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Assessing the utility of NAIP digital aerial photogrammetric point clouds for estimating canopy height of managed loblolly pine plantations in the southeastern United States
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-10 , DOI: 10.1016/j.jag.2022.103012
Alison L. Ritz, Valerie A. Thomas, Randolph H. Wynne, P. Corey Green, Todd A. Schroeder, Timothy J. Albaugh, Harold E. Burkhart, David R. Carter, Rachel L. Cook, Otávio C. Campoe, Rafael A. Rubilar, Jim Rakestraw

Remote sensing offers many advantages to supplement traditional, ground-based forest measurements, such as limiting time in the field and fast spatial coverage. Data from airborne laser scanning (lidar) have provided accurate estimates of forest height, where, and when available. However, lidar is expensive to collect, and wall-to-wall coverage in the United States is lacking. Recent studies have investigated whether point clouds derived from digital aerial photogrammetry (DAP) can supplement lidar data for estimating forest height due to DAP’s lower costs and more frequent acquisitions. We estimated forest heights using point clouds derived from the National Agricultural Imagery Program (NAIP) DAP program in the United States to create a predicted height map for managed loblolly pine stands. For 534 plots in Virginia and North Carolina, with stand age ranging from 1 year to 42 years old, field-collected canopy heights were regressed against the 90th percentile of heights derived from NAIP point clouds. Model performance was good, with an R2 of 0.93 and an RMSE of 1.44 m. However, heights in recent heavily thinned stands were consistently underestimated, likely due to between-row shadowing leading to a poor photogrammetric solution. The model was applied to non-thinned evergreen areas in Virginia, North Carolina, and Tennessee to produce a multi-state 5 m × 5 m canopy height map. NAIP-derived point clouds are a viable means of predicting canopy height in southern pine stands that have not been thinned recently.



中文翻译:

评估 NAIP 数字航空摄影测量点云在估计美国东南部管理的火炬松种植园冠层高度的效用

遥感提供了许多优势来补充传统的基于地面的森林测量,例如限制现场时间和快速的空间覆盖。机载激光扫描(激光雷达)的数据提供了对森林高度、地点和时间的准确估计。然而,激光雷达的收集成本很高,而且美国缺乏全覆盖。最近的研究调查了来自数字航空摄影测量 (DAP) 的点云是否可以补充激光雷达数据以估计森林高度,因为 DAP 成本较低且采集频率更高。我们使用源自美国国家农业影像计划 (NAIP) DAP 计划的点云估计森林高度,以创建管理火炬松林分的预测高度图。对于弗吉尼亚州和北卡罗来纳州的 534 个地块,林龄从 1 岁到 42 岁不等,田间采集的冠层高度与 NAIP 点云得出的高度的 90% 进行了回归。模型性能很好,有一个 R2为 0.93,RMSE 为 1.44 m。然而,最近严重稀疏的林分的高度一直被低估,可能是由于行间阴影导致摄影测量解决方案不佳。该模型应用于弗吉尼亚州、北卡罗来纳州和田纳西州的非稀疏常绿地区,以生成多州 5 m × 5 m 的树冠高度图。NAIP 派生的点云是预测最近没有变薄的南方松林冠层高度的可行方法。

更新日期:2022-09-10
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