Geometric accuracy of LANDSAT-4 MSS image data

Analyses of the LANDSAT-4 MSS image data of North Georgia provided by the EDC in CCT-p formats reveal that errors of approximately + or - 30 m in the raw data can be reduced to about + or - 55 m based on rectification procedures involving the use of 20 to 30 well-distributed GCPs and 2nd or 3rd degree polynomial equations. Higher order polynomials do not appear to improve the rectification accuracy. A subscene area of 256 x 256 pixels was rectified with a 1st degree polynomial to yield an RMSE sub xy value of + or - 40 m, indicating that USGS 1:24,000 scale quadrangle-sized areas of LANDSAT-4 data can be fitted to a map base with relatively few control points and simple equations. The errors in the rectification process are caused by the spatial resolution of the MSS data, by errors in the maps and GCP digitizing process, and by displacements caused by terrain relief. Overall, due to the improved pointing and attitude control of the spacecraft, the geometric quality of the LANDSAT-4 MSS data appears much improved over that of LANDSATS -1, -2 and -3

Application of remote sensing to study nearshore circulation

Immediate use of drogued buoy tracking was made when the Virginia State Highway Department requested assistance in selecting the best route for a new bridge-tunnel complex across the James River at Newport News. The result was that the Highway Department acted and chose a preferred route from several alternatives. It was also observed that the drogues did not follow the channel as predicted by the James River hydraulic model. This permitted telling the Navy why it is that part of their channel always silts up. The Hampton Roads Sanitation District asked help locate the best route and position of an ocean sewer outfall. Biological activities are focused primarily on delineating biological interaction between the marsh and continental shelf waters on Virginia's Eastern Shore. Information derived is helpful in categorizing the relative biological value of different marsh areas so that meaningful use and management decisions can be made concerning their eventual disposition

Application of the Trend Filtering Algorithm on the MACHO Database

Due to the strong effect of systematics/trends in variable star observations, we employ the Trend Filtering Algorithm (TFA) on a subset of the MACHO database and search for variable stars. TFA has been applied successfully in planetary transit searches, where weak, short-lasting periodic dimmings are sought in the presence of noise and various systematics (due to, e.g., imperfect flat fielding, crowding, etc). These latter effects introduce colored noise in the photometric time series that can lead to a complete miss of the signal. By using a large number of available photometric time series of a given field, TFA utilizes the fact that the same types of systematics appear in several/many time series of the same field. As a result, we fit each target time series by a (least-square-sense) optimum linear combination of templates and frequency-analyze the residuals. Once a signal is found, we reconstruct the signal by employing the full model, including the signal, systematics and noise. We apply TFA on the brightest ~5300 objects from subsets of each of the MACHO Large Magellanic Cloud fields #1 and #79. We find that the Fourier frequency analysis performed on the original data detect some 60% of the objects as trend-dominated. This figure decreases essentially to zero after using TFA. Altogether, We detect 387 variables in the two fields, 183 of which would have remained undetected without using TFA. Where possible, we give preliminary classification of the variables found.Comment: 12 pages, 15 figures, 3 tables with online material; to appear in Astronomy and Astrophysic