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題 名 | 以空載光達資料推估柳杉人工林地上部生物量=Using Airborne Laser Scanning Data to Estimate Forest Biomass of Cryptomeria japonica |
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作 者 | 魏浚紘; 吳守從; 黃冠理; 陳朝圳; 陳建璋; | 書刊名 | 國立臺灣大學生物資源暨農學院實驗林研究報告 |
卷 期 | 26:2=276 2012.06[民101.06] |
頁 次 | 頁113-123 |
分類號 | 436.12 |
關鍵詞 | 生物量; 三維像元; 空載光達; Biomass; Voxel-based LiDAR; Airborne LiDAR; |
語 文 | 中文(Chinese) |
中文摘要 | 本研究以溪頭營林區70年生柳杉不同栽植距離之生長量試驗區為研究對象,進行地面林木材積量調查,並依照IPCC推估方法進行地上部生物量估算。光達資料為工研院2006年6月於溪頭樣區所拍攝,資料經過濾及正規化處理後,建構樹冠高度模型,進而推估林分高、林分枝下高、回波比例等相關衍生變數,並將光達所推估之變數與地面調查所推估之林分生物量進行多元迴歸分析,建立以光達樹冠高度模型(Canopy Height Model, CHM)為基礎之林分生物量推估模式;另本研究引用三維像元的概念,依照1 m等間距高度,分割研究樣區範圍內點雲資料,計算各分層中每一個1 m × 1 m網格內出現之點雲數佔該立方柱內總點雲數量之比值,輸出成網格式影像,再以15個樣區邊界為範圍,計算每個分層中各樣區之林分高、林分枝下高、回波比例平均值等變數,並與15個地面調查樣區所推估之林分生物量進行迴歸分析,建立以三維像元為基礎之森林生物量推估模式。研究結果顯示,CHM衍生變數所建立之生物量推估模式,以空載光達枝下高(樹冠基礎高)與60%林分高兩變數之相關性最高R = 0.84,判定係數R^2 = 0.6994;三維像元變數之生物量推估模式,則以取樣體積1 m^3配合高度21、22、23與28 m處之點雲資料所得結果最理想(R^2 = 0.7448)。未來對兩種模式之運用,可根據林地狀況與光達點雲分布加以調整,藉以獲得更加準確之森林林木地上部生物量推估值。 |
英文摘要 | The 70 years old, different planting density of ”Cryptomeria japonica” experiment area was selected for this study in Xitou area. The timber volume was measured by ground survey and the biomass was transferred from the volume by the conversion coefficient of IPCC method. A small-footprint LiDAR dataset was collected from Industrial Technology Research Institute (ITRI) during June 2006. The raw dataset was processed using filtering and normalized, and constructed the parameters of stand height, crown base height, echo ratio with Canopy Height Model (CHM). Multiple regression analysis was used for establish the relationship between the biomass and parameters of estimating from the LiDAR CHM. The results indicated that the LiDAR crown base and 60% stand height are high correlation with biomass (R^2 = 0.6994). LiDAR height bins were generated as multiband images of 1m height intervals and 1 m × 1 m pixel dimensions (i.e. 1 × 1 × 1 m voxels). A pixel value represents the number of laser points as a percentage of the total number of points summed up for all pixels in the stack of this position. Average pixel values of different dimension voxels were calculated with 15 plots as independent variable for regression with stand biomass. The results indicated that average pixel value of 1 × 1 × 1 m voxels of 21, 22, 23, 28 bins were the best fitting with biomass (R^2 = 0.7448). In order to estimating biomass precisely, both approaches should follow the site situation and status of point cloud distribution accordingly. |
本系統中英文摘要資訊取自各篇刊載內容。