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USGS2015

North America's net terrestrial CO 2 exchange with the atmosphere 1990–2009

Scientific understanding of the global carbon cycle is required for developing national and international policy to mitigate fossil fuel CO 2 emissions by managing terrestrial carbon uptake. Toward that understanding and as a contribution to the REgional Carbon Cycle Assessment and Processes (RECCAP) project, this paper provides a synthesis of net land–atmosphere CO 2 exchange for North America (Canada, United States, and Mexico) over the period 1990–2009. Only CO 2 is considered, not methane or other greenhouse gases. This synthesis is based on results from three different methods: atmospheric inversion, inventory-based methods and terrestrial biosphere modeling. All methods indicate that the North American land surface was a sink for atmospheric CO 2 , with a net transfer from atmosphere to land. Estimates ranged from −890 to −280 Tg C yr −1 , where the mean of atmospheric inversion estimates forms the lower bound of that range (a larger land sink) and the inventory-based estimate using the production approach the upper (a smaller land sink). This relatively large range is due in part to differences in how the approaches represent trade, fire and other disturbances and which ecosystems they include. Integrating across estimates, "best" estimates (i.e., measures of central tendency) are −472 ± 281 Tg C yr −1 based on the mean and standard deviation of the distribution and −360 Tg C yr −1 (with an interquartile range of −496 to −337) based on the median. Considering both the fossil fuel emissions source and the land sink, our analysis shows that North America was, however, a net contributor to the growth of CO 2 in the atmosphere in the late 20th and early 21st century. With North America's mean annual fossil fuel CO 2 emissions for the period 1990–2009 equal to 1720 Tg C yr −1 and assuming the estimate of −472 Tg C yr −1 as an approximation of the true terrestrial CO 2 sink, the continent's source : sink ratio for this time period was 1720:472, or nearly 4:1.

Biogeosciences↗

USGS2009

Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30??m to 1??km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600??ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400??m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine-resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire landscape. Failure to account for wetlands had little impact on landscape-scale estimates, because vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

Remote Sensing of Environment↗