Estimating plot-level tree heights with lidar : local filtering with a canopy-height based variable window size
Estimating plot-level tree heights with lidar : local filtering with a canopy-height based variable window size
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DOI：
10.1016/s0168-1699(02)00121-7
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发表时间：
2002-12
影响因子：
8.3
通讯作者：
S. Popescu;R. Wynne;R. Nelson
中科院分区：
文献类型：
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作者：
S. Popescu;R. Wynne;R. Nelson
近年来，利用机载激光雷达技术测量森林生物物理特性的应用迅速增加。本文讨论了利用多次回波、高密度、小面积激光雷达数据集来获取地形模型和估计树高的处理算法。激光雷达数据是在美国东南部典型的不同年龄段和环境的落叶、针叶和混交林上获得的。具体目标是：(1)开发和测试利用激光雷达数据估计地块水平树高的算法，以及(2)调查地面测量如何有助于在激光雷达数据的处理阶段进行树高评估。研究区域位于美国弗吉尼亚州皮埃蒙特地貌省，包括阿波马托克斯-白金汉州立森林的一部分(北纬37°25‘，西经78°41’)。讨论了两种激光雷达处理算法，一种是基于局部滤波的可变窗口大小的单树冠识别算法，另一种是基于地面实况数据覆盖区域内所有激光脉冲高度的算法。用这两种算法处理激光雷达数据得到的高度估计值与美国农业部林业局森林调查和分析(FIA)现场数据布局后的样地设计获得的现场测量结果进行了比较。线性回归被用来建立方程，将激光雷达估计的参数与每个FIA地块的实地清单联系起来。正如预期的那样，每个地块上的最大高度预测准确率最高(第一种算法和第二种算法的R2值分别为85%和90%)。与基于所有激光高度的算法(R2值57-73%)相比，可变窗口大小算法对胸径大于12.7 cm(5英寸)的优势树和共优势树的高度(R2值84-85%)的预测效果更好。在处理激光雷达数据时，使用基于现场的高度阈值并没有在解释与树高相关的总变化方面带来显著的收益。可变窗口大小的局部滤波技术考虑了基本的森林生物识别关系，总体上证明比所有激光拍摄的技术提供了更好的结果。
In recent years, the use of airborne lidar technology to measure forest biophysical characteristics has been rapidly increasing. This paper discusses processing algorithms for deriving the terrain model and estimating tree heights by using a multiple return, high–density, small-footprint lidar data set. The lidar data were acquired over deciduous, coniferous, and mixed stands of varying age classes and settings typical of the southeastern US. The specific objectives were: (1) to develop and test algorithms to estimate plot level tree height using lidar data, and (2) to investigate how ground measurements can help in the processing phase of lidar data for tree height assessment. The study area is located in the Piedmont physiographic province of Virginia, USA and includes a portion of the Appomattox-Buckingham State Forest (37°25′N, 78°41′W). Two lidar processing algorithms are discussed—the first based on single tree crown identification using a variable window size for local filtering, and the second based on the height of all laser pulses within the area covered by the ground truth data. Height estimates resulted from processing lidar data with both algorithms were compared to field measurements obtained with a plot design following the USDA Forest Service Forest Inventory and Analysis (FIA) field data layout. Linear regression was used to develop equations relating lidar-estimated parameters with field inventories for each of the FIA plots. As expected, the maximum height on each plot was predicted with the highest accuracy (R2values of 85 and 90%, for the first and the second algorithm, respectively). The variable window size algorithm performed better for predicting heights of dominant and co-dominant trees (R2values 84–85%), with a diameter at breast height (dbh) larger than 12.7 cm (5 in), when compared with the algorithm based on all laser heights (R2values 57–73%). The use of field-based height thresholds when processing lidar data did not bring significant gains in explaining the total variation associated with tree height. The technique of local filtering with a variable window size considers fundamental forest biometrics relationships and overall proved to give better results than the technique of all laser shots.