Applying a Four-Way Factorial Experimental Model to Diagnose Optimum kNN Parameters for Precise Aboveground Biomass Mapping

The k-nearest neighbors (kNN) algorithm is a versatile tool for mapping forest attributes. However, the effects of using inadequate reference plots for modeling have not been thoroughly investigated. The interaction between topographic and biological factors results in a more complex distribution of forest attributes, leading to significant uncertainty and challenges in obtaining reliable information. This study presents a protocol that uses a 4-way factorial experimental design to establish appropriate sampling schemes aimed at reducing uncertainty and bias in estimating aboveground biomass (AGB) using the kNN technique. The research was conducted in a mixed forest within a subtropical region affected by wildfires, pine wilt disease, and agricultural activities. A total of 252 sampling schemes, incorporating various sampling methods, feature counts, neighbor counts (k), and reference-target distances (RTD), were utilized to generate corresponding AGB-kNN models. The performance of the models was assessed against measured AGB using a tree-based IPCC-compliant method with a high-resolution orthoimage and canopy-height-model data. Results indicated that these sampling schemes produced AGB estimations with average error rates ranging from 13% to 241%. The optimal kNN model utilized spectral, biophysical, and topographic features through a systematic sampling approach, with k set at 30 and RTD at 900 meters. Findings suggest that systematic sampling outperformed random and cluster sampling, with models that used moderate k and RTD generally providing less biased estimates. This protocol effectively identifies suitable kNN-AGB models, enhancing the ability to delineate areas with low biomass productivity for precision management, while also supporting forest improvement initiatives and biomass-related studies.