Influence of plot and sample sizes on aboveground biomass estimations in plantation forests using very high resolution stereo satellite imagery

IF 3 2区农林科学 Q1 FORESTRY

Forestry Pub Date : 2020-07-24 DOI:10.1093/forestry/cpaa028

Z. Hosseini, Hooman Latifi, H. Naghavi, Siavash Bakhtiarvand Bakhtiari, F. Fassnacht

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引用次数: 6

Abstract

Regular biomass estimations for natural and plantation forests are important to support sustainable forestry and to calculate carbon-related statistics. The application of remote sensing data to estimate biomass of forests has been amply demonstrated but there is still space for increasing the efficiency of current approaches. Here, we investigated the influence of field plot and sample sizes on the accuracy of random forest models trained with information derived from Pleiades very high resolution (VHR) stereo images applied to plantation forests in an arid environment. We collected field data at 311 locations with three different plot area sizes (100, 300 and 500 m2). In two experiments, we demonstrate how plot and sample sizes influence the accuracy of biomass estimation models. In the first experiment, we compared model accuracies obtained with varying plot sizes but constant number of samples. In the second experiment, we fixed the total area to be sampled to account for the additional effort to collect large field plots. Our results for the first experiment show that model performance metrics Spearman’s r, RMSErel and RMSEnor improve from 0.61, 0.70 and 0.36 at a sample size of 24–0.79, 0.51 and 0.15 at a sample size of 192, respectively. In the second experiment, highest accuracies were obtained with a plot size of 100 m2 (most samples) with Spearman’s r =0.77, RMSErel =0.59 and RMSEnor =0.15. Results from an analysis of variance type-II suggest that the overall most important factors to explain model performance metrics for our biomass models is sample size. Our results suggest no clear advantage for any plot size to reach accurate biomass estimates using VHR stereo imagery in plantations. This is an important finding, which partly contradicts the suggestions of earlier studies but requires validation for other forest types and remote sensing data types (e.g. LiDAR).

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利用非常高分辨率立体卫星图像估算人工林地上生物量时样地和样本量的影响

定期估算天然林和人工林的生物量对于支持可持续林业和计算与碳有关的统计数据非常重要。遥感数据在估算森林生物量方面的应用已得到充分证明，但目前的方法仍有提高效率的空间。本文研究了野外样地和样本量对Pleiades甚高分辨率(VHR)立体图像信息训练的随机森林模型精度的影响，并将其应用于干旱环境下的人工林。我们在311个地点收集了三种不同地块面积(100,300和500 m2)的现场数据。在两个实验中，我们展示了图和样本量如何影响生物量估计模型的准确性。在第一个实验中，我们比较了在不同地块大小但样本数量不变的情况下获得的模型精度。在第二个实验中，我们固定了要采样的总面积，以考虑到收集大块田地的额外努力。我们的第一个实验结果表明，模型性能指标Spearman’s r、RMSErel和RMSEnor在24 - 192样本量下分别从0.61、0.70和0.36提高到0.79、0.51和0.15。在第二个实验中，当样块面积为100 m2(大多数样本)时，获得了最高的精度，Spearman 's r =0.77, RMSErel =0.59, RMSEnor =0.15。方差类型ii的分析结果表明，解释我们的生物量模型的模型性能的最重要的因素是样本大小。我们的研究结果表明，在人工林中使用VHR立体图像获得准确的生物量估算对于任何地块大小都没有明显的优势。这是一个重要的发现，它部分地与早期研究的建议相矛盾，但需要对其他森林类型和遥感数据类型(例如激光雷达)进行验证。

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

Forestry 农林科学-林学

CiteScore

6.70

自引率

7.10%

发文量

审稿时长

12-24 weeks

期刊介绍： The journal is inclusive of all subjects, geographical zones and study locations, including trees in urban environments, plantations and natural forests. We welcome papers that consider economic, environmental and social factors and, in particular, studies that take an integrated approach to sustainable management. In considering suitability for publication, attention is given to the originality of contributions and their likely impact on policy and practice, as well as their contribution to the development of knowledge. Special Issues - each year one edition of Forestry will be a Special Issue and will focus on one subject in detail; this will usually be by publication of the proceedings of an international meeting.