Search NASA⌕ Search

SEARCH · Search NASA

Results for “Spatial Exclusions”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records

Cooperative microbial interactions drive spatial segregation in porous environments

The role of microbial interactions and the underlying mechanisms that shape complex biofilm communities are poorly understood. Here we employ a microfluidic chip to represent porous subsurface environments and show that cooperative microbial interactions between free-living and biofilm-forming bacteria trigger active spatial segregation to promote their respective dominance in segregated microhabitats. During initial colonization, free-living and biofilm-forming microbes are segregated from the mixed planktonic inoculum to occupy the ambient fluid and grain surface. Contrary to spatial exclusion through competition, the active spatial segregation is induced by cooperative interactions which improves the fitness of both biofilm and planktonic populations. We further show that free-living Arthrobacter induces the surface colonization by scavenging the biofilm inhibitor, D-amino acids and receives benefits from the public goods secreted by the biofilm-forming strains. Collectively, our results reveal how cooperative microbial interactions may contribute to microbial coexistence in segregated microhabitats and drive subsurface biofilm community succession.

59 BASIC BIOLOGICAL SCIENCES↗

The Renewable Energy Potential (reV) Model: A Geospatial Platform for Technical Potential and Supply Curve Modeling

The Renewable Energy Potential (reV) model is a platform for detailed assessment of renewable energy (RE) resources and their geospatial intersection with grid infrastructure and land use characteristics. The reV model currently supports photovoltaic (PV), concentrating solar power (CSP) and land-based wind turbine technologies. Modules in the reV framework function at different spatial and temporal resolutions, allowing for assessment of resource potential, technical potential and supply curves at varying levels of detail. The platform runs on NREL's High Performance Computing system, providing scalable and efficient performance from a single location all the way up to continental scales, for a single year or decades of time series resource data. Coupled with NREL's System Advisor Model (SAM), reV supports resource assessment from 5-minute to hourly temporal resolution and provides for analysis of long-term (i.e., year-on-year) variability of RE generation (e.g., interannual variability and exceedance probabilities). Technical potential is measured as a function of resource potential and limitations put on developable land area defined by the user. For example, the user can limit development by land ownership, terrain, land use/cover, and urban areas, as well as custom inputs. Technology, grid interconnection and operation costs, based on the latest market data and future projections, are also embedded in the model. The supply curve module is a spatial sorting algorithm based on plant siting, grid interconnection cost, and regional competition, which provides a geographically discrete estimate of levelized cost of electricity (LCOE) and supply (i.e., capacity) for specific renewable technologies. The reV model currently provides broad coverage across North America, South and Central Asia, South America and South Africa to inform national- and international-scale analyses as well as regional infrastructure and deployment planning.

13 HYDRO ENERGY↗

Trapping-induced quantum beats in a van-der-Waals heterostructure microcavity observed by two-dimensional micro-spectroscopy

Spatial confinement has been frequently engineered to control the flow and relaxation dynamics of exciton polaritons. While widely investigated in GaAs microcavities, exciton-polariton coupling between discretized polariton modes arising from spatially confined 2D crystals been has been less exhaustively studied. Here, we use coherent 2D photoluminescence-detected micro-spectroscopy to detect oscillating 2D peaks exclusively from a spatial trap in a microcavity with an embedded van-der-Waals heterostructure at room temperature. We observe a wide variation of oscillatory phases as a function of spectral position within the 2D spectrum, which suggests the existence of a coupling between the discretized polariton modes. The latter is accompanied by the generation of coherent phonons.

Materials Science↗

Multi-decade high-resolution regional hindcasts for wave energy resource characterization in U.S. coastal waters

Long-term, high-resolution, regional wave hindcast datasets were generated using unstructured-grid Simulating WAves Nearshore (SWAN) models for the U.S. coastal waters to support nearshore wave energy development in the U.S. including those bordering U.S. territorial islands. The model domains resolved the entire U.S. exclusive economic zones, with a spatial resolution of approximately 200 m nearshore. The regional SWAN models were driven by the global WAVEWATCH III® model outputs and run for a 42-year period from 1979 to 2020. Extensive model validations were performed using buoy observations and altimeter data. Regional resource characterization was performed based on hindcast data points at 2 km from shore and along the 100 m isobath. Aggregations of wave resource parameters were produced, and spatial and seasonal variations were analyzed for all the regions. Wave resource metrics recommended by international standards, including a 3-hour time series of six resource parameters, hourly frequency- and directionally resolved wave spectra at selected “virtual buoy” locations, and average-annual values of omni-directional wave power, significant wave height, and energy period are publicly disseminated through an Amazon Web Service and a Marine Energy Atlas web application tool to facilitate wave energy research and a wide range of coastal ocean applications.

16 TIDAL AND WAVE POWER↗

Advances in PFAS Monitoring and Remediation Using a Functionalized Material Approach

The growing global concerns about the effects to public health from human exposure to per- and polyfluoroalkyl substances (PFAS) motivates the development of strategies for reliable monitoring of PFAS in environmental streams, as well as for their rapid, effective removal if detected. For the continuous PFAS monitoring, an inexpensive, field-deployable, in situ sensor is urgently needed; yet the prevalent in situ techniques often struggle to strike a balance between the practical sensitivity and selectivity demands of the real world. Similarly, for effective PFAS removal, strategies for their fast, selective, and quantitative capture are desired, yet the present commercially available sorbents are unable to meet the requirements of rapid, quantitative capture of all PFAS components, and are notably inefficient in removing the more toxic smaller chains. To address these twin challenges, Pacific Northwest National Laboratory is developing strategies for improved detection and remediation of PFAS. For the rapid, selective, quantitative removal of PFAS from environmental streams, the strategy relies on designing capture probes with exclusively tailored electronic and spatial affinities for the PFAS that are able to selectively capture them from environmental streams. For the in situ detection and quantification of PFAS in complex, multicomponent matrices such as groundwater, the approach relies on the targeted capture of specific PFAS by these PFAS-specific capture probes immobilized on a platform. The platform acts as an electrode to directly measure PFAS concentration through a proportional change in electrical response upon their capture. A combination of optimization of platform design and incorporation of additional, sensitive detection modalities have allowed us to achieve detection limits as low as 0.5 ng/L for detection of PFAS compounds (compared to the 70 ng/L Health Advisory Limit of the U.S. Environmental Protection Agency).

Per- and poly-fluorinated alkyl substances (PFAS),↗

Advances in PFAS Monitoring and Remediation Using a Functionalized Material Approach - 20080

The growing global concerns about the effects to public health from human exposure to per- and polyfluoroalkyl substances (PFAS) motivates the development of strategies for reliable monitoring of PFAS in environmental streams, as well as for their rapid, effective removal if detected. For the continuous PFAS monitoring, an inexpensive, field-deployable, in situ sensor is urgently needed; yet the prevalent in situ techniques often struggle to strike a balance between the practical sensitivity and selectivity demands of the real world. Similarly, for effective PFAS removal, strategies for their fast, selective, and quantitative capture are desired, yet the present commercially available sorbents are unable to meet the requirements of rapid, quantitative capture of all PFAS components, and are notably inefficient in removing the more toxic smaller chains. To address these twin challenges, Pacific Northwest National Laboratory is developing strategies for improved detection and remediation of PFAS. For the rapid, selective, quantitative removal of PFAS from environmental streams, the strategy relies on designing capture probes with exclusively tailored electronic and spatial affinities for the PFAS that are able to selectively capture them from environmental streams. For the in situ detection and quantification of PFAS in complex, multicomponent matrices such as groundwater, the approach relies on the targeted capture of specific PFAS by these PFAS-specific capture probes immobilized on a platform. The platform acts as an electrode to directly measure PFAS concentration through a proportional change in electrical response upon their capture. A combination of optimization of platform design and incorporation of additional, sensitive detection modalities have allowed us to achieve detection limits as low as 0.5 ng/L for detection of PFAS compounds (compared to the 70 ng/L Health Advisory Limit of the U.S. Environmental Protection Agency). (authors)

47 OTHER INSTRUMENTATION↗

Offshore application of landslide susceptibility mapping using gradient-boosted decision trees: a Gulf of Mexico case study

Abstract Among natural hazards occurring offshore, submarine landslides pose a significant risk to offshore infrastructure installations attached to the seafloor. With the offshore being important for current and future energy production, there is a need to anticipate where future landslide events are likely to occur to support planning and development projects. Using the northern Gulf of Mexico (GoM) as a case study, this paper performs Landslide Susceptibility Mapping (LSM) using a gradient-boosted decision tree (GBDT) model to characterize the spatial patterns of submarine landslide probability over the United States Exclusive Economic Zone (EEZ) where water depths are greater than 120 m. With known spatial extents of historic submarine landslides and a Geographic Information System (GIS) database of known topographical, geomorphological, geological, and geochemical factors, the resulting model was capable of accurately forecasting potential locations of sediment instability. Results of a permutation modelling approach indicated that LSM accuracy is sensitive to the number of unique training locations with model accuracy becoming more stable as the number of training regions was increased. The influence that each input feature had on predicting landslide susceptibility was evaluated using the SHapely Additive exPlanations (SHAP) feature attribution method. Areas of high and very high susceptibility were associated with steep terrain including salt basins and escarpments. This case study serves as an initial assessment of the machine learning (ML) capabilities for producing accurate submarine landslide susceptibility maps given the current state of available natural hazard-related datasets and conveys both successes and limitations.

Dyer, Alec S. (ORCID:0000000219813904)↗

Mass enhancement in 3 d and s - p perovskites from symmetry breaking

In some d-electron oxides, the measured effective mass m* exptl has long been known to be significantly larger than the model effective mass m* model deduced from mean-field band theory, i.e., m* exptl =βm* model , where β>1 is the “mass-enhancement” or “mass-renormalization” factor. Previous applications of density functional theory (DFT), based on a symmetry-restricted structure with the smallest number of possible magnetic, orbital, and structural degrees of freedom, missed such mass enhancement. This fact has been taken as evidence of strong electronic correlation, often described via the symmetry-restricted dynamic mean-field approach of the many-body theory, being the exclusive enabling physics. This paper uses instead a static density functional approach that does not restrict positional or spin degrees of freedom (symmetry-broken structures). This approach analyzes the contributions of different symmetry-broken modalities to mass enhancement for a few nominally highly correlated d-electron perovskites as well as the nominally uncorrelated, closed-shell s-p bonding perovskites. It shows that the energy-lowering symmetry-broken spin effects (e.g., nonzero local moment in the paramagnetic phase) and structural effects (e.g., atomic displacement) as described in mean-field DFT already manifest mass enhancement for both electrons and holes in a range of d-electron perovskites SrVO 3 , SrTiO 3 , BaTiO 3 , and LaMnO 3 , as well as p-electron perovskites CsPbI 3 and SrBiO 3 , including both metals (SrVO 3 ) and insulators (the rest). This is revealed only when enlarged unit cells of the same parent global symmetry, which are large enough to allow for symmetry-breaking distortions and concomitant variations in spin order, are explored for their ability to lower the total energy. Positional symmetry breaking that leads to mass enhancement includes octahedral rotation in halide perovskites such as CsPbI 3 , Jahn-Teller-like Q${+}\atop{2}$ distortion in LaMnO 3 , and bond disproportionation in SrBiO 3 , while magnetic symmetry breaking resulting in mass enhancement includes the formation of a distribution of local moments in SrVO3 that averages to zero in the paramagnetic phase. Not all symmetry breaking leads to significant mass enhancement, e.g., the rather small octahedral rotations in the nearly perfectly cubic SrTiO 3 cause negligible mass enhancement, as do the paraelectric displacements in BaTiO 3 . In principle, by ergodicity, the two descriptions, i.e., the symmetry-restricted dynamic approach with a single time-fluctuating magnetic moment and the symmetry-broken mean-field approach with a static distribution of spatially fluctuated local moments, are not mutually exclusive but are a choice of representation and consequently, a choice of computational efficiency. In approximate implementations, the symmetry-broken mean-field approach appears to remove much of what was strong correlation in dynamically correlated symmetry-restricted solutions, leaving smaller (“weak”) residual correlation with respect to the exact solution.

36 MATERIALS SCIENCE↗

Elucidating mechanisms of Photosystem I assembly and repair (Final Technical Report)

Oxygenic photosynthesis generates the carbohydrates and oxygen that support life on earth. Central to this process are photosystems I and II (PSI and PSII), large protein-pigment complexes that are among the most complex macromolecular structures in nature. Impressive advances have been made in understanding the structure and photochemical mechanisms of PSI and PSII. However, less is known about the mechanisms underlying their biogenesis. The overarching goal of this project was to elucidate mechanisms of photosystem assembly. Several proteins that are not found in mature PSI or PSII are essential for their assembly, but the assembly pathways and the role of each assembly factor are just starting to emerge. During the course of this project, we advanced understanding of this problem in several ways. First, we addressed the spatial organization of photosystem assembly by localizing many known assembly factors to specific subdomains of thylakoid membranes in plant chloroplasts. This work revealed that the "curvature" domain - the highly curved periphery of stacked granal membrane disks - is a center of photosystem assembly. However, whereas some PSI assembly factors localized exclusively to the curvature domain, another was found exclusively in granal margins and unstacked membranes. This spatial partitioning suggests an assembly line along which different assembly intermediates are physically segregated. Most assembly factors localized similarly in dark-adapted and illuminated plants, but one PSII assembly factor shifted from the curvature domain to unstacked membranes upon illumination, suggesting an unanticipated role in the repair of light-induced PSII damage. Whereas most PSII subunits were found primarily in grana stacks (as expected), one PSII subunit, PsbP, was prominent also in the curvature fraction, suggesting that PsbP has functions beyond its role in mature PSII, possibly in biogenesis or repair.

59 BASIC BIOLOGICAL SCIENCES↗

Maximum Impact of Ionic Strength on Acid‐Catalyzed Reaction Rates Induced by a Zeolite Microporous Environment

Abstract The intracrystalline ionic environment in microporous zeolite can remarkably modify the excess chemical potential of adsorbed reactants and transition states, thereby influencing the catalytic turnover rates. However, a limit of the rate enhancement for aqueous‐phase dehydration of alcohols appears to exist for zeolites with high ionic strength. The origin of such limitation has been hypothesized to be caused by the spatial constraints in the pores via, e.g., size exclusion effects. It is demonstrated here that the increase in turnover rate as well as the formation of a maximum and the rate drop are intrinsic consequences of the increasingly dense ionic environment in zeolite. The molecularly sized confines of zeolite create a unique ionic environment that monotonically favors the formation of alcohol‐hydronium ion complexes in the micropores. The zeolite microporous environment determines the kinetics of catalytic steps and tailors the impact of ionic strength on catalytic rates.

Liu, Qiang↗

Maximum Impact of Ionic Strength on Acid‐Catalyzed Reaction Rates Induced by a Zeolite Microporous Environment

Abstract The intracrystalline ionic environment in microporous zeolite can remarkably modify the excess chemical potential of adsorbed reactants and transition states, thereby influencing the catalytic turnover rates. However, a limit of the rate enhancement for aqueous‐phase dehydration of alcohols appears to exist for zeolites with high ionic strength. The origin of such limitation has been hypothesized to be caused by the spatial constraints in the pores via, e.g., size exclusion effects. It is demonstrated here that the increase in turnover rate as well as the formation of a maximum and the rate drop are intrinsic consequences of the increasingly dense ionic environment in zeolite. The molecularly sized confines of zeolite create a unique ionic environment that monotonically favors the formation of alcohol‐hydronium ion complexes in the micropores. The zeolite microporous environment determines the kinetics of catalytic steps and tailors the impact of ionic strength on catalytic rates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Advancing stream temperature prediction with a generalizable large-sample framework across CONUS river reaches

Accurately predicting stream temperature in ungauged basins remains a critical challenge for water resource management, thermoelectric power plant cooling, and ecosystem conservation. Large-sample machine learning models trained on hundreds of well-monitored river basins have shown remarkable performance; however, such models have yet to be developed solely using forcing data that can be readily extracted to simulate stream temperatures anywhere in the contiguous United States (CONUS). In this study, we present a scalable, large-sample deep learning framework using Long Short-Term Memory (LSTM) networks to simulate daily stream temperatures in ungauged basins across the CONUS. The framework leverages both modeled reanalysis of meteorological and streamflow inputs as well as static attributes available for all 2.7 million CONUS river reaches in the National Hydrography Dataset Plus (NHDPlusV2). By generating dynamical inputs from predefined thermally relevant upstream contributing areas, rather than the entire upstream basin, the model also offers improvements in very large basins where full-basin averaging can dilute the most important influences on stream temperature. Evaluated across 300 basins, the model achieves a median Mean Absolute Error (MAE) of 1.1 °C and a Nash-Sutcliffe Efficiency (NSE) of 0.95 on temporally and spatially distinct test folds—comparable to models trained exclusively using meteorological and streamflow observational data. The flexible, high-performing framework generalizes to any unmonitored river reach without significant regulation or unnatural thermal input immediately upstream, substantially expanding predictive capabilities in data-scarce regions.

Hydrology↗

Identifying Potential Candidates for Renewable Energy Zones (REZs) in Bangladesh

Bangladesh faces several hurdles to achieving its renewable energy objectives, such as land availability and transmission congestion. Renewable Energy Zones (REZ), which are geographic areas with high-quality utility-scale renewable resources, suitable land topography, and commercial interest, can help address these challenges and support long-term generation and transmission planning. This study focuses on solar PV (fixed-tilt) and onshore wind, leveraging recently developed high temporal and spatial resolution resource data. In the moderate land exclusion scenario, large "study areas" are identified in Bangladesh with capacity factors in the top 25% for the entire country - 5 for wind and 8 for solar. Within these study areas, 19 candidates for REZ are identified based on the overlap between these study areas and upazilas (i.e., administrative subdivisions) containing economic development zones. Four of the identified candidate zones are opportunities for priority development, given the combination of strong wind and solar resources and the presence of economic zones. Furthermore, the geographic diversity of wind and solar resources in Bangladesh could help increase grid resilience by not concentrating all renewable energy development in the same region. Finally, pairing REZ with economic zones can bolster economic development, take advantage of large electricity demand, and leverage existing infrastructure investments.

Bangladesh↗

High-Resolution South American Wind Resource Data Downscaled with Generative Machine Learning Conditioned on Near-Surface Observations

High-resolution historical wind data was developed for the entirety of South America using the innovative Super-Resolution for Renewable Resource Data (sup3r) machine learning framework. The publicly available Sup3rWind South America dataset represents a significant advancement in wind resource data generation, leveraging generative machine learning conditioned on near-surface observations from the Meteorological Assimilation Data Ingest System (MADIS) to efficiently and accurately downscale coarse reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA5). This approach produces fine-scale, spatially and temporally coherent wind and meteorological fields hundreds of times more computationally efficient than traditional numerical weather modeling methods, enabling access to high-fidelity wind information across both continental and offshore regions. Sup3rWind South America builds on the earlier Sup3rWind Ukraine dataset through improvements in model architecture and outputs conditioned on near-surface observation inputs. As with the Ukraine data release, this dataset includes wind speed, wind direction, temperature, relative humidity, and pressure at a horizontal resolution of ~2 km, representing a 15x spatial enhancement relative to the 31 km ERA5 grid. Wind speed and direction are provided at 5-minute resolution, a 12x temporal refinement compared to the hourly ERA5 data, while temperature, relative humidity, and pressure remain at hourly resolution. The data covers all years from 2005 to 2024. Before downscaling, ERA5 inputs were bias-corrected using long-term monthly means and a limited number of quality-controlled observations to align large-scale statistics with regional conditions. The resulting dataset is the first publicly available high-resolution timeseries wind record that provides full spatial coverage of South America. Model validation demonstrates strong agreement with observations across several statistical metrics, consistent with other state-of-the-art high-resolution wind resource datasets. The potential applications of Sup3rWind South America span renewable energy resource assessment, energy system modeling, and grid resilience analysis. The 20-year record and high spatial and temporal resolution support accurate estimation of long-term energy yield and the economic feasibility of potential wind development sites. Continuous coverage across both continental and offshore regions enables comprehensive site prospecting within exclusive economic zones. The 2 km, 5-minute resolution data provide the spatial and temporal variability required for power system simulation, operational planning, and regional risk assessments.

17 WIND ENERGY↗

Predicted range expansion of Prostephanus truncatus (Coleoptera: Bostrichidae) under projected climate change scenarios

Abstract The larger grain borer (Prostephanus truncatus [Horn] [Coleoptera: Bostrichidae]) is a wood-boring insect native to Central America and adapted to stored maize and cassava. It was accidentally introduced to Tanzania and became a pest across central Africa. Unlike many grain pests, P. truncatus populations can establish and move within forests. Consequently, novel infestations can occur without human influence. The objectives of our study were to (i) develop an updated current suitability projection for P. truncatus, (ii) assess its potential future distribution under different climate change scenarios, and (iii) identify climate variables that best inform the model. We used WALLACE and MaxEnt to predict potential global distribution by incorporating bioclimatic variables and occurrence records. Future models were projected for 2050 and 2070 with Representative Concentration Pathways (RCPs) 2.6 (low change) and 8.5 (high change). Distribution was most limited by high precipitation and cold temperatures. Globally, highly suitable areas (> 75%) primarily occurred along coastal and equatorial regions with novel areas in northern South America, India, southeastern Asia, Indonesia, and the Philippines, totaling 7% under current conditions. Highly suitable areas at RCPs 2.6 and 8.5 are estimated to increase to 12% and 15%, respectively, by 2050 and increase to 19% in 2070 under RCP 8.5. Centroids of highly suitable areas show distribution centers moving more inshore and away from the equator. Notably, the result is a range expansion, not a shift. Results can be used to decrease biosecurity risks through more spatially explicit and timely surveillance programs for targeting the exclusion of this pest.

Entomology↗

Investigation of the background in coherent J/ψ production at the EIC

Understanding various fundamental properties of nucleons and nuclei is among the most important scientific goals at the upcoming Electron-Ion Collider (EIC). With the unprecedented opportunity provided by the next-generation machine, the EIC might provide definitive answers to many standing puzzles and open questions in modern nuclear physics. We investigate one of the golden measurements proposed at the EIC, which is to obtain the spatial gluon density distribution within a lead (Pb) nucleus. The proposed experimental process is the exclusive J/ψ vector-meson production off the Pb nucleus: e + Pb → e' + J/ψ + Pb'. The Fourier transformation of the momentum transfer |t| distribution of the coherent diffraction is the transverse gluon spatial distribution. In order to measure it, the experiment has to overcome an overwhelmingly large background arising from the incoherent diffractive production, where the nucleus Pb' mostly breaks up into fragments of particles in the far-forward direction close to the hadron-going beam rapidity. We systematically study the rejection of incoherent J/ψ production by vetoing products from these nuclear breakups—protons, neutrons, and photons—which is based on the BeAGLE event generator and the most up-to-date EIC Far-forward Interaction Region design. The achieved vetoing efficiency, the ratio between the numbers of vetoed events and total incoherent events, ranges from about 80% to 99% depending on |t|. Assuming a 5% smearing applied to the reconstructed |t| resolution in the Sar t re model, this vetoing efficiency can suppress the incoherent background to at least the first minimum of the coherent |t| distribution. Experimental and accelerator machine challenges as well as potential improvements are discussed herein.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Cloud classification from satellite data using a fuzzy sets algorithm: A polar example

Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine likely areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.

Key, J. R.↗

Cloud classification from satellite data using a fuzzy sets algorithm - A polar example

Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine like areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.

Key, J. R.↗