UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA

Sankey, Temuulen Tsagaan; Donager, Jonathon; McVay, Jason; Sankey, Joel B.

doi:10.1016/j.rse.2017.04.007

Cited by 368 publications

(253 citation statements)

References 61 publications

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“…Sankey et al [30] successfully estimated canopy cover (R 2 = 0.74; RMSE = 8.5%), individual tree height (R 2 of 0.64 to 0.93; RMSE = 1.5 m to 2.9 m), and crown diameter (R 2 of 0.66 to 0.70; RMSE of 0.72 to 1.9 m) in a ponderosa pine forest area similar to this study. However, the individual tree delineation showed weaker correlations with field-based measurements (R 2 of 0.36-0.53; RMSE of 0.83 trees/100 m 2 and 2.2 trees/100 m 2 ) [30]. Our study tested the accuracies in measuring tree canopy base height and canopy bulk density using SfM methods.…”

Section: Introductionsupporting

confidence: 76%

“…In Australia, UAV image-derived individual tree segmentation detected 70% of the dominant trees, and 35% of suppressed trees, but with a low R 2 of 0.15 in aboveground biomass estimates [37]. Sankey et al [30] successfully estimated canopy cover (R 2 = 0.74; RMSE = 8.5%), individual tree height (R 2 of 0.64 to 0.93; RMSE = 1.5 m to 2.9 m), and crown diameter (R 2 of 0.66 to 0.70; RMSE of 0.72 to 1.9 m) in a ponderosa pine forest area similar to this study. However, the individual tree delineation showed weaker correlations with field-based measurements (R 2 of 0.36-0.53; RMSE of 0.83 trees/100 m 2 and 2.2 trees/100 m 2 ) [30].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand

et al. 2018

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Forests in the Southwestern United States are becoming increasingly susceptible to large wildfires. As a result, forest managers are conducting forest fuel reduction treatments for which spatial fuels and structure information are necessary. However, this information currently has coarse spatial resolution and variable accuracy. This study tested the feasibility of using unmanned aerial vehicle (UAV) imagery to estimate forest canopy fuels and structure in a southwestern ponderosa pine stand. UAV-based multispectral images and Structure-from-Motion point clouds were used to estimate canopy cover, canopy height, tree density, canopy base height, and canopy bulk density. Estimates were validated with field data from 57 plots and aerial photography from the U.S. Department of Agriculture National Agriculture Imaging Program. Results indicate that UAV imagery can be used to accurately estimate forest canopy cover (correlation coefficient (R 2 ) = 0.82, root mean square error (RMSE) = 8.9%). Tree density estimates correctly detected 74% of field-mapped trees with a 16% commission error rate. Individual tree height estimates were strongly correlated with field measurements (R 2 = 0.71, RMSE = 1.83 m), whereas canopy base height estimates had a weaker correlation (R 2 = 0.34, RMSE = 2.52 m). Estimates of canopy bulk density were not correlated to field measurements. UAV-derived inputs resulted in drastically different estimates of potential crown fire behavior when compared with coarse resolution LANDFIRE data. Methods from this study provide additional data to supplement, or potentially substitute, traditional estimates of canopy fuel.

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Section: Introductionsupporting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand

et al. 2018

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“…2018, 10, 641 8 of 28 weight is still challenging). There is particular interest in UAS LiDAR for forestry applications, especially in relation to classifying and quantifying structural parameters (e.g., forest height, crown dimensions; [58]). …”

Section: Sensorsmentioning

confidence: 99%

On the Use of Unmanned Aerial Systems for Environmental Monitoring

et al. 2018

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Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small-and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air-and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challenges.

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“…3. Hardy et al (2017) Mapping malaria vector habitats Pulver et al (2016) Transporting automated external defibrillators Chabot and Francis (2016) Bird detection Hodgson et al (2017) Surveying marine fauna Sankey et al (2017) Forest monitoring Casella et al (2017) Mapping coral reefs Szantol et al (2017) Mapping orangutan habitat Chowdhury et al (2017) Disaster response and relief Restas (2015) Supporting disaster management (earthquakes, floods, fires) Source Own elaboration from different sources …”

Section: Us$) Sourcementioning

confidence: 99%

The Drone Sector in Europe

Molina¹,

Oña²

2017

SpringerBriefs in Law

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The aim of this chapter is to review the industry sector in Europe, which involves big companies as well as SMEs. The leading European countries, the main competitors, and the main competitive advantages of the industry are analysed. We have identified five different segments in which companies compete based on distinctive features (toys, hobby/leisure, professional, commercial, and military). The homogenization of the industry, especially in the toy, hobby, and commercial subsectors, is mainly led by big companies, which at the same time also represent the largest market share. We have concluded that there are no entry barriers besides technology and commercial distribution, but the main difficulty that is hampering the industry's expansion is regulation. This affects in particular the hobby/leisure and commercial drone segments, some areas more than others; while agricultural and military drones are expanding and evolving rapidly, professional filming and photography and toys are still too dependent on safety or legal issues, such as privacy concerns. It is also remarkable that companies from China and the USA are reaching the top positions. This should be taken into consideration by European policy makers, as the decisions made in the next years will be the key to the development of the sector in Europe.

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UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA

Cited by 368 publications

References 61 publications

Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand

Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand

On the Use of Unmanned Aerial Systems for Environmental Monitoring

The Drone Sector in Europe

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