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Continuous Change Detection and Classification—Spectral Trajectory Breakpoint Recognition for Forest Monitoring

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  • Yangjian Zhang

    (State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Li Wang

    (State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Quan Zhou

    (State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Feng Tang

    (School of Land Science and Technology, China University of Geosciences, Beijing 100083, China)

  • Bo Zhang

    (State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Ni Huang

    (State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Biswajit Nath

    (Department of Geography and Environmental Studies, Faculty of Biological Sciences, University of Chittagong, Chittagong 4331, Bangladesh)

Abstract

Forest is one of the most important surface coverage types. Monitoring its dynamics is of great significance in global ecological environment monitoring and global carbon circulation research. Forest monitoring based on Landsat time-series stacks is a research hotspot, and continuous change detection is a novel approach to real-time change detection. Here, we present an approach, continuous change detection and classification-spectral trajectory breakpoint recognition, running on Google Earth Engine (GEE) for monitoring forest disturbance and forest long-term trends. We used this approach to monitor forest disturbance and the change in forest cover rate from 1987 to 2020 in Nanning City, China. The high-resolution Google Earth images are collected for the validation of forest disturbance. The classification accuracy of forest, non-forest, and water maps by using the optima classification features was 95.16%. For disturbance detection, the accuracy of our map was 86.4%, significantly higher than 60% of the global forest change product. Our approach can successfully generate high-accuracy classification maps at any time and detect the forest disturbance time on a monthly scale, accurately capturing the thinning cycle of plantations, which earlier studies failed to estimate. All the research work is integrated into GEE to promote the use of the approach on a global scale.

Suggested Citation

  • Yangjian Zhang & Li Wang & Quan Zhou & Feng Tang & Bo Zhang & Ni Huang & Biswajit Nath, 2022. "Continuous Change Detection and Classification—Spectral Trajectory Breakpoint Recognition for Forest Monitoring," Land, MDPI, vol. 11(4), pages 1-20, March.
  • Handle: RePEc:gam:jlands:v:11:y:2022:i:4:p:504-:d:784199
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    References listed on IDEAS

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    1. Susan C. Cook-Patton & Sara M. Leavitt & David Gibbs & Nancy L. Harris & Kristine Lister & Kristina J. Anderson-Teixeira & Russell D. Briggs & Robin L. Chazdon & Thomas W. Crowther & Peter W. Ellis & , 2020. "Mapping carbon accumulation potential from global natural forest regrowth," Nature, Nature, vol. 585(7826), pages 545-550, September.
    2. Brice B. Hanberry, 2021. "Timing of Tree Density Increases, Influence of Climate Change, and a Land Use Proxy for Tree Density Increases in the Eastern United States," Land, MDPI, vol. 10(11), pages 1-17, October.
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