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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/15/2440 |
| _version_ | 1797560927566954496 |
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| author | Stéphanie Landry Martin-Hugues St-Laurent Gaetan Pelletier Marc-André Villard |
| author_facet | Stéphanie Landry Martin-Hugues St-Laurent Gaetan Pelletier Marc-André Villard |
| author_sort | Stéphanie Landry |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T18:07:01Z |
| format | Article |
| id | doaj.art-838d32e687884f4887d3ff3e27a95d02 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T18:07:01Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-838d32e687884f4887d3ff3e27a95d022023-11-20T08:24:55ZengMDPI AGRemote Sensing2072-42922020-07-011215244010.3390/rs12152440The Best of Both Worlds? Integrating Sentinel-2 Images and airborne LiDAR to Characterize Forest RegenerationStéphanie Landry0Martin-Hugues St-Laurent1Gaetan Pelletier2Marc-André Villard3Northern Hardwood Research Institute Inc., 165 boulevard Hébert, Edmundston, NB E3V 2S8, CanadaDépartement de Biologie, Chimie et Géographie, Université du Québec à Rimouski 300 allée des Ursulines, Rimouski, QC G5L 3A1, CanadaNorthern Hardwood Research Institute Inc., 165 boulevard Hébert, Edmundston, NB E3V 2S8, CanadaParc national d’Oka, Oka, QC J0N 1E0, CanadaSustainable 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.https://www.mdpi.com/2072-4292/12/15/2440Acadian forestcanopy coverforest regenerationintegration of sensorsrandom forest regression |
| spellingShingle | Stéphanie Landry Martin-Hugues St-Laurent Gaetan Pelletier Marc-André Villard The Best of Both Worlds? Integrating Sentinel-2 Images and airborne LiDAR to Characterize Forest Regeneration Remote Sensing Acadian forest canopy cover forest regeneration integration of sensors random forest regression |
| title | The Best of Both Worlds? Integrating Sentinel-2 Images and airborne LiDAR to Characterize Forest Regeneration |
| title_full | The Best of Both Worlds? Integrating Sentinel-2 Images and airborne LiDAR to Characterize Forest Regeneration |
| title_fullStr | The Best of Both Worlds? Integrating Sentinel-2 Images and airborne LiDAR to Characterize Forest Regeneration |
| title_full_unstemmed | The Best of Both Worlds? Integrating Sentinel-2 Images and airborne LiDAR to Characterize Forest Regeneration |
| title_short | The Best of Both Worlds? Integrating Sentinel-2 Images and airborne LiDAR to Characterize Forest Regeneration |
| title_sort | best of both worlds integrating sentinel 2 images and airborne lidar to characterize forest regeneration |
| topic | Acadian forest canopy cover forest regeneration integration of sensors random forest regression |
| url | https://www.mdpi.com/2072-4292/12/15/2440 |
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