Next Article in Journal
Increasing the Accuracy and Automation of Fractional Vegetation Cover Estimation from Digital Photographs
Next Article in Special Issue
Characteristics and Diurnal Cycle of GPM Rainfall Estimates over the Central Amazon Region
Previous Article in Journal
Sixteen Years of Agricultural Drought Assessment of the BioBío Region in Chile Using a 250 m Resolution Vegetation Condition Index (VCI)
Previous Article in Special Issue
Distinguishing Land Change from Natural Variability and Uncertainty in Central Mexico with MODIS EVI, TRMM Precipitation, and MODIS LST Data
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(7), 535; doi:10.3390/rs8070535

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
Daniel A. Griffith is an Ashbel Smith Professor.
*
Author to whom correspondence should be addressed.
Academic Editors: Yudong Tian, Ken Harrison, Yoshio Inoue and Prasad S. Thenkabail
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)
View Full-Text   |   Download PDF [5947 KB, uploaded 23 June 2016]   |  

Abstract

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
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top