Accurate Measurement of Tropical Forest Canopy Heights and Aboveground Carbon Using Structure From Motion
Accurate Measurement of Tropical Forest Canopy Heights and Aboveground Carbon Using Structure From Motion
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DOI：
10.3390/rs11080928
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发表时间：
2019-04
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通讯作者：
T. Swinfield;J. Lindsell;Jonathan Williams;R. Harrison;Agustiono;Habibi;E. Gemita;C. Schönlieb;D. Coomes
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文献类型：
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作者：
T. Swinfield;J. Lindsell;Jonathan Williams;R. Harrison;Agustiono;Habibi;E. Gemita;C. Schönlieb;D. Coomes
无人驾驶飞行器越来越多地用于监测森林。热带雨林冠层的三维模型可以使用运动恢复结构（SfM）从重叠的照片中构建，但通常不可能直接从这些数据中绘制地面高程，因为冠层间隙在雨林中很少见。因此，如果不了解地形海拔，就很难准确测量树冠高度或森林特性，包括恢复阶段和地上碳密度。在印度尼西亚的生态系统恢复景观中工作，我们评估了SfM与机载激光扫描（也称为LiDAR）基准相比，如何获得树冠高度和地上碳密度的估计。SfM系统地低估了树冠高度，平均偏差约为5米。线性模型表明，偏差增加二次冠层高度短，均匀的年龄，林分，但高，结构复杂的冠层（>10米）的线性。基于简单线性模型的预测密切相关的实地测量的高度时，该方法被应用到一个独立的调查在不同的位置（R 2 = 67%和RMSE = 1.85米），但仍然存在0.89米的负偏差，这表明需要完善模型参数与额外的训练数据。模型，包括冠层复杂性的指标偏差较小，但减少了R2.列入地面控制点（GCPs）被认为是重要的，在准确登记空间的SfM测量，这是必不可少的，如果调查要求是产生小规模的恢复干预措施或跟踪随着时间的变化。然而，在几公顷的尺度上，即使没有GCP，SfM和LiDAR的冠层高度和地上碳密度估计值也非常相似。从SfM产生准确的树冠高度和碳储量测量的能力改变了森林管理者和恢复从业者的游戏规则，提供了在不需要LiDAR的情况下对数百公顷进行快速，低成本调查的方法。
Unmanned aerial vehicles are increasingly used to monitor forests. Three-dimensional models of tropical rainforest canopies can be constructed from overlapping photos using Structure from Motion (SfM), but it is often impossible to map the ground elevation directly from such data because canopy gaps are rare in rainforests. Without knowledge of the terrain elevation, it is, thus, difficult to accurately measure the canopy height or forest properties, including the recovery stage and aboveground carbon density. Working in an Indonesian ecosystem restoration landscape, we assessed how well SfM derived the estimates of the canopy height and aboveground carbon density compared with those from an airborne laser scanning (also known as LiDAR) benchmark. SfM systematically underestimated the canopy height with a mean bias of approximately 5 m. The linear models suggested that the bias increased quadratically with the top-of-canopy height for short, even-aged, stands but linearly for tall, structurally complex canopies (>10 m). The predictions based on the simple linear model were closely correlated to the field-measured heights when the approach was applied to an independent survey in a different location ( R 2 = 67% and RMSE = 1.85 m), but a negative bias of 0.89 m remained, suggesting the need to refine the model parameters with additional training data. Models that included the metrics of canopy complexity were less biased but with a reduced R 2 . The inclusion of ground control points (GCPs) was found to be important in accurately registering SfM measurements in space, which is essential if the survey requirement is to produce small-scale restoration interventions or to track changes through time. However, at the scale of several hectares, the top-of-canopy height and above-ground carbon density estimates from SfM and LiDAR were very similar even without GCPs. The ability to produce accurate top-of-canopy height and carbon stock measurements from SfM is game changing for forest managers and restoration practitioners, providing the means to make rapid, low-cost surveys over hundreds of hectares without the need for LiDAR.