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Mapping mountain pine beetle mortality through growth trend analysis of time-series landsat data

Remote Sensing

By:
, , , , and
DOI: 10.3390/rs6065696

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Abstract

Disturbances are key processes in the carbon cycle of forests and other ecosystems. In recent decades, mountain pine beetle (MPB; Dendroctonus ponderosae) outbreaks have become more frequent and extensive in western North America. Remote sensing has the ability to fill the data gaps of long-term infestation monitoring, but the elimination of observational noise and attributing changes quantitatively are two main challenges in its effective application. Here, we present a forest growth trend analysis method that integrates Landsat temporal trajectories and decision tree techniques to derive annual forest disturbance maps over an 11-year period. The temporal trajectory component successfully captures the disturbance events as represented by spectral segments, whereas decision tree modeling efficiently recognizes and attributes events based upon the characteristics of the segments. Validated against a point set sampled across a gradient of MPB mortality, 86.74% to 94.00% overall accuracy was achieved with small variability in accuracy among years. In contrast, the overall accuracies of single-date classifications ranged from 37.20% to 75.20% and only become comparable with our approach when the training sample size was increased at least four-fold. This demonstrates that the advantages of this time series work flow exist in its small training sample size requirement. The easily understandable, interpretable and modifiable characteristics of our approach suggest that it could be applicable to other ecoregions.

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Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Mapping mountain pine beetle mortality through growth trend analysis of time-series landsat data
Series title:
Remote Sensing
DOI:
10.3390/rs6065696
Volume
6
Issue:
6
Year Published:
2014
Language:
English
Publisher:
MDPI
Contributing office(s):
Geosciences and Environmental Change Science Center
Description:
21 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Remote Sensing
First page:
5696
Last page:
5716
Country:
United States
State:
Colorado
County:
Grand County