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首頁(yè)> 外文期刊>International journal of remote sensing >Tree height mapping and crown delineation using LiDAR, large format aerial photographs, and unmanned aerial vehicle photogrammetry in subtropical urban forest
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Tree height mapping and crown delineation using LiDAR, large format aerial photographs, and unmanned aerial vehicle photogrammetry in subtropical urban forest

機(jī)譯：使用激光雷達(dá)，大幅面空中照片和無(wú)人空中車輛攝影測(cè)量在亞熱帶城市森林中的樹高度映射和皇冠描繪

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Accurately identified trees can serve as a basis of estimating forest variables through the individual tree-based approach. Increasing richness of remote sensing data also provides opportunities to explore the potential uses of various types of data sources. This study adopted three widely used remote sensing data, including airborne light detection and ranging (LiDAR), unmanned aerial vehicle (UAV) photography and traditional digital aerial photos (DAP), and aimed to investigate their potentials on estimating tree heights and extracting individual tree information in four forested sites in Hong Kong with different tree compositions. Image-based point clouds were generated through photogrammetry. Local maxima and region growing methods were adopted to identify treetops and delineate tree crowns, respectively, with different fixed and variable window size settings. Tree heights obtained from remote sensing datasets resulted in correlation coefficients (r) = 0.58-0.94 and root-mean-square errors (RMSE) = 1.33-3.78 m compared to field-measured values and similar levels of correspondences among the datasets. Point cloud characteristics were highlighted through point-based and profile-based analysis. The highest F-scores for treetop detections in each site ranged from 0.53 to 0.69 with variations caused by different window sizes and data sources. Matched rates of reference trees were positively correlated (r = 0.19-0.49) with geometric properties including diameter at breast height (DBH), tree height, crown area, and distance to the nearest neighbour. No single remote sensing dataset was clearly superior in all methodologies in this study, but unique properties were demonstrated in terms of both data acquisitions and analysis. Knowledge and testing on both characteristics of study areas and data sources were important when deciding the best window size parameters. Heterogeneity of forest environment could be a major factor hindering the delineation performance with further influences on plot-level difference and tree-level detectability.
機(jī)譯：準(zhǔn)確識(shí)別的樹木可以通過(guò)基于各自的基于樹的方法估算森林變量的基礎(chǔ)。增加遙感數(shù)據(jù)的豐富性也提供了探索各種類型數(shù)據(jù)源的潛在用途的機(jī)會(huì)。本研究采用了三種廣泛使用的遙感數(shù)據(jù)，包括空中光檢測(cè)和測(cè)距（LIDAR），無(wú)人駕駛飛行器（UAV）攝影和傳統(tǒng)的數(shù)字空中照片（DAP），并旨在調(diào)查估計(jì)樹高度和提取單個(gè)樹的潛力在香港四個(gè)森林地點(diǎn)的信息，有不同的樹木組成。通過(guò)攝影測(cè)量生成基于圖像的點(diǎn)云。采用本地最大值和地區(qū)生長(zhǎng)方法來(lái)分別識(shí)別樹梢和描繪樹冠，具有不同的固定和可變窗口大小設(shè)置。與遠(yuǎn)程傳感數(shù)據(jù)集獲得的樹高，導(dǎo)致相關(guān)系數(shù)（R）= 0.58-0.94和根均方誤差（RMSE）= 1.33-3.78m與實(shí)地測(cè)量值和數(shù)據(jù)集之間的類似相應(yīng)的相應(yīng)程度相比。點(diǎn)云特征通過(guò)基于點(diǎn)和基于概況的分析來(lái)突出顯示。每個(gè)站點(diǎn)中的樹梢檢測(cè)的最高F分?jǐn)?shù)范圍為0.53至0.69，具有由不同窗口尺寸和數(shù)據(jù)源引起的變化。匹配的參考樹速率呈正相關(guān)（r = 0.19-0.49），幾何屬性包括乳房高度（dbh），樹高，冠區(qū)域和與最近鄰居的距離處的直徑。在本研究中的所有方法中，沒(méi)有單一遙感數(shù)據(jù)集明顯優(yōu)越，但在數(shù)據(jù)采集和分析方面都證明了獨(dú)特的性質(zhì)。在決定最佳窗口尺寸參數(shù)時(shí)，研究區(qū)域和數(shù)據(jù)源的兩個(gè)特征的知識(shí)和測(cè)試都很重要。森林環(huán)境的異質(zhì)性可能是阻礙描繪性能的主要因素，進(jìn)一步影響繪圖級(jí)別差異和樹級(jí)可檢測(cè)性。

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  • 來(lái)源
    《International journal of remote sensing》 |2020年第14期|5228-5256|共29頁(yè)
  • 作者

    Kwong Ivan H. Y.; Fung Tung;

  • 作者單位

    Chinese Univ Hong Kong Dept Geog & Resource Management Shatin Hong Kong Peoples R China;

    Chinese Univ Hong Kong Dept Geog & Resource Management Shatin Hong Kong Peoples R China|Chinese Univ Hong Kong Inst Future Cities Shatin Hong Kong Peoples R China;

  • 收錄信息 美國(guó)《科學(xué)引文索引》(SCI);美國(guó)《工程索引》(EI);
  • 原文格式 PDF
  • 正文語(yǔ)種 eng
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