台灣樹林資源豐富，為了能有效獲得樹林平面及高程資訊，整合不同型態的資料已經是目前的趨勢，空載光達能快速提供高精度的地物三維坐標，但並無法提供地物的光譜資訊，而遙測影像可提供豐富的光譜資訊而取得樹林的分佈位置，因此整合空載光達及遙測影像的資料特性，可助益於樹林資訊的萃取。

本研究採用空載光達點雲及遙測影像來估算樹林分佈面積及覆蓋體積，對於樹林覆蓋體積的推估需要兩個主要的素材，一個是數值高程模型（DEM），一個是樹冠高度模型（CHM），估算樹林體積方法之概念是由空載光達點雲先過濾非地面點雲建立DEM，以DEM為計算樹林覆蓋體積的基面，再利用影像光譜特性結合共線式過濾出樹林點雲，以樹林點雲與DEM產生樹冠高度模型（CHM），利用Extended Simpson's Rule計算樹冠網格面到地表網格面的體積，其概念是累計兩個面中的每個子網格體積，計算中以高程差距為高度，網格大小為底面的柱體體積，累加成為樹林覆蓋體積。

實驗資料可分為都市區的成大校園及森林區的南化水庫，並分別取得樹高、樹冠幅、樹冠投影面積、樹（林）體積，以地面驗證資料比對樹冠高度模型中獲得的樹林觀測資料，分析不同樹林觀測資料的關係，最後推估整體實驗區的樹林分佈及體積。實驗成果顯示出空載點雲的資料中能有效獲得對樹林的資訊，對樹林觀測資料的均方根誤差百分比都在16％以下，而實驗區整體的樹林體積，南化水庫的樹林體積密度約是成大區域的14～16倍左右。在萃取樹林觀測參數時，點雲密度對樹冠幅越小的樹種影響越大，樹林觀測資料中，精度最高的是樹高，展現出空載光達在高程上精度的優勢的確有利於獲取林木的高度，不過實驗結果亦顯示出估計出的樹高普遍都有低估的情形。

Efficiently obtaining information regarding forest regions and tree canopy volume is important for forestry management. Light Detection and Ranging (LiDAR) data is able to provide high resolution three dimensional coordinates of surface features, but does not contain spectrum information. In contrast, remote sensing imagery offers copious spectrum information that can be used to locate forest regions. Therefore, integrating the complementary LiDAR data and remote sensing imagery is an effective strategy for surveying forest area and canopy volume.

Two data sets are required to canopy volume: digital elevation models (DEMs) and canopy height models (CHMs). In this study, non-ground point clouds are first filtered to generate a DEM. Subsequently, the DEM is utilized as the basic datum for calculating the canopy volume. LiDAR data and remote sensing imagery are then combined to filter the canopy LiDAR data. The sub-grid volume accumulated between the canopy grid surface and the DEM provides the canopy volume.

The study sites include the NCKU campus and Nan Wha Reservoir forest area. The tree height, crown diameter, crown projected area, and canopy volume are determined for both study areas. Furthermore, the forest area and canopy volume are estimated over the study areas using the proposed techniques. The results show forest information can be acquired effectively using LiDAR data and that the root mean square error for all tree parameters is less than 16%

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