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Open AccessArticle

Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data

School of Economic, Political and Policy Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX 75080, USA
Author to whom correspondence should be addressed.
Daniel A. Griffith is an Ashbel Smith Professor.
Academic Editors: Yudong Tian, Ken Harrison, Yoshio Inoue and Prasad S. Thenkabail
Remote Sens. 2016, 8(7), 535;
Received: 5 March 2016 / Revised: 17 May 2016 / Accepted: 14 June 2016 / Published: 23 June 2016
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
Virtually all remotely sensed data contain spatial autocorrelation, which impacts upon their statistical features of uncertainty through variance inflation, and the compounding of duplicate information. Estimating the nature and degree of this spatial autocorrelation, which is usually positive and very strong, has been hindered by computational intensity associated with the massive number of pixels in realistically-sized remotely-sensed images, a situation that more recently has changed. Recent advances in spatial statistical estimation theory support the extraction of information and the distilling of knowledge from remotely-sensed images in a way that accounts for latent spatial autocorrelation. This paper summarizes an effective methodological approach to achieve this end, illustrating results with a 2002 remotely sensed-image of the Florida Everglades, and simulation experiments. Specifically, uncertainty of spatial autocorrelation parameter in a spatial autoregressive model is modeled with a beta-beta mixture approach and is further investigated with three different sampling strategies: coterminous sampling, random sub-region sampling, and increasing domain sub-regions. The results suggest that uncertainty associated with remotely-sensed data should be cast in consideration of spatial autocorrelation. It emphasizes that one remaining challenge is to better quantify the spatial variability of spatial autocorrelation estimates across geographic landscapes. View Full-Text
Keywords: spatial autocorrelation; spatial variability; NDVI; NBR; Florida Everglades spatial autocorrelation; spatial variability; NDVI; NBR; Florida Everglades
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MDPI and ACS Style

Griffith, D.A.; Chun, Y. Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data. Remote Sens. 2016, 8, 535.

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