(jī)(posvm))分類(lèi)后比較法的森林資源變化監(jiān)測(cè)技術(shù)。研究結(jié)果表明:引入多元特征和穩(wěn)健、優(yōu)化的非參數(shù)分類(lèi)器,可顯著提高分類(lèi)精度和分類(lèi)結(jié)果的可信度,降低類(lèi)別混淆和結(jié)果的不確定性。兩種分類(lèi)方法均取得了較好的分類(lèi)效果,具有較高的空間一致性,且時(shí)序分類(lèi)結(jié)果及逐期變化分析結(jié)果可準(zhǔn)確、客觀地反映該區(qū)域近30年來(lái)森林資源時(shí)空動(dòng)態(tài)變化。隨機(jī)森林(rf)分類(lèi)方法在分類(lèi)精度、效率、計(jì)算量和穩(wěn)定性方面明顯優(yōu)于參數(shù)優(yōu)化支持向量機(jī)(posvm)分類(lèi)方法,隨機(jī)森林(rf)方法對(duì)于復(fù)雜地形、破碎地貌區(qū)域和典型植被(森林-灌草-草地)交錯(cuò)過(guò)渡區(qū)具有的較強(qiáng)的適應(yīng)性,可應(yīng)用于大區(qū)域、復(fù)雜地形、過(guò)渡區(qū)域的植被/森林制圖和動(dòng)態(tài)變化監(jiān)測(cè)。監(jiān)測(cè)結(jié)果表明:1990年~1996年林地轉(zhuǎn)化為非林地為3.764%,非林地轉(zhuǎn)化為林地為3.024%。林地面積凈減少0.74%。1996年~2002年林地轉(zhuǎn)化為非林地為5.648%,非林地轉(zhuǎn)化為林地為2.914%。林地面積凈減少2.734%,林地減少呈現(xiàn)加劇趨勢(shì)。2002年~2008年林地轉(zhuǎn)化為非林地為5.574%,非林地轉(zhuǎn)化為林地為6.631%。林地面積凈增加1.057%。2008年~2015年林地轉(zhuǎn)化為非林地為6.563%,非林地轉(zhuǎn)化為林地為15.446%。林地面積凈增加為8.883%。該區(qū)域近30年來(lái)森林資源變化的總體趨勢(shì):以2002年(2002期影像)為界,林地面積為先減少后增加,2002年后林地面積增加顯著。林地面積增加主要原因?yàn)槠渌��?lèi)型向有林地轉(zhuǎn)化,由于1999年以后,隨著天然林保護(hù)工程、退耕還林工程等林業(yè)重點(diǎn)工程的實(shí)施,使得該區(qū)域森林覆蓋率上升趨勢(shì)明顯,林地面積顯著擴(kuò)大。林地面積明顯減少的區(qū)域在空間上主要集中在武山縣和張家川回族自治縣,在林地與其他地類(lèi)交錯(cuò)過(guò)渡地帶、林緣區(qū)域表現(xiàn)尤為明顯,尤其在2002年之后。林地面積減少的可能原因?yàn)樽匀灰蛩丶叭祟?lèi)活動(dòng)影響使該區(qū)域原有的森林、灌木群落等植被遭到破壞,林地轉(zhuǎn)變?yōu)楦�地、草地和建設(shè)用地等,局部生態(tài)環(huán)境狀況有進(jìn)一步惡化的風(fēng)險(xiǎn)和可能。(3)在對(duì)地類(lèi)、林地-非林地信息提取與變化監(jiān)測(cè)結(jié)果分析的基礎(chǔ)上,進(jìn)一步對(duì)“常綠針葉林”、“落葉闊葉林”、“其他林地”三類(lèi)林地內(nèi)類(lèi)型的動(dòng)態(tài)變化(轉(zhuǎn)化)進(jìn)行深入分析,對(duì)四個(gè)時(shí)間段內(nèi)三類(lèi)林地內(nèi)類(lèi)型的“增加”(轉(zhuǎn)入)、“減少”(轉(zhuǎn)出)變化區(qū)域、空間分布和變化程度狀況進(jìn)行過(guò)程分析。研究結(jié)果表明:(1)1990年~1996年間,“常綠針葉林”類(lèi)型增加(轉(zhuǎn)入)、減少(轉(zhuǎn)出)均較為明顯。增加區(qū)域分布于秦嶺南坡小隴山林區(qū)的各個(gè)林場(chǎng);減少區(qū)域位于秦嶺北坡、沿渭河流域的帶狀區(qū)域,以及嘉陵江上游低海拔區(qū)域的河流及山谷兩側(cè)。1996年~2002年間、2002年~2008年間,在整體上“常綠針葉林”類(lèi)型增加、減少均不明顯。2008年~2015年間,“常綠針葉林”類(lèi)型增加十分顯著。增加區(qū)域分布于小隴山林區(qū)、西秦嶺林區(qū)和關(guān)山林區(qū)的所有林場(chǎng),僅在龍門(mén)林場(chǎng)中部有小片減少區(qū)域。(2)1990年~1996年間,“落葉闊葉林”類(lèi)型增加(轉(zhuǎn)入)、減少(轉(zhuǎn)出)均較為明顯。增加區(qū)域主要分布于太碌、立遠(yuǎn)、東岔林場(chǎng),以及黨川、觀音林場(chǎng)、龍門(mén)林場(chǎng)南部;減少區(qū)域分布于秦嶺南坡小隴山林區(qū)的各個(gè)林場(chǎng),尤其在小隴山林區(qū)北部邊緣區(qū)域和林區(qū)中部表現(xiàn)尤為明顯。1996年~2002年間、2002年~2008年間,“落葉闊葉林”類(lèi)型增加區(qū)域主要分布于小隴山林區(qū)的麥積林場(chǎng)、東岔林場(chǎng)、立遠(yuǎn)林場(chǎng)等�！奥淙~闊葉林”類(lèi)型減少主要分布于東岔、立遠(yuǎn)林場(chǎng)及小隴山林區(qū)北部邊緣區(qū)域。2008年~2015年間,“落葉闊葉林”類(lèi)型增加區(qū)域主要分布在秦嶺北坡渭河南岸區(qū)域、麥積林場(chǎng),以及天水市轄溫泉、尖山林場(chǎng)�！奥淙~闊葉林”類(lèi)型減少主要分布在麥積林場(chǎng)和灘歌林場(chǎng)。(3)1990年~2015年間,“其他林地”類(lèi)型增加(轉(zhuǎn)入)、減少(轉(zhuǎn)出)區(qū)域均主要分布于林區(qū)邊緣,增減變化幅度不大�；�于兩種非參數(shù)分類(lèi)器分類(lèi)后比較法的遙感影像變化監(jiān)測(cè)技術(shù),探討了典型黃土高原丘陵溝壑與隴山-西秦嶺山地交接過(guò)渡區(qū)域近30年來(lái)森林資源空間分布規(guī)律、時(shí)間變化趨勢(shì)及變化影響因素,以期為該區(qū)域森林動(dòng)態(tài)變化定量分析及綜合評(píng)價(jià)、森林資源空間配置與優(yōu)化調(diào)整、經(jīng)營(yíng)管理與輔助決策、林業(yè)工程進(jìn)展監(jiān)測(cè)、生態(tài)環(huán)境評(píng)價(jià)以及森林保護(hù)措施制定等提供一定的參考。
[Abstract]:In recent years, with the development of remote sensing technology and the in-depth application of remote sensing image forestry, the application of high spatial resolution remote sensing images to carry out fine recognition of forest types and monitoring the dynamic change of forest resources has become one of the hotspots of current research. Although the remote sensing image classification technology has made great progress, the existing research has shown that forest type In the acquisition of type information, there are still some technical difficulties, such as low precision, detailed degree and poor reliability, and the dynamic change monitoring of forest resources has not completely overcome the long period of information acquisition, the difficulty in extracting the change information, the low application of new methods, the low degree of automation, the poor quality of the results and the poor precision. In order to provide a reliable support for the timely and accurate grasp of the current situation and dynamic change trend of forest resources and provide technical support for the spatial allocation of forest resources, and the technical support for the optimization adjustment and auxiliary decision, the main content and the main content of this paper are the main content and the main content of this paper. The conclusions are as follows: (1) taking the Baihua forest farm of the Xiaolong Forestry Experiment Bureau of the upper reaches of the Jialingjiang River in the upper reaches of Gansu Province as an example, the detailed classification method of the high spatial resolution remote sensing image forest types was studied under the support of the multi source data, and the remote sensing image of SPOT5 and GF-1 was used as the main data source, and the image spectral characteristics were used synthetically. The index features, texture features and temporal features, terrain features, forest resources "two types of survey" data and forest phase map and other auxiliary information, as well as typical and main forest types of survey sample data, developed a typical natural secondary forest area of warm temperate zone, high spatial resolution remote sensing image under the complex mountainous terrain conditions. The effective method of multi level information extraction and fine recognition of forest types was used. The independent test samples of stratified random sampling were used to verify the accuracy of 7 types of forest and forest types in the classification results, and the area statistics of the fine recognition results of the 5 types of main types of forest types were carried out, and each type of "two types of investigation" and the results of image interpretation were used. The results show that the classification method developed in this paper has high precision for extracting forest type information. The overall classification accuracy of 7 types of woodland and forest types, including woodland, other woodlands and nursery fields, is 92.28%, the total Kappa coefficient is 0.8996, oil pine forest, Huashan pine forest, Japanese Larix forest and oak deciduous broad-leaved forest, The average relative accuracy of the statistical results of 5 major forest types, such as other deciduous broad-leaved mixed forests, is the precise classification method of multi level forest types supported by the multi source data supported by 92.4%., which is an effective method for accurate monitoring of forest type information, with high accuracy and high credibility, and the fine recognition of forest types. It is an effective means to solve the information extraction and fine recognition of forest types in complex mountainous areas. It can meet the needs of forest resources survey, change monitoring and digital updating. (2) taking Tianshui, Gansu as an example, the landsattm/oli remote sensing image of winter and summer in 1990 ~2015 year is taken as an example. On the basis of comprehensive analysis of spectral features, exponential features and temporal features, the main data sources, based on the comprehensive analysis of spectral features, exponential features and temporal features, are based on the combined analysis of four indices, such as NDVI, NDWI, NDI and mtvi, and develop two nonparametric classifiers (random forest (RF) and parameter optimization support vector machine (posvm)). The research results show that the introduction of multiple features and robust and optimized non parametric classifiers can significantly improve the classification accuracy and the reliability of the classification results, reduce the confusion of categories and the uncertainty of the results. The two classification methods have achieved a better classification effect and have a higher space. Consistency, time series classification results and phase by phase analysis results can be accurate, objectively reflecting the temporal and spatial dynamic changes of forest resources in the region during the last 30 years. The stochastic forest (RF) classification method is obviously superior to the parameter optimization support vector machine (posvm) classification method in the classification accuracy, efficiency, calculation and stability, and the random forest (RF) method is used. Complex terrain, broken landform area and typical vegetation (forest - grasses and grassland) interlaced transitional zone have strong adaptability. It can be applied to large area, complex terrain, vegetation / forest mapping and dynamic change monitoring in transition region. The results of monitoring show that in 1990, 3.764% of forest land was converted to non woodland in ~1996 and 3. of non woodland to Forestland in 1990. 024%. woodland area net reduction in 0.74%.1996 year ~2002 forest land conversion to non woodland 5.648%, non woodland conversion to woodland to 2.914%. woodland area net reduction of 2.734%, woodland decrease trend in.2002 year ~2008 year ~2008 forest conversion to non woodland is 5.574%, non woodland to woodland to 6.631%. woodland area net increase 1.057%.2008 year ~2015 1.057%.2008 year ~2015 The conversion of woodland to non woodland was 6.563% in the year, and the net area of non woodland converted to woodland was 15.446%. forest area net increase in the total trend of forest resources change in the area of 8.883%. in the last 30 years. In 2002 (2002 period images), the area of woodland was reduced first and then increased, and the forest land accumulation increased significantly after 2002. The main reason for the increase of forest land area was the others. Since 1999, since 1999, with the implementation of the key forestry projects such as natural forest protection project, returning farmland to forest engineering, the forest coverage rate of the region is rising obviously, the area of forest land is greatly enlarged. The area of the forest area is obviously reduced in the space mainly concentrated in Wushan County and Zhangjiachuan Hui Autonomous County, in the forest area. After 2002, the possible reasons for the reduction of forest land area are the natural factors and the influence of human activities, which cause the destruction of the original forest, shrub community and other vegetation, the transformation of the woodland into arable land, the grassland and the construction land, and the local ecological environment. The risk and possibility of one step worsening. (3) on the basis of the analysis of the information extraction and change monitoring results of the land class, woodland and non woodland, the dynamic changes (transformation) of the three types of woodland types in the "evergreen coniferous forest", "deciduous broad-leaved forest" and "other woodland" were further analyzed, and the three types of woodland types within the four time periods were analyzed. "Increase" (transfer), "reduce" (turn out) change area, space distribution and change degree state of the process analysis. The results show: (1) in 1990 ~1996, the "evergreen coniferous forest" type increased (transfer), reduce (transfer) is more obvious. Increase the area distribution in the small Longshan Forest Area of the south slope of Qinling Mountains; reduce the area On the northern slope of Qinling Mountains, the zonal region along the Weihe River Basin and the rivers and valleys on the upper reaches of the Jialing River in the upper reaches of the Jialing River in ~2002 years of.1996 years, the "evergreen coniferous forest" type increased in the period of ~2008 2002, and the decrease of the "evergreen coniferous forest" type is very significant in the ~2015 years of.2008. The small Longshan Forest Area, the western Qinling Mountains forest area and the Guan Shan Forest area all forest farms, only in the middle of the Longmen forest farm, there are small areas. (2) in 1990 ~1996, the type of "deciduous broad-leaved forest" increased (transfer), and the decrease (transfer) was more obvious. The region is distributed in every forest farm in the small Longshan Forest Area of the southern slope of Qinling Mountains, especially in the northern edge of the Xiaolong forest area and the middle of the forest area, especially in the middle of.1996 year ~2002. In 2002, the increasing area of "deciduous broad-leaved forest" was mainly distributed in the Maiji forest farm in the Xiaolong forest area, the East Fork forest farm and the Li Yuan forest farm, etc. " The type of deciduous broad-leaved forest is mainly distributed in Dong Cha, Li Yuan forest farm and the northern edge of Xiaolong mountain forest area in.2008 year ~2015, the increasing area of "deciduous broad-leaved forest" is mainly distributed in the South Bank of Weihe area of Qinling Mountains north slope, Maiji forest farm, Tianshui jurisdiction hot spring, Jianshan forest farm and the type of "deciduous broadleaf forest". In the Maiji forest farm and the tan song forest farm. (3) during the period of ~2015 in 1990, the types of "other woodlands" were increased (transferred), and the regions were mainly distributed on the edge of the forest area, and the changes were not significant. Based on the remote sensing image changing monitoring techniques of two non parametric classifiers, the typical Loess Plateau hilly and gully and longs were discussed. The spatial distribution law of forest resources in the transition region of mountain and West Qinling Mountains mountain area over the past 30 years, the time change trend and the influence factors, in order to be the quantitative analysis and comprehensive evaluation of the forest dynamic changes in this region, the spatial allocation and optimization of forest resources, the management and auxiliary decision, the progress monitoring of forestry engineering, and the ecological environment evaluation And provide some reference for the formulation of forest protection measures.

【學(xué)位授予單位】：中國(guó)林業(yè)科學(xué)研究院
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：S757;S771.8

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 林海晏;岳彩榮;吳曉暉;胥輝;鄭欣;;基于EnMAP-Box的遙感圖像分類(lèi)研究[J];西南林業(yè)大學(xué)學(xué)報(bào);2014年02期

2 董曉峰;馬如蘭;龐國(guó)錦;李波;宋翔;;近30年隴東黃土高原溝壑區(qū)林地時(shí)空變化及其驅(qū)動(dòng)力分析[J];西北師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2011年03期

3 張超;黃清麟;朱雪林;張曉紅;普布頓珠;旦增;;基于ETM+和DEM的西藏灌木林遙感分類(lèi)技術(shù)[J];林業(yè)科學(xué);2011年01期

4 許文杰;邢朝輝;李菲菲;;基于參數(shù)選擇優(yōu)化SVM算法的研究[J];山東交通學(xué)院學(xué)報(bào);2010年02期

5 陳旭;徐佐榮;余世孝;;基于對(duì)象的QuickBird遙感圖像多層次森林分類(lèi)[J];遙感技術(shù)與應(yīng)用;2009年01期

6 鄧書(shū)斌;武紅敢;江濤;;GIS輔助下的基于數(shù)據(jù)挖掘的林型遙感分類(lèi)方法研究[J];測(cè)繪科學(xué);2008年03期

7 覃先林;李增元;易浩若;陳爾學(xué);龐勇;;基于ENVISAT-MERIS數(shù)據(jù)的過(guò)火區(qū)制圖方法研究[J];遙感技術(shù)與應(yīng)用;2008年01期

8 高曉嵐;汪小欽;;多源遙感數(shù)據(jù)在植被識(shí)別和提取中的應(yīng)用[J];資源科學(xué);2008年01期

9 仝慧杰;馮仲科;張彥林;;樹(shù)種在遙感信息上的差異分析[J];北京林業(yè)大學(xué)學(xué)報(bào);2007年S2期

10 劉清旺;武紅敢;石進(jìn);蔣麗雅;葉勤文;;基于TM影像的森林病蟲(chóng)災(zāi)害遙感監(jiān)測(cè)系統(tǒng)[J];遙感信息;2007年02期

相關(guān)碩士學(xué)位論文 前3條

1 張智超;基于多分類(lèi)器組合的塔河林區(qū)森林類(lèi)型信息提取技術(shù)[D];東北林業(yè)大學(xué);2015年

2 楊辰;區(qū)域森林植被擾動(dòng)遙感監(jiān)測(cè)研究[D];南京信息工程大學(xué);2013年

3 鄧書(shū)斌;林冠狀態(tài)變化遙感監(jiān)測(cè)方法研究[D];山東科技大學(xué);2007年

，

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