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

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2025, Vol.18, p.479-495
• Main Authors: Lin, Chinsu, Doyog, Nova D.
• Format: Article
• Language: English
• Subjects: Adaptable precision management; Airborne laser scanning (ALS) lidar; Biological effects; Biological system modeling; Biomass; Carbon; carbon accounting; Design of experiments; Estimation; Experimental design; forest degradation; Forest management; Forestry; information uncertainty; k-nearest neighbor (kNN) approach estimated AGB (kNN-AGB) modeling; K-nearest neighbors algorithm; Mapping; Measurement; Measuring instruments; Mixed forests; Optimization; Physiographic features; Random forests; Sampling; Sampling methods; sampling scheme; Systematics; Topography; Tropical forests; Uncertainty; Wildfires; Wilt
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2024.3486737

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.