Combining Kriging Interpolation to Improve the Accuracy of Forest Aboveground Biomass Estimation Using Remote Sensing Data

The accurate estimation of forest biomass is very significant to the study of regional ecosystems. China's National Forest Continuous Inventory data and Landsat 8 data were used to establish a linear regression (LR) model for the estimation of forest aboveground biomass (AGB) based on forest type. The original results of the LR yielded upon conducting AGB estimation were insufficient. Therefore, an interpolation map of the residuals of the observed and predicted AGB was used to correct the error of the original LR model. There is a highly positive result of the accuracy of the corrected AGB map. First, the AGB estimation based on forest type could effectively improve its accuracy. For example, significant improvements were made in the estimations of broadleaf, coniferous, and mixed forests compared to that of the total vegetation conducted using the original LR model. Second, semivariance analysis should be conducted before spatial interpolation using the Kriging method to determine optimal semivariogram models and parameters. The optimal semivariogram model for broadleaf and total forests was the exponential model, while that for the coniferous and mixed forests was the spherical model. Third, combining Kriging interpolation predicted the AGB map effectively and reduced the under- and overestimation of AGB, although it did not fully eliminate this limitation. The R
²
values of broadleaf, coniferous, and mixed forests were improved to 0.897, 0.856, and 0.826, respectively. Overall, the methods used in this study provide an effective approach towards improving the accuracy of AGB estimations by reducing under- and overestimation based on remote sensing data and increasing the ability to monitor the forest ecosystem.