Abstract
Mangrove is located in the tropical and subtropical regions and brings good services for native people. Mangrove in the world has been lost with a rapid rate. Therefore, monitoring a spatiotemporal distribution of mangrove is thus critical for natural resource management. This research objectives were: (i) to map the current extent of mangrove in the West and Central Africa and in the Sundarbans delta, and (ii) to identify change of mangrove using Landsat data. The data were processed through four main steps: (1) data pre-processing including atmospheric correction and image normalization, (2) image classification using supervised classification approach, (3) accuracy assessment for the classification results, and (4) change detection analysis. Validation was made by comparing the classification results with the ground reference data, which yielded satisfactory agreement with overall accuracy 84.1% and Kappa coefficient of 0.74 in the West and Central Africa and 83.0% and 0.73 in the Sundarbans, respectively. The result shows that mangrove areas have changed significantly. In the West and Central Africa, mangrove loss from 1988 to 2014 was approximately 16.9%, and only 2.5% was recovered or newly planted at the same time, while the overall change of mangrove in the Sundarbans increased approximately by 900 km<sup>2</sup> of total mangrove area. Mangrove declined due to deforestation, natural catastrophes deforestation and mangrove rehabilitation programs. The overall efforts in this study demonstrated the effectiveness of the proposed method used for investigating spatiotemporal changes of mangrove and the results could provide planners with invaluable quantitative information for sustainable management of mangrove ecosystems in these regions.
Highlights
Mangrove grows in river deltas, estuarine complexes and coasts in the tropical and subtropical regions throughout the world
The methodology was adopted for four main steps: (1) data preprocessing including atmospheric and geometric corrections, and reflectance normalization, (2) image classification using Support Vector Machine (SVM), (3) accuracy assessment, and (4) change detection analysis
This research successfully applied to extract mangrove forests based on characteristics, singularities, and distribution as well as reflectance values and spectral properties of mangrove forests in the images
Summary
Mangrove grows in river deltas, estuarine complexes and coasts in the tropical and subtropical regions throughout the world. It inhabits on the shorelines and islands in sheltered coastal areas with locally variable topography and hydrology (Lugo & Snedaker, 1974). Mangrove is one of the most threatened and vulnerability ecosystems. Based on the importance and vulnerable of mangrove ecosystems faced, many studies on mangrove have been conducted to solve these issues in difference scales, long-term monitoring and detecting mangrove by using remote sensing techniques (Blasco et al, 2001; Everitt et al, 2008; Giri et al, 2007; Green, 1998; Seto & Fragkias, 2007; and Vaiphasa et al, 2006)
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