The Dynamics, Ecological Variability and Estimated Carbon Stocks of Mangroves in Mahajamba Bay, Madagascar

Abstract

Mangroves are found throughout the tropics, providing critical ecosystem goods and services to coastal communities and supporting rich biodiversity. Globally, mangroves are being rapidly degraded and deforested at rates exceeding loss in many tropical inland forests. Madagascar contains around 2% of the global distribution, >20% of which has been deforested since 1990, primarily from over-harvest for forest products and conversion for agriculture and aquaculture. While historically not prominent, mangrove loss in

Madagascar’s Mangroves: Quantifying Nation-Wide and Ecosystem Specific Dynamics, and Detailed Contemporary Mapping of Distinct Ecosystems

“…As described in later sections, to improve upon the shortcomings associated with available maps for MHJ, Jones et al [61] used Landsat data acquired in 2011 to produce a detailed, contemporary AOI-specific map of MHJ. Using similar methodologies, Jones et al [25] produced an AOI-specific map for AAB from Landsat data acquired in 2010.…”

“…The results of nation-wide and ecosystem-specific dynamics from 1990 to 2010, and ecosystem-specific maps for TMD and BdA are presented in this study for the first time. The production of ecosystem-specific maps builds on previous studies which employed similar methods for AAB and MHJ (i.e., Jones et al [25]; Jones et al [61]). …”

“…Atmospheric correction was undertaken using the Cos(t) model [92], which estimates the impact of absorption by atmospheric gases and Rayleigh scattering, minimizes systematic haze, and converts image units to at-surface reflectance. Following the methodology described in Jones et al [25] for AAB, and Jones et al [61] for MHJ, for mapping efforts in TMD and BdA, 30 m spatial resolution Landsat scenes were downloaded from the United States Geological Survey (USGS) Earth Resources Observation and Science Center (Sioux Falls, SD, USA) ( Table 1). All Landsat scenes were orthorectified to a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM).…”

“…Reducing the image extent based on habitat requirement through masking can increase classification accuracy by decreasing spectral confusion amongst target classes [57]. Emulating studies that establish the effectiveness of SRTM data to estimate mangrove forest canopy height (e.g., [94][95][96][97][98]), an SRTM height mask further eliminated unnecessary scene components based on a 30 m threshold, above which mangrove habitat were observed in each AOI not to exist [25,61]. Adhering to the methods described in Jones et al [25] for AAB, and Jones et al [61] for MHJ, to produce initial maps for TMD and BdA, an unsupervised iterative self-organizing classification algorithm (i.e., ISOCLUST) was employed to group pixels in to dominant cover types based on shared spectral properties in bands 1-5 and 7 (i.e., TM and ETM) or bands 2-7 (i.e., OLI).…”

“…Emulating studies that establish the effectiveness of SRTM data to estimate mangrove forest canopy height (e.g., [94][95][96][97][98]), an SRTM height mask further eliminated unnecessary scene components based on a 30 m threshold, above which mangrove habitat were observed in each AOI not to exist [25,61]. Adhering to the methods described in Jones et al [25] for AAB, and Jones et al [61] for MHJ, to produce initial maps for TMD and BdA, an unsupervised iterative self-organizing classification algorithm (i.e., ISOCLUST) was employed to group pixels in to dominant cover types based on shared spectral properties in bands 1-5 and 7 (i.e., TM and ETM) or bands 2-7 (i.e., OLI). Unsupervised classification for mapping mangroves and closely related ecosystem types is proven, and has produced both preliminary and final maps described in numerous studies [25,57,59,61,68,96,97,[99][100][101][102].…”