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Abstract Changing wildfire regimes in the western US and other fire-prone regions pose considerable risks to human health and ecosystem function. However, our understanding of wildfire behavior is still limited by a lack of data products that systematically quantify fire spread, behavior and impacts. Here we develop a novel object-based system for tracking the progression of individual fires using 375 m Visible Infrared Imaging Radiometer Suite active fire detections. At each half-daily time step, fire pixels are clustered according to their spatial proximity, and are either appended to an existing active fire object or are assigned to a new object. This automatic system allows us to update the attributes of each fire event, delineate the fire perimeter, and identify the active fire front shortly after satellite data acquisition. Using this system, we mapped the history of California fires during 2012–2020. Our approach and data stream may be useful for calibration and evaluation of fire spread models, estimation of near-real-time wildfire emissions, and as means for prescribing initial conditions in fire forecast models. 

As the majority of fossil fuel carbon dioxide (CO₂) emissions originate from cities, the use of novel techniques to leverage available satellite observations of CO₂and proxy species to constrain urban CO₂is of great importance. In this study, we seek to empirically determine relationships between satellite observations of CO₂and the proxy species nitrogen dioxide (NO₂), applying these relationships to NO₂fields to generate NO₂‐derived CO₂fields (NDCFs) from which CO₂emissions can be estimated. We first establish this method using simulations of CO₂and NO₂for the cities of Buenos Aires, Melbourne, and Mexico City, finding that the method is viable throughout the year. For the same three cities, we next calculate empirical relationships (slopes) between co‐located observations of NO₂from the Tropospheric Monitoring Instrument and Snapshot Area Mode observations of CO₂from Orbiting Carbon Observatory‐3. Applying varying combinations of slopes to generate NDCFs, we evaluate methodological uncertainties for each slope application method and use a simple mass balance method to estimate CO₂emissions from NDCFs. We demonstrate monthly urban CO₂emissions estimates that are comparable to emissions inventory estimates. We additionally prove the utility of our method by demonstrating how large uncertainties at a grid cell level (equivalent to ∼1–3 ppm) can be reduced substantially when aggregating emissions estimates from NDCFs generated from all NO₂swaths (about 1%–6%). Rather than rely on prior knowledge of emission ratios, our method circumvents such assumptions and provides a valuable observational constraint on urban CO₂emissions.

 

Mangroves buffer inland ecosystems from hurricane winds and storm surge. However, their ability to withstand harsh cyclone conditions depends on plant resilience traits and geomorphology. Using airborne lidar and satellite imagery collected before and after Hurricane Irma, we estimated that 62% of mangroves in southwest Florida suffered canopy damage, with largest impacts in tall forests (>10 m). Mangroves on well-drained sites (83%) resprouted new leaves within one year after the storm. By contrast, in poorly-drained inland sites, we detected one of the largest mangrove diebacks on record (10,760 ha), triggered by Irma. We found evidence that the combination of low elevation (median = 9.4 cm asl), storm surge water levels (>1.4 m above the ground surface), and hydrologic isolation drove coastal forest vulnerability and were independent of tree height or wind exposure. Our results indicated that storm surge and ponding caused dieback, not wind. Tidal restoration and hydrologic management in these vulnerable, low-lying coastal areas can reduce mangrove mortality and improve resilience to future cyclones.

 

Fire emissions of gases and aerosols alter atmospheric composition and have substantial impacts on climate, ecosystem function, and human health. Warming climate and human expansion in fire‐prone landscapes exacerbate fire impacts and call for more effective management tools. Here we developed a global fire forecasting system that predicts monthly emissions using past fire data and climate variables for lead times of 1 to 6 months. Using monthly fire emissions from the Global Fire Emissions Database (GFED) as the prediction target, we fit a statistical time series model, the Autoregressive Integrated Moving Average model with eXogenous variables (ARIMAX), in over 1,300 different fire regions. Optimized parameters were then used to forecast future emissions. The forecast system took into account information about region‐specific seasonality, long‐term trends, recent fire observations, and climate drivers representing both large‐scale climate variability and local fire weather. We cross‐validated the forecast skill of the system with different combinations of predictors and forecast lead times. The reference model, which combined endogenous and exogenous predictors with a 1 month forecast lead time, explained 52% of the variability in the global fire emissions anomaly, considerably exceeding the performance of a reference model that assumed persistent emissions during the forecast period. The system also successfully resolved detailed spatial patterns of fire emissions anomalies in regions with significant fire activity. This study bridges the gap between the efforts of near‐real‐time fire forecasts and seasonal fire outlooks and represents a step toward establishing an operational global fire, smoke, and carbon cycle forecasting system.