The Best of Both Worlds? Integrating Sentinel-2 Images and airborne LiDAR to Characterize Forest Regeneration

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 mu...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2020-08, Vol.12 (15), p.2440
Main Authors: Landry, Stéphanie, St-Laurent, Martin-Hugues, Pelletier, Gaetan, Villard, Marc-André
Format: Article
Language:English
Subjects:
Acadian forest
Accuracy
Canopies
canopy cover
Commercial species
Density
Estimates
Foliage
Forest management
forest regeneration
Image acquisition
Image resolution
Integration
integration of sensors
Lidar
Model accuracy
random forest regression
Regeneration
Remote sensing
Satellite imagery
Sensors
Software
Studies
Sustainability management
Sustainable forestry
Trees
Variables
Vegetation
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs12152440