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Copyright of Canadian Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Knowledge about the condition and location of forest roads is important for forest management. Coupling accurate forest road information with planning and conservation strategies supports forest resource management. In Canada, spatial data of forestry road networks are available provincially; however, they lack spatial accuracy, and up-to-date information on key attributes such as road width is missing. In this study, we apply a novel approach to update forest road networks and characterize road conditions in Ontario's Boreal and Great Lakes—St. Lawrence (GLSL) Forest regions. We use airborne laser scanning (ALS), to facilitate the identification of forest roads across densely forested landscapes. We categorized roads into four classes based on driveable width, edge vegetation, as well as surface and edge degradation as derived from high-density Single Photon LiDAR (SPL) data. Using a novel road extraction method, we produced a road probability raster and map road centerlines. We validated road location and attribute information using Global Navigation Satellite System (GNSS) ground truth data in two Ontario forest management units, in the boreal forest and the GLSL. Road segments in some regions have been altered to account for land cover changes, such as flooding or fallen trees. In other situations, the road path may deviate from the planned layout of the road, which is not always followed in the field. Our results highlight inaccuracies in the existing road networks, with 30 per cent of 'Full access' roads and 29 per cent of 'Partial access' roads being undriveable by standard vehicles and 45 per cent of 'Status unknown' roads, which make up 48 per cent of the pre-existing network, being driveable by standard vehicles. Results show that the average positional accuracy of updated road centerlines is 0.4 m, and the average road width error is 2 m. The production of spatially accurate forest road networks and road attribute information is important for characterizing large road networks for which often minimal information is available. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An increasing number of jurisdictions are integrating airborne laser scanning (ALS) into forest inventory programs to produce spatially explicit and accurate inventories of forest resources. However, wall-to-wall ALS coverage relative to the total area of managed forest remains limited in large forest nations such as Canada, wherein logistics, cost and acquisition capacity can be limiting factors. Technologies such as single photon light detection and ranging (SPL) have emerged commercially, which have the capacity to provide efficient ALS acquisitions over large areas and with a greater point density than conventional linear-mode ALS. However, the large-scale operational application of SPL in a forest inventory still needs to be effectively demonstrated. In this study, we used wall-to-wall SPL data (collected with a Leica SPL100) across a 630 000 ha boreal forest in Ontario, Canada to develop a forest inventory. Specifically, we used a structurally guided sampling approach enabled via a principal component analysis of the SPL100 data to establish a network of 250 ground plots. Random forest models were then used to produce area-based estimates of forest attributes of interest. Results demonstrated that the sampling approach enabled the optimization and enhancement of the existing plot network by extending the range of sampled structural types and reducing the number of plots in oversampled forest types. Moreover, Lorey's height, basal area, quadratic mean diameter at breast height, stem density, gross and merchantable volume and above-ground biomass were estimated with a relative root mean square error of 8.5, 19.76, 13.97, 30.82, 21.53, 23.79 and 22.87 per cent, respectively, and relative bias <1 per cent. Model accuracies achieved using the SPL100 were comparable with those obtained using linear-mode ALS in a previous forest inventory. This study demonstrates the utility of the SPL100 for the complete development of a forest inventory over large forest areas, from ground plot establishment through to the production of forest attribute estimates. [ABSTRACT FROM AUTHOR]