VandendaeleFournierVepakommaEtAl2021

Reference

Vandendaele, B., Fournier, R.A., Vepakomma, U., Pelletier, G., Lejeune, P., Martin‐ducup, O. (2021) Estimation of northern hardwood forest inventory attributes using uav laser scanning (Uls): Transferability of laser scanning methods and comparison of automated approaches at the tree‐ and stand‐level. Remote Sensing, 13(14). (Scopus )

Abstract

UAV laser scanning (ULS) has the potential to support forest operations since it provides high‐density data with flexible operational conditions. This study examined the use of ULS systems to estimate several tree attributes from an uneven‐aged northern hardwood stand. We investigated: (1) the transferability of raster‐based and bottom‐up point cloud‐based individual tree detection (ITD) algorithms to ULS data; and (2) automated approaches to the retrieval of tree‐level (i.e., height, crown diameter (CD), DBH) and stand‐level (i.e., tree count, basal area (BA), DBH‐distribution) forest inventory attributes. These objectives were studied under leaf‐on and leaf‐off canopy conditions. Results achieved from ULS data were cross‐compared with ALS and TLS to better understand the potential and challenges faced by different laser scanning systems and methodological approaches in hardwood forest environments. The best results that characterized individual trees from ULS data were achieved under leaf‐off conditions using a point cloud‐based bottom‐up ITD. The latter outperformed the raster‐based ITD, improving the accuracy of tree detection (from 50% to 71%), crown delineation (from R2 = 0.29 to R2 = 0.61), and prediction of tree DBH (from R2 = 0.36 to R2 = 0.67), when compared with values that were estimated from reference TLS data. Major improve-ments were observed for the detection of trees in the lower canopy layer (from 9% with raster‐based ITD to 51% with point cloud‐based ITD) and in the intermediate canopy layer (from 24% with raster-based ITD to 59% with point cloud‐based ITD). Under leaf‐on conditions, LiDAR data from aerial systems include substantial signal occlusion incurred by the upper canopy. Under these conditions, the raster‐based ITD was unable to detect low‐level canopy trees (from 5% to 15% of trees detected from lower and intermediate canopy layers, respectively), resulting in a tree detection rate of about 40% for both ULS and ALS data. The cylinder‐fitting method used to estimate tree DBH under leaf-off conditions did not meet inventory standards when compared to TLS DBH, resulting in RMSE = 7.4 cm, Bias = 3.1 cm, and R2 = 0.75. Yet, it yielded more accurate estimates of the BA (+3.5%) and DBH‐distribution of the stand than did allometric models −12.9%), when compared with in situ field measurements. Results suggest that the use of bottom‐up ITD on high‐density ULS data from leaf-off hardwood forest leads to promising results when estimating trees and stand attributes, which opens up new possibilities for supporting forest inventories and operations. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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@ARTICLE { VandendaeleFournierVepakommaEtAl2021,
    AUTHOR = { Vandendaele, B. and Fournier, R.A. and Vepakomma, U. and Pelletier, G. and Lejeune, P. and Martin‐ducup, O. },
    JOURNAL = { Remote Sensing },
    TITLE = { Estimation of northern hardwood forest inventory attributes using uav laser scanning (Uls): Transferability of laser scanning methods and comparison of automated approaches at the tree‐ and stand‐level },
    YEAR = { 2021 },
    NOTE = { cited By 0 },
    NUMBER = { 14 },
    VOLUME = { 13 },
    ABSTRACT = { UAV laser scanning (ULS) has the potential to support forest operations since it provides high‐density data with flexible operational conditions. This study examined the use of ULS systems to estimate several tree attributes from an uneven‐aged northern hardwood stand. We investigated: (1) the transferability of raster‐based and bottom‐up point cloud‐based individual tree detection (ITD) algorithms to ULS data; and (2) automated approaches to the retrieval of tree‐level (i.e., height, crown diameter (CD), DBH) and stand‐level (i.e., tree count, basal area (BA), DBH‐distribution) forest inventory attributes. These objectives were studied under leaf‐on and leaf‐off canopy conditions. Results achieved from ULS data were cross‐compared with ALS and TLS to better understand the potential and challenges faced by different laser scanning systems and methodological approaches in hardwood forest environments. The best results that characterized individual trees from ULS data were achieved under leaf‐off conditions using a point cloud‐based bottom‐up ITD. The latter outperformed the raster‐based ITD, improving the accuracy of tree detection (from 50% to 71%), crown delineation (from R2 = 0.29 to R2 = 0.61), and prediction of tree DBH (from R2 = 0.36 to R2 = 0.67), when compared with values that were estimated from reference TLS data. Major improve-ments were observed for the detection of trees in the lower canopy layer (from 9% with raster‐based ITD to 51% with point cloud‐based ITD) and in the intermediate canopy layer (from 24% with raster-based ITD to 59% with point cloud‐based ITD). Under leaf‐on conditions, LiDAR data from aerial systems include substantial signal occlusion incurred by the upper canopy. Under these conditions, the raster‐based ITD was unable to detect low‐level canopy trees (from 5% to 15% of trees detected from lower and intermediate canopy layers, respectively), resulting in a tree detection rate of about 40% for both ULS and ALS data. The cylinder‐fitting method used to estimate tree DBH under leaf-off conditions did not meet inventory standards when compared to TLS DBH, resulting in RMSE = 7.4 cm, Bias = 3.1 cm, and R2 = 0.75. Yet, it yielded more accurate estimates of the BA (+3.5%) and DBH‐distribution of the stand than did allometric models −12.9%), when compared with in situ field measurements. Results suggest that the use of bottom‐up ITD on high‐density ULS data from leaf-off hardwood forest leads to promising results when estimating trees and stand attributes, which opens up new possibilities for supporting forest inventories and operations. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. },
    AFFILIATION = { Department of Applied Geomatics, Centre d’Applications et de Recherches en Télédétection (CARTEL), Université de Sherbrooke, 2500, Boul. de l’Université, Sherbrooke, QC J1K 2R1, Canada; FPInnovations, 570 Boul. Saint‐Jean, Pointe‐Claire, QC H9R 3J9, Canada; Northern Hardwoods Research Institute Inc., 165 boulevard Hébert, Edmundston, NB E3V 2S8, Canada; TERRA Teaching and Research Centre (Forest Is Life), Gembloux Agro‐Bio Tech, University of Liege, Passage des Déportés 2, Gembloux, 5030, Belgium; AMAP, IRD, CNRS, CIRAD, INRA, University Montpellier, botAnique et Modélisation de l’Architecture, des Plantes et des Végétations, TA A51/PS2, CEDEX 05, Montpellier, 34398, France },
    ART_NUMBER = { 2796 },
    AUTHOR_KEYWORDS = { Airborne laser scanning (ALS); Diameter at breast height (DBH); Forest inventory; Hardwood; Individual tree detection and delineation (ITD); Open‐source analytic tools; Terrestrial laser scanning (TLS); UAV laser scanning (ULS); Uneven‐aged forest },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.3390/rs13142796 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111305476&doi=10.3390%2frs13142796&partnerID=40&md5=d9fae8f41b27db327333e43dee72689c },
}

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