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.
Suggested Citation
Myint, S.W., Franklin, J., Buenemann, M., Kim, W., and Giri, C., 2014, Examining change detection approaches for tropical mangrove monitoring: Photogrammetric Engineering and Remote Sensing, v. 10, p. 983-993, https://doi.org/10.14358/PERS.80.10.983.
| 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 |