Andrés Varhola

The influence of ground- and lidar-derived forest structure metrics on snow accumulation and ablation in disturbed forests

The current mountain pine beetle infestation in British Columbias lodgepole pine forests has raised concerns about potential impacts on water resources. Changes in forest structure resulting from defoliation, windthrow, and salvage harvesting may increase snow accumulation and ablation (i.e., spring runoff and flooding risk) below the forest canopy be- cause of reduced snow interception and higher levels of radiation reaching the surface. Quantifying these effects requires a better understanding of the link between forest structure and snow processes. Light detection and ranging (lidar) is an in- novative technology capable of estimating forest structure metrics in a detailed, three-dimensional approach not easily ob- tained from manual measurements. While a number of previous studies have shown that increased snow accumulation and ablation occur as forest cover decreases, the potential improvement of these relationships based on lidar metrics has not been quantified. We investigated the correlation between lidar-derived and ground-based traditional canopy metrics with snow accumulation and ablation indicators, demonstrating that a lidar-derived forest cover parameter was the strongest pre- dictor of peak snow accumulation (r 2 = 0.70, p < 0.001) and maximum snow ablation rate (r 2 = 0.59, p < 0.01). Improving our ability to quantify changes in forest structure in extensive areas will assist in developing more robust models of water- shed processes.

A New Low-Cost, Stand-Alone Sensor System for Snow Monitoring

Abstract Monitoring continuous changes in snowpack dynamics and its meteorological drivers is critical for understanding key aspects of water resources, climate variability, and ecology. While manual snow surveys have traditionally been used to evaluate snow processes, their high costs and discrete measurements can lead to biased estimations of accumulation and ablation rates. Ultrasonic range sensors offer an alternative to continuously monitor snow depth but their widespread employment has been limited because of high prices. This paper describes the development of an inexpensive prototype ultrasonic sensor suite characterized by a ready-to-use stand-alone design and flexibility to incorporate additional meteorological instruments. The performance of 48 units was tested during a winter season in central British Columbia, recording snow depth and air temperature data consistent with those from nearby weather stations and manual measurements. Despite a relatively small underestimation of snow depth due to...

Assessing differences in tree and stand structure following beetle infestation using lidar data

The current mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation in British Columbia is the largest in recorded history and has caused unprecedented damage to the lodgepole pine (Pinus contorta Dougl. var. latifolia Engelm.) forests of the interior of the province. During the early years after attack, changes to overall crown structure are relatively minor due to low needle loss; within several years, however, needle loss can be substantial, even at the stand level. Needle loss can affect snow hydrology due to the role of the canopy in interception and accumulation and its impacts on radiation transmission, wind speed, and the overall snowmelt energy balance. In addition, the infestation is impacting other forest attributes such as wildlife habitat, forest fire risk and behaviour, and biogeochemistry. In this paper we investigate variations in light detection and ranging (lidar) return hit densities and distributions, analyzed with high spatial resolution digital camera imagery, in response to changes in forest cover and structure due to beetle infestation at both the individual tree level and the stand level. Results indicate that the density of lidar returns from tree crowns is impacted by the later health status of the tree, with a larger number of returns from green and early attack phases and a significantly smaller number of returns from grey-attack crowns. At the stand level, there are a number of significant relationships between plot-level indicators of infestation and lidar-derived structural metrics, in particular with vegetation cover (r2  = 0.76, p < 0.001). The total number and vertical distribution of returns from vegetation in green, red-attacked, and grey-attacked pine stands were distinct. We conclude that the potential to combine the structure information derived from lidar technology with assessment of heath status from aerial imagery provides unique quantitative data that may be used to map lodgepole pine stands according to structural attributes relevant to both silviculturalists and hydrologists.

Forest inventory stand height estimates from very high spatial resolution satellite imagery calibrated with lidar plots

Many areas of forest across northern Canada are challenging to monitor on a regular basis as a result of their large extent and remoteness. Although no forest inventory data typically exist for these northern areas, detailed and timely forest information for these areas is required to support national and international reporting obligations. We developed and tested a sample-based approach that could be used to estimate forest stand height in these remote forests using panchromatic Very High Spatial Resolution (VHSR, < 1 m) optical imagery and light detection and ranging (lidar) data. Using a study area in central British Columbia, Canada, to test our approach, we compared four different methods for estimating stand height using stand-level and crown-level metrics generated from the VHSR imagery. ‘Lidar plots’ (voxel-based samples of lidar data) are used for calibration and validation of the VHSR-based stand height estimates, similar to the way that field plots are used to calibrate photogrammetric estimates of stand height in a conventional forest inventory or to make empirical attribute estimates from multispectral digital remotely sensed data. A k-nearest neighbours (k-NN) method provided the best estimate of mean stand height (R 2 = 0.69; RMSE = 2.3 m, RMSE normalized by the mean value of the estimates (RMSE-%) = 21) compared with linear regression, random forests, and regression tree methods. The approach presented herein demonstrates the potential of VHSR panchromatic imagery and lidar to provide robust and representative estimates of stand height in remote forest areas where conventional forest inventory approaches are either too costly or are not logistically feasible. While further evaluation of the methods is required to generalize these results over Canada to provide robust and representative estimation, VHSR and lidar data provide an opportunity for monitoring in areas for which there is no detailed forest inventory information available.