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Article

Measuring Spatial and Temporal Gravelled Forest Road Degradation in the Boreal Forest

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Institut de Recherche sur les Forêts, Université du Québec en Abitibi-Témiscamingue, 445 Boul. de l’Université, Rouyn-Noranda, QC J9X 5E4, Canada
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Centre d’étude de la forêt, Case Postale 8888, Succursale Centre-Ville, Montréal, QC H3C 3P8, Canada
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Hémera Centro de Observación de la Tierra, Escuela de Ingeniería Forestal, Facultad de Ciencias, Universidad Mayor, Camino La Pirámide 5750, Huechuraba 8580745, Santiago, Chile
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Département de Géographie, Université de Montréal, Campus MIL, 1375 Avenue Thérèse-Lavoie-Roux, Montréal, QC H2V 0B3, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Luis A. Ruiz
Remote Sens. 2022, 14(3), 457; https://doi.org/10.3390/rs14030457
Received: 30 December 2021 / Revised: 10 January 2022 / Accepted: 16 January 2022 / Published: 19 January 2022
(This article belongs to the Section Forest Remote Sensing)
Degradation of forest roads in Canada has strong negative effects on access to forestlands, together with economic (e.g., increased maintenance costs), environmental (e.g., erosion of materials and subsequent habitat contamination), and social (e.g., use risks) impacts. Maintaining sustainable and safe access to forestland requires a better understanding and knowledge of forest road degradation over time and space. Our study aimed to identify relevant spatiotemporal variables regarding the state of eastern Canadian forest road networks by (1) building predictive models of gravel forest road degradation and assessing effects of the slope, time, loss of the road surface, and road width (field approach), and (2) evaluating the potential of topography, roughness and vegetation indices obtained from Airborne Laser Scanning (ALS) data and Sentinel-2 optical images to estimate degradation rates (remote sensing approach). The field approach (n = 207 sample plots) confirmed that only four variables were efficient to estimate degradation rates (pseudo-R2 = 0.43 with ±8% error). Simulations that were conducted showed that after about five years without maintenance, the rate of degradation on a road, regardless of its width, increased exponentially, exacerbated by a high slope gradient and loss of road surface. The narrowest roads tended to degrade more rapidly over time. The remote sensing approach performed quite well (pseudo-R2 = 0.34 with ±9% error) in terms of predicting road degradation, giving us the valuable tools to spatialise the state of gravel forest road degradation in eastern Canadian forest. This study provided new knowledge and tools that are critical for maintaining and sustaining access to Canada’s boreal forest territory in both the short- and the long-term. View Full-Text
Keywords: access; airborne LiDAR; management; roughness; Sentinel-2; spatial indices; topography access; airborne LiDAR; management; roughness; Sentinel-2; spatial indices; topography
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MDPI and ACS Style

Girardin, P.; Valeria, O.; Girard, F. Measuring Spatial and Temporal Gravelled Forest Road Degradation in the Boreal Forest. Remote Sens. 2022, 14, 457. https://doi.org/10.3390/rs14030457

AMA Style

Girardin P, Valeria O, Girard F. Measuring Spatial and Temporal Gravelled Forest Road Degradation in the Boreal Forest. Remote Sensing. 2022; 14(3):457. https://doi.org/10.3390/rs14030457

Chicago/Turabian Style

Girardin, Patricia, Osvaldo Valeria, and François Girard. 2022. "Measuring Spatial and Temporal Gravelled Forest Road Degradation in the Boreal Forest" Remote Sensing 14, no. 3: 457. https://doi.org/10.3390/rs14030457

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