Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review
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 purpo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/13/6/911 |
| _version_ | 1797487389985210368 |
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| author | André Duarte Nuno Borralho Pedro Cabral Mário Caetano |
| author_facet | André Duarte Nuno Borralho Pedro Cabral Mário Caetano |
| author_sort | André Duarte |
| collection | DOAJ |
| description | 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–green–blue (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. |
| first_indexed | 2024-03-09T23:46:58Z |
| format | Article |
| id | doaj.art-a077b5a200d744cbb9b504c1872e7739 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-09T23:46:58Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-a077b5a200d744cbb9b504c1872e77392023-11-23T16:41:12ZengMDPI AGForests1999-49072022-06-0113691110.3390/f13060911Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic ReviewAndré Duarte0Nuno Borralho1Pedro Cabral2Mário Caetano3RAIZ—Forest and Paper Research Institute, Quinta de S. Francisco, Rua José Estevão (EN 230-1), Eixo, 3800-783 Aveiro, PortugalRAIZ—Forest and Paper Research Institute, Quinta de S. Francisco, Rua José Estevão (EN 230-1), Eixo, 3800-783 Aveiro, PortugalNOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisboa, PortugalNOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisboa, PortugalUnmanned 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–green–blue (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.https://www.mdpi.com/1999-4907/13/6/911insect pest and disease monitoringforestunmanned aerial vehiclesremote sensingPRISMA protocol |
| spellingShingle | André Duarte Nuno Borralho Pedro Cabral Mário Caetano Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review Forests insect pest and disease monitoring forest unmanned aerial vehicles remote sensing PRISMA protocol |
| title | Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review |
| title_full | Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review |
| title_fullStr | Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review |
| title_full_unstemmed | Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review |
| title_short | Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review |
| title_sort | recent advances in forest insect pests and diseases monitoring using uav based data a systematic review |
| topic | insect pest and disease monitoring forest unmanned aerial vehicles remote sensing PRISMA protocol |
| url | https://www.mdpi.com/1999-4907/13/6/911 |
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