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Spatial structure, sampling design and scale in remotely-sensed imagery of a California savanna woodlandThis article describes research related to sampling techniques for establishing linear relations between land surface parameters and remotely-sensed data. Predictive relations are estimated between percentage tree cover in a savanna environment and a normalized difference vegetation index (NDVI) derived from the Thematic Mapper sensor. Spatial autocorrelation in original measurements and regression residuals is examined using semi-variogram analysis at several spatial resolutions. Sampling schemes are then tested to examine the effects of autocorrelation on predictive linear models in cases of small sample sizes. Regression models between image and ground data are affected by the spatial resolution of analysis. Reducing the influence of spatial autocorrelation by enforcing minimum distances between samples may also improve empirical models which relate ground parameters to satellite data.
Document ID
19930072022
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Mcgwire, K.
(NASA Headquarters Washington, DC United States)
Friedl, M.
(NASA Headquarters Washington, DC United States)
Estes, J. E.
(California Univ. Santa Barbara, United States)
Date Acquired
August 16, 2013
Publication Date
July 20, 1993
Publication Information
Publication: International Journal of Remote Sensing
Volume: 14
Issue: 11
ISSN: 0143-1161
Subject Category
Earth Resources And Remote Sensing
Accession Number
93A56019
Funding Number(s)
CONTRACT_GRANT: NAGW-1743
Distribution Limits
Public
Copyright
Other

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