A review of the application of multispectral remote sensing in the study of mangrove ecosystems with special emphasis on image processing techniques

다국어 초록 (Multilingual Abstract)

Much progress has been made since the first published report on application of remote sensing in mangrove mapping nearly 40 years ago. Remote sensing is now a widely used proxy in mangrove ecosystem related research mostly due to increased affordability of data and ease of access. This work reviews previous works on mangrove forests using remote sensing techniques with an aim to identify the best combinations of sensors, methods of image processing and vegetation indices for future studies. It was observed that higher accuracies were directly related to sensor resolution and expertise of the authors. From the imageries used, Quickbird had the highest accuracies (* 100%) whereas aerial photos had * 93% accuracy and IRS LISS recorded (76–97%).
Accuracies for Landsat images classified using hybrid approaches were up to 88% while AVIRIS and ASTER, gave up to 99% accuracies. Sentinel images also produced very high accuracy levels. Non parametric methods using machine learning techniques produced the highest levels of accuracy (up to 99%). But not all datasets are freely available and the cost of procuring datasets varies from country to country. This suggests that remote sensing of mangrove vegetation pose some serious challenges that require careful considerations in order to obtain better results. An in-depth understanding of the factors affecting the interaction between electro-magnetic radiation and mangrove vegetations in the environment, selection of images with appropriate spatial and spectral resolution and use of proper processing techniques for extracting spectral information is a must. This review paper thus, can be considered as a convenient asset to the researchers involved in assessing the mangrove environment by remote sensing technique.

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