An Assessment of Mangroves in Guinea, West Africa, Using a Field and Remote Sensing Based Approach

Abstract

We provide a baseline account as to the type of mangrove that is typical for Guinea, Africa using field based and remotely sensed data. Specifically, the mangroves of the estuarine islands of Mabala and Yélitono were classified using satellite and airborne optical remote sensing data. Mangroves were mapped according to four classes: tall red (Rhizophora racemosa), medium red (R. racemosa), dwarf red (R. mangle and R. harisonii), and black mangrove (Avicennia germinans). Producer's and user's accuracies for the mapping of mangrove from nonmangrove areas were both 98%. When separating amongst the mangrove classes most of the confusion resulted from the medium red mangrove class. Of the 10,442 ha of mangrove mapped, approximately 30% were classified as riverine, dominated by tall R. racemosa. The remaining mangrove areas were dominated by dwarf mangrove of either Rhizophora or A. germinans. Biophysical parameter data collected from 56 transects varied considerably amongst the classes. For the tallest mangrove class, the mean values of height, DBH, estimated LAI, stem density and basal area recorded were 13 m, 15.1 cm, 4.3, 838 stems/ha, and 25.9 m 2 /ha, respectively. In contrast, for A. germinans, values of 3 m, 4.6 cm, 1.5, 2,877 stems/ha, and 6.0 m 2 /ha were calculated, respectively.

Mangrove Forests Mapping in the Southern Part of Japan Using Landsat ETM+ with DEM

“…Many papers have produced to analyze the change detection of mangrove forests using aerial photographs [5], and to monitor coastal ecosystem changes supported by multi-temporal and multi-spatial resolution remote sensing data [6]. In addition, several studies have been proposed for mapping mangroves at local scale using different spatial resolution satellite data, including Landsat Thematic Mapper (TM) [7], Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) [3], Satellite Pour l'Observation de la Terre (SPOT) [1], Synthetic-aperture radar (SAR) [8], Compact Airborne Spectrographic Imager (CASI) [2], IKONOS [9] and Quickbird [10]. On the other hand many methods have been proposed for identifying and mapping mangrove forests from remote sensing data.…”

“…[11] used principle component analysis and band ratios in addition to other techniques to compare the relative effectiveness of multi-spectral remote sensing data in mapping mangrove forests in Turks and Caicos Islands, and [12] compared different classifiers for SPOT XS and Terra ASTER data. More recently, [9] applied an ISO-DATA unsupervised classification technique on IKONOS and QuickBird images to distinguish different species of mangrove forests in Mabala and Yélitono mangrove islands of Guinea, West Africa.…”

An object-oriented classification method for mapping mangroves in Guinea, West Africa, using multipolarized ALOS PALSAR L-band data

“…According to the FAO (2007) and Kovacs et al (2010), there are six mangrove species that can be found on the southern coast of Guinea: the red mangroves (Rhizophora mangle, R. racemosa, and R. harisonii), the black mangrove (Avicennia germinans), the white mangrove (Laguncularia racemosa), and the button wood (Conocarpus erectus). Kovacs et al (2010) reported that white and button wood mangroves were rarely found and that no uniform stands were encountered in this area.…”

Remote sensing for multi-scale mangrove mapping

“…The biophysical properties that are commonly investigated using remote sensing images in mangrove environments include extent and composition, species type, LAI, canopy height, canopy closure, diameter of breast height (DBH) and basal area (Jensen et al 1991;Ramsey III & Jensen 1996;Green et al 1997;Davis & Jensen 1998;Manson et al 2001;Díaz & Blackburn 2003;Jean-Baptiste & Jensen 2006;Kovacs et al 2010). Table 1.1 provides a synopsis of the data and methods used to map selected mangrove structural features and biophysical properties for the purpose of this research.…”

“…It provides an estimate of LAI at repeated times over local to global scales (Fang & Liang 2008). Although still limited in number, several studies have indicated the successful implementation of optical remote sensing data for mangrove LAI mapping from various sensors, for example Landsat TM or ETM+ (Ramsey III & Jensen 1996;Green et al 1997;Díaz & Blackburn 2003;Ishil & Tateda 2004), SPOT XS (Ramsey III & Jensen 1996;Green et al 1997), AVHRR (Ramsey III & Jensen 1996), ASTER (Jean-Baptiste & Jensen 2006), IKONOS (Kovacs et al 2004;Kovacs et al 2005;Kovacs et al 2010), QuickBird (Kovacs et al 2009;Kovacs et al 2010), CASI (Green et al 1998a), Leica-ADS40 (Kovacs et al 2010), and ALOS PALSAR (Kovacs et al 2013). …”