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At least 109 records · Page 6

Transitioning Earth Remote Sensing Data to Benefit Society: A Paradigm for a Center of Excellence

Over the past decade there has been a substantial increase in the number of Earth remote sensing satellites launched for research and operational usage and numerous others planned by the international community. These satellites have been used to varying degrees by their supporting agencies for weather and environmental monitoring, climate studies, disaster monitoring and response, and other humanitarian activities. While there are success stories on useful applications of remote sensing data, the broader use of these satellite assets by other organizations and entities has been limited for a number of reasons including lack of data services, data dissemination issues, and a general failure to engage the broader end user community with useful data access and knowledge of how to use the data and products. This paper describes some of these current limitations on the broader use of Earth remote sensing data by the international community and describes the concept of a general "Center of Excellence" to facilitate the development, transition, and utilization of these Earth remote sensing observations by the broader international community.

Transition to operations↗

A method for classifying multispectral remote sensing data using context

The paper describes a method of classifying multispectral remote sensing data using a context classifier. Because the computational requirements of the context classifier are very large, its implementation on parallel/pipelined multiprocessor systems is being investigated, so that the types of computations can be efficiently implemented. Special considerations necessary for such implementations are discussed, with particular reference to implementation on an array of Control Data Corporation Flexible Processors.

Swain, P. H.↗

Interpretation of remotely sensed data and its applications in oceanography

The methodology of interpretation of remote sensing data and its oceanographic applications are described. The elements of image interpretation for different types of sensors are discussed. The sensors utilized are the multispectral scanner of LANDSAT, and the thermal infrared of NOAA and geostationary satellites. Visual and automatic data interpretation in studies of pollution, the Brazil current system, and upwelling along the southeastern Brazilian coast are compared.

Parada, N. D. J.↗

On strategies for inverting remote sensing data

Attention is given to a number of methods for inverting remote sensing data obtained in a variety of astronomical applications. Applications include image restoration, inversion of helioseismological data to obtain the internal rotation rate of stars such as the sun, fitting of spectra (especially thermal line spectra) to grating or other dispersed observed spectra, differential emission measure analysis, and reconstruction of images derived from interferometric observations. The results consider the tradeoff between resolution and variance and the stability properties for each method and propose an inversion stragegy using the available techniques.

Jeffrey, W.↗

Machine processing of remotely sensed data; Proceedings of the Fifth Annual Symposium, Purdue University, West Lafayette, Ind., June 27-29, 1979

Papers are presented on techniques and applications for the machine processing of remotely sensed data. Specific topics include the Landsat-D mission and thematic mapper, data preprocessing to account for atmospheric and solar illumination effects, sampling in crop area estimation, the LACIE program, the assessment of revegetation on surface mine land using color infrared aerial photography, the identification of surface-disturbed features through a nonparametric analysis of Landsat MSS data, the extraction of soil data in vegetated areas, and the transfer of remote sensing computer technology to developing nations. Attention is also given to the classification of multispectral remote sensing data using context, the use of guided clustering techniques for Landsat data analysis in forest land cover mapping, crop classification using an interactive color display, and future trends in image processing software and hardware.

Tendam, I. M.↗

Bhutan Agriculture II: Creating a Graphical User Interface, Crop Mask, and Data Collection Protocol for Analysis of Rice Crop in Bhutan Using Remotely Sensed Data

Agriculture is an important sector in Bhutan, accounting for 19.63% of Bhutan’s GDP in 2020 (World Bank) while also providing livelihoods for approximately 57% of the population (World Bank, 2017). The Department of Agriculture (DoA) in Bhutan still relies on in-field reporting for crop monitoring, which is time-consuming and labour intensive. To promote efficiency in these efforts, the team partnered with the DoA, the Bhutan Foundation, and the Ugyen Wangchuck Institute of Conservation and Environmental Research (UWICER). The team, with the help of the science advisors from NASA SERVIR, expanded the crop mask created in the previous term to the whole country of Bhutan and streamlined the sampling protocols for applicability to any available crop data. The team also created a graphical user interface (GUI) which provided a visual representation of current trends and rice distribution across Bhutan. The team utilized NASA Earth observations, including Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), and Shuttle Radar Topography Mission (SRTM), as well as other Earth observations including Sentinel-1 C-band Synthetic Aperture Radar (C-SAR). This project refined the previous term’s methodology to help supplement crop monitoring and increase the frequency of data collected to aid decision-making processes with the use of remote sensing data.

Wangdrak Dorji​↗

Recent developments in lithologic mapping using remote sensing data

Major development trends noted in remote sensing scanners are toward greater spatial and spectral resolution, as well as the acquisition of data over a broader portion of the electromagnetic spectrum. Attention is presently given to representative samples of the product of two new-generation satellite sensors, the Landsat-4 Thematic Mapper and SPOT, as well as the status of airborne scanner research aimed at the exploration of multispectral data in the thermal IR wavelength region (which encompasses the diagnostic spectral features of silicates and carbonates). Testing is underway for scanners having spectral bands as narrow as 0.01 micron in the visible and near-IR, which will be capable of identifying specific minerals.

Abrams, M.↗

A conceptual design for an integrated data base management system for remote sensing data

The requirements of potential users were considered in the design of an integrated data base management system, developed to be independent of any specific computer or operating system, and to be used to support investigations in weather and climate. Ultimately, the system would expand to include data from the agriculture, hydrology, and related Earth resources disciplines. An overview of the system and its capabilities is presented. Aspects discussed cover the proposed interactive command language; the application program command language; storage and tabular data maintained by the regional data base management system; the handling of data files and the use of system standard formats; various control structures required to support the internal architecture of the system; and the actual system architecture with the various modules needed to implement the system. The concepts on which the relational data model is based; data integrity, consistency, and quality; and provisions for supporting concurrent access to data within the system are covered in the appendices.

Maresca, P. A.↗

Method of interpretation of remotely sensed data and applications to land use

Instructional material describing a methodology of remote sensing data interpretation and examples of applicatons to land use survey are presented. The image interpretation elements are discussed for different types of sensor systems: aerial photographs, radar, and MSS/LANDSAT. Visual and automatic LANDSAT image interpretation is emphasized.

Parada, N. D. J.↗

Image processing in remote sensing data analysis - The state of the art

Image analysis techniques applicable to remote sensing data and covering image models, feature detection, segmentation and classification, texture analysis, and matching are studied. Model types for characterizing images examined include random-field, mosaic, and facet models. Edge and corner detection as well as global extraction of linear features are discussed. Pixel clustering and classification are covered in addition to the regional approach to segmentation. Autocorrelation, second-order gray level probability density, and the use of primitive element statistics are discussed in relation to texture analysis. Finally, reducing the cost of (sub)imaging matching methods (e.g., pixelwise comparison of gray levels and normalized cross-correlation between two images) as well as improving match sharpness is considered.

Rosenfeld, A.↗

OhioView: Distribution of Remote Sensing Data Across Geographically Distributed Environments

Various issues associated with the distribution of remote sensing data across geographically distributed environments are presented in viewgraph form. Specific topics include: 1) NASA education program background; 2) High level architectures, technologies and applications; 3) LeRC internal architecture and role; 4) Potential GIBN interconnect; 5) Potential areas of network investigation and research; 6) Draft of OhioView data model; and 7) the LeRC strategy and roadmap.

Ramos, Calvin T.↗

Method for Identifying Probable Archaeological Sites from Remotely Sensed Data

Archaeological sites are being compromised or destroyed at a catastrophic rate in most regions of the world. The best solution to this problem is for archaeologists to find and study these sites before they are compromised or destroyed. One way to facilitate the necessary rapid, wide area surveys needed to find these archaeological sites is through the generation of maps of probable archaeological sites from remotely sensed data. We describe an approach for identifying probable locations of archaeological sites over a wide area based on detecting subtle anomalies in vegetative cover through a statistically based analysis of remotely sensed data from multiple sources. We further developed this approach under a recent NASA ROSES Space Archaeology Program project. Under this project we refined and elaborated this statistical analysis to compensate for potential slight miss-registrations between the remote sensing data sources and the archaeological site location data. We also explored data quantization approaches (required by the statistical analysis approach), and we identified a superior data quantization approached based on a unique image segmentation approach. In our presentation we will summarize our refined approach and demonstrate the effectiveness of the overall approach with test data from Santa Catalina Island off the southern California coast. Finally, we discuss our future plans for further improving our approach.

Tilton, James C.↗

Implementation and operation of three fractal measurement algorithms for analysis of remote-sensing data

Fractal geometry is increasingly becoming a useful tool for modeling natural phenomena. As an alternative to Euclidean concepts, fractals allow for a more accurate representation of the nature of complexity in natural boundaries and surfaces. The purpose of this paper is to introduce and implement three algorithms in C code for deriving fractal measurement from remotely sensed data. These three methods are: the line-divider method, the variogram method, and the triangular prism method. Remote-sensing data acquired by NASA's Calibrated Airborne Multispectral Scanner (CAMS) are used to compute the fractal dimension using each of the three methods. These data were obtained as a 30 m pixel spatial resolution over a portion of western Puerto Rico in January 1990. A description of the three methods, their implementation in PC-compatible environment, and some results of applying these algorithms to remotely sensed image data are presented.

Jaggi, S.↗

Environmental Public Health Survelliance for Exposure to Respiratory Health Hazards: A Joint NASA/CDC Project to Use Remote Sensing Data for Estimating Airborne Particulate Matter Over the Atlanta, Georgia Metropolitan Area

Describes the public health surveillance efforts of NASA, in a joint effort with the Center for Disease Control (CDC). NASA/MSFC and the CDC are partners in linking nvironmental and health data to enhance public health surveillance. The use of NASA technology creates value - added geospatial products from existing environmental data sources to facilitate public health linkages. The venture sought to provide remote sensing data for the 5-country Metro-Atlanta area and to integrate this environmental data with public health data into a local network, in an effort to prevent and control environmentally related health effects. Remote sensing data used environmental data (Environmental Protection Agency [EPA] Air Quality System [AQS] ground measurements and MODIS Aerosol Optical Depth [AOD]) to estimate airborne particulate matter over Atlanta, and linked this data with health data related to asthma. The study proved the feasibility of linking environmental data (MODIS particular matter estimates and AQS) with health data (asthma). Algorithms were developed for QC, bias removal, merging MODIS and AQS particulate matter data, as well as for other applications. Additionally, a Business Associate Agreement was negotiated for a health care provider to enable sharing of Protected Health Information.

Quattrochi, Dale A.↗

Reconstruction of atmospheric pollutant concentrations from remote sensing data - An application of distributed parameter observer theory

The reconstruction of a concentration distribution from spatially averaged and noise-corrupted data is a central problem in processing atmospheric remote sensing data. Distributed parameter observer theory is used to develop reconstructibility conditions for distributed parameter systems having measurements typical of those in remote sensing. The relation of the reconstructibility condition to the stability of the distributed parameter observer is demonstrated. The theory is applied to a variety of remote sensing situations, and it is found that those in which concentrations are measured as a function of altitude satisfy the conditions of distributed state reconstructibility.

Koda, M.↗

Applications of remote-sensing data in Alaska

Public and private agencies were introduced to the use of remotely sensed data obtained by both satellite and aircraft, and benefitted from facilities for data processing enhancement and interpretation as well as from the institute's data library. Cooperative ventures involving the performance of operational activities included assistance to the Bureau of Land Management in the suppression of wildfires; the selection of sites for power line right-of-way; the mapping of leads in sea ice; determination of portions of public lands to be allocated for small scale farming; the identification of areas for large scale farming of barley; the observation of coastal processes and sediment transport near Prudhoe Bay; the establishment of a colar infrared file of the entire state; and photomapping for geological surveys. Monitoring of the outer continental shelf environment and reindeer herds was also conducted. Institutional constraints to full utilization of satellite remote sensing in the state are explored and plans for future activites include the generation of awareness by government agencies, the training of state personnel, and improving coordination and communication with users.

Miller, J. M.↗