LandryStLaurentPelletierEtAl2020

Référence

Landry, S., St-Laurent, M.-H., Pelletier, G., Villard, M. (2020) The best of both worlds? Integrating sentinel-2 images and airborne LiDAR to characterize forest regeneration. Remote Sensing, 12(15). (Scopus )

Résumé

Sustainable forest management relies on practices ensuring vigorous post-harvest regeneration. Data on regeneration structure and composition are often collected through intensive field surveys. Remote sensing technologies (e.g., Light Detection and Ranging (LiDAR), satellite imagery) can cover a much larger spatial extent, but their ability to estimate regeneration characteristics is often challenged by the obstruction associated with canopy foliage. Here, we determined whether the integration of LiDAR and Sentinel-2 images can increase the accuracy of sapling density estimates and whether this accuracy decreased with canopy cover in the Acadian forest of New Brunswick, Canada. Using random forest regression, we compared the accuracy of three models (LiDAR and Sentinel-2 images alone or combined) to estimate sapling density for two species groups: saplings of all species or commercial species only. The integration of both sensors did not increase the accuracy of sapling density estimates, nor did it reduce the negative influence of canopy cover for either species group compared to LiDAR, but it increased the accuracy by approximately 15% relative to Sentinel-2 images. Under very high canopy cover, the accuracy of density estimates for all species combined was significantly lower with Sentinel-2 images only. We recommend using LiDAR and high-resolution satellite images acquired in the fall to obtain more accurate estimates of sapling density. © 2020 by the authors.

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@ARTICLE { LandryStLaurentPelletierEtAl2020,
    AUTHOR = { Landry, S. and St-Laurent, M.-H. and Pelletier, G. and Villard, M. },
    JOURNAL = { Remote Sensing },
    TITLE = { The best of both worlds? Integrating sentinel-2 images and airborne LiDAR to characterize forest regeneration },
    YEAR = { 2020 },
    NOTE = { cited By 0 },
    NUMBER = { 15 },
    VOLUME = { 12 },
    ABSTRACT = { Sustainable forest management relies on practices ensuring vigorous post-harvest regeneration. Data on regeneration structure and composition are often collected through intensive field surveys. Remote sensing technologies (e.g., Light Detection and Ranging (LiDAR), satellite imagery) can cover a much larger spatial extent, but their ability to estimate regeneration characteristics is often challenged by the obstruction associated with canopy foliage. Here, we determined whether the integration of LiDAR and Sentinel-2 images can increase the accuracy of sapling density estimates and whether this accuracy decreased with canopy cover in the Acadian forest of New Brunswick, Canada. Using random forest regression, we compared the accuracy of three models (LiDAR and Sentinel-2 images alone or combined) to estimate sapling density for two species groups: saplings of all species or commercial species only. The integration of both sensors did not increase the accuracy of sapling density estimates, nor did it reduce the negative influence of canopy cover for either species group compared to LiDAR, but it increased the accuracy by approximately 15% relative to Sentinel-2 images. Under very high canopy cover, the accuracy of density estimates for all species combined was significantly lower with Sentinel-2 images only. We recommend using LiDAR and high-resolution satellite images acquired in the fall to obtain more accurate estimates of sapling density. © 2020 by the authors. },
    AFFILIATION = { Northern Hardwood Research Institute Inc., 165 boulevard Hébert, Edmundston, NB E3V 2S8, Canada; Département de Biologie, Chimie et Géographie, Université du Québec à Rimouski, 300 allée des Ursulines, Rimouski, QC G5L 3A1, Canada; Centre for Forest Research, C.P. 8888, succursale Centre-Ville, Montréal, QC H3C 3P8, Canada; Centre for Northern Studies, 300 allée des Ursulines, Rimouski, QC G5L 3A1, Canada; Parc National d'Oka, Oka, QC J0N 1E0, Canada },
    ART_NUMBER = { 2440 },
    AUTHOR_KEYWORDS = { Acadian forest; canopy cover; forest regeneration; integration of sensors; random forest regression },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.3390/RS12152440 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089706172&doi=10.3390%2fRS12152440&partnerID=40&md5=2c9d33a0bca730356da6c8a9f560dc36 },
}

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