A practical implementation for a data dictionary in an environment of diverse data sets

Karla K. Sprenger; Dana M. Larsen

Home/Archive/USGS

USGS · 70113750

A practical implementation for a data dictionary in an environment of diverse data sets

Karla K. Sprenger·Dana M. Larsen

View original source ↗Export metadata ↓

Abstract

The need for a data dictionary database at the U.S. Geological Survey's EROS Data Center (EDC) was reinforced with the Earth Observing System Data and Information System (EOSDIS) requirement for consistent field definitions of data sets residing at more than one archive center. The EDC requirement addresses the existence of multiple sets with identical field definitions using various naming conventions. The EDC is developing a data dictionary database to accomplish the following foals: to standardize field names for ease in software development; to facilitate querying and updating of the date; and to generate ad hoc reports. The structure of the EDC electronic data dictionary database supports different metadata systems as well as many different data sets. A series of reports is used to keep consistency among data sets and various metadata systems.

Explore related subjects

Pecora 12 Symposium Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Karla K. Sprenger, Dana M. Larsen. 1993. A practical implementation for a data dictionary in an environment of diverse data sets. https://pubs.usgs.gov/publication/70113750

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS1993

Identification of requirements and sources for global digital topographic data

Many of the physical processes being studied by global change researchers are affects by land surface topography and consequently topographic data are an important requirement for these investigations. Remotely sensed data, especially those that will be collected by the instruments of the Earth Observing System, require significant correction to remove topographic effects. Although some requirements are met by existing topographic data, there are serious data shortages that will affect global change science. The interdisciplinary and multi temporal natural of global change research requires that remotely sensed data be processed using a consistent, highly accurate global topographic database so that information extracted from these data for different areas and times can be compared quantitively. Cartographic and remote sensing sources for the generation of new topographic data exist or are planned and will be helpful for fulfilling these requirements. More consistent use of accuracy statement terminology by data users and producers is necessary to better compare the requirements with existing or future data sets.

Pecora 12 Symposium↗

USGS1993

Identifying high production, low production and degraded rangelands in Senegal with normalized difference vegetation index data

Seasonal herbaceous vegetation production on Senegal's native rangelands exhibits high spatial and temporal variability. This variability can be monitored using normalized difference vegetation index (NDVI) data computed from 1-km resolution Advanced Very High Resolution Radiometer (AVHRR) image data. Although annual fluctuations in rainfall account for some of the variability, numerous long-term production patterns are evident in the AVHRR time-series data. Different n productivity reflect variations in the region's climate, topography, soils, and land use. Areas of overgrazing and intensive cultivation have caused long-term soil and vegetation degradation. Rangelands of high and low productivity, and degraded rangelands were identified using NDVI. Time-series image data from 1987 though 1992 were used to map relative rangeland productivity. The results were compared to detailed resource maps on soils, vegetation and land use. Much of the variation in rangeland productivity correlated well to the known distribution of resources. The study developed an approach that identified a number of areas of degraded soils and low vegetation production.

Pecora 12 Symposium↗

USGS1993

Compositing multitemporal remote sensing data sets

To eliminate cloud and atmosphere-affected pixels, the compositing of multi temporal remote sensing data sets is done by selecting the maximum vale of the normalized different vegetation index (NDVI) within a compositing period. The NDVI classifier, however, is strongly affected by surface type and anisotropic properties, sensor viewing geometries, and atmospheric conditions. Consequently, the composited, multi temporal, remote sensing data contain substantial noise from these external conditions. Consequently, the composited, multi temporal, remote sensing data contain substantial noise from these external effects. To improve the accuracy of compositing products, two key approaches can be taken: one is to refine the compositing classifier (NDVI) and the other is to improve existing compositing algorithms. In this project, an alternative classifier was developed and an alternative pixel selection criterion was proposed for compositing. The new classifier and the alternative compositing algorithm were applied to an advanced very high resolution radiometer data set of different biome types in the United States. The results were compared with the maximum value compositing and the best index slope extraction algorithms. The new approaches greatly reduced the high frequency noises related to the external factors and repainted more reliable data. The results suggest that the geometric-optical canopy properties of specific biomes may be needed in compositing. Limitations of the new approaches include the dependency of pixel selection on the length of the composite period and data discontinuity.

Pecora 12 Symposium↗