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Age Identification of Farmland Shelterbelt Using Growth Pattern Based on Landsat Time Series Images

Xing Zhang,Jie Li,Ying Li,Rongxin Deng,Gao Yang,Jing Tang

Published 2023 in Remote Sensing

ABSTRACT

Farmland shelterbelt, as a category of shelterbelt in forestry ecological engineering, has an important influence on agricultural sustainability in agricultural systems. Timely and accurate acquisition of farmland shelterbelt age is not only essential to understanding their shelter effects but also directly relates to the adjustment of subsequent shelterbelt projects. In this study, we developed an age identification method using growth pattern to extract the age of shelterbelt (i.e., years after planting) based on Landsat time series images. This method was applied to a typical area of shelterbelt construction in the north of Changchun, China. The results indicated that the accuracy of age identification reached a stable situation when the permissible age error exceeded 3 years, achieving an accuracy of approximately 90%. Moreover, the accuracy at different growth phases (1–3 years, 4–15 years, 16–30 years, and >30 years) decreased with increasing age, and the accuracy of each growth phase can reach more than 80% when the permissible age error is beyond 7 years. Compared to building the typically weak statistical relationship between the shelterbelt age and remote sensing characteristic information to derive age, this method presented a direct age identification method for fine-scale age extraction of the shelterbelt. It introduced a novel perspective for shelterbelt age identification and the assessment of shelterbelt project advancement on the regional scale.

PUBLICATION RECORD

  • Publication year

    2023

  • Venue

    Remote Sensing

  • Publication date

    2023-09-28

  • Fields of study

    Agricultural and Food Sciences, Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/rs15194750
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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