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Estimating forest AGB at large scales and fine spatial resolutions has becomeincreasingly important for greenhouse gas accounting, monitoring, andverification efforts to mitigate climate change. Airborne LiDAR is highlyvaluable for modeling attributes of forest structure including AGB, yet mostLiDAR collections take place at local or regional scales covering irregular,non-contiguous footprints, resulting in a patchwork of different landscapesegments at various points in time. Here, as part of a statewide forest carbonassessment for New York State (USA), we addressed common obstacles inleveraging a LiDAR patchwork for AGB mapping at landscape scales, includingselection of training data, the investigation of regional or coverage specificpatterns in prediction error, and map agreement with field inventory acrossmultiple scales. Three machine learning algorithms and an ensemble model weretrained with FIA field measurements, airborne LiDAR, and topographic, climaticand cadastral geodata. Using a strict set of plot selection criteria, 801 FIAplots were selected with co-located point clouds drawn from a patchwork of 17leaf-off LiDAR coverages (2014-2019). Our ensemble model was used to produce 30m AGB prediction surfaces within a predictor-defined area of applicability (98%of LiDAR coverage), and the resulting AGB maps were compared with FIAplot-level and areal estimates at multiple scales of aggregation. Our model wasoverall accurate (% RMSE 22-45%; MAE 11.6-29.4 Mg ha$^{-1}$; ME 2.4-6.3 Mgha$^{-1}$), explained 73-80% of field-observed variation, and yielded estimatesthat were consistent with FIA's design-based estimates (89% of estimates withinFIA's 95% CI). We share practical solutions to challenges faced in usingspatiotemporal patchworks of LiDAR to meet growing needs for AGB mapping insupport of applications in forest carbon accounting and ecosystem.

Fine-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coverages