Remote Sens. 2013, 5(12), 6408-6426; doi:10.3390/rs5126408
Article

Multi-Decadal Mangrove Forest Change Detection and Prediction in Honduras, Central America, with Landsat Imagery and a Markov Chain Model

1 Center for Space and Remote Sensing Research, National Central University, Taoyuan County 32001, Taiwan 2 Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA 3 Department of Civil Engineering, National Central University, Taoyuan County 32001, Taiwan 4 Servicio de Información Agroalimentaria, Tegucigalpa 4710, Honduras 5 National Secretary of Natural Resources and Environment, Tegucigalpa 4710, Honduras 6 Institute of Forest Conservation and Development, Tegucigalpa 4710, Honduras
* Author to whom correspondence should be addressed.
Received: 30 September 2013; in revised form: 22 November 2013 / Accepted: 25 November 2013 / Published: 27 November 2013
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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.
Keywords: Landsat; mangrove forests; image classification; change detection; change projection

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MDPI and ACS Style

Chen, C.-F.; Son, N.-T.; Chang, N.-B.; Chen, C.-R.; Chang, L.-Y.; Valdez, M.; Centeno, G.; Thompson, C.A.; Aceituno, J.L. Multi-Decadal Mangrove Forest Change Detection and Prediction in Honduras, Central America, with Landsat Imagery and a Markov Chain Model. Remote Sens. 2013, 5, 6408-6426.

AMA Style

Chen C-F, Son N-T, Chang N-B, Chen C-R, Chang L-Y, Valdez M, Centeno G, Thompson CA, Aceituno JL. Multi-Decadal Mangrove Forest Change Detection and Prediction in Honduras, Central America, with Landsat Imagery and a Markov Chain Model. Remote Sensing. 2013; 5(12):6408-6426.

Chicago/Turabian Style

Chen, Chi-Farn; Son, Nguyen-Thanh; Chang, Ni-Bin; Chen, Cheng-Ru; Chang, Li-Yu; Valdez, Miguel; Centeno, Gustavo; Thompson, Carlos A.; Aceituno, Jorge L. 2013. "Multi-Decadal Mangrove Forest Change Detection and Prediction in Honduras, Central America, with Landsat Imagery and a Markov Chain Model." Remote Sens. 5, no. 12: 6408-6426.

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