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林业科学 ›› 2015, Vol. 51 ›› Issue (6): 81-92.

• 论文与研究报告 • 上一篇    下一篇

基于机载小光斑全波形LiDAR的亚热带林分特征反演

曹林, 佘光辉   

  1. 南京林业大学南方现代林业协同创新中心 南京 210037
  • 收稿日期:2014-08-04 修回日期:2014-10-18 出版日期:2015-06-25 发布日期:2015-07-10
  • 基金资助:
    江苏省高校自然科学研究项目(14KJB220002); 国家自然科学基金青年科学基金项目(31400492); 江苏省高校优势学科建设工程资助项目(PAPD)。

Inversion of Forest Stand Characteristics Using Small-Footprint Full-Waveform Airborne LiDAR in a Subtropical Forest

Cao Lin, She Guanghui   

  1. Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University Nanjing 210037
  • Received:2014-08-04 Revised:2014-10-18 Online:2015-06-25 Published:2015-07-10
  • Contact: 佘光辉

摘要: [目的] 研究通过集成波形信号处理、空间解析和重构建模以及综合波形信息提取方法,探索基于小光斑全波形LIDAR特征变量高精度反演林分特征的新方法。[方法] 以江苏南部丘陵地区的亚热带天然次生林为研究对象,在预处理和分析小光斑全波形LIDAR数据的基础上,首先基于体元空间框架分解和提取波形的振幅能量信息,并构建伪垂直波形模型; 然后,从中提取空间位置信息(即点云)及几何辐射变量,计算LiDAR点云和波形特征变量,并通过相关性分析筛选特征变量; 最后,结合地面实测林分特征参数构建反演模型并验证精度。[结果] 1) 各LiDAR特征变量对Lorey's树高的敏感性最高,对蓄积量和地上生物量次之,对胸高断面积最低,而返回脉冲总能量和返回脉冲峰值点数对胸高断面积的敏感性却高于其他林分特征因子; 在点云特征变量组中,平均高、高度百分位数及冠层上部的返回点云密度与各林分特征之间的相关性较高,而在波形特征变量组中,能量中值高度的均值、返回脉冲长度的标准差和冠层粗糙度的标准差与各林分特征之间的相关性较高; 2) Lorey's树高的模型估算精度最高(RMSE为实测均值的7.26%),而蓄积量、地上生物量和胸高断面积的模型估算精度略低且较相近(RMSE为实测均值的15.91%~19.82%); 模型自变量的数量都在3个以内,选中的自变量为高度百分位数、冠层返回点云密度、返回脉冲长度和冠层粗糙度的标准差; 3) 各林分特征实测值与交叉验证估算值的拟合结果表明,Lorey's树高的拟合效果最好(R2=0.85),地上生物量(R2=0.68)和蓄积量(R2=0.59)次之,而胸高断面积(R2=0.45)最低; 4) Lorey's 树高、蓄积量和地上生物量的空间分布状况基本一致,源于它们内在的相关性; 相比其他3个特征变量,胸高断面积的空间分布不够连续,这可能是由于其预测模型精度较低所致。[结论] 各林分特征综合回归模型的拟合效果和精度都高于仅使用点云特征变量拟合模型的精度,表明了波形特征变量提取森林中下层信息的潜力。点云特征变量描述了森林冠层及上部的三维结构及密度信息,而波形特征变量则获得了森林冠层及以下部分完整的垂直分布和能量信息,二者互补可提升林分特征反演的精度。

关键词: 小光斑全波形LiDAR, 林分特征, 反演, 点云特征变量, 波形特征变量, 体元

Abstract: [Objective] By integration the methods of waveform signal processing, spatial information analyzing and re-construction, and the comprehensive information extraction, a novel approach for the inversion of forest stand characteristics, based on the metrics extracted from the small-footprint full-waveform LiDAR data, was explored in this research.[Method] The subtropical secondary forests in the hilly area of southern Jiangsu were set as a research objective, and based on the pre-processing and analysis of small-footprint full-waveform LiDAR data, the voxel spatial framework was firstly decomposed and the waveform amplitude information was extracted to form a pseudo-vertical waveform model; then, the spatial location and geometric variables were extracted to calculated the point-cloud and waveform based metrics, following by an analysis of correlations for selecting metrics; finally, the inversion models were built by coupling the field-measured forest stand characteristics and validated for accuracy. [Result] 1) For all the LiDAR metrics, Lorey's tree height has the highest sensitivity, following by the volume and above-ground biomass. The basal area has the lowest sensitivity. Whereas, the total return amplitude and the number of peaks have a higher sensitivity of basal area than other metrics. In the group of point-cloud metrics, mean height, height percentiles and upper canopy return point cloud density have a relatively higher correlation with each forest stand characteristic than other metrics; whereas in the group of waveform metrics, the average of medium energy, the standard deviation of return waveform width and the canopy roughness have a high relationship with the forest stand characteristics. 2) Lorey's tree height has the highest estimation accuracy (RMSE=7.26% of the mean of field-measured value), whereas volume, above-ground biomass and basal area have a relatively similar low accuracy (RMSE=15.91%-19.82% of the mean of field-measured value). The number of independent variables was less than 3, and the selected metrics were height percentiles, canopy return point-cloud density, return waveform width and the canopy roughness. 3) The fitted results of the field-measured forest stand characteristics and the cross-validated estimated values showed that Lorey's tree height fitted best (R2=0.85), followed by above-ground biomass (R2=0.68) and volume (R2=0.59), whereas basal area has the lowest R2=0.45. 4) Lorey's tree height, volume and above-ground biomass have a similar spatial distribution, which may be attributed to their inner correlations. Compared with other 3 metrics, the spatial distribution of basal area appeared discontinuous, which may be resulted from the low prediction accuracy.[Conclusion] The results and accuracies of the fitted combo regression model of each forest stand characteristic were better and higher than the point-cloud metrics based models, demonstrated that waveform metrics has a potential to extract the information of the medium and lower level of forest stands. The point-cloud metrics described the forest canopy and the upper 3-D structure and density information, whereas the waveform metrics acquired the canopy and the complete vertical distribution and energy information of the lower part forest. The combination of them enhances the inversion accuracy of the forest stand characteristics.

Key words: small-footprint full-waveform LiDAR, forest stand characteristics, inversion, point-cloud based metrics, waveform based metrics, voxel

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