Examining change detection approaches for tropical mangrove monitoring
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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.
| 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 |
| Google Analytic Metrics | Metrics page |