Accurate estimation of aboveground forest biomass stocks is required to assess the impacts of land use changes such as deforestation and subsequent regrowth on concentrations of atmospheric CO2. The Global Ecosystem Dynamics Investigation (GEDI) is a lidar mission launched by NASA to the International Space Station in 2018. GEDI was specifically designed to retrieve vegetation structure within a novel, theoretical sampling design that explicitly quantifies biomass and its uncertainty across a variety of spatial scales. In this paper we provide the estimates of pan-tropical and temperate biomass derived from two years of GEDI observations. We present estimates of mean biomass densities at 1 km resolution, as well as estimates aggregated to the national level for every country GEDI observes, and at the sub-national level for the United States. For all estimates we provide the standard error of the mean biomass. These data serve as a baseline for current biomass stocks and their future changes, and the mission’s integrated use of formal statistical inference points the way towards the possibility of a new generation of powerful monitoring tools from space.
Accurate estimation of aboveground forest biomass stocks is required to assess the impacts of land use changes such as deforestation and subsequent regrowth on concentrations of atmospheric CO2. The Global Ecosystem Dynamics Investigation (GEDI) is a lidar mission launched by NASA to the International Space Station in 2018. GEDI was specifically designed to retrieve vegetation structure within a novel, theoretical sampling design that explicitly quantifies biomass and its uncertainty across a variety of spatial scales. In this paper we provide the estimates of pan-tropical and temperate biomass derived from two years of GEDI observations. We present estimates of mean biomass densities at 1 km resolution, as well as estimates aggregated to the national level for every country GEDI observes, and at the sub-national level for the United States. For all estimates we provide the standard error of the mean biomass. These data serve as a baseline for current biomass stocks and their future changes, and the mission’s integrated use of formal statistical inference points the way towards the possibility of a new generation of powerful monitoring tools from space.
Great Basin bristlecone pine (Pinus longaeva) and foxtail pine (Pinus balfouriana) are valuable paleoclimate resources due to their longevity and climatic sensitivity of their annually-resolved rings. Treeline research has shown that growing season temperatures limit tree growth at and just below the upper treeline. In the Great Basin, the presence of precisely dated remnant wood above modern treeline shows that the treeline ecotone shifts at centennial timescales tracking long-term changes in climate; in some areas during the Holocene climatic optimum treeline was 100 meters higher than at present. Regional treeline position models built exclusively from climate data may identify characteristics specific to Great Basin treelines and inform future physiological studies, providing a measure of climate sensitivity specific to bristlecone and foxtail pine treelines. This study implements a topoclimatic analysis-using topographic variables to explain patterns in surface temperatures across diverse mountainous terrain-to model the treeline position of three semi-arid bristlecone and/or foxtail pine treelines in the Great Basin as a function of growing season length and mean temperature calculated from in situ measurements. Results indicate: (1) the treeline sites used in this study are similar to other treelines globally, and require a growing season length of between 147-153 days and average temperature ranging from 5.5°C-7.2°C, (2) site-specific treeline position models may be improved through topoclimatic analysis and (3) treeline position in the Great Basin is likely out of equilibrium with the current climate, indicating a possible future upslope shift in treeline position.
Tree‐ring chronologies from bristlecone pine (Pinus longaeva) are a unique proxy used to understand climate variability over the middle to late Holocene. The annual rings from trees growing toward the species' lower elevational range are sensitive to precipitation variability. Interpretation of the ring‐width signal at the upper forest border has been more difficult. We evaluate differences in climate induced by topography (topoclimate) to better understand the dual signals of temperature and moisture. We unmix signals from trees growing at and near the upper forest border based on the seasonal mean temperature (SMT) experienced by each tree. We find that trees growing in exposures with SMT <7.5 ∘C are limited by temperature, while trees with SMT > 7.5 ∘C are limited by moisture. We demonstrate this independently through analysis of growth in the frequency and time domains and using a process model of xylogenesis. Furthermore, we identify increasing moisture sensitivity in trees formerly limited by temperature.
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