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首页> 外文期刊>iForest: Biogeosciences and Forestry >Comparing image-based point clouds and airborne laser scanning data for estimating forest heights
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Comparing image-based point clouds and airborne laser scanning data for estimating forest heights

机译：比较基于图像的点云和机载激光扫描数据以估计森林高度

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Abstract: Accurate and updated knowledge of forest tree heights is fundamental in the context of forest management. However, measuring canopy height over large forest areas using traditional inventory techniques is laborious, time-consuming and excessively expensive. In this study, image-based point clouds produced from stereo aerial photographs (AP) were used to estimate forest height, and compared to Airborne Laser Scanning (ALS) data. We generated image-based Canopy Height Models (CHM) using different image-matching algorithms (SGM: Semi-Global Matching; eATE: enhanced Automatic Terrain Extraction), which were compared with a pure ALS-derived CHM. Additionally, plot-level height and density metrics were extracted from CHMs and used as explanatory variables for predicting the Lorey’s mean height (LMH), which was measured at 296 reference points on the ground. CHMSGM and CHMALS showed similar results in predicting LMH at sample plot locations (RMSE% = 8.54 vs. 7.92, respectively), while CHMeATE had lower accuracy (RMSE% = 13.23). Similarly, CHMSGM showed a lower normalized median absolute deviation (NMAD) from CHMALS (0.68 m) compared to CHMeATE (1.1 m). Our study revealed that image-based point clouds using SGM in the presence of high-resolution ALS-derived digital terrain model (DTM) provide comparable results with ALS data, while the performance of image-based point clouds using eATE is poorer than ALS for forest height estimation. The findings of this study provide a viable and cost-effective option for assessing height-related forest structural parameters. The proposed methodology can be usefully applied in all those countries where AP are updated on a regular basis and pre-existing historical ALS-derived DTMs are available.
机译：摘要：在森林管理的背景下，准确和更新的林木高度知识至关重要。但是，使用传统的清单技术来测量大片森林面积的树冠高度是费力，费时且过高的。在这项研究中，从立体航拍照片（AP）生成的基于图像的点云用于估计森林高度，并与机载激光扫描（ALS）数据进行比较。我们使用不同的图像匹配算法（SGM：半全局匹配; eATE：增强的自动地形提取）生成了基于图像的树冠高度模型（CHM），并将其与纯ALS派生的CHM进行了比较。此外，从CHM中提取地块高度和密度度量，并将其用作解释Lorey平均高度（LMH）的解释变量，该平均高度是在地面上的296个参考点处测量的。 CHMSGM和CHMALS在预测样地处的LMH方面显示出相似的结果（分别为RMSE％= 8.54和7.92），而CHMeATE的准确性较低（RMSE％= 13.23）。同样，与CHMeATE（1.1 m）相比，CHMSGM显示出相对于CHMALS（0.68 m）更低的归一化中值绝对偏差（NMAD）。我们的研究表明，在存在高分辨率ALS衍生的数字地形模型（DTM）的情况下，使用SGM的基于图像的点云可以提供与ALS数据相当的结果，而使用eATE的基于图像的点云的性能却不如ALS森林高度估计。这项研究的结果为评估与高度相关的森林结构参数提供了可行且具有成本效益的选择。提议的方法可以有效地应用在那些定期更新AP并且可以使用已有ALS历史DTM的国家。

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