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Related Experiment Video

Updated: Mar 30, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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[An object-based information extraction technology for dominant tree species group types].

Tian Tian, Wen-yi Fan, Wei Lu

    Ying Yong Sheng Tai Xue Bao = the Journal of Applied Ecology
    |November 18, 2015
    PubMed
    Summary

    Object-oriented classification using remote sensing data accurately identifies tree types. This method, incorporating spectral, texture, and vegetation indices, achieved 91.3% classification accuracy.

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    Area of Science:

    • Remote Sensing
    • Forestry
    • Image Classification

    Background:

    • Accurate extraction of dominant tree group types from remote sensing imagery is challenging.
    • Object-oriented classification offers a novel approach for precise information extraction using high spatial resolution data.

    Purpose of the Study:

    • To apply an object-oriented classification method for identifying various land cover types, including specific tree species.
    • To evaluate the effectiveness of incorporating spectral, texture, and vegetation indices in improving classification accuracy.

    Main Methods:

    • Utilized Quickbird satellite imagery from 2013 for the Jiangle Forest Farm.
    • Employed an object-oriented classification approach with a class hierarchy.
    • Integrated spectral, texture, and vegetation indices with membership functions and decision tree rules.

    Main Results:

    • Achieved an overall classification accuracy of 91.3% for identifying farmland, shrub-herbaceous plants, young afforested land, Pinus massoniana, Cunninghamia lanceolata, and broad-leaved trees.
    • Demonstrated a 5.7% accuracy improvement compared to methods using only texture and spectral factors.

    Conclusions:

    • The object-oriented classification method, enhanced by spectral, texture, and vegetation indices, is highly effective for detailed land cover classification.
    • This approach significantly improves the accuracy of identifying dominant tree group types in remote sensing.