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題 名 | Using Small-Footprint and Multiple-Return LiDAR Data to Characterize and Classify Four Temperate Forest Cover Types=小覆蓋面多重回波光達資料應用於四個溫帶森林覆蓋類型之特徵與分類探討 |
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作 者 | 謝依達; 陳朝圳; 吳守從; | 書刊名 | 臺灣林業科學 |
卷 期 | 29:1 2014.03[民103.03] |
頁 次 | 頁53-68 |
分類號 | 436.718 |
關鍵詞 | 空載光達; 多重回波; 強度值; 分類; LiDAR; Multiple-return; Intensity; Classification; |
語 文 | 英文(English) |
中文摘要 | 樹葉覆蓋與枝條分佈的資訊為理解森林垂直結構空間變異的基本要素,然而透過傳統的田野調查研究或航空照片判釋,卻很難有效獲取森林垂直結構與冠層以下的相關資訊;因此,本研究旨在評估利用空載光達資料探討冠層下森林垂直結構的可行性,並進行山區森林植群分類。本研究以阿里山地區為範圍,其內計有紅檜(Chamaecyparis formosensis)、柳杉(Cryptomeria japonica)、闊葉樹混淆林與裸露地等四種類型,因此透過空載光達多重回波與強度值資料,結合統計分析與影像分類探討其差異性。研究指出,回波比率模型(REM)與回波強度模型(EIM)能有效區分土地利用類型的不同,同時證明回波比率與強度和林分冠層密度具有相關性,其中第一回波的回波比率與強度值最能充分反應冠層資訊。至於影像分類方面,單一影像的分類準確度介於50.5至68.5%之間,且第一回波強度(EIM(下標FE))、第一回波比率(REM(下標FE))、最終回波比率(REM(下標LE))與單一回波比率(REM(下標OE))具有較高的分類潛力;不過融合較多變數的混合影像,其分類準確度更高,達81.5%。因此本研究證實,透過空載光達的多重回波與強度值資料,確可有效進行土地覆蓋類型的分類與製圖工作。 |
英文摘要 | The information of the foliage cover and the distribution of branches are essential sources for understanding the spatial variability of the vertical forest structure. But it is difficult to use traditional research methods, such as doing field surveys and interpreting aerial photographs, to obtain related information on the vertical forest structure and conditions below the canopy. Therefore, this study attempted to evaluate the possibilities of using airborne LiDAR data to examine the forest vertical structure below the canopy, and utilize LiDAR data to classify land cover types in mountain areas. Red cypress (Chamaecyparis formosensis), Sugi (Cryptomeria japonica), mixed hardwoods, and bare land were the 4 categories analyzed in the investigated area, the Alishan region of central Taiwan. The analytical methods were based on LiDAR multiple-return and intensity data, and statistical analyses and image classification were used to describe the diversities of the investigated land types. The ratio of echo model (REM) and echo intensity model (EIM) were effective in distinguishing the divergences of different land types. Results of this study demonstrated the proportion of echo return and intensity data related to the canopy density. Among the types of test echoes used in the study, plentiful information for land cover classification using both the ratio of echo returns and the intensity of echoes was acquired from the first echo returns. The results of applying single-image classification showed a classification accuracy of 50.5~68.5%. The EIM(subscript FE), REM(subscript FE), REM(subscript LE), and REM(subscript OE) showed a higher potential for classifying land types. The classification results of stack images indicated that combining more LiDAR-derived variables yielded a more-accurate classification accuracy (81.5%). This study corroborates the high feasibility for mapping land cover types using LiDAR multiple-return and intensity data. |
本系統中英文摘要資訊取自各篇刊載內容。