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Fire is the dominant disturbance agent in Alaskan and Canadian boreal ecosystems and releases large amounts of carbon into the atmosphere. Burned area and carbon emissions have been increasing with climate change, which have the potential to alter the carbon balance and shift the region from a historic sink to a source. It is therefore critically important to track the spatiotemporal changes in burned area and fire carbon emissions over time. Here we developed a new burned-area detection algorithm between 2001–2019 across Alaska and Canada at 500 m (meters) resolution that utilizes finer-scale 30 m Landsat imagery to account for land cover unsuitable for burning. This method strictly balances omission and commission errors at 500 m to derive accurate landscape- and regional-scale burned-area estimates. Using this new burned-area product, we developed statistical models to predict burn depth and carbon combustion for the same period within the NASA Arctic–Boreal Vulnerability Experiment (ABoVE) core and extended domain. Statistical models were constrained using a database of field observations across the domain and were related to a variety of response variables including remotely sensed indicators of fire severity, fire weather indices, local climate, soils, and topographic indicators. The burn depth and aboveground combustion models performed best, with poorer performance for belowground combustion. We estimate 2.37×106 ha (2.37 Mha) burned annually between 2001–2019 over the ABoVE domain (2.87 Mha across all of Alaska and Canada), emitting 79.3 ± 27.96 Tg (±1 standard deviation) of carbon (C) per year, with a mean combustion rate of 3.13 ± 1.17 kg C m−2. Mean combustion and burn depth displayed a general gradient of higher severity in the northwestern portion of the domain to lower severity in the south and east. We also found larger-fire years and later-season burning were generally associated with greater mean combustion. Our estimates are generally consistent with previous efforts to quantify burned area, fire carbon emissions, and their drivers in regions within boreal North America; however, we generally estimate higher burned area and carbon emissions due to our use of Landsat imagery, greater availability of field observations, and improvements in modeling. The burned area and combustion datasets described here (the ABoVE Fire Emissions Database, or ABoVE-FED) can be used for local- to continental-scale applications of boreal fire science.

Climate change and land-use activities are increasing fire activity across much of the Siberian boreal forest, yet the climate feedbacks from forest disturbances remain difficult to quantify due to limited information on forest biomass distribution, disturbance regimes and post-disturbance ecosystem recovery. Our primary objective here was to analyse post-fire accumulation of Cajander larch (Larix cajanderi Mayr.) aboveground biomass for a 100 000 km2 area of open forest in far northeastern Siberia. In addition to examining effects of fire size and topography on post-fire larch aboveground biomass, we assessed regional fire rotation and density, as well as performance of burned area maps generated from MODIS satellite imagery. Using Landsat imagery, we mapped 116 fire scar perimeters that dated c. 1966–2007. We then mapped larch aboveground biomass by linking field biomass measurements to tree shadows mapped synergistically from WorldView-1 and Landsat 5 satellite imagery. Larch aboveground biomass tended to be low during early succession (≤ 25 yr, 271 ± 26 g m−2, n = 66 [mean ± SE]) and decreased with increasing elevation and northwardly aspect. Larch aboveground biomass tended to be higher during mid-succession (33–38 yr, 746 ± 100 g m−2, n = 32), though was highly variable. The high variability was not associated with topography and potentially reflected differences in post-fire density of tree regrowth. Neither fire size nor latitude were significant predictors of post-fire larch aboveground biomass. Fire activity was considerably higher in the Kolyma Mountains (fire rotation = 110 yr, fire density = 1.0 ± 1.0 fires yr−1 × 104 km−2) than along the forest-tundra border (fire rotation = 792 yr, fire density = 0.3 ± 0.3 fires yr−1 × 104 km−2). The MODIS burned area maps underestimated the total area burned in this region from 2000–2007 by 40%. Tree shadows mapped jointly using high and medium resolution satellite imagery were strongly associated (r2 ≈ 0.9) with field measurements of forest structure, which permitted spatial extrapolation of aboveground biomass to a regional extent. Better understanding of forest biomass distribution, disturbances and post-disturbance recovery is needed to improve predictions of the net climatic feedbacks associated with landscape-scale forest disturbances in northern Eurasia.

No abstract available.

Biodiversity-structure relationships (BSRs), which describe the correlation between biodiversity and three-dimensional forest structure, have been used to map spatial patterns in biodiversity based on forest structural attributes derived from lidar. However, with the advent of spaceborne lidar like the Global Ecosystem Dynamics Investigation (GEDI), investigators are confronted with how to predict biodiversity from discrete GEDI footprints, sampled discontinuously across the Earth surface and often spatially offset from where diversity was measured in the field. In this study, we used National Ecological Observation Network data in a hierarchical modeling framework to assess how spatially-coincident BSRs (where field-observed taxonomic diversity measurements and structural data from airborne lidar coincide at a single plot) compare with BSRs based on statistical aggregates of proximate, but spatially-dispersed GEDI samples of structure. Despite substantial ecoregional variation, results confirm cross-biome consistency in the relationship between plant/tree alpha diversity and spatially-coincident lidar data, including structural data from outside the field plot where diversity was measured. Moreover, we found that generalized forest structural profiles derived from GEDI footprint aggregates were consistently related to tree alpha diversity, as well as cross-biome patterns in beta and gamma diversity. These findings suggest that characteristic forest structural profiles generated from aggregated GEDI footprints are effective for BSR diversity prediction without incorporation of more standard predictors of biodiversity like climate, topography, or optical reflectance. Cross-scale comparisons between airborne- and GEDI-derived structural profiles provide guidance for balancing scale-dependent trade-offs between spatial proximity and sample size for BSR-based prediction with GEDI gridded products. This study fills a critical gap in our understanding of how generalized forest structural attributes can be used to infer specific field-observed biodiversity patterns, including those not directly observable from remote sensing instruments. Moreover, it bolsters the empirical basis for global-scale biodiversity prediction with GEDI spaceborne lidar.