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Unmanned aerial vehicles (UAV) based methods for reconnaissance activities aim to update wetlands' health status and are safer and cost-effective considering that wetlands normally have saturated soils, dense vegetation and wildlife. Vegetation survey work in wetland areas needs many staff to ensure the safety of researchers and equipment. This paper describes the application of UAV technology to identify and demarcate vegetation communities in a dense BRIS (beach ridges interspersed with swales) coastal wetland. The methodology employed in this research has two steps. The first step involves the utilisation of UAV imagery and Geographic Information System (GIS) technology. The second step entails ground truthing, which involves validating tree species using 15 quadrants. The utilisation of UAVs in conjunction with ArcGIS 10.3 demonstrated that the unique characteristics of tree canopy morphology and tree heights could be assessed and analysed. The UAV-GIS results are compared to ground truthing results to validate tree communities' demarcation. The findings indicate that integrating two datasets, particularly tree canopy morphology and tree height, produced an acceptably accurate vegetation community demarcation. The tree canopy form of Hibiscus tiliaceus (Bebaru) was differentiated from the canopies of Acacia mangium and Nypa fruticans by analysing the image. The tree height analysis involved using ArcGIS 10.3 software to generate the digital surface model (DSM) and digital terrain model (DTM). Validation results indicate an 87% accuracy in the demarcation work. Hence, identification and demarcation of the vegetation communities could be achieved by utilising both tree canopy morphology and tree height data obtained from UAV. [ABSTRACT FROM AUTHOR]

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]

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]

This study examined Fuzzy Sigmoidal, Bayesian, and Demspter-Shafer classifications to map shorelines in Kuala Terengganu, Malaysia for different types of coasts (CT). These three soft classification methods were applied to simulated Satellite Pour l’Observation de la Terre-5 (SPOT-5 with 10 and 20 m spatial resolutions) images covering the three shoreline locations which were typically different in shape and orientation: (a) rocky breakwater coasts (CT-1), (b) rocky and sandy airport coasts (CT-2), and (c) sandy beach coasts (CT-3) for predicting shorelines. Visual inspection and statistical measures showed that variations in accuracies were evident, predominantly due to differences in CTs; mapping accuracy decreased with an increase of spatial resolution, but accuracy increased if shorelines were aligned exact parallel to the column of pixel grid. Sigmoidal can predict shoreline over the CT-3 areas with greater accuracy than the other two methods and has less dependence on spatial resolutions for across the CTs. But effective use of Bayesian membership function for mapping shoreline over certain small and narrow jetty-like structures makes this classifier also efficient. The soft classifier assessed in this study produced reliable shoreline maps and should help shoreline change detection and monitoring from coarser spatial resolution imagery. [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]