Earth Resources Observation Research Articles

THE NATIONAL BASIN DELINEATION PROJECT

The National Basin Delineation Project (NBDP) was undertaken by the National Severe Storms Laboratory to define flash-flood-scale basin boundaries for the country in support of the National Weather Service (NWS) Flash Flood Monitoring and Prediction (FFMP) system. FFMP-averaged basin rainfall calculations allow NWS forecasters to monitor precipitation in flash-flood-scale basins, improving their ability to make accurate and timely flash-flood-warning decisions. The NBDP was accomplished through a partnership with the U.S. Geological Survey Earth Resources Observation Systems (EROS) Data Center (EDC). The one-arc-second (approximately 30 m)-resolution digital terrain data in the EDC's National Elevation Dataset provided the basis for derivation of the following digital maps using a geographic information system: 1) a grid of hydrologically conditioned elevation values (all grid cells have a defined flow direction), 2) a grid of flow direction indicating which of eight directions water will travel based on slope, 3) a grid of flow accumulation containing a count of the number of upstream grid cells contributing flow to each grid cell, 4) synthetic streamlines derived from the flow accumulation grid, and 5) flash-flood-scale basin boundaries. Special techniques were applied in coastal areas and closed basins (basins with no outflow) to ensure the accuracy of derived basins and streams. Codifying each basin with a unique identifier and including hydrologic connectivity information produced a versatile, seamless dataset for use in FFMP and other national applications.

Soil moisture estimation using an artificial neural network: a feasibility study

An artificial neural network (ANN) based algorithm is implemented and tested for soil moisture estimation. The ANN model is calibrated (trained) and validated (tested) with data including National Centers for Environmental Protection (NCEP) daily precipitation; normalized difference vegetation index (NDVI) data processed by the US Geological Survey Earth Resources Observations Systems (USGS EROS) data center; Geostationary Operational Environmental Satellite (GOES) based, cloud-masked infrared (IR) skin temperature produced by the University of Maryland; and soil moisture profiles measured over the Oklahoma (OK) Mesonet. The performance of the ANN model is evaluated by direct comparison between the soil moisture estimated by the ANN model and the Mesonet measurements and by examining the correlation between them. Strong correlation is demonstrated between the ANN estimates and Mesonet measurements for spatially averaged data. This work suggests that the ANN model is a promising alternative to soil moisture estimation. The advantage of the ANN approach to soil moisture estimation is that it can provide estimates having resolution commenmmensurate with remotely sensed IR data and has the potential for worldwide coverage.

Revised landsat-5 tm radiometric calibration procedures and postcalibration dynamic ranges

Effective May 5, 2003, Landsat-5 (L5) Thematic Mapper (TM) data processed and distributed by the U.S. Geological Survey (USGS) Earth Resources Observation System (EROS) Data Center (EDC) will be radiometrically calibrated using a new procedure and revised calibration parameters. This change will improve absolute calibration accuracy, consistency over time, and consistency with Landsat-7 (L7) Enhanced Thematic Mapper Plus (ETM+) data. Users will need to use new parameters to convert the calibrated data products to radiance. The new procedure for the reflective bands (1-5,7) is based on a lifetime radiometric calibration curve for the instrument derived from the instrument's internal calibrator, cross-calibration with the ETM+, and vicarious measurements. The thermal band will continue to be calibrated using the internal calibrator. Further updates to improve the relative detector-to-detector calibration and thermal band calibration are being investigated, as is the calibration of the Landsat-4 (L4) TM.

Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data

An algorithm based on the physics of radiative transfer in vegetation canopies for the retrieval of vegetation green leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) from surface reflectances was developed and implemented for operational processing prior to the launch of the moderate resolution imaging spectroradiometer (MODIS) aboard the TERRA platform in December of 1999. The performance of the algorithm has been extensively tested in prototyping activities prior to operational production. Considerable attention was paid to characterizing the quality of the product and this information is available to the users as quality assessment (QA) accompanying the product. The MODIS LAI/FPAR product has been operationally produced from day one of science data processing from MODIS and is available free of charge to the users from the Earth Resources Observation System (EROS) Data Center Distributed Active Archive Center. Current and planned validation activities are aimed at evaluating the product at several field sites representative of the six structural biomes. Example results illustrating the physics and performance of the algorithm are presented together with initial QA and validation results. Potential users of the product are advised of the provisional nature of the product in view of changes to calibration, geolocation, cloud screening, atmospheric correction and ongoing validation activities.

The work of the US National Satellite Land Remote Sensing Data Archive Advisory Committee: 1998–2000

Earth observation data have been acquired and stored since the early 1970 s. One of the world's largest, and most important, repositories for land satellite data is the Earth Resources Observation Systems (EROS) Data Center (EDC). It is a data management, systems development and research field center for the US Geological Survey's (USGS) National Mapping Division in Sioux Falls, SD, USA. It was established in the early 1970 s and, in 1992, the US Congress established the National Satellite Land Remote Sensing Data Archive at EDC. Although data have been acquired and stored for decades, the world's remote sensing community has only recently begun to address long-term data preservation and access. One such effort was made recently by remote sensing leaders from academia, industry and government as members of a federal advisory committee from 1998 to 2000. 2 2 http://edc.usgs.gov/programs/nslrsda/advisory/index.html. This paper provides a brief account of the Committee's work product.

Research Article: A Comparison of Land Use and Land Cover Data in Watersheds of the Mid-Atlantic Region, USA

We compared different sources of land use/land cover data using the percentages of forest, agriculture, urban, and mined lands in approximately 400 watersheds of the U.S. Mid-Atlantic region. The primary data sources were digital Land Use/Land Cover maps (LULC) from the U.S. Geological Survey (USGS) and data derived from Landsat Thematic Mapper (TM) imagery obtained by the Multi-Resolution Land Characteristics (MRLC) Consortium and interpreted at the Earth Resources Observation Systems (EROS) Data Center (version 2). The latter is referred to as MRLC-TM. We also used aerial photographs (AP), topographic maps, field comments, and qualitative watershed assessments as ancillary information. The LULC and MRLC-TM data differed in age, source material, classifications, format (vector vs. raster), and resolution. However, Pearson correlation coefficients between LULC and MRLC-TM were high for broad (composite) forest, agriculture, and urban categories (0.95, 0.96, and 0.92, respectively). Correspondence varied by forest type and by percentage. Where deciduous forests comprised less than 50% of the watershed, MRLC-TM data depicted greater amounts than did LULC, while the reverse was true where deciduous forests were dominant. Agriculture and forest percentages were inversely related in all cases. Urban land percentages were consistently lower for MRLC-TM than LULC. Relatively few watersheds contained strip mines, quarries, or gravel pits and the MRLC-TM vs. LULC relationship was weak (correlation coefficient = 0.41). Detection of reclaimed mines seemed problematic for LULC and MRLC-TM, but these were readily discerned on AP. A single “snapshot” of land cover data will inadequately characterize a watershed if rapid landscape changes occur (e.g., forest clearcuts). Each data type has strengths and weaknesses, depending on a study's objectives, but individual limitations may be alleviated by using multiple data sources.

North American landscape characterization project: The production of a continental scale three‐decade Landsat data set

The North American Landscape Characterization (NALC) project is a component of the National Aeronautics and Space Administration (NASA) Landsat Pathfinder program. Pathfinder projects are focused on the investigation of global change utilizing current remote sensing technologies. The NALC project is a cooperative effort between the U. S. Environmental Protection Agency (EPA), the U.S. Geological Survey (USGS), and NASA to make Landsat data available to the widest possible user community for scientific research and general public interest. The NALC project is principally funded by the EPA Office of Research and Development and the USGS's Earth Resources Observation Systems (EROS) Data Center (EDC). The objectives of the NALC project are to produce standardized remote sensing data sets, develop standardized analysis methods, and derive standardized land cover change products for a large portion of the North American continent (the conterminous United States and Mexico) (Lunetta and Sturdevant, 1993). The standard product is the NALC “triplicate”;, consisting of co‐registered Landsat multispectral scanner data for the years 1973, 1986, and 1991 (plus or minus one year), plus co‐registered 3 arcsecond digital terrain elevation data. Processing began with the 1986 scene, which was precision corrected (with full terrain correction) to a 60 meter Universal Transverse Mercator base. Automated cross‐correlation procedures were used to co‐register the 1970's and 1990's data to the 1980's base, and independent verifications of registration quality were performed on all triplicate components. The pertinent metadata were compiled in a relational database, which includes WRS2 path/rows, scene ID's, image dates, solar azimuth and elevation, verification RMSE's, and the number of verification control points. NALC triplicate data sets are being used for a number of applications, including the analysis of urbanization patterns, dynamics of climatic fluctuations, deforestation studies, and vegetation classification and mapping. These data are being distributed through the Earth Observing System Data and Information System (EOSDIS) Information Management System (IMS) at a cost of $15(U.S.) for each triplicate.

IMPACT OF USGS VEGETATION MAP ON GCM SIMULATIONS OVER THE UNITED STATES

A global atmospheric general circulation model (GCM) coupled with a biosphere model (SSiB, simplified simple biosphere model) was used to study the impact of vegetation on simulations over the United States during summer. Ensembles of 90-d integrations were performed from early June initial conditions with different vegetation maps and different biophysical characteristics. Monthly and seasonal mean differences among these ensembles were analyzed. Incorporation of a new SiB vegetation map produced from the latest available data by the U.S. Geological Survey’s EROS (Earth Resources Observation Systems) Data Center has a significant impact on monthly and seasonal simulations of evaporation, surface air temperature, and precipitation over some regions of the United States. The impact is greater over the western half of the United States than over the eastern half, where moisture convergence plays a stronger role in the hydrological cycle. Systematic errors in the model simulations appear to be related to the use of a single crop vegetation type in SiB (simple biosphere model). Replacing the crops over the United States with broadleaf deciduous trees reduces the systematic errors. It appears that the strong seasonality of the SiB crop vegetation type makes it an unsuitable representative for crops in general. The importance of vegetation specification in monthly and seasonal predictions is emphasized.

Impact of USGS Vegetation Map on GCM Simulations over the United States

A global atmospheric general circulation model (GCM) coupled with a biosphere model (SSiB, simplified simple biosphere model) was used to study the impact of vegetation on simulations over the United States during summer. Ensembles of 90-d integrations were performed from early June initial conditions with different vegetation maps and different biophysical characteristics. Monthly and seasonal mean differences among these ensembles were analyzed. Incorporation of a new SiB vegetation map produced from the latest available data by the U.S. Geological Survey’s EROS (Earth Resources Observation Systems) Data Center has a significant impact on monthly and seasonal simulations of evaporation, surface air temperature, and precipitation over some regions of the United States. The impact is greater over the western half of the United States than over the eastern half, where moisture convergence plays a stronger role in the hydrological cycle. Systematic errors in the model simulations appear to be related to the use of a single crop vegetation type in SiB (simple biosphere model). Replacing the crops over the United States with broadleaf deciduous trees reduces the systematic errors. It appears that the strong seasonality of the SiB crop vegetation type makes it an unsuitable representative for crops in general. The importance of vegetation specification in monthly and seasonal predictions is emphasized.

UNITED STATES LAND USE INVENTORY FOR ESTIMATING BIOGENIC OZONE PRECURSOR EMISSIONS

The U.S. Geological Survey’s (USGS) EROS (Earth Resources Observation System) Data Center’s (EDC) 1-km classified land cover data are combined with other land use data using a Geographic Information System (GIS) to create the Biogenic Emissions Landcover Database (BELD). The land cover data are being used to estimate biogenic emissions in the contiguous United States. These emissions include volatile organic compound (VOC) emissions from vegetation and nitric oxide (NO) from soils. The EDC data are used predominately in the western United States, while other sources, such as the U.S. Department of Agriculture’s Census of Agriculture and the U.S. Forest Service Eastwide Forest Inventory and Analysis Database (EWDB), are used in the eastern United States. The EROS Data Center land cover classifications must be used with caution in heterogenous areas. Emission factors vary drastically by specific crop and tree genera, and mixed classes in the EDC scheme may not always accurately reflect the actual crop/genus mix. However, future use of satellite-derived vegetation indices and other land cover characteristics may prove useful in understanding geographic distributions of foliar mass and seasonal variation in Leaf Area Index (LAI), which are important drivers in biogenic emission models.

Accuracy Assessment of DCW DEM

The Earth Resources Observation Systems (EROS) Data Center of the U.S. Geological Survey has been providing a 30arc second Digital Elevation Model (DEM) data set of the whole world (DCW DEM) through the Internet since 1995. Although the accuracy is one of the most important meta-information of the DEM data set, no such information has been provided by the data producer except for nominal accuracies of the original topographic maps. This paper summarizes a result of accuracy assessment of the part of the data covering Japan using existing higher accuracy DEM data sets developed by the Geographical Survey Institute (GSI). The assessment includes the investigation of the datum difference and the comparison between DCW DEM and GSI's DEM data. The calculated accuracy of DCW DEM is 89m RMSE. The datum investigation revealed that the datum of DCW DEM of Japan was Tokyo Datum instead of nominal WGS-84.

Speed Vector Measurement of Moving Objects Using JERS-1/OPS Data

OPS (OPtical Sensor) on JERS-1 (Japanese Earth Resources Observation Satellite) has a special function of observing the stereo pair image by nadir and forward viewing. When this stereo pair image is observed, the gap of the observation time of about 20 seconds is included. Therefore, one can obtain not only terrain information, but also the vector data of objects which move in minute time, using JERS-1/OPS data. >

Landsat data commercialized

Data for about 600,000 Landsat scenes—all those acquired by multispectral scanners (MSS) aboard Landsat satellites more than 2 years ago—are now available from the Earth Resources Observation Systems (EROS) Data Center (EDC) in Sioux Falls, S. Dak., at reduced prices. Landsat images are valuable for planning, change detection, and monitoring of Earth resources and environment and provide unique information about land conditions and changes during the 1970s and 1980s that is not available from other sources. The availability of the data are expected to benefit substantially the study of global climate change as an early indicator of climate change and as a contributor to changes in the chemical composition of the atmosphere.An agreement was recently signed between the National Oceanic and Atmospheric Administration and the Earth Observation Satellite, whereby MSS data more than 2 years old will be distributed through both EOSAT and the U.S. Geological Survey. EOSAT retains exclusive sales rights to MSS data less than 2 years old.

Landsat data conversion cut from budget

The conversion of a set of early Landsat data from wide band videotape to digitized, computer‐compatible form is slated to end prematurely when this fiscal year ends on September 30, 1986. The National Aeronautics and Space Administration (NASA) is shutting down the project to save money because of the Gramm‐Rudman‐Hollings deficit reduction act (Eos, January 28, 1986, p. 41) and in view of the strain on NASA's budget caused by the loss of the space shuttle Challenger, according to Joseph Bishop, program manager for data processing in NASA's Office of Space Tracking and Data Systems. Moreover, the only equipment that can be used to read the wide band videotape data and convert it to computer‐compatible tape (CCT) has become obsolete, and the tapes themselves are deteriorating, so it seems unlikely that this project could be renewed in the future, according to Allen H. Watkins, chief of the Earth Resources Observation System (EROS) Data Center of the U.S. Geological Survey in Sioux Falls, S. Dak.

DIGITAL PROCESSING OF MULTISPECTRAL SPACE IMAGERY

A final installment in a series of articles devoted to the Soviet earth resource observation system and the “Meteor” and “Meteor-Priroda” satellites focuses on the processing of multispectral information after it is transmitted to ground receiving and processing stations. Among the many aspects of digital image processing described from the Soviet perspective are methods of improving data quality (filtration and correction), data compression and encoding, geometric and radiational transformation, cloud penetration and feature enhancement, texture analysis, and feature recognition and enhancement. The paper closes with a description of Soviet image display system capabilities. Translated from: Priroda Zemli iz kosmosa, A. P. Tishchenko and S. V. Viktorov, eds., Leningrad: Gidrometeoizdat, 1984, pp. 39-55.

SUB-SATELLITE COLLECTION OF SUPPLEMENTAL REMOTE SENSING INFORMATION

This paper, the third in a series on the design, payloads, and operation of Soviet earth resource observation satellites [see MSRS, 1984, No. 4 and 1985, No. 1], describes the collection of supplemental sub-satellite information. More specifically, it outlines how the Soviets use satellite-synchronized aerial, ground, and water-borne sensors to gather auxiliary information used in the improvement of space imagery (data verification and correction, instrument calibration, etc.). A later section demonstrates procedures for selection and preparation of field test sites (for ground truth), and design of sub-satellite information measurement complexes. Translated from: Priroda Zemli iz kosmosa, A. P. Tishchenko and S. V. Viktorov, eds. Leningrad: Gidrometeoizdat, 1984, pp. 35-38.

USGS expands Alaska activities

The U.S. Geological Survey has moved its Alaska geology branch headquarters from Menlo Park, California, to Anchorage, Alaska. In addition, it has added three new offices in Anchorage: an Earth Resources Observation Systems field office to serve users of satellite imagery, a National Mapping Division field office, and a regional Conservation Division headquarters with a special focus on oil and gas leasing.

Scanning System Tradeoffs for Remote Optical Sensing from Geosynchronous Orbit

The scanning and telescope field of view requirements for making Earth Resources (ER) and Meteorological (MET) phenomena observations from a Synchronous Earth Observation Satellite (SEOS) have been defined. The equations relating spacecraft scan capability to user scan field of view requirements are given and evaluated using four different telescope fields of view. The tradeoffs between telescope field-of-view and user scan fields of view requirements are shown, and it was determined that a 0.6 degree by 1.2 degree telescope FOV is optimum for the SEOS telescope. It was shown that an internal scan as well as external scan is required to satisfy both ER and MET users.

Surveying Earth and Its Environment From Space: ABSTRACT

Data from the Earth Resources Technology Satellite (ERTS) are being utilized for cartographic, geologic, and other earth-science purposes by most nations of the world. The Earth Resources Observation Systems (EROS) Data Center in Sioux Falls, South Dakota, is supplying data directly to 96 nations, many from the Circum-Pacific region. The cartographic qualities of data from ERTS, especially that from MSS (multispectral scanner), have exceeded prelaunch expectations. MSS data have been used to produce photo maps of parts of the United States that have root mean square positional errors of less than 150 m. Structural geologic information has been obtained from Nimbus and later ERTS data have led to revision of the metallogenic map of Alaska and are providing focus on potential locations of metallic ores in other areas of the western hemisphere. Observations of the distributions, shapes, and orientations of lakes, stream valleys, and other water features as seen in the synoptic images from ERTS provide new insight into the presence of deep-seated structures that may have metals or petroleum significance. Tectolinear interpretations of ERTS-1 data, in conjunction with seismic epicentral data from other sources, may provide a new approach in defining hazardous areas for future development. Recent work involving special processing of ERTS images of parts of Nevada have led to a capability to detect and map areas of surface alteration--a well-known guide to ore deposits. The Data Collection System (DCS) experiment aboard ERTS is being used to receive and retransmit information from a variety of in-situ sensors, including tiltmeters and seismic event counters on 16 volcanoes in North and Central America. Precursor earthquakes of 80 events per day recently signalled the eruption of Volcan Fuego in Guatemala. End_of_Article - Last_Page 1436------------

Galloping glaciers monitored from space

‘Galloping’ or surging glaciers, as well as nonsurging glaciers up to 40 miles in length around Mt. McKinley in south central Alaska, have been identified clearly in images returned from the first experimental Larth Resources Technology Satellite (ERTS 1).The images, also revealing the extent of sediment plumes from coastal glaciers, offer considerable promise as a tool for monitoring the behavior of glaciers. The Earth Resources Observation Systems (EROS) program is aimed at applying high‐altitude photographic data to a variety of natural resource and environmental problems.