A Bayesian geostatistical estimation of biomass in semi-arid rangelands by combining airborne and terrestrial laser scanning data

Biomass of vegetation is critical for carbon cycle research. Estimating biomass from field survey data is laborious and/or destructive and thus retrieving biomass from remote sensing data may be advantageous. Most remote sensing biomass studies have focused on forest ecosystems, while few have focused on low stature vegetation, such as grasses in semi-arid environments. Biomass estimates for grass are significant for studying wildlife habitat, assessing fuel loads, and studying climate change response in semi-arid regions. Recent research has demonstrated the ability of small footprint airborne laser scanning (ALS) data to extract sagebrush height characteristics and the ability of terrestrial laser scanning (TLS) data to estimate vegetation volume over semi-arid rangelands. ALS has somewhat lower resolution than TLS, but has improved spatial coverage over TLS. Combining ALS and TLS is a powerful tool to estimate biomass on regional scales. Bayesian geostatistics, also known as Bayesian Maximum Entropy (BME), can fuse multiple data sources across scales and provide estimation uncertainties for the integration of ALS and TLS data for grass biomass. Regression models are used to approximately delineate the relationship between field biomass measurements and TLS derived height and shape metrics. We then consider TLS plot-level data at the point scale with ALS data at the area scale. The regularization method is utilized to establish the scaling relations between TLS-derived and ALS-derived metrics. The metric maps from the ALS level are reconstructed using a BME method based on regularized variograms. We gain biomass and estimation uncertainty on the regional scale by introducing updated metrics into the model. In order to evaluate the effectiveness of the BME method, we develop simple independent regression models by assuming the TLS-derived metrics as ground reference data. Therefore, the regression model is used to correct the ALS-estimated values and we retrieve biomass at the ALS scale based on the relationship between the derived metrics and biomass. The results are expected to lead to a better understanding of the scaling characteristics of biomass estimation and demonstrate the potential of integrating TLS and ALS data for improved vegetation characterization.