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Nurfashareena Muhamad

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Review Open access Jul 2026

Floods From Space: How Remote Sensing, AI , and Cloud Platforms Are Reshaping Disaster Risk Reduction

Flooding remains one of the most damaging climate‐related hazards globally, yet flood remote sensing has historically developed largely along sensor‐specific pathways. This review addresses the need for a systematic and decision‐oriented synthesis of how space‐borne remote sensing has evolved toward integrated flood monitoring and disaster risk reduction frameworks. Moving beyond conventional sensor inventories, the study examines sensor integration pathways, temporal evolution, and decision relevance. Four research questions guide the analysis: (RQ1) how space‐borne sensors have been applied and advanced in flood studies, (RQ2) how multi‐sensor fusion architectures have evolved, (RQ3) what limitations persist, and (RQ4) what future directions are emerging. Following PRISMA guidelines, 176 peer‐reviewed studies published between 2001 and 2024 were systematically analysed. Quantitative synthesis indicates a marked post‐2018 increase in multi‐sensor approaches, alongside a growing share of studies adopting fusion frameworks. Synthetic Aperture Radar (SAR)‐centred integration systems represent a dominant share of recent applications; most commonly combined with precipitation products and digital elevation models. Analytical synthesis further indicates that SAR plays a central role in flood detection, while precipitation and topographic data provide key hydrological drivers and terrain constraints. These findings indicate a transition from static flood mapping toward process‐aware, multi‐sensor, and algorithm‐driven flood intelligence systems. The review also highlights the growing role of artificial intelligence and cloud‐based platforms in enabling scalable, near‐real‐time flood analysis. It concludes that future flood resilience increasingly depends on the coordinated integration of space‐borne observations, advanced analytics, and operational decision‐support architectures. This synthesis provides a unifying framework to guide next‐generation flood monitoring and disaster risk reduction under accelerating climate change.

R. Kemarau, Nurfashareena Muhamad, Aida Soraya Shamsuddin et al. · 0 citations
Open access Jul 2026

Assessing multi-decadal shoreline change and future sea level projections to support coastal adaptation in Selangor, Malaysia

Coastal zones are increasingly exposed to climate change and rapid urbanization, yet the geomorphological stability of tropical, mud-dominated coastlines remains poorly understood. This study evaluates multi-decadal shoreline dynamics along a 250 km corridor of the Selangor coastline, Peninsular Malaysia, from 1990 to 2020. Using seven multi-temporal, sensor-homogenized Landsat datasets, baseline change metrics were computed via the Digital Shoreline Analysis System (DSAS v5.1). Historical shoreline shifts were quantified using the End Point Rate (EPR) and Linear Regression Rate (LRR) metrics to construct a process-based Geomorphological Stability Index (GSI). These historical vectors were coupled with IPCC AR6 sea-level rise anomalies to simulate scenario-based exposure projections for the 2030, 2040, and 2050 horizons. Baseline results revealed severe localized erosion hotspots exceeding -75 m/yr near the highly engineered Port Klang complex. The EPR network yielded a net erosional tendency of -0.15 m/yr, whereas the LRR model revealed a marginal net accretionary trend of +0.30 m/yr. The GSI framework classified 53.19% of the transects as highly stable/accreting (Rank 5) and 32.92% as severely eroding (Rank 1). Validation against an independent hazard inventory demonstrated a framework sensitivity of 100.0%. Scenario-based projections reveal intensifying erosive trends by 2050, accelerating mean EPR erosion to -5.14 m/yr and destabilizing up to 49% of the shoreline footprint. The findings provide actionable screening data to guide climate adaptation strategies.

N. Batmanathan, Joy Jacqueline Pereira, A. Shah et al. · 0 citations

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