Species identification is a critical factor for obtaining accurate forest inventories. This paper compares the same method of tree species identification (at the individual crown level) across three different types of airborne laser scanning systems (ALS): two linear lidar systems (monospectral and multispectral) and one single-photon lidar (SPL) system to ascertain whether current individual tree crown (ITC) species classification methods are applicable across all sensors. SPL is a new type of sensor that promises comparable point densities from higher flight altitudes, thereby increasing lidar coverage. Initial results indicate that the methods are indeed applicable across all of the three sensor types with broadly similar overall accuracies (Hardwood/Softwood, 83–90%; 12 species, 46–54%; 4 species, 68–79%), with SPL being slightly lower in all cases. The additional intensity features that are provided by multispectral ALS appear to be more beneficial to overall accuracy than the higher point density of SPL. We also demonstrate the potential contribution of lidar time-series data in improving classification accuracy (Hardwood/Softwood, 91%; 12 species, 58%; 4 species, 84%). Possible causes for lower SPL accuracy are (a) differences in the nature of the intensity features and (b) differences in first and second return distributions between the two linear systems and SPL. We also show that segmentation (and field-identified training crowns deriving from segmentation) that is performed on an initial dataset can be used on subsequent datasets with similar overall accuracy. To our knowledge, this is the first study to compare these three types of ALS systems for species identification at the individual tree level.

Increase in forest disturbance due to land use as well as climate change has led to an expansion of young forests worldwide, which drives global carbon dynamics and timber allocation. This study presents a method that combines a single airborne LiDAR acquisition and time since harvest maps to model height growth of post-logged black spruce-dominated forests in a 1700 km2 eastern Canadian boreal landscape. We developed a random forest model in which forest height at a 20 m × 20 m pixel resolution is a function of stand age, combined with environmental variables (e.g., slope, site moisture, surface deposit). Our results highlight the model's strong predictive power: least-square regression between predicted and observed height of our validation dataset was very close to the 1:1 relation and strongly supported by validation metrics (R2 = 0.74; relative RMSE = 19%). Environmental variables thus allowed to accurately predict forest productivity with a high spatial resolution (20 m × 20 m pixels) and predicted forest height growth in the first 50 years after logging ranged between 16 and 27 cm·year−1 across the whole study area, with a mean of 20.5 cm·year−1. The spatial patterns of potential height growth were strongly linked to the effect of topographical variables, with better growth rates on mesic slopes compared to poorly drained soils. Such models could have key implications in forest management, for example to maintain forest ecosystem services by adjusting the harvesting rates depending on forest productivity across the landscapes.

Context

Although logging has affected circumboreal forest dynamics for nearly a century, very few studies have reconstructed its influence on landscape structure at the subcontinental scale.

Objectives

This study aims to document spatiotemporal patterns of logging and fire since the introduction of logging in the early twentieth-century, and to evaluate the effects of these disturbances on landscape structure.

Methods

We used historical (1940–2009) logging and fire maps to document disturbance patterns across a 195,000-km2 boreal forest landscape of eastern Canada. We produced multitemporal (1970s–2010s) mosaics providing land cover status using Landsat imagery.

Results

Logging significantly increased the rate of disturbance (+74 %) in the study area. The area affected by logging increased linearly with time resulting in a significant rejuvenation of the landscape along the harvesting pattern (south–north progression). From 1940 to 2009, fire was the dominant disturbance and showed a more random spatial distribution than logging. The recent increase of fire influence and the expansion of the proportion of area classified as unproductive terrestrial land suggest that regeneration failures occurred.

Conclusions

This study reveals how logging has modified the disturbances dynamics, following the progression of the logging frontier. Future management practices should aim for a dispersed spatial distribution of harvests to generate landscape structures that are closer to natural conditions, in line with ecosystem-based management. The challenges of defining sustainable practices will remain complex with the predicted increase in fire frequency, since this factor, in combination with logging, can alter both the structure and potentially the resilience of boreal forest.

We explore the possibility of predicting wood fiber attributes across Newfoundland for two commercial species: black spruce (Picea mariana (Mill.) B.S.P.) and balsam fir (Abies balsamea (L.) Mill.). Estimates of key fiber attributes (including wood density, coarseness, fiber length, and modulus of elasticity) were derived from measurements of wood cores taken from sample plots representing a wide structural gradient of forest stands. Candidate models for predicting fiber attributes at plot and landscape scales were developed using an information-theoretical approach and compared based on Akaike’s information criterion. The most influential variables were stand age and the presence of precommercial thinning. Other significant explanatory variables included those that characterize vegetation structure (mean diameter at breast height, dominant height), climate (annual precipitation, mean temperature of the growing season), and geography (elevation, latitude) depending on the species and fiber attribute being modeled. At the plot level, model inference gave root mean square errors of 5.3–11.9% for all attributes. At the landscape level, prediction errors were similar (5.4–12.1%), with the added benefit of being suitable for mapping fiber attributes across the landscape. The results obtained demonstrate the potential for predicting and mapping fiber attributes over a large region of boreal forest in Newfoundland, Canada.

Sustainable management of boreal ecosystems involves the establishment of vigorous tree regeneration after harvest. However, two groups of understory plants influence regeneration success in eastern boreal Canada. Ericaceous shrubs are recognized to rapidly dominate susceptible boreal sites after harvest. Such dominance reduces recruitment and causes stagnant conifer growth, lasting decades on some sites. Additionally, peat accumulation due to Sphagnum growth after harvest forces the roots of regenerating conifers out of the relatively nutrient rich and warm mineral soil into the relatively nutrient poor and cool organic layer, with drastic effects on growth. Shifts from once productive black spruce forests to ericaceous heaths or paludified forests affect forest productivity and biodiversity. Under natural disturbance dynamics, fires severe enough to substantially reduce the organic layer thickness and affect ground cover species are required to establish a productive regeneration layer on such sites. We succinctly review how understory vegetation influences black spruce ecosystem dynamics in eastern boreal Canada, and present a multi-scale research model to understand, limit the loss and restore productive and diverse ecosystems in this region. Our model integrates knowledge of plant-level mechanisms in the development of silvicultural tools to sustain productivity. Fundamental knowledge is integrated at stand, landscape, regional and provincial levels to understand the distribution and dynamics of ericaceous shrubs and paludification processes and to support tactical and strategic forest management. The model can be adapted and applied to other natural resource management problems, in other biomes.