Dataset Information

TreeMap, a tree-level model of conterminous US forests circa 2014 produced by imputation of FIA plot data.

ABSTRACT: A 30?×?30m-resolution gridded dataset of forest plot identifiers was developed for the conterminous United States (CONUS) using a random forests machine-learning imputation approach. Forest plots from the US Forest Service Forest Inventory and Analysis program (FIA) were imputed to gridded c2014 landscape data provided by the LANDFIRE project using topographic, biophysical, and disturbance variables. The output consisted of a raster map of plot identifiers. From the plot identifiers, users of the dataset can link to a number of tree- and plot-level attributes stored in the accompanying tables and in the publicly available FIA DataMart, and then produce maps of any of these attributes, including number of trees per acre, tree species, and forest type. Of 67,141 FIA plots available, 62,758 of these (93.5%) were utilized at least once in imputation to 2,841,601,981 forested pixels in CONUS. Continuous high-resolution forest structure data at a national scale will be invaluable for analyzing carbon dynamics, habitat distributions, and fire effects.

SUBMITTER: Riley KL

PROVIDER: S-EPMC7810723 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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TreeMap, a tree-level model of conterminous US forests circa 2014 produced by imputation of FIA plot data.

Riley Karin L KL Grenfell Isaac C IC Finney Mark A MA Wiener Jason M JM

Scientific data 20210115 1

A 30 × 30m-resolution gridded dataset of forest plot identifiers was developed for the conterminous United States (CONUS) using a random forests machine-learning imputation approach. Forest plots from the US Forest Service Forest Inventory and Analysis program (FIA) were imputed to gridded c2014 landscape data provided by the LANDFIRE project using topographic, biophysical, and disturbance variables. The output consisted of a raster map of plot identifiers. From the plot identifiers, users of th ...[more]

PMID: 33452261

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Project description:BackgroundWith the introduction of the Trillion Trees Initiative and similar programs, forests' ability to absorb carbon dioxide is increasingly in the spotlight. Many states have mandates to develop climate action plans, of which forest carbon is an important component, and planners need current information on forest carbon stocks and rates of change at relevant spatial scales. To this end, we examine rates of average annual change in live aboveground tree carbon in different forest type groups and provide state-wide and regional summaries of current live tree carbon stock and rates of change for the forests of the conterminous United States. Forest carbon summaries are presented in a format designed to meet the needs of managers, policymakers, and others requiring current estimates of aboveground live tree carbon at state and regional scales.ResultsRegional average aboveground live tree carbon stocks (represented on a per area basis) are generally between 40 and 75 tC/ha but range from 12.8 tC/ha in the Great Plains to 130 tC/ha in the Pacific Northwest West (west-side of Cascades). Regional average annual change in live aboveground tree carbon varies from a low of - 0.18 mtC/ha/y in the Rocky Mountain South to a high value of 1.74 mtC/ha/y in Pacific Northwest West. For individual states, carbon per unit area varies widely, from a low of 11.9 tC/ha in Nevada to a high of 96.4 tC/ha in Washington, with half the states falling between 50 and 75 tC/ha. Rates of average annual change in live aboveground tree carbon vary from a high of 1.82 tC/ha/y in Mississippi to a low of - 0.47 tC/ha/y in Colorado.ConclusionsAboveground live tree carbon stocks and rates of average annual change vary by forest type within regions. While softwood forest types currently exhibit a higher rate of increase in the amount of carbon in aboveground live tree biomass, the current standing stock of carbon per unit area does not consistently follow this pattern. For this reason, we recommend computing and considering both measures -standing stock and average annual change-of carbon storage. The relative importance of each component will depend on management and policy objectives and the time frame related to those objectives. Harvesting and natural disturbance also affect forest carbon stock and change and may need to be considered if developing projections of potential carbon storage.

Project description:Tracking the response of forest ecosystems to climate change demands large (≥1 ha) monitoring plots that are repeatedly measured over long time frames and arranged across macro-ecological gradients. Continental scale networks of permanent forest plots have identified links between climate and carbon fluxes by monitoring trends in tree growth, mortality and recruitment. The relationship between tree growth and climate in Australia has been recently articulated through analysis of data from smaller forest plots, but conclusions were limited by (a) absence of data on recruitment and mortality, (b) exclusion of non-eucalypt species, and (c) lack of knowledge of stand age or disturbance histories. To remedy these gaps we established the Ausplots Forest Monitoring Network: a continental scale network of 48 1 ha permanent plots in highly productive tall eucalypt forests in the mature growth stage. These plots are distributed across cool temperate, Mediterranean, subtropical and tropical climates (mean annual precipitation 850 to 1900 mm per year; mean annual temperature 6 to 21°C). Aboveground carbon stocks (AGC) in these forests are dominated by eucalypts (90% of AGC) whilst non-eucalypts in the understorey dominated species diversity and tree abundance (84% of species; 60% of stems). Aboveground carbon stocks were negatively related to mean annual temperature, with forests at the warm end of the temperature range storing approximately half the amount of carbon as forests at the cool end of the temperature range. This may reflect thermal constraints on tree growth detected through other plot networks and physiological studies. Through common protocols and careful sampling design, the Ausplots Forest Monitoring Network will facilitate the integration of tall eucalypt forests into established global forest monitoring initiatives. In the context of projections of rapidly warming and drying climates in Australia, this plot network will enable detection of links between climate and growth, mortality and carbon dynamics of eucalypt forests.

Dataset Information

TreeMap, a tree-level model of conterminous US forests circa 2014 produced by imputation of FIA plot data.

Publications

TreeMap, a tree-level model of conterminous US forests circa 2014 produced by imputation of FIA plot data.

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