Monitoring Wildfire Risk with a Near-Real-Time Live Fuel Moisture Content System: A Review and Roadmap for Operational Application in New Zealand

Abstract

Highlights: What are the main findings? A comprehensive evaluation of multispectral, hyperspectral, thermal, and mi-crowave sensors, undertaken individually and in combination, shows how LFMC and fuel types can be monitored with specific applicability to New Zealand. The review clarifies trade-offs (resolution, cadence, spectral sensitivity), outlines limitations and identifies opportunities to improve mapping of LFMC and fuel type. What are the implications of the main findings? The synthesis provides the technical groundwork for an operational, near-real-time LFMC prediction system in New Zealand with relevance to other fire-prone regions. Such a system will enable more reliable, timely wildfire risk assessment and strengthen decision-making for fire management and emergency response. Live fuel moisture content (LFMC) is a critical variable influencing wildfire behavior, ignition potential, and suppression difficulty, yet it remains challenging to monitor consistently across landscapes due to sparse field observations, rapid temporal changes, and vegetation heterogeneity. This study presents a comprehensive review of satellite-based approaches for estimating LFMC, with emphasis on methods applicable to New Zealand, where wildfire risk is increasing due to climate change. We assess the suitability of different remote sensing data sources, including multispectral, thermal, and microwave sensors, and evaluate their integration for characterizing both LFMC and fuel types. Particular attention is given to the trade-offs between data resolution, revisit frequency, and spectral sensitivity. As knowledge of fuel type and structure is critical for understanding wildfire behavior and LFMC, the review also outlines key limitations in existing land cover products for fuel classification and highlights opportunities for improving fuel mapping using remotely sensed data. This review lays the groundwork for the development of an operational LFMC prediction system in New Zealand, with broader relevance to fire-prone regions globally. Such a system would support real-time wildfire risk assessment and enhance decision-making in fire management and emergency response.