Title of article :
Statistical inference for remote sensing-based estimates of net deforestation
Author/Authors :
McRoberts، نويسنده , , Ronald E. and Walters، نويسنده , , Brian F.، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2012
Pages :
8
From page :
394
To page :
401
Abstract :
Statistical 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.
Keywords :
Landsat , Forest inventory , Error matrix , Model-assisted estimator
Journal title :
Remote Sensing of Environment
Serial Year :
2012
Journal title :
Remote Sensing of Environment
Record number :
1632406
Link To Document :
بازگشت