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Understanding historical forest dynamics, specifically changes in forestbiomass and carbon stocks, has become critical for assessing current forestclimate benefits and projecting future benefits under various policy,regulatory, and stewardship scenarios. Carbon accounting frameworks basedexclusively on national forest inventories are limited to broad-scaleestimates, but model-based approaches that combine these inventories withremotely sensed data can yield contiguous fine-resolution maps of forestbiomass and carbon stocks across landscapes over time. Here we describe afundamental step in building a map-based stock-change framework: mappinghistorical forest biomass at fine temporal and spatial resolution (annual, 30m)across all of New York State (USA) from 1990 to 2019, using freely availabledata and open-source tools.  Using Landsat imagery, US Forest Service Forest Inventory and Analysis (FIA)data, and off-the-shelf LiDAR collections we developed three modelingapproaches for mapping historical forest aboveground biomass (AGB): training onFIA plot-level AGB estimates (direct), training on LiDAR-derived AGB maps(indirect), and an ensemble averaging predictions from the direct and indirectmodels. Model prediction surfaces (maps) were tested against FIA estimates atmultiple scales. All three approaches produced viable outputs, yet tradeoffswere evident in terms of model complexity, map accuracy, saturation, andfine-scale pattern representation. The resulting map products can help identifywhere, when, and how forest carbon stocks are changing as a result of bothanthropogenic and natural drivers alike. These products can thus serve asinputs to a wide range of applications including stock-change assessments,monitoring reporting and verification frameworks, and prioritizing parcels forprotection or enrollment in improved management programs.

Mapping historical forest biomass for stock-change assessments at parcel to landscape scales