Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective

Timely and accurate information on tree species (TS) is crucial for developing strategies for sustainable management and conservation of artificial and natural forests. Over the last four decades, advances in remote sensing technologies have made TS classification possible. Since many studies on the...

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Autor principal:	Ruiliang Pu
Formato:	article
Lenguaje:	EN
Publicado:	American Association for the Advancement of Science (AAAS) 2021
Materias:	Environmental sciences GE1-350 Physical geography GB3-5030
Acceso en línea:	https://doaj.org/article/16c174d2a3ad4302bb114cb995a505c1
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spelling	oai:doaj.org-article:16c174d2a3ad4302bb114cb995a505c12021-11-15T08:22:28ZMapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective2694-158910.34133/2021/9812624https://doaj.org/article/16c174d2a3ad4302bb114cb995a505c12021-01-01T00:00:00Zhttp://dx.doi.org/10.34133/2021/9812624https://doaj.org/toc/2694-1589Timely and accurate information on tree species (TS) is crucial for developing strategies for sustainable management and conservation of artificial and natural forests. Over the last four decades, advances in remote sensing technologies have made TS classification possible. Since many studies on the topic have been conducted and their comprehensive results and novel findings have been published in the literature, it is necessary to conduct an updated review on the status, trends, potentials, and challenges and to recommend future directions. The review will provide an overview on various optical and light detection and ranging (LiDAR) sensors; present and assess current various techniques/methods for, and a general trend of method development in, TS classification; and identify limitations and recommend future directions. In this review, several concluding remarks were made. They include the following: (1) A large group of studies on the topic were using high-resolution satellite, airborne multi-/hyperspectral imagery, and airborne LiDAR data. (2) A trend of “multiple” method development for the topic was observed. (3) Machine learning methods including deep learning models were demonstrated to be significant in improving TS classification accuracy. (4) Recently, unmanned aerial vehicle- (UAV-) based sensors have caught the interest of researchers and practitioners for the topic-related research and applications. In addition, three future directions were recommended, including refining the three categories of “multiple” methods, developing novel data fusion algorithms or processing chains, and exploring new spectral unmixing algorithms to automatically extract and map TS spectral information from satellite hyperspectral data.Ruiliang PuAmerican Association for the Advancement of Science (AAAS)articleEnvironmental sciencesGE1-350Physical geographyGB3-5030ENJournal of Remote Sensing, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Environmental sciences GE1-350 Physical geography GB3-5030
spellingShingle	Environmental sciences GE1-350 Physical geography GB3-5030 Ruiliang Pu Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective
description	Timely and accurate information on tree species (TS) is crucial for developing strategies for sustainable management and conservation of artificial and natural forests. Over the last four decades, advances in remote sensing technologies have made TS classification possible. Since many studies on the topic have been conducted and their comprehensive results and novel findings have been published in the literature, it is necessary to conduct an updated review on the status, trends, potentials, and challenges and to recommend future directions. The review will provide an overview on various optical and light detection and ranging (LiDAR) sensors; present and assess current various techniques/methods for, and a general trend of method development in, TS classification; and identify limitations and recommend future directions. In this review, several concluding remarks were made. They include the following: (1) A large group of studies on the topic were using high-resolution satellite, airborne multi-/hyperspectral imagery, and airborne LiDAR data. (2) A trend of “multiple” method development for the topic was observed. (3) Machine learning methods including deep learning models were demonstrated to be significant in improving TS classification accuracy. (4) Recently, unmanned aerial vehicle- (UAV-) based sensors have caught the interest of researchers and practitioners for the topic-related research and applications. In addition, three future directions were recommended, including refining the three categories of “multiple” methods, developing novel data fusion algorithms or processing chains, and exploring new spectral unmixing algorithms to automatically extract and map TS spectral information from satellite hyperspectral data.
format	article
author	Ruiliang Pu
author_facet	Ruiliang Pu
author_sort	Ruiliang Pu
title	Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective
title_short	Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective
title_full	Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective
title_fullStr	Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective
title_full_unstemmed	Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective
title_sort	mapping tree species using advanced remote sensing technologies: a state-of-the-art review and perspective
publisher	American Association for the Advancement of Science (AAAS)
publishDate	2021
url	https://doaj.org/article/16c174d2a3ad4302bb114cb995a505c1
work_keys_str_mv	AT ruiliangpu mappingtreespeciesusingadvancedremotesensingtechnologiesastateoftheartreviewandperspective
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Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective

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