Uncertainties in the Geostationary Ocean Color Imager (GOCI) Remote Sensing Reflectance for Assessing Diurnal Variability of Biogeochemical Processes
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Javier Concha↗
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Results for “remote assessment”
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.
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Remote sensing and the urban environment: An assessment of usefulness
The usefulness of remote sensing in urban planning and development relative to social and economic factors as well as physical factors is examined. Particular attention was given to environmental quality assessment and land use problems.
Howard, W. B.↗
Using Remotely Sensed Information for Near Real-Time Landslide Hazard Assessment
The increasing availability of remotely sensed precipitation and surface products provides a unique opportunity to explore how landslide susceptibility and hazard assessment may be approached at larger spatial scales with higher resolution remote sensing products. A prototype global landslide hazard assessment framework has been developed to evaluate how landslide susceptibility and satellite-derived precipitation estimates can be used to identify potential landslide conditions in near-real time. Preliminary analysis of this algorithm suggests that forecasting errors are geographically variable due to the resolution and accuracy of the current susceptibility map and the application of satellite-based rainfall estimates. This research is currently working to improve the algorithm through considering higher spatial and temporal resolution landslide susceptibility information and testing different rainfall triggering thresholds, antecedent rainfall scenarios, and various surface products at regional and global scales.
remote sensing↗
Clear-Sky Remote Sensing in the Vicinity of Clouds: What Can Be Learned About Aerosol Changes?
Studies on aerosol direct and indirect effects require a precise separation of cloud-free and cloudy air. However, separation between cloud-free and cloudy areas from remotely-sensed measurements is ambiguous. The transition zone in the regions around clouds often stretches out tens of km, which are neither precisely clear nor precisely cloudy. We study the transition zone between cloud-free and cloudy air using MODerate-resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements. Both instruments show enhanced clear-sky reflectance (MODIS) and clear-sky backscatterer (CALIPSO) near clouds. Analyzing a large dataset of MODIS observations we examine the effect of three-dimensional (3D) radiative interactions between clouds and cloud-free areas, also known as a cloud adjacency effect. Comparing with CALIPSO clear-sky backscatterer measurements, we show that the cloud adjacency effect may be responsible for a large portion of the enhanced clear sky reflectance observed by MODIS. While aerosol particles are responsible for a large part of the near-cloud enhancements in CALIPSO observations, misidentified or undetected cloud particles are also likely to contribute. As a result, both the nature of these particles (cloud vs. aerosol) and the processes creating them need to be clarified using a quantitative assessment of remote sensing limitations in particle detection and identification. "The width and ubiquity of the transition zone near clouds imply that studies of-aerosol-cloud -interactions and aerosol direct radiative effects need to account for aerosol changes near clouds. Not accounted, these changes can cause systematic biases toward smaller aerosol radiative forcing. On the other hand, including aerosol products near clouds despite their uncertainties may overestimate aerosol radiative forcing. Therefore, there is an urgent need for developing methods that can assess and account for remote sensing challenges and thus allow for including the transition zone into the study. We describe a simple model that estimates the cloud-induced enhanced reflectances of cloud-free areas in the vicinity of clouds. The model assumes that the enhancement is due entirely to Rayleigh scattering and is therefore bigger at shorter wavelengths, thus creating a so-called apparent "bluing" of aerosols in remote sensing retrievals.
Marshak, Alexander↗
Clear-Sky Remote Sensing in the Vicinity of Clouds: What We Learned from MODIS and CALIPSO
Studies on aerosol direct and indirect effects require a precise separation of cloud-free and cloudy air. However, separation between cloud-free and cloudy areas from remotely-sensed measurements is ambiguous. The transition zone in the regions around clouds often stretches out tens of km, which are neither precisely clear nor precisely cloudy. We study the transition zone between cloud-free and cloudy air using MODerate-resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements. Both instruments show enhanced clear-sky reflectance (MODIS) and clear-sky backscatterer (CALIPSO) near clouds, Analyzing a large dataset of MODIS observations we examine the effect of three-dimensional (3D) radiative interactions between clouds and cloud-free areas, also known as a cloud adjacency effect. Comparing with CALIPSO clear-sky backscatterer measurements, we show that the cloud adjacency effect may be responsible for a large portion of the enhanced clear sky reflectance observed by MODIS. While aerosol particles are responsible for a large part of the near-cloud enhancements in CALIPSO observations, misidentified or undetected cloud particles are also likely to contribute. As a result, both the nature of these particles (cloud vs. aerosol) and the processes creating them need to be clarified using a quantitative assessment of remote sensing limitations in particle detection and identification. The width and ubiquity of the transition zone near clouds imply that studies of aerosol-cloud interactions and aerosol direct radiative effects need to account for aerosol changes near clouds. Not accounted, these changes can cause systematic biases toward smaller aerosol radiative forcing. On the other hand, including aerosol products near clouds despite their uncertainties may overestimate aerosol radiative forcing. Therefore, there is an urgent need for developing methods that can assess and account for remote sensing challenges and thus allow for including the transition zone into the study. We describe a simple model that estimates the cloud-induced enhanced reflectances of cloud-free areas in the vicinity of clouds. The model assumes that the enhancement is due entirely to Rayleigh scattering and is therefore bigger at shorter wavelengths, thus creating a so-called apparent "bluing" of aerosols in remote sensing retrievals.
Marshak, Alexander↗
Few-shot Learning for Post-disaster Structure Damage Assessment
Automating post-disaster damage assessment with remote sensing data is critical for faster surveys of structures impacted by natural disasters. One significant obstacle to training state-of-the-art deep neural networks to support this automation is that large quantities of labelled data are often required. However, obtaining those labels is particularly unrealistic to support post-disaster damage assessment in a timely manner. Few-shot learning methods could help to mitigate this by reducing the amount of labelled data required to successfully train a model while achieving satisfactory results. To this end, we explore a feature reweighting method to the YOLOv3 object detection architecture to achieve few-shot learning of damage assessment models on the xBD dataset. Our results show that the feature reweighting approach yield improved mAP over the baseline with significantly fewer labelled samples. In addition, we use t-SNE to analyze the class-specific reweighting vectors generated by the reweighting module in order to evaluate their inter-class and intra-class similarity. We find that the vectors form clusters based on class, and that these clusters overlap with visually similar classes. Those results show the potential to employ this few-shot learning strategy for rapid damage assessment with post-event remote sensing images.
Bowman, Jordan↗
Needs Assessment for the Use of NASA Remote Sensing Data for Regulatory Water Quality
This slide presentation reviews the assessment of the needs that NASA can use for the remote sensing of water quality. The goal of this project is to provide information for decision-making activities (water quality standards) using remotely sensed/satellite based water quality data from MODIS and Landsat data.
Spiering, Bruce↗
Assessment of the role of remote sensing in the study of inland and coastal waters
Several problems within Great Lakes, coastal, and continental shelf water were selected and organized under the topical headings of Productivity, Sedimentation, Water Dynamics, Eutrophication, and Hazardous Substances. The measurements required in the study of each of the problems were identified. An assessment was made of the present capability and the potential of remote sensing to make these measurements. The relevant remote-sensing technology for each of these classifications was discussed and needed advancements indicated.
Curfman, H. J.↗
Energy Remote Sensing Applications Projects at the NASA Ames Research Center
The NASA Ames Research Center is active in energy projects primarily in the role of providing assistance to users in the solution of a number of problems related to energy. Data bases were produced which can be used, in combination with other sources of information, to solve spatially related energy problems. Six project activities at Ames are described which relate to energy and remote sensing. Two projects involve power demand forecasting and estimations using remote sensing and geographic information systems; two others involve transmission line routing and corridor analysis; one involves a synfuel user needs assessment through remote sensing; and the sixth involves the siting of energy facilities.
Norman, S. D.↗
Analysis of Spacecraft Materials Discrimination Using Color Indices for Remote Sensing for Space Situational Awareness
The increasing number of space missions have resulted in an augmented density of artificial objects positioned in orbital domains near Earth. Knowing the material composition of the resident space object can be of value in object identification and risk assessment using remote sensing techniques. To perform this task, it is increasingly imperative to optically characterize spacecraft materials to identify unique material-specific spectroscopic markers. In this work, a variety of materials frequently utilized by the aerospace industry in spacecraft design and construction were analyzed using reflectance spectroscopy. The collected data provide a spectral characterization baseline for modern-day and historical spacecraft materials. The color index was computed for standard astronomical and suggested theoretical filter passbands and compared to each other in their capability to discriminate materials belonging to different classes. The color index was calculated from reflectance spectra of common spacecraft materials in their pristine, as-received conditions that fell under different family groupings. Different color-index combinations were studied using color-color diagrams. Visual and quantitative analysis of the color-color diagrams were used to evaluate the possibility of discriminating materials from one another by means of optical measurements. Results of the analysis show that polyimide and photovoltaic materials are most easy to discriminate from all other materials via color indices using different filter passbands evaluated in this study.
spectral reflectance↗
Ground Truth Sampling and LANDSAT Accuracy Assessment
It is noted that the key factor in any accuracy assessment of remote sensing data is the method used for determining the ground truth, independent of the remote sensing data itself. The sampling and accuracy procedures developed for nuclear power plant siting study are described. The purpose of the sampling procedure was to provide data for developing supervised classifications for two study sites and for assessing the accuracy of that and the other procedures used. The purpose of the accuracy assessment was to allow the comparison of the cost and accuracy of various classification procedures as applied to various data types.
Robinson, J. W.↗
Neural network uncertainty assessment using Bayesian statistics: a remote sensing application
Neural network (NN) techniques have proved successful for many regression problems, in particular for remote sensing; however, uncertainty estimates are rarely provided. In this article, a Bayesian technique to evaluate uncertainties of the NN parameters (i.e., synaptic weights) is first presented. In contrast to more traditional approaches based on point estimation of the NN weights, we assess uncertainties on such estimates to monitor the robustness of the NN model. These theoretical developments are illustrated by applying them to the problem of retrieving surface skin temperature, microwave surface emissivities, and integrated water vapor content from a combined analysis of satellite microwave and infrared observations over land. The weight uncertainty estimates are then used to compute analytically the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of an NN model. The uncertainties on the NN Jacobians are then considered in the third part of this article. Used for regression fitting, NN models can be used effectively to represent highly nonlinear, multivariate functions. In this situation, most emphasis is put on estimating the output errors, but almost no attention has been given to errors associated with the internal structure of the regression model. The complex structure of dependency inside the NN is the essence of the model, and assessing its quality, coherency, and physical character makes all the difference between a blackbox model with small output errors and a reliable, robust, and physically coherent model. Such dependency structures are described to the first order by the NN Jacobians: they indicate the sensitivity of one output with respect to the inputs of the model for given input data. We use a Monte Carlo integration procedure to estimate the robustness of the NN Jacobians. A regularization strategy based on principal component analysis is proposed to suppress the multicollinearities in order to make these Jacobians robust and physically meaningful.
Neural Networks (Computer)↗
Smart Ultrasound Remote Guidance Experiment (SURGE) Preliminary Findings
To date, diagnostic quality ultrasound images were obtained aboard the International Space Station (ISS) using the ultrasound of the Human Research Facility (HRF) rack in the Laboratory module. Through the Advanced Diagnostic Ultrasound in Microgravity (ADUM) and the Braslet-M Occlusion Cuffs (BRASLET SDTO) studies, non-expert ultrasound operators aboard the ISS have performed cardiac, thoracic, abdominal, vascular, ocular, and musculoskeletal ultrasound assessments using remote guidance from ground-based ultrasound experts. With exploration class missions to the lunar and Martian surfaces on the horizon, crew medical officers will necessarily need to operate with greater autonomy given communication delays (round trip times of up to 5 seconds for the Moon and 90 minutes for Mars) and longer periods of communication blackouts (due to orbital constraints of communication assets). The SURGE project explored the feasibility and training requirements of having non-expert ultrasound operators perform autonomous ultrasound assessments in a simulated exploration mission outpost. The project aimed to identify experience, training, and human factors requirements for crew medical officers to perform autonomous ultrasonography. All of these aims pertained to the following risks from the NASA Bioastronautics Road Map: 1) Risk 18: Major Illness and Trauna; 2) Risk 20) Ambulatory Care; 3) Risk 22: Medical Informatics, Technologies, and Support Systems; and 4) Risk 23: Medical Skill Training and Maintenance.
Hurst, Victor↗
Utilizing Remote Sensing to Detect and Assess the Impacts of Estuarine Breach Events for Improved Coastal Wetland Monitoring and Management
Estuaries are extremely dynamic environments that provide a host of vital ecosystem services. California’s Marine Life Protection Act protects such ecosystems by creating Marine Protected Areas (MPAs). California has approximately 440,000 acres of estuarine habitat as well as 23 Estuarine Marine Protected Areas (EMPAs). Thus, in situ data collection is often difficult due to time and resource constraints. This project used remote sensing to gather data that examined the health and dynamics of California EMPAs to supplement ground-based field measurements. Through the use of Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Sentinel-2 MultiSpectral Instrument (MSI), and Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), this project assessed mouth state, inundation extent, turbidity, temperature, and tidal measurements for observable estuaries. The Normalized Difference Water Index (NDWI) from Sentinel-2 MSI captured estuary mouth state and inundation extent. Landsat 8 OLI and Sentinel-2 MSI detected differences in water quality metrics that correlated to changes in estuary mouth state (i.e., open or closed) through algorithms in Google Earth Engine (GEE). The GEE California Estuary Assessment (CEA) tool was created with partner input throughout development and culminated in a graphical user interface tailored to management needs. The CEA tool will allow the partners, the Ocean Protection Council, Moss Landing Marine Laboratories’ Central Coast Wetlands Group, the Southern California Coastal Water Research Project, and University of California Los Angeles (UCLA) and Davis (UCD), to better understand estuary dynamics and facilitate informed management decisions through satellite-based Earth observations.
Alex Gunnerson↗
Synegies Between Visible/Near-Infrared Imaging Spectrometry and the Thermal Infrared in an Urban Environment: An Evaluation of the Hyperspectral Infrared Imager (HYSPIRI) Mission
A majority of the human population lives in urban areas and as such, the quality of urban environments is becoming increasingly important to the human population. Furthermore, these areas are major sources of environmental contaminants and sinks of energy and materials. Remote sensing provides an improved understanding of urban areas and their impacts by mapping urban extent, urban composition (vegetation and impervious cover fractions), and urban radiation balance through measures of albedo, emissivity and land surface temperature (LST). Recently, the National Research Council (NRC) completed an assessment of remote sensing needs for the next decade (NRC, 2007), proposing several missions suitable for urban studies, including a visible, near-infrared and shortwave infrared (VSWIR) imaging spectrometer and a multispectral thermal infrared (TIR) instrument called the Hyperspectral Infrared Imagery (HyspIRI). In this talk, we introduce the HyspIRI mission, focusing on potential synergies between VSWIR and TIR data in an urban area. We evaluate potential synergies using an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and MODIS-ASTER (MASTER) image pair acquired over Santa Barbara, United States. AVIRIS data were analyzed at their native spatial resolutions (7.5m VSWIR and 15m TIR), and aggregated 60 m spatial resolution similar to HyspIRI. Surface reflectance was calculated using ACORN and a ground reflectance target to remove atmospheric and sensor artifacts. MASTER data were processed to generate estimates of spectral emissivity and LST using Modtran radiative transfer code and the ASTER Temperature Emissivity Separation algorithm. A spectral library of common urban materials, including urban vegetation, roofs and roads was assembled from combined AVIRIS and field-measured reflectance spectra. LST and emissivity were also retrieved from MASTER and reflectance/emissivity spectra for a subset of urban materials were retrieved from co-located MASTER and AVIRIS pixels. Fractions of Impervious, Soil, Green Vegetation (GV) and Non-photosynthetic Vegetation (NPV), were estimated using Multiple Endmember Spectral Mixture Analysis (MESMA) applied to AVIRIS data at 7.5, 15 and 60 m spatial scales. Surface energy parameters, including albedo, vegetation cover fraction, broadband emissivity and LST were also determined for urban and natural land-cover classes in the region. Fractions were validated using 1m digital photography.
Roberts, Dar A.↗
Cyber Threat Assessment Methodology for Autonomous and Remote Operations for Advanced Reactors (Conference Presentation)
The next generation of Advanced Reactors include planned capabilities for both Autonomous (operating without human interaction for a set period-of-time) and Remote (operating with human interaction from a separate physical location) Operations. Existing Nuclear Reactor architectures include a set of safety and security constraints tightly coupled with personnel policies and procedures. As Advanced Reactors are fielded with these new Autonomous and Remote operational capabilities, the architectures and associated infrastructure services and components will perceivably expand the overall attack surface and risk calculations with regards to safe and secure operations. This paper is part of an FY21 work program focused on ensuring Advanced Reactor designs are informed with threat-based guidance on design and operation of Secure Architectures with a specific focus on the deployment of Autonomous Systems in support of Advanced Reactor Operations. The next phase of this research program is to complement produce a methodology for assessment of the cyber threat against these architectures as well as a catalogue of Use Cases to support the Advanced Reactor community in their implementation of Autonomous and Remote Operations.
97 MATHEMATICS AND COMPUTING↗
Application of remote sensing for fishery resource assessment and monitoring
The author has identified the following significant results. A plot was drawn of the dolphin catch and the water discontinuities observed in the aerial photography. This plot was similar in format to the one made earlier on the white marlin catch relative to the water rips. Neither plot substantiates (as far as white marlin and dolphin are concerned) an opinion held by fishermen that better fishing may be found in the vicinity of the rips. Remotely inferred values for sea surface temperature, chlorophyll-a and turbidity were substituted for sea truth measurements in prediction models developed in previous analysis. Model performance, using the new values, was disappointing.
Savastano, K. J.↗
Estimation of intensity, footprint, and capacity of surface urban heat islands using a direction-enhanced adaptive synchronous extraction (DEASE) method
Here, the surface urban heat island (SUHI) effect, assessed through remotely sensed land surface temperature (LST), remains a focal point in urban climate research. Conventional indicators like SUHI intensity (SUHII) and footprint (SUHIF) capture peak values and spatial extent but fail to account for the cumulative thermal load—a critical dimension reflecting the total heat exposure imposed by spatially continuous warming, which directly limits a holistic assessment of ecological and societal impacts of the SUHI effect. Therefore, this study introduces an indicator termed SUHI capacity (SUHIC), designed to quantify the aggregated SUHI effect by integrating the magnitude of the warming signal across all affected areas, thereby enabling a more comprehensive evaluation of urban thermal environments. Furthermore, a direction-enhanced adaptive synchronous extraction (DEASE) method is proposed for the quantification of SUHIC. This method can dynamically identify the optimal background reference area based on the urban-rural LST gradients in various directions within the city, without relying on predefined mathematical models as previously. The results from 102 European cities first confirm that the directional variations in urban-rural LST gradients, and the DEASE method can effectively capture these distinctions for the simultaneous estimation of SUHII, SUHIF, and SUHIC. Secondly, the spatial patterns of absolute SUHIC values show strong associations with those of SUHIF (R2>0.86), while its relative values (normalized by the area of urban) align more closely with SUHII (R2 > 0.64). More importantly, SUHIC can serve as a crucial reference for assessing the urban thermal signal when SUHII and SUHIF diverge. The proposed method and framework contribute to standardizing the quantification of the SUHI effect.
Indicator↗