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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

The minimal time detection algorithm

An aerospace vehicle may operate throughout a wide range of flight environmental conditions that affect its dynamic characteristics. Even when the control design incorporates a degree of robustness, system parameters may drift enough to cause its performance to degrade below an acceptable level. The object of this paper is to develop a change detection algorithm so that we can build a highly adaptive control system applicable to aircraft systems. The idea is to detect system changes with minimal time delay. The algorithm developed is called Minimal Time-Change Detection Algorithm (MT-CDA) which detects the instant of change as quickly as possible with false-alarm probability below a certain specified level. Simulation results for the aircraft lateral motion with a known or unknown change in control gain matrices, in the presence of doublet input, indicate that the algorithm works fairly well as theory indicates though there is a difficulty in deciding the exact amount of change in some situations. One of MT-CDA distinguishing properties is that detection delay of MT-CDA is superior to that of Whiteness Test.

Kim, Sungwan↗
NASA NTRS1989

Thematic Mapper detection of changes in the leaf area of closed canopy pine plantations in central Massachusetts

Consideration is given to the possibility of using Landsat TM data to detect differences and changes in the leaf area index (LAI) of closed canopy pine plantations in central Massachusetts. Field measurements of the LAI were obtained using locally-derived allometric relationships between leaf area and trunk diameter. The relationship between the Landsat TM Band 3/Band 4 ratio and regional differences in LAI is examined. A thinning treatment was conducted to demonstrate the ability of TM to detect a known reduction in LAI. It is concluded that, for the detection of moderate changes and differences in the LAI of closed canopy pine plantations at local scales, the TM sensor is better than field measurements involving allometric equations.

Herwitz, Stanley R.↗
NASA NTRS1981

Urban area update procedures using Landsat data

Landsat digital enhancements and classification maps are shown to be useful for updating the urban expansion of standard metropolitan statistical areas on a macro scale. Automated procedures for detecting nonurban to urban land coverage change using multitemporal Landsat data are investigated for five metropolitan areas, showing an overall delineation similar to that obtained from large scale aerial photography. The evaluated change detection procedures include image differencing, principal component transformation prior to differencing, and post classification comparison. Results show that image differencing techniques in MSS band 5 provide the most accurate land cover change detections.

Toll, D. L.↗
NASA NTRSDate not supplied

Cloud-Based Solutions for Monitoring Coastal Ecosystems and Prioritization of Restoration Efforts Across Belize

In recent years the availability of automated change detection algorithms in Google Earth Engine has permitted cloud-based processing of large time series of satellite imagery. Models such as the Continuous Change Detection and Classification (CCDC), CCDC-Spectral Mixture Analysis (CCDC-SMA), and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) allow users to exploit decades of Earth Observations (EO) , leveraging the Landsat archive and data from other sensors to detect disturbance in forest ecosystems. Despite the wide adoption of these methods, robust documentation and growing community of users, little research has explored their use in mangrove environments. Mangroves are dynamic environments subject to changes due to not only the natural migration of mudflats but also coastal erosion, urban expansion, aquaculture practices, etc. This work aims to identify best practices for the application of these models to identify and monitor changes in the Belizean mangroves, which experienced an estimated 5.4% decrease in extent between 1980 and 2017 (Cherrington et al. 2020). Partnering directly with the Belizean Forest Department, our team will develop a replicable, efficient methodology to annually update the country’s mangrove extent employing EO-based change detection. This collaboration supports Belize’s marine conservation and climate resilience efforts, such as its Blue Bond agreement with the Nature Conservancy, building on national processes to monitor these critical ecosystems.

Mangroves↗
NASA NTRS2019

Comprehensive Severe Weather Impact Assessment and Monitoring using Synthetic Aperture Radar and Auxiliary Data

Remote sensing datasets, particularly acquired by Synthetic Aperture Radar (SAR) sensors, have become increasingly important in severe weather disaster impact studies given their ability to observe the Earth largely irrespective of weather and sunlight conditions. The reliability of existing SAR change detection products applied on a pair of SAR images is constrained by the limitation of current methods to differentiate and classify disaster specific changes from anthropogenic surface alterations. Moreover, the inherent properties of the sensors, variations in SAR backscatter due to changes in surface conditions, and other factors exacerbate these limitations. We proposed a novel procedure expanding on earlier SAR-based change detection methods to exclude anthropogenic alterations and other sources of ambiguity that might lead to inaccurate mapping of the impacts of severe weather disasters. We applied the proposed procedure that is based on long term interferometric and amplitude-based change detection analyses of Sentinel- 1 SAR imagery to two study sites recently impacted by severe weather disasters (Flooding post severe weather events in urban centers; Hailstorm damage on crops). For the first case study, Sentinel-1 SLC scenes from two flood events in the Houston area (April 2016 flooding event and Hurricane Harvey of August-September 2017) were used to construct a flood map depicting areas repeatedly affected by the flood. Pixels with consistent coherence values in the pre-disaster coherence stack were retained for comparison with the pre- and post-disaster coherence stack and pixels with significant decline (greater than 60%) in coherence values were retained in the final flood map. The findings of the applied technique were calibrated and validated through datasets from NOAA/NWS Service storm reports, aerial imaging (NOAA and Civil Air Patrol), Federal Emergency Management Agency (FEMA) reporting, and targeted collections of NASA’s L-band UAVSAR data. Findings and products derived from the adopted methodology can be useful in disaster response and mitigation activities.

Gebremichael, Esayas↗
DOE OSTI2022

A Review on the State of the Art of Machine Learning and Satellite Imaging: Detecting Scene Changes in Selected Nuclear Fuel Cycle Datasets

The timely detection of clandestine nuclear facilities is one of the greatest challenges faced by the International Atomic Energy Agency’s (IAEA). Idaho National Laboratory is currently applying machine learning (ML) to existing satellite imagery (SI) datasets to find facilities within the nuclear fuel life cycle, with primary focus placed on identifying critical predecessor (i.e., fuel fabrication and fuel enrichment) and successor (i.e., nuclear power plants) facilities. This could provide a satellite image methodology that the IAEA could leverage to discover clandestine facilities. The work presented in this paper describes the evolution of a workflow developed by this team for object detection related to critical infrastructure by expanding that workflow for the purpose of identifying nuclear fuel cycle components and automating dependency assessments. This will be done using two methods housed within a single pipeline. The first method involves implementing a DenseNet161 convolutional neural network to classify the images and explain the results using Local Interpretable Model-Agnostic Explanations (LIME). The second method implements You Only Look Once version 5 (YoloV5), to detect objects within images, provide a probability for the detection, and provide a bounding box that corresponds to the object of interest. The results of this work are anticipated to provide a clear picture of this portion of the nuclear fuel cycle and perform as a stand-alone tool for image assessment that can be expanded to additional fuel cycle components and implemented in international safeguards and national security domains. This capability addresses the IAEA’s need to detect undeclared nuclear materials and activities within a state while encompassing the entire nuclear fuel cycle.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Discovering hydrothermalism from Afar: In Situ methane instrumentation and change-point detection for decision-making

Seafloor hydrothermalism plays a critical role in fundamental interactions between geochemical and biological processes in the deep ocean. A significant number of hydrothermal vents are hypothesized to exist, but many of these remain undiscovered due in part to the difficulty of detecting hydrothermalism using standard sensors on rosettes towed in the water column or robotic platforms performing surveys. Here, we use in situ methane sensors to complement standard sensing technology for hydrothermalism discovery and compare sensors on a towed rosette and an autonomous underwater vehicle (AUV) during a 17 km long transect in the Northern Guaymas Basin in the Gulf of California. This transect spatially intersected with a known hydrothermally active venting site. These data show that methane signalled possible hydrothermal-activity 1.5–3 km laterally (100–150 m vertically) from a known vent. Methane as a signal for hydrothermalism performed similarly to standard turbidity sensors (plume detection 2.2–3.3 km from reference source), and more sensitively and clearly than temperature, salinity, and oxygen instruments which readily respond to physical mixing in background seawater. We additionally introduce change-point detection algorithms—streaming cross-correlation and regime identification—as a means of real-time hydrothermalism discovery and discuss related data supervision technologies that could be used in planning, executing, and monitoring explorative surveys for hydrothermalism.

58 GEOSCIENCES↗
DOE OSTI2021

Screening candidate supplementary cementitious materials under standard and accelerated curing through time-series surface resistivity measurements and change-point detection

Time-series surface resistivity (SR) measurements are evaluated as an indicator of pozzolanicity in concretes containing 13 materials of both known and unknown pozzolanicity, under standard and accelerated curing conditions, and the results were validated against portlandite consumption obtained through thermogravimetric analysis. Change-point detection (CPD) analysis is applied to the data to provide a rigorous indicator of hydration age of the onset and rate of the pozzolanic reaction, if occurring. The SR measurements were able to capture the onset of the pozzolanic reactions in concretes curing at elevated temperature regimes, which allows determination of pozzolanicity weeks earlier than at standard curing temperatures. Using CPD along with slope analysis allows a comprehensive analysis of a material's reactivity and determination of pozzolanicity in as quickly as two days. Time-series SR measurements at elevated curing conditions, along with limits on material composition, are recommended for determination of pozzolanic reactivity in novel or variable materials.

36 MATERIALS SCIENCE↗
NASA NTRS1989

Detection of changes in leaf water content using near- and middle-infrared reflectances

A method to detect plant water stress by remote sensing is proposed using indices of near-IR and mid-IR wavelengths. The ability of the Leaf Water Content Index (LWCI) to determine leaf relative water content (RWC) is tested on species with different leaf morphologies. The way in which the Misture Stress Index (MSI) varies with RWC is studied. On test with several species, it is found that LWCI is equal to RWC, although the reflectances at 1.6 microns for two different RWC must be known to accurately predict unknown RWC. A linear correlation is found between MSI and RWC with each species having a different regression equation. Also, MSI is correlated with log sub 10 Equivalent Water Thickness (EWT) with data for all species falling on the same regression line. It is found that the minimum significant change of RWC that could be detected by appying the linear regression equation of MSI to EWT is 52 percent. Because the natural RWC variation from water stress is about 20 percent for most species, it is concluded that the near-IR and mid-IR reflectances cannot be used to remotely sense water stress.

Hunt, E. Raymond, Jr.↗
DOE OSTI2023

Biogeophysical Effects of Land-Use and Land-Cover Change Not Detectable in Warmest Month

Land-use and land-cover changes (hereafter simply “land use”) alter climates biogeophysically by affecting surface fluxes of energy and water. Yet, near-surface temperature responses to land use across observational versus model-based studies and spatial-temporal scales can be inconsistent. Here we assess the prevalence of the historical land use signal of daily maximum temperatures averaged over the warmest month of the year (t LU ) using regularized optimal fingerprinting for detection and attribution. We use observations from the Climatic Research Unit and Berkeley Earth alongside historical simulations with and without land use from phase 6 of the Coupled Model Intercomparison Project to reconstruct an experiment representing the effects of land use on climate. To assess the signal of land use at spatially resolved continental and global scales, we aggregate all input data across reference regions and continents, respectively. At both scales, land use does not comprise a significantly detectable set of forcings for two of four Earth system models and their multimodel mean. Furthermore, using a principal component analysis, we find that t LU is mostly composed of the nonlocal effects of land use rather than its local effects. These findings show that, at scales relevant for climate attribution, uncertainties in Earth system model representations of land use are too high relative to the effects of internal variability to confidently assess land use.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1997

Using Laser Altimetry to Detect Topographic Change in Long Valley Caldera, California

Long Valley caldera California, is a site of extensive volcanism, persistent seismicity, and uplift of a resurgent dome, currently at a rate of about 3 cm/year. Airborne laser altimetry was used to determine the surface topography of the region in 1993. A repeat mission occurred in 1995. Three different laser altimeters were flown, dubbed ATLAS, SLICER and RASCAL. Data processing consists of the combination of the aircraft trajectory and attitude data with the laser range, the determination of an atmospheric delay, laser pulse timing errors, laser system biases, and data geolocation to obtain the position of the laser spot on the ground. Results showed that using the ATLAS and SLICER instruments, the elevation of an overflown lake is determined to precisions of 3.3 cm and 2.9 cm from altitudes of 500 m and 3 km above the ground, and about 10 cm using the RASCAL instrument from 500 m above ground. Comparison with tide gauge data showed the laser measurements are able to resolve centimeter-level changes in the lake elevation over time. Repeat pass analysis of tracks over flat surfaces indicate no systematic biases affect the measurement procedure of the ATLAS and SLICER instruments. Comparison of GPS and laser-derived elevations of easily-identifiable features in the caldera confirm the horizontal accuracy of the measurement is within the diameter of the laser footprint, and vertical accuracy is within the error inherent in the measurement. Crossover analysis shows that the standard error of the means at track intersection points within the caldera, and dome (i.e., where zero and close to the maximum amount of uplift is expected) are about I cm, indicating elevation change at the 3 cm/year level should be detectable. We demonstrate one of the powerful advantages of scanning laser altimetry over other remote sensing techniques; the straightforward creation of precise digital elevation maps of overflown terrain. Initial comparison of the 1993-1995 data indicates uplift occurred, but filtering is required to remove vegetation effects. Although research continues to utilize the full potential of laser altimetry data, the results constitute a successful demonstration that the technique may be used to perform geodetic monitoring of surface topographic change.

Hofton, M. A.↗
NASA NTRS1997

Using Laser Altimetry to Detect Topographic Change at Long Valley Caldera, California

Long Valley caldera, California, is a site of extensive volcanism, persistent seismicity, and uplift of a resurgent dome, currently at a rate of approximately 3 cm/year. Airborne laser altimetry was used to determine the surface topography of the region in 1993. A repeat mission occurred in 1995. Three different laser altimeters were flown, dubbed ATLAS, SLICER and RASCAL. Data processing consists of the combination of the aircraft trajectory and attitude data with the laser range, the determination of an atmospheric delay, laser pulse timing errors, laser system biases, and data geolocation to obtain the position of the laser spot on the ground. Results showed that using the ATLAS and SLICER instruments, the elevation of an overflown lake is determined to precisions of 3.3 cm and 2.9 cm from altitudes of 500 m and 3 km above the ground, and approximately 10 cm using the RASCAL instrument from 500 m above ground. Comparison with tide gauge data showed the laser measurements are able to resolve centimeter-level changes in the lake elevation over time. Repeat pass analysis of tracks over flat surfaces indicate no systematic biases affect the measurement procedure of the ATLAS and SLICER instruments. Comparison of GPS and laser-derived elevations of easily-identifiable features in the caldera confirm the horizontal accuracy of the measurement is within the diameter of the laser footprint, and vertical accuracy is within the error inherent in the measurement. Crossover analysis shows that the standard error of the means at track intersection points within the caldera and dome (i.e., where zero and close to the maximum amount of uplift is expected) are about 1 cm, indicating elevation change at the 3 cm/year level should be detectable. We demonstrate one of the powerful advantages of scanning laser altimetry over other remote sensing techniques; the straightforward creation of precise digital elevation maps of overflown terrain. Initial comparison of the 1993-1995 data indicates uplift occurred, but filtering is required to remove vegetation effects. Although research continues to utilize the full potential of laser altimetry data, the results constitute a successful demonstration that the technique may be used to perform geodetic monitoring of surface topographic change.

Hofton, M. A.↗
DOE OSTI2023

Spectral induced polarization signatures of smoldering remediation enhanced with colloidal activated carbon: An experimental study

Monitoring the remediation of soil and groundwater contaminated by organic compounds remains highly challenging. The spectral induced polarization (SIP) method exhibits significant promise for monitoring changes to the electrical properties of soils that are undergoing remediation. Thermal treatments, such as smoldering combustion, have become established remediation techniques for destroying contaminants. Smoldering combustion is now being supported by colloidal activated carbon (CAC), with CAC able to adsorb contaminants and supplement the fuel source for destroying contaminants. The objective of this study is to investigate the potential of SIP for tracking the smoldering remediation of imitated field soils supplemented with CAC. SIP column experiments were first conducted to assess the response of SIP to varying concentrations of CAC in field soils that contain, or do not contain, organic material (OM). Here these results demonstrate that increasing OM and CAC contents increase both the real and imaginary components of the complex conductivity, with the imaginary conductivity also showing frequency dependence. Next, a suite of smoldering and SIP column experiments was conducted to investigate if SIP can detect changes in imitated field soils of varying OM and CAC contents that have been remediated by smoldering combustion. The SIP results on the examined soils both before and after smoldering show that SIP can detect changes in the real conductivity, and particularly the imaginary conductivity, between different soil compositions and different stages of the remediation process. High resolution scanning electron microscopy (SEM) imaging was performed on all samples to validate the SIP and smoldering experiments, confirming significant reductions in carbon after smoldering. Overall, this study suggests that SIP has potential to detect changes in the electrical properties of field soils due to the addition of remedial fluids like CAC and contaminant destruction by smoldering remediation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2006

An improved method for precise automatic co-registration of moderate and high-resolution spacecraft imagery

Improvements to the automated co-registration and change detection software package, AFIDS (Automatic Fusion of Image Data System) has recently completed development for and validation by NGA/GIAT. The improvements involve the integration of the AFIDS ultra-fine gridding technique for horizontal displacement compensation with the recently evolved use of Rational Polynomial Functions/ Coefficients (RPFs/RPCs) for image raster pixel position to Latitude/Longitude indexing. Mapping and orthorectification (correction for elevation effects) of satellite imagery defies exact projective solutions because the data are not obtained from a single point (like a camera), but as a continuous process from the orbital path. Standard image processing techniques can apply approximate solutions, but advances in the state-of-the-art had to be made for precision change-detection and time-series applications where relief offsets become a controlling factor. The earlier AFIDS procedure required the availability of a camera model and knowledge of the satellite platform ephemeredes. The recent design advances connect the spacecraft sensor Rational Polynomial Function, a deductively developed model, with the AFIDS ultrafine grid, an inductively developed representation of the relationship raster pixel position to latitude /longitude. As a result, RPCs can be updated by AFIDS, a situation often necessary due to the accuracy limits of spacecraft navigation systems. An example of precision change detection will be presented from Quickbird.

co-registration↗
NASA NTRS2000

Using Airborne Laser Altimetry to Detect Topographic Change at Long Valley Caldera California

The topography of the Long Valley caldera, California, was sampled using airborne laser altimetry in 1993, 1995, and 1997 to test the feasibility of using airborne laser altimetry for monitoring deformation of volcanic origin. Results show the laser altimeters are able to resolve subtle topographic features such as a gradual slope and to detect small transient changes in lake elevation. Crossover and repeat pass analyses of laser tracks indicate decimeter-level vertical precision is obtained over flat and low-sloped terrain for altimeter systems performing waveform digitization. Comparisons with complementary, ground-based CPS data at a site close to Bishop airport indicate that the laser and GPS-derived elevations agree to within the error inherent in the measurement and that horizontal locations agree to within the radius of the laser footprint. A comparison of the data at two sites, one where no change and the other where the maximum amount of vertical uplift is expected, indicates approximately 10 cm of relative uplift occurred 1993-1997, in line with predictions from continuous CPS measurements in the region. Extensive terrain mapping flights during the 1995 and 1997 missions demonstrate some of the unique abilities of laser altimetry; the straightforward creation of high resolution, high accuracy digital elevation models of overflown terrain, and the ability to determine ground topography in the presence of significant ground cover such as dense tree canopies. These capabilities make laser altimetry an attractive technique for quantifying topographic change of volcanic origin, especially in forested regions of the world where other remote sensing instruments have difficulty detecting the underlying topography.

Hofton, M. A.↗
NASA NTRS1999

Using Airborne Laser Altimetry to Detect Topographic Change at Long Valley Caldera, California

The topography of the Long Valley caldera, California, was sampled using airborne laser altimetry in 1993, 1995, and 1997 to test the feasibility of using airborne laser altimetry for monitoring deformation of volcanic origin. Results show the laser altimeters are able to resolve subtle topographic features such as a gradual slope and to detect small transient changes in lake elevation. Crossover and repeat pass analyses of laser tracks indicate decimeter-level vertical precision is obtained over flat and low-sloped terrain for altimeter systems performing waveform digitization. Comparisons with complementary, ground-based GPS data at a site close to Bishop airport indicate that the laser and GPS-derived elevations agree to within the error inherent in the measurement and that horizontal locations agree to within the radius of the laser footprint. A comparison of the data at two sites, one where no change and the other where the maximum amount of vertical uplift is expected, indicates approximately 10 cm of relative uplift occurred 1993-1997, in line with predictions from continuous GPS measurements in the region. Extensive terrain mapping flights during the 1995 and 1997 missions demonstrate some of the unique abilities of laser altimetry; the straightforward creation of high resolution, high accuracy digital elevation models of overflown terrain, and the ability to determine ground topography in the presence of significant ground cover such as dense tree canopies. These capabilities make laser altimetry an attractive technique for quantifying topographic change of volcanic origin, especially in forested regions of the world where other remote sensing instruments have difficulty detecting the underlying topography.

Hofton, M. A.↗
NASA NTRS1986

Theory of optimal weighting of data to detect climatic change

A search for climatic change predicted by climate models can easily yield unconvincing results because of 'climatic noise,' the inherent, unpredictable variability of time-average atmospheric data. A weighted average of data that maximizes the probability of detecting predicted climatic change is presented. To obtain the optimal weights, an estimate of the covariance matrix of the data from a prior data set is needed. This introduces additional sampling error into the method. This is presently taken into account. A form of the weighted average is found whose probability distribution is independent of the true (but unknown) covariance statistics of the data and of the climate model prediction.

Bell, T. L.↗
DOE OSTI2024

Defining Golden Batches in Biomanufacturing Processes From Internal Metabolic Activity to Detect Process Changes That May Affect Product Quality

ABSTRACT Cellular metabolism plays a role in the observed variability of a drug substance's Critical Quality Attributes (CQAs) made by biomanufacturing processes. Therefore, here we describe a new approach for monitoring biomanufacturing processes that measures a set of metabolic reaction rates (named Critical Metabolic Parameters (CMP) in addition to the macroscopic process conditions currently being used as Critical Process Parameters (CPP) for biomanufacturing. Constraint‐based systems biology models like Flux Balance Analysis (FBA) are used to estimate metabolic reaction rates, and metabolic rates are used as inputs for multivariate Batch Evolution Models (BEM). Metabolic activity was reproducible among batches and could be monitored to detect a deliberately induced macroscopic process shift (i.e., temperature change). The CMP approach has the potential to enable “golden batches” in biomanufacturing processes to be defined from the internal metabolic activity and to aid in detecting process changes that may impact the quality of the product. Overall, the data suggested that monitoring of metabolic activity has promise for biomanufacturing process control.

Biotechnology & Applied Microbiology↗