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

Abstract

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.

Determination of Riparian Vegetation Biomass from an Unmanned Aerial Vehicle (UAV)

“…Although LiDAR could measure the riparian forest dendrometric features accurately, this technology is quite expensive. The authors of [90] reported a major drawback in comparison to LiDAR and photogrammetry, where the latter is limited to the characterization of the outer canopy envelope, while LiDAR can acquire the vertical profile of vegetation also operating in under-canopy conditions. Regarding the costs, [91] estimated a total cost, including field crew, of about 9300 € for UAV-LiDAR and 6800 € for UAV-SfM to measure vegetation height for 30 sites on seismic lines.…”

UAV-Supported Forest Regeneration: Current Trends, Challenges and Implications

“…Along with this, satellite data can also help with site analysis and selection. For instance, they can be utilized for determining fire perimeters and/or classes/severity of forest loss (by analyzing pre-and post-fire scenarios), and this can be incorporated as a parameter while selecting flight paths, dispersal intensities and seed types [74,75]. Moreover, in several cases, when partial cover image acquisitions occur (due to low lighting, wind speed, etc.)…”

Extending Our Scientific Reach in Arboreal Ecosystems for Research and Management

“…Traditional remote sensing uses satellite imagery and it has improved in terms of the type of sensors, spatial and temporal resolution (Chavana-Bryant et al, 2019;Lechner et al, 2020). UAVs provide relevant canopy information at flexible times, with a higher spatial resolution and a relatively cheaper price when compared to satellite data (Lechner et al, 2020;Dainelli et al, 2021). Arboreal camera traps are effective at capturing photos of canopy vertebrates if foliage is removed from the immediate vicinity of the camera to prevent false triggering (Di Cerbo and Biancardi, 2013;Gregory et al, 2014;Whitworth et al, 2016;Bowler et al, 2017;Nazir and Kaleem, 2021).…”

“…The combination of machine learning techniques with remote sensing data allows several canopy studies ranging from semi and automatic identification and quantification of canopy species using conventional RGB cameras (Tagle Casapia et al, 2019;Ferreira et al, 2020;Wang et al, 2021), multispectral cameras (Gini et al, 2014;Wagner et al, 2020) or hyperspectral sensors (Dalponte et al, 2014), to assess health status (Dainelli et al, 2021), phenology (Feng et al, 2021), above-ground biomass estimation/quantification using RGB, radar or Lidar (Marks et al, 2014;Brede et al, 2019;Dainelli et al, 2021), canopy traits (Thomson et al, 2018;Ganivet and Bloomberg, 2019), or to detect fauna with thermal sensors (Spaan et al, 2019;Zhang et al, 2020). In all the cases, to be most effective, calibration against carefully chosen samples at the top of the canopy is required to provide accurate results (Käslin et al, 2018;Chavana-Bryant et al, 2019;Schweiger et al, 2020).…”