Status of the Landsat thematic mapper and multispectral scanner archive conversion system

Darla J. Werner

Home/Archive/USGS

USGS · 70113764

Status of the Landsat thematic mapper and multispectral scanner archive conversion system

Darla J. Werner

View original source ↗Export metadata ↓

Abstract

The U.S. Geological Survey's EROS Data Center (EDC) manages the National Satellite Land Remote Sensing Data Archive. This archive includes Landsat thematic mapper (TM) multispectral scanner (MSS) data acquired since 1972. The Landsat archive is an important resource to global change research. To ensure long-term availability of Landsat data from the archive, the EDC specified requirements for a Thematic Mapper and Multispectral Scanner Archive Conversion System (TMACS) that would preserve the data by transcribing it to a more durable medium. In addition to media conversion, hardware and software was installed at EDC in July 1992. In December 1992, the EDC began converting Landsat MSS data from high-density, open reel instrumentation tapes to digital cassette tapes. Almost 320,000 MSS images acquired since 1979 and more than 200,000 TM images acquired since 1982 will be converted to the new medium during the next 3 years. During the media conversion process, several high-density tapes have exhibited severe binder degradation. Even though these tapes have been stored in environmentally controlled conditions, hydrolysis has occurred, resulting in "sticky oxide shed". Using a thermostatically controlled oven built at EDC, tape "baking" has been 100 percent successful and actually improves the quality of some images.

Explore related subjects

Pecora 12 Symposium Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Darla J. Werner. 1993. Status of the Landsat thematic mapper and multispectral scanner archive conversion system. https://pubs.usgs.gov/publication/70113764

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↗