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6 results for “global forest biomass”
Dataset linking to the paper "Exploring characteristics of national forest inventories for integration with global space-based forest biomass data"
<p>The dataset links to the study titled “Exploring characteristics of national forest inventories for integration with global space-based forest biomass data”. This study is published in the journal “Science of the Total Environment” and the publication can be found at <a href="https://doi.org/10.1016/j.scitotenv.2022.157788">https://doi.org/10.1016/j.scitotenv.2022.157788</a>. The dataset contains four csv files that were used to produce the results and other figures in the paper. The description of the individual data files contained in the dataset is given below.</p> <p><strong>NFI availability and characteristics data: </strong>The data file “NFI_availability_characteristics.csv” contains data on the total number of NFIs, the NFI extent, and the year of the most recent NFI in countries with NFI as reported in FRA 2020 country reports. The respective data variables in the data file are termed as Number_of_NFI, Latest_NFI_extent_FRA2020, and Latest_NFI_year_FRA2020 (NFI years generally refer to the years of data collection). In addition, the data file contains data on the region and tropical domain per country. The tropical and subtropical countries were considered tropical in the analysis and interpretation of the results. These data were used to produce Figure 2 of the study. ArcMap 10.7.1 was used for this purpose. </p> <p><strong>National biomass intercomparison data: </strong>The data file “national_biomass_intercomparison.csv” contains national forest AGB data for the year 2018 from FRA 2020 and CCI Biomass product that were used in the national biomass intercomparison analysis. The total (tons) and average space-based AGB (tons/ha) are extracted directly from the CCI Biomass Map 2018 for each country included in the study. The processing is done in Python and R environments. The spatial resolution of the map is 100 m. The average FRA AGB data in tons per ha was compiled from FRA 2020 country reports. The total FRA AGB data (tons) was estimated by multiplying each country's average FRA AGB data with FRA forest area data (in ha).</p> <p>The data unit for total AGB was converted from tons to gigaton (Gt) in intercomparison analysis. The total CCI Map AGB estimates used in the analysis are termed as CCI_MAP_AGB_Gt in the data file and the average as CCI_Map_AGB_tons.ha. Similarly, the total FRA AGB data are termed as FRA_AGB_Gt and the average as FRA_AGB_ton.ha. The NFI availability and temporality were also used in intercomparison analysis and this data is termed as Latest_NFI_year_FRA2020 in the data file. The data were used to produce Figure 3 of the study in the R environment.</p> <p><strong>NFI plot design characteristics: </strong>The data file named “NFI_plot_design_characteristics.csv” contains data on variables that were used in the analysis of NFI plot designs in 46 tropical countries. This data file mainly contains the data that was used to produce Figure 4 and Figure 6 in the R environment. The value “uniform” in the sampling_stratification variable means no stratification was used in the sampling design. The variable name “psu” stands for primary sampling unit (both cluster and single plots), “psu_distance_km” for the distance between primary sampling units in km, “cluster_plotdis_m” for the distance between plots in meter in the cluster, “plotsize_ha” for plot (single and cluster plots ) size in ha, “plotshape” for plot shapes (single and cluster plots), “ILUA” for Integrated Land Use Assessment. The data were compiled from the latest NFI design manuals and NFI reports.</p> <p><strong>NFI years: </strong>The data file “NFI_years_tropical_countries_data.csv” contains data on NFI years of the latest NFI in 46 tropical countries that were used to produce Figure 1 using ArcMap 10.7.1. The years generally refer to the last years of data collection. Data were compiled from the latest country NFI design manual or NFI report. This included both ongoing and completed NFI.</p>
Data from: Functional identity regulates aboveground biomass better than trait diversity along abiotic conditions in global forest metacommunities
<p>Although several studies have identified the effects of functional trait diversity (FTD) and/ or identity, i.e., the community-weighted mean (CWM) of a trait, on aboveground biomass (AGB) along abiotic conditions, these effects on AGB in global forest metacommunities are still largely unexplored. Here, we modelled the effects of abiotic (i.e., climate, soil and plot physical conditions) and biotic [i.e., FTD, CWM of conservative traits (CWMCT), CWM of acquisitive traits (CWMAT), and functional dominance (FunDom; based on CWM of plant maximum height or diameter)] factors on AGB in 76 forest metacommunities (from 24 studies). Using multiple linear regression models and piecewise structural equation modeling (pSEM), we tested the hypothesis that both abiotic and biotic factors regulate AGB, but that the mass ratio mechanism underpins AGB of metacommunities in global forests better than the niche complementarity mechanism. We found that abiotic and biotic factors contributed 45.39% and 54.07%, respectively, to the explained variance in AGB (<i>R<sup>2</sup></i> = 0.59), and as such, abiotic factors shaped FTD (<i>R<sup>2</sup></i> = 0.42 to 0.48), CWMCT (<i>R<sup>2</sup></i> = 0.33 to 0.36), CWMAT (<i>R<sup>2</sup></i> = 0.27 to 0.33), and FunDom (<i>R<sup>2</sup></i> = 0.59 to 0.61) through divergent effect sizes and directions. The final best-fitted pSEM showed that FunDom increased (<i>β</i> = 0.49) but CWMCT (<i>β</i> = -0.35) and CWMAT (<i>β</i> = -0.11) decreased AGB (<i>R<sup>2</sup></i> = 0.52) as compared to the negligible effect of FTD (<i>β</i> = 0.04). This study supports the mass ratio effect, specifically the overruling role of tall-stature or dominant trees on AGB, at a macroecological scale, and hence, suggests that a suitable species' functional strategy is important to promote carbon sequestration in forest metacommunities that underpins human well-being. We expect that our study will advance the field of biodiversity – ecosystem functioning at a macroecological scale by using the metacommunity concept and approach.</p>
Data from: Functional identity regulates aboveground biomass better than trait diversity along abiotic conditions in global forest metacommunities
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Classification of Global Forests for IPCC Aboveground Biomass Tier 1 Estimates, 2020
This dataset provides classes of global forests delineated by status/condition in 2020 at approximately 30-m resolution. The data support generating Tier 1 estimates for Aboveground dry woody Biomass Density (AGBD) in natural forests in the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Forest classes include primary, young secondary (<=20 years), and old secondary forests (>20 years). Classification was based on a Boolean combination of a suite of existing Earth Observation (EO) products of forest tree cover, height, age, and land use classification layers representing years 2000 to 2020. This forest status/condition classification prioritizes the reduction of potential errors of commission in the delineations by minimizing the inclusion of ambiguous pixels. Hence, it provides a conservative estimate of global forest area, identifying approximately 3.26 billion ha of forests worldwide. The classification was created on the collaborative open-science cloud-computing system, the ESA-NASA Multi-mission Analysis and Algorithm Platform (MAAP). The data are provided in cloud-optimized GeoTIFF format.
Global 1-degree Maps of Forest Area, Carbon Stocks, and Biomass, 1950-2010
This data set provides global forest area, forest growing stock, and forest biomass data at 1-degree resolution for the period 1950-2010. The data set is based on a compilation of forest area and growing stock data reported in international assessments performed by FAO, MCPFE (now Forest Europe), and UNECE. Data of different assessments are to the extent possible harmonized to reflect both forest area and other wooded land, to be comparable between countries and assessments.
A 5-km/5-year global forest aboveground biomass product from 1985-2015
<p>This dataset is associated with a research article entitled "A 5-km/5-year global forest aboveground biomass product from 1985-2015 based on integrated optical, radar and Lidar satellite data".</p>
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