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Purpose of Review <p>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.</p> Recent Findings <p>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.</p> Summary <p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年4月22日

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  • Felipe Gomes Moreira,
  • Ivana Pires de Sousa-Baracho,
  • Maria Luiza de Azevedo,
  • Sally Deborah Pereira da Silva,
  • Fernando Coelho Eugenio

摘要

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.