Texture as a Property of Remote-sensed Images: Augmenting Standard Spectral Classification Techniques Identification of Built Patches on the Upper San Pedro Basin Landscape

2003 ◽  
pp. 145-157
Author(s):  
Ward Brady ◽  
Ryan Miller
Keyword(s):  
Spectral Classification ◽  
Classification Techniques ◽  
San Pedro ◽  
San Pedro Basin

Using Analytic Signal Analysis On Aeromagnetic Data To Constrain Amt Inversions, Sonora San Pedro Basin, Mexico

2003 ◽  
Author(s):  
J.C. Wynn ◽  
Floyd Gray ◽  
T.E. Nordstrom ◽  
Dexin Liu ◽  
E.V. Reed ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Analytic Signal ◽  
Aeromagnetic Data ◽  
San Pedro ◽  
San Pedro Basin

Temporal variability in sediment fluxes in the San Pedro Basin, southern California bight

1994 ◽  
Vol 14 (4) ◽  
pp. 333-352 ◽  
Author(s):  
Robert C. Thunell ◽  
Cynthia H. Pilskaln ◽  
Eric Tappa ◽  
Leslie Reynolds Sautter
Keyword(s):  
Temporal Variability ◽  
Southern California ◽  
Southern California Bight ◽  
Sediment Fluxes ◽  
San Pedro ◽  
San Pedro Basin

New constraints on the sedimentary and tectonic evolution of the San Pedro basin (southern Dominican republic offshore margin), new opportunities for the hydrocarbon exploration?

2016 ◽  
Author(s):  
Jose Miguel Gorosabel ◽  
Andrés Carbó Gorosabel ◽  
José Luis Granja Bruña ◽  
Álvaro Rodríguez Zurrunero ◽  
Alfonso Muñoz Martín ◽  
...  
Keyword(s):  
Dominican Republic ◽  
Tectonic Evolution ◽  
Hydrocarbon Exploration ◽  
San Pedro ◽  
San Pedro Basin

scholarly journals Adjusting Satellite Precipitation Data to Facilitate Hydrologic Modeling

2010 ◽  
Vol 11 (4) ◽  
pp. 966-978 ◽  
Author(s):  
Kenneth J. Tobin ◽  
Marvin E. Bennett
Keyword(s):  
Hydrological Modeling ◽  
Tropical Rainfall Measuring Mission ◽  
Probability Of Detection ◽  
Precipitation Data ◽  
Satellite Precipitation ◽  
Precipitation Product ◽  
San Pedro ◽  
Precipitation Analysis ◽  
San Pedro Basin ◽  
Daily Time

Abstract Significant concern has been expressed regarding the ability of satellite-based precipitation products such as the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 products (version 6) and the U.S. National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center’s (CPC) morphing technique (CMORPH) to accurately capture rainfall values over land. Problems exist in terms of bias, false-alarm rate (FAR), and probability of detection (POD), which vary greatly worldwide and over the conterminous United States (CONUS). This paper directly addresses these concerns by developing a methodology that adjusts existing TMPA products utilizing ground-based precipitation data. The approach is not a simple bias adjustment but a three-step process that transforms a satellite precipitation product. Ground-based precipitation is used to develop a filter eliminating FAR in the authors’ adjusted product. The probability distribution function (PDF) of the satellite-based product is adjusted to the PDF of the ground-based product, minimizing bias. Failure of precipitation detection (POD) is addressed by utilizing a ground-based product during these periods in their adjusted product. This methodology has been successfully applied in the hydrological modeling of the San Pedro basin in Arizona for a 3-yr time series, yielding excellent streamflow simulations at a daily time scale. The approach can be applied to any satellite precipitation product (i.e., TRMM 3B42 version 7) and will provide a useful approach to quantifying precipitation in regions with limited ground-based precipitation monitoring.

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