Monsoonal storms cause coastal erosion of worldwide sandy beaches, including coasts in Malaysia. Although hard engineering structures are effective in mitigating erosion, those constructions can ...create several environmental issues such as down-drift erosion. The Effective Sand Fence (also known as E-Fence) is considered one of the sustainable alternative structures to protect beach erosion. Therefore, the objective of the current study is to identify the effectiveness of E-Fence for dune restoration. In this study, we measured beach profile survey, grain size distribution, and wind speed. In addition, XBeach simulation was used to determine sediment accumulation under the E-Fence protection. Results of the beach profile survey (i.e., slope and dune volume) indicate dune restoration in protected areas of the E-Fence. Grain size distribution and wind speed suggest the decreasing of wind velocities from the swash zone to the backshore. Accordingly, the E-Fence acts as a barrier, and the reduction of energy leads to accumulate sediments by passing through gaps in the structure. The E-Fence is thus capable of sustaining against wave attack and can maintain stable coastal ecosystems. Consequently, this coastal protection structure assists in developing cheaper coastal erosion mitigation strategies in Malaysia and elsewhere.
•The E-Fence (Effective Sand Fence), a sustainable alternative structure, aim to mitigate coastal erosion.•The presence of the E-Fence has positively influenced the restoration of dunes.•The simulation-based assessment of storm impact within the E-Fence provides crucial insights into coastal protection.
Brunei Bay, which receives freshwater discharge from four major rivers, namely Limbang, Sundar, Weston and Menumbok, hosts a luxuriant mangrove cover in East Malaysia. However, this relatively ...undisturbed mangrove forest has been less scientifically explored, especially in terms of vegetation structure, ecosystem services and functioning, and land-use/cover changes. In the present study, mangrove areal extent together with species composition and distribution at the four notified estuaries was evaluated through remote sensing (Advanced Land Observation Satellite-ALOS) and ground-truth (Point-Centred Quarter Method-PCQM) observations. As of 2010, the total mangrove cover was found to be ca. 35,183.74 ha, of which Weston and Menumbok occupied more than two-folds (58%), followed by Sundar (27%) and Limbang (15%). The medium resolution ALOS data were efficient for mapping dominant mangrove species such as
,
,
,
and
in the vicinity (accuracy: 80%). The PCQM estimates found a higher basal area at Limbang and Menumbok-suggestive of more mature vegetation, compared to Sundar and Weston. Mangrove stand structural complexity (derived from the complexity index) was also high in the order of Limbang > Menumbok > Sundar > Weston and supporting the perspective of less/undisturbed vegetation at two former locations. Both remote sensing and ground-truth observations have complementarily represented the distribution of
spp. as pioneer vegetation at shallow river mouths,
in the areas of strong freshwater discharge,
in the areas of strong neritic incursion and
at interior/elevated grounds. The results from this study would be able to serve as strong baseline data for future mangrove investigations at Brunei Bay, including for monitoring and management purposes locally at present.
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.
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.
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.
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