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Most spaceborne sensors have an inevitable tradeoff between spatial and spectral resolutions. This is a typical ill-posed inverse problem in the field of image fusion. To solve this problem, this letter proposes an image fusion method using spatial principal component analysis and modulation transfer function-based filters. The key behind the proposed fusion method is to efficiently estimate the missing spatial details by considering the spatial structures of the low-resolution multispectral (MS) imagery. Also, this letter proposes a newly developed injection gain model to resolve the local and global dissimilarity between panchromatic and MS imageries, which could prevent over- and under-injections. Finally, spatial details, optimized to be injected into the MS images, were constructed and paired with the developed injection gain model to produce high-resolution MS images. Two data sets acquired by WorldView-2 are employed for validation. The experimental results demonstrate that the proposed fusion method generates high-quality imagery in terms of both qualitative and quantitative standards.

Meteorological satellite images provide crucial information on solar irradiation and weather conditions at spatial and temporal resolutions which are ideal for short-term photovoltaic (PV) power forecasts. Following the introduction of next-generation meteorological satellites, investigating their application on PV forecasts has become imminent. In this study, Communications, Oceans, and Meteorological Satellite (COMS) and Himawari-8 (H8) satellite images were inputted in a deep neural network (DNN) model for 2 hour (h)- and 1 h-ahead PV forecasts. A one-year PV power dataset acquired from two solar power test sites in Korea was used to directly forecast PV power. H8 was used as a proxy for GEO-KOMPSAT-2A (GK2A), the next-generation satellite after COMS, considering their similar resolutions, overlapping geographic coverage, and data availability. In addition, two different data sampling setups were designed to implement the input dataset. The first setup sampled chronologically ordered data using a relatively more inclusive time frame (6 a.m. to 8 p.m. in local time) to create a two-month test dataset, whereas the second setup randomly sampled 25% of data from each month from the one-year input dataset. Regardless of the setup, the DNN model generated superior forecast performance, as indicated by the lowest normalized mean absolute error (NMAE) and normalized root mean squared error (NRMSE) results in comparison to that of the support vector machine (SVM) and artificial neural network (ANN) models. The first setup results revealed that the visible (VIS) band yielded lower NMAE and NRMSE values, while COMS was found to be more influential for 1 h-ahead forecasts. For the second setup, however, the difference in NMAE results between COMS and H8 was not significant enough to distinguish a clear edge in performance. Nevertheless, this marginal difference and similarity of the results suggest that both satellite datasets can be used effectively for direct short-term PV forecasts. Ultimately, the comparative study between satellite datasets as well as spectral bands, time frames, forecast horizons, and forecast models confirms the superiority of the DNN and offers insights on the potential of transitioning to applying GK2A for future PV forecasts.

Fire spread models (FSMs) are used to reproduce fire behavior and can simulate fire propagation over landscapes. As wildfires have emerged into a global phenomenon with far-reaching impacts on the natural and built environments, FSM simulations provide crucial information to better understand and predict fire behavior in various landscapes. In this study, we tested Cell2Fire, a recently developed cellular automata-based FSM, against benchmarking models used in the U.S., Canada, and Chile. We experimented on synthetically generated landscapes (homogeneous and heterogeneous mix of fuels), applying Cell2Fire for the first time on U.S. landscapes, and found a high level of agreement between Cell2Fire and existing FSMs. However, FSMs may not always produce realistic simulations. In response, we used two optimization methods to improve the simulation’s accuracy. First, we adopted a multi-objective optimization algorithm that scales the elliptical shape of the Cell2Fire’s output based on rate of spread (ROS) and eccentricity. Second, we optimized four adjustment factors related to the fire spread (head ROS, back ROS, flank ROS, and eccentricity) using blackbox optimization (i.e., derivative-free optimization), minimizing the discrepancy of the output with respect to real burn data. We assessed the effectiveness of the optimization on the 2001 Dogrib Fire in Alberta, Canada and found that the optimized Cell2Fire result more accurately predicted the real burn in comparison to Prometheus (standard Canadian FSM), increasing F1-score from 0.74 to 0.83. Further, Cell2Fire exhibited better computational efficiency, with simulation runtime increasing linearly compared to Prometheus’ runtime increasing exponentially. From these results, users can adjust Cell2Fire and simulate more realistic burns and surpass the capabilities of benchmark FSMs, integrating local or custom-made FSM data to expand the simulator’s application.