Traditional in situ observation interpolation techniques that provide rainfall data from rain gauges have limitations because they are discrete point-based data records, which may not be sufficient to assess droughts from a spatiotemporal perspective. Considering this limitation, this study has developed a run-off model—a fully satellite-based method for monitoring drought in Peninsular Malaysia. The formulation of the run-off deficit uses a water balance equation based on satellite-based rainfall and evapotranspiration data extracted respectively from calibrated TRMM multi-satellites precipitation analysis data (TMPA) and moderate resolution imaging spectroradiometer data (MODIS). The run-off deficit was calculated based on per pixel spatial scale and allowed to produce the continuous and regular run-off maps. The run-off model was tested and evaluated in a one drought year (2005) within a span of three years (2003–2005) over the Kelantan (3448 km²) and Hulu Perak (3672 km²) catchments of Peninsular Malaysia. The validation results show that (1) monthly TMPA rainfall and MODIS evapotranspiration data significantly improved after calibration; (2) satellite-based run-off data is not only strongly correlated with actual steam flow, but also with spatiotemporal variation of run-off in drought-affected forest catchments. The most severely drought-affected forest catchments that experienced the run-off deficits were Hulu Perak, Ulu Gading, Gunung Stong and Relai over Kelantan. The real time run-off change analysis shows that drought started in January and reached its peak in July of 2005. It was therefore demonstrated that this fully satellite-based run-off deficit model is as good as a conventional drought-monitoring indicator, and can provide not only drought distribution information, but it also can reflect the drought-induced impacts on stream flow, forest catchment and land-use.

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.

This study examined Fuzzy Sigmoidal, Bayesian, and Demspter-Shafer classifications to map shorelines in Kuala Terengganu, Malaysia for different types of coasts. 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.