請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/28218
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 邱祈榮(Chyi-Rong Chiou) | |
dc.contributor.author | Rui-Wen Chang | en |
dc.contributor.author | 張瑞文 | zh_TW |
dc.date.accessioned | 2021-06-13T00:02:57Z | - |
dc.date.available | 2007-08-03 | |
dc.date.copyright | 2007-08-03 | |
dc.date.issued | 2007 | |
dc.date.submitted | 2007-07-30 | |
dc.identifier.citation | 邱祈榮,2004。森林資源碳吸存資料庫建置計畫。國土資訊系統通訊第五十期。
邱祈榮、黃愷茹、潘孝隆,2006。台灣地區柳杉生長模式研究之回顧。中華林學會95年度學術論文發表會論文集。P 733-744. 林國銓、洪富文、游漢明、馬復京,1994。福山試驗林林生態系生物量與葉面積指數的累積與分佈。林業試驗所研究報告季刊 9(4): p299-315. 林務局,1997。台灣林產物處分調查用立木材積表。行政院農業委員會林務局。台北。 陳良健、江采薇、張智安,2006。整合影像及光達點雲建立三維林木模型。第二十五屆測量及空間資訊研討會。8 pp. 馬仕穆,2000。以SPOT衛星影像資料推估南仁山森林生態系之葉面積指數及凋落物。屏東科技大學碩士論文。74 pp. 湯國安、楊琋,2006。ARCGIS地理信息系統空間分析實驗教程。科學出版社。 鄭祈全、邱祈榮、陳燕章,1997。應用遙測方法估測台灣杉林分之葉面積指數。台灣林業科學 12(3): p309-317. 劉進金,2003。雷射掃描之地質應用(Lidar for Geology),工研院能資所。 A. Barilotti, S. Turco, G. Alberti, 2006. LAI determination in Forestry ecosystems by LiDAR data analysis. 3D Remote Sensing in Forestry. Daniel A. Zimble, David L. Evans, Geroge C. Carlson, Robert C. Parker, Stephen C. Grado, Patric D. Gerard, 2003. Characterizing vertical forest structure using small-footprint airborne Lidar. Remote Sensing of Environment 87, p171-182. Daniel T. Larose, 2005. Discovering Knowledge in Data, an introduction to data mining. Wiley Inter-science. D.S. Kimes, K.J. Ranson, G. Sun, J.B. Blair, 2006. Predicting Lidar measured forest vertical structure from multi-angle spectral data. Remote Sensing of Environment 100, p503-511. G. Patenaude, R.A Hill, R. Milne, D.L.A. Gaveau, B.B.J. Briggs, T.P. Dawson, 2004. Quantifying forest above ground carbon content using LiDAR remote sensing. Remote Sensing of Environment 93(2004) 368-380. Gary Smith, Åsa Persson, Johan Holmgren, Björn Hallberg, Johan E. S. Fransson, Lars M. H. Ulander, 2002. Forest Stem Volume Estimation Using High-Resolution Lidar and SAR Data. Geoscience and Remote Sensing Symposium, 2002. Johan Holmgren, Mats Nilsson, Håkan Olsson, 2003. Estimation of Tree Height and Stem Volume on Plots Using Airborne Laser Scanning. Forest Science 49(3). Juan C. Suárez, Carlos Ontiveros, Steve Smith, Stewart Snape, 2005. Use of airborne LiDAR and aerial photography in the estimation of individual tree heights in forestry. Computers & Geosciences 31, pp. 253-262. Juha Hyyppä, Olavi Kelle, Mikko Lehikoinen, Mikko Inkinen, 2001. A Segmentation-Based Method to Retrieve Stem Volume Estimates from 3-D Tree Height Models Produced by Laser Scanners. IEEE Transactions on geoscience and remote sensing, vol. 39, No. 5. pp 969-975. Matthew L. Clark, David B. Clark, Dar. A. Roberts, 2004. Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape. Remote Sensing of Environment 91, pp. 68-89. Michael G. Wing, Rebecca Johnson, 2001. Quantifying Forest Visibility with Spatial Data. Environmental Management Vol. 27, No. 3, pp. 411-420. M. Maltamo, K. Eerikäinen, J. Pitkänen, J. Hzzppä, M. Vehmas, 2004. Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions. Remote Sensing of Environment 90, pp 319-330. S. C. Anderson, J. A. Kupfer, R. R. Wilson, R. J. Cooper, 2000. Estimating forest crown area removed by selection cutting: a linked regression-GIS approach based on stump diameters. Forest Ecology and Management 137, pp. 171-177. Sorin C. Popescu, Randolph H. Wynne, John A. Scrivani, 2004. Fusion of Small-Footprint Lidar and Multispectral Data to Estimate Plot-Level Volume and Biomass in Deciduous and Pine Forests in Virginia, USA. Forest Science 50(4), p551-563. Xuexia Chen, Lee Vierling, Eric Rowell, Thomas DeFelice, 2004. Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest. Remote Sensing of Environment 91, p14-26. Y. Hirata, 2004. The effects of footprint size and sampling density in airborne laser scanning to extract individual trees in mountainous terrain. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVI-8/W2. Y. Hirata, K. Sato, S. Kuramoto and A. Sakai, 2004. Extracting Forest Patch Attributes at the Landscape Level Using New Remote Sensing Techniques – An Integrated Approach of High-Resolution Satellite Data, Airborne Lidar Data and GIS Data for Forest Conservation. Monitoring and Indicators of Forest Biodiversity in Europe, pp. 360-366. Y. Hirata, Y. Akiyama, H. Saito, A. Miyamoto, M. Fukuda, T. Nishizono, 2003. Estimating forest canopy structure using helicopter-borne LIDAR measurement. Advances in Forest Inventory for Sustainable Forest Management and Biodiversity Monitoring, pp. 125-134. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/28218 | - |
dc.description.abstract | 在大面積的森林資源調查中,採用遙測(分為多光譜、多解像度、多時序性遙測影像)之調查方法因具有可大幅度減少人力經費等物資的消耗之優點,因而近年來多為各森林調查所採用。本研究採用工研院能資所提供的台灣大學實驗林溪頭營林區柳杉實驗林的光達影像作為研究材料,探討使用原始光達資料利用於森林資源調查之可行性。研究內容分為兩大部分,首先是光達資料的準確性評估。基於不同栽植密度的15個研究樣區的特性,以不同搜尋面積的局部最大值演算法、樹冠層分離法、光達體積參數的統計方式比較各樣區內單株立木的位置(水平誤差)及樹高(垂直誤差)之準確性、樹冠面積的篩選與分層。基於三種篩選原則,固定光達林分高界限值、林分高圖層相對斜率變化、光達百分比分層體積。初步研究結果確立光達圖層的準確度與誤差範圍後,進入樣區為基準的材積估算。採取柳杉材積通式與迴歸樹高式比較我們迴歸的結果。研究結果顯示光達是一個準確性高的高程量測工具,局部最大值的個數與樣區株數相關性有0.80,且使用光達分層體積、林分百分位高、局部最大值個數應用於林分中估算樣區材積的相關性達0.72。顯示使用光達資訊估算樣區林分材積有不錯的結果,光達百分位林分高與光達百分比分層體積是不可或缺的樣區材積估算參數。光達材積式應用於估算樣區材積如有樣區樹高、栽植密度分佈均勻林分的特性可得較準確的估算結果。 | zh_TW |
dc.description.abstract | Recent researches have concluded that it is better to use remote sensing data to gain a time and effort-consuming result when applying in a large scale forest canopy monitoring. The LiDAR data is one of the remote sensing data that can be classified as multi-spectral, multi-scale and multi-temporal. The LiDAR system has many advantages, allowing quick access to large scale 3-dimensional information that is not available from field investigations. In this study, we used the LiDAR data provided by Industrial Technology Research Institute (ITRI) to understand its effects and abilities when applied to the NTU Chi-Tou experimental forest. To understand the accuracy of LiDAR data, field investigations are still required for calibration. The primary results in horizontal and vertical errors are rather good, and the vertical residuals caused by the inaccuracy of the measuring equipment resulted in the majority of the errors in this study.
When applying to the different plantation density of the 15 sampling plots, we used different searching ranges in the LiDAR local maximum algorithm, which are also the basis in the separation of tree crowns. In the scale of plot, to understand the stem volume concerning the volumes of LiDAR DCHM, we used single threshold value, relative slope variation and classified percentile volumes in DCHM to subtract layers of pixels within a plot. The argument will be more persuasive when some background data such as field investigation and canopy mean height is served. To sum up, using LiDAR data is a powerful tool to obtain the multi-layer forest vertical structure and to gain a large-scale canopy height. In this study, we can use LiDAR local maximum number to predict number of trees in a plot, and predict canopy volume using LiDAR local maximum number, LiDAR CHM percentile and classified LiDAR volume, with the former correlation is up to 0.8 and the later 0.72. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T00:02:57Z (GMT). No. of bitstreams: 1 ntu-96-R92625019-1.pdf: 5744652 bytes, checksum: 9ca278ca3cf7ddd120d234208c35da4d (MD5) Previous issue date: 2007 | en |
dc.description.tableofcontents | 謝誌………………………………………………………I
中文摘要………………………………………………………Ⅱ Abstract………………………………………………………Ⅲ 目錄………………………………………………………Ⅳ 圖目錄………………………………………………………VI 表目錄………………………………………………………Ⅸ 壹、 前言………………………………………………………………1 一、研究動機…………………………………………………………3 二、研究目的…………………………………………………………5 貳、 文獻回顧…………………………………………………………7 一、光達系統分類及成像原理………………………………………7 二、光達在林業方面的相關研究…………………………………13 1. 光達應用於地面生物量的估算………………………………13 2. 光達應用於林分垂直結構的模擬……………………………17 參、 研究材料與方法…………………………………………………23 一、研究材料………………………………………………………23 二、研究流程………………………………………………………25 三、研究方法………………………………………………………31 肆、 研究結果…………………………………………………………39 一、 光達資料的前處理……………………………………………39 1. DSM與DEM分群法……………………………………………39 2. 不同資料來源誤差大小的評估……………………………45 二、 樣區調查資訊與光達局部最大點的套合與比對……………51 1. 樣區調查資訊…………………………………………………51 2. 光達局部最大值與每木位置套合抽取光達樹高…………………55 3. 兩種解析度光達於高程DCHM上的準確度比較……………………57 4. 兩種解析度光達局部最大值於水平距離上的準確度比較………61 三、 柳杉樣區的胸徑、樹高、冠面積相互關係……………………65 四、 光達林分高界限值的界定………………………………………69 五、 光達分層體積與樣區材積關係…………………………………75 伍、 結論與建議………………………………………………………85 陸、 參考文獻…………………………………………………………87 柒、 附錄………………………………………………………………93 | |
dc.language.iso | zh-TW | |
dc.title | 應用光達資料於溪頭柳杉人工林分調查之研究 | zh_TW |
dc.title | A study on investigating Cryptomeria japonica stand in Chitou using LiDAR data | en |
dc.type | Thesis | |
dc.date.schoolyear | 95-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林世宗(Shi-Zong Lin),陳朝圳(Chaur-Tzuhn Chen) | |
dc.subject.keyword | 光達林分高,局部最大值演算法,光達百分位林分高,光達林分體積, | zh_TW |
dc.subject.keyword | LiDAR digital canopy height model,local maximum algorithm,LiDAR DCHM percentile,LiDAR volume, | en |
dc.relation.page | 108 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2007-07-31 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 森林環境暨資源學研究所 | zh_TW |
顯示於系所單位: | 森林環境暨資源學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-96-1.pdf 目前未授權公開取用 | 5.61 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。