Enhancing forest inventories with mountain pine beetle infestation information

Polygon decomposition is an approach for integrating different data sources within a GIS. We use this approach to understand the impacts associated with mountain pine beetle red attack. Three different sources of red attack information are considered: aerial overview sketch mapping, helicopter GPS surveys, and Landsat imagery. Existing inventory polygons are augmented with estimates of the proportion and area of red attack damage. Although differences are found in the area of the infestation, the affected forest stands have similar characteristics. Polygon decomposition adds value to existing forest inventories through update and the incorporation of new attributes applicable to forest management. Key words: polygon decomposition, forest inventory, GIS, mountain pine beetle, red attack, remote sensing, Landsat

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The Mountain Pine Beetle Epidemic in the Black Hills, South Dakota: The Consequences of Long Term Fire Policy, Climate Change and the Use of Remote Sensing to Enhance Mitigation

Journal of Geography and Geology ◽

10.5539/jgg.v10n1p69 ◽

2018 ◽

Vol 10 (1) ◽

pp. 69 ◽

Cited By ~ 2

Author(s):

Kyle Mullen ◽

Fei Yuan ◽

Martin Mitchell

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Ponderosa Pine ◽

Mountain Pine Beetle ◽

Black Hills ◽

Fire Policy ◽

Mountain Pine ◽

Pine Beetle ◽

Ponderosa Pine Forest

The recent and intense outbreak (first decade of 2000s) of the mountain pine beetle in the Black Hills of South Dakota and Wyoming, which impacted over 33% of the 1.2 million acre (486,000 ha) Black Hills National Forest, illustrates what can occur when forest management practices intersect with natural climatic oscillations and climate change to create the “perfect storm” in a region where the physical environment sets the stage for a plethora of economic activities ranging from extractive industries to tourism. This study evaluates the potential of WorldView-2 satellite imagery for green-attacked tree detection in the ponderosa pine forest of the Black Hills, USA. It also discusses the consequences of long term fire policy and climate change, and the use of remote sensing technology to enhance mitigation. It was found that the near-infrared one (band 7) of WorldView-2 imagery had the highest influence on the green-attack classification. The Random Forest classification produced the best results when transferred to the independent dataset, whereas the Logistic Regression models consistently yielded the highest accuracies when cross-validated with the training data. Lessons learned include: (1) utilizing recent advances in remote sensing technologies, most notably the use of WorldView-2 data, to assist in more effectively implementing mitigation measures during an epidemic, and (2) implementing pre-emptive thinning strategies; both of which can be applied elsewhere in the American West to more effectively blunt or preclude the consequences of a mountain pine beetle outbreak on an existing ponderosa pine forest.

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Hyperspectral Remote Sensing of Mountain Pine Beetle with an Emphasis on Previsual Assessment

Canadian Journal of Remote Sensing ◽

10.1080/07038992.2015.1065707 ◽

2015 ◽

Vol 41 (3) ◽

pp. 191-202 ◽

Cited By ~ 11

Author(s):

K. Olaf Niemann ◽

G. Quinn ◽

R. Stephen ◽

F. Visintini ◽

D. Parton

Keyword(s):

Remote Sensing ◽

Mountain Pine Beetle ◽

Hyperspectral Remote Sensing ◽

Mountain Pine ◽

Pine Beetle

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MPB Critical Forest Inventory

The Forestry Chronicle ◽

10.5558/tfc2014-096 ◽

2014 ◽

Vol 90 (04) ◽

pp. 475-478

Keyword(s):

Species Composition ◽

Land Management ◽

Forest Inventory ◽

Mountain Pine Beetle ◽

Tree Height ◽

Forest Products ◽

Growth And Yield ◽

Yield Analysis ◽

Mountain Pine ◽

Pine Beetle

The Mountain Pine Beetle epidemic in Alberta has been substantial, with several forest products companies facing a potential decrease in fibre supply as a result. Accurate forest inventory is integral in developing management strategies that effectively address the infestation. Within this context, forest inventory must provide enough species composition detail to allow the design of appropriate harvesting activities. The project evaluated the use of softcopy photo-interpretation and a semi-automated inventory approach to create a forest inventory with a higher level of detail, and looked to advance these methodologies to explore whether metrics such as tree height and volume could also be included. The project also aimed to demonstrate the benefits that such an inventory could provide in growth and yield analysis and within the general framework of integrated land management. Results indicate that the more detailed inventory is useful in addressing forest management challenges associated with the Mountain Pine Beetle infestation and in improving growth and yield analysis, resulting in an overall enhancement to strategic and operational planning. The inventory can also be used for integrated land management, allowing for species composition to be spatially identified within the stand and the identification of other features including anthropogenic disturbance and microsites.

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Integrating remotely sensed and ancillary data sources to characterize a mountain pine beetle infestation

Remote Sensing of Environment ◽

10.1016/j.rse.2006.06.007 ◽

2006 ◽

Vol 105 (2) ◽

pp. 83-97 ◽

Cited By ~ 41

Author(s):

Nicholas C. Coops ◽

Michael A. Wulder ◽

Joanne C. White

Keyword(s):

Mountain Pine Beetle ◽

Remotely Sensed ◽

Data Sources ◽

Ancillary Data ◽

Mountain Pine ◽

Pine Beetle

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

The Forestry Chronicle ◽

10.5558/tfc85032-1 ◽

2009 ◽

Vol 85 (1) ◽

pp. 32-38 ◽

Cited By ~ 22

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

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