Very high resolution aboveground carbon mapping in subtropical thicket

Abstract

Aboveground carbon (AGC) maps will assist with the monitoring and verification of carbon stored through restoration of subtropical thicket. Field methods are required to capture AGC ground truth data at the plot scale, but they are highly impractical for large area mapping of carbon stocks. Against this background, a remote sensing method to estimate AGC from very high spatial resolution multi-spectral imagery was developed. AGC ground truth was acquired for 85 plots in a small study area in the Baviaanskloof, South Africa. Using the ground truth, univariate and multivariate snapshot models were developed to predict AGC from features in a 2017 WorldView-3 (0.34-m resolution 8-band) satellite image. Informative features were selected from a large set of spectral, textural, and vegetation index features using stepwise forward selection. Using this approach, the multivariate model produced a coefficient of determination (R2) of 0.886 and root mean square error of 2.862 t C ha − 1. This study demonstrates the efficacy of regression approaches for estimating AGC from multi-spectral imagery and provides a foundation for the spatial and temporal extension of AGC remote sensing in the thicket biome.