Performance of Four Optical Methods in Estimating Leaf Area Index at Elementary Sampling Unit of Larix principis-rupprechtii Forests
Performance of Four Optical Methods in Estimating Leaf Area Index at Elementary Sampling Unit of Larix principis-rupprechtii Forests
复制标题
四种光学方法估算华北落叶松林基本样单位叶面积指数的性能
作者：
Zou Jie;Zuo Yong;Zhong Peihong;Hou Wei;Leng Peng;Chen Bin
光学方法经常被用作获取森林基本采样单位（ESU）叶面积指数（LAI）的常规方法。然而，很少有研究尝试评估通过光学方法获得的 ESU LAI 是否符合 LAI 地图产品验证社区所需的精度。本研究采用数字半球摄影（DHP）、数字覆盖摄影（DCP）、冠层和建筑辐射追踪（TRAC）和多光谱冠层成像仪（MCI）四种常用的光学方法，估算了5个具有对比结构特征的华北落叶松林的ESU（25 m × 25 m）LAI。分析了反演模型、冠层单元或木质成分丛集指数（Ω e 或 Ω w ）算法、木质成分校正方法三个因素对四种光学方法 ESU LAI 估计的影响。然后，使用从垃圾收集测量中获得的 LAI 来评估从四种光学方法得出的 LAI。结果表明，四种光学方法估算五种森林ESU LAI的性能很大程度上受这三个因素的影响。 DHP 和 MCI 获得的 LAI 的准确性强烈依赖于反演模型、Ω e 或 Ω w 算法以及估算中采用的木质成分校正方法。然后确定了最佳的Ω e 或Ω w 算法、反演模型和木本成分校正方法，以获得华北落叶松林ESU LAI 的均方根误差(RMSE) 和平均绝对误差(MAE) 最小。在本研究评估的三种典型木质成分校正方法中，通过破坏性测量获得的木质与总面积比是DHP推导具有最小RMSE和MAE的ESU LAI的最有效方法。相比之下，使用从落叶DHP或DCP图像获得的木质面积指数作为木质成分校正方法会导致LAI大幅低估。在五片森林的 ESU LAI 估计中，TRAC 和 MCI 的表现优于 DHP 和 DCP，其 RMSE 和 MAE 最小。除DCP外，所有光学方法均能够获得华北落叶松林的ESU LAI，其MAE<20%，符合全球气候观测系统的要求。除 TRAC 外，所有光学方法都没有显示出获得华北落叶松森林 ESU LAI 的潜力，其 MAE 小于 5%。
Optical methods are frequently used as a routine method to obtain the elementary sampling unit (ESU) leaf area index (LAI) of forests. However, few studies have attempted to evaluate whether the ESU LAI obtained from optical methods matches the accuracy required by the LAI map product validation community. In this study, four commonly used optical methods, including digital hemispherical photography (DHP), digital cover photography (DCP), tracing radiation of canopy and architecture (TRAC) and multispectral canopy imager (MCI), were adopted to estimate the ESU (25 m × 25 m) LAI of five Larix principis-rupprechtii forests with contrasting structural characteristics. The impacts of three factors, namely, inversion model, canopy element or woody components clumping index ( Ω e or Ω w ) algorithm, and the woody components correction method, on the ESU LAI estimation of the four optical methods were analyzed. Then, the LAI derived from the four optical methods was evaluated using the LAI obtained from litter collection measurements. Results show that the performance of the four optical methods in estimating the ESU LAI of the five forests was largely affected by the three factors. The accuracy of the LAI obtained from the DHP and MCI strongly relied on the inversion model, the Ω e or Ω w algorithm, and the woody components correction method adopted in the estimation. Then the best Ω e or Ω w algorithm, inversion model and woody components correction method to be used to obtain the ESU LAI of L. principis-rupprechtii forests with the smallest root mean square error (RMSE) and mean absolute error (MAE) were identified. Amongst the three typical woody components correction methods evaluated in this study, the woody-to-total area ratio obtained from the destructive measurements is the most effective method for DHP to derive the ESU LAI with the smallest RMSE and MAE. In contrast, using the woody area index obtained from the leaf-off DHP or DCP images as the woody components correction method would result in a large LAI underestimation. TRAC and MCI outperformed DHP and DCP in the ESU LAI estimation of the five forests, with the smallest RMSE and MAE. All the optical methods, except DCP, are qualified to obtain the ESU LAI of L. principis-rupprechtii forests with an MAE of <20% that is required by the global climate observation system. None of the optical methods, except TRAC, show the potential to obtain the ESU LAI of L. principis-rupprechtii forests with an MAE of <5%.
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影响因子：
6.2
作者：
Motomu Toda;A. Richardson
通讯作者：
Motomu Toda;A. Richardson
影响因子：
4.9
作者：
Tao Yan;X. Lü;Jiaojun Zhu;Kai Yang;Li-zhong Yu;Tian Gao
通讯作者：
Tao Yan;X. Lü;Jiaojun Zhu;Kai Yang;Li-zhong Yu;Tian Gao
影响因子：
8.2
作者：
Wenze Yang;D. Huang;B. Tan;J. Stroeve;N. Shabanov;Y. Knyazikhin;R. Nemani;R. Myneni
通讯作者：
Wenze Yang;D. Huang;B. Tan;J. Stroeve;N. Shabanov;Y. Knyazikhin;R. Nemani;R. Myneni
影响因子：
3.7
作者：
Cutini, A;Matteucci, G;Mugnozza, GS
通讯作者：
Mugnozza, GS
影响因子：
4.4
作者：
Chen, JM;Rich, PM;Plummer, S
通讯作者：
Plummer, S