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基于無人機LiDAR的天然林與人工林林隙提取
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國家重點研發(fā)計劃項目(2017YFD0600902)和中央高校基本科研業(yè)務(wù)費專項資金項目(2572018BA02)


Extraction of Forest Gaps in Natural Forest and Man-made Forest Based on UAV LiDAR
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    摘要:

    為研究主動遙感進行森林地物分類和林隙提取的效果,,分別在天然林和人工林中比較了無人機激光雷達(LiDAR)數(shù)據(jù)的閾值法,、逐像元法和面向?qū)ο蠓?種方法的分類精度和適用性。選取天然林(黑龍江省哈爾濱市帽兒山林場)和人工林(內(nèi)蒙古自治區(qū)赤峰市旺業(yè)甸林場)兩處試驗區(qū),,應(yīng)用閾值法、逐像元法和面向?qū)ο蠓?種方法,對兩個試驗區(qū)采集的無人機LiDAR數(shù)據(jù)進行林隙、非林隙,、其他類型劃分。研究結(jié)果表明,,面向?qū)ο蠓ㄔ谔烊涣趾腿斯ち衷囼瀰^(qū)中的分類精度和Kappa系數(shù)均最高,,天然林為82.43%、0.73,,人工林為91.74%,、0.88;逐像元法次之,,天然林為76.62%,、0.64,人工林為78.68%,、0.68,;閾值法的分類精度和Kappa系數(shù)差異較大,在天然林中的精度極低,,為50.54%,、0.27,人工林的精度較高,,為79.12%,、0.69。面向?qū)ο蠓ê椭鹣裨ㄔ谔烊涣趾腿斯ち制毡檫m用,,均可以達到理想的分類精度和Kappa系數(shù),。閾值法在天然林的精度較低,,更適合于人工林的分類,即林分高度趨于一致,,且建筑,、道路等其他類型干擾較少的區(qū)域。天然林的最佳分類方法為面向?qū)ο蠓?,人工林的最佳分類方法為閾值法?/p>

    Abstract:

    Aiming to explore results of classification and extraction of forest gaps in forest based on active remote sensing, the classification accuracy and applicability of the threshold method were compared, including the pixeloriented method and the objectoriented method in natural forest and man-made forest based on unmanned aerial vehicle (UAV) light detection and ranging (LiDAR). Maoershan Forest Farm in Harbin, Heilongjiang Province and Wangyedian Forest Farm in Chifeng, Inner Mongolia Autonomous Region were selected as the natural forest experimental site and the manmade forest experimental site respectively. The threshold method, the pixel-oriented method and the object-oriented method were applied to classify the two experimental sites into three classes based on UAV LiDAR acquired, which were forest gap, nonforest gap and others. The research results indicated that the object-oriented method produced the highest classification accuracy and Kappa coefficient in both experimental sites, which were 82.43% and 0.73 in natural forest and 91.74% and 0.88 in man-made forest, respectively. The accuracy and Kappa coefficient of the pixel-oriented method was lower than that of the objectoriented method, which was 76.62% and 0.64 in natural forest and 78.68% and 0.68 in man-made forest. The accuracy and Kappa coefficient of the threshold method had larger difference. It produced the lowest accuracy in natural forest (50.54% and 0.27) and the higher accuracy in man-made forest (79.12% and 0.69). The objectoriented method and the pixel-oriented method were the methods with general applicability in classification of natural forest and man-made forest and could produce ideal accuracy and Kappa coefficient. The threshold method produced lower accuracy in natural forest and was more suitable for classification of man-made forest, forest height of which was similar and where others such as buildings and roads were rare. The best classification method of natural forest was the object-oriented method and the best of man-made forest was the threshold method. The research results provided method reference and technology support for extraction of forest gaps in natural forest and man-made forest.

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毛學(xué)剛,杜子涵,劉家倩,陳樹新.基于無人機LiDAR的天然林與人工林林隙提取[J].農(nóng)業(yè)機械學(xué)報,2020,51(3):232-240. MAO Xuegang, DU Zihan, LIU Jiaqian, CHEN Shuxin. Extraction of Forest Gaps in Natural Forest and Man-made Forest Based on UAV LiDAR[J]. Transactions of the Chinese Society for Agricultural Machinery,2020,51(3):232-240.

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  • 收稿日期:2019-07-16
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  • 在線發(fā)布日期: 2020-03-10
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