No abstract

Fine-scale height-growth response of boreal trees to canopy openings is difficult to measure from the ground, and there are important limitations in using stereophotogrammetry in defining gaps and determining individual crowns and height. However, precise knowledge on height growth response to different openings is critical for refining partial harvesting techniques. In this study, we question whether conifers and hardwoods respond equally in terms of sapling growth or lateral growth to openings. We also ask to what distance gaps affect tree growth into the forest. We use multi-temporal lidar to characterize tree/sapling height and lateral growth responses over five years to canopy openings and high resolution images to identify conifers and hardwoods. Species-class-wise height-growth patterns of trees/saplings in various neighborhood contexts were determined across a 6-km matrix of Canadian boreal mixed deciduous coniferous forests. We then use statistical techniques to probe how these growth responses vary by spatial location with respect to the gap edge. Results confirm that both mechanisms of gap closure contribute to the closing of canopies at a rate of 1.2% per annum. Evidence also shows that both hardwood and conifer gap edge trees have a similar lateral growth (average of 22 cm/yr) and similar rates of height growth irrespective of their location and initial height. Height growth of all saplings, however, was strongly dependent on their position within the gap and the size of the gap. Results suggest that hardwood and softwood saplings in gaps have greatest growth rates at distances of 0.5-2 m and 1.5-4 m from the gap edge and in openings smaller than 800 m2 and 250 m2, respectively. Gap effects on the height growth of trees in the intact forest were evident up to 30 m and 20 m from gap edges for hardwood and softwood overstory trees, respectively. Our results thus suggest that foresters should consider silvicultural techniques that create many small openings in mixed coniferous deciduous boreal forests to maximize the growth response of both residual and regenerating trees.

Pine processionary moth (PPM) is one of the most destructive insect defoliators in the Mediterranean for many conifers, causing losses of growth, vitality and eventually the death of trees during outbreaks. There is a growing need for cost-effective monitoring of the temporal and spatial impacts of PPM in forest ecology to better assess outbreak spread patterns and provide guidance on the development of measures targeting the negative impacts of the species on forests, industry and human health. Remote sensing technology mounted on unmanned aerial systems (UASs) with high-resolution image processing has been proposed to assess insect outbreak impacts at local and forest stand levels. Here, we used UAS-acquired RGB imagery in two pine sites to quantify defoliation at the tree-level and to verify the accuracy of the estimates. Our results allowed the identification of healthy, infested and completely defoliated trees and suggested that pine defoliation estimates using UASs are robust and allow high-accuracy (79%) field-based infestation indexes to be derived that are comparable to those used by forest technicians. When compared to current field-based methods, our approach provides PPM impact assessments with an efficient data acquisition method in terms of time and staff, allowing the quantitative estimation of defoliation at tree-level scale. Furthermore, our method could be expanded to a number of situations and scaled up in combination with satellite remote sensing imagery or citizen science approaches.

<p><strong>Abstract.</strong> Tree species classification at individual tree level is a challenging problem in forest management. Deep learning, a cutting-edge technology evolved from Artificial Intelligence, was seen to outperform other techniques when it comes to complex problems such as image classification. In this work, we present a novel method to classify forest tree species through high resolution RGB images acquired with a simple consumer grade camera mounted on a UAV platform using Residual Neural Networks. We used UAV RGB images acquired over three years that varied in numerous acquisition parameters such as season, time, illumination and angle to train the neural network. To begin with, we have experimented with limited data towards the identification of two pine species namely red pine and white pine from the rest of the species. We performed two experiments, first with the images from all three acquisition years and the second with images from only one acquisition year. In the first experiment, we obtained 80% classification accuracy when the trained network was tested on a distinct set of images and in the second experiment, we obtained 51% classification accuracy. As a part of this work, a novel dataset of high-resolution labelled tree species is generated that can be used to conduct further studies involving deep neural networks in forestry.</p>

Summary1. Mixed-wood boreal forests are often considered to undergo directional succession from shadeintolerant to shade-tolerant species. It is thus expected that overstorey gaps should lead to the recruitment of shade-tolerant conifers into the canopy in all stand development stages and that the recruitment of shade-intolerant hardwoods would be minimal except in the largest gaps. 2. We analysed short-term gap dynamics over a large 6-km 2 spatial area of mixed-wood boreal forest across a gradient of stands in different developmental stages with different times of origin since fire (expressed as stand 'age') that were affected differentially by the last spruce budworm (SBW) outbreak. Structural measurements of the canopy from lidar data were combined with spectral classification of broad species groups to characterize the gap disturbance regime and to evaluate the effect of gap openings on forest dynamics. 3. Estimated annual gap opening rates increased from 0.16% for 84-year-old stands to 0.88% for 248-year-old stands. Trees on gap peripheries in all stands were more vulnerable to mortality than interior canopy trees. 4. Due to recovery from the last SBW outbreak 16 years previously, gap closure rates were higher than opening rates, ranging from 0.44% to 2.05% annually, but did not show any relationship with stand age. There was, however, a continuing legacy of the last SBW outbreak in old-conifer stands in terms of a continued high mortality of conifers. In all stands, the majority of the openings were filled from below, although a smaller but significant proportion filled from lateral growth of gap edge trees. 5. Synthesis. The forest response to moderate-to small-scale disturbances in old-growth boreal forest counters the earlier assumption that the transition from one forest state to the next is slow and directional with time since the last fire. Overall, a small 6% increase in hardwoods was observed over 5 years, largely due to regeneration in-filling of hardwoods in gaps instead of successional transition to more shade-tolerant conifers. Gaps are vital for hardwood maintenance while transition to softwoods can occur without perceived gap-formation as overstorey trees die, releasing understorey trees.