| Summary: | Accurately estimating aboveground biomass (AGB) is important in many applications, including monitoring carbon stocks, investigating deforestation and forest degradation, and designing sustainable forest management strategies. Although lidar provides critical three-dimensional forest structure information for estimating AGB, acquiring comprehensive lidar coverage is often cost prohibitive. This research focused on developing a lidar sampling framework to support AGB estimation from Landsat images. Two sampling strategies, systematic and classification-based, were tested and compared. The proposed strategies were implemented over a temperate forest study site in northern New York State and the processes were then validated at a similar site located in central New York State. Our results demonstrated that while the inclusion of lidar data using systematic or classification-based sampling supports AGB estimation, the systematic sampling selection method was highly dependent on site conditions and had higher accuracy variability. Of the 12 systematic sampling plans, R<sup>2</sup> values ranged from 0.14 to 0.41 and plot root mean square error (RMSE) ranged from 84.2 to 93.9 Mg ha<sup>−1</sup>. The classification-based sampling outperformed 75% of the systematic sampling strategies at the primary site with R<sup>2</sup> of 0.26 and RMSE of 70.1 Mg ha<sup>−1</sup>. The classification-based lidar sampling strategy was relatively easy to apply and was readily transferable to a new study site. Adopting this method at the validation site, the classification-based sampling also worked effectively, with an R<sup>2</sup> of 0.40 and an RMSE of 108.2 Mg ha<sup>−1</sup> compared to the full lidar coverage model with an R<sup>2</sup> of 0.58 and an RMSE of 96.0 Mg ha<sup>−1</sup>. This study evaluated different lidar sample selection methods to identify an efficient and effective approach to reduce the volume and cost of lidar acquisitions. The forest type classification-based sampling method described in this study could facilitate cost-effective lidar data collection in future studies.
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