Background: Complex interactions between fuel structure and fire substantially affect fire spread and spatial variability in fire behaviour. Heterogeneous arrangement of the fuel coupled with variability in fuel characteristics can impact heat transfer efficiency, preheating of unburned fuel and consequent ignition and spread. Aim: Study the influence of pre-burn fuel structure (canopy height, spatial arrangement) on fire behaviour (rate of spread, flame residence time) derived from high-resolution video of a prescribed gorse fire. Method: Rate of spread and flame residence time are calculated and mapped from high-resolution overhead visible-spectrum video, and compared with the Canopy Height Model derived from pre-burn Light Detection and Ranging (Lidar) scans. Results: Geospatial analytics can provide precision observations of fire behaviour metrics. Rates of spread under high wind conditions are influenced by local changes in canopy height and may be more dependent on other fuel characteristics, while flame residence time is better correlated with canopy height. Conclusions: These observational technology and spatio-temporal analytical techniques highlight how detailed fire behaviour characteristics can be derived from these data. Implications: The results have implications for wildfire modelling and Wildland–Urban Interface (WUI) building design engineers, as the reported dataset is suitable for model validation and the analysis contributes to further understanding of gorse fire hazard.

Background: Wildfires propagate through vegetation exhibiting complex spread patterns modulated by ambient atmospheric wind turbulence. Wind gusts at the fire-front extend and intensify flames causing direct convective heating towards unburnt fuels resulting in rapid acceleration of spread. Aims: To characterise ambient and fire turbulence over gorse shrub and explore how this contributes to fire behaviour. Methods: Six experimental burns were carried out in Rakaia, New Zealand under varying meteorological conditions. The ignition process ensured a fire-line propagating through dense gorse bush (1 m high). Two 30-m sonic anemometer towers measured turbulent wind velocity at six different levels above the ground. Visible imagery was captured by cameras mounted on uncrewed aerial vehicles at 200 m AGL. Key results: Using wavelet decomposition, we identified different turbulent time scales that varied between 1 and 128 s relative to height above vegetation. Quadrant analysis identified statistical distributions of atmospheric sweeps (downbursts of turbulence towards vegetation) with sustained events emanating from above the vegetation canopy and impinging at the surface with time scales up to 10 s. Conclusions: Image velocimetry enabled tracking of ‘fire sweeps’ and characterised for the first time their lifetime and dynamics in comparison with overlying atmospheric turbulent structures. Implications: This methodology can provide a comprehensive toolkit when investigating coupled atmosphere–fire interactions.

This study presents two new remote sensing approaches that can be used to derive rate of spread and flaming zone velocities of a wildfire at very high spatiotemporal resolution. Time sequential image tracking from thermal or visible video collected on uncrewed aerial vehicles is used to estimate instantaneous spatial rate of spread of a surface fire. The techniques were developed using experimental wheat‐stubble burns carried out near Darfield, New Zealand, in March 2019. The thermal tracking technique is based on Thermal Image Velocimetry, which tracks evolving temperature patterns within an infrared video. The visible tracking technique uses colour thresholding, and tracks fire perimeter progression through time at pixel resolution. Results show that the visible perimeter tracking creates a higher mean rate of spread compared to thermal image velocimetry. The visible perimeter tracking provides rate of spread measurements for fire front progression whereas the thermal tracking techniqueis computationally more expensive, but can resolve velocities of thermal structures within the flaming zone and provides spatiotemporal rate of spread measurements. Both techniques are available as open‐source code and providevital scientific data for new studies concerning e.g. fire–atmospheric interactions or model validation. They may be adapted for operational purposes providing rate of spread at high spatiotemporal resolution. This study presents two new remote sensing approaches that can be used to derive rate of spread and flaming zone velocities of a wildfire at very high spatiotemporal resolution. Time sequential image tracking from thermal or visible video collected on uncrewed aerial vehicles is used to estimate instantaneous spatial rate of spread of a surface fire. The techniques were developed using experimental wheat‐stubble burns carried out near Darfield, New Zealand, in March 2019. The thermal tracking technique is based on Thermal Image Velocimetry, which tracks evolving temperature patterns within an infrared video. The visible tracking technique uses colour thresholding, and tracks fire perimeter progression through time at pixel resolution. Results show that the visible perimeter tracking creates a higher mean rate of spread compared to thermal image velocimetry. The visible perimeter tracking provides rate of spread measurements for fire front progression whereas the thermal tracking techniqueis computationally more expensive, but can resolve velocities of thermal structures within the flaming zone and provides spatiotemporal rate of spread measurements. Both techniques are available as open‐source code and providevital scientific data for new studies concerning e.g. fire–atmospheric interactions or model validation. They may be adapted for operational purposes providing rate of spread at high spatiotemporal resolution.

The Mt. Kilimanjaro ecosystem depends on atmospheric moisture transport and recycling of water, yet limited knowledge on the relation between advective moisture pathways and local water recycling is available. This study uses a regional atmospheric model to analyse moisture source and sink processes as well as precipitation recycling at Mt. Kilimanjaro. The results indicate that locally produced precipitation is deposited mainly in June and July while trans‐regional effects are weak. Results also revealed the importance of tropical storm systems for local convection in the Kilimanjaro region and the effect of the Indian Ocean Zonal Mode phases on East African moisture pathways. The analysis further revealed connections between the Madagascar tip jet and the Turkana jet with implications for regional moisture pathways that have not been described before.

An adequate compute and storage infrastructure supporting the full exploitation of Copernicus and Earth Observation datasets is currently not available in Europe. This paper presents the cross-disciplinary open-source technologies being leveraged in the C-SCALE project to develop an open federation of compute and data providers as an alternative to monolithic infrastructures for processing and analysing Copernicus and Earth Observation data. Three critical aspects of the federation and the chosen technologies are elaborated upon: (1) federated data discovery, (2) federated access and (3) software distribution. With these technologies the open federation aims to provide homogenous access to resources, thereby enabling its users to generate meaningful results quickly and easily. This will be achieved by abstracting the complexity of infrastructure resource access provisioning and orchestration, including discovery of data across distributed archives, away from the end-users. Which is needed because end-users wish to focus on analysing ready-to-use data products and models rather than spending their time on the setup and maintenance of complex and heterogeneous IT infrastructures. The open federation will support processing and analysing the vast amounts of Copernicus and Earth Observation data that are critical for the implementation of the Destination Earth resp. Digital Twins vision for a high precision digital model of the Earth to model, monitor and simulate natural phenomena and related human activities.

Thermal image velocimetry (TIV) is a near-target remote sensing technique for estimating two-dimensional (2D) near-surface wind velocity based on spatio-temporal displacement of fluctuations in surface brightness temperature captured by an infrared camera. The addition of an automated parameterization and the combination of ensemble TIV results into one output made the method more suitable to changing meteorological conditions and less sensitive to noise stemming from the airborne sensor platform. Three field campaigns were carried out to evaluate the algorithm over turf, dry grass, and wheat stubble. The derived velocities were validated with independently acquired observations from fine-wire thermocouples and sonic anemometers. It was found that the TIV technique correctly derives atmospheric flow patterns close to the ground. Moreover, the modified method resolves wind speed statistics close to the surface at a higher resolution than the traditional measurement methods. Adaptive thermal image velocimetry (A-TIV) is capable of providing contactless spatial information about near-surface atmospheric motion and can help to be a useful tool in researching turbulent transport processes close to the ground.