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Recent Advances in Unmanned Aerial Vehicles Forest Remote Sensing-A Systematic Review. Part II: Research Applications

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FORESTS
卷 12, 期 4, 页码 -

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MDPI
DOI: 10.3390/f12040397

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UAV; drone; precision forestry; forest management; meta-analysis; inventory parameters; biomass; hyperspectral; machine learning; technology transfer

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The use of UAV-RS in forestry management is becoming increasingly popular due to its accuracy in obtaining information. However, there are still critical issues in transferring research products and practical applications, such as underutilization of hyperspectral sensors and lack of interoperability in sensors and platforms.
Forest sustainable management aims to maintain the income of woody goods for companies, together with preserving non-productive functions as a benefit for the community. Due to the progress in platforms and sensors and the opening of the dedicated market, unmanned aerial vehicle-remote sensing (UAV-RS) is improving its key role in the forestry sector as a tool for sustainable management. The use of UAV (Unmanned Aerial Vehicle) in precision forestry has exponentially increased in recent years, as demonstrated by more than 600 references published from 2018 until mid-2020 that were found in the Web of Science database by searching for UAV + forest. This result is even more surprising when compared with similar research for UAV + agriculture, from which emerge about 470 references. This shows how UAV-RS research forestry is gaining increasing popularity. In Part II of this review, analyzing the main findings of the reviewed papers (227), numerous strengths emerge concerning research technical issues. UAV-RS is fully applicated for obtaining accurate information from practical parameters (height, diameter at breast height (DBH), and biomass). Research effectiveness and soundness demonstrate that UAV-RS is now ready to be applied in a real management context. Some critical issues and barriers in transferring research products are also evident, namely, (1) hyperspectral sensors are poorly used, and their novel applications should be based on the capability of acquiring tree spectral signature especially for pest and diseases detection, (2) automatic processes for image analysis are poorly flexible or based on proprietary software at the expense of flexible and open-source tools that can foster researcher activities and support technology transfer among all forestry stakeholders, and (3) a clear lack exist in sensors and platforms interoperability for large-scale applications and for enabling data interoperability.

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