Using landsat time series imagery to detect forest disturbance in selectively logged tropical forests in Myanmar

Katsuto Shimizu, Raul Ponce-Hernandez, Oumer S. Ahmed, Tetsuji Ota, Zar Chi Win, Nobuya Mizoue, Shigejiro Yoshida

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Detecting forest disturbances is an important task in formulating mitigation strategies for deforestation and forest degradation in the tropics. Our study investigated the use of Landsat time series imagery combined with a trajectory-based analysis for detecting forest disturbances resulting exclusively from selective logging in Myanmar. Selective logging was the only forest disturbance and degradation indicator used in this study as a causative force, and the results showed that the overall accuracy for forest disturbance detection based on selective logging was 83.0% in the study area. The areas affected by selective logging and other factors accounted for 4.7% and 5.4%, respectively, of the study area from 2000 to 2014. The detected disturbance areas were underestimated according to error assessments; however, a significant correlation between areas of disturbance and numbers of harvested trees during the logging year was observed, indicating the utility of a trajectory-based, annual Landsat imagery time series analysis for selective logging detection in the tropics. A major constraint of this study was the lack of available data for disturbances other than selective logging. Further studies should focus on identifying other types of disturbances and their impacts on future forest conditions.

Original languageEnglish
Pages (from-to)289-296
Number of pages8
JournalCanadian Journal of Forest Research
Volume47
Issue number3
DOIs
Publication statusPublished - Jan 1 2017

Fingerprint

Myanmar
Landsat
selective logging
tropical forests
logging
tropical forest
time series analysis
imagery
time series
disturbance
trajectories
tropics
trajectory
degradation
deforestation
mitigation

All Science Journal Classification (ASJC) codes

  • Global and Planetary Change
  • Forestry
  • Ecology

Cite this

Using landsat time series imagery to detect forest disturbance in selectively logged tropical forests in Myanmar. / Shimizu, Katsuto; Ponce-Hernandez, Raul; Ahmed, Oumer S.; Ota, Tetsuji; Win, Zar Chi; Mizoue, Nobuya; Yoshida, Shigejiro.

In: Canadian Journal of Forest Research, Vol. 47, No. 3, 01.01.2017, p. 289-296.

Research output: Contribution to journalArticle

Shimizu, Katsuto ; Ponce-Hernandez, Raul ; Ahmed, Oumer S. ; Ota, Tetsuji ; Win, Zar Chi ; Mizoue, Nobuya ; Yoshida, Shigejiro. / Using landsat time series imagery to detect forest disturbance in selectively logged tropical forests in Myanmar. In: Canadian Journal of Forest Research. 2017 ; Vol. 47, No. 3. pp. 289-296.
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