Design and development of a fixed and a mobile data collection platform
Mobile and fixed data collection platforms for constant level balloons and remote ground locations, for platform-satellite-ground station system
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International Symposium on Remote Sensing of Environment, 13th, Ann Arbor, Mich., April 23-27, 1979, Proceedings. Volumes 1, 2 & 3
The presentations document current activities in the field of remote sensing. Papers include those concerned with data collection, processing, and analysis hardware and methodology, as well as the application of this technology to monitoring and managing the earth's resources and man's global environment. Ground-based, airborne, and spaceborne sensor systems and both manual and machine-assisted data analysis and interpretation are considered.
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Remote sensing for exploration - An overview
The use of remote sensing in resource exploration is reviewed, with emphasis placed on new developments in high spectral resolution remote-sensing techniques for mineralogic and vegetation mapping. Topics discussed include aerial photography and satellite remote sensing, concepts and principles of spectral data collection, spectral properties of rocks and minerals, spectral properties of vegetation, and botanical aspects of geochemical stress. The discussion also covers applications of Landsat multispectral scanner data to lithologic and geobotanic studies and the future development of data acquisition and data interpretation techniques.
Goetz, A. F. H.↗
International Symposium on Remote Sensing of Environment, 17th, University of Michigan, Ann Arbor, MI, May 9-13, 1983, Proceedings. Volumes 1, 2 & 3
The collection, processing, and analysis of remote-sensing data from ground-based, airborne, and spaceborne instruments for application to the monitoring and management of the earth and environment and resources are examined in reviews and reports, some in summary form. Subject areas covered include US policy and directions on remote sensing (RS); the future of terrestrial RS from space; RS of land, oceans, and atmosphere from a global perspective; RS in hydrological modeling; microprocessing technology; array processors; geobased information systems; artificial intelligence; the Shuttle imaging radar; and current results from Landsat-4. Among the specific topics discussed are RS application to hydrocarbon exploration, airborne gamma-radiation assessment of snow water equivalent, surface-vegetation-biomass modeling from AVHRR and Landsat data, Landsat imagery of Mediterranean pollution, fast two-dimensional filtering of thermal-scanner data, RS of severe convective storms, registration of rotated images by invariant moments, and the geometric accuracy of Landsat-4 Thematic-Mapper P-tapes.
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Science program for an imaging radar receiving station in Alaska. Report of the science working group
It is argued that there would be broad scientific benefit in establishing in Alaska an imaging radar receiving station that would collect data from the European Space Agency's Remote Sensing Satellite, ERS-1. This station would acquire imagery of the ice cover from the American territorial waters of the Beaufort, Chukchi, and Bering Seas. This station, in conjunction with similar stations proposed for Kiruna, Sweden, and Prince Albert, Canada would provide synoptic coverage of nearly the entire Arctic. The value of such coverage to aspects of oceanography, geology, glaciology, and botany is considered.
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Software agents for the dissemination of remote terrestrial sensing data
Remote terrestrial sensing (RTS) data is constantly being collected from a variety of space-based and earth-based sensors. The collected data, and especially 'value-added' analyses of the data, are finding growing application for commercial, government, and scientific purposes. The scale of this data collection and analysis is truly enormous; e.g., by 1995, the amount of data available in just one sector, NASA space science, will reach 5 petabytes. Moreover, the amount of data, and the value of analyzing the data, are expected to increase dramatically as new satellites and sensors become available (e.g., NASA's Earth Observing System satellites). Lockheed and other companies are beginning to provide data and analysis commercially. A critical issue for the exploitation of collected data is the dissemination of data and value-added analyses to a diverse and widely distributed customer base. Customers must be able to use their computational environment (eventually the National Information Infrastructure) to obtain timely and complete information, without having to know the details of where the relevant data resides and how it is accessed. Customers must be able to routinely use standard, widely available (and, therefore, low cost) analyses, while also being able to readily create on demand highly customized analyses to make crucial decisions. The diversity of user needs creates a difficult software problem: how can users easily state their needs, while the computational environment assumes the responsibility of finding (or creating) relevant information, and then delivering the results in a form that users understand? A software agent is a self-contained, active software module that contains an explicit representation of its operational knowledge. This explicit representation allows agents to examine their own capabilities in order to modify their goals to meet changing needs and to take advantage of dynamic opportunities. In addition, the explicit representation allows agents to advertize their capabilities and results to other agents, thereby allowing the collection of agents to reuse each others work.
Toomey, Christopher N.↗
Mission Operations Insights
The mission description Polar Operational Environmental Satellites (POES): I) Collect and disseminate worldwide meteorological and environmental data: a) Provide day and night information (AVHRR): 1) cloud cover distribution and type; 2) cloud top temperature; 3) Moisture patterns and ice/snow melt. b) Provide vertical temperature and moisture profiles of atmospheres (HIRS, AMSU, MHS. c) Measure global ozone distribution and solar UV radiation (SBUV). d) Measure proton, electro, and charged particle density to provide solar storm warnings (SEM). d) Collect environmental data (DCS): 1) Stationary platforms in remote locations; 2) Free floating platforms on buoys, balloons, migratory animals. II) Provide Search and Rescue capabilities (SARR, SARP): a) Detection and relay of distress signals. b) Has saved thousands of lives around the world.
Littman, Dave↗
Multipoint sensing by intelligent collectives
Many remote sensing applications require that multiple sensors collect data simultaneously at spatially distributed locations and their information combined in order to characterize the phenomena of interest. Several basic classes of such multipoint measurement systems may be identified. For each, centralized methods exist for combining the raw data from the various sensors. However, recent advancements have given rise to small, integrated nodes comprised of one or more miniaturized sensors, processor, wireless communications capability and power supplies. Collections of these may be deployed and self-organized into intelligent sensor networks capable of performing cooperative signal processing locally, thereby providing substantial benefits.
multipoint measurement sensor networks↗
NASA DEVELOP Students Rev Up Response to Gulf Oil Spill
After the April 20th explosion aboard the Deepwater Horizon drilling rig in the Gulf of Mexico, the world witnessed one of the worst oil spill catastrophes in global history. In an effort to mitigate the disaster, the U.S. government moved quickly to establish a unified command for responding to the spill. Some of the command's most immediate needs were to track the movement of the surface oil slick, establish a baseline measurement of pre-oil coastal ecosystem conditions, and assess potential air quality and water hazards related to the spill. To help address these needs and assist the Federal response to the disaster, NASA deployed several of its airborne and satellite research sensors to collect an unprecedented amount of remotely-sensed data over the Gulf of Mexico region. Although some of these data were shared with the public via the media, much of the NASA data on the disaster was not well known to the Gulf Coast community. The need existed to inform the general public about these datasets and help improve understanding about how NASA's science research was contributing to oil spill response and recovery. With its extensive experience conducting community-oriented remote sensing projects and close ties to organizations around Gulf of Mexico, the NASA DEVELOP National Program stood in a unique position to meet this need.
Jones, Jason B.↗
NASA Wrangler: Automated Cloud-Based Data Assembly in the RECOVER Wildfire Decision Support System
NASA Wrangler is a loosely-coupled, event driven, highly parallel data aggregation service designed to take advantageof the elastic resource capabilities of cloud computing. Wrangler automatically collects Earth observational data, climate model outputs, derived remote sensing data products, and historic biophysical data for pre-, active-, and post-wildfire decision making. It is a core service of the RECOVER decision support system, which is providing rapid-response GIS analytic capabilities to state and local government agencies. Wrangler reduces to minutes the time needed to assemble and deliver crucial wildfire-related data.
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Maximizing the Volume of Collocated Data from Two Coordinated Suborbital Platforms
Suborbital (e.g., airborne) campaigns that carry advanced remote sensing and in situ payloads provide detailed observations of atmospheric processes, but can be challenging to use when it is necessary to geographically collocate data from multiple platforms that make repeated observations of a given geographic location at different altitudes. This study reports on a data collocation algorithm that maximizes the volume of collocated data from two coordinated suborbital platforms and demonstrates its value using data from the NASA Aerosol Cloud Meteorology Interactions Over the western Atlantic Experiment (ACTIVATE) suborbital mission. A robust data collocation algorithm is critical for the success of the ACTIVATE mission goal to develop new and improved remote sensing algorithms, and quantify their performance. We demonstrate the value of these collocated data to quantify the performance of a recently developed vertically resolved lidar + polarimeter–derived aerosol particle number concentration (Na ) product, resulting in a range-normalized mean absolute deviation (NMAD) of 9% compared to in situ measurements. We also show that this collocation algorithm increases the volume of collocated ACTIVATE data by 21% compared to using only nearest-neighbor finding algorithms alone. Additional to the benefits demonstrated within this study, the data files and routines produced by this algorithm have solved both the critical collocation and the collocation application steps for researchers who require collocated data for their own studies. This freely available and open-source collocation algorithm can be applied to future suborbital campaigns that, like ACTIVATE, use multiple platforms to conduct coordinated observations, e.g., a remote sensing aircraft together with in situ data collected from suborbital platforms.
Atmosphere↗
Development and Flight Testing of an Adaptable Vehicle Health-Monitoring Architecture
Development and testing of an adaptable wireless health-monitoring architecture for a vehicle fleet is presented. It has three operational levels: one or more remote data acquisition units located throughout the vehicle; a command and control unit located within the vehicle; and a terminal collection unit to collect analysis results from all vehicles. Each level is capable of performing autonomous analysis with a trained adaptable expert system. The remote data acquisition unit has an eight channel programmable digital interface that allows the user discretion for choosing type of sensors; number of sensors, sensor sampling rate, and sampling duration for each sensor. The architecture provides framework for a tributary analysis. All measurements at the lowest operational level are reduced to provide analysis results necessary to gauge changes from established baselines. These are then collected at the next level to identify any global trends or common features from the prior level. This process is repeated until the results are reduced at the highest operational level. In the framework, only analysis results are forwarded to the next level to reduce telemetry congestion. The system's remote data acquisition hardware and non-analysis software have been flight tested on the NASA Langley B757's main landing gear.
Woodard, Stanley E.↗
The NASA NEESPI Data Portal: Products, Information, and Services
Studies have indicated that land cover and use changes in Northern Eurasia influence global climate system. However, the procedures are not fully understood and it is challenging to understand the interactions between the land changes in this region and the global climate. Having integrated data collections form multiple disciplines are important for studies of climate and environmental changes. Remote sensed and model data are particularly important die to sparse in situ measurements in many Eurasia regions especially in Siberia. The NASA GES DISC (Goddard Earth Sciences Data and Information Services Center) NEESPI data portal has generated infrastructure to provide satellite remote sensing and numerical model data for atmospheric, land surface, and cryosphere. Data searching, subsetting, and downloading functions are available. ONe useful tool is the Web-based online data analysis and visualization system, Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure), which allows scientists to assess easily the state and dynamics of terrestrial ecosystems in Northern Eurasia and their interactions with global climate system. Recently, we have created a metadata database prototype to expand the NASA NEESPI data portal for providing a venue for NEESPI scientists fo find the desired data easily and leveraging data sharing within NEESPI projects. The database provides product level information. The desired data can be found through navigation and free text search and narrowed down by filtering with a number of constraints. In addition, we have developed a Web Map Service (WMS) prototype to allow access data and images from difference data resources.
Shen, Suhung↗
Coastal California Water Resources II: Utilizing NASA Earth Observations to Detect and Assess the Impacts of Estuarine Breach Events for Improved Coastal Wetland Monitoring and Management
Estuaries are dynamic environments that provide a host of vital ecosystem services. California’s Marine Life Protection Act protects such ecosystems by creating Marine Protected Areas. California has approximately 440,000 acres of estuarine habitats 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 in order 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 Water Difference Index 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). The team’s California Estuary Assessment (CEA) tool in Google Earth Engine was successful in analyzing estuary mouth state, inundation, and water quality. It was most effective when breach events were larger than 10 meters in resolution, water surface was smooth, and imagery was unimpeded by algae or sun glint. The CEA tool will allow the partners, the Ocean Protection Council, Central Coast Wetlands Group, Southern California Coastal Water Research Project, and University of California Los Angeles (UCLA) and Davis (UCD), to better understand estuary dynamics and more effectively conduct in situ estuary monitoring.
Sarah Payne↗
Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Using Neural Networks
A number of satellite sensor systems will collect large data sets of the Earth's surface during NASA's Earth Observing System (EOS) era. Efforts are being made to develop efficient algorithms that can incorporate a wide variety of spectral data and ancillary data in order to extract vegetation variables required for global and regional studies of ecosystem processes, biosphere-atmosphere interactions, and carbon dynamics. These variables are, for the most part, continuous (e.g. biomass, leaf area index, fraction of vegetation cover, vegetation height, vegetation age, spectral albedo, absorbed photosynthetic active radiation, photosynthetic efficiency, etc.) and estimates may be made using remotely sensed data (e.g. nadir and directional optical wavelengths, multifrequency radar backscatter) and any other readily available ancillary data (e.g., topography, sun angle, ground data, etc.). Using these types of data, neural networks can: 1) provide accurate initial models for extracting vegetation variables when an adequate amount of data is available; 2) provide a performance standard for evaluating existing physically-based models; 3) invert multivariate, physically based models; 4) in a variable selection process, identify those independent variables which best infer the vegetation variable(s) of interest; and 5) incorporate new data sources that would be difficult or impossible to use with conventional techniques. In addition, neural networks employ a more powerful and adaptive nonlinear equation form as compared to traditional linear, index transformations, and simple nonlinear analyses. These neural networks attributes are discussed in the context of the authors' investigations of extracting vegetation variables of ecological interest.
Kimes, Daniel S.↗
Application of computer processed multispectral data to the discrimination of land collapse (sinkhole) prone areas in Florida
Data were collected near Bartow, Florida, for the purpose of studying land collapse phenomena using remote sensing techniques. Data obtained using the multispectral scanner system consisted of various combinations of 18 spectral bands ranging from 0.4-14.0 microns and several types of photography. The multispectral data were processed on a special-purpose analog computer in order to detect moisture-stressed vegetation and to enhance terrain surface temperatures. The processed results were printed on film to show the patterns of distribution of the proposed hydrogeologic indicators.
Coker, A. E.↗
Radar Ocean Wave Spectrometer (ROWS) preprocessing program (PREROWS2.EXE). User's manual and program description
This Technical Memorandum is a user's manual with additional program documentation for the computer program PREROWS2.EXE. PREROWS2 works with data collected by an ocean wave spectrometer that uses radar (ROWS) as an active remote sensor. The original ROWS data acquisition subsystem was replaced with a PC in 1990. PREROWS2.EXE is a compiled QuickBasic 4.5 program that unpacks the recorded data, displays various variables, and provides for copying blocks of data from the original 8mm tape to a PC file.
Vaughn, Charles R.↗
Polarimetric remote sensing of sea ice in the Beaufort Sea
Data were collected in March 1988 with the Jet Propulsion Laboratory (JPL) polarimetric airborne synthetic aperture radar (SAR). Full covariance matrices were obtained from the scattering data with proper consideration of the polarimetric calibration. Images were processed to form the sea ice scenes in the Beaufort sea. These scenes contain various ice types including thin ice, thick first-year ice, and multiyear ice. Sea ice was modeled as a layer random medium and the polarimetric backscattering coefficients were calculated under the distorted Born approximation with effective permittivities obtained from the strong fluctuation theory. Sea ice signatures remotely observed by the JPL SAR over the Beaufort sea ice scenes were interpreted by comparing their behavior at different incident angles with results obtained from the theoretical model.
Nghiem, S. V.↗