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Visualizing 2D Probability Distributions from Satellite Image-Derived DataCreating maps of biophysical and geophysical variables using Earth Observing System (EOS) satellite image data is an important component of Earth science. These 2D maps have a single value at every location and standard techniques are used to visualize them. Current tools fall short, however, when it is necessary to describe a distribution of values at each location. Distributions may represent a frequency of occurrence over time, frequency of occurrence from multiple runs of an ensemble forecast or possible values from an uncertainty model. 'Distribution data sets' are described, then a case study is presented to visualize such 2D distributions. Distribution data sets are different from multivariate data sets in the sense that the values are for a single variable instead of multiple variables. Our case study data consists of multiple realizations of percent forest cover, generated using a geostatistical technique that combines ground measurements and satellite imagery to model uncertainty about forest cover. We present several approaches for analyzing and visualizing such data sets. The first is a pixel-wise analysis of the probability density functions for the 2D image while the second is an analysis of features identified within the image. Such pixel-wise and feature-wise views will give Earth scientists a more complete understanding of distribution data sets.
Document ID
20030001842
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Kao, David
(NASA Ames Research Center Moffett Field, CA United States)
Dungan, Jennifer
(NASA Ames Research Center Moffett Field, CA United States)
Pang, Alex
(California Univ. Santa Cruz, CA United States)
Biegel, Bryan
Date Acquired
August 21, 2013
Publication Date
September 23, 2002
Subject Category
Documentation And Information Science
Funding Number(s)
PROJECT: RTOP 704-40-42
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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