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Impact of Base-to-Height Ratio on Canopy Height Estimation Accuracy of Hemiboreal Forest Tree Species by Using Satellite and Airborne Stereo Imagery
Remote Sensing ( IF 5 ) Pub Date : 2021-07-27 , DOI: 10.3390/rs13152941
Grigorijs Goldbergs

The present study assessed the large-format airborne (UltraCam) and satellite (GeoEye1 and Pleiades1B) image-based digital surface model (DSM) performance for canopy height estimation in predominantly mature, closed-canopy Latvian hemiboreal forestland. The research performed the direct comparison of calculated image-based DSM models with canopy peaks heights extracted from reference LiDAR data. The study confirmed the tendency for canopy height underestimation for all satellite-based models. The obtained accuracy of the canopy height estimation GeoEye1-based models varied as follows: for a pine (−1.49 median error, 1.52 m normalised median absolute deviation (NMAD)), spruce (−0.94 median, 1.97 m NMAD), birch (−0.26 median, 1.96 m NMAD), and black alder (−0.31 median, 1.52 m NMAD). The canopy detection rates (completeness) using GeoEye1 stereo imagery varied from 98% (pine) to >99% for spruce and deciduous tree species. This research has shown that determining the optimum base-to-height (B/H) ratio is critical for canopy height estimation efficiency and completeness using image-based DSMs. This study found that stereo imagery with a B/H ratio range of 0.2–0.3 (or convergence angle range 10–15°) is optimal for image-based DSMs in closed-canopy hemiboreal forest areas.

中文翻译:

使用卫星和机载立体图像的基高比对半球森林树种冠层高度估计精度的影响

本研究评估了大型机载 (UltraCam) 和卫星 (GeoEye1 和 Pleiades1B) 基于图像的数字表面模型 (DSM) 性能,用于估计主要成熟的封闭冠层拉脱维亚半北方林地的冠层高度。该研究直接比较了基于图像的 DSM 模型与从参考 LiDAR 数据中提取的冠层峰高。该研究证实了所有基于卫星的模型都存在冠层高度低估的趋势。获得的冠层高度估计基于 GeoEye1 模型的准确度变化如下:松树(-1.49 中值误差,1.52 m 归一化中值绝对偏差 (NMAD))、云杉(-0.94 中值、1.97 m NMAD)、桦木(-0.26 中值,1.96 m NMAD)和黑桤木(-0.31 中值,1.52 m NMAD)。对于云杉和落叶树种,使用 GeoEye1 立体图像的冠层检测率(完整性)从 98%(松树)到 >99%不等。这项研究表明,确定最佳基高 (B/H) 比对于使用基于图像的 DSM 估计冠层高度的效率和完整性至关重要。该研究发现,B/H 比范围为 0.2-0.3(或会聚角范围为 10-15°)的立体图像最适合封闭树冠半北方森林区域中基于图像的 DSM 。
更新日期:2021-07-27
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