This article presents the waveform retracking technique for two predominant waveform classes near the coast: quasi-specular and multi-peak echoes. The technique retracks the truncated sub-waveform rather than the full-waveform. Sub-waveforms, which are only based on returns reflected from the water surface, are extracted and retracked using the Brown model. The retracker is applied to simulated waveforms and Jason-1 and Jason-2 waveforms in the Great Barrier Reef. When using it as a supplement to the full-waveform retracker, results show that retracked SSHs can be extended further to the coastline, up to ∼2–6 km for Jason-1 and ∼1–7 km for Jason-2.

The most devastating flood event in Kota Bharu was recorded in December 2014, which affected several properties worth millions of dollars and thousands of homes. Damage to physical properties, especially buildings, is identified as a significant contributor to flood disasters in Malaysia. Therefore, it is essential to address physical flood vulnerability by developing an integrated approach for modeling buildings’ flood vulnerability to decrease the flood consequences. This study aims at developing a flood vulnerability assessment approach using an indicator-based model (IBM) for individual buildings in Kota Bahru, Kelantan, Malaysia. An intensive literature review and expert opinions were used to determine suitable indicators that contribute to the physical flood vulnerability of buildings. The indicators were grouped into three components, i.e., flood hazard intensity (I), building characteristics (C), and effect of the surrounding environment (E). The indicators were further refined based on expert opinions and Relative Importance Index (RII) analysis. Based on their contribution to the Malaysia local building flood vulnerability, priority weight is assigned by the experts to each of the selected indicators using the participatory Analytical Hierarchy Process (AHP). A spatial database of buildings in Kota Bharu is developed through field surveys and manually digitizing building footprints from satellite imageries. The identified indicators and their weight are added to each building footprint. The Weighted Linear Combination (WLC) aggregation method combined the weight of indicators into a vulnerability index and maps. The results of a physical flood vulnerability were validated using building damage information obtained through interviews with the community that experienced previous flood in the study area. The result showed that about 98% of the study area’s buildings have either moderate or low vulnerability to flooding. The flood vulnerability map has an overall accuracy of 75.12% and 0.63 kappa statistics. In conclusion, the IBM approach has been used successfully to develop a physical flood vulnerability for buildings in Kota Bharu. The model contributes to support different structural and non-structural approaches in the flood mitigations process.

Spatial information on benthic habitats in Wangiwangi island waters, Wakatobi District, Indonesia was very limited in recent years. However, this area is one of the marine tourism destinations and one of the Indonesia’s triangle coral reef regions with a very complex coral reef ecosystem. The drone technology that has rapidly developed in this decade, can be used to map benthic habitats in this area. This study aimed to map shallow-water benthic habitats using drone technology in the region of Wangiwangi island waters, Wakatobi District, Indonesia. The field data were collected using a 50 × 50 cm squared transect of 434 observation points in March–April 2017. The DJI Phantom 3 Pro drone with a spatial resolution of 5.2 × 5.2 cm was used to acquire aerial photographs. Image classifications were processed using object-based image analysis (OBIA) method with contextual editing classification at level 1 (reef level) with 200 segmentation scale and several segmentation scales at level 2 (benthic habitat). For level 2 classification, we found that the best algorithm to map benthic habitat was the support vector machine (SVM) algorithm with a segmentation scale of 50. Based on field observations, we produced 12 and 9 benthic habitat classes. Using the OBIA method with a segmentation value of 50 and the SVM algorithm, we obtained the overall accuracy of 77.4% and 81.1% for 12 and 9 object classes, respectively. This result improved overall accuracy up to 17% in mapping benthic habitats using Sentinel-2 satellite data within the similar region, similar classes, and similar method of classification analyses.

This paper presents the Coastal Altimetry Waveform Retracking Expert System (CAWRES), a novel method to optimise the Jason satellite altimetric sea levels from multiple retracking solutions. CAWRES’ aim is to achieve the highest possible accuracy of coastal sea levels, thus bringing measurement of radar altimetry data closer to the coast. The principles of CAWRES are twofold. The first is to reprocess altimeter waveforms using the optimal retracker, which is sought based on the analysis from a fuzzy expert system. The second is to minimise the relative offset in the retrieved sea levels caused by switching from one retracker to another using a neural network. The innovative system is validated against geoid height and tide gauges in the Great Barrier Reef, Australia for Jason-1 and Jason-2 satellite missions. The regional investigations have demonstrated that the CAWRES can effectively enhance the quality of 20 Hz sea level data and recover up to 16% more data than the standard MLE4 retracker over the tested region. Comparison against tide gauge indicates that the CAWRES sea levels are more reliable than those of Sensor Geophysical Data Records (SGDR) products, because the former has a higher (≥0.77) temporal correlation and smaller (≤19 cm) root mean square errors. The results demonstrate that the CAWRES can be applied to coastal regions elsewhere as well as other satellite altimeter missions.

A new approach for improving the accuracy of altimetry-derived sea level anomalies (SLAs) near the coast is presented. Estimation of SLAs is optimized using optimal waveform retracking through a fuzzy multiple retracking system and the most appropriate detiding method. With the retracking system, fuzzy-retracked SLAs become available within 5 km of the coast; meanwhile it becomes more important to use pointwise tide modelling rather than state-of-the-art global tidal models, as the latter leave residual ocean tide signals in retracked SLAs. These improvements are demonstrated for Jason-2 waveforms in the area of the Great Barrier Reef, Australia. Comparing the retrieved SLAs with in situ tide gauge data from Townsville and Bundaberg stations showed that the SLAs from this study generally outperform those from conventional methods, demonstrating that adequate waveform retracking and detiding are equally important in bringing altimetry SLAs closer to the coast

This paper presents the assessment of altimetric data from Sentinel-3A satellite operating in Synthetic Aperture Radar (SAR) mode for sea level research studies and applications over the largest archipelagos at Southeast Asia. Both qualitative and quantitative assessments are conducted by analysing the physical shapes of waveforms, comparing with quasi-independent geoidal height data and independent tide gauge measurements. The results identified the percentage of ocean like and non-ocean like waveforms are 91% and 9%, respectively. Off 9% of non-ocean like waveforms, the generic class is multi-peak (7%) followed by the quasi-specular waveforms (2%) observed near the coastline (< 10 km). Ocean like waveforms typically appear beyond 500 m from the coastline. When comparing with geoidal heights and tide gauge measurements, the performance of sea levels from several retrackers are assessed. The SAMOSA+ (SAR Altimetry Mode Studies and Applications) retracker outperforms other retrackers (i.e. sub-waveform and modified threshold retrackers with 30%, 20%, and 10%). That is, the standard deviation of differences against geoidal heights, and the temporal correlation against tide gauges are superior in most cases. In terms of root mean square error (RMSE), all retrackers are ranging with RMSE ≤ 20 cm in all cases. It can be concluded that in general, the SAMOSA+ retracked sea levels are accurate over the complicated regions at the Southeast Asia.