Assessing the accuracy of regional LiDAR-based biomass estimation using a simulation approach

To meet the increasing need for reliable and timely timber resources and carbon stock estimates at intermediate and local decision levels, a sampling approach using airborne laser scanning (ALS) as a strip sampling tool has been proposed as a supplement to the conventional field-based National Forest Inventory system. This idea led to a large-scale biomass survey project undertaken in Hedmark County, Norway, an area encompassing 27390 km2. The field biomass estimates were provided by the Norwegian NFI, and the ALS measurements were acquired in parallel strips using a systematic (SYS) design. The ALS-based biomass  estimation was performed using regression estimators under design-based and the model-based inferential frameworks. A possible approach to assess the validity of inference when complex designs are involved is to use a sampling simulator where an artificial population represents the ‘ground truth’ and the properties of the estimators are investigated via simulated sampling. To create the artificial population, a large multivariate dataset containing NFI field observations and ALS metrics was generated using a copula function fitted to the empirical observations, and then it was generalized over the study area using satellite imagery and nearest neighbor imputations. The properties of several design-based model-assisted and model-based variance estimators were investigated using simulated sampling and the accuracy of ALS-based and ground-based estimates under simple random sampling without replacement (SRSwoR) and SYS designs were compared. The simulation results indicated that the ALS-based survey produced valid inference under design-based and model-based frameworks. The variance estimators performed well under two-phase SRSwoR, but the real standard errors were overestimated approximately 4.7 times under two-phase SYS. Compared to the pure ground-based inventories, the estimated standard errors of the ALS-based estimates were approximately 1.8 times larger, while the real accuracy (in terms of root mean squared error) improved with 59%.

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