Examining change detection approaches for tropical mangrove monitoring

Photogrammetric Engineering and Remote Sensing
By: , and 

Links

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.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Examining change detection approaches for tropical mangrove monitoring
Series title Photogrammetric Engineering and Remote Sensing
DOI 10.14358/PERS.80.10.983
Volume 10
Year Published 2014
Language English
Publisher American Society of Photogrammetry and Remote Sensing
Contributing office(s) Earth Resources Observation and Science (EROS) Center
Description 11 p.
First page 983
Last page 993