{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T20:44:06Z","timestamp":1775076246772,"version":"3.50.1"},"reference-count":15,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2016,6,23]],"date-time":"2016-06-23T00:00:00Z","timestamp":1466640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs8070535","type":"journal-article","created":{"date-parts":[[2016,6,24]],"date-time":"2016-06-24T11:03:11Z","timestamp":1466766191000},"page":"535","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5125-6450","authenticated-orcid":false,"given":"Daniel","family":"Griffith","sequence":"first","affiliation":[{"name":"School of Economic, Political and Policy Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4957-1379","authenticated-orcid":false,"given":"Yongwan","family":"Chun","sequence":"additional","affiliation":[{"name":"School of Economic, Political and Policy Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX 75080, USA"}]}],"member":"1968","published-online":{"date-parts":[[2016,6,23]]},"reference":[{"key":"ref_1","first-page":"3","article-title":"Positive spatial autocorrelation impacts on attribute variable frequency distributions","volume":"2","author":"Griffith","year":"2011","journal-title":"Chil. J. Stat."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.1538-4632.1972.tb00475.x","article-title":"Testing for spatial autocorrelation among regression residuals","volume":"4","author":"Cliff","year":"1972","journal-title":"Geogr. Anal."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Anselin, L. (1988). Spatial Econometrics: Methods and Models, Kluwer Academic Publishers.","DOI":"10.1007\/978-94-015-7799-1"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.rse.2005.11.016","article-title":"Remote sensing image-based analysis of the relationship between urban heat island and land use\/cover changes","volume":"104","author":"Chen","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1080\/13658816.2015.1068318","article-title":"Approximation of Gaussian spatial autoregressive models for massive regular square tessellation data","volume":"29","author":"Griffith","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1093\/biomet\/76.2.289","article-title":"On multimodality of the likelihood in the spatial linear model","volume":"76","author":"Mardia","year":"1989","journal-title":"Biometrika"},{"key":"ref_7","unstructured":"Kitchin, R., and Thrift, N. (2009). International Encyclopedia of Human Geography, Elsevier."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1080\/01621459.1975.10480272","article-title":"Estimation methods for models of spatial interactions","volume":"70","author":"Ord","year":"1975","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1080\/02331888.2013.801480","article-title":"On asymptotic approximation of inverse moments for a class of nonnegative random variables","volume":"48","author":"Shen","year":"2014","journal-title":"Statistics"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1111\/j.1467-8306.2005.00484.x","article-title":"Effective geographic sample size in the presence of spatial autocorrelation","volume":"95","author":"Griffith","year":"2005","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1006\/jema.2000.0369","article-title":"Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA","volume":"59","author":"Brown","year":"2000","journal-title":"J. Environ. Manag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0034-4257(02)00199-2","article-title":"The use of time-integrated NOAA NDVI data and rainfall to assess landscape degradation in the arid shrubland of Western Australia","volume":"85","author":"Holm","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1080\/01431160500169098","article-title":"Relationship between herbaceous biomass and 1 km2 Advanced Very High Resolution Radiometer (AVHRR) NDVI in Kruger National Park, South Africa","volume":"27","author":"Wessels","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1080\/01431160412331331012","article-title":"Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data","volume":"26","author":"Lee","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/S0024-3795(97)10009-X","article-title":"Monte Carlo estimates of the log determinant of large sparse matrices","volume":"289","author":"Barry","year":"1999","journal-title":"Linear Algebra Appl."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/7\/535\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:24:38Z","timestamp":1760210678000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/7\/535"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,6,23]]},"references-count":15,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2016,7]]}},"alternative-id":["rs8070535"],"URL":"https:\/\/doi.org\/10.3390\/rs8070535","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,6,23]]}}}