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NASA NTRS2019

Thermal History Mapping Technology for Turbine Engine Diagnostics

The infusion of ceramic matrix composites (CMCs) in next generation gas turbines represents daunting challenges as the failure of protective coating, known as Environmental Barrier Coating (EBC), means little remnant life of CMC components. Consequently reliable lifing is critical to the success of CMC components. Accurate thermal mapping of EBC in rig and engine test is of paramount importance because temperature is the most critical variable for EBC life. Current temperature measurement technologies include thermocouples, pyrometers, IR cameras, and thermal paints. Thermocouples require a passage to access the components and pyrometers and IR cameras require line-of-sight. Thermal paints are based on color-sensitive pigments that visually indicate the temperature that a component experienced. Thermal paints do not require line-of-sight, however, they have a low resolution and a limited temperature capability (~1400°C). Thermal history coating is based on oxide ceramic which luminesces when excited with a light source. The luminescence changes when the material's structure changes triggered by heat impact. The maximum temperature to which the material was exposed can be determined from the luminescence change. This paper describes the principles of this new technique and demonstrates its capabilities for EBCs exposed to excess of 1500°C.

Lee, Kang N.↗
NASA NTRS2019

Deep Learning Method for Detecting Precursors to Adverse Events

With the recent advancements in Deep Learning methods, the ability to model large complex heterogeneous data sets are fundamentally changing industry and research. Coupled with hardware improvements, and ease of implementation, a wide variety of deep neural network architectures can quickly be developed to solve a sweeping range of problems such as: object detection in images, automatic healthcare diagnosis using heterogenous data sources, real time language translating and sentence prediction, upscaling low resolution images, and forecasting of multivariate timeseries. Generally, many of these architectures outperform classical machine learning approaches in their respective tasks, however, this typically comes at a cost of interpretability. These black box algorithms generally suffer from lack of transparency in both model complexity as well as the rationale behind the prediction. This lack of comprehension, is driving an emerging area of interest in “Explainable AI”. An algorithm called: “Deep Temporal Multiple Instance Learning”1 was a recently developed to identify precursors to adverse events and has been applied in the aviation domain. The deep learning architecture is designed to capture the evolution of the probability of the outcome over the time preceding the adverse event using a multiple instance learning approach as illustrated in Figure 1. Precursors are defined when the probability of the event has exceeded a threshold at some point in the timeseries, at which point, a sensitivity analysis is performed to determine contributing factors. The contributing factors are used to explain and define the precursor during the periods where the probability score is high. The identified contributing factors are then presented to subject matter experts to provide objective insights into the leading factors associated with the particular adverse event. The algorithm has been tested on flight data from a commercial airline and has the ability to discover precursors to known adverse events that take the form of safety critical operations, such as unstable approach events on final approach. Apart from detecting precursors to adverse events, the converse can also be leveraged to discover corrective actions. These positive actions manifest themselves as periods in the timeseries when the precursor score has been lowered from an elevated state; meaning that if the system had been left uncorrected, it would have eventually reached the adverse event state. Characterizing these state changes can help identify successful interventions that may not have been known before. Policy makers and procedure designers can use this additional knowledge to craft more safety and efficient resilient procedures for future operations and therefore improve the overall performance of the National Airspace.

Matthews, Bryan L.↗
NASA NTRS2020

Sensitive Probing of Exoplanetary Oxygen via Mid-Infrared Collisional Absorption

The collision-induced fundamental vibration–rotation band at 6.4 μm is the strongest absorption feature from O2 in the infrared, yet it has not been previously incorporated into exoplanet spectral analyses for several reasons. Either collision-induced absorptions (CIAs) were not included or incomplete/obsolete CIA databases were used. Also, the current version of HITRAN does not include CIAs at 6.4 μm with other collision partners (O2–X). We include O2–X CIA features in our transmission spectroscopy simulations by parameterizing the 6.4-μm O2–N2 CIA based on ref. 3 and the O2–CO2 CIA based on ref. 4. Here we report that the O2–X CIA may be the most detectable O2 feature for transit observations. For a potential TRAPPIST-1 e analogue system within 5 pc of the Sun, it could be the only O2 signature detectable with the James Webb Space Telescope (JWST) (using MIRI LRS (Mid-Infrared Instrument low-resolution spectrometer)) for a modern Earth-like cloudy atmosphere with biological quantities of O2. Also, we show that the 6.4-μm O2–X CIA would be prominent for O2-rich desiccated atmospheres and could be detectable with JWST in just a few transits. For systems beyond 5 pc, this feature could therefore be a powerful discriminator of uninhabited planets with non-biological ‘false-positive’ O2 in their atmospheres, as they would only be detectable at these higher O2 pressures.

Thomas J Fauchez↗
NASA NTRS2020

Sensitive Probing of Exoplanetary Oxygen via Mid-Infrared Collisional Absorption

The collision-induced fundamental vibration–rotation band at 6.4 μm is the strongest absorption feature from O2 in the infrared1,2,3, yet it has not been previously incorporated into exoplanet spectral analyses for several reasons. Either collision-induced absorptions (CIAs) were not included or incomplete/obsolete CIA databases were used. Also, the current version of HITRAN does not include CIAs at 6.4 μm with other collision partners (O2–X). We include O2–X CIA features in our transmission spectroscopy simulations by parameterizing the 6.4-μm O2–N2 CIA based on ref. 3 and the O2–CO2 CIA based on ref. 4. Here we report that the O2–X CIA may be the most detectable O2 feature for transit observations. For a potential TRAPPIST-1 e analogue system within 5 pc of the Sun, it could be the only O2 signature detectable with the James Webb Space Telescope (JWST) (using MIRI LRS (Mid-Infrared Instrument low-resolution spectrometer)) for a modern Earth-like cloudy atmosphere with biological quantities of O2. Also, we show that the 6.4-μm O2–X CIA would be prominent for O2-rich desiccated atmospheres5 and could be detectable with JWST in just a few transits. For systems beyond 5 pc, this feature could therefore be a powerful discriminator of uninhabited planets with non-biological ‘false-positive’ O2 in their atmospheres, as they would only be detectable at these higher O2 pressures.

Thomas Fauchez↗
NASA NTRS2000

The spectrum of TMR-1C is consistent with a background star

...This paper presents near-infrared grism spectroscopy to constrain the efective temperature of TMR-1C, obtained with the Near-Infrared Camera (NIRC) at Keck Observatory. To interpret the data, we construct a grid of extincted M dwarf spectra to compare with the low-resolution (RD120) NIRC spectrum. The assumed extinction corresponds to standard interstellar dust. With the additional assumption that no near-infrared dust excess contributes to the spectrum, then M4.5 is the latest spectral type TMR-1C can have within the uncertainties. ...

Padgett, D. L.↗
NASA NTRS2019

MultiLayer Clustered Sampling (MLCS) technique for near-earth asteroid impact hazard assessment

Because of planetary encounters, the motion of near-Earth asteroids is chaotic and small differences in the initial conditions tend to diverge exponentially. Linear approximations for propagating orbital uncertainties can lead to inaccurate estimates of the probability of an Earth collision. We present a novel fully nonlinear strategy for estimating the probability of an asteroid impact using sequential Monte Carlo layers. The method first explores a low-resolution layer to locate potentially relevant regions. Then, we conduct localized searches on deeper layers with higher resolution. The method retains the accuracy of brute-force Monte Carlo sampling while reducing the computational cost by only sampling relevant regions.

Farnocchia, Davide↗
NASA NTRS2018

HabEx Space Telescope Exoplanet Instruments

The HabEx (Habitable Exoplanet) space telescope mission concept carries two complementary optical systems as part of its baseline design, a coronagraph and a starshade, that are designed to detect and characterize planetary systems around nearby stars. The starshade is an external occulter which would be 72 m in diameter and fly some 124,000 km ahead of the telescope. A starshade instrument on board the telescope enables formation flying to maintain the starshade within 1 m of the line of sight to the star. The starshade instrument has various modes, including imaging from the near UV through to the near infrared and integral field spectroscopy in the visible band. The coronagraph would provide imaging and integral field spectroscopy in the visible band and would reach out to 1800 nm for low resolution spectroscopy in the near infrared. To provide the necessary stability for the coronagraph, the telescope would be equipped with a laser metrology system allowing measurement and control of the relative positions of the principal mirrors. In addition, a fine guidance sensor is needed for precision attitude control. The requirements for telescope stability for coronagraphy are discussed. The design and requirements on the starshade will also be discussed.

Marchen, Luis↗
NASA NTRS2020

Understanding and Improving Model Representation of Aerosol Optical Properties for a Chinese Haze Event Measured During KORUS-AQ

KORUS-AQ was an international cooperative air quality field study in South Korea that measured local and remote sources of air pollution affecting the Korean Peninsula during May–June 2016. Some of the largest aerosol mass concentrations were measured during a Chinese haze transport event (24 May). Air quality forecasts using the WRF-Chem model with aerosol optical depth (AOD) data assimilation captured AOD during this pollution episode but overpredicted surface particulate matter concentrations in South Korea, especially PM2.5, often by a factor of 2 or larger. Analysis revealed multiple sources of model deficiency related to the calculation of optical properties from aerosol mass that explain these discrepancies. Using in situ observations of aerosol size and composition as inputs to the optical properties calculations showed that using a low-resolution size bin representation (four bins) underestimates the efficiency with which aerosols scatter and absorb light (mass extinction efficiency). Besides using finer-resolution size bins (8–16 bins), it was also necessary to increase the refractive indices and hygroscopicity of select aerosol species within the range of values reported in the literature to achieve better consistency with measured values of the mass extinction efficiency (6.7 m2 g−1 observed average) and light-scattering enhancement factor (f(RH)) due to aerosol hygroscopic growth (2.2 observed average). Furthermore, an evaluation of the optical properties obtained using modeled aerosol properties revealed the inability of sectional and modal aerosol representations in WRF-Chem to properly reproduce the observed size distribution, with the models displaying a much wider accumulation mode. Other model deficiencies included an underestimate of organic aerosol density (1.0 g cm−3 in the model vs. observed average of 1.5 g cm−3) and an overprediction of the fractional contribution of submicron inorganic aerosols other than sulfate, ammonium, nitrate, chloride, and sodium corresponding to mostly dust (17 %–28 % modeled vs. 12 % estimated from observations). These results illustrate the complexity of achieving an accurate model representation of optical properties and provide potential solutions that are relevant to multiple disciplines and applications such as air quality forecasts, health impact assessments, climate projections, solar power forecasts, and aerosol data assimilation.

Pablo E. Saide↗
NASA NTRSDate not supplied

Compositional Analysis of Titan’s Atmosphere Using Spitzer Infrared Spectrograph Data

We present, for the first time, infrared spectra from the Spitzer Space Telescope’s Infrared Spectrograph (IRS) (2004-2008) of Titan in both the short wavelength-low resolution(SL, R=60~127,5.13-14.29 μm) and short wavelength-high resolution channels (SH, R=600, 9.89-19.51 μm) showing the emissions of CH4, C2H4, C2H2, C2H6, HCN, CO2, HC3N, C3H4, C4H2, and C3H8. Spitzer IRS data has been used to measure atmospheric composition of various Solar System bodies, including Neptune [1] and Uranus [2],[3]. Although Spitzer took multiple dedicated observations of Titan, none of the results have been modeled before. We conduct our own investigation of these datasets and search for new results. We retrieve temperature and gas composition profiles and compare the results obtained for Titan to those of the Cassini Composite Infrared Spectrometer (CIRS) and the Infrared Space Observatory Short Wavelength Spectrometer [4], and comment on the effect of spectral resolution on retrieved information content. We conclude by recommending gaps in current spectroscopic knowledge of molecular bands that could be addressed by theoretical and laboratory study to aid future astronomical studies of Titan, for example the James Webb Space Telescope (JWST) and the Stratospheric Observatory for Infrared Astronomy (SOFIA) Acknowledgments: Data analyzed in this project is public available online on the Spitzer Heritage Archive (sha.ipac.caltech.edu). This research has made use of the NASA/IPAC Infrared Science Archive, which is funded by the National Aeronautics and Space Administration and operated by the California Institute of Technology.

Titan↗
NASA NTRS2021

Contribution of Meteorological Downscaling to Skill and Precision of Seasonal Drought Forecasts

Research in meteorological prediction on sub-seasonal to seasonal (S2S) timescales has seen growth in recent years. Concurrent with this, demand for seasonal drought forecasting has risen. While there is obvious synergy between these fields, S2S meteorological forecasting has typically focused on low resolution global models, while the development of drought can be sensitive to the local expression of weather anomalies and their interaction with local surface properties and processes. This suggests that downscaling might play an important role in the application of meteorological S2S forecasts to skillful forecasting of drought. Here, we apply the Generalized Analog Regression Downscaling (GARD) algorithm to downscale meteorological hindcasts from the NASA Goddard Earth Observing System (GEOS) global S2S forecast system. Downscaled meteorological fields are then applied to drive offline simulations with the Catchment Land Surface Model (CLSM) to forecast United States Drought Monitor (USDM) style drought indicators derived from simulated surface hydrology variables. We compare the representation of drought in these downscaled hindcasts to hindcasts that are not downscaled, using the North American Land Data Assimilation System Phase 2 (NLDAS-2) dataset as an observational reference. We find that downscaling using GARD improves hindcasts of temperature and temperature anomalies, but the results for precipitation are mixed and generally small. Overall, GARD downscaling led to improved hindcast skill for total drought across the Contiguous United States (CONUS), and improvements were greatest for extreme (D3) and exceptional (D4) drought categories.

Ryan A Zamora↗
NASA NTRSDate not supplied

New Constraints on the Volatile Deposit in Mercury’s North Polar Crater, Prokofiev

There is strong evidence from multi-wavelength, multi-instrument observations and thermal models for the presence of ice and other volatiles in Mercury’s north polar permanently shadowed regions (PSRs)[1-6]. Ground-based radar measurements identified bright regions in some polar craters interpreted to be water ice on or near the surface[1]. The MESSENGER spacecraft found that some radar-bright deposits appeared bright in the optical and near-infrared while others appeared dark[4-6]. The bright regions were interpreted to be exposed surface ice while the dark regions were hypothesized to be complex organic volatile lag deposits overlaying buried ice[3]. One example of the former is in the 112 km diameter crater, Prokofiev, for which the Mercury Dual Imaging System (MDIS) acquired broadband images while in Sun shadow but illuminated by scattered light from the crater walls[5]. These images revealed a relatively high-albedo region spatially correlated with the PSR. Subsequent work[7] found a small offset between the boundary of the radar-bright region and that of the PSR predicted by an illumination model based on the polar digital elevation model (DEM)from the Mercury Laser Altimeter (MLA). Such an offset, if true, could have implications for the ice properties and its delivery and destruction mechanisms. However, the low resolution (1 km/pix) of the polar MLA DEM precluded a definitive conclusion. In the present work, we build new higher-resolution topographic, illumination, and thermal models of Prokofiev to place stronger constraints on its PSR and volatile deposits. We also study the MLA reflectance data in this crater to quantitatively estimate the ice abundance.

Michael K Barker↗
NASA NTRSDate not supplied

A Search for Butane in Titan’s Atmosphere using Cassini CIRS Infrared Spectra

The atmosphere of Saturn’s moon, Titan, is unlike any other. Photochemistry and ion chemistry in the upper atmosphere process the major atmospheric constituents, molecular nitrogen (N2) and methane (CH4), into larger and more complex molecules including hydrocarbons and nitriles. To date there are ten hydrocarbons heavier than methane that have been detected, including large quantities of the saturated hydrocarbons (alkanes) ethane (C2H6) and propane (C3H8). In addition to being significant trace gases of the atmosphere, these species condense, rain out and add to the volume of Titan’s hydrocarbon lakes and seas. The fourth lightest alkane, butane (C4H10),comes in two structural forms: an unbranched carbon chain (n-butane) and a branched chain (i-butane). The relative production rates of both these isomers are presently unknown, although the circumstantial case for butane to be present in detectable amounts is strong, based on photochemical models and from Cassini low-resolution mass spectrometry of the ionosphere. Butane, like its lighter siblings ethane and propane, is thought to be present in Titan’s lakes and seas where it may be active in forming molecular ‘co-crystals’ with acetylene (C2H2). We report on our recent attempts to detect both forms of butane in Titan’s atmosphere by analysis of Cassini infrared spectra collected during 2004-2017 by the Composite Infrared Spectrometer(CIRS) instrument. This multi-year effort has encompassed the collection of new laboratory spectra of both isomers at several institutions, followed by a concerted attempt to find their signatures in CIRS spectra. We search for butane by high precision modeling and removal of overlying emissions due to other, already-known gases to reveal signatures that match those of the butane isomers. The current status of the search will be discussed, including improved upper limits for both isomers, and directions of future research to detect both species.

Conor Nixon↗
NASA NTRSDate not supplied

Additional characterization of Libya-4 in support of post-launch vicarious calibration of satellite imagers

Libya-4 (28.55° N and 23.39° E) is one of the most characterized and utilized Pseudo Invariant Calibration Site (PICS) for post-launch radiometer drift monitoring and sensor pair radiometric scaling. Libya-4 is one of the driest and most reflective PICS located in an extensive sand dune region void of any vegetation with an elevation of 118 m. It is positioned near the northeast border with Egypt. With minimal cloud cover and the monthly mean precipitation of ~ 1mm, Libya-4 is one of the temporally, spectrally, and spatially stable CEOS recommended PICS and Cosnefroy et al. 1996 identified sites. The Libya-4 PICS has been extensively used for post-launch radiometric calibration and validation of high-, medium, and low-resolution satellite imagers, including Landsat, MODIS, VIIRS, and Meteosats. The CERES Imager and Geostationary Calibration Group (IGCG) at NASA LaRC utilizes Libya-4 to perform an independent assessment of the radiometric stability of the MODIS and VIIRS L1B products. The site is also used for absolute radiometric scaling between MODIS, VIIRS, and geostationary imagers to ensure consistent cloud and radiative flux retrievals. Multi-year Terra-MODIS, Aqua-MODIS , NPP-VIIRS and Metoesat-7 observations over Libya-4 show very similar TOA reflectance temporal variability. Although, the surface reflectance and atmospheric column varies seasonally, the inter-annual variability of the seasonal cycle should be small. Especially during 2013, the Libya-4 TOA reflectance was found to be greater than usual in all 4 satellite records. Preliminary comparisons with mean wind speed and aerosol optical depth (AOD) indicate that the year 2013 is marked by elevated levels of both conditions. The goal of this study is to tie the Libya-4 visible reflectance inter-annual variability with corresponding meteorological measurements to further improve the characterization of the site.

David R. Doelling↗
NASA NTRS2022

Ultraviolet Spectropolarimetry: on the Origin of Rapidly Rotating B Stars

UV spectroscopy and spectropolarimetry hold the key to understanding certain aspects of massive stars that are largely inaccessible (or exceptionally difficult) with optical or longer wavelength observations. As we demonstrate, this is especially true for the rapidly-rotating Be and Bn stars, owing to their high temperatures, geometric asymmetries, binary properties, evolutionary history, as well as mass ejection and disks (in the case of Be stars). UV spectropolarimetric observations are extremely sensitive to the photospheric consequences of rapid rotation (i.e. oblateness, temperature, and surface gravity gradients), far beyond the reach of optical wavelengths. Our polarized radiative-transfer modelling predicts that with low-resolution UV spectropolarimetry covering 120–300 nm, and with a reasonable SNR, the inclination angle of a rapid rotator can be determined to within 5 degrees, and the rotation rate to within 1%. The origin of rapid rotation in Be/n stars can be explained by either single-star or binary evolution, but their relative importance is largely unknown. Some Be stars have hot sub-luminous (sdO) companions, which at an earlier phase transferred their envelope (and with it mass and angular momentum) to the present-day rapid rotator. Although sdO stars are small and relatively faint, their flux peaks in the UV making this the optimal observational wavelength regime. Through spectral modelling of a wide range of simulated Be/n+sdO configurations, we demonstrate that high-resolution high-signal-to-noise ratio UV spectroscopy can detect an sdO star even when ∼1,000 times fainter in the UV than its Be/n star companion. This degree of sensitivity is needed to more fully explore the parameter space of Be/n+sdO binaries, which so far has been limited to about a dozen systems with relatively luminous sdO stars. We suggest that a UV spectropolarimetric survey of Be/n stars is the next step forward in understanding this population. Such a dataset would, when combined with population synthesis models, allow for the determination of the relative importance of the possible evolutionary pathways traversed by these stars, which is also crucial for understanding their future evolution and fate.

Ultraviolet astronomy↗
NASA NTRSDate not supplied

Reanalysis Activities at the NASA Global Modeling and Assimilation Office

This talk presents an overview of recent reanalysis activities at the NASA Global Modeling and Assimilation Office (GMAO) as part of a multi-faceted strategy towards an Integrated Earth System retrospective analysis, coupling components of the atmosphere, ocean, chemistry, land, and ice. While elements of the atmosphere-ocean coupled Goddard Earth Observing System (GEOS) model and data assimilation are being actively developed, a suite of reanalysis products is designed to provide further understanding of key aspects of Earth system coupling in a reanalysis context: The baseline atmospheric reanalysis, the GEOS Retrospective analysis for the early 21st Century (GEOS-R21C), features recent advances in the GEOS model and data assimilation, and targets the NASA’s Earth Observing System EOS and post-EOS satellite observations; GEOS-IT, a user-tailored low-resolution atmospheric reanalysis, serves as a second baseline to the NASA Instrument Teams for validation and calibration and drives a one-way coupled ocean reanalysis, GEOSIT-Ocean; PolarMERRA, a high-resolution downscaled product for the polar regions, focuses on improving the representation of polar atmospheric processes with an assessment of current cryospheric biases, and targeted improvements to surface sea ice and glacier conditions; Finally, R21C-Chem, an off-line atmospheric chemistry and composition reanalysis, includes both tropospheric and stratospheric trace gases. The diversity of these reanalysis activities presents unique opportunities for collaborations cross-teams/institutions, with new commercial data partners, and with end-user groups. This talk will discuss these opportunities and explore leveraging the lessons learned along the way on key drivers in Earth system interactions as we converge towards the next generation of the Modern-Era Retrospective analysis for Research and Applications (MERRA) suite.

Amal El Akkraoui↗
NASA NTRSDate not supplied

Martian B Storm Evolution: Modeling Dust Activity over the Receding South Polar CO 2 Ice Cap at Southern Hemisphere Summer Solstice

Observations of annually recurring, elevated (25 km) atmospheric warming and associated dust concentrations over the south polar cap near Southern Hemisphere (SH) summer solstice are the defining features of the regional dust storm known as the B storm [1]. Our observational analysis of MCS and TES temperature and dust retrievals include all Mars Years (MY) lacking a global dust storm (GDS) between MY 26 and MY 33 (Figure 1). The data indicate the B storm is initiated around perihelion (L s =252°) in the southern midlatitudes and reaches maximum height and intensity over the south pole near SH summer solstice at L s ~267°. At peak intensity, mid-level atmospheric temperatures are elevated by as much as 30 K and mid-level atmospheric dust mixing ratios are more than triple pre-B storm levels (1.5-2 ppm to 7-8 ppm). Cap edge lifting may contribute significantly to initial dust mobilization in the B storm since surface wind stress along the receding cap edge is likely strengthened by the combined effects of the sea breeze circulation and katabatic winds at this time of year [2, 3, 4]. We are investigating dust lifting and lofting near the south pole of Mars during SH summer using the NASA Ames Mars Global Climate Model (MGCM). The NASA Ames MGCM is a finite-volume numerical model with a cubed sphere grid ideal for modeling processes at high latitudes. Initial simulations at low resolution (4x4 degrees) capture some elevated dust and associated warming at the south pole around L s =265°, but the model fails to capture the intensity of the B storm. We will examine and present the model-predicted surface stress and vertical velocities near the receding seasonal CO 2 cap edge and at the south pole during solstice. Finally, we will present higher resolution simulations to better capture small-scale dynamics and features at the pole.

Courtney M. L. Batterson↗
NASA NTRSDate not supplied

Impact Of Rotation on the Sun’s Subsurface Structure

It is well known that the dynamical evolution of subsurface flows of the Sun and magnetic fields is strongly linked to rotation. Observational investigation of sub surface structuring and dynamics can be performed only with relatively low resolution using helioseismic inversion techniques. To confidently model the coupling of turbulent convection, magnetic fields, and rotation in the solar interior, we perform advanced radiative hydrodynamic and MHD modeling of local scales. To explore the coupling effects in the upper layers of the convection zone, we present results of analysis from a series of hydrodynamic simulations performed for a 25-Mm-deep computational domain at different latitudes obtained for 1) the Carrington rotation rate at all latitudes and 2) the realistic rotation rate from the global helioseismic inversions during the solar minimum. Using additional 3D radiative hydrodynamic models, we reveal how physical properties of the solar convection are affected by rotation.

Heliophysics↗
NASA NTRS2026

Proactive Wildfire Management: A Remote Sensing and Multimodal CNN-MLP Architecture for Ignition Risk Forecasting

As the frequency and intensity of wildfires increase, with fire seasons now starting earlier and ending later than they have over the past decades, current monitoring systems, such as lookout towers and satellites, are hindered by cloud cover, low-resolution imagery, and static data gaps that fail to track vegetation moisture levels fast enough to catch rapid pre-ignition changes. This report proposes a Machine Learning-enabled Wildfire Ignition Prediction framework that combines satellite monitoring with dynamic and high-resolution remote sensing from Unmanned Aerial Vehicle (UAV) swarms. The method would use multispectral and thermal data from the Landsat program to create a baseline for vegetation health, calculating a two-band Enhanced Vegetation Index (EVI2) and the moisture content of the vegetation. These inputs will later be fused with microscale UAV weather data, including thermal hotspots found through thick canopies, hyperspectral chemical signatures of pre-visual combustion, and local weather streams. The multispectral satellite, multispectral Light Detection and Ranging (LiDAR), and thermal data would then be processed through a Convolutional Neural Network (CNN), alongside a Multilayer Perceptron (MLP) for the micro-weather telemetry. The outputs of these networks would be fused into a single feature representation and passed through a final prediction network to generate real-time ignition risk scores and hotspot alerts. Model performance would be assessed using standard classification metrics, including a Receiver Operating Characteristic - Area Under the Curve (ROC AUC) and F1 score. This system would allow first responders to identify high-risk zones and intervene before ignition occurs, improving emergency response time compared to current approaches.

machine learning↗