Seasonal optimisation of drone-based photogrammetry in a heterogeneous boreal landscape

IF 2 3区环境科学与生态学 Q3 ECOLOGY

Applied Vegetation Science Pub Date : 2024-07-23 DOI:10.1111/avsc.12797

Ian A. Brown, Mark Ghaly, Caroline Greiser, Norris Lam, Philipp Lehmann

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引用次数: 0

Abstract

Aims

Uncrewed aerial vehicles (UAV), or drones, have become more affordable and easier to use, resulting in increased UAV applications in ecology and conservation. However, solar illumination, vegetation phenology and prevailing weather conditions will impact the quality of the derived products to differing degrees. In this study, we investigate how seasonal differences in solar illumination, tree foliage and weather conditions impact the accuracy of digital elevation models (DEM) and canopy height models (CHM) in a heterogeneous boreal landscape.

Methods

We compared DEMs and CHMs derived from drone photogrammetry with DEMs and CHMs produced from a drone-mounted laser scanner across three seasons with different solar illumination, tree foliage and weather conditions during leaf-off and leaf-on seasons. Photogrammetric height models were evaluated across three land-cover classes consisting of open areas, sparse-forest and forest. The most accurate CHM for sparse-forest was produced during summer under overcast conditions, whereas for the forest class, summer under clear skies was best.

Results

Structure from motion (SfM) photogrammetry performed well against the LiDAR survey in most cases with correlations between sampled points of up to R² = 0.995. Root mean square errors (RMSEs) were <1.5 m in all DEMs and as low as 0.31 m in autumn clear-sky data over open terrain. CHM RMSEs were somewhat higher in all cases except under winter overcast conditions when the RMSE for sparse-forest reached 6.03 m.

Conclusions

We have shown that SfM photogrammetry is surprisingly robust to variations in vegetation type, tree phenology and weather, and performs well in comparison with a reference LiDAR data set. Our results show that, in boreal forests, autumn is the preferred season under clear-sky conditions for DEM generation from SfM photogrammetry across all land-cover classes, whereas summer is preferred for CHM modelling with a small trade-off between overcast and clear-sky conditions over different vegetation types. These results can help potential SfM users in ecology and forestry plan missions and review the quality of products derived from drone photogrammetry products.

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基于无人机的摄影测量在异质北方地貌中的季节性优化

无人驾驶飞行器（UAV）或无人机的价格越来越低廉，使用也越来越方便，因此无人驾驶飞行器在生态学和保护领域的应用越来越多。然而，太阳光照、植被物候和当时的天气条件会对衍生产品的质量产生不同程度的影响。在这项研究中，我们调查了太阳光照度、树木叶片和天气条件的季节性差异如何影响异质北方地貌中数字高程模型（DEM）和树冠高度模型（CHM）的准确性。我们比较了无人机摄影测量得出的 DEM 和 CHM，以及无人机安装的激光扫描仪在落叶期和落叶期三个季节不同的太阳光照度、树木叶片和天气条件下生成的 DEM 和 CHM。对空旷地区、稀疏森林和森林三个土地覆盖等级的摄影测量高度模型进行了评估。在大多数情况下，运动结构（SfM）摄影测量与激光雷达测量相比表现良好，采样点之间的相关性高达 R2 = 0.995。所有 DEM 的均方根误差 (RMSE) 均小于 1.5 米，开阔地形上的秋季晴空数据的均方根误差更低至 0.31 米。我们的研究表明，SfM 摄影测量对植被类型、树木物候和天气的变化具有惊人的稳健性，与参考 LiDAR 数据集相比表现良好。我们的研究结果表明，在北方森林中，在晴空条件下，秋季是利用 SfM 摄影测量法生成所有土地覆盖类别的 DEM 的首选季节，而在不同植被类型中，夏季则是 CHM 建模的首选季节，在阴天和晴空条件之间的权衡较小。这些结果有助于生态学和林业规划任务中潜在的 SfM 用户，并审查无人机摄影测量产品的质量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Vegetation Science 环境科学-林学

CiteScore

6.00

自引率

10.70%

发文量

审稿时长

3 months

期刊介绍： Applied Vegetation Science focuses on community-level topics relevant to human interaction with vegetation, including global change, nature conservation, nature management, restoration of plant communities and of natural habitats, and the planning of semi-natural and urban landscapes. Vegetation survey, modelling and remote-sensing applications are welcome. Papers on vegetation science which do not fit to this scope (do not have an applied aspect and are not vegetation survey) should be directed to our associate journal, the Journal of Vegetation Science. Both journals publish papers on the ecology of a single species only if it plays a key role in structuring plant communities.