DOI: 10.5061/dryad.nvx0k6dp5
title: Data from: Deriving accurate AGC pool map of Bokod Benguet

description: 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.
description: <b><i>Ground data of the reference plots for the k-NN method</i></b> Fifty-six plots with an area of approximately 400 m<sup>2</sup> (11.3m radius) and with a distance of more or less 2km from one plot to another were determined through a random sampling method and served as reference plots for this study. 100% of the trees larger than 5cm in diameter found inside the plot was measured at 1.3m above the ground for the diameter at breast height (D). The GPS receiver was used to determine the geographical coordinates of each plots. The AGB estimates of the plots were calculated with the use of the allometric equation developed by Brown (1997) while the AGC of the reference plots were computed using the formula recommended by the IPCC (2006).  <b><i>Satellite image of the study site</i></b> The satellite image that was used in this study is the Landsat-8 Operational Land Imager and Thermal Infrared Sensor (Landsat 8 OLI &TIRS). The prepocessing namely reflectance normalization and data fusion was performed in the satellite image before data analysis.  <b><i>AGC estimation of the target plots using the k-NN method </i></b> The <i>k</i>-NN method in determining the AGC of the target plots of the natural pine forests was used in this study. It is a nonparametric algorithm that had been extensively used for calculating forest resources with the use of Landsat images (Yim, Kong, Kim, & Shin, 2007; McRoberts, 2012; Lumbres & Lee, 2014; Doyog et al., 2018). <b><i>Accuracy assessment using confusion matrices</i></b> The 56 plots in this study were randomly split into two: 80% (45 plots) for estimating the AGC and the 20% (11 plots) for confirmation in the confusion matrix for the accuracy assessment. This study used the confusion matrix to evaluate the accurateness of the <i>k</i>-NN method in estimating the AGC of the natural pine forest.