A Lightweight Leddar Optical Fusion Scanning System (FSS) for Canopy Foliage Monitoring

Xi, Zhouxin; Hopkinson, C.; Rood, Stewart B.; Barnes, Celeste; Xu, Fang; Pearce, David; Jones, Emily A.

doi:10.3390/s19183943

Cited by 5 publications

(4 citation statements)

References 53 publications

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“…6) Other: 9.1% of the papers [148][149][150][151][152][153][154][155][156][157][158][159][160] include a variety of applications, such as mapping the pigment distribution and quantifying taxonomic, functional, and phylogenetic diversity, tree age estimation etc. 7) Fuel load: 6.3% of the papers [161][162][163][164][165][166][167][168][169] include applications that deal with fuel load and forest fire modeling.…”

Section: Applicationsmentioning

confidence: 99%

LiDAR Data Fusion to Improve Forest Attribute Estimates: A Review

Balestra,

Marselis,

Sankey

et al. 2024

Curr. For. Rep.

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Purpose of the Review Many LiDAR remote sensing studies over the past decade promised data fusion as a potential avenue to increase accuracy, spatial-temporal resolution, and information extraction in the final data products. Here, we performed a structured literature review to analyze relevant studies on these topics published in the last decade and the main motivations and applications for fusion, and the methods used. We discuss the findings with a panel of experts and report important lessons, main challenges, and future directions. Recent Findings LiDAR fusion with other datasets, including multispectral, hyperspectral, and radar, is found to be useful for a variety of applications in the literature, both at individual tree level and at area level, for tree/crown segmentation, aboveground biomass assessments, canopy height, tree species identification, structural parameters, and fuel load assessments etc. In most cases, gains are achieved in improving the accuracy (e.g. better tree species classifications), and spatial-temporal resolution (e.g. for canopy height). However, questions remain regarding whether the marginal improvements reported in a range of studies are worth the extra investment, specifically from an operational point of view. We also provide a clear definition of “data fusion” to inform the scientific community on data fusion, combination, and integration. Summary This review provides a positive outlook for LiDAR fusion applications in the decade to come, while raising questions about the trade-off between benefits versus the time and effort needed for collecting and combining multiple datasets.

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Section: Applicationsmentioning

confidence: 99%

LiDAR Data Fusion to Improve Forest Attribute Estimates: A Review

Balestra,

Marselis,

Sankey

et al. 2024

Curr. For. Rep.

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“…They derived gap probabilities and vertical plant profiles from the TLS point clouds, and calculated plant area index (PAI) as a total canopy integrated PAI and as values at different horizontal layers. Xi et al (2019) used a compact and low-cost 3D fusion scanning system with a multi-segment Leddar (light emitting diode detection and ranging, LeddarTech, Quebec City, Quebec, Canada), a monocular camera, and rotational robotics to recover hemispherical, colored point clouds. The system was applied to scan a poplar (Populus spp.)…”

Section: Tree and Forest Growthmentioning

confidence: 99%

Monitoring Forests in Space and Time Using Close-Range Sensing

Vastaranta¹,

Saarinen²,

Yrttimaa³

et al. 2020

Preprint

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In this review, we summarize the current state-of-the-art in the utilization of close-range sensing in forest monitoring. We include technologies, such as terrestrial and mobile laser scanning as well as unmanned aerial vehicles, which are mainly used for collecting detailed information from single trees, forest patches or small forested landscapes. Based on the current published scientific literature, the capacity to characterize changes in forest ecosystems using close-range sensing has clearly been recognized. Forest growth has been the most investigated cause for changes and terrestrial laser scanner the most applied sensor for capturing forest structural changes. Unmanned aerial vehicles, on the other hand, have been used to acquire aerial imagery for detecting tree height growth and monitoring forest health. Mobile laser scanning has not yet been used in forest change monitoring except for a few early investigations. Considering the length of the forest growth process, investigated time spans have been rather short, less than 10 years. In addition, data from only two time points have been used in many of the studies, which has further been limiting the capability of understanding dynamics related to forest growth. In general, method development and quantification of changes have been the main interests so far regardless of the driver of change. This shows that the close-range remote sensing community has just started to explore the time dimension and its possibilities for forest characterization.

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“…"Crosstalk removal" and "object demerging" were both enabled to reduce return signal degradation from objects detected in other segments. The control and logger system for the Leddar sensor had a similar design to that of [30], wherein the Leddar unit was used for 3D digital canopy foliage sampling. The Leddar unit (Figure 1b) was connected to a Raspberry Pi 3 (RP) running the Raspbian OS through a 2.0, 12 Mbits/s USB cable (Figure 3).…”

Section: Leddartech Is16 (Leddar) Configuration and Laboratory Calibrationmentioning

confidence: 99%

In-Situ LED-Based Observation of Snow Surface and Depth Transects

Barnes

Hopkinson

Porter

et al. 2020

Sensors

Self Cite

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As part of a new snowpack monitoring framework, this study evaluated the feasibility of using an LED LIDAR (Leddar) time of flight sensor for snowpack depth measurement. The Leddar sensor has two additional features over simple sonic ranging sensors: (i) the return signal is divided into 16 segments across a 48° field of view, each recording individual distance-to-target (DTT) measurements; (ii) an index of reflectance or intensity signal is recorded for each segment. These two features provide information describing snowpack morphology and surface condition. The accuracy of Leddar sensor DTT measurements for snow depth monitoring was found to be < 20 mm, which was better than the 50 mm quoted by the manufacturer, and the precision was < 5 mm. Leddar and independent sonic ranger snow depth measurement showed strong linear agreement (r2 = 0.98). There was also a strong linear relationship (r2 = 0.98) between Leddar and manual field snow depth measurements. The intensity signal response was found to correlate with snow surface albedo and inversely with air temperature (r = 0.77 and −0.77, respectively).

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A Lightweight Leddar Optical Fusion Scanning System (FSS) for Canopy Foliage Monitoring

Cited by 5 publications

References 53 publications

LiDAR Data Fusion to Improve Forest Attribute Estimates: A Review

LiDAR Data Fusion to Improve Forest Attribute Estimates: A Review

Monitoring Forests in Space and Time Using Close-Range Sensing

In-Situ LED-Based Observation of Snow Surface and Depth Transects

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