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

2012 ◽  
Vol 6 (1) ◽  
pp. 062527 ◽  
Author(s):  
Joanne C. White
2004 ◽  
Vol 80 (6) ◽  
pp. 743-745 ◽  
Author(s):  
Joanne C White ◽  
Michael A Wulder ◽  
Darin Brooks ◽  
Richard Reich ◽  
Roger D Wheate

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


Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1785
Author(s):  
Benjamin A. Jones

The mountain pine beetle (MPB) destroys millions of coniferous trees annually throughout Western US forests. Coniferous forests are important air pollutant sinks, removing pollutants from the air such as PM2.5 (particulate matter < 2.5 μm in diameter), O3 (ozone), SO2 (sulfur dioxide), NO2 (nitrogen dioxide), and CO (carbon monoxide). In this paper, US Forest Service data on MPB tree mortality in the Western US is combined with a forest air pollution model (i-Tree Eco) and standard health impact functions to assess the human mortality and morbidity impacts of MPB-induced tree mortality. Modeling results suggest considerable spatial and temporal heterogeneity of impacts across the Western US. On average, MPB is associated with 10.0–15.7 additional deaths, 6.5–40.4 additional emergency room (ER) visits, and 2.2–10.5 additional hospital admissions per year over 2005–2011 due to lost PM2.5 sinks. For every 100 trees killed by MPB, the average PM2.5 mortality health costs are $418 (2019$). Impacts on other criteria pollutants are also estimated. Several sensitivity checks are performed on model inputs. These results have important policy implications for MPB management and on our understanding of the complex couplings between forest pests, forest health, and human health.


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