Stephanie M. Ortlepp

Evaluation of Landsat-7 SLC-off image products for forest change detection

Since July 2003, Landsat-7 ETM+ has been operating without the scan line corrector (SLC), which compensates for the forward motion of the satellite in the imagery acquired. Data collected in SLC-off mode have gaps in a systematic wedge-shaped pattern outside of the central 22 km swath of the imagery; however, the spatial and spectral quality of the remaining portions of the imagery are not diminished. To explore the continued use of Landsat-7 ETM+ SLC-off imagery to characterize change in forested environments, we compare the change detection results generated from a reference image pair (a 1999 Landsat-7 ETM+ image and a 2003 Landsat-5 TM image) with change detection results generated from the same 1999 Landsat-7 ETM+ image coupled with three different 2003 Landsat-7 SLC-off products: unremediated SLC-off (i.e., with gaps); histogram-based gap-filled; and segment-based gap-filled. The results are compared on both a pixel and polygon basis; on a pixel basis, the unremediated SLC-off product missed 35% of the change identified by the reference data, and the histogram- and segment-based gap-filled products missed 23% and 21% of the change, respectively. When using forest inventory polygons as a context for change (to reduce commission error), the amount of change missed was 31%, 14%, and 12% for the each of the unremediated, histogram-based gap-filled, and segment-based gap-filled products, respectively. Our results indicate that over the time period considered, and given the types and spatial distribution of change events within our study area, the gap-filled products can provide a useful data source for change detection in forested environments. The selection of which product to use is, however, very dependent on the nature of the application and the spatial configuration of change events.

Investigating the effectiveness of Mountain Pine Beetle mitigation strategies

We review a broad range of mitigation strategies associated with the management of Mountain Pine Beetle (Dendroctonus ponderosae Hopkins). We consider: methods that are currently utilised or have been proposed for controlling beetle populations; the manner in which the effectiveness of these approaches is monitored and assessed; and the role that remotely sensed data may play in a large-area monitoring system. To this end, we first examine the goals of effectiveness monitoring and introduce a general classification system to clarify the purpose and practice of efficacy monitoring. Based on these principles, the review is then structured around effectiveness evaluations for managing forest pests, primarily Mountain, Southern (Dendroctonus frontalis Zimmermann), and Western Pine Beetles (Dendroctonus brevicomis LeConte) throughout North America, and grouped by management strategy: silvicultural treatments; prescribed burns; and the use of attractants, repellants and insecticides. Finally, we propose the use of remotely sensed data as a complementary tool for monitoring changes in the extent and severity of Mountain Pine Beetle damage across large areas. Use of such data enables assessment of the efficacy of landscape level management practices, directing the application of new mitigation activities, and reducing the risk of future infestations.

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.