Zonation and Directional Dynamics of Mangrove Forests Derived from Time-Series Satellite Imagery in Mai Po, Hong Kong

Abstract

Mangrove deforestation is occurring globally at a rapid rate, and is causing serious ecological and economic losses on all scales. Monitoring mangrove changes is the first important step for mangrove management and conservation. Zonation of mangrove species (ZMS) is the predictable and discrete ordering of mangrove species caused by a unique, intertidal environment. Mapping the ZMS is critical to understanding the mangrove community at a species level. In this paper, the Standard Deviational Ellipse (SDE) was proposed as a method to evaluate mangrove species from a new dimension of directional changes. Three dominant mangrove species, Kandelia obovata (KO), Avicennia marina (AM), and Acanthus ilicifolius (AI), in Mai Po, Hong Kong were analyzed using SDEs based on the time series Système Pour l'Observation de la Terre (SPOT) and Gaofen-1 (GF-1) satellite images. The SDE results demonstrated that in the past 25 years: (1) The overall spatial extent of the mangroves in Mai Po expanded significantly, approximately from 150 to 350 Ha, and show a zonation pattern with a clear sequence of species perpendicular to the shoreline; (2) KO was the dominant species in most years, showing the strongest directional characteristic; (3) All three species zones have moved toward the north and west, as observed by the SDE centers. The SDE was proved to be a useful tool for understanding the temporal and spatial changes of mangrove zonation.

The Mangrove Forests Change and Impacts from Tropical Cyclones in the Philippines Using Time Series Satellite Imagery

“…Other factors or activities should be included to increase the mangroves, such as reforestation. For instance, the increase of mudflat areas derived from rapid urbanization and soil erosion was identified as an important contribution to the increase of mangrove forest [9]. The mangroves in these two study sites have experienced fluctuations in the long term.…”

“…Several methods have been proposed and applied to the classification of mangrove forests at both community and species levels [1,[23][24][25]. Among all existing methods, the support vector machine (SVM) technique, a linear supervised non-parametric learning algorithm, is among the best methods due to its effective use of limited numbers of training data sets [9,12,23,26]. Therefore, SVM was employed as the classifier in this research and two key parameters, the Gamma of the kernel function, (e.g., radial basis function) and the penalty parameter for non-linear cases, were optimized with a cross-validation strategy according to previous studies [9,10].…”

“…Among all existing methods, the support vector machine (SVM) technique, a linear supervised non-parametric learning algorithm, is among the best methods due to its effective use of limited numbers of training data sets [9,12,23,26]. Therefore, SVM was employed as the classifier in this research and two key parameters, the Gamma of the kernel function, (e.g., radial basis function) and the penalty parameter for non-linear cases, were optimized with a cross-validation strategy according to previous studies [9,10]. The series of Landsat images were classified into four categories: mangroves, non-mangroves land cover, sea water and clouds.…”

“…A decreasing trend is mainly observed for the global mangrove forest. On the other hand, an expansion of mangrove forests was also reported in some cases [9,31]. Conservation is a critical factor for the expansion of mangrove forests.…”

“…There are several major advantages of remote sensing monitoring of mangrove forests. First, remote sensing is able to obtain the mangrove distribution at a large scale, from tens of hectare to millions of hectares [8,9]. Second, the cost of satellite remote sensing over mangrove forests is much lower than a traditional field survey, especially when an increasing number of satellites are making observations with freely accessible data.…”