Status, advancements and prospects of deep learning methods applied in forest studies

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dc.contributor.authorYun, Tingen
dc.contributor.authorLi, Jianen
dc.contributor.authorMa, Lingfeien
dc.contributor.authorZhou, Jien
dc.contributor.authorWang, Ruishengen
dc.contributor.authorEichhorn, Markus P.en
dc.contributor.authorZhang, Huaiqingen
dc.contributor.funderNational Natural Science Foundation of Chinaen
dc.contributor.funderNatural Science Foundation of Jiangsu Provinceen
dc.contributor.funderJiangsu Provincial Agricultural Science and Technology Independent Innovation Funden
dc.contributor.funderMinistry of Natural Resources of the People's Republic of Chinaen
dc.date.accessioned2025-01-28T10:53:01Z
dc.date.available2025-01-28T10:53:01Z
dc.date.issued2024-06-04en
dc.description.abstractDeep learning, which has exhibited considerable potential and effectiveness in forest resource assessment, is vital for comprehending and managing forest resources and ecosystems. However, extensive assessment of forest resources is highly challenging due to the complex and varied nature of forest types sourced from diverse remote sensing platforms, which include images, point clouds, and fusion data. To facilitate further study, we systematically review the current status, applications and prospects of deep learning technologies for different types of forest remote sensing data. After considering more than two hundred forest-related papers published over the past decade, we introduce sensors and devices for forest data acquisition, classify deep learning methods based on their data processing methods and operational principles, and categorize diverse instances of these methods with various forest applications. Moreover, we summarize available datasets related primarily to forest data and examine the global geographic distribution of the relevant literature. Comprehensive insights into the advantages and limitations of each method are described, offering a forward-looking perspective on the trend of applying deep learning technology to forest research. In this paper, we aim to provide an overview of the current trends and challenges of deep learning techniques applied to forest research, creating a comprehensive picture for use as a reference by both academia and industry professionals.en
dc.description.sponsorshipNational Natural Science Foundation of China (grant numbers 32371876; 32271877; 42101451); Natural Science Foundation of Jiangsu Province (BK20221337); Jiangsu Provincial Agricultural Science and Technology Independent Innovation Fund (Project CX(22)3048); Ministry of Natural Resources of the People's Republic of China (KLSMNR-G202208)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid103938en
dc.identifier.citationYun, T., Li, J., Ma, L., Zhou, J., Wang, R., Eichhorn, M. P. and Zhang, H. (2024) 'Status, advancements and prospects of deep learning methods applied in forest studies', International Journal of Applied Earth Observation and Geoinformation, 131, 103938 (20pp). https://doi.org/10.1016/j.jag.2024.103938en
dc.identifier.doihttps://doi.org/10.1016/j.jag.2024.103938en
dc.identifier.endpage20en
dc.identifier.issn1569-8432en
dc.identifier.journaltitleJournal of Applied Earth Observation and Geoinformationen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/16905
dc.identifier.volume131en
dc.language.isoenen
dc.publisherElsevier Ltd.en
dc.relation.ispartofInternational Journal of Applied Earth Observation and Geoinformationen
dc.rights© 2024, the Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectDeep learning networken
dc.subjectForest applicationen
dc.subjectRemote sensingen
dc.subjectPoint clouden
dc.subjectSatellite imageryen
dc.subjectAerial photographyen
dc.titleStatus, advancements and prospects of deep learning methods applied in forest studiesen
dc.typeArticle (peer-reviewed)en
oaire.citation.volume131en
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