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Dryad

Data from: Deriving accurate AGC pool map of Bokod Benguet

Abstract

The accuracy of the number (k) of Nearest Neighbor (NN) technique in estimating the aboveground carbon (AGC) pool of a naturally grown pine forest as affected by reflectance normalization and data fusion was evaluated in this study. Accurate AGC map provides detail information for the measurement, reporting, and verification of the greenhouse gases (GHG) that are accumulated in the atmosphere. This study utilized the established 56 plots and Landsat 8 OLI & TIRS. The reflectance normalization was conducted with the use of the FLAASH technique and the data fusion process using the NNDiffuse technique was performed in the Landsat image for enhancement. 

This study indicated that the AGC estimation accuracy of k-NN method using Landsat image with combination of reflectance normalization and data fusion is 81.82% with an average AGC of 71.00 tC/ha as compared with that of reflectance normalization conducted in the original image without data fusion which has an accuracy of 54.54% and with an average AGC of 69.20tC/ha. This study concludes that reflectance normalization and data fusion of Landsat images provide relevant accuracy on AGC estimation using the k-NN method. Besides, the k-NN method could offer reliable AGC estimates addressing the hindrances of conducted field inventory in a tropical forest with irregular topography.