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Remote Sens. 2014, 6(6), 5696-5716; doi:10.3390/rs6065696

Mapping Mountain Pine Beetle Mortality through Growth Trend Analysis of Time-Series Landsat Data

1
Department of Environmental Science, Policy and Management, University of California, Berkeley, CA 94720, USA
2
Geological Survey, P.O. Box 25046, DFC, MS 980, Denver, CO 80225, USA
3
Geological Survey, 12201 Sunrise Valley Drive, Reston, VA 20192, USA
4
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China
5
Joint Center for Global Change Studies, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Received: 14 March 2014 / Revised: 9 June 2014 / Accepted: 9 June 2014 / Published: 18 June 2014
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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. View Full-Text
Keywords: Landsat; mountain pine beetle; time-series classification; temporal segmentation; decision tree; sample size Landsat; mountain pine beetle; time-series classification; temporal segmentation; decision tree; sample size
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Liang, L.; Chen, Y.; Hawbaker, T.J.; Zhu, Z.; Gong, P. Mapping Mountain Pine Beetle Mortality through Growth Trend Analysis of Time-Series Landsat Data. Remote Sens. 2014, 6, 5696-5716.

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