Work

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

Year: 2024

Type: article

Abstract: 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, withkset at 30 and RTD at 900 meters. Findings suggest that systematic sampling outperformed random and cluster sampling, with models that used moderatekand 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.

Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI: 10.1109/jstars.2024.3486737

Authors: Chinsu Lin, Nova D. Doyog

Institutions: National Chiayi University, Benguet State University

Language: English

Topic: Remote Sensing and LiDAR Applications

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Life on land

Open Access status: gold

Estimated APC paid: $1,496