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NASA NTRS2004

Merging the MODIS and NESDIS Monthly Snow-Cover Records to Study Decade-Scale Changes in Northern Hemisphere Snow Cover

A decade-scale record of Northern Hemisphere snow cover has been available from the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite Data and Information Service (NESDIS) and has been reconstructed and validated by Rutgers University following adjustments for inconsistencies that were discovered in the early years of the data set. This record provides weekly, monthly (and, in recent years, daily) snow cover from 1966 to the present for the Northern Hemisphere. With the December 1999 launch of NASA's Earth observing System (EOS) Terra satellite, snow maps are being produced globally, using automated algorithms, on a daily, weekly and monthly basis from the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument. The resolution of the MODIS monthly snow maps (0.05deg or about 5 km) is an improvement over that of the NESDIS-derived monthly snow maps (>approx.10 km) the maps, it is necessary to study the datasets carefully to determine if it is possible to merge the datasets into a continuous record. The months in which data are available for both the NESDIS and MODIS maps (March 2000 to the present) will be compared quantitatively to analyze differences in North American and Eurasian snow cover. Results from the NESDIS monthly maps show that for North America (including all 12 months), there is a trend toward slightly less snow cover in each succeeding decade. Interannual snow-cover extent has varied significantly since 2000 as seen in both the NESDIS and MODIS maps. As the length of the satellite record increases through the MODIS era, and into the National Polar-orbiting Environmental Satellite System (NPOESS) era, it should become easier to identify trends in areal extent of snow cover, if present, that may have climatic significance. Thus it is necessary to analyze the validity of merging the NESDIS and MODIS, and, in the future, the NPOESS datasets for determination of long-term continuity in measurement of Northern Hemisphere snow cover.

Hall, Dorothy K.↗

NASA NTRS2004

Tree-mendous Timber Evaluation

Funded and administered by NASA, the Affiliated Research Center (ARC) program transfers geospatial technologies from the Space Agency and participating universities to commercial companies, non-profit and trade organizations, and tribal governments. The origins of the ARC program date back to 1988, when NASA's Stennis Space Center initiated the Visiting Investigator Program to bring industry closer to spatial information technologies. The success of this trial program led to an expansion into the ARC program, whose goal is to enhance competitiveness of U.S. industries through more efficient use of remote sensing and related technologies. NASA's ARC program served as the foundation for the development of International Hardwood Resources, which then grew into Falcon Informatics with the acquisition of a technology from a European software company and a change of business models. Doylestown, Pennsylvania-based Falcon Informatics is now a world-leading information services company that combines in-depth timber industry experience with state-of-the-art software to serve the needs of national governments, international paper companies, and timber-investment management organizations.

Source record↗

NASA NTRS2004

Distributive On-line Processing, Visualization and Analysis System for Gridded Remote Sensing Data

The ability to use data stored in the current Earth Observing System (EOS) archives for studying regional or global phenomena is highly dependent on having a detailed understanding of the data's internal structure and physical implementation. Gaining this understanding and applying it to data reduction is a time- consuming task that must be undertaken before the core investigation can begin. This is an especially difficult challenge when science objectives require users to deal with large multi-sensor data sets that are usually of different formats, structures, and resolutions, for example, when preparing data for input into modeling systems. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has taken a major step towards meeting this challenge by developing an infrastructure with a Web interface that allows users to perform interactive analysis online without downloading any data, the GES-DISC Interactive Online Visualization and Analysis Infrastructure or "Giovanni." Giovanni provides interactive, online, analysis tools for data users to facilitate their research. There have been several instances of this interface created to serve TRMM users, Aerosol scientists, Ocean Color and Agriculture applications users. The first generation of these tools support gridded data only. The user selects geophysical parameters, area of interest, time period; and the system generates an output on screen in a matter of seconds. The currently available output options are: Area plot averaged or accumulated over any available data period for any rectangular area; Time plot time series averaged over any rectangular area; Time plots image view of any longitude-time and latitude-time cross sections; ASCII output for all plot types; Image animation for area plot. In the future, we will add correlation plots, GIS-compatible outputs, etc. This allow user to focus on data content (i.e. science parameters) and eliminate the need for expensive learning, development and processing tasks that are redundantly incurred by an archive's user community. The current implementation utilizes the GrADS-DODS Server (GDS), a stable, secure data server that provides subsetting and analysis services across the Internet for any GrADS-readable dataset. The subsetting capability allows users to retrieve a specified temporal and/or spatial subdomain from a large dataset, eliminating the need to download everything simply to access a small relevant portion of a dataset. The analysis capability allows users to retrieve the results of an operation applied to one or more datasets on the server. In our case, we use this approach to read pre-processed binary files and/or to read and extract the needed parts from HDF or HDF-EOS files. These subsets then serve as inputs into GrADS processing and analysis scripts. It can be used in a wide variety of Earth science applications: climate and weather events study and monitoring; modeling. It can be easily configured for new applications.

Leptoukh, G.↗

NASA NTRS2005

Automated Wildfire Detection Through Artificial Neural Networks

Wildfires have a profound impact upon the biosphere and our society in general. They cause loss of life, destruction of personal property and natural resources and alter the chemistry of the atmosphere. In response to the concern over the consequences of wildland fire and to support the fire management community, the National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite, Data and Information Service (NESDIS) located in Camp Springs, Maryland gradually developed an operational system to routinely monitor wildland fire by satellite observations. The Hazard Mapping System, as it is known today, allows a team of trained fire analysts to examine and integrate, on a daily basis, remote sensing data from Geostationary Operational Environmental Satellite (GOES), Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors and generate a 24 hour fire product for the conterminous United States. Although assisted by automated fire detection algorithms, N O M has not been able to eliminate the human element from their fire detection procedures. As a consequence, the manually intensive effort has prevented NOAA from transitioning to a global fire product as urged particularly by climate modelers. NASA at Goddard Space Flight Center in Greenbelt, Maryland is helping N O M more fully automate the Hazard Mapping System by training neural networks to mimic the decision-making process of the frre analyst team as well as the automated algorithms.

Miller, Jerry↗

NASA NTRS2005

NASA GES DISC On-line Visualization and Analysis System for Gridded Remote Sensing Data

The ability to use data stored in the current NASA Earth Observing System (EOS) archives for studying regional or global phenomena is highly dependent on having a detailed understanding of the data's internal structure and physical implementation. Gaining this understanding and applying it to data reduction is a time-consuming task that must be undertaken before the core investigation can begin. This is an especially difficult challenge when science objectives require users to deal with large multi-sensor data sets that are usually of different formats, structures, and resolutions. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has taken a major step towards meeting this challenge by developing an infrastructure with a Web interface that allows users to perform interactive analysis online without downloading any data, the GES-DISC Interactive Online Visualization and Analysis Infrastructure or "Giovanni." Giovanni provides interactive, online, analysis tools for data users to facilitate their research. There have been several instances of this interface created to serve TRMM users, Aerosol scientists, Ocean Color and Agriculture applications users. The first generation of these tools support gridded data only. The user selects geophysical parameters, area of interest, time period; and the system generates an output on screen in a matter of seconds. The currently available output options are: Area plot averaged or accumulated over any available data period for any rectangular area; Time plot time series averaged over any rectangular area; Hovmoller plots image view of any longitude-time and latitude-time cross sections; ASCII output for all plot types; Image animation for area plot. Another analysis suite deals with parameter intercomparison: scatter plots, temporal correlation maps, GIs-compatible outputs, etc. This allow user to focus on data content (i.e. science parameters) and eliminate the need for expensive learning, development and processing tasks that are redundantly incurred by an archive's user community. The current implementation utilizes the GrADS-DODS Server (GDS), and provides subsetting and analysis services across the Internet for any GrADS-readable dataset. The subsetting capability allows users to retrieve a specified temporal and/or spatial subdomain from a large dataset, eliminating the need to download everything simply to access a small relevant portion of a dataset. The analysis capability allows users to retrieve the results of an operation applied to one or more datasets on the server. We use this approach to read pre-processed binary files and/or to read and extract the needed parts directly from HDF or HDF-EOS files. These subsets then serve as inputs into GrADS analysis scripts. It can be used in a wide variety of Earth science applications: climate and weather events study and monitoring; modeling. It can be easily configured for new applications.

Leptoukh, Gregory G.↗

NASA NTRS2005

NASA Remote Sensing Data in Earth Sciences: Processing, Archiving, Distribution, Applications at the GES DISC

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of the major Distributed Active Archive Centers (DAACs) archiving and distributing remote sensing data from the NASA's Earth Observing System. In addition to providing just data, the GES DISC/DAAC has developed various value-adding processing services. A particularly useful service is data processing a t the DISC (i.e., close to the input data) with the users' algorithms. This can take a number of different forms: as a configuration-managed algorithm within the main processing stream; as a stand-alone program next to the on-line data storage; as build-it-yourself code within the Near-Archive Data Mining (NADM) system; or as an on-the-fly analysis with simple algorithms embedded into the web-based tools (to avoid downloading unnecessary all the data). The existing data management infrastructure at the GES DISC supports a wide spectrum of options: from data subsetting data spatially and/or by parameter to sophisticated on-line analysis tools, producing economies of scale and rapid time-to-deploy. Shifting processing and data management burden from users to the GES DISC, allows scientists to concentrate on science, while the GES DISC handles the data management and data processing at a lower cost. Several examples of successful partnerships with scientists in the area of data processing and mining are presented.

Leptoukh, Gregory G.↗

NASA NTRS2005

Giovanni: A System for Rapid Access, Visualization and Analysis of Earth Science Data Online

Collecting data and understanding data structures traditionally are the first steps that a user must take, before the core investigation can begin. This is a time-consuming and challenging task, especially when science objectives require users to deal with large multi-sensor data that are usually in different formats and internal structures. The Goddard Earth Sciences Data and Information Services Center (GES DISC) has created the GES DISC Interactive Online Visualization and ANalysis Infrastructure, Giovanni, to enable Web-based visualization and analysis of satellite remotely sensed meteorological, oceanographic, and hydrologic data sets, without users having to download data. The current operational Giovanni interfaces provide the capability to process a number of important satellite measurements, such as (1) ozone and other trace gases from TOMS, OMI, HALOE, and MLS; (2) air temperature, water vapor, and geopotential height from AIRS; (3) aerosols from MODIS TerrdAqua, and GOCART model; (4) precipitation from TRMM and ground measurements; (5) chlorophyll and other ocean color products from SeaWiFS and MODIS Aqua; and (6) sea surface temperature from MODIS Aqua. Depending on the input data structure, the system provides simple statistical analysis and creates time-averaged area plot, area-averaged time series, animations, Hovmoller latitude vs. time and longitude vs. time plots, as well as vertical profiles. The inter-comparison interfaces allow a user to compare observations from different instruments, to conduct anomaly analysis, and to study basic relationships between physical parameters. Giovanni handles data with different temporal and spatial resolutions and, thus, enables both regional and global long-term climate research and short-term special events investigation, as well as data validations and assessments. Because of its simplicity of usage, Giovanni is powerful and versatile, able to assist a wide range of users, from the discipline scientists conducting preliminary research in various fields, to students in the classroom learning about weather, climate, and other natural phenomena. Giovanni can be accessed from: http://disc. esfc.nasa.gov/techlab/giovanni/index.shtml

Shen, S.↗

NASA NTRS2005

Invasive Species Forecasting System: A Decision Support Tool for the U.S. Geological Survey: FY 2005 Benchmarking Report v.1.6

The National Institute of Invasive Species Science (NIISS), through collaboration with NASA's Goddard Space Flight Center (GSFC), recently began incorporating NASA observations and predictive modeling tools to fulfill its mission. These enhancements, labeled collectively as the Invasive Species Forecasting System (ISFS), are now in place in the NIISS in their initial state (V1.0). The ISFS is the primary decision support tool of the NIISS for the management and control of invasive species on Department of Interior and adjacent lands. The ISFS is the backbone for a unique information services line-of-business for the NIISS, and it provides the means for delivering advanced decision support capabilities to a wide range of management applications. This report describes the operational characteristics of the ISFS, a decision support tool of the United States Geological Survey (USGS). Recent enhancements to the performance of the ISFS, attained through the integration of observations, models, and systems engineering from the NASA are benchmarked; i.e., described quantitatively and evaluated in relation to the performance of the USGS system before incorporation of the NASA enhancements. This report benchmarks Version 1.0 of the ISFS.

Stohlgren, Tom↗

NASA NTRS2006

A-Train Data Depot: Integrating and Visualizing Atmospheric Measurements Along the A-Train Tracks

The succession of US and international satellites that follow each other, seconds to minutes apart, across the local afternoon equator crossing is called the ATrain. The A-Train consists of the following satellites, in order of equator crossing: OCO, EOS Aqua, CloudSat, CALIPSO, PARASOL, and EOS Aura. Flying in such formation increases the number of observations, validates observations, and enables coordination between science observations, resulting in a more complete virtual science platform (Kelly, 2000) The goal of this project is to create the first ever A-Train virtual data portal/center, the A-Train Data Depot, to process, archive, access, visualize, analyze and correlate distributed atmosphere measurements from various A-Train instruments along A-Train tracks. The A-Train Data Depot (ATDD) will enable the free movement of remotely located A-Train data so that they are combined to create a consolidated vertical view of the Earth s Atmosphere along the A-Train tracks. Once the infrastructure of the ATDD is in place, it will be easily evolved to serve data from all A-Train data measurements: one stop shopping. The innovative approach of analyzing and visualizing atmospheric profiles along the platforms track (i.e., time) will be accommodated by reusing the GSFC Atmospheric Composition Data and Information Services Center (ACDISC) visualization and analysis tool, GIOVANNI, existing data reduction tools, on-line archwing for fast data access, and Cooperative Institute for Research in the Atmosphere (CRA) data co-registration tools. Initial measurements utilized include CALIPSO lidar backscatter, CloudSat radar reflectivity, clear air relative humidity, water vapor and temperature from AIRS, and cloud properties and aerosols from both MODIS. This will be followed by associated measurements from MLS, OMI, HIRDLS, and TES. Given the independent nature of instrument/platform development, the ATDD project has been met with many interesting challenges that, once resolved, will provide a much greater understanding of the relative flight dynamics and data co-registration of the suite of A-Train instruments, thus greatly increasing the accuracy of A-Train data analysis. Some of these challenges will be discussed. The project s resulting visualizations and analysis illustrate the importance of managing data so that measurements from various missions can be combined to enhance the understanding of the atmosphere. A-Train data management coordination, as performed here, is extremely significant in facilitating the A-Train science of clouds, precipitation, aerosol and chemistry.

Kempler, Steven↗

NASA NTRS2007

Online Analysis Enhances Use of NASA Earth Science Data

Giovanni, the Goddard Earth Sciences Data and Information Services Center (GES DISC) Interactive Online Visualization and Analysis Infrastructure, has provided researchers with advanced capabilities to perform data exploration and analysis with observational data from NASA Earth observation satellites. In the past 5-10 years, examining geophysical events and processes with remote-sensing data required a multistep process of data discovery, data acquisition, data management, and ultimately data analysis. Giovanni accelerates this process by enabling basic visualization and analysis directly on the World Wide Web. In the last two years, Giovanni has added new data acquisition functions and expanded analysis options to increase its usefulness to the Earth science research community.

Acker, James G.↗

NASA NTRS2006

A-Train Data Depot: Integrating, Visualizing, and Extracting Cloudsat, CALIPSO, MODIS, and AIRS Atmospheric Measurements Along the A-Train Tracks

The succession of US and international Earth observing satellites that follow each other, seconds to minutes apart, across the local afternoon equator crossing is called the ATrain. The A-Train consists of the following satellites, in order of equator crossing: OCO, EOS Aqua, CloudSat, CALIPSO, PARASOL, and EOS Aura. Flying in such formation increases the number of observations, validates observations, and enables coordination between science observations, resulting in a more complete virtual science platform (Kelly, 2000). The goal of this project is to create the first ever A-Train virtual data portal/center, the A-Train Data Depot (ATDD), to process, archive, access, visualize, analyze and correlate distributed atmosphere measurements from various A-Train instruments along A-Train tracks. The ATDD will enable the free movement of remotely located A-Train data so that they are combined to create a consolidated vertical view of the Earth's Atmosphere along the A-Train tracks. Once the infrastructure of the ATDD is in place, it will be easily evolved to serve data from all A-Train data measurements: one stop shopping. The innovative approach of analyzing and visualizing atmospheric profiles along the platforms track (i.e., time) will be accommodated by reusing the GSFC Atmospheric Composition Data and Information Services Center (ACDISC) visualization and analysis tool, GIOVANNI, existing data reduction tools, on-line archiving for fast data access, access to remote data without unnecessary data transfers, and data retrieval by users finding data desirable for further study. Initial measurements utilized include CALIPSO lidar backscatter, CloudSat radar reflectivity, clear air relative humidity, water vapor and temperature from AIRS, and cloud properties and aerosols from both MODIS. This will be foilowed by associated measurements from TVILS, =MI, HIRDLS, sad TES. Given the independent nature of instrumentlplatform development, the ATDD project has been met with many interesting challenges that, once resolved, will provide a much greater understanding of the relative flight dynamics and data co-registration of the suite of A-Train instruments, thus greatly increasing the accuracy of A-Train data analysis. Some of these challenges will be illustrated and discussed. The project's early visualizations and analysis efforts illustrate the importance of managing data so that measurements from various missions can be combined to enhance the understanding of the atmosphere. A-Train data management coordination, as performed here, is extremely significant in facilitating the A-Train science of clouds, precipitation, aerosol and chemistry.

Kempler, Steven↗

NASA NTRS2007

Application of NASA Giovanni to Coastal Zone Remote Sensing Search

The Goddard Earth Sciences Data and Information Services Center (GES DISC) Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) provides rapid access to, and enables effective utilization of, remotely-sensed data that are applicable to investigations of coastal environmental processes. Data sets in Giovanni include precipitation data from the Tropical Rainfall Measuring Mission (TRMM), particularly useful for coastal storm investigations; ocean color radiometry data from the Sea-viewing Wide Field-of-view Sensor (SeaWIFS) and Moderate Resolution Imaging Spectroradiometer (MODIS), useful for water quality evaluation, phytoplankton blooms, and terrestrial-marine interactions; and atmospheric data from MODIS and the Advanced Infrared Sounder (AIRS), providing the capability to characterize atmospheric variables. Giovanni provides a simple interface allowing discovery and analysis of environmental data sets with accompanying graphic visualizations. Examples of Giovanni investigations of the coastal zone include hurricane and storm impacts, hydrologically-induced phytoplankton blooms, chlorophyll trend analysis, and dust storm characterization. New and near-future capabilities of Giovanni will be described.

Acker, James↗

NASA NTRS2007

Application of NASA Giovanni to Coastal Zone Remote Sensing Research

Acker, James↗

NASA NTRS2007

Spatial Aspects of Multi-Sensor Data Fusion: Aerosol Optical Thickness

The Goddard Earth Sciences Data and Information Services Center (GES DISC) investigated the applicability and limitations of combining multi-sensor data through data fusion, to increase the usefulness of the multitude of NASA remote sensing data sets, and as part of a larger effort to integrate this capability in the GES-DISC Interactive Online Visualization and Analysis Infrastructure (Giovanni). This initial study focused on merging daily mean Aerosol Optical Thickness (AOT), as measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, to increase spatial coverage and produce complete fields to facilitate comparison with models and station data. The fusion algorithm used the maximum likelihood technique to merge the pixel values where available. The algorithm was applied to two regional AOT subsets (with mostly regular and irregular gaps, respectively) and a set of AOT fields that differed only in the size and location of artificially created gaps. The Cumulative Semivariogram (CSV) was found to be sensitive to the spatial distribution of gap areas and, thus, useful for assessing the sensitivity of the fused data to spatial gaps.

Leptoukh, Gregory↗

NASA NTRS2008

The NPOESS Preparatory Project (NPP) Science Data Segment (SDS) Data Depository and Distribution Element (SD3E) System Architecture

The National Polar-orbiting Operational Environmental Satellite System (NPOESS), the U.S. Government's future low-Earth orbiting satellite system, will monitor global weather and environmental conditions. Serving as a risk reduction for NPOESS, the NPOESS Preparatory Project (NPP) will provide remotely sensed atmospheric, land, ocean, ozone, and sounder data that will serve the meteorological and global climate change scientific communities. The National Aeronautics and Space Administration (NASA) NPP Science Data Segment's (SDS) primary role is to independently assess the quality of the NPP science and environmental data records for their ability to support climate research. The SDS is composed of nine elements; an input element that receives data from the operational agencies and acts as a buffer, a calibration analysis element, five elements devoted to measurement based quality assessment, an element used to test algorithmic improvements, and an element that provides overall science direction. Each element requires a set of sensor specific science data products for their evaluation. There are four NPP sensors that will be flown on the NPP observatory. They are the Visible Infrared Imagining Radiometer Suite (VIIRS), the Advanced Technology Microwave Sounder (ATMS), the Cross-Track Infrared Sounder (CrIS), and the Ozone Mapper/Profiler Suite (OMPS). It is estimated that these four sensors combined will make daily data requests for approximately six terabytes of NPP science products from the operational data providers. As a result, issues associated with duplicate data requests, data transfers of large volumes of diverse products, and data transfer failures raised concerns with respect to the network traffic and bandwidth consumption. Therefore, a central data broker system for receiving and buffering data requests and data products for the SDS was developed. The data element for this system is called the SDS Data Depository and Distribution Element (SD3E). It supports science mission data assessment by assuring the timely and validated acquisition and subsequent transfer of the NPP Science Mission data to the SDS Elements and NPP Science Team. The six science elements that interface with the SD3E span across the NASA Goddard Space Flight Center (GSFC), the NASA Jet Propulsion Laboratory (JPL), and the University of Wisconsin. As the primary communication vehicle for the science elements and science team, the SD3E has an interface to the operational data providers: National Environment Satellite, Data, and Information Service (NESDIS) Interface Data Processing System (IDPS) and the National Oceanic Atmospheric Administration's (NOAA) Comprehensive Large Array-data Stewardship system (CLASS) Archive Data System (ADS), that are responsible for product generation and archive and distribution respectively. The SD3E is designed to be a semi-customizable and semi-automated system. This system is designed to provide flexibility and ease of use for the science users in accessing the latest data products by creating a rolling data cache that temporarily stores the products locally before transferring the data to the SDS Measurement based elements for the land, ocean, atmosphere, sounder, and ozone. This paper describes the design and architecture of one of the nine SDS elements, the SD3E, and how this system has provided a mechanism for efficient data exchange, how it has helped in alleviating some of the network traffic and usage, and how it has contributed to reducing operational costs.

Ho, Evelyn L.↗

NASA NTRS2007

Using Selection Pressure as an Asset to Develop Reusable, Adaptable Software Systems

The Goddard Earth Sciences Data and Information Services Center (GES DISC) at NASA has over the years developed and honed several reusable architectural components for supporting large-scale data centers with a large customer base. These include a processing system (S4PM) and an archive system (S4PA) based upon a workflow engine called the Simple Scalable Script based Science Processor (S4P) and an online data visualization and analysis system (Giovanni). These subsystems are currently reused internally in a variety of combinations to implement customized data management on behalf of instrument science teams and other science investigators. Some of these subsystems (S4P and S4PM) have also been reused by other data centers for operational science processing. Our experience has been that development and utilization of robust interoperable and reusable software systems can actually flourish in environments defined by heterogeneous commodity hardware systems the emphasis on value-added customer service and the continual goal for achieving higher cost efficiencies. The repeated internal reuse that is fostered by such an environment encourages and even forces changes to the software that make it more reusable and adaptable. Allowing and even encouraging such selective pressures to software development has been a key factor In the success of S4P and S4PM which are now available to the open source community under the NASA Open source Agreement

Berrick, Stephen↗

NASA NTRS2008

Accessing and Utilizing Remote Sensing Data for Vectorborne Infectious Diseases Surveillance and Modeling

Background: The transmission of vectorborne infectious diseases is often influenced by environmental, meteorological and climatic parameters, because the vector life cycle depends on these factors. For example, the geophysical parameters relevant to malaria transmission include precipitation, surface temperature, humidity, elevation, and vegetation type. Because these parameters are routinely measured by satellites, remote sensing is an important technological tool for predicting, preventing, and containing a number of vectorborne infectious diseases, such as malaria, dengue, West Nile virus, etc. Methods: A variety of NASA remote sensing data can be used for modeling vectorborne infectious disease transmission. We will discuss both the well known and less known remote sensing data, including Landsat, AVHRR (Advanced Very High Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer), TRMM (Tropical Rainfall Measuring Mission), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), EO-1 (Earth Observing One) ALI (Advanced Land Imager), and SIESIP (Seasonal to Interannual Earth Science Information Partner) dataset. Giovanni is a Web-based application developed by the NASA Goddard Earth Sciences Data and Information Services Center. It provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data. After remote sensing data is obtained, a variety of techniques, including generalized linear models and artificial intelligence oriented methods, t 3 can be used to model the dependency of disease transmission on these parameters. Results: The processes of accessing, visualizing and utilizing precipitation data using Giovanni, and acquiring other data at additional websites are illustrated. Malaria incidence time series for some parts of Thailand and Indonesia are used to demonstrate that malaria incidences are reasonably well modeled with generalized linear models and artificial intelligence based techniques. Conclusions: Remote sensing data relevant to the transmission of vectorborne infectious diseases can be conveniently accessed at NASA and some other websites. These data are useful for vectorborne infectious disease surveillance and modeling.

Kiang, Richard↗

NASA NTRS2008

Mirador: A Simple, Fast Search Interface for Remote Sensing Data

A major challenge for remote sensing science researchers is searching and acquiring relevant data files for their research projects based on content, space and time constraints. Several structured query (SQ) and hierarchical navigation (HN) search interfaces have been develop ed to satisfy this requirement, yet the dominant search engines in th e general domain are based on free-text search. The Goddard Earth Sci ences Data and Information Services Center has developed a free-text search interface named Mirador that supports space-time queries, inc luding a gazetteer and geophysical event gazetteer. In order to compe nsate for a slightly reduced search precision relative to SQ and HN t echniques, Mirador uses several search optimizations to return result s quickly. The quick response enables a more iterative search strateg y than is available with many SQ and HN techniques.

Lynnes, Christopher↗