The Role of RPAS in Vegetation Height Estimation: Challenges and Future Perspectives in the Forestry Context

IF 9 1区 农林科学 Q1 FORESTRY
Felipe Gomes Moreira, Ivana Pires de Sousa-Baracho, Maria Luiza de Azevedo, Sally Deborah Pereira da Silva, Fernando Coelho Eugenio
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引用次数: 0

Abstract

Purpose of Review

This study aims to systematically examine the application of Remotely Piloted Aircraft Systems (RPAS) for estimating vegetation height in natural and planted forests, aiming to understand the critical challenges encountered by identifying the methods and technologies employed.

Recent Findings

Since 2018, the use of RPAS for vegetation height estimation has grown substantially, spanning diverse applications ranging from direct height measurements to biomass modelling. Researchers widely favour multirotor platforms because of their versatility and affordability. Moreover, LiDAR technology stands out for its high accuracy in estimating vegetation height. Despite their potential, accurate segmentation of individual trees within dense canopies remains a significant challenge, necessitating further research into advanced algorithms and sensor integration. The article further emphasises analytical methodologies– such as segmentation, classification, and machine learning techniques — that enhance tree delineation, species identification, and overall forest structure analysis.

Summary

The increasing demand for efficient and cost-effective forest monitoring methods has driven the adoption of RPAS. This systematic review analyses 133 publications (2013–2024) concerning the use of RPAS in estimating vegetation height in natural and planted forests. The findings highlight the prevalence of multirotor platforms, which are valued for their affordability and versatility, and the extensive application of LiDAR sensors, which are renowned for their precision. A growing trend in the combined use of sensors enhances estimation accuracy and broadens potential applications. Despite these advancements, challenges such as segmentation within dense canopies and identifying individual trees persist. Integrating sensors with machine learning algorithms is a promising solution, potentially optimising forest inventories and sustainable management practices. This study also identifies research opportunities in underexplored areas, such as the measurement of seedlings at early growth stages, underscoring the strategic role of RPAS in contemporary forestry.

RPAS在植被高度估算中的作用:林业背景下的挑战和未来展望
本研究旨在系统研究远程驾驶飞机系统(RPAS)在天然林和人工林植被高度估算中的应用,旨在通过确定所采用的方法和技术来了解面临的关键挑战。自2018年以来,RPAS用于植被高度估计的应用大幅增长,涵盖了从直接高度测量到生物量建模的各种应用。研究人员普遍青睐多旋翼平台,因为它们的通用性和可负担性。此外,激光雷达技术在估算植被高度方面具有很高的精度。尽管具有潜力,但在茂密的树冠中准确分割单个树木仍然是一个重大挑战,需要进一步研究先进的算法和传感器集成。文章进一步强调了分析方法,如分割、分类和机器学习技术,这些方法可以增强树木描绘、物种识别和整体森林结构分析。对高效、经济的森林监测方法的需求日益增长,推动了RPAS的采用。本系统综述分析了133篇(2013-2024)关于利用RPAS估算天然林和人工林植被高度的论文。该研究结果突出了多旋翼平台的流行,其价格合理,多功能性很有价值,而激光雷达传感器的广泛应用以其精度而闻名。传感器组合使用的趋势日益增长,提高了估计精度,拓宽了潜在的应用。尽管取得了这些进步,但诸如密集树冠内的分割和识别单个树木等挑战仍然存在。将传感器与机器学习算法相结合是一个很有前途的解决方案,有可能优化森林资源清单和可持续管理实践。本研究还确定了在未充分开发的领域的研究机会,例如在早期生长阶段测量幼苗,强调了RPAS在当代林业中的战略作用。
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
Current Forestry Reports
Current Forestry Reports Agricultural and Biological Sciences-Ecology, Evolution, Behavior and Systematics
CiteScore
15.90
自引率
2.10%
发文量
22
期刊介绍: Current Forestry Reports features in-depth review articles written by global experts on significant advancements in forestry. Its goal is to provide clear, insightful, and balanced contributions that highlight and summarize important topics for forestry researchers and managers. To achieve this, the journal appoints international authorities as Section Editors in various key subject areas like physiological processes, tree genetics, forest management, remote sensing, and wood structure and function. These Section Editors select topics for which leading experts contribute comprehensive review articles that focus on new developments and recently published papers of great importance. Moreover, an international Editorial Board evaluates the yearly table of contents, suggests articles of special interest to their specific country or region, and ensures that the topics are up-to-date and include emerging research.
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