Data from: Deriving accurate AGC pool map of Bokod Benguet

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.

本研究评估了k近邻（k-Nearest Neighbor，k-NN）技术在估算天然生长松林地上碳库（aboveground carbon，AGC）时的精度，该精度受反射率归一化与数据融合方案的影响。精准的地上碳库分布图可为大气中累积的温室气体（greenhouse gases，GHG）的测量、报告与核查提供详实的基础信息。本研究采用了已建立的56块样地数据，以及陆地卫星8号OLI与TIRS（Landsat 8 OLI & TIRS）传感器获取的影像数据。研究过程中，通过FLAASH技术完成反射率归一化处理，并采用NNDiffuse技术对陆地卫星影像进行数据融合以提升影像质量。本研究结果表明，结合反射率归一化与数据融合的陆地卫星影像用于k近邻法的AGC估算时，精度可达81.82%，平均AGC为71.00 tC/ha；而仅对原始影像进行反射率归一化、未开展数据融合的方案，其估算精度仅为54.54%，平均AGC为69.20 tC/ha。本研究得出结论：对陆地卫星影像进行反射率归一化与数据融合，可有效提升k近邻法开展AGC估算的精度。此外，k近邻法能够提供可靠的AGC估算结果，解决了地形不规则的热带森林开展野外实地调查所面临的阻碍。