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David Meyer

Publications and source records attributed to David Meyer.

10 recordsLinked to original sources
USGS2016

Validation of the ASTER Global Digital Elevation Model version 3 over the conterminous United States

The ASTER Global Digital Elevation Model Version 3 (GDEM v3) was evaluated over the conterminous United States in a manner similar to the validation conducted for the original GDEM Version 1 (v1) in 2009 and GDEM Version 2 (v2) in 2011. The absolute vertical accuracy of GDEM v3 was calculated by comparison with more than 23,000 independent reference geodetic ground control points from the U.S. National Geodetic Survey. The root mean square error (RMSE) measured for GDEM v3 is 8.52 meters. This compares with the RMSE of 8.68 meters for GDEM v2. Another important descriptor of vertical accuracy is the mean error, or bias, which indicates if a DEM has an overall vertical offset from true ground level. The GDEM v3 mean error of −1.20 meters reflects an overall negative bias in GDEM v3. The absolute vertical accuracy assessment results, both mean error and RMSE, were segmented by land cover type to provide insight into how GDEM v3 performs in various land surface conditions. While the RMSE varies little across cover types (6.92 to 9.25 meters), the mean error (bias) does appear to be affected by land cover type, ranging from −2.99 to +4.16 meters across 14 land cover classes. These results indicate that in areas where built or natural aboveground features are present, GDEM v3 is measuring elevations above the ground level, a condition noted in assessments of previous GDEM versions (v1 and v2) and an expected condition given the type of stereo-optical image data collected by ASTER. GDEM v3 was also evaluated by differencing with the Shuttle Radar Topography Mission (SRTM) dataset. In many forested areas, GDEM v3 has elevations that are higher in the canopy than SRTM. The overall validation effort also included an evaluation of the GDEM v3 water mask. In general, the number of distinct water polygons in GDEM v3 is much lower than the number in a reference land cover dataset, but the total areas compare much more closely.

The International Archives of the Photogrammetry, ↗
USGS2015

Advanced Spaceborne Thermal Emission and Reflection Radiometer Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) Product, algorithm theoretical basis document

This document provides an overview of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) supplemental algorithms in conjunction with the reuse of Landsat geometric algorithms modified by the National Aeronautics and Space Administration (NASA Land Processes Distributed Active Archive Center (LP DAAC) to create an ASTER Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) product. Implementation of these algorithms occurs within the AST_L1T product generation executable (PGE) as part of the open source Simple, Scalable, Script-based Science Processor for Missions (S4PM) processing software subsystem. The AST_L1T algorithms include the following: Generation of the AST_L1A input product via supplemental algorithms Application of the radiometric and geometric corrections appended in the AST_L1A product Application of cross-talk correction coefficients Generation and application of affine transformation coefficients Modification and reuse of Landsat’s geometric algorithms including: Systematic—generates the systematic grid Resampling—multi-use; only a single resample of input scene Gpyramid—scale input image to reference image (if necessary) Ground Control Point (GCP) Correlate—computes x,y offsets for GCPs to be used for precision grid generation Precision Refine—generates the precision grid GVERIFY—geometric verification

Open-File Report↗
USGS2000

Synthetic aperture radar interferometry of Okmok volcano, Alaska: radar observations

ERS-1/ERS-2 synthetic aperture radar interferometry was used to study the 1997 eruption of Okmok volcano in Alaska. First, we derived an accurate digital elevation model (DEM) using a tandem ERS-1/ERS-2 image pair and the preexisting DEM. Second, by studying changes in interferometric coherence we found that the newly erupted lava lost radar coherence for 5-17 months after the eruption. This suggests changes in the surface backscattering characteristics and was probably related to cooling and compaction processes. Third, the atmospheric delay anomalies in the deformation interferograms were quantitatively assessed. Atmospheric delay anomalies in some of the interferograms were significant and consistently smaller than one to two fringes in magnitude. For this reason, repeat observations are important to confidently interpret small geophysical signals related to volcanic activities. Finally, using two-pass differential interferometry, we analyzed the preemptive inflation, coeruptive deflation, and posteruptive inflation and confirmed the observations using independent image pairs. We observed more than 140 cm of subsidence associated with the 1997 eruption. This subsidence occurred between 16 months before the eruption and 5 months after the eruption, was preceded by ∼18 cm of uplift between 1992 and 1995 centered in the same location, and was followed by ∼10 cm of uplift between September 1997 and 1998. The best fitting model suggests the magma reservoir resided at 2.7 km depth beneath the center of the caldera, which was ∼5 km from the eruptive vent. We estimated the volume of the erupted material to be 0.055 km 3 and the average thickness of the erupted lava to be ∼7.4 m. Copyright 2000 by the American Geophysical Union.

Journal of Geophysical Research B: Solid Earth↗
USGS1995

The effect of surface anisotropy and viewing geometry on the estimation of NDVI from AVHRR

Since terrestrial surfaces are anisotropic, all spectral reflectance measurements obtained with a small instantaneous field of view instrument are specific to these angular conditions, and the value of the corresponding NDVI, computed from these bidirectional reflectances, is relative to the particular geometry of illumination and viewing at the time of the measurement. This paper documents the importance of these geometric effects through simulations of the AVHRR data acquisition process, and investigates the systematic biases that result from the combination of ecosystem-specific anisotropies with instrument-specific sampling capabilities. Typical errors in the value of NDVI are estimated, and strategies to reduce these effects are explored. -from Authors

Remote Sensing Reviews↗
USGS1993

Processing techniques for global land 1-km AVHRR data

The U.S. Geological Survey's (USGS) Earth Resources Observation Systems (EROS) Data Center (EDC) in cooperation with several international science organizations has developed techniques for processing daily Advanced Very High Resolution Radiometer (AVHRR) 1-km data of the entire global land surface. These techniques include orbital stitching, geometric rectification, radiometric calibration, and atmospheric correction. An orbital stitching algorithm was developed to combine consecutive observations acquired along an orbit by ground receiving stations into contiguous half-orbital segments. The geometric rectification process uses an AVHRR satellite model that contains modules for forward mapping, forward terrain correction, and inverse mapping with terrain correction. The correction is accomplished by using the hydrologic features coastlines and lakes from the Digital Chart of the World. These features are rasterized into the satellite projection and are matched to the AVHRR imagery using binary edge correlation techniques. The resulting coefficients are related to six attitude correction parameters: roll, roll rate, pitch, pitch rate, yaw, and altitude. The image can then be precision corrected to a variety of map projections and user-selected image frames. Because the AVHRR lacks onboard calibration for the optical wavelengths, a series of time-variant calibration coefficients derived from vicarious calibration methods and are used to model the degradation profile of the instruments. Reducing atmospheric effects on AVHRR data is important. A method has been develop that will remove the effects of molecular scattering and absorption from clear sky observations, using climatological measurements of ozone. Other methods to remove the effects of water vapor and aerosols are being investigated.

Pecora 12 Symposium↗
USGS1993

An evaluation of atmospheric corrections to advanced very high resolution radiometer data

A data set compiled to analyze vegetation indices is used to evaluate the effect of atmospheric correction to AVHRR measurement in the solar spectrum. Such corrections include cloud screening and "clear sky" corrections. We used the "clouds from AVHRR" (CLAVR) method for cloud detection and evaluated its performance over vegetated targets. Clear sky corrections, designed to reduce the effects of molecular scattering and absorption due to ozone, water vapor, carbon dioxide, and molecular oxygen, were applied to data values determine to be cloud free. Generally, it was found that the screening and correction of the AVHRR data did not affect the maximum NDVI compositing process adversely, while at the same time improving estimates of the land-surface radiances over a compositing period.

Pecora 12 Symposium↗
USGS1987

Image restoration techniques as applied to Landsat MSS and TM data

Two factors are primarily responsible for the loss of image sharpness in processing digital Landsat images. The first factor is inherent in the data because the sensor's optics and electronics, along with other sensor elements, blur and smear the data. Digital image restoration can be used to reduce this degradation. The second factor, which further degrades by blurring or aliasing, is the resampling performed during geometric correction. An image restoration procedure, when used in place of typical resampled techniques, reduces sensor degradation without introducing the artifacts associated with resampling. The EROS Data Center (EDC) has implemented the restoration proceed for Landsat multispectral scanner (MSS) and thematic mapper (TM) data. This capability, developed at the University of Arizona by Dr. Robert Schowengerdt and Lynette Wood, combines restoration and resampling in a single step to produce geometrically corrected MSS and TM imagery. As with resampling, restoration demands a tradeoff be made between aliasing, which occurs when attempting to extract maximum sharpness from an image, and blurring, which reduces the aliasing problem but sacrifices image sharpness. The restoration procedure used at EDC minimizes these artifacts by being adaptive, tailoring the tradeoff to be optimal for individual images.

Pecora XI Symposium↗