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.

Context

Forest management alters patterns generated by natural disturbances, particularly in ecosystems with infrequent fires. Management effects can differ according to spatial scale and affect ecological processes.

Objectives

To assess the effect of 80 years of forest management at both the landscape and burn/harvest scales on forest age, composition, density, spatial pattern and heterogeneity.

Methods

Forest inventory maps and satellite images were used to compare two contiguous landscapes, respectively managed and unmanaged, of the eastern boreal forest of Canada, in a region with infrequent fires. Burns and harvests occurring from 1920–1950 were also compared.

Results

In addition to reducing the proportion of old-growth stands in the landscape, forest management changed forest composition at both scales, favouring the late-successional species balsam fir. Landscape metrics indicated that old-growth forests and spruce-dominated ones were more fragmented, less connected, and confined to smaller patches in the managed landscape than in the unmanaged one. Forest management increased heterogeneity at the landscape scale, but decreased it at the burn/harvest scale. Logging had a homogenizing effect at the burn/harvest scale by attenuating the effect of the physical environment on forest density.

Conclusions

This study provides knowledge to help reduce effects of forest management at both scales. In this forest region with low fire recurrence, the goal should be to manage for greater forest heterogeneity at the burn/harvest scale whereas, at the landscape scale, restoration strategies should aim to create large contiguous patches of coniferous forests to increase spatial continuity as these were reduced by past management activities.

The opening and closure of “gaps” in forest canopies plays an important role in the structure, turnover, and overall ecological processes of natural forests. Gap characterization was until recently mostly based on field studies and relied on sampling approaches. ALS (Airborne Laser Scanning) has now revolutionized this field of scientific enquiry by giving researchers the capacity to detect and measure gaps rapidly over large areas. We first provide a brief scientific background on gaps and then succinctly review field and other conventional remote sensing methods to characterize them. We then turn our attention to the principles of ALS-based gap detection and review different methods of automated gap delineation and measurement. We explain how gap types can be automatically classified, and how multitemporal ALS can be used to not only monitor gap dynamics, but also to reveal the complex role of gaps in influencing tree growth within and around them.

Biodiversity monitoring is increasingly being bolstered with high resolution data derived from remote sensing such as LIDAR (Light Detection and Ranging). We derived a series of topographical variables, including slope, azimuth, ground curvature and flow accumulation from LIDAR images and compared these to captures of female carabids in pitfall traps in Eastern boreal mixedwood forests. We developed a series of species-specific logistic models predicting the proportion of females for eight dominant species, including Agonum retractum, Calathus ingratus, Platynus decentis, Pterostichus adstrictus, Pterostichus coracinus, Pterostichus pensylvanicus, Sphaeroderus nitidicollis and Synuchus impunctatus. We used these models to test three hypotheses related to how the modest topography in boreal forests could influence the availability of microhabitats and possibly potential sites for oviposition and larval development. In general, topographic features such as north facing slopes and high flow accumulation were important predictors of the proportion of females. Models derived from larger scale topography, such as hillsides or small watersheds on the order of ¼-1 ha were better predictors of the proportion of females than were models derived from finer scale topography such as hummocks and small depressions. We conclude that topography likely influences the distribution of carabids based on hydrological mechanisms rather than factors related to temperature. We further suggest based on the scale of responses that these hydrological mechanisms may be linked to the attenuation of past disturbances by wildfire and the propensity of unburned forest patches and fire skips.

1. Mixed-wood boreal forests are often considered to undergo directional succession from shade-intolerant 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-km2 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.

The combined analysis of lidar and image datasets for information extraction of forest structural attributes and composition requires that the image-to-lidar geometric correspondence be known accurately. We propose a series of methods for producing a 10 cm high quality true orthomosaic of Vexcel UltraCam images perfectly adjusted to the lidar digital surface model (DSM). First, we introduce a technique for filling the small cavities visible on lidar raster DSMs. We then assess the image-to-lidar registration using visualization and quantitative approaches. The small geometric discrepancy measured between the two datasets is then corrected. In the image overlap areas, the true orthomosaic is created by choosing the contributing image that has the smallest distance to the corresponding DSM pixel. Occluded pixels that can not be seen from any centre of perspective are then filled with synthetic values calculated according to their sunlit/shadowed state at the time the images were taken. The resulting true orthomosaic is perfectly registered to the lidar dataset, is complete (considering occluded pixels receive synthetic values), is not radiometrically altered, and shows no visible cut lines. The proposed process should greatly help the simultaneous analysis of lidar and image datasets.

Large canopy gaps in old-growth forests, formed as a result of tree fall events over time, could be composed of regeneration in various stages of growth different from that of single mortality events. Though important to understand forest dynamics such complex processes are rarely monitored due to limited techniques. Applying object-based techniques on a series of three lidar datasets acquired over nine years in boreal forests, we characterised gap events into old gaps, gap expansions and new random gaps. Combining broad species class from high resolution images, and individually locating gap saplings on the lidar surface, specieswise height growth across gradients of height was estimated. The results indicate distinct height growth patterns of both hardwood and softwood gap saplings in different gap events. The methods can potentially be extended to develop accurate juvenile growth patterns.

Field data describing the height growth of trees or stands over several decades are very scarce. Consequently, our capacity of analyzing forest dynamics over large areas and long periods of time is somewhat limited. This study proposes a new method for retrospectively reconstructing plot-wise average dominant tree height based on a time series of high-resolution canopy height maps, termed canopy height models (CHMs). The absolute elevation of the canopy surface, or digital surface model (DSM), was first reconstructed by applying image-matching techniques to stereo-pairs of aerial photographs acquired in 1945, 1965, 1983, and 2003. The historical CHMs were then created by subtracting the bare earth elevation provided from a recent lidar survey from the DSMs. A method for estimating average dominant tree height from these historical CHMs was developed and calibrated for each photographic year. The accuracy of the resulting remote sensing height estimates was compared to age–height data reconstructed based on dendrometric measurements. The height bias of the remote sensing estimates relative to the verification data ranged from 0.52 m to 1.55 m (1.16 m on average). The corresponding root-mean-square errors varied between 1.49 m and 2.88 m (2.03 m average). Despite being slightly less accurate than historical field data, the quality of the remote sensing estimates is sufficient for many types of forest dynamics studies. The procedures for implementing this method, with the exception of the calibration phase, are entirely automated such that forest height growth curves can be reconstructed and mapped over large areas for which recent lidar data and historical photographs exist.

Our objective was to assess the accuracy of the forest height and biomass estimates derived from an Ikonos stereo pair and a lidar digital terrain model (DTM). After the Ikonos scenes were registered to the DTM with submetric accuracy, tree heights were measured individually by subtracting the photogrammetric elevation of the treetop from the lidar ground-level elevation of the tree base. The low residual error (1.66 m) of the measurements confirmed the joint geometric accuracy of the combined models. Matched images of the stereo pair were then used to create a digital surface model. The latter was transformed to a canopy height model (CHM) by subtracting the lidar DTM. Plotwise height percentiles were extracted from the Ikonos-lidar CHM and used to predict the average dominant height and above-ground biomass. The coefficient of determination reached 0.91 and 0.79 for average height and biomass, respectively. In both cases, the accuracy of the Ikonos-lidar CHM predictions was slightly lower than that of the all-lidar reference CHM. Although the CHM heights did not saturate at moderate biomass levels, as do multispectral or radar images, values above 300Mgha21 could not be predicted accurately by the Ikonos-lidar or by the all-lidar CHM.

Understanding a disturbance regime such as gap dynamics requires that we study its spatial and temporal characteristics. However, it is still difficult to observe and measure canopy gaps extensively in both space and time using field measurements or bi-dimensional remote sensing images, particularly in open and patchy boreal forests. In this study, we investigated the feasibility of using small footprint lidar to map boreal canopy gaps of sizes ranging from a few square meters to several hectares. Two co-registered canopy height models (CHMs) of optimal resolution were created from lidar datasets acquired respectively in 1998 and 2003. Canopy gaps were automatically delineated using an object-based technique with an accuracy of 96%. Further, combinatorics was applied on the two CHMs and the delineated gaps to provide information on the area of old and new gaps, gap expansions, new random gap openings, gap closure due to lateral growth of adjacent vegetation or due to vertical growth of regeneration. The results obtained establish lidar as an excellent tool for rapidly acquiring detailed and spatially extensive short-term dynamics of canopy gaps. © 2007 Elsevier Inc. All rights reserved.