Extraction of Mangrove Biophysical Parameters Using Airborne LiDAR
Extraction of Mangrove Biophysical Parameters Using Airborne LiDAR
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DOI：
10.3390/rs5041787
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发表时间：
2013-04
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影响因子：
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通讯作者：
Wasinee Wannasiri;M. Nagai;K. Honda;P. Santitamnont;P. Miphokasap
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文献类型：
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作者：
Wasinee Wannasiri;M. Nagai;K. Honda;P. Santitamnont;P. Miphokasap
利用机载光探测和测距（LiDAR）技术确定树木参数已经在许多森林类型中进行了研究，包括针叶林、针叶林和落叶林。然而，讨论激光雷达在单树水平上的红树林生物物理参数提取中的应用的科学文章很少。本研究的主要目的是探讨利用激光雷达数据在单树尺度上估计红树林生物物理参数的潜力。通过比较变窗滤波（VWF）和逆分水岭分割（IWS）方法在单株树检测和利用激光雷达衍生的冠层高度模型（CHM）提取树的位置、树冠直径和树高方面的性能，对两种方法进行了研究。结果表明，在树冠重叠率较低的红树林中，每种方法的效果都很好。与IWS方法相比，VWF方法从LiDAR数据中提取红树林参数的精度略高。这是因为VWF方法使用基于异速关系的自适应圆形滤波窗口大小。由于VWF方法的结果，单个树的位置测量显示平均距离误差值为1.10 m。单株检测的一致性kappa系数(K)值为0.78。冠径估计的决定系数（R2）为0.75，估计的均方根误差（RMSE）为1.65 m，相对误差（RE）为19.7%。激光雷达测量树高的R2值为0.80，RMSE值为1.42 m， RE值为19.2%。然而，激光雷达获取的红树林参数存在一定的局限性。结果表明，随着树冠重叠比例（COL）的增加，从lidar衍生的CHM中提取的红树林参数的精度降低，特别是树冠测量。在本研究中，使用VWF和IWS方法获得红树林激光雷达生物物理参数的精度低于针叶林、北方针叶林、松林和落叶林。为提高VWF方法的预测精度，提出了一种针对树木密度水平和树冠重叠率的自适应异速生长方程。
Tree parameter determinations using airborne Light Detection and Ranging (LiDAR) have been conducted in many forest types, including coniferous, boreal, and deciduous. However, there are only a few scientific articles discussing the application of LiDAR to mangrove biophysical parameter extraction at an individual tree level. The main objective of this study was to investigate the potential of using LiDAR data to estimate the biophysical parameters of mangrove trees at an individual tree scale. The Variable Window Filtering (VWF) and Inverse Watershed Segmentation (IWS) methods were investigated by comparing their performance in individual tree detection and in deriving tree position, crown diameter, and tree height using the LiDAR-derived Canopy Height Model (CHM). The results demonstrated that each method performed well in mangrove forests with a low percentage of crown overlap conditions. The VWF method yielded a slightly higher accuracy for mangrove parameter extractions from LiDAR data compared with the IWS method. This is because the VWF method uses an adaptive circular filtering window size based on an allometric relationship. As a result of the VWF method, the position measurements of individual tree indicated a mean distance error value of 1.10 m. The individual tree detection showed a kappa coefficient of agreement (K) value of 0.78. The estimation of crown diameter produced a coefficient of determination (R2) value of 0.75, a Root Mean Square Error of the Estimate (RMSE) value of 1.65 m, and a Relative Error (RE) value of 19.7%. Tree height determination from LiDAR yielded an R2 value of 0.80, an RMSE value of 1.42 m, and an RE value of 19.2%. However, there are some limitations in the mangrove parameters derived from LiDAR. The results indicated that an increase in the percentage of crown overlap (COL) results in an accuracy decrease of the mangrove parameters extracted from the LiDAR-derived CHM, particularly for crown measurements. In this study, the accuracy of LiDAR-derived biophysical parameters in mangrove forests using the VWF and IWS methods is lower than in coniferous, boreal, pine, and deciduous forests. An adaptive allometric equation that is specific for the level of tree density and percentage of crown overlap is a solution for improving the predictive accuracy of the VWF method.