, 28:336 | Cite as

Monitoring mangrove forest changes using remote sensing and GIS data with decision-tree learning

  • Kai Liu
  • Xia Li
  • Xun Shi
  • Shugong Wang


This paper presents a decision-tree method for identifying mangroves in the Pearl River Estuary using multi-temporal Landsat TM data and ancillary GIS data. Remote sensing can be used to obtain mangrove distribution information. However, serious confusion in mangrove classification using conventional methods can develop because some types of land cover (e.g., agricultural land and forests) have similar spectral behaviors and distribution features to mangroves. This paper develops a decisiontree learning method for integrating Landsat TM data and ancillary GIS data (e.g., DEM and proximity variables) to solve this problem. The analysis has demonstrated that this approach can produce superior mangrove classification results to using only imagery or ancillary data. Three temporal maps of mangroves in the Pearl River Estuary were obtained using this decision-tree method. Monitoring results indicated a rapid decline of mangrove forest area in recent decades because of intensified human activities.

Key Words

change detection DEM Landsat TM image Pearl River Estuary 

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Copyright information

© The Society of Wetland Scientists 2008

Authors and Affiliations

  • Kai Liu
    • 1
    • 5
    • 6
  • Xia Li
    • 2
  • Xun Shi
    • 3
  • Shugong Wang
    • 4
  1. 1.Guangzhou Institute of GeochemistryChinese Academy of SciencesGuangzhouP.R. China
  2. 2.School of Geography and PlanningSun Yat-sen UniversityGuangzhouP.R. China
  3. 3.Department of GeographyDartmouth CollegeHanoverUSA
  4. 4.Institute of Environmental ScienceSun Yat-sen UniversityGuangzhouP.R. China
  5. 5.Guangzhou Institute of GeographyGuangzhouP.R. China
  6. 6.Graduate School of Chinese Academy of SciencesBeijingP.R. China

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