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Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

Shanshan Liu,Xinliang Wei,Dengqiu Li,D. Lu

Published 2017 in Remote Sensing

ABSTRACT

Detection of forest disturbance and recovery has received much attention during the last two decades due to its important influence on forest carbon budget estimation. This research used Landsat time-series data from 1984 to 2015 to examine forest disturbance and recovery in a subtropical region of eastern Zhejiang Province, China, through the LandTrendr algorithm. Field inventory data and high spatial resolution images were used to evaluate the disturbance and recovery results. This research indicates that high producer and user accuracies for both disturbance and recovery classes were obtained and three levels of disturbance and recovery each can be detected. Through incorporation of climate data and disturbance results, drought events also can be successfully detected. More research is needed to incorporate multisource data for detection of forest disturbance types in subtropical regions.

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    Remote Sensing

  • Publication date

    2017-05-14

  • Fields of study

    Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/rs9050479
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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