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