Skip to Main Content
Statistical inference for remote sensing-based estimates of net deforestationAuthor(s): Ronald E. McRoberts; Brian F. Walters
Source: Remote Sensing of Environment. 124: 394-401.
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
View PDF (850.97 KB)
DescriptionStatistical inference requires expression of an estimate in probabilistic terms, usually in the form of a confidence interval. An approach to constructing confidence intervals for remote sensing-based estimates of net deforestation is illustrated. The approach is based on post-classification methods using two independent forest/non-forest classifications because sufficient numbers of observations of forest/non-forest change were not available for direct classification. Further, the approach uses a model-assisted estimator with information from a traditional error matrix for the forest/non-forest classifications to compensate for bias as the result of classification errors and to estimate variances. Classifications were obtained using a logistic regression model, forest inventory data, and two dates of Landsat imagery, although the approach to inference can be used with multiple classification approaches. For the study area in northeastern Minnesota, USA, overall pixel-level accuracies for the year 2002 and 2007 forest/non-forest classifications were 0.85-0.88, and estimates of proportion net deforestation for the 2002-2007 interval were less in absolute value than 0.015. However, standard errors for the remote sensing-based estimates of net deforestation were on the order of 0.02-0.04, meaning that the estimates were not statistically significantly different from zero. Particular attention is directed to the potentially severe sample size and classification accuracy requirements necessary for estimates of net deforestation to be detected as statistically significantly different from zero.
- Check the Northern Research Station web site to request a printed copy of this publication.
- Our on-line publications are scanned and captured using Adobe Acrobat.
- During the capture process some typographical errors may occur.
- Please contact Sharon Hobrla, email@example.com if you notice any errors which make this publication unusable.
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationMcRoberts, Ronald E.; Walters, Brian F. 2012. Statistical inference for remote sensing-based estimates of net deforestation. Remote Sensing of Environment. 124: 394-401.
KeywordsLandsat, Forest inventory, Error matrix, Model-assisted estimator
- Post-classification approaches to estimating change in forest area using remotely sense auxiliary data.
- Estimation for inaccessbile and non-sampled forest areas using model-based inference and remotely sense auxiliary information.
- Using satellite image-based maps and ground inventory data to estimate the area of the remaining Atlantic forest in the Brazilian state of Santa Catarina
XML: View XML