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[Object-oriented aquatic vegetation extracting approach based on visible vegetation indices.]

Ran Jing1,2, Lei Deng1,2, Wen Ji Zhao1,2

  • 1Ministry of Education Key Laboratory of 3D-Information Acquisition and Application, Capital Normal University, Beijing 100048, China.

Ying Yong Sheng Tai Xue Bao = the Journal of Applied Ecology
|May 8, 2018
PubMed
Summary

Object-oriented image analysis (OBIA) significantly improves aquatic vegetation mapping accuracy using mini-UAV data. This method enhances extraction precision compared to traditional supervised classification, offering a feasible approach for similar environments.

Keywords:
aquatic vegetationestimation of scale parametermini-UAV imageobject-oriented image classificationsupervised classificationvisible vegetation index

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Area of Science:

  • Remote Sensing
  • Environmental Monitoring
  • Geographic Information Systems (GIS)

Background:

  • Accurate aquatic vegetation mapping is crucial for environmental management.
  • Traditional pixel-based classification methods often struggle with complex spatial patterns.
  • Mini-unmanned aerial vehicle (mini-UAV) data offers high-resolution imagery for detailed environmental analysis.

Purpose of the Study:

  • To develop and evaluate an object-oriented image analysis (OBIA) method for aquatic vegetation extraction.
  • To compare the accuracy of OBIA with traditional supervised classification using mini-UAV data.
  • To determine the optimal image segmentation scale for improved classification results.

Main Methods:

  • Utilized the estimation of scale parameters (ESP) tool for optimal image segmentation scale determination.
  • Applied multi-scale segmentation to create optimally segmented images.
  • Selected visible vegetation indices from mini-UAV data and developed a decision tree rule.
  • Employed a membership function for automatic area classification and aquatic vegetation mapping.

Main Results:

  • Object-oriented image analysis (OBIA) achieved an overall classification accuracy of 91.7%, significantly higher than supervised classification (53.7%).
  • The Kappa value for OBIA was 0.9, compared to 0.4 for supervised classification, indicating substantial improvement.
  • The OBIA method demonstrated superior performance in extracting aquatic vegetation compared to pixel-based methods.

Conclusions:

  • The developed OBIA method, utilizing visible vegetation indices from mini-UAV data, is a feasible and accurate approach for aquatic vegetation extraction.
  • OBIA significantly enhances classification accuracy and extraction precision for aquatic vegetation mapping.
  • This methodology can be effectively applied to other environmentally similar regions for vegetation monitoring.