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spatial_analysis_methods:regression_kriging

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<sup>[[:bug_reporting|Report a bug, broken link, or incorrect content]]</sup>[[http://methods.landscapetoolbox.org|{{ field_methods:methodsguide3.png?220x120|}}]] ====== Regression Kriging ====== [[http://www.landscapetoolbox.org/about/get_involved|{{:abstract_in_dev.gif|}}]]\\ Written by Jason Karl ===== Other Names: ===== Kriging with External Drift ===== Description ===== [[glossary:home#kriging|Kriging]] is a method of [[glossary:home#interpolating|interpolating]] the values of a variable at points between field observations. Unlike classical statistics which assumes that data points are independent, kriging relies on the fact that observations are NOT independent. Regression kriging is a variation that uses additional – secondary – datasets such as imagery or other correlated observations to improve the quality of the predictions. The method proceeds by using multiple [[glossary:home#regression|regression]] to describe the relationship between the variable observed in the field and the secondary data. The kriging then occurs with the regression residuals, and the regression and kriging results are combined to produce the prediction. ===== Similar Methods ===== [[Ordinary Kriging]], [[Universal Kriging]] ===== Output ===== Prediction map, map of prediction [[glossary:home#variance]], summary statistics of model performance ===== Successful Rangeland Uses ===== Predicting/mapping soil characteristics, mapping continuous vegetation attributes (e.g., percent canopy cover), mapping insect outbreaks [[regression_kriging#References|Cousens et al. (2002)]]- weed mapping [[regression_kriging#References|Karl (in press)]] - mapping attributes of rangeland condition [[regression_kriging#References|Mutanga and Rugege (2006)]] - biomass estimation [[regression_kriging#References|Voltz et al. (1997)]] - predicting soil properties ===== Application References ===== [[regression_kriging#References|Cousens et al. (2002)]], [[regression_kriging#References|Cigliano et al. (1995)]], [[regression_kriging#References|Mutanga and Rugege (2006)]], [[regression_kriging#References|Karl et al. (in review)]], [[regression_kriging#References|Voltz et al. (1997)]] ===== Technical References ===== *[[regression_kriging#References|Bailey and Gatrell (1995)]] - general reference on kriging - see section on Universal Kriging *[[regression_kriging#References|Hengl et al. (2003)]] - good treatment of the specifics of regression kriging. Presents a framework of process steps for implementing regression kriging. - Very useful reference. ===== Limitations ===== Works poorly if there is little correlation between field observations and the secondary data (e.g., imagery). If there is little [[glossary:home#spatial_autocorrelation|spatial autocorrelation]] among the field observations, then regression-kriging will not produce results that are any better than multiple regression. Also, kriging requires enough data points to be able to estimate the spatial dependence among the field observations. A general rule of thumb is that you should have 100 points at varying distances from each other (e.g., some points close, others far away). ===== Data Inputs ===== Field observations with latitude/longitude coordinates, correlated data measured at same locations, or imagery correlated to the field observations. ===== Software/Hardware Requirements ===== Whereas standard kriging can be done in several GIS applications like ESRI’s ArcGIS Geospatial Analyst, regression kriging is a more specialized method and requires a statistics program like R or SAS. Regression kriging can be done on most desktop or laptop PCs, although high-resolution imagery might need to be aggregated to a coarser resolution to avoid out-of-memory errors. ===== Sample Graphic ===== | {{:remote_sensing_methods:regression_krig_pred.jpg?250|}} | {{:remote_sensing_methods:regression_krig_var.jpg?400|}} | |Predictions of percent sagebrush cover made via regression kriging of 147 field observations. | Regression kriging gives variance estimates that vary with distance from the sample point. The maximum variance that is achieved furthest from the sample points is equal to the variance estimate of a standard regression without kriging.| ===== Additional Information ===== * http://spatial-analyst.net/wiki/index.php?title=Regression-kriging ===== Who Is Using This Method? ===== * Idaho Chapter of The Nature Conservancy Landscape Toolbox Project Contact: Jason Karl, [[jkarl@tnc.org]] ===== References ===== * Bailey, T.C., Gatrell, A.C. 1995. Interactive spatial data analysis. Addison-Wesley. * Cigliano, M.M., Kemp, W.P., Kalaris, T.M. 1995. Spatiotemporal characteristics of rangeland grasshopper (Orthoptera: Acrididae) regional outbreaks. Journal of Orthoptera Research * Cousens RD, Brown RW, McBratney AB, Whelan B, Moerkerk M. 2002. Sampling strategy is important for producing weed maps: a case study using kriging. Weed Science 50:542-6. * Hengl, T., Heuvelink, G.B.M., Stein, A. 2004. A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma 120:75-93. * Karl, J.W. //in review// Using regression kriging to make spatial predictions of rangeland condition attributes. Rangeland Ecology and Management. [[jkarl@tnc.org]] * Mutanga O, Rugege D. 2006. Integrating remote sensing and spatial statistics to model herbaceous biomass distribution in a tropical savanna. International Journal of Remote Sensing 27(16):3499-514. * Voltz M, Lagacherie P, Louchart X. 1997. Predicting soil properties over a region using sample information from a mapped reference area. European Journal of Soil Science 48:19-30. ===== Web Search Results ===== <html> <script src="http://www.google.com/jsapi"></script> <script language="Javascript" type="text/javascript">//<![CDATA[ google.load('search', '1.0'); function OnLoad() { var searchString = '"regression kriging"'; // create a tabbed mode search control var searchControl = new google.search.SearchControl(); options = new google.search.SearcherOptions(); options.setExpandMode(google.search.SearchControl.EXPAND_MODE_OPEN); options.setRoot(document.getElementById("web_results")); searchControl.addSearcher(new google.search.WebSearch(), options); var drawOptions = new google.search.DrawOptions(); options2 = new google.search.SearcherOptions(); options2.setExpandMode(google.search.SearchControl.EXPAND_MODE_OPEN); searchControl.addSearcher(new google.search.BookSearch(), options2); searchControl.draw(document.getElementById("book_results"), drawOptions); // execute searches searchControl.execute(searchString); } google.setOnLoadCallback(OnLoad, true); //]]> </script> <table border="0"> <tr><td width="50%"> <div class="search-control" id="book_results">Loading...</div> </td> <td width="50%"> <div class="search-control" id="web_results">Loading...</div> </td> </tr> </table> </html> ===== Discussion/Comments ===== <sub>**You must have an account and be logged in to post or reply to the discussion topics below. 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spatial_analysis_methods/regression_kriging.1331247906.txt.gz · Last modified: 2012/03/08 16:05 by jgillan

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