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This study deals with the mixed-pixel problem of detecting benthic habitat class membership and evaluates two soft classifiers for coral habitat mapping on Lang Tengah island (Malaysia). A comparison was made between the Bayesian and Dempster–Shafer (D–S) with a traditional maximum likelihood (ML). The heterogeneous pattern of reef environment, established by field observation, four classes of coral habitats containing various combinations of live coral, dead coral with algae, rubble coral and sand. Posterior probability and belief maps, generated by Bayesian and D–S, respectively, were evaluated by visual inspection and final coral habitat distribution maps were validated via accuracy assessment estimates. The accuracy validation tests agreed with the visual inspection of the probability, uncertainty and coral distribution maps. The Bayesian algorithm performed better, with a 34.7–68.5% improvement in accuracy compared to D–S and ML, respectively. Probability maps demonstrate the advantages of the soft classifier over the hard classifier for coral mapping. [ABSTRACT FROM AUTHOR]

Hossain, M.S.; Muslim, A.M.; Pour, A.B.; Mohamad, M.N.; Alam, S.M.R.; Nadzri, M.I., and Khalil, I., 2021. Mapping different types of shorelines from coarse-resolution imagery: Fuzzy classification method can deliver greater accuracy. Journal of Coastal Research, 37(2), 433–441. Coconut Creek (Florida), ISSN 0749-0208. Coastal zones are among the most structurally complex ecosystems, though their complex shorelines are threatened because of both natural and anthropocentric influences. There are a smaller number of studies that dealt with developing remote-sensing techniques for detecting and mapping different shoreline types (ST) using coarse-resolution imagery. This study examined fuzzy c-means (FCM), wavelet interpolation, and fuzzy maximum likelihood to map shorelines over Seberang Takir (Malaysia) for different STs. These three fuzzy classification methods were applied to simulated IKONOS (with 16- and 32-m spatial resolutions) image covering the four STs. The positional accuracy of shorelines was assessed in terms of root mean square error (RMSE). The visual inspection and RMSE values showed that variations in accuracies were evident, predominantly due to differences in STs; fuzzy algorithm improved the accuracy. FCM can predict shoreline positions with greater accuracy than the other two methods. [ABSTRACT FROM AUTHOR]

Remote sensing has potential in studies of the benthic habitat and extracting the reflectance from the data of multispectral sensors, but traditional image classification techniques cannot provide coral habitat maps with adequate accuracy. This study tested five traditional and three ensemble classification techniques on QuickBird for mapping the benthic composition of coral reefs on the Lang Tengah Island (Malaysia). The common techniques, minimum distance, maximum likelihood, K-nearest neighbour, Fisher and parallelepiped techniques were compared with ensemble classifiers, such as majority voting (MV), simple averaging, and mode combination. The per-class accuracy of the habitat detection improved in the ensemble classifiers; in particular, the MV classifier achieved 95%, 65%, 75% and 95% accuracies for coral, sparse coral, coral rubble and sand, respectively. Ensembles increased the accuracy of the habitat mapping classification by 28%, relative to conventional techniques. Thus, the ensemble techniques can be preferred over the traditional for benthic habitat mapping. [ABSTRACT FROM AUTHOR]

Although methods were proposed for eliminating sun glint effects from airborne and satellite images over coral reef environments, a method was not proposed previously for unmanned aerial vehicle (UAV) image data. De-glinting in UAV image analysis may improve coral distribution mapping accuracy result compared with an uncorrected image classification technique. The objective of this research was to determine accuracy of coral reef habitat classification maps based on glint correction methods proposed by Lyzenga et al., Joyce, Hedley et al., and Goodman et al. The UAV imagery collected from the coral-dominated Pulau Bidong (Peninsular Malaysia) on 20 April 2016 was analyzed in this study. Images were pre-processed with the following two strategies: Strategy-1 was the glint removal technique applied to the whole image, while Strategy-2 used only the regions impacted by glint instead of the whole image. Accuracy measures for the glint corrected images showed that the method proposed by Lyzenga et al. following Strategy-2 could eliminate glints over the branching coral—Acropora (BC), tabulate coral—Acropora + Montipora (TC), patch coral (PC), coral rubble (R), and sand (S) with greater accuracy than the other four methods using Strategy-1. Tested in two different coral environments (Site-1: Pantai Pasir Cina and Site-2: Pantai Vietnam), the glint-removed UAV imagery produced reliable maps of coral habitat distribution with finer details. The proposed strategies can potentially be used to remove glint from UAV imagery and may improve usability of glint-affected imagery, for analyzing spatiotemporal changes of coral habitats from multi-temporal UAV imagery. [ABSTRACT FROM AUTHOR]