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首页> 外文期刊>Journal of Forest Planning >Stemwood Volume Estimates for a Mixed Temperate Forest using Satellite LiDAR
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Stemwood Volume Estimates for a Mixed Temperate Forest using Satellite LiDAR

机译：利用卫星LiDAR估算温带混交林的材材体积

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Data from the Geoscience Laser Altimeter System (GLAS) aboard the Ice Cloud and Land Elevation Satellite (ICESat) were used to explore the potential of satellite LiDAR for the estimation of forest parameters such as vegetation height and stemwood volume. This was carried out for the Forest of Dean, Gloucestershire, UK, a semi-ancient, highly mixed, temperate forest. Previous research suggests use of Waveform Extent (the difference of alternate model fit Signal Begin and Signal End) and a Terrain Index (maximum minus minimum elevations from a 7 x 7 matrix, 10m resolution DTM) to provide the most robust estimate of maximum canopy height. These waveform-based maximum vegetation height estimations were used to investigate the potential of satellite LiDAR for the estimation of stemwood volume for the tallest species within each footprint. Relationships were established with predictions of stemwood volume calculated from Forestry Commission yield models. These equations succeeded in explaining 68% of variance with 88.7m~3/ha RMSE for coniferous species and R2 of 0.65 with 68.2m~3/ha RMSE for broadleaf species. The ability of satellite LiDAR waveforms to account for stemwood volume within mixed composition stands was also investigated. Area under the waveform canopy return, maximum canopy height, dominant canopy height and height of cumulative energy percentiles were considered. The height of the 90th percentile of cumulative energy was found to best represent the weighted stemwood volume of heterogeneous stands producing R~2 of 0.57, 92.3m~3/ha RMSE and R~2 of 0.59, 67.5m~3/ha RMSE for stands dominated by coniferous and broadleaf species respectively. The results of this local study indicate the potential for similar methods to be applied to regionalor national scales.
机译：冰云和陆地高程卫星（ICESat）上的地球科学激光测高系统（GLAS）的数据被用于探索LiDAR卫星在估计森林参数（如植被高度和树干木材量）方面的潜力。这是针对英国格洛斯特郡的迪恩森林（Dean Forest）进行的，这是一个半古老，高度混合的温带森林。先前的研究建议使用波形范围（替代模型拟合信号开始和信号结束的差）和地形指数（最大负最小仰角，从7 x 7矩阵，分辨率为10m的DTM）提供最大树冠高度的最可靠估计。这些基于波形的最大植被高度估计被用于调查卫星LiDAR估计每个足迹内最高树种的树干木材量的潜力。建立了与从林业委员会产量模型计算得出的树干木材量预测之间的关系。这些方程式成功地解释了针叶树种的方差为68％，其中RMSE为88.7m〜3 / ha，阔叶树种的R2为0.65，RMSE为68.2m〜3 / ha。还研究了卫星LiDAR波形用于解释混合成分林中材材体积的能力。考虑了波形冠层下的面积，最大冠层高度，优势冠层高度和累积能量百分位数的高度。发现累积能量的第90个百分数的高度最能代表异种林的加权茎木体积，其中R〜2为0.57，92.3m〜3 / ha RMSE，R〜2为0.59，67.5m〜3 / ha RMSE。分别以针叶和阔叶树种为主的林分。这项本地研究的结果表明，类似方法可能会应用于区域或国家规模。

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