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AbstractIntegrating airborne laser scanning (ALS) forest attributes with photo-interpreted forest stand age and species attributes can provide managers with the best information to drive estate planning, growth and yield projections, and forest operations. Photo-interpreted forest inventories provide certain forest attributes that are difficult to measure with ALS, yet are subjective and irregularly updated. ALS is objective and provides detailed estimates of forest structure attributes, but poorly estimates age and species composition. We used Generic Region Merging segmentation and k-nearest neighbor imputation to integrate photo-interpreted and ALS-derived forest attributes into a contemporary stand-based forest inventory. We first segmented gridded ALS attributes into forest stand polygons across a ∼630,000 ha managed forest in Ontario, Canada. We next applied imputation to a photo-interpreted inventory, assessing the influence of model parameters and imputed vs. observed values of age and species using leave-one-out cross-validation. Compared to the photo-interpreted inventory, the optimal imputation model estimated age with a mean absolute and mean bias difference of 16.06 and −0.14 years, and classified leading species with 65.46% accuracy. We lastly integrated imputed age and species attributes into the automatically segmented forest stand polygons, finding similar age and species distributions across the landscape when compared to the photo-interpreted inventory.

ABSTRACTSingle-photon lidar (SPL100) is a recently commercialized airborne lidar system facilitating efficient wide-area acquisitions of high-density point clouds due to its capacity for higher altitude acquisitions compared to traditional linear-mode lidar (LML) systems. Increased acquisition efficiency and point densities make SPL100 attractive for forest management applications. SPL100 utilizes 532 nm (green wavelength) lasers, wherein there is reduced reflectance from vegetation, increased sensitivity to solar noise, and increased signal attenuation, which may impact the vertical distribution of SPL100 returns in forest canopies. We assessed SPL100 data acquisitions over managed forests in north-eastern Ontario, Canada, using high-density unmanned aerial vehicle-borne laser scanning (ULS) data as reference over a range of forest conditions with variable vertical structure. Signal attenuation depth of individual SPL100 returns was estimated through a surface model normalization approach stratified by a ULS-derived structural index that compared densities of returns in the upper canopy to low vegetation and near ground. Canopy signal attenuation was closely matched in both systems, particularly in the upper canopy and near the ground surface; however, results showed a 31% reduction in the relative characterization of mid-canopy vegetation layers by SPL100 under conditions identified by the structural index as closed canopy, compared to the ULS system.

AbstractThe advent of CubeSat constellations is revolutionizing the ability to observe Earth systems through time. The improved spatial and temporal resolutions from these data could assist in tracking forest harvesting by forest management companies or government organizations interested in monitoring the sustainable management of forest resources. However, differing characteristics of individual satellites in each constellation requires study into geometric and radiometric normalization of the imagery and tuning parameters for change detection algorithms. In this study, a method for the spatial and temporal detection of forest harvest operations using images from the PlanetScope constellation was developed and implemented for a managed forest in Ontario, Canada. Temporal smoothing was applied on Landsat-normalized PlanetScope values of the Normalized Differential Vegetation Index (NDVI), and change points were detected based on the first derivative of the NDVI trend. Detected changes were compared to known locations of harvesting machines. Results indicate that 80–90% of harvested areas were detected, with temporal errors of approximately 9–10 days for two sites. Overall, this study demonstrated that forest harvesting can be detected with relative accuracy, deriving previously unavailable levels of spatial and temporal detail and enhancing the ability of forest stakeholders to monitor the sustainable use of forest resources.

AbstractAirborne light detection and ranging (LiDAR) data are increasingly used to inform sustainable forest management practices. Information about species composition is needed for a range of applications; however, commonly used area-based summaries of LiDAR data are limited to accurately differentiate tree species. The objective of this study was to map dominant species groups across a large (>580,000 ha) boreal forest by combining area-based and individual tree metrics derived from single photon LiDAR data with multispectral information derived from Sentinel-2 imagery. Classification of the forest into jack pine (Pinus banksiana), black spruce (Picea mariana), mixed conifer, mixedwood, and hardwood species groups resulted in an overall accuracy of 67.8% compared with field data. A more generalized classification into softwood, hardwood, and mixedwood had an overall accuracy of 83.2%. The reflectance in the red edge region of the electromagnetic spectrum (λ = 740 nm), the area and volume of tree crowns, and the cumulative distribution of LiDAR returns in the canopy were particularly important variables to discriminate between species groups. Wall-to-wall predictions of species groups based on remotely sensed data—as derived herein—could provide a spatially-detailed alternative to stand-level species composition information traditionally derived from photo-interpreted aerial imagery.