Examining change detection approaches for tropical mangrove monitoring

Soe Myint, Janet Franklin, Michaela Buenemann, Won K. Kim, Chandra P. Giri

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

This study evaluated the effectiveness of different band combinations and classifiers (unsupervised, supervised, object-oriented nearest neighbor, and object-oriented decision rule) for quantifying mangrove forest change using multitemporal Landsat data. A discriminant analysis using spectra of different vegetation types determined that bands 2 (0.52 to 0.6 μm), 5 (1.55 to 1.75 μm), and 7 (2.08 to 2.35 μm) were the most effective bands for differentiating mangrove forests from surrounding land cover types. A ranking of thirty-six change maps, produced by comparing the classification accuracy of twelve change detection approaches, was used. The object-based Nearest Neighbor classifier produced the highest mean overall accuracy (84 percent) regardless of band combinations. The automated decision rule-based approach (mean overall accuracy of 88 percent) as well as a composite of bands 2, 5, and 7 used with the unsupervised classifier and the same composite or all band difference with the object-oriented Nearest Neighbor classifier were the most effective approaches.

Original languageEnglish (US)
Pages (from-to)983-993
Number of pages11
JournalPhotogrammetric Engineering and Remote Sensing
Volume80
Issue number10
DOIs
StatePublished - Oct 1 2014

ASJC Scopus subject areas

  • Computers in Earth Sciences

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