{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T13:22:54Z","timestamp":1777555374758,"version":"3.51.4"},"reference-count":133,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"national funds through Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia (FCT)","doi-asserted-by":"publisher","award":["UIDB\/04152\/2020"],"award-info":[{"award-number":["UIDB\/04152\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Forests"],"abstract":"<jats:p>Unmanned aerial vehicles (UAVs) are platforms that have been increasingly used over the last decade to collect data for forest insect pest and disease (FIPD) monitoring. These machines provide flexibility, cost efficiency, and a high temporal and spatial resolution of remotely sensed data. The purpose of this review is to summarize recent contributions and to identify knowledge gaps in UAV remote sensing for FIPD monitoring. A systematic review was performed using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) protocol. We reviewed the full text of 49 studies published between 2015 and 2021. The parameters examined were the taxonomic characteristics, the type of UAV and sensor, data collection and pre-processing, processing and analytical methods, and software used. We found that the number of papers on this topic has increased in recent years, with most being studies located in China and Europe. The main FIPDs studied were pine wilt disease (PWD) and bark beetles (BB) using UAV multirotor architectures. Among the sensor types, multispectral and red\u2013green\u2013blue (RGB) bands were preferred for the monitoring tasks. Regarding the analytical methods, random forest (RF) and deep learning (DL) classifiers were the most frequently applied in UAV imagery processing. This paper discusses the advantages and limitations associated with the use of UAVs and the processing methods for FIPDs, and research gaps and challenges are presented.<\/jats:p>","DOI":"10.3390\/f13060911","type":"journal-article","created":{"date-parts":[[2022,6,11]],"date-time":"2022-06-11T09:33:26Z","timestamp":1654940006000},"page":"911","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":107,"title":["Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2852-379X","authenticated-orcid":false,"given":"Andr\u00e9","family":"Duarte","sequence":"first","affiliation":[{"name":"RAIZ\u2014Forest and Paper Research Institute, Quinta de S. Francisco, Rua Jos\u00e9 Estev\u00e3o (EN 230-1), Eixo, 3800-783 Aveiro, Portugal"},{"name":"NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5606-7878","authenticated-orcid":false,"given":"Nuno","family":"Borralho","sequence":"additional","affiliation":[{"name":"RAIZ\u2014Forest and Paper Research Institute, Quinta de S. Francisco, Rua Jos\u00e9 Estev\u00e3o (EN 230-1), Eixo, 3800-783 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8622-6008","authenticated-orcid":false,"given":"Pedro","family":"Cabral","sequence":"additional","affiliation":[{"name":"NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8913-7342","authenticated-orcid":false,"given":"M\u00e1rio","family":"Caetano","sequence":"additional","affiliation":[{"name":"NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal"},{"name":"DGT\u2014Dire\u00e7\u00e3o Geral do Territ\u00f3rio, 1099-052 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"eaaz7005","DOI":"10.1126\/science.aaz7005","article-title":"Climate-Driven Risks to the Climate Mitigation Potential of Forests","volume":"368","author":"Anderegg","year":"2020","journal-title":"Science"},{"key":"ref_2","unstructured":"FAO (2011). 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