Peatland fires are among the most severe disturbances in tropical regions and represent a major source of greenhouse gas emissions in Southeast Asia, including Malaysia. Extensive drainage of peatlands for agriculture and settlement has transformed naturally waterlogged and fire-resistant peat swamp forests into highly combustible landscapes. These changes highlight the need for an integrated understanding of the interactions among soil properties, hydrology, vegetation and human activities to support effective fire management and ecological restoration. This review synthesises existing research on peatland fire behaviour in Malaysia, drawing on studies in hydrology, ecology, fire science and policy. Evidence has consistently demonstrated that fire ignition risk increases sharply as peat moisture declines and groundwater tables fall. Smouldering fires can propagate beyond 1 m below the surface and release substantial amounts of carbon per hectare. Moreover, recurrent burning can lead to biodiversity loss, soil subsidence and the conversion of peat swamp forests into fire-prone grasslands. The review highlights hydrological restoration, particularly canal blocking and revegetation with native species, as a key strategy for reducing fire frequency and severity. In addition, emerging technologies, including unmanned aerial vehicle thermal detection, the GS Mark III deep-water injection system and Internet of Things sensor networks, demonstrate strong potential for early detection and improved suppression of ground fire. Sustainable peatland fire management in Malaysia requires a holistic framework that integrates community engagement, hydrological restoration and ecological rehabilitation. Strengthening data sharing, monitoring and adaptive management within existing initiatives, such as the National Action Plan for Peatlands and regional ASEAN haze mitigation strategies, can further reduce emissions and enhance ecosystem resilience. Together, these approaches provide a pathway towards more climate-resilient peatland fire management in Malaysia and across Southeast Asia.

Accurate assessment of fuel conditions is a prerequisite for fire ignition and behavior prediction, and risk management. The method proposed herein leverages diverse data sources including Landsat-8 optical imagery, Sentinel-1 (C-band) Synthetic Aperture Radar (SAR) imagery, PALSAR (L-band) SAR imagery, and terrain features to capture comprehensive information about fuel types and distributions. An ensemble model was trained to predict landscape-scale fuels such as the 'Scott and Burgan 40' using the as-received Forest Inventory and Analysis (FIA) field survey plot data obtained from the USDA Forest Service. However, this basic approach yielded relatively poor results due to the inadequate amount of training data. Pseudo-labeled and fully synthetic datasets were developed using generative AI approaches to address the limitations of ground truth data availability. These synthetic datasets were used for augmenting the FIA data from California to enhance the robustness and coverage of model training. The use of an ensemble of methods including deep learning neural networks, decision trees, and gradient boosting offered a fuel mapping accuracy of nearly 80\%. Through extensive experimentation and evaluation, the effectiveness of the proposed approach was validated for regions of the 2021 Dixie and Caldor fires. Comparative analyses against high-resolution data from the National Agriculture Imagery Program (NAIP) and timber harvest maps affirmed the robustness and reliability of the proposed approach, which is capable of near-real-time fuel mapping.

High-resolution soil moisture (SM) observations are critical for agricultural monitoring, forestry management, and hazard prediction, yet current satellite passive microwave missions cannot directly provide retrievals at tens-of-meter spatial scales. Unmanned aerial vehicle (UAV) mounted microwave radiometry presents a promising alternative, but most evaluations to date have focused on agricultural settings, with limited exploration across other land covers and few efforts to quantify retrieval uncertainty. This study addresses both gaps by evaluating SM retrievals from a drone-based Portable L-band Radiometer (PoLRa) across shrubland, bare soil, and forest strips in Central Illinois, U.S., using a 10-day field campaign in 2024. Controlled UAV flights at altitudes of 10 m, 20 m, and 30 m were performed to generate brightness temperatures (TB) at spatial resolutions of 7 m, 14 m, and 21 m. SM retrievals were carried out using multiple tau-omega-based algorithms, including the single channel algorithm (SCA), dual channel algorithm (DCA), and multi-temporal dual channel algorithm (MTDCA). A Bayesian inference framework was then applied to provide probabilistic uncertainty characterization for both SM and vegetation optical depth (VOD). Results show that the gridded TB distributions consistently capture dry-wet gradients associated with vegetation density variations, and spatial correlations between polarized observations are largely maintained across scales. Validation against in situ measurements indicates that PoLRa derived SM retrievals from the SCAV and MTDCA algorithms achieve unbiased root-mean-square errors (ubRMSE) generally below 0.04 m3/m3 across different land covers. Bayesian posterior analyses confirm that reference SM values largely fall within the derived uncertainty intervals, with mean uncertainty ranges around 0.02 m3/m3 and 0.11 m3/m3 for SCA and DCA related retrievals.

Understanding fire behavior is a crucial step in wildfire risk assessment and management. Accurate and near real-time knowledge of the spatio-temporal characteristics of fuels is critical for analyzing pre-fire risk mitigation and managing active-fire emergency response. Geospatial modeling and land cover mapping using remote sensing combined with artificial intelligence techniques can provide fuel information at regional scales with high accuracy and resolution, as evidenced by the extensive recent work in the literature that appeared with increasing volume in the open literature. This paper provides a comprehensive survey of the state-of-the-art in wildfire fuel mapping, focusing on the research frontier of fire fuel models, fuel mapping methods, remote sensing data sources, existing datasets/reference maps, and applicable artificial intelligence techniques. The main findings highlight the increasing research on fire fuel mapping worldwide, with a considerable emphasis on multispectral imagery and the Random Forest classifier for its efficacy with limited data. The majority of these studies concentrate on relatively limited geographical scales spanning a small variety of fuel types, thus leaving a gap in regional and national-scale mapping. Further, this review focuses on identifying the major challenges in wildfire fuel mapping and viable solutions as they relate to (i) ground truth data scarcity, (ii) mapping understory vegetation, (iii) temporal latency, and (iv) lack of uncertainty-aware models. Lastly, this paper identifies potential AI-driven solutions that promise a significant leap in fuel mapping and discusses the latest developments and potential future trends in AI-based fuel mapping applications.

Deadwood is a critical component of forest ecosystems, storing nutrients for plants and serving as a carbon store and emission source. Climate change influences forest ecosystem dynamics with the potential for deadwood to emit carbon more rapidly due to accelerated decay and increased wildfires and increased inputs via mass forest mortality and disturbance events. To objectively inform our understanding of wildfires and associated carbon emissions, this study estimates the carbon content of dead fine woody debris (FWD) using multimodal data, such as Landsat-8 multispectral imagery, Sentinel-1 (C-band) and PALSAR (L-band) synthetic aperture radar (SAR) imagery, and terrain features to estimate the FWD of less than 0.25 in (1 h), 0.25-1 in (10 h), and 1-3 in (100 h). This data fusion provides spectral information to assess vegetation health that correlates with deadwood, as well as penetrability from SAR, resulting in structural information and biomass sensitivity. An ensemble machine learning (ML) model was trained using measurements from the Forest Inventory and Analysis (FIA) Database. A feature importance analysis was also performed to investigate the importance of input features to the model's performance. A super learner regression (SLR) model composed of 9 base learners, including an ElasticNet model as meta-learner, was proposed and achieved the R-2 values of 0.75, 0.72, and 0.62 to estimate 1-, 10-, and 100-h FWD, respectively. The validated model was then used to estimate deadwood carbon in the 2021 Dixie Fire region of California, demonstrating the effectiveness of our approach, emphasizing the value of multimodal data for real-time FWD carbon stock estimation.

Wildfires have the potential to cause severe damage to vegetation, property and most importantly, human life. In order to minimize these negative impacts, it is crucial that wildfires are detected at the earliest possible stages. A potential solution for early wildfire detection is to utilize unmanned aerial vehicles (UAVs) that are capable of tracking the chemical concentration gradient of smoke emitted by wildfires. A spatiotemporal model of wildfire smoke plume dynamics can allow for efficient tracking of the chemicals by utilizing both real-time information from sensors as well as future information from the model predictions. This study investigates a spatiotemporal modeling approach based on subspace identification (SID) to develop a data-driven smoke plume dynamics model for the purposes of early wildfire detection. The model was learned using CO2 concentration data which were collected using an air quality sensor package onboard a UAV during two prescribed burn experiments. Our model was evaluated by comparing the predicted values to the measured values at random locations and showed mean errors of 6.782 ppm and 30.01 ppm from the two experiments. Additionally, our model was shown to outperform the commonly used Gaussian puff model (GPM) which showed mean errors of 25.799 ppm and 104.492 ppm, respectively.

Accurate estimation of sub-surface properties such as moisture content and depth of soil and vegetation layers is crucial for applications spanning sub-surface condition monitoring, precision agriculture, and effective wildfire risk assessment. Soil in nature is often covered by overlaying vegetation and surface organic material, making its characterization challenging. In addition, the estimation of the properties of the overlaying layer is crucial for applications like wildfire risk assessment. This study thus proposes a Bayesian model-updating-based approach for ground penetrating radar (GPR) waveform inversion to predict moisture contents and depths of soil and overlaying material layer. Due to its high correlation with moisture contents, the dielectric permittivity of both layers were predicted with the proposed method, along with other parameters, including depth and electrical conductivity of layers. The proposed Bayesian model updating approach yields probabilistic estimates of these parameters that can provide information about the confidence and uncertainty related to the estimates. The methodology was evaluated for a diverse range of experimental data collected through laboratory and field investigations. Laboratory investigations included variations in soil moisture values, depth of the overlaying surface layer, and coarseness of its material. The field investigation included measurement of field soil moisture for sixteen days. The results demonstrated predictions consistent with time-domain reflectometry (TDR) measurements and conventional gravimetric tests. The depth of the surface layer could also be predicted with reasonable accuracy. The proposed method provides a promising approach for uncertainty-aware sub-surface parameter estimation that can enable decision-making for risk assessment across a wide range of applications.

A novel Portable L-band radiometer (PoLRa), compatible with tower-, vehicle- and drone-based platforms, can provide gridded soil moisture estimations from a few meters to several hundred meters yet its retrieval accuracy has rarely been examined. This study aims to provide an initial assessment of the performance of PoLRa-derived soil moisture at a spatial resolution of approximately 0.7 m × 0.7 m at a set of sampling pixels in central Illinois, USA. This preliminary evaluation focuses on (1) the consistency of PoLRa-measured brightness temperatures from different viewing directions over the same area and (2) whether PoLRa-derived soil moisture retrievals are within an acceptable accuracy range. As PoLRa shares many aspects of the L-band radiometer onboard NASA’s Soil Moisture Active Passive (SMAP) mission, two SMAP operational algorithms and the conventional dual-channel algorithm (DCA) were applied to calculate volumetric soil moisture from the measured brightness temperatures. The vertically polarized brightness temperatures from the PoLRa are typically more stable than their horizontally polarized counterparts across all four directions. In each test period, the standard deviations of observed dual-polarization brightness temperatures are generally less than 5 K. By comparing PoLRa-based soil moisture retrievals against the simultaneous moisture values obtained by a handheld capacitance probe, the unbiased root mean square error (ubRMSE) and the Pearson correlation coefficient (R) are mostly below 0.05 m3/m3 and above 0.7 for various algorithms adopted here. While SMAP models and the DCA algorithm can derive soil moisture from PoLRa observations, no single algorithm consistently outperforms the others. These findings highlight the significant potential of ground- or drone-based PoLRa measurements as a standalone reference for the calibration and validation of spaceborne L-band synthetic aperture radars and radiometers. The accuracy of PoLRa-yielded high-resolution soil moisture can be further improved via standardized operational procedures and appropriate tau-omega parameters.

Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses. These losses have underscored the urgent need to improve public knowledge and advance existing techniques in wildfire management. Recently, the use of Artificial Intelligence (AI) in wildfires, propelled by the integration of Unmanned Aerial Vehicles (UAVs) and deep learning models, has created an unprecedented momentum to implement and develop more effective wildfire management. Although existing survey papers have explored learning-based approaches in wildfire, drone use in disaster management, and wildfire risk assessment, a comprehensive review emphasizing the application of AI-enabled UAV systems and investigating the role of learning-based methods throughout the overall workflow of multi-stage wildfire management, including pre-fire (e.g., vision-based vegetation fuel measurement), active-fire (e.g., fire growth modeling), and post-fire tasks (e.g., evacuation planning) is notably lacking. This survey synthesizes and integrates state-of-the-science reviews and research at the nexus of wildfire observations and modeling, AI, and UAVs - topics at the forefront of advances in wildfire management, elucidating the role of AI in performing monitoring and actuation tasks from pre-fire, through the active-fire stage, to post-fire management. To this aim, we provide an extensive analysis of the existing remote sensing systems with a particular focus on the UAV advancements, device specifications, and sensor technologies relevant to wildfire management. We also examine the pre-fire and post-fire management approaches, including fuel monitoring, prevention strategies, as well as evacuation planning, damage assessment, and operation strategies. Additionally, we review and summarize a wide range of computer vision techniques in active-fire management, with an emphasis on Machine Learning (ML), Reinforcement Learning (RL), and Deep Learning (DL) algorithms for wildfire classification, segmentation, detection, and monitoring tasks. Ultimately, we underscore the substantial advancement in wildfire modeling through the integration of cutting-edge AI techniques and UAV-based data, providing novel insights and enhanced predictive capabilities to understand dynamic wildfire behavior.

In the soil moisture active passive (SMAP) mission's soil moisture retrieval algorithms, the effects of surface roughness and vegetation scattering on the brightness temperature are conventionally modeled using time-invariant parameters: roughness intensity (h) and effective scattering albedo (omega). Such simplification neglects the variability of h and omega over time, potentially compromising the accuracy of soil moisture estimates at the satellite footprint scale. This study aims to derive dynamic, pixel-scale h and omega parameters specifically for the SMAP single-channel algorithm (SCA) and the regularized dual-channel algorithm (RDCA). This is achieved through an iterative inverse procedure that minimizes the differences between the simulated brightness temperatures from spatially representative 9 km soil moisture and SMAP observations across the SMAP core validation sites. The results demonstrated that the incorporation of dynamic h and omega parameters, derived on a daily scale, markedly enhanced the soil moisture retrieval performance with an average unbiased root-mean-square error (ubRMSE) of 0.01 (0.02) m(3)/m(3) and Pearson correlation (R) of 0.95 (0.90) for the SCA (RDCA) algorithms, indicating that dynamic parameterization holds significant promise for improving retrieval accuracy. The daily scale h parameters are generally above the static values utilized in the SMAP SCA. Within the SMAP SCA framework, the accuracy of soil moisture estimates employing daily scale h and omega parameters-randomly selected from the SCA range (h is an element of [0.03, 0.16] and omega is an element of [0, 0.08])-demonstrates notable stability and is comparable with the SMAP level 3 product. Furthermore, the daily scale parameters were temporally contracted to generate a monthly climatology for h and omega. While soil moisture values derived from these climatological h and omega parameters exhibit reduced absolute bias, their ubRMSE and R slightly degrade relative to SMAP level 3 product. This degradation likely suggests that the climatological parameters' gradual variations are insufficient to capture the fluctuations of those daily parameters. Moreover, the static h and omega values for the RDCA are systematically higher than those for the SCA. However, there is no consistent trend in the magnitudes of dynamic h and omega between different algorithms. Identifying the most effective dynamic h and omega parameters within the SMAP algorithmic framework necessitates not only selecting an appropriate parameter range but also accurately tracking the temporal evolutions of surface roughness and vegetation scattering. Potential applications arising from improvements in retrieved soil moisture include the management of agricultural lands and forecasts of their productivity, quantification of global water and energy fluxes at the land surface, and management of forests, particularly in instances where disturbances, such as droughts, floods, or wildfire, are concerned.

Fire ecology is a complex discipline that can only be understood by integrating biological, physical, and social sciences. The science of fire ecology explores wildland fire's mechanisms and effects across all scales of time and space. However, the lack of defined, organizing concepts in fire ecology dilutes its collective impact on knowledge and management decision-making and makes the discipline vulnerable to misunderstanding and misappropriation. Fire ecology has matured as a discipline and deserves an enunciation of its unique emergent principles of organization. Most scientific disciplines have established theories, laws, and principles that have been tested, debated, and adopted by the discipline's practitioners. Such principles reflect the consensus of current knowledge, guide methodology and interpretation, and expose knowledge gaps in a coherent and structured way. In this manuscript, we introduce five comprehensive principles to define the knowledge fire ecology has produced and provide a framework to support the continued development and impact of the fire ecology discipline.

Soil moisture sensing through biomass or vegetation canopy has challenged researchers, even those who use SAR sensors with penetration capabilities. This is mainly due to the imposed extra time and phase offsets on Radio Frequency (RF) signals as they travel through the canopy. These offsets depend on the vegetation canopy moisture and height, both of which are typically unknown in agricultural and forest fields. In this paper, we leverage the mobility of an unmanned aerial system (UAS) to collect spatially-diverse radar measurements, enabling the joint estimation of soil moisture, above-ground biomass moisture, and biomass height, all without assuming any calibration steps. We leverage the changes in time-of-flight (ToF) and angle-of-arrival (AoA) measurements of reflected radar signals as the UAS flies above a reflector buried under the soil. We demonstrate the effectiveness of our algorithm by simulating its performance under realistic measurement noises as well as conducting lab experiments with different types of above-ground biomass. Our simulation results conclude that our algorithm is capable of estimating volumetric soil moisture to less than 1% median absolute error (MAE), vegetation height to 11.1cm MAE, and vegetation relative permittivity to 0.32 MAE. Our experimental results demonstrate the effectiveness of the proposed method in practical scenarios for varying biomass moistures and heights.

The use of coarse scale and temporally static effective roughness and single scattering albedo parameters in the Soil Moisture Active Passive (SMAP) operational algorithms falls short in achieving high spatial resolution in soil moisture (SM) retrieval. To address this issue, this study leverages in-situ SM measurements from 2016 to 2018 at the SMAP core validation sites to derive monthly parameters. These parameters were then applied to estimate SM for subsequent periods. Our findings suggest that SM retrievals from these monthly-adjusted parameters exhibit a marginal improvement in mean absolute error relative to the standard SMAP retrievals. This scheme will be applied to develop high spatiotemporal resolution parameters for passive microwave SM retrieval from instruments aboard uncrewed aerial systems (UAS). The proposed method also allows for these parameters to be tailored to targeted agricultural and forested areas for applications ranging from precision agriculture to intelligent wildfire management. This adaptation promises to refine our ability to retrieve SM with greater accuracy and resolution.

Background Characterisation of fuel consumption provides critical insights into fire behaviour, effects, and emissions. Stand-replacing prescribed fire experiments in central Utah offered an opportunity to generate consumption estimates in coordination with other research efforts.Aims We sought to generate fuel consumption maps using pre- and post-fire airborne laser scanning (ALS) and ground measurements and to test the spatial transferability of the ALS-derived fuel models.Methods Using random forest (RF), we empirically modelled fuel load and estimated consumption from pre- and post-fire differences. We used cross-validation to assess RF model performance and test spatial transferability.Key results Consumption estimates for overstory fuels were more precise and accurate than for subcanopy fuels. Transferring RF models to provide consumption estimates in areas without ground training data resulted in loss of precision and accuracy.Conclusions Fuel consumption maps were produced and are available for researchers who collected coincident fire behaviour, effects, and emissions data. The precision and accuracy of these data vary by fuel type. Transferability of the models to novel areas depends on the user's tolerance for error.Implications This study fills a critical need in the broader set of research efforts linking fire behaviour, effects, and emissions. We used airborne laser scanning (ALS) data and ground measurements to create fuel consumption maps for prescribed fires in central Utah. The data, methods, and accuracy assessments we produced are useful for researchers who collected data on fire behaviour, effects, and emissions on the same fires.

Accurate fuel condition assessment is crucial for predicting fire behavior, enhancing operational decision support, and improving overall fire management. Our approach utilizes diverse data sources, such as Landsat-8 optical imagery, Sentinel-1 (C-band) SAR imagery, PALSAR (L-band) SAR imagery, and terrain features, to estimate time-lag fuel loadings (1 hour, 10 hours, and 100 hours). Optical data mainly captures the characteristics of leaf and forest canopy, while SAR data is more sensitive to forest vertical structures due to its strong penetrability. An ensemble model was trained on the Forest Inventory and Analysis (FIA) plots and spectral indices. Followed by, feature importance analysis and the inclusion of polynomial features were undertaken. The ensemble strategy, involving neural networks, decision trees, gradient boosting, and ensemble methods, achieved R 2 values of 0.72, 0.70, and 0.60 for 1-hour, 10-hour, and 100-hour fuel loads. Extensive experimentation in the 2021 Dixie Fire incident validates the effectiveness of our approach, emphasizing the value of leveraging multimodal data and ensemble machine learning models for real-time fuel load estimation.

Spaceborne microwave passive soil moisture products are known for their accuracy but are often limited by coarse spatial resolutions. This limits their ability to capture finer soil moisture gradients and hinders their applications. The Portable L band radiometer (PoLRa) offers soil moisture measurements from submeter to tens of meters depending on the altitude of measurement. Given that the assessments of soil moisture derived from this sensor are notably lacking, this study aims to evaluate the performance of submeter soil moisture retrieved from PoLRa mounted on poles at four different locations in central Illinois, USA. The evaluation focuses on the consistency of PoLRa measured brightness temperatures from different directions relative to the same area, and the accuracy of PoLRa derived soil moisture. As PoLRa shares many aspects of the L band radiometer onboard the NASA Soil Moisture Active Passive (SMAP) mission, two SMAP operational algorithms and the conventional dual channel algorithm were applied to calculate soil moisture from the measured brightness temperatures. The vertically polarized brightness temperatures from the PoLRa are typically more stable than their horizontally polarized counterparts. In each test period, the standard deviations of observed dual polarization brightness temperatures are generally less than 5 K. By comparing PoLRa based soil moisture retrievals against the moisture values obtained by handheld time domain reflectometry, the unbiased root mean square error and the Pearson correlation coefficient are mostly below 0.04 and above 0.75, confirming the high accuracy of PoLRa derived soil moisture retrievals and the feasibility of utilizing SMAP algorithms for PoLRa data. These findings highlight the significant potential of ground or drone based PoLRa measurements as a standalone reference for future spaceborne L band sensors.

Smoke from wildland fires contains more diverse, viable microbes than typical ambient air, yet little is known about the sources and sinks of smoke-borne microorganisms. Data from molecular-based surveys suggest that smoke-borne microorganisms originate from material associated with the vegetation and underlying soils that becomes aerosolized during combustion, however, the sources of microbes in smoke have not yet been experimentally assessed. To elucidate this link, we studied high-intensity forest fires in the Fishlake National Forest, Utah, USA and applied source-sink modeling to assemblages of 16S ribosomal RNA (rRNA) gene sequences recovered from samples of smoke, vegetation, and soil. Our results suggest that 70% of the bacterial taxa in smoke originated from the local aspen (Populus tremuloides) (33%) and soil (37%) communities. In comparison, 42% of bacteria in air sampled prior to the fires could be attributed to these terrestrial sources. When the bacterial assemblages in smoke were modeled as sources to the local communities, they contributed an average of 25% to the terrestrial sinks versus an estimated contribution of <4% from ambient air. Our results provide support for the role of wildland fire in bacterial dispersal and the working hypothesis that smoke is an environmental reservoir of microbes for receiving ecosystems.

Moisture estimation of sub-surface soil and the overlaying biomass layer is pivotal in precision agriculture and wildfire risk assessment. However, the characterization of layered material is nontrivial due to the radar penetration-resolution tradeoff. Here, a waveform inversion-based method was proposed to predict the dielectric permittivity (as a moisture proxy) of the bottom soil layer and the top biomass layer from radar signals. Specifically, the use of a combination of a higher and a lower frequency radar compared to a single frequency in predicting the permittivity of both the soil and the overlaying layer was investigated in this study. The results show that each layer was best characterized via one of the frequencies. However, for the simultaneous prediction of both layers' permittivity, the most consistent results were achieved by inversion of data from a combination of both frequencies, showing better correlation with in situ permittivity and reduced prediction errors.

Coordination across multiple disciplines is necessary for current and future generations of modeling systems and the decision-support tools used for understanding and managing wildland fires. This coordination manifests in active-fire campaigns involving practitioners, modelers, and data-collection and management groups to produce datasets for training and evaluating models and underlying theories upon which they are built. New approaches and technology often are involved in these efforts, and in many cases their development is specified or driven by requirements identified by data-collection or model-evaluation activities. The Fire and Smoke Model Evaluation Experiment (FASMEE) is an effort to 1) conduct large-scale, active-fire data collection in order to produce a library of wildland fire model inputs 2) to advance wildland fire and smoke models, decision tools, and underlying science, and 3) to encourage disciplinary cross-training, diversity of backgrounds, and the development of new partnerships and technology in wildland fire science.

Background Prescribed fire is vital for fuel reduction and ecological restoration, but the effectiveness and fine-scale interactions are poorly understood. Aims We developed methods for processing uncrewed aircraft systems (UAS) imagery into spatially explicit pyrometrics, including measurements of fuel consumption, rate of spread, and residence time to quantitatively measure three prescribed fires. Methods We collected infrared (IR) imagery continuously (0.2 Hz) over prescribed burns and one experimental calibration burn, capturing fire progression and combustion for multiple hours. Key results Pyrometrics were successfully extracted from UAS-IR imagery with sufficient spatiotemporal resolution to effectively measure and differentiate between fires. UAS-IR fuel consumption correlated with weight-based measurements of 10 1-m2 experimental burn plots, validating our approach to estimating consumption with a cost-effective UAS-IR sensor (R2 = 0.99; RMSE = 0.38 kg m-2). Conclusions Our findings demonstrate UAS-IR pyrometrics are an accurate approach to monitoring fire behaviour and effects, such as measurements of consumption. Prescribed fire is a fine-scale process; a ground sampling distance of <2.3 m2 is recommended. Additional research is needed to validate other derived measurements. Implications Refined fire monitoring coupled with refined objectives will be pivotal in informing fire management of best practices, justifying the use of prescribed fire and providing quantitative feedback in an uncertain environment.