KEYWORDS: Geographic information systems, Network architectures, Raster graphics, Data storage, Computer networks, Data centers, Internet, Data processing, Computer architecture, Data transmission
We have witnessed the accumulation of petabyes of geospatial data in the past decades, and, currently, terabytes of data are collected every day. These geospatial data are crucial in supporting decision making and emergency response. It becomes increasingly important to deliver these datasets in a timely fashion to the decision support or emergency response systems. Network GIS provides a vehicle to facilitate this delivering process. But to deliver efficiently such large volume of data and to handle large number of concurrent users, the performance of Network GIS needs to be improved to a level that different types of applications, especially near real time applications, can be satisfied. In this circumstance, we 1) review
selected research on improving the performance of Network GIs; 2) provide insides on implementing the techniques; and 3) illustrate how to adopt the techniques in Network GIs. We expect the research and development reported here can be easily adopted by different users to
accelerate the performance of various Network GIS software and applications, as well as to support the building of spatial data infrastructure to support the sharing of heterogeneous geospatial information.
Global pollution aerosol monitoring is a very important climatic and environmental problem. It affects not only human health but also ecological systems. Because most pollution aerosols are concentrated in the atmospheric boundary layer where human, animal and vegetation live, global pollution aerosol stuides have been an important topic since about a decade ago. Recently, many new chemistry remote sensing satellite systems, such as NASA's Aura (EOS-CHEM), have been established. However, pollution aerosols in the atmospheric boundary layer cannot be detected using current remote sensing technologies. George Mason University (GMU) proposes to design scientific algorithms and technologies to monitor the atmospheric boundary layer pollution aerosols, using both satellite remote sensing measurements and ground measurements, collaborating with NASA and the United States Department of Agriculture (USDA)/Forest Services (FS). Boundary layer pollution aerosols result from industrial pollution, desert dust storms, smoke from wildfires and biomass burning, volcanic eruptions, and from other trace gases. The current and next generation satellite instruments, such as The Ozone Mapping and Profiler Suite (OMPS), Ozone Monitoring Instrument (OMI), Thermal Emission Spectrometer (TES), and High Resolution Dynamics Limb Sounder (HIRDLS) can be used for this study. Some surface measurements from USDA/FS and other agencies may also be used in this study. We will discuss critical issues for GPAM in the boundary layer using Earth observing satellite remote sensing in detail in this paper.
The Normalized Difference Vegetation Index (NDVI), provide synoptic information on environmental conditions in agricultural regions such as in Ukraine and Russia. However, past studies have raised questions whether the Maximum Value Compositing (MVC) method, used to produce time-series of NDVI, is affected by Bidirectional Reflectance Distributions (BRD), as a result of view geometry, possibly leading to erroneous NDVI values. This study uses Angular Dependence Models (ADM), derived the NOAA/NASA Pathfinder AVHRR Land (PAL) data set, to correct PAL Channels 1 & 2 reflectance for BR. MVC is used to produce ten day NDVI composites, from both the pre-BRD correct channels and the BRD corrected channels. Comparisons of the two NDVI time-series show a slight increase in NDVI for the
Virginia Access (VAccess) is a regional, remote sensing and Geographical Information Sciences project among several educational institutions. It is a prototype for regional projects in other states and other countries, and is funded by NASA's applications program. The user communities VAccess serves are the Commonwealth of Virginia and State of Maryland, local and regional users represented in a Technical Advisory Committee. Remote sensing data include global NASA and NOAA data tailored for regional applications as well as high-resolution multispectral (Landsat, MODIS, etc.), hyperspectral, LIDAR and SAR data sets. Broad beam LIDAR technology can provide canopy structure as well as other information for environmental concerns such as the state of wetlands. The data information system is based on a distributed architecture to serve remote sensing and GIS data to a variety of users via the WWW. Several remote sensing and GIS-based environmental and Earth systems science applications projects are discussed here, including flood and fire hazard mitigation, forestry, land use/land cover and epidemiology projects; as well as innovative data fusion, data access and analysis and various tools serving the users and their applications.
Many researchers consider coral reefs the 'rainforests of the oceans' because they cover such a small area and yet provide homes for literally thousands of unique marine species. A multispectral or hyperspectral remote sensing satellite, with its spectral coverage, offers iadvantages over traditional methodologies for coral reef surveying, monitoring, and mapping. This apper presents research into the suitabilty of spectral remote sensing for coral reed surveying, monitoring and mapping. This paper presents research into the suitability of spectral remote sensing for coral reef surveying, monitoring and mapping using the SeaWiFS multispectral ocean color data for illustration. We describe the information technology developed to support this research and provide an overview of the database driven web application, which was developed to allow live interaction with the data. A database of in situ observations from the ReefBase web site was used as validation data as part of this investigation. This discussion includes details on the XML representation of the satellite and in situ data and metadat. It also introduces a dynamic Java Visualization applet developed to allow the users to visually interact wiht the data. The paper concludes wiht a discussion of the suitability and additional advantages of using hyperspectral remote sensing technology for this application that exploits the full spectral characteristics of submerged coral reefs.
The multiple scattering theory which produces Doppler-like wavelength shift,even, when the source and the intervening medium are at rest with respect to the observer has been recently verified in laboratory experiments. In the experiments, light from Hg lines propagating through anisotropic plasma medium has been shown to produce redshift as well as broadening. These results may signify important developments in statistical optics as well as in observational cosmology.
Some types of clay, esp. montmorillonite, become slippery when getting wet. Clay movement is very harmful for various constructions and can also cause trouble for both wheeled and tracked vehicles in military operations at some rural areas when raining. We present a summary of a project using hyperspectral imaging in assisting earth roads construction planning and cross-country trafficability analysis. Spectral signature libraries are used to help identify materials and define those areas to be avoided, which have significant montmorillonite content. We perform a case study in this kind of application; some methods of data processing and analyzing are discussed. We also discussed the problems we met in this application. Hyperspectral sensing is a relatively new but mature technology; development of applications and corresponding analyzing procedures will be the major impetus of this technology.
Different research groups have recently studied the concept of wavelet image fusion between panchromatic and multispectral images using different approaches. In this paper, a new approach using the wavelet based method for data fusion between hyperspectral and multispectral images is presented. Using this wavelet concept of hyperspectral and multispectral data fusion, we performed image fusion between two spectral levels of a hyperspectral image and one band of multispectral image. The reconstructed image has a root mean square error of 2.8 per pixel and a signal-to- noise ratio of 36 dB. We achieved our goal of creating a composite image that has the same spectral resolution as the hyperspectral image and the same spatial resolution as the multispectral image with minimum artifacts.
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