臺灣博碩士論文加值系統

隨著遙測科技的發展，各類遙測資料及技術廣泛應用於森林資源調查研究。傳統皆以AVHRR、MODIS衛星影像進行植被第一生產力估算，因為估算未考慮植生穿透率問題，所以只獲得植生冠層的植被第一生產力。本研究目的為利用光達資料探討物件式分類法對第一生產力資源參數萃取。以物件式分類法進行影像分割以及區塊分類兩個步驟，在影像分割中，原始影像經影像分割步驟後產生由眾多區塊組成的區塊影像；在區塊分類步驟中，利用知識庫系統，對區塊影像中的各個區塊進行類別的判釋。運用空載光達具有高密度測點與可分離植生多回波訊號之特性，迅速獲取植生之三度空間資料，光達多重反射功能對於植被之穿透亦是能計算陽光穿透力之主要因素。本研究流程主要分為四個階段，第一階段為為資料前處理。第二階段以物件式分類法進行植生區域萃取。第三階段以光達資料進行植生區穿透率演算。第四階段為福衛二號衛星影像及融合光達資料與高解析影像之第一生產力估算。本研究針對山區與平地分別進行探討，經由研究成果顯示以融合光達與航照影像運算之NPP值，平地試驗區較以福衛二號衛星影像運算值高出+10.7278 %，而山區試驗區NPP值較以福衛二號衛星影像運算值多 +9.7889 %。平地試驗區植生較山區試驗區稀疏，穿透率較山區高(17.50848% > 15.87155%)，造成平地的地面層NPP估算結果高於山區；主要差別在於平地與山地樣區中有發生陽光穿透引起的地面層NPP之比率並不相同。

Due to the rapid development of remote sensing technology, the remote sensed image data had widely used in evaluating the forest resource inventory. Traditional method to estimate the NPP with AVHRR or MODIS satellite images. The penetrating rate of plantation did not considered in such process. Therefore, only the NPP from canopy of tree is estimated.In this study, the object-oriented classification process consists of two various steps: image segmentation and object classification. In the image segmentation step, the process group the pixels into numerous sub-regions thus transform the source image into an objective image. The second step is designed to classify every sub-region according to the database of experts. This classification is based on knowledge-based system which was designed by spectrum and texture information. LiDAR is capable of measuring objects with very high density and multiple return signal among tree crowns, therefore it can rapidly obtain the 3D data about forest stands. Capability ofpenetrating over forestry is a primary factor for this techniques being recognized in recent years.The proposed schema in the study comprises four major steps: (1) LiDAR data preprocessing, (2) Object-Oriented Classification by vegetated regional extraction, (3) LiDAR data in vegetated regional penetration rate calculation. (4) Estimate NPP by FORMOSAT-2 images and LiDAR data.There are two study areas, mountains and plains, in the study. Research results show that the NPP estimated by LiDAR is other than NPP calculated from FORMOSAT-2 images. In the plains area +10.7278% more and in the mountains area +9.7889% increased value was found. Since the vegetation in plains region is sparser than in mountains, according to the data of this research, penetrating rate 17.50848% in plains, while the same phenomena of 15.87155% found in mountain area. The calculated NPP at plains is larger then region of mountain.

摘要 I
ABSTRACT II
致謝 IV
目錄 IV
圖目錄 VII
表目錄 X
第一章 緒論 1
1-1 研究動機 1
1-2 研究目的 2
1-3 研究構想及內容 4
1-4 研究流程 6
第二章 文獻回顧 7
2-1 以遙測影像為基礎 7
2-1-1 氣候生產力模式 7
2-1-2 生理生態過程模式 8
2-1-3 光能利用率模式(遙測資料運用模式) 9
2-2 以融合光達資料及航照影像為基礎 15
第三章 研究方法 21
3-1 航照影像及LIDAR資料前處理 21
3-1-1 航照影像正射化處理 22
3-1-2 LiDAR資料處理 23
3-2 植生區域影像萃取分類 29
3-2-1 影像分割(lmage segmentation) 30
3-2-2 知識庫區塊分類 36
3-3樹植生區之穿透率運算 40
3-3-1 樹植生區套繪方式分析 41
3-3-2 樹植生區地面點分類分析 42
3-3-3 樹植生區穿透率分析 45
3-4 植被第一性生產力(NPP)估算 47
3-4-1 以遙測影像(福衛二號)為基礎之NPP估算 50
3-4-2 以融合光達資料與航照影像為基礎之NPP估算 56
第四章 研究成果 57
4-1 航照影像及LIDAR資料前處理成果 59
4-1-1 航照影像正射化處理成果 59
4-1-2 LiDAR資料處理成果 60
4-2 植生區域影像萃取分類成果 64
4-2-1 影像分割(lmage segmentation)成果 64
4-2-2 知識庫區塊分類成果 66
4-2-3 分類成果分析 73
4-3 樹植生區之穿透率運算成果 75
4-4 植被第一性生產力(NPP)估算成果 82
4-4-1 以福衛二號為基礎之NPP估算成果 82
4-4-2 以融合光達資料與航照影像為基礎之NPP估算成果 90
4-4-3 研究區之NPP值估算統整成果 91
第五章 結果與討論 94
第六章 參考文獻 97
自述 103

1.王宗明、梁銀麗，植被淨第一性生產力模型研究進展，西北林學院學報，17(2)，P22-P25，2002。
2.王文俊 編著，認識Fuzzy，全華科技圖書，1986。
3.王軍邦、牛錚、胡秉民、王長耀、王政權，定量遙感在生態學研究中的基礎應用，生態學雜誌， 23(2)，P152-P157，2004。
4.史天元、何心瑜，數值高程模型及數值覆蓋模型產製品質研究，內政部辦理LiDAR測區之高精度及高解析度數值地形測繪、資料庫建置與應用推廣工作案，P129-P142，2005。
5.朱文泉、潘耀忠、何浩、于德永、扈海波，中國典型植被最大光利用率模擬，科學通報，51(6)，P700-P706，2006。
6.何心瑜、史天元、徐偉城、陳大科，空載光達點雲密度探討，內政部辦理LiDAR測區之高精度及高解析度數值地形測繪、資料庫建置與應用推廣工作案，P151-P158，2005。
7.李世華、牛錚、李壁成，植被淨第一性生產力遙感過程模型研究，水土保持研究 12(3)，P126-P128，2005。
8.吳紹禎，不同時期空載光達成果比對探討以新竹地區為例，國立交通大學碩士論文，2006。
9.林國銓，以不同尺度談森林對碳的吸收，2001。
10.徐歷鵬，生態系生態學Ecosystem Ecology， 取自網路資料http://www.dyu.edu.tw/~lphsu/htm/ecosystem.htm，(2008年05月10日查)。
11.孫睿、朱后疆，中國陸地植被淨第一性生產力及季節變化研究，地理學報，55(1)，P36-P45，2000。
12.陳利軍、劉高煥、馮險峰，遙感在植被淨第一性生產力研究中的應用，生態學雜誌 21(2)，P53-P57，2002。
13.陳月淑，以光達與數位影像資料進行建物與樹木重疊區的地物特徵提取之研究，國立中山大學碩士論文，2007。
14.陳大科、廖子毅、李惠容、陳浩志、莊敬業，應用空載光達系統獲取棲蘭山林區數值地形模型與樹高模型，農委會94年度遙測應用計畫成果發表會專刊，2005。
15.陳良健、江采薇、張智安，融合光達及空照影像建立三維森林覆蓋模型，內政部辦理LiDAR測區之高精度及高解析度數值地形測繪、資料庫建置與應用推廣工作案，P169-P178，2005。
16.張智安、陳良健，整合光達資料與高解析衛星影像於建物偵測，航測及遙測學刊，第十卷，第四期，P361-P371，2005。
17.彭曉鵑、鄧孺孺、劉小平，遙感尺度轉換研究進展。地理與地理信息科學 20(5)， P6-P14，2004。
18.鄭鼎耀、王弘基、鄒慶敏、劉正倫，空載光達系統應用不同掃描參數於密植被區穿透率研究，第五屆海峽兩岸測繪發展研討會，2007。
19.蘇理宏、李小文、黃裕霞，遙感尺度問題研究進展。地球科學進展16(4)，P544-P548，2001。
20.Axelsson, P.. Processing of laser scanner data – algorithms and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2-3), P138-P147, 1999.
21.Behan, A., On the matching accuracy rasterised scanning laser altimeter data, International Archives of Photogrammetry and Remote Sensing, Vol. XXXIII, Part B2 , P75-P82, 2000.
22.Briese, C., Pfeifer, N., and Dorninger ,P., Application of the Robust Interpolation for DTM Determination. IAPRS, vol. XXXIII, P55-P61, 2000.
23.DEFINIENS AG, eCoginition user’s guide. http://www.definiens.com. (accessed 23,Dec,2003) , 2003.
24.Dubayah, R.O. and Drake, J.B.. LIDAR remote sensing for forestry. Journal of Forestry, 98, P44-P46, 2000.
25.D. Turner. et al., Site-level evaluation of satellite-based global terrestrial gross primary production and net primary production monitoring, Global Change Biology, vol. 11, P666-P684, 2005.
26.E. Raymond Hunt Jr. and Brian A. Miyake., Comparison of Stocking Rates From Remote Sensing and Geospatial Data, Rangeland Ecol Manage,59, P11-P18, 2006.
27.F. Rottensteiner. et a5, Using the Dempster–Shafer method for the fusion of LIDAR data and multi-spectral images for building detection, Information Fusion, 6, P283-P300 l, 200.
28.F. A. Kruse., Multi-resolution segmentation for improved hyperspectral mapping, 2005.
29.Field C B, Randerson J T, Malmstrom CM, Global net primary production :Combining ecology and remote sensing[J], Remote Sensing Environ., 51, P74-P88, 1995.
30.Foley J A, Net primary productivity in the terrestrial biosphere : the application of a global model. J. Geophys. Res., 99(D10), P20773-P20783., 1994.
31.G. GUTMAN and A. IGNATOV, The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models, int. j. remote sensing, vol. 19, no. 8, P1533-P1543, 1998.
32.Goetz S J, Prince S D., Remote sensing of net primary production boreal forest stands. Agricultural and Forest Meteorology, 78, P149-P179, 1996.
33.Hendrik S. and Frank V., Estimation of Net Primary and Net Ecosystem Productivity of European terrestrial ecosystems by means of the C-Fix model and NOAA/AVHRR data, 2000.
34.Hofmann, A.D., Mass, H., and Streilein, A., Knowledge-based building detection based on laser scanner data and topographic map information, International Archives of Photogrammetry and Remote Sensing, Vol.34, Part 3A+B, P163-P169, 2002.
35.Jingyun Fang et al, Increasing net primary production in China from 1982 to 1999, Front Ecol Environ 1(6), P293–P297. , 2003
36.Jonathan A Foley, Net primary productivity in the terrestrial biosphere: The application of a global model. 99(D10), P20773-P20783, 1994.
37.Juan C. Suárez, Tree counting analysis using eCognition, 2004.
38.Kaduk J , Heimann M ., A prognostic phenology scheme for global terrestrial carbon cycle models., Clim. Res., 6, P1-P19, 1996.
39.Kristian, W.M.. Calibration of Airborne Laser Scanners. Master thesis, Department of Geomatics Engineering, Calgary, Alberta, November , 2002.
40.Kergoat L ., A model for hydrologic equilibrium of leaf area index on a global scale. , J . Hydro. , P212-P213, P267-P286, 1999.
41.Matsushita B., M. Xu, J. Chen, S. Kameyama, M. Tamura., Estimation of regional net primary productivity (NPP) using a process-based ecosystem model: How important is the accuracy of climate data, Ecological Modelling 178, P371-P388, 2004.
42.Lieth H F H., Modeling the primary productivity of the world. Neture and Resources, 8 (2), P5-P10, 1972.
43.Lim, K., Treitz, P., Wulder, M., St-Onge, B. and Flood, M., LIDAR remote sensing of forest structure. Physical Geography, 27(1), P88-P106, 2003.
44.Liu J. Chen J M. Cihlar J et al, A process-based boreal ecosystem productivity simulator using remote sensing inputs, Remote Sensing of Environment, 62(2), P158-P175, 1997.
45.Los S O, Justice C O, Tucker C J., A global 1 by 1 NDVI dataset for climate studies derived from the GIMMS continental NDVI data. International Journal of Remote Sensing, 15, P3493-P3518, 1994.
46.McGuire AD, Melilo J M , Kickilghter D W et al.,Equilibrium responses of global net primary production and carbon storage to doubled atmospheric carbon dioxide., Global Biogeochem. Cyc.11(2), P173P189, 1997.
47.Mynenir B. and Williams D.L.. On the relationship between fAPAR and NDVI. Remote Sensing of Environment, 49, P200-P211, 1994.
48.Parton W J, Scurlock J MO ,Ojima D S. et al..Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide., Global Biogeochem .Cy.,7, P 785-P890, 1993.
49.Paruelo J M, Epstei H E, Lauenroth W K, et al., ANPP estimates from NDVI for the central grassland region of the United States. Ecology, 78(3), P953-P958, 1997.
50.Popescu, S.C. and Wynne, R.H.. Seeing the trees in the forest:using LIDAR and multispectral data fusion with local filtering andvariable window size for estimating tree height, PE&RS,70(5), P589-P604, 2004.
51.R. J. Olson. et al.,Global and Regional Ecosystem Modeling: Databases of Model Drivers and Validation Measurements , Environmental Sciences Division, 2001.
52.Raymond E, Hunt J R., Relationship between woody biomass and PAR conversion efficiency for estimating net primary production from NDVI. International Journal of Remote Sensing, 15, P1725-P1730, 1994.
53.Ruimy A, Saugier B., Methodology for the estimation of terrestrial net primary production from remotely sensed data. Journal of Geophysical Research, 97, P18515-P18521, 1994.
54.Running S W, H unt E R., Generailzation of a forest ecosystem process model for other biomes, BIOME-BGC ,and an application for global-scale models[A]. In : Ehleringer JR ,eds . Scailng Physiological Processes :Leaf to Globe[C].San Diego ,CA ,Academic Press, P141-P158, 1993.
55.St-Onge, B. A. and N. Achaichia.. Measuring forest canopy height using a combination of LIDAR and aerial photography data, Workshop on Land Surface Mapping and Characterization Using Laser Altimetry, Annapolis, Maryland, USA,2001.
56.Song, J., Han, S., Yu, K., Kim, Y.. Assessing the possibility of land-cover classification using LiDAR intensity data. ISPRS Commission III, September ,Graz, Austria, P9-P13, 2002.
57.Sithole, G. and G. Vosselman. Experimental comparison of filter algorithms forbare-Earth extraction from airborne laser scanning point clouds, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 59, P 85-P101, 2004.
58.Terrasolid , TerrScan User Guide (18.11.2004), Terrasolid , 2004a.
59.Terrasolid, TerrMatch User Guide , Terrasolid, 2004b.
60.Uchijima Z, Seino H., An agroclimating net primary productivity of natural vegetation., J Agric. Res. Q. , 214, P244-250, 1985.
61.Warnant P, Francois L, Strivay D. et al., CARAIB: A global model of terrestrial biological productivity, Global Biogeochem. Cyc., 8, P255-P270, 1994.