Mangrove species and stand mapping in Gazi bay (Kenya) using quickbird satellite imagery

Abstract

Stand recognition (delineation and labelling) and species mapping are cornerstones of forest inventory mapping and key elements to forest management decision making. We present an automated method for mangrove stand recognition and species mapping based on fuzzy per-pixel classification techniques.Mapping of the present distribution of mangrove species in Gazi Bay (Kenya) was done using supervised Maximum Likelihood fuzzy classification of a QuickBird satellite image. Species recordings were obtained during a field mission in July-August 2003 with the Point-Centred-Quarter-Method (PCQM). The overall accuracy if the species map is 72%, where the two socio-economically most important species are mapped with user accuracies above 85%. Mangrove stand maps are obtained through supervised fuzzy classification of the multispectral satellite image, convolution of an appropriate window size and subsequent elimination of patches covering less than 0.05ha. The automated stand boundaries were compared to visual delineations done by an expert interpreter. The quality of the correspondence between visual and automated stand boundaries was assessed based on the quantity of overlap one has with the other. The correspondence varied from perfect, over good, to poor matches. An overall correspondence of 64% was obtained for visual labelling of stands versus automated labelling (classification) based on dominant species and total cover. When only dominant species were taken into account, the overall accuracy of stand labelling increased to 86%. Automated stand delineation and labelling are of a quality suitable for operational use in mangrove forest management.

Discrimination of Tropical Mangroves at the Species Level with EO-1 Hyperion Data

“…In light of the existing literature, it is found that many scientists in the field of remote sensing have already tried to discriminate and map mangroves at the species level [25,26,44,46,51,62], but their efforts have been inconclusive in application to tropical mangrove species discrimination. The authors of [26] should have been the first to conclude this problem, if their hyperspectral image had not been obscured by cloud.…”

“…It is evident that conventional remote sensing instruments are now operationally used for mapping and monitoring mangroves at the broad level [31][32][33][34][35][36][37][38][39][40][41]. However, the spatial and spectral information provided by this conventional equipment may not be sufficient for studying mangrove ecosystems and their diversity in details [14,23,26,31,35,[42][43][44][45][46]. As a result, new generation sensors that possess higher spatial and spectral resolutions are therefore needed for a finer level of mangrove studies [17,35,42,43,[45][46][47].…”

“…However, the spatial and spectral information provided by this conventional equipment may not be sufficient for studying mangrove ecosystems and their diversity in details [14,23,26,31,35,[42][43][44][45][46]. As a result, new generation sensors that possess higher spatial and spectral resolutions are therefore needed for a finer level of mangrove studies [17,35,42,43,[45][46][47].…”

“…Some of these reports try to use the hyperspectral data for discriminating mangroves at the species level [25,26,44,51,62]. However, it is unfortunate that the outcome of these studies are still inconclusive as their study sites are covered by only a few mangrove species, and a recent conclusion [46] is even in doubt as a result of a poor choice of the GPS measurement.…”

Artificial Mangrove Species Mapping Using Pléiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms

“…Wang et al (2008) [10] showed that clustering-based neural network classification utilizing 4-m IKONOS imagery with textural information from the panchromatic band could obtain an 0.93 Kappa value for discriminating mangrove species. Neukermans et al (2008) [11] found that the pixel-based fuzzy classification using pan-sharpened QuickBird imagery could produce a 72% overall accuracy for the detection of mangrove species. Object-based approaches have been frequently applied for mapping mangrove species in recent years.…”