|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|4460033||1621318||2009||13 صفحه PDF||سفارش دهید||دانلود رایگان|
New model-based estimators of the uncertainty of pixel-level and areal k-nearest neighbour (knn) predictions of attribute Y from remotely-sensed ancillary data X are presented. Non-parametric functions predict Y from scalar ‘Single Index Model’ transformations of X. Variance functions generated estimates of the variance of Y. Three case studies, with data from the Forest Inventory and Analysis program of the U.S. Forest Service, the Finnish National Forest Inventory, and Landsat ETM+ ancillary data, demonstrate applications of the proposed estimators. Nearly unbiased knn predictions of three forest attributes were obtained. Estimates of mean square error indicate that knn is an attractive technique for integrating remotely-sensed and ground data for the provision of forest attribute maps and areal predictions.
Journal: Remote Sensing of Environment - Volume 113, Issue 3, 16 March 2009, Pages 476–488