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.
Highlights
Regeneration is essential to establish or renew a stand after a disturbance, whether of natural or anthropogenic origin [1]
The integration of LiDAR and Sentinel-2 images did not increase the accuracy of sapling density estimates, nor it did for the variance explained compared to LiDAR for both species groups, i.e., all species combined and commercial species only (Table 3)
The integration of both sensors, as for LiDAR, yielded higher accuracy and pseudo R-squared values compared to models using Sentinel-2 images for both species groups (Table 3)
Summary
Regeneration is essential to establish or renew a stand after a disturbance, whether of natural or anthropogenic origin [1]. Even though field surveys are more accurate [2], they are more time-consuming [3] and their spatial extent is limited compared to remote sensing [4,5] Remote sensing technologies such as satellite images have been used for decades in forestry to estimate various stand characteristics [6]. Overstory density [14,15,16,17], canopy height [18], proportion of hardwoods in the overstory [19], and canopy cover [20] all have been shown to decrease the accuracy of estimates of overstory tree characteristics. A few authors have attempted to characterize regeneration under high values of canopy cover [25,26] and, not surprisingly, the accuracy of estimates decreased with canopy cover
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