Augmenting the existing survey hierarchy for mountain pine beetle red-attack damage with satellite remotely sensed data

2006 ◽  
Vol 82 (2) ◽  
pp. 187-202 ◽  
Author(s):  
M A Wulder ◽  
J C White ◽  
B J Bentz ◽  
T. Ebata
Keyword(s):  
Mountain Pine Beetle ◽  
Survey Methods ◽  
Remotely Sensed ◽  
Detailed Data ◽  
Remotely Sensed Data ◽  
Temporal Precision ◽  
Management Objectives ◽  
Mountain Pine ◽  
Pine Beetle ◽  
Data Source

Estimates of the location and extent of the red-attack stage of mountain pine beetle (Dendroctonus ponderosae Hopkins) infestations are critical for forest management. The degree of spatial and temporal precision required for these estimates varies according to the management objectives and the nature of the infestation. This paper outlines the range of information requirements associated with mountain pine beetle infestations, from the perspectives of forest inventory, planning, and modeling. Current methods used to detect and map red-attack damage form a hierarchy of increasingly detailed data sources. The capability of satellite-based remotely sensed data to integrate into this hierarchy and provide data that is complementary to existing survey methods is presented, with specific examples using medium (Landsat) and high (IKONOS) spatial resolution imagery. The importance of matching the information requirement to the appropriate data source is emphasized as a means to reduce the overhead associated with data collection and processing. Key words: mountain pine beetle, red-attack, remote sensing, detection, Landsat, IKONOS

scholarly journals Challenges for the operational detection of mountain pine beetle green attack with remote sensing

2009 ◽  
Vol 85 (1) ◽  
pp. 32-38 ◽  
Author(s):  
Michael A Wulder ◽  
Joanne C White ◽  
Allan L Carroll ◽  
Nicholas C Coops
Keyword(s):  
Remote Sensing ◽  
Mountain Pine Beetle ◽  
Survey Methods ◽  
Remotely Sensed ◽  
Attack Detection ◽  
High Spectral Resolution ◽  
Remotely Sensed Data ◽  
Mountain Pine ◽  
Pine Beetle ◽  
Attack Trees

Mountain pine beetle infestations are spatially correlated; current (green) attack is often located near previous (red) attack. This spatial correlation between the green and red attack stages enables operational survey methods, as detection of red attack trees—typically from an airborne survey such as a helicopter GPS survey or aerial photography—guides the location of subsequent ground surveys for green attack trees. Forest managers, in an attempt to understand beetle movement and infestation patterns, hope to utilize remotely sensed data to detect and map green attack trees, with the expectation that the spatial extent, accuracy, and timeliness afforded by remotely sensed data will greatly improve the efficacy of beetle treatment and control. In this communication, we present the biological, logistical, and technological factors that limit the operational utility of remotely sensed data for green attack detection and mapping. To provide context for these limitations, we identify the operational information needs associated with green attack and discuss how these requirements dictate the characteristics of any potential remotely sensed data source (e.g., spatial, spectral, and temporal characteristics). Based upon our assessment, we conclude that the remote detection of green attack is not operationally viable, and is unlikely to become so unless the limiting factors we have identified are altered substantially or removed. Key words: green attack, remote sensing, operational, insect survey, high spatial resolution, high spectral resolution

Assessing forest management strategies under a mountain pine beetle attack in Alberta: exploring the impacts

2010 ◽  
Vol 40 (4) ◽  
pp. 597-610 ◽  
Author(s):  
Anne-Hélène Mathey ◽  
Harry Nelson
Keyword(s):  
Forest Management ◽  
Mountain Pine Beetle ◽  
Management Strategies ◽  
Timber Supply ◽  
Growing Stock ◽  
Management Objectives ◽  
Mountain Pine ◽  
Resource Managers ◽  
Recourse Approach ◽  
Pine Beetle

We explore how forest resource managers can respond to a potential outbreak of mountain pine beetle ( Dendroctonus ponderosae Hopkins, 1902) by assessing how well different forest management strategies achieve various management objectives over time. Strategies include targeting at-risk stands as well as increasing harvest levels. Outcomes are evaluated on the basis of volume flows, net revenues, and the age class structure of the ending inventory. We use a spatially and temporally explicit model to simulate forest management outcomes and consider two different scenarios, one in which the attack occurs early and one where it is delayed. The model utilizes a planning with recourse approach in which the firm can reevaluate its harvesting schedule following the attack. We use company data from west-central Alberta for a 40-year planning exercise. The timing of the attack resulted in small differences in timber supply. However, most strategies performed better financially under an early attack, which limits the harvest of marginal stands. Increasing harvest levels performed better in economic terms but resulted in a very young growing stock with little old forest. The success of any strategy is linked to the timing of the attack and how it affects the growing stock, subsequently impacting timber and revenue flows.

scholarly journals Mapping mountain pine beetle infestation with high spatial resolution satellite imagery

2004 ◽  
Vol 80 (6) ◽  
pp. 743-745 ◽  
Author(s):  
Joanne C White ◽  
Michael A Wulder ◽  
Darin Brooks ◽  
Richard Reich ◽  
Roger D Wheate
Keyword(s):  
Spatial Resolution ◽  
Mountain Pine Beetle ◽  
High Spatial Resolution ◽  
Unsupervised Classification ◽  
Remotely Sensed ◽  
Validation Data ◽  
Mountain Pine ◽  
Pine Beetle ◽  
Attack Trees

The on-going mountain pine beetle outbreak in British Columbia has reached historic proportions. Recently, management efforts at the local level shifted from exhaustive mapping of the infestation, to detection and mitigation of sites with minimal levels of infestation, creating an operational need for efficient and cost-effective methods to identify red-attack trees in these areas. High spatial resolution remotely sensed imagery has the potential to satisfy this information need. This paper presents the unsupervised classification of 4 metre IKONOS multispectral imagery, for the detection of mountain pine beetle red-attack, at sites with minimal infestation (< 20% of trees infested). A 4-metre buffer (analogous to a single IKONOS pixel) was applied to the red-attack trees identified on the IKONOS imagery in order to account for positional errors. When compared to the independent validation data collected from the aerial photography, it was found that 70.1% (lightly infested sites) and 92.5% (moderately infested sites) of the red-attack trees existing on the ground were correctly identified through the classification of the remotely sensed IKONOS imagery. These results demonstrate the operational potential of using an unsupervised classification of IKONOS imagery to detect and map mountain pine beetle red-attack at sites with minimal levels of infestation. Key words: mountain pine beetle, remote sensing, accuracy assessment, IKONOS, red-attack

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