Authors

C. F. Chen; N. T. Son; N. B. Chang; C. R. Chen; L. Y. Chang; M. Valdez; G. Centeno; C. A. Thompson;J. L. Aceituno

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

Remote Sens.

Keywords

Landsat; mangrove forests; image classification; change detection; change projection; ARTIFICIAL NEURAL-NETWORK; CELLULAR-AUTOMATA; COVER CHANGE; SATELLITE; DATA; ENVIRONMENTAL-IMPACT; SHRIMP AQUACULTURE; URBAN-GROWTH; DYNAMICS; REGION; OPTIMIZATION; Remote Sensing

Abstract

Mangrove forests play an important role in providing ecological and socioeconomic services for human society. Coastal development, which converts mangrove forests to other land uses, has often ignored the services that mangrove may provide, leading to irreversible environmental degradation. Monitoring the spatiotemporal distribution of mangrove forests is thus critical for natural resources management of mangrove ecosystems. This study investigates spatiotemporal changes in Honduran mangrove forests using Landsat imagery during the periods 1985-1996, 1996-2002, and 2002-2013. The future trend of mangrove forest changes was projected by a Markov chain model to support decision-making for coastal management. The remote sensing data were processed through three main steps: (1) data pre-processing to correct geometric errors between the Landsat imageries and to perform reflectance normalization; (2) image classification with the unsupervised Otsu's method and change detection; and (3) mangrove change projection using a Markov chain model. Validation of the unsupervised Otsu's method was made by comparing the classification results with the ground reference data in 2002, which yielded satisfactory agreement with an overall accuracy of 91.1% and Kappa coefficient of 0.82. When examining mangrove changes from 1985 to 2013, approximately 11.9% of the mangrove forests were transformed to other land uses, especially shrimp farming, while little effort (3.9%) was applied for mangrove rehabilitation during this 28-year period. Changes in the extent of mangrove forests were further projected until 2020, indicating that the area of mangrove forests could be continuously reduced by 1,200 ha from 2013 (approximately 36,700 ha) to 2020 (approximately 35,500 ha). Institutional interventions should be taken for sustainable management of mangrove ecosystems in this coastal region.

Journal Title

Remote Sensing

Volume

5

Issue/Number

12

Publication Date

1-1-2013

Document Type

Article

Language

English

First Page

6408

Last Page

6426

WOS Identifier

WOS:000330318900013

ISSN

2072-4292

Download

Share

COinS