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Biomass estimates derived from sector subsampling of 360° spherical images
Forestry ( IF 2.8 ) Pub Date : 2021-05-10 , DOI: 10.1093/forestry/cpab023
Xiao Dai 1 , Mark J Ducey 2 , Haozhou Wang 1 , Ting-Ru Yang 1 , Yung-Han Hsu 1 , Jae Ogilvie 1 , John A Kershaw 1
Affiliation  

Efficient subsampling designs reduce forest inventory costs by focusing sampling efforts on more variable forest attributes. Sector subsampling is an efficient and accurate alternative to big basal area factor (big BAF) sampling to estimate the mean basal area to biomass ratio. In this study, we apply sector subsampling of spherical images to estimate aboveground biomass and compare our image-based estimates with field data collected from three early spacing trials on western Newfoundland Island in eastern Canada. The results show that sector subsampling of spherical images produced increased sampling errors of 0.3–3.4 per cent with only about 60 trees measured across 30 spherical images compared with about 4000 trees measured in the field. Photo-derived basal area was underestimated because of occluded trees; however, we implemented an additional level of subsampling, collecting field-based basal area counts, to correct for bias due to occluded trees. We applied Bruce’s formula for standard error estimation to our three-level hierarchical subsampling scheme and showed that Bruce’s formula is generalizable to any dimension of hierarchical subsampling. Spherical images are easily and quickly captured in the field using a consumer-grade 360° camera and sector subsampling, including all individual tree measurements, were obtained using a custom-developed python software package. The system is an efficient and accurate photo-based alternative to field-based big BAF subsampling.

中文翻译:

从 360° 球形图像的扇区二次采样得出的生物量估计

有效的二次抽样设计通过将抽样工作集中在更多可变的森林属性上来降低森林库存成本。扇区二次抽样是一种有效且准确的替代大基底面积因子(大 BAF)抽样的方法,用于估计平均基底面积与生物量的比率。在这项研究中,我们应用球形图像的扇区二次抽样来估计地上生物量,并将我们基于图像的估计与从加拿大东部纽芬兰岛西部的三个早期间隔试验收集的现场数据进行比较。结果表明,球面图像的扇区二次采样产生的采样误差增加了 0.3% 至 3.4%,在 30 幅球面图像中仅测量了约 60 棵树,而在野外测量的约 4000 棵树。由于树木被遮挡,照片衍生的基底面积被低估了;然而,我们实施了额外级别的子采样,收集基于字段的基础面积计数,以纠正由于树木遮挡造成的偏差。我们将用于标准误差估计的布鲁斯公式应用于我们的三级分层子采样方案,并表明布鲁斯公式可推广到分层子采样的任何维度。使用消费级 360° 相机在现场轻松快速地捕获球形图像,并使用定制开发的 python 软件包获得包括所有单个树木测量在内的扇区二次采样。该系统是基于现场的大 BAF 二次采样的有效且准确的基于照片的替代方案。我们将用于标准误差估计的布鲁斯公式应用于我们的三级分层子采样方案,并表明布鲁斯公式可推广到分层子采样的任何维度。使用消费级 360° 相机可以轻松快速地在现场捕获球形图像,并使用定制开发的 python 软件包获得包括所有单个树木测量在内的扇区二次采样。该系统是基于现场的大 BAF 二次采样的有效且准确的基于照片的替代方案。我们将用于标准误差估计的布鲁斯公式应用于我们的三级分层子采样方案,并表明布鲁斯公式可推广到分层子采样的任何维度。使用消费级 360° 相机可以轻松快速地在现场捕获球形图像,并使用定制开发的 python 软件包获得包括所有单个树木测量在内的扇区二次采样。该系统是基于现场的大 BAF 二次采样的有效且准确的基于照片的替代方案。是使用定制开发的python软件包获得的。该系统是基于现场的大 BAF 二次采样的有效且准确的基于照片的替代方案。是使用定制开发的python软件包获得的。该系统是基于现场的大 BAF 二次采样的有效且准确的基于照片的替代方案。
更新日期:2021-05-10
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