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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Use of the Earth Observing One (EO-1) Satellite for the Namibia SensorWeb Flood Early Warning Pilot

The Earth Observing One (EO-1) satellite was launched in November 2000 as a one year technology demonstration mission for a variety of space technologies. After the first year, it was used as a pathfinder for the creation of SensorWebs. A SensorWeb is the integration of variety of space, airborne and ground sensors into a loosely coupled collaborative sensor system that automatically provides useful data products. Typically, a SensorWeb is comprised of heterogeneous sensors tied together with a messaging architecture and web services. Disasters are the perfect arena to use SensorWebs. One SensorWeb pilot project that has been active since 2009 is the Namibia Early Flood Warning SensorWeb pilot project. The Pilot Project was established under the auspices of the Namibian Ministry of Agriculture Water and Forestry (MAWF)/Department of Water Affairs, the Committee on Earth Observing Satellites (CEOS)/Working Group on Information Systems and Services (WGISS) and moderated by the United Nations Platform for Space-based Information for Disaster Management and Emergency Response (UN-SPIDER). The effort began by identifying and prototyping technologies which enabled the rapid gathering and dissemination of both space-based and ground sensor data and data products for the purpose of flood disaster management and water-borne disease management. This was followed by an international collaboration to build small portions of the identified system which was prototyped during that past few years during the flood seasons which occurred in the February through May timeframe of 2010 and 2011 with further prototyping to occur in 2012. The SensorWeb system features EO-1 data along with other data sets from such satellites as Radarsat, Terra and Aqua. Finally, the SensorWeb team also began to examine the socioeconomic component to determine the impact of the SensorWeb technology and how best to assist in the infusion of this technology in lesser affluent areas with low levels of basic infrastructure. This paper provides an overview of these efforts, highlighting the EO-1 usage in this SensorWeb.

Mandl, Daniel↗
DOE OSTI2025

Flood Susceptibility Mapping Using Machine Learning and Geospatial-Sentinel-1 SAR Integration for Enhanced Early Warning Systems

This study presents a comprehensive framework for flood susceptibility mapping by integrating geospatial factors with both statistical and machine learning models. Thirteen Flood-related factors, including DEM, slope, TWI, NDVI, etc., are extracted as features of models, and historical flood data derived from Sentinel-1 SAR from 2018 to 2023 are used as the target variables of the models. These datasets are analyzed using a frequency-based statistical model and three machine learning models, including Random Forest, XGBoost, and CNN, to generate flood susceptibility maps. The performance of each model is evaluated through AUC; and SHAP scores are separately generated for Machine learning (ML) models to explain each feature contribution in the ML model. The generated susceptibility maps are validated by high-flood-risk locations monitored by flood sensors, BLE inundation models, and flood-prone areas suggested by the Local Community Task Force. The results indicate that the XGBoost model outperforms all other models, with an AUC of 0.92 and demonstrates the highest alignment with recommended high-flood-risk locations, while the frequency-based statistical model showed the weakest performance with an AUC of 0.65. SHAP value graphs highlight the elevation, slope, and TWI as the most influential features across all models. The susceptibility maps generated by the machine learning model show strong agreement with the BLE map and high-flood-risk areas identified by the local Community Task Force.

Google Engine↗
NASA NTRS1996

Early Warning: Development of Confidential Incident Reporting Systems

Accidents hardly ever happen without warning. The combination, or sequence, of failures and mistakes that cause an accident may indeed be unique but the individual failures and mistakes rarely are. In the USA in 1974 the crews on two different aircraft misunderstood the same aeronautical chart and descended towards their destination dangerously early towards a mountain. The first crew were in good weather conditions and could see the mountain and resolved their misinterpretation of the chart. The second crew six weeks later were not so lucky. In cloud they had no clues to point out their mistake nor the presence of the mountain. The resulting crash and the ensuing inquiry, which brought to light the previous incident, shocked the country but gave it the impetus to instigate a safety reporting system. This system eventually became the NASA's Aviation Safety Reporting System (ASRS). The programme collects incident reports from pilots, controllers, mechanics, cabin attendants and many others involved in aviation operations. By disseminating this safety information the ASRS has helped enormously to give US airlines and airspace the highest safety standards. Accident prevention is a goal sought by everyone in the aviation industry and establishing effective incident reporting programmes can go a long way toward achieving that goal. This article will describe the steps and issues required to establish an incident reporting system. The authors summarize the lessons learned from the ASRS, now in its twentieth year of operation and from the Confidential Human Factors Reporting (HER) Programme run by British Airways, an airline that is a recognized world leader in safety reporting and analysis. The differences between government and airline operation of confidential safety reporting systems will be addressed.

OLeary, Mike J.↗
NASA NTRSDate not supplied

The Application of Remote Sensing and Machine Learning to Improve Early Warning Systems for Harmful Algal Events in the Highland Lake Chain, TX

Beginning in 2019, harmful algal events have caused canine deaths in both Lady Bird Lake and Lake Travis located near Austin, Texas. These two reservoirs are part of the larger Highland Lakes chain, managed by the City of Austin Department of Watershed Protection (COA DWP) and the Lower Colorado River Authority (LCRA), which fulfill municipal, commercial, and agricultural water demands. Given the recent increase in favorable environmental conditions for algal events in central Texas, NASA DEVELOP partnered with LCRA and COA DWP to improve monitoring and early detection of algal events, utilizing satellite remote sensing and machine learning. Spatially and temporally varied chlorophyll a concentrations, cyanobacteria detections, turbidity, and water surface temperature products are used as environmental proxies. Landsat 8 Operational Land Imager (OLI) and the Sentinel-2 MultiSpectral Instrument (MSI) data were used to provide a combined revisit time of up to ~3 days and < 30 m per pixel products. Chlorophyll a concentrations were estimated using a pre-trained Mixture Density Network, and cyanobacteria detection was accomplished using the Broad Wavelength Algae Index, which can differentiate algal blooms from algal proliferations (mats). In situ data were used to validate remotely sensed measurements and quantify uncertainties. Preliminary results show a good fit between the modeled output and in situ observations, suggesting that remote sensing data can be used to retrieve biogeochemical properties and/or inherent optical properties (IOPs) of water columns in these inland human-made lakes. Uncertainties were introduced from the sensitivity to atmospheric correction, inherent mismatch between satellite and sampling data, and a relatively lower signal-to-noise ratio over water. The resulting products enable near real-time monitoring of environmental proxies relevant to algal event presence in the Highland Lakes chain, and will ultimately support water management, decision making, and risk communication.

Shuyu Chang↗
NASA NTRS1980

Crop calendars for the US, USSR, and Canada in support of the early warning project

New crop calendars are produced for U.S. regions where several years of periodic growth stage observations are available on a CRD basis. Preexisting crop calendars from the LACIE are also collected as are U.S. crop calendars currently being created for the Foreign Commodities Production Forecast project. For the U.S.S.R. and Canada, no new crop calendars are created because no new data are available. Instead, LACIE crop calendars are compared against simulated normal daily temperatures and against the Robertson wheat and Williams barley phenology models run on the simulated normal temperatures. Severe inconsistencies are noted and discussed. For the U.S.S.R., spring and fall planting dates can probably be estimated accurately from satellite or meteorological data. For the starter model problem, the Feyerherm spring wheat model is recommended for spring planted small grains, and the results of an analysis are presented. For fall planted small grains, use of normal planting dates supplemented by spectral observation of an early stage is recommended. The importance of nonmeteorological factors as they pertain to meteorological factors in determining fall planting is discussed. Crop calendar data available at the Johnson Space Center for the U.S., U.S.S.R., Canada, and other countries are inventoried.

Hodges, T.↗
DOE OSTI2025

Rapid wavefield forecasting for earthquake early warning via deep sequence to sequence learning

We propose a deep learning model, WaveCastNet, to forecast high-dimensional wavefields. WaveCastNet integrates a convolutional long expressive memory architecture into a sequence-to-sequence forecasting framework, enabling it to model long-term dependencies and multiscale patterns in both space and time. By sharing weights across spatial and temporal dimensions, WaveCastNet requires significantly fewer parameters than more resource-intensive models such as transformers, resulting in faster inference times. Crucially, WaveCastNet also generalizes better than transformers to rare and critical seismic scenarios, such as high-magnitude earthquakes. Here, we show the ability of the model to predict the intensity and timing of destructive ground motions in real time, using simulated data from the San Francisco Bay Area. Furthermore, we demonstrate its zero-shot capabilities by evaluating WaveCastNet on real earthquake data. Our approach does not require estimating earthquake magnitudes and epicenters, steps that are prone to error in conventional methods, nor does it rely on empirical ground-motion models, which often fail to capture strongly heterogeneous wave propagation effects.

Geophysics↗
NASA NTRS1972

A system for early warning of bearing failure

System for detecting incipient failure in ball bearings is described. Ultrasonic equipment detects bearing system resonance and provides warning signal through electronic circuitry. Detector can be used to evaluate performance of newly installed bearings. Schematic diagram is provided to show components.

Broderick, J. J.↗
NASA NTRS1972

Design and fabrication of prototype system for early warning of impending bearing failure

Ball bearing performance tests run on several identical ball bearings under a variety of load, speed, temperature, and lubrication conditions are reported. Bearing temperature, torque, vibration, noise, strain, cage speed, etc., were monitored to establish those measurements most suitable as indicators of ball bearing health. Tape records were made under steady-state conditions of a variety of speeds and loads. Sample sections were selected for narrowband spectral analysis with a real time analyzer. An artificial flow was created across the inner race surface of one bearing using an acid etch technique to produce the scratch. Tape records obtained before and after established a characteristic frequency response that identifies the presence of the flow. The signals found most useful as indicators of performance degradation were ultrasonic outputs.

Broderick, J. J.↗
NASA NTRS1974

Design and fabrication of prototype system for early warning of impending bearing failure

A test program was conducted with the objective of developing a method and equipment for on-line monitoring of installed ball bearings to detect deterioration or impending failure of the bearings. The program was directed at the spin-axis bearings of a control moment gyro. The bearings were tested at speeds of 6000 and 8000 rpm, thrust loads from 50 to 1000 pounds, with a wide range of lubrication conditions, with and without a simulated fatigue spall implanted in the inner race ball track. It was concluded that a bearing monitor system based on detection and analysis of modulations of a fault indicating bearing resonance frequency can provide a low threshold of sensitivity.

Meacher, J.↗
NASA NTRS1975

Synthesis and evaluation of polymers for use in early warning fire alarm devices

Conjugated polyacetylene polymers and one condensation polyene, all containing a high degree of conjugated unsaturation, were synthesized. These polymers were characterized by chemical analysis and by thermogravimetric analysis, as well as for their film-forming capability and gas/polymer interactions. It was found that those that had a high degree of conjugated unsaturation and had resonance - stabilizing groups were very thermally stable to 200 C, e.g., poly(dicyanoacetylene), poly(ethynylferrocene) and poly(phenylacetylene); while those with labile moieties, such as poly(p-formamidophenylacetylene), among others, suffered some degradation when heated in air. When subjected to gas/polymer interaction effects, the greatest change in electrical conductance was observed when ammonia was used as the gas and poly(p-nitrophenylacetylene) was the detector. Other polymers showed similar behavior. For example, poly(ethynylcarborane), considered to be an electron acceptor also showed a change in electrical conductance when exposed to ammonia, while poly(ethynylpyridine) and poly(ethylidenepyridazine) responded to carbon monoxide. However, for "fire gases" (gases from smoldering cotton), poly(ethynylferrocene) was the most responsive. Thus, the concept of polymers with different electronegativities forming charge-transfer complexes with different gases was found to be operable.

Byrd, N. R.↗
NASA NTRS1977

Charge-flow structures as polymeric early-warning fire alarm devices

The charge-flow transistor (CFT) and its applications for fire detection and gas sensing were investigated. The utility of various thin film polymers as possible sensing materials was determined. One polymer, PAPA, showed promise as a relative humidity sensor; two others, PFI and PSB, were found to be particularly suitable for fire detection. The behavior of the charge-flow capacitor, which is basically a parallel-plate capacitor with a polymer-filled gap in the metallic tip electrode, was successfully modeled as an RC transmission line. Prototype charge-flow transistors were fabricated and tested. The effective threshold voltage of this metal oxide semiconductor was found to be dependent on whether surface or bulk conduction in the thin film was dominant. Fire tests with a PFI-coated CFT indicate good sensitivity to smouldering fires.

Sechen, C. M.↗
NASA NTRS1980

Canadian crop calendars in support of the early warning project

The Canadian crop calendars for LACIE are presented. Long term monthly averages of daily maximum and daily minimum temperatures for subregions of provinces were used to simulate normal daily maximum and minimum temperatures. The Robertson (1968) spring wheat and Williams (1974) spring barley phenology models were run using the simulated daily temperatures and daylengths for appropriate latitudes. Simulated daily temperatures and phenology model outputs for spring wheat and spring barley are given.

Trenchard, M. H.↗
NASA NTRS1981

AgRISTARS: Supporting research. US crop calendars in support of the early warning project

The crop calendars produced for the Large Area Crop Inventory Experiment (LACIE) and crop calendar samples for Colorado, Iowa, Kansas, Minnesota, Montana, Nebraska, North Dakota, South Dakota, and Texas are presented. These calendars are based on weekly crop reporting district level observations of the percentage of various crops at several growth stages. A sample of the statistical treatments of the weekly data is provided. Four to five years of 50-percent dates for stages on a crop reporting district level for Arkansas, Iowa, Kentucky, Louisiana, Michigan, Mississippi, Ohio and Wisconsin are also given.

Hodges, T.↗
NASA NTRS1981

AgRISTARS: Early warning and crop condition assessment. Plant cover, soil temperature, freeze, water stress, and evapotranspiration conditions

Emissive (10.5 to 12.5 microns) and reflective (0.55 to 1.1 microns) data for ten day scenes and infrared data for six night scenes of southern Texas were analyzed for plant cover, soil temperature, freeze, water stress, and evapotranspiration. Heat capacity mapping mission radiometric temperatures were: within 2 C of dewpoint temperatures, significantly correlated with variables important in evapotranspiration, and related to freeze severity and planting depth soil temperatures.

Wiegand, C. L.↗
NASA NTRS1982

Development of an early warning system of crop moisture conditions using passive microwave

Emissivities were calculated from the Nimbus 5 electrically scanning microwave radiometer (ESMR) over 25 km grid cells for the southern Great Plains includin the western two-thirds of Kansas and Oklahoma and northwest Texas. These emissivities, normalized for seasonal temperature changes, were in excellent agreement with theory and measurements made from aircraft and truck sensors at the 1.55 cm wavelength of ESMR. These emissivities were related to crop moisture conditions of the winter wheat in the major wheat producing counties of the three states. High correlations were noted between emissitivity and an antecedent precipitation index (API) used to infer soil moisture for periods when the soils were essentially bare. The emissivities from ESMR were related through API and actual crop condition reports to progress of fall planting, adequacy of crop moisture for stand establishment, and periods of excessive moisture that necessitated replanting. Periods of prolonged frozen soil in the winter were observable at several grid points. The average emissivities of the canopy/soil surface during the maximum canopy development times in the spring showed a good agreement with moisture stress inferred from rainfall and yield data.

Mcfarland, M. J.↗
NASA NTRS1991

Real-time earthquake monitoring: Early warning and rapid response

A panel was established to investigate the subject of real-time earthquake monitoring (RTEM) and suggest recommendations on the feasibility of using a real-time earthquake warning system to mitigate earthquake damage in regions of the United States. The findings of the investigation and the related recommendations are described in this report. A brief review of existing real-time seismic systems is presented with particular emphasis given to the current California seismic networks. Specific applications of a real-time monitoring system are discussed along with issues related to system deployment and technical feasibility. In addition, several non-technical considerations are addressed including cost-benefit analysis, public perceptions, safety, and liability.

Source record↗