Brycen D. Pearl

Brycen D. Pearl, Joshua G. Warner, Hang Woon Lee

Wildfires are becoming increasingly frequent, with potentially devastating consequences, including loss of life, infrastructure destruction, and severe environmental damage. Low Earth orbit satellites equipped with onboard sensors can capture critical information relative to active wildfires and enable near real-time detection through machine learning algorithms applied to the acquired data. We propose a framework that automates the complete wildfire detection and satellite scheduling pipeline, entitled the WildFire-applicable Intelligent and Responsive Ensemble for Detection and Scheduling (WildFIRE-DS). This paper develops an algorithm to realize the vision of the WildFIRE-DS as a proof of concept, integrating three key components: wildfire detection in satellite imagery, statistical updating that incorporates data from repeated flyovers, and multi-satellite scheduling optimization. The algorithm enables wildfire detection using convolutional neural networks with sensor fusion techniques, incorporates subsequent flyover information via Bayesian statistics, and schedules a constellation of satellites using the state-of-the-art Reconfigurable Earth Observation Satellite Scheduling Problem. Simulated experiments conducted using real-world wildfire locations and the orbits of operational Earth observation satellites to demonstrate that this autonomous detection and scheduling approach effectively enhances wildfire monitoring capabilities.

Brycen D. Pearl, Logan P. Gold, Hang Woon Lee

Tropical cyclones (TCs) are highly dynamic natural disasters that travel vast distances and occupy a large spatial scale, leading to loss of life, economic strife, and destruction of infrastructure. The severe impact of TCs makes them crucial to monitor such that the collected data contributes to forecasting their trajectory and severity, as well as the provision of information to relief agencies. Among the various methods used to monitor TCs, Earth observation satellites are the most flexible, allowing for frequent observations with a wide variety of instruments. Traditionally, satellite scheduling algorithms assume nadir-directional observations, a limitation that can be alleviated by incorporating satellite agility and constellation reconfigurability -- two state-of-the-art concepts of operations (CONOPS) that extend the amount of time TCs can be observed from orbit. This paper conducts a systematic comparative analysis between both CONOPS to present the performance of each relative to baseline nadir-directional observations in monitoring TCs. A dataset of 100 historical TCs is used to provide a benchmark concerning real-world data through maximizing the number of quality observations. The results of the comparative analysis indicate that constellation reconfigurability allowing plane-change maneuvers outperforms satellite agility in the majority of TCs analyzed.

Brycen D. Pearl, Joseph M. Miller, Hang Woon Lee

Earth observation satellites (EOSs) play a pivotal role in capturing and analyzing planetary phenomena, ranging from natural disasters to societal development. The EOS scheduling problem (EOSSP), which optimizes the schedule of EOSs, is often solved with respect to nadir-directional EOS systems, thus restricting the observation time of targets and, consequently, the effectiveness of each EOS. This paper leverages state-of-the-art constellation reconfigurability to develop the reconfigurable EOS scheduling problem (REOSSP), wherein EOSs are assumed to be maneuverable, forming a more optimal constellation configuration at multiple opportunities during a schedule. This paper develops a novel mixed-integer linear programming formulation for the REOSSP to optimally solve the scheduling problem for given parameters. Additionally, since the REOSSP can be computationally expensive for large-scale problems, a rolling horizon procedure (RHP) solution method is developed. The performance of the REOSSP is benchmarked against the EOSSP, which serves as a baseline, through a set of random instances where problem characteristics are varied and a case study in which Hurricane Sandy is used to demonstrate realistic performance. These experiments demonstrate the value of constellation reconfigurability in its application to the EOSSP, yielding solutions that improve performance, while the RHP enhances computational runtime for large-scale REOSSP instances.