UAV-based forest health monitoring: a systematic review

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dc.contributor.authorEcke, Simonen_ZA
dc.contributor.authorDempewolf, Janen_ZA
dc.contributor.authorFrey, Julianen_ZA
dc.contributor.authorSchwaller, Andreasen_ZA
dc.contributor.authorEndres, Ewalden_ZA
dc.contributor.authorKlemmt, Hans-Joachimen_ZA
dc.contributor.authorTiede, Dirken_ZA
dc.contributor.authorSeifert, Thomasen_ZA
dc.date.accessioned2022-07-05T10:42:11Zen_ZA
dc.date.available2022-07-05T10:42:11Zen_ZA
dc.date.issued2022-07en_ZA
dc.descriptionCITATION: Ecke, S. et al. 2022. UAV-Based forest health monitoring : a systematic review. Remote Sensing, 14(13):3205, doi:10.3390/rs14133205.en_ZA
dc.descriptionThe original publication is available at https://www.mdpi.comen_ZA
dc.description.abstractIn recent years, technological advances have led to the increasing use of unmanned aerial vehicles (UAVs) for forestry applications. One emerging field for drone application is forest health monitoring (FHM). Common approaches for FHM involve small-scale resource-extensive fieldwork combined with traditional remote sensing platforms. However, the highly dynamic nature of forests requires timely and repetitive data acquisition, often at very high spatial resolution, where conventional remote sensing techniques reach the limits of feasibility. UAVs have shown that they can meet the demands of flexible operation and high spatial resolution. This is also reflected in a rapidly growing number of publications using drones to study forest health. Only a few reviews exist which do not cover the whole research history of UAV-based FHM. Since a comprehensive review is becoming critical to identify research gaps, trends, and drawbacks, we offer a systematic analysis of 99 papers covering the last ten years of research related to UAV-based monitoring of forests threatened by biotic and abiotic stressors. Advances in drone technology are being rapidly adopted and put into practice, further improving the economical use of UAVs. Despite the many advantages of UAVs, such as their flexibility, relatively low costs, and the possibility to fly below cloud cover, we also identified some shortcomings: (1) multitemporal and long-term monitoring of forests is clearly underrepresented; (2) the rare use of hyperspectral and LiDAR sensors must drastically increase; (3) complementary data from other RS sources are not sufficiently being exploited; (4) a lack of standardized workflows poses a problem to ensure data uniformity; (5) complex machine learning algorithms and workflows obscure interpretability and hinders widespread adoption; (6) the data pipeline from acquisition to final analysis often relies on commercial software at the expense of open-source tools.en_ZA
dc.description.urihttps://www.mdpi.com/2072-4292/14/13/3205en_ZA
dc.description.versionPublisher's versionen_ZA
dc.format.extent45 pagesen_ZA
dc.identifier.citationEcke, S. et al. 2022. UAV-Based forest health monitoring : a systematic review. Remote Sensing, 14(13):3205, doi:10.3390/rs14133205.en_ZA
dc.identifier.issn2072-4292 (online)en_ZA
dc.identifier.otherdoi:10.3390/rs14133205en_ZA
dc.identifier.urihttp://hdl.handle.net/10019.1/125471en_ZA
dc.language.isoen_ZAen_ZA
dc.publisherMDPIen_ZA
dc.rights.holderAuthors retain copyrighten_ZA
dc.subjectForest monitoringen_ZA
dc.subjectRemote sensing -- Equipment and suppliesen_ZA
dc.subjectDrone aircraften_ZA
dc.subjectForest healthen_ZA
dc.subjectTechnological innovationsen_ZA
dc.titleUAV-based forest health monitoring: a systematic reviewen_ZA
dc.typeArticleen_ZA
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