Sam B. Coggins

Monitoring Tree-level Insect Population Dynamics with Multi-scale and Multi-source Remote Sensing

Long term monitoring of the rate‐of‐change of mountain pine beetle (Dendroctonus ponderosae Hopkins) populations requires detailed tree‐level information over large areas. This information is used to assess the status of an infestation (e.g., increasing, stable or decreasing), and to select and evaluate mitigation approaches. In this research project, we develop and demonstrate a prototype monitoring system, which enables the extrapolation of tree level estimates of beetle damage from field data to a larger study area using a double sampling approach, and multi‐scale, multi‐source, high spatial resolution remotely sensed data.

Digital high spatial resolution aerial imagery to support forest health monitoring: the mountain pine beetle context

We summarize the capacity of high spatial resolution ( < 1     m ) digital aerial imagery to support forest health monitoring. We review the current use of digital aerial imagery in the context of the recent mountain pine beetle epidemic in western Canada. Supported by this review, we posit that high spatial resolution digital aerial imagery can play at least two critical roles in forest health monitoring. First, the capacity to characterize damage at the individual tree level directly supports a broad range of forest health information needs (e.g., tree-level attributes for estimating the population at risk and for inputs to models, estimates of mortality, rates of population growth). Second, the level of detail afforded by the digital high spatial resolution aerial imagery provides critical calibration and validation data for lower spatial resolution remotely sensed imagery (e.g., QuickBird, Landsat) for large-area detection and mapping of forest damage and can be used in a double sampling scheme as a bridge between detailed field measures and landscape-level estimates of mortality. In an era with increasing numbers of commercially deployed sensors capable of acquiring high spatial resolution satellite imagery, the flexibility and cost-effectiveness of aerial image options should not be disregarded. Moreover, experiences with airborne imagery can continue to inform applications using high spatial resolution satellite imagery for forest health information needs.

Estimates of bark beetle infestation expansion factors with adaptive cluster sampling

Insects have infested over 37 million hectares of forested land, the most aggressive forest insect pest in North America is the mountain pine beetle that has attacked 14 million hectares. To determine infestation extent and spread rates, we examined mountain pine beetle damage at two sites over two consecutive years (2007–2008). High spatial resolution (20 cm) airborne digital imagery was acquired over a range of infestation intensities (High: site A; Low: site B). An adaptive cluster sampling approach assessed the extent and severity of damage from the imagery. In 2007, site A contained 5.22 infested trees per hectare (variance: 10.65) increasing in 2008 to 11.02 trees per hectare (variance: 24.83). In contrast, site B had 0.25 infested trees per hectare in 2007 (variance: 0.02), which increased in 2008 to 0.47 trees per hectare, with a variance of 0.08 trees per hectare. At both sites, infestations approximately doubled over a 1-year period. Adaptive cluster sampling applied to high spatial resolution airborne imagery can provide estimates of the severity of attack on the landscape.

Augmenting forest inventory attributes with geometric optical modelling in support of regional susceptibility assessments to bark beetle infestations

Assessment of the susceptibility of forests to mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation is based upon an understanding of the characteristics that predispose the stands to attack. These assessments are typically derived from conventional forest inventory data; however, this information often represents only managed forest areas. It does not cover areas such as forest parks or conservation regions and is often not regularly updated resulting in an inability to assess forest susceptibility. To address these shortcomings, we demonstrate how a geometric optical model (GOM) can be applied to Landsat-5 Thematic Mapper (TM) imagery (30 m spatial resolution) to estimate stand-level susceptibility to mountain pine beetle attack. Spectral mixture analysis was used to determine the proportion of sunlit canopy and background, and shadow of each Landsat pixel enabling per pixel estimates of attributes required for model inversion. Stand structural attributes were then derived from inversion of the geometric optical model and used as basis for susceptibility mapping. Mean stand density estimated by the geometric optical model was 2753 (standard deviation ± 308) stems per hectare and mean horizontal crown radius was 2.09 (standard deviation ± 0.11) metres. When compared to equivalent forest inventory attributes, model predictions of stems per hectare and crown radius were shown to be reasonably estimated using a Kruskal–Wallis ANOVA (p < 0.001). These predictions were then used to create a large area map that provided an assessment of the forest area susceptible to mountain pine beetle damage.