Comparison of variable extraction methods using surface field data and its key influencing factors: A case study on aboveground biomass of Pinus densata forest using the original bands and vegetation indices of Landsat 8

Abstract

Insufficient sample data is a challenge when estimating forest aboveground biomass (AGB) using large-scale remote sensing. Extracting remote sensing information from sub-compartments could rectify such defects, but the corresponding method, its accuracy, and influencing factors still need to be clarified. We combined Landsat 8 data with a Pinus densata forest sub-compartment to extract remote sensing information that matched the sample plots. Six sub-compartment based methods, including the centroid point extraction method, and the minimum, mean, maximum, majority, and median statistic extraction methods were used to extract sub-compartment remote sensing information and compare the differences between each method and the true values. For each method, structural equation modelling (SEM) was used to explore the effect of sub-compartment topography, shape, and forest stand factors on the extraction error. Mean statistic was the best extraction method, with the highest consistency index, and the lowest mean relative error, between the extracted and true values. All three factors affected extraction accuracy, with forest stand being the dominant one. When sub-compartment data are sufficient, but sample plots are insufficient, it is an effective extrapolation method for large-scale AGB estimation.