Multi-Decadal Mangrove Forest Change Detection and Prediction in Honduras, Central America, with Landsat Imagery and a Markov Chain Model
Multi-Decadal Mangrove Forest Change Detection and Prediction in Honduras, Central America, with Landsat Imagery and a Markov Chain Model
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DOI：
10.3390/rs5126408
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发表时间：
2013-11
期刊：
影响因子：
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通讯作者：
Chi-Farn Chen;N. Son;N. Chang;Cheng-Ru Chen;L. Chang;M. Valdez;Gustavo Centeno;C. A. Thompson;J. Aceituno
中科院分区：
文献类型：
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作者：
Chi-Farn Chen;N. Son;N. Chang;Cheng-Ru Chen;L. Chang;M. Valdez;Gustavo Centeno;C. A. Thompson;J. Aceituno
红树林在为人类社会提供生态和社会经济服务方面发挥着重要作用。沿海开发将红树林转变为其他土地用途，往往忽视了红树林可能提供的服务，导致不可逆转的环境退化。因此，监测红树林的时空分布对于红树林生态系统的自然资源管理至关重要。本研究调查了1985-1996年，1996-2002年和2002-2013年期间使用Landsat图像的海南红树林的时空变化。利用马尔可夫链模型预测了红树林的未来变化趋势，为海岸带管理提供决策支持。通过三个主要步骤对遥感数据进行处理：（1）数据预处理，以校正Landsat图像之间的几何误差并执行反射率归一化;（2）采用无监督大津方法进行图像分类和变化检测;（3）采用马尔可夫链模型进行红树林变化投影。通过与2002年的地面参考数据进行比较，验证了无监督的大津方法，取得了令人满意的协议，总体精度为91.1%，Kappa系数为0.82。在研究1985年至2013年的红树林变化时，大约11.9%的红树林被转化为其他土地用途，特别是养虾，而在这28年期间，红树林的恢复工作很少（3.9%）。对红树林范围的变化作了进一步预测，直至2020年，这表明红树林面积可能从2013年（约36 700公顷）到2020年（约35 500公顷）持续减少1 200公顷。应采取机构干预措施，对这一沿海地区的红树林生态系统进行可持续管理。
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.