This dataset shows the modelled global patterns of above-ground biomass of mangrove forests. The dataset was developed by the Department of Zoology, University of Cambridge, with support from The Nature Conservancy. The work is based on a review of 95 field studies on carbon storage and fluxes in mangroves world-wide. A climate-based model for potential mangrove above-ground biomass was developed, with almost four times the explanatory power of the only previous published model. The map highlights the high variability in mangrove above-ground biomass and indicates areas that could be prioritised for mangrove conservation and restoration.

Citation: Hutchison J, Manica A, Swetnam R, Balmford A, Spalding M (2014) Predicting global patterns in mangrove forest biomass. Conservation Letters 7(3): 233–240. doi: 10.1111/conl.12060; http://data.unep-wcmc.org/datasets/39

Use Constraints: UNEP-WCMC General Data License. For commercial use, please contact business-support@unep-wcmc.org.

This dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at a 300-m spatial resolution. The aboveground biomass map integrates land-cover specific, remotely sensed maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed from the published literature and, where necessary, updated to cover the focal extent or time period. The belowground biomass map similarly integrates matching maps derived from each aboveground biomass map and land-cover specific empirical models. Aboveground and belowground maps were then integrated separately using ancillary maps of percent tree cover and landcover and a rule-based decision tree. Maps reporting the accumulated uncertainty of pixel-level estimates are also provided. Provider's note: The UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) carbon biomass dataset represents conditions between 1982 and 2010 depending on land cover type. The relative patterns of carbon stocks are well represented with this dataset. The NASA/ORNL carbon biomass dataset represents biomass conditions for 2010, with uncertainty estimates at the pixel-level. Additional biomass of non-dominant land cover types are represented within each pixel. For more detailed information, please refer to the papers describing each dataset: WCMC (Soto-Navarro et al. 2020) and NASA/ORNL (Spawn et al. 2020).

Seychelles Ecosystem Services: Seagrass and Mangrove Blue CarbonMangroves and seagrasses represent rich sources of blue carbon, that is carbon stored and sequestered by coastal and marine ecosystems. In mangroves, carbon is stored and sequestered in living aboveground biomass and in the soil.Model OutputsMangrove carbon: Estimates of mangrove carbon have been calculated for Seychelles using the global mangrove map developed by Global Mangrove Watch (GMW) 2016. Unfortunately, the GMW misses several key mangrove areas in the Seychelles, most notably in the Aldabra group. While these errors are being amended in newer versions of the global mangrove map, the GMW base-map is relatively low resolution while the mangrove layer created for the Seychelles MSP (Klaus 2015) provides higher resolution and an estimate of 30.7 km2 of total mangroves. Existing estimates of mangrove carbon for the Seychelles from the global extents are thus major undestimates. To improve estimates of carbon stored in Seychelles’ mangroves, we used the Global Mangrove Watch models of aboveground biomass (AGB) (derived from Simard et al. 2019) and soil organic carbon (SOC) (derived from Sanderman et al. 2018) and applied them to the locally-derived mangrove map layer (Klaus 2015). For areas of the local-scale layer that overlapped with the global carbon estimates, we used zonal statistics to find the mean AGB and SOC values (expressed in MgC per ha) per mangrove polygon. We then multiplied this value by the area (in ha) for each polygon to get the total values AGB and SOC values per polygon. For local-scale mangrove polygons that did not overlap with the global carbon estimates, we used a spatial join to assign the nearest AGB and SOC values to each polygon, and converted the values from MgC per ha by multiplying the value by the polygon area to obtain total AGB and SOC. To convert AGB to aboveground carbon (AGC), we used a conversion factor of 0.451 (Simard et al. 2019); AGC and SOC values were summed to get total carbon values. Seagrass carbon: As no known global or local-scale estimates of seagrass carbon exist for Seychelles, we provide an estimate based on maps of seagrass derived for the MSP (Klaus 2015). These maps assign a density class (high, medium, low) to each mangrove polygon. To estimate the above and belowground biomass for each seagrass polygon, we used aboveground and belowground dry weight biomass estimates per unit area (m2) for low, medium, an high density seagrass from Mallombasi et al. (2020). These biomass values were then converted to carbon using a conversion factor of 0.35 (from Fourqueran et al. 2012) and then converted to total carbon by multiplying by the area of the seagrass polygon. Model Output Datasets Seagrass Blue Carbon Dataset name: Seychelles_Seagrass_Blue_Carbon.shp Dataset type: ESRI File Geodatabase, polygon feature class Values: Estimated seagrass blue carbon (summed by polygon) Field ValuesAGgDWm2 Above-ground dry weight biomass estimates per unit area (meters squared) for low, medium and high density seagrassBGDWm2 Below-ground dry weight biomass estimates per unit area (meters squared) for low, medium and high density seagrassAGMgCha AGgDWm2 converted to aboveground carbon (MgC) per unit area (hectare). Biomass converted to carbon using 0.35 carbon conc from Fourqueran et al (2012)BGMgCha BGgDWm2 converted to aboveground carbon (MgC) per unit area (hectare). Biomass converted to carbon using 0.35 carbon concentration from Fourqueran et al (2012)socMgCha Mean soil organic carbon (Mg) per unit area (hectare) TotMgCha AGC + BGC + SOC per unit area (hectares)TotMgC TotMgCha Mean soil organic carbon (Mg) per unit area (hectare) multiplied by the area estimate of each unique polygon (MgCha * ha)TotTgC TotMgC converted to teragrams (TgC) Mangrove Blue Carbon Dataset name: Seychelles_Mangrove_Blue_Carbon.shp Dataset type: ESRI File Geodatabase, polygon feature class Values: Estimated mangrove blue carbon (summed by polygon) Field ValuesAGC_ton Aboveground carbon, metric tonnesSOC_ton Soil organic carbon, metric tonnesTotal_ton AGC_ton + SOC_tonHa Unique polygon areal estimate in hectares References: Fourqurean, J. W., Duarte, C. M., Kennedy, H., Marbà, N., Holmer, M., Mateo, M. A., ... & Serrano, O. (2012). Seagrass ecosystems as a globally significant carbon stock. Nature geoscience, 5(7), 505-509. Klaus, R. (2015). Strengthening Seychelles ’ protected area system through NGO management modalities. Mallombasi, A., Mashoreng, S., & La Nafie, Y. A. (2020). The relationship between seagrass Thalassia hemprichii percentage cover and their biomass. Jurnal Ilmu Kelautan SPERMONDE, 6(1), 7-10. Palacios, M. M., Waryszak, P., de Paula Costa, M. D., Wartman, M., Ebrahim, A., & Macreadie, P. I. (2021). Literature Review: Blue Carbon research in the Tropical Western Indian Ocean.Simard, M., Fatoyinbo, T., Smetanka, C., Rivera-Monroy, V. H., CASTANEDA, E., Thomas, N., & Van der Stocken, T. (2019). Global Mangrove Distribution, Aboveground Biomass, and Canopy Height. ORNL DAAC. Bunting P., Rosenqvist A., Lucas R., Rebelo L-M., Hilarides L., Thomas N., Hardy A., Itoh T., Shimada M. and Finlayson C.M. (2018). The Global Mangrove Watch – a New 2010 Global Baseline of Mangrove Extent. Remote Sensing 10(10): 1669. doi: 10.3390/rs1010669.

Thomas N, Lucas R, Bunting P, Hardy A, Rosenqvist A, Simard M. (2017). Distribution and drivers of global mangrove forest change, 1996-2010. PLOS ONE 12: e0179302. doi: 10.1371/journal.pone.0179302

This Global Ecosystem Dynamics Investigation (GEDI) L4B product provides 1 km x 1 km (1 km, hereafter) estimates of mean aboveground biomass density (AGBD) based on observations from mission week 19 starting on 2019-04-18 to mission week 138 ending on 2021-08-04. The GEDI L4A Footprint Biomass product converts each high-quality waveform to an AGBD prediction, and the L4B product uses the sample present within the borders of each 1 km cell to statistically infer mean AGBD. The gridding procedure is described in the GEDI L4B Algorithm Theoretical Basis Document (ATBD). Patterson et al. (2019) describes the hybrid model-based mode of inference used in the L4B product. Corresponding 1 km estimates of the standard error of the mean are also provided in the L4B product. Uncertainty is due to both GEDI's sampling of the 1 km area (as opposed to making wall-to-wall observations) and the fact that L4A biomass values are modeled in a process subject to error instead of measured in a process that may be assumed to be error-free.

This repository contains the global datasets of aboveground biomass, canopy height and cover accompanying the publication:

Weber, M.; Beneke, C.; Wheeler, C. Unified Deep Learning Model for Global Prediction of Aboveground Biomass, Canopy Height, and Cover from High-Resolution, Multi-Sensor Satellite Imagery. Remote Sens. 2025, 17, 1594. https://doi.org/10.3390/rs17091594

The dataset consists of GeoTIFF files covering a latitude range from 57° S to 67° N in splits of 3° x 3° per file. Each file contains 6 bands corresponding to the model outputs with the following order:

1. aboveground biomass density (Mg/ha), AGBD
2. canopy height (cm), CH
3. canopy cover (%), CC
4. standard error of AGBD
5. standard error of CH
6. standard error of CC

In addition, an alpha band is included indicating the valid pixels. Non-valid pixels are masked based on the following conditions:

1. is water or
2. is urban build up

Due to the storage limit on Zenodo, we provide a sub-sample of 9 globally distributed files (48 GB) of the full dataset (5 TB) in this repository.

The files have the following naming convention: earthdaily_agbd_{lon}_{lat}-[data, alpha].tif
We provide the full list of files contained in this dataset in filelist.txt. The sub-sample of files contained in this repository are listed in filelist_sample.txt.

The full dataset can be retrieved from a public S3 bucket on AWS as a Requester-Pays service. Note that no transfer costs are incurred if downloading to an AWS resource within the same region (us-west-2). For further details on data transfer costs we refer to the AWS documentation. We encourage users to create their own AWS account (if not already existing) and transfer individual files within the same region by:

aws s3 cp s3://eda-appsci-open-access/biomass/earthdaily_agbd_{lon}_{lat}-[data, alpha].tif DESTINATION_PATH --request-payer requester

or the full dataset by:

aws s3 sync s3://eda-appsci-open-access/biomass/ DESTINATION_PATH --request-payer requester

For access without an AWS account, please contact the corresponding author (Manuel Weber).

Files in this repository:

• AGBD_CH_CC_sample_2023.zip; archive of 9 globally distributed sample GeoTIFFs
• filelist_sample.txt; S3 paths of the sample files
• filelist.txt; S3 paths of the full dataset

This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) Version 2 predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. In this version, the granules are in sub-orbits. The algorithm setting group selection used for GEDI02_A Version 2 has been modified for Evergreen Broadleaf Trees in South America to reduce false positive errors resulting from the selection of waveform modes above ground elevation as the lowest mode. The footprints are located within the global latitude band observed by the International Space Station (ISS), nominally 51.6 degrees N and S and reported for the period 2019-04-18 to 2021-08-05. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. Footprint AGBD was derived from parametric models that relate simulated GEDI Level 2A (L2A) waveform relative height (RH) metrics to field plot estimates of AGBD. Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFTs) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands). For each of the eight beams, additional data are reported with the AGBD estimates, including the associated uncertainty metrics, quality flags, model inputs, and other information about the GEDI L2A waveform for this selected algorithm setting group. Model inputs include the scaled and transformed GEDI L2A RH metrics, footprint geolocation variables and land cover input data including PFTs and the world region identifiers. Additional model outputs include the AGBD predictions for each of the six GEDI L2A algorithm setting groups with AGBD in natural and transformed units and associated prediction uncertainty for each GEDI L2A algorithm setting group. Providing these ancillary data products will allow users to evaluate and select alternative algorithm setting groups. Also provided are outputs of parameters and variables from the L4A models used to generate AGBD predictions that are required as input to the GEDI04_B algorithm to generate 1-km gridded products. Note that there are 351 granules in this release affected by duplicate GEDI shots for selected days (2020-297 to 2020-300, 2020-365, and 2021-106).

Above-ground biomass in forests is critical to the global carbon cycle as it stores and sequesters carbon from the atmosphere. Climate change will disrupt the carbon cycle hence understanding how climate and other abiotic variables determine forest biomass at broad spatial scales is important for validating and constraining Earth System models and predicting the impacts of climate change on forest carbon stores. We examined the importance of climate and soil variables to explaining above-ground biomass distribution across the Australian continent using publicly available biomass data from 3130 mature forest sites, in 6 broad ecoregions, encompassing tropical, subtropical, and temperate biomes. We used the Random Forest algorithm to test the explanatory power of 14 abiotic variables (8 climate, 6 soil) and to identify the best-performing models based on climate-only, soil-only, and climate plus soil. The best performing models explained ~50% of the variation (climate-only: R² = 0.47 ± 0.04, and climate plus soils: R² = 0.49 ± 0.04). Mean temperature of the driest quarter was the most important climate variable, and bulk density was the most important soil variable. Climate variables were consistently more important than soil variables in combined models, and model predictive performance was not substantively improved by the inclusion of soil variables. This result was also achieved when the analysis was repeated at the ecoregion scale. Predicted forest above-ground biomass ranged from 18 to 1066 Mg ha^-1, often under-predicting measured above-ground biomass, which ranged from 7 to 1500 Mg ha^-1. This suggested that other non-climate, non-edaphic variables impose a substantial influence on forest above-ground biomass, particularly in the high biomass range. We conclude that climate is a strong predictor of above-ground biomass at broad spatial scales and across large environmental gradients, yet to predict forest above-ground biomass distribution under future climates, other non-climatic factors must also be identified.

This dataset provides maps of aboveground forest biomass (AGB) of living trees and standing dead trees in Mg/ha across portions of Northwestern United States, including Washington, Oregon, Idaho, and Montana, at a spatial resolution of 30 m. Forest inventory data were compiled from 29 stakeholders that had overlapping lidar imagery. The collection totaled 3805 field plots with lidar imagery for 176 collections acquired between 2002 and 2016. Plot-level AGB estimates were calculated from tree measurements using the default allometric equations found in the Fire Fuels Extension (FFE) of the Forest Vegetation Simulator (FVS). The random forest algorithm was used to model AGB from lidar height and density metrics that were generated from the lidar returns within fixed-radius field plot footprints, gridded climate metrics obtained from the Climate-FVS Ready Data Server, and topographic estimates extracted from Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global elevation rasters. AGB was then mapped from the same lidar metrics gridded across the extent of the lidar collections at 30-m resolution. The standard deviation of estimated AGB of the terminal nodes from the random forest predictions was also mapped to show pixel-level model uncertainty. Note that the AGB estimates are, for the most part, a single snapshot in time and that the forest conditions are not necessarily representative of the larger study area.

Aim Existing global models to predict standing woody biomass are based on trees characterized by a single principal stem, well-developed in height. However, their use in open woodlands and shrublands, characterized by multistemmed species with substantial crown development, generates a high level of uncertainty in biomass estimates. This limitation led us to i) develop global predictive models of shrub individual aboveground biomass based on simple allometric variables; ii) to compare the fit of these models with existing global biomass models; and iii) to assess whether models fit change when bioclimatic variables are considered. Location Global. Time period Present. Major taxa studied 118 species. Methods We compile a database of 3243 individuals across 49 sites distributed worldwide. Including basal diameter, height and crown diameter as predictor variables, we built potential models and compared their fit using generalized least squares. We used mixed effects models to determine if ...

This dataset includes maps of canopy height and aboveground biomass at spatial resolutions of 25 m and 100 m for Mexico, Gabon, French Guiana, and the Amazon Basin. The GEDI-TanDEM-X (GTDX) fusion maps were created by combining data from NASA's Global Ecosystem Dynamics Investigation (GEDI) Version 2 footprint data (from 2019-04-18 to 2021-08-18) and TanDEM-X (abbreviated as TDX) Interferometric Synthetic Aperture Radar (InSAR) images (from 2011-01-06 to 2020-12-31). The GTDX canopy height maps were generated by using the TDX coherence maps to invert the TDX height and subsequently using GEDI canopy height as reference data to calibrate the inverted height. The GTDX aboveground biomass maps were produced based on a generalized hierarchical model-based (GHMB) framework that utilizes GEDI biomass as training data to establish models for estimating biomass based on the GTDX canopy height. The dataset also includes maps of canopy height uncertainty, biomass uncertainty, and ancillary data including a regional modeling parameter and forest disturbance. The uncertainty of GTDX canopy height was estimated for each pixel by propagating the GEDI-TDX model error to each GTDX pixel prediction. The uncertainty of GTDX aboveground biomass was estimated by considering the error in both the GEDI footprint biomass data and the GEDI-TDX model, and then applying it to each GTDX biomass pixel prediction. The regional model parameter indicates the size of the analysis window (2 to 50 km or country wide) used for each pixel. The forest disturbance information identifies pixels where disturbance occurred between 2011 and 2020, and provides the year of last disturbance.

This is the initial version of the data and code used in the paper, "Canopy height and biomass distribution across the forests of Iberian Peninsula".AuthorsYang Su a, b, c, Martin Schwartz b, Ibrahim Fayad b, García Alonso Mariano d, Miguel A. Zavala e, Julián Tijerín-Triviño e, Julen Astigarraga e, Verónica Cruz f, Siyu Liu g, Xianglin Zhang c, h, Songchao Chen h,i, François Ritter b, Nikola Besic j, Alexandre d'Aspremont a, Philippe Ciais bAffiliationsa Département d'Informatique, École Normale Supérieure – PSL, 45 Rue d'Ulm, 75005 Paris, Franceb Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ Orme des Merisiers, 91190 Gif-sur-Yvette, Francec UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 91120 Palaiseau, Franced University of Alcalá, Department of Geology, Geography and the Environment, Enviromental Remote Sensing Research Group, 28801 Alcalá de Henares, Spaine University of Alcalá, Department of Life Sciences, 28801 Alcalá de Henares, Spainf Department of Biodiversity, Ecology and Evolution, Complutense University of Madrid, 28040 Madrid, Spaing Department of Geosciences and Natural Resource Management, Copenhagen University, 1958 Frederiksberg, Denmarkh College of Environmental and Resource Sciences, Zhejiang University, 310058 Hangzhou, Chinai ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, 311215 Hangzhou, Chinaj IGN, ENSG, Laboratoire d'inventaire forestier (LIF), 54000 Nancy, FranceCorresponding AuthorYang Suyang.su@ens.fr+33 1 89 10 07 67École normale supérieure - PSLTo use the data and code, please cite this study, and in case of difficulties when using the model and data, please contact the corresponding author for further details and possible assistance.The maps of canopy height and above-ground biomass provided by this study can be found on GEE, detailed information about how to access those datasets can be found in Table 1 and Table 2 in this study. A preview of those maps can be found here: https://ens-yangsu-forest-spain-als.projects.earthengine.app/view/ai4forest-iberian-peninsula

Global climate change has markedly influenced the structure and distribution of mid-high-latitude forests. In the forest region of Northeast China, the magnitude of climate warming surpasses the global average, which presents immense challenges to the survival and habitat sustainability of dominant tree species. We predicted the potential changes in aboveground biomass, dominant tree species composition, and distribution in the forest region of Northeast China over the next century under different climatic conditions encompassing the current scenario and future scenarios (RCP2.6, RCP4.5, and RCP8.5). Forest ecosystem process model LINKAGES 3.0 was used to simulate dynamic changes in species-level aboveground biomass under four climate scenarios at the homogeneous land-type unit level. The potential spatial distribution of tree species was investigated based on three indicators: extinction, colonization, and persistence. The results showed that LINKAGES 3.0 model effectively simulated the aboveground biomass of 17 dominant tree species in the forest region of Northeast China, achieving a high accuracy with R² = 0.88. Under the current, RCP2.6, and RCP4.5 climate scenarios, the dominant tree species presented gradual increases in aboveground biomass, whereas under RCP8.5, an initial increase and subsequent decline were observed. With increasing warming magnitude, cold-temperate coniferous tree species will gradually be replaced by other temperate broad-leaved tree species. Furthermore, a large temperature increase under RCP8.5 will likely produce a significant contraction in the potential distribution range of tree species like Larch, Scotch pine, Ribbed birch, Spruce and Fir, while most temperate broad-leaved tree species and Korean pine are expected to demonstrate a northward migration. These findings provide guidance for enhancing the adaptability and resilience of forest ecosystems in middle and high latitudes and addressing the threats posed by climate warming.

This dataset provides a record of fuel characteristics at high spatiotemporal resolution: ~9km, daily. The two main variable groups are fuel load and fuel moisture, both of which are further divided by live/dead and wood/foliage fractions. The dataset combines state-of-the-art model data (ERA5-Land) with observations from multiple satellites and in-situ variables into a globally complete and consistent dataset. The data provides high spatiotemporal resolution data of fuel load, which is essential for modelling wildfire activity, which contributes directly to the derivation of multiple Essential Climate Variables (ECVs). The data is relevant not only for the wildfire community but also for studying various biogeochemical processes related to land-atmosphere interactions. The fuel load is initially informed using the static European Space Agency Climate Change Initiative biomass product (ESA-CCI), which provides high-resolution estimates of above ground biomass. Crucially our dataset adds a time evolution of the dataset to provide a daily product with accurate seasonality based on modelled Carbon Dixoide (CO₂) exchange. Furthermore, by collecting estimates on biomass allocation and modelling techniques the dataset allocates the biomass into 4 categories of fuel load, which is relevant for wildfires but not currently estimated from the ESA-CCI product. Dead fuel moisture content is based on existing modelling principles which have not been scaled up to the global scale nor have they used the high accuracy mapping and output data available within the Integrated Forecast System (IFS) or the the fifth generation of reanalysis data produced by the European Centre for Medium-Range Weather Forecasts (ERA5). The live fuel moisture model was trained on a global insitu dataset which is based on sampling and does not provide a global product, this is the first attempt to achieve such a product using modelling. All variables were validated using independent observations when available. This is a first version of the dataset which will be updated to provide improved accuracy and expanded through time.

Aim: Despite mounting empirical evidence regarding the positive effects of forest structural diversity (STRDIV) on forest functioning, the underlying biotic mechanisms and controlling abiotic factors remain poorly understood. This study provides the first assessment of the interactive effects of STRDIV and diversity in species and functional traits on aboveground biomass (AGB) in natural forests in West and East Africa. Location: West and East Africa Time period: 2014-2020 Major taxa studied: Woody plants Methods: Using data from 276 plots and 7993 trees of 207 species distributed across various types of natural forests and major climatic zones of Africa, linear mixed-effects and structural equation models, we have evaluated how alternative causal relationships between STRDIV and taxonomic and functional diversity attributes influence AGB, while accounting for the effects of environmental covariates. We also assessed the consistency of these relationships across floristically and enviro..., The dataset results from forest inventories conducted in 276 plots, distributed across four West African countries (Benin, Burkina Faso, Togo, and Ghana) and one East African country (Ethiopia) from 5°40ʹ W to 48°23 ʹE longitude and 3°18ʹ N to 15°03ʹ N latitude. The plots spanned various climate types of the region, including arid, semi-arid, dry sub-humid, and humid, and covered a broad range of topographic and edaphic gradients. The forest inventories were conducted between 2014 and 2020. The plot size was on average 0.1 ha and ranged from 0.02 to 0.25 ha. Within each plot, several key dendrometric parameters including height and diameter at breast height (DBH) were systematically recorded for all living individual trees that met specific criteria. Environmental factors were also either recorded in the field or downloaded from publicly available databses., , # Both the selection and complementarity effects underpin the effect of structural diversity on aboveground biomass in tropical forests

https://doi.org/10.5061/dryad.dfn2z3582

Description of the data and file structure

The zipped file in Dryad contains the data necessary to reproduce the statistical analyses published in the manuscript "Both the selection and complementarity effects underpin the effect of structural diversity on aboveground biomass in tropical forests" in Global Ecology and Biogeography by Noulèkoun et al.

The file includes 3 files, whose content is described below.

1- Main database "dataall_Noulekoun_GEB" This is .csv document that contains all the variables used in the statistical analysis are displayed along with their values per plot. The names of the variables are abbreviated in this document and their description is provided in the second file entitled "Description_abbreviations_Noulekoun" (see also Table bel...

Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate “wall-to-wall” remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution. Forest Inventories used for AGB estimates in French GuianaThe xls file contains one readme and two datasets derived from two forest surveys : "inventaire papetier" inventory done by CTFT between 1974 and 1976 (coded "pap"), and "inventaire habitat" inventory done by ONF between 2006 and 2013 (coded "hab").The datasets include plots coordinates and areas, estimates of mean wood specific gravity (WSG) for each plots, and the number of trees per DBH classes for each plots.DataAGB.xlsx

This dataset and R script accompany the published paper Bruns et al. (2024) in Geophysical Research Letters. The data are from the first six years of a field manipulation of whole-ecosystem warming and elevated CO2 experiment (Salt Marsh Accretion Response to Temperature eXperiment, or SMARTX) in the Smithsonian's Global Change Research Wetland (GCReW), a brackish, microtidal wetland site on a subestuary of the Chesapeake Bay. These data were generated to understand how warming and elevated CO2 interact to structure ecosystem-level responses to global change, particularly in terms of carbon sequestration. The dataset covers 2017-2022 and includes peak annual above ground biomass, annual belowground fine root productivity, and porewater NH4 for each experimental plot. The overall experiment is replicated in two locations on the marsh, a lower elevation zone dominated the C3 sedge S. Americanus and a higher elevation plot dominated by the C4 species. This paper and its data release is only for the C3 plot. Variable descriptions for data file is available in variable_descriptions.pdf. The R script Bruns_et_al_2024_GRL_make_figures.Rmd contains model code and other scripts used to generate all paper figures.

This is the data used or generated in the paper, "Canopy height and biomass distribution across the forests of Iberian Peninsula".

Authors

Yang Su ^{a, b, c}, Martin Schwartz ^b, Ibrahim Fayad ^b, García Alonso Mariano ^d, Miguel A. Zavala ^e, Julián Tijerín-Triviño ^e, Julen Astigarraga ^e, Verónica Cruz ^f, Siyu Liu ^g, Xianglin Zhang ^{c, h}, Songchao Chen ^h,i, François Ritter ^b, Nikola Besic ^j, Alexandre d'Aspremont ^a, Philippe Ciais ^b

Affiliations

^a Département d'Informatique, École Normale Supérieure – PSL, 45 Rue d'Ulm, 75005 Paris, France

^b Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ Orme des Merisiers, 91190 Gif-sur-Yvette, France

^c UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 91120 Palaiseau, France

^d University of Alcalá, Department of Geology, Geography and the Environment, Enviromental Remote Sensing Research Group, 28801 Alcalá de Henares, Spain

^e University of Alcalá, Department of Life Sciences, 28801 Alcalá de Henares, Spain

^f Department of Biodiversity, Ecology and Evolution, Complutense University of Madrid, 28040 Madrid, Spain

^g Department of Geosciences and Natural Resource Management, Copenhagen University, 1958 Frederiksberg, Denmark

^h College of Environmental and Resource Sciences, Zhejiang University, 310058 Hangzhou, China

ⁱ ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, 311215 Hangzhou, China

^j IGN, ENSG, Laboratoire d'inventaire forestier (LIF), 54000 Nancy, France

Corresponding Author

Yang Su

yang.su@ens.fr

+33 1 89 10 07 67

École normale supérieure - PSL

To use the data, please cite this study, and in case of difficulties when using the model and data, please contact the corresponding author for further details and possible assistance

The maps of canopy height and above-ground biomass provided by this study can be found on Zenodo, detailed information about how to access those datasets can be found in Table 1-5 in this study. A preview of those maps can be found here: https://ens-yangsu-forest-spain-als.projects.earthengine.app/view/ai4forest-iberian-peninsula

Funding

Artificial Intelligence for forest monitoring from space – AI4Forests

Agence Nationale de la Recherche

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MAZ, JTT, JA and VCA acknowledge support from the Spanish Ministry of Science and Innovation (grant LARGE, Nº PID2021-123675OB-C41).

MG acknowledges support from the Spanish Ministry of Science and Innovation (grant REMOTE, Nº PID2021-123675OB-C42).

VCA was supported by the Ministry of Universities, Spain, and Next Generation-EU, with “Maria Zambrano” fellowship.

Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of field measurements to map woody above-ground biomass (AGB) across tropical Africa. A mosaic of best-quality cloud-free MODIS satellite reflectance observations [MODIS Nadir bidirectional reflectance distribution function adjusted reflectances (NBAR) product (MOD43B4.V4)] for the period 2000–2003 provided cloud-free spectral reflectance data for the entire region, about 20 million km2 of tropical Africa, covered by 19 MODIS tiles. The region is characterized by a diverse range of moist tropical forest, seasonal and semi-arid woodland, savanna, and wetland forests. Field measurements were then used to calibrate a regression tree model that estimated AGB for each 1 km2 pixel as a function of the spectral information derived from MODIS data. The results were cross-validated using a reserved set of field data, as well as independent lidar measurements from the Geoscience Laser Altimeter System (GLAS). The results showed a strong positive correlation (R2 = 0. 90) between the GLAS height metrics and predicted AGB. Reference: Baccini, A., N. Laporte, S.J., Goetz, M. Sun, H. Don. 2008. A first map of Tropical Africa’s above-ground biomass derived from satellite imagery. Environmental Research Letters 3 – 045011.

Distribution of harvested trees across tree size used in biomass estimation models.

This dataset consists of seagrass shoot height and biomass assessments from an experiment which compared control plots to exclusion cage plots which prevented megaherbivore grazing. This experiment …Show full descriptionThis dataset consists of seagrass shoot height and biomass assessments from an experiment which compared control plots to exclusion cage plots which prevented megaherbivore grazing. This experiment ran at two sites from September 2021 to April 2022. We use a short-term field study, adapting recent methods applied in the Great Barrier Reef to investigate the role of megaherbivore grazing in two key locations where seagrass declines have been most dramatic: the Orman Reefs and Mabuyag Island. Koey Maza on Orman Reefs is an intertidal reef-top meadow dominated by the common reef-associated seagrass species Thalassia hemprichii. Mabuyag Island is a diverse intertidal meadow with up to seven species present; in recent surveys this meadow has been dominated by either Cymodocea serrulata or T. hemprichii. Megaherbivore exclusion cages were used to prevent green turtles and dugong grazing on small areas of seagrass. Six steel megaherbivore exclusion cages 2 x 2 x 0.5 m were deployed in the seagrass at each location and secured with steel pegs, six control plots 2 x 2 m were established adjacent to exclusion cages and corners were marked with star pickets. Seagrass metrics (biomass and canopy height) inside cages and adjacent control plots were measured at the beginning (September 2021), during the experiment at two months (November 2021) and six months, (March 2022) and at the end of the experiment after seven months (April 2022) to understand the grazing pressure on seagrass meadows in both locations. Previous studies in tropical locations using the same exclusion cages have shown that experimental units do not impact the light environment (Scott et al 2020, 2021). Within each plot, three replicate 0.5m2 quadrats were used to collect data on the seagrass meadow. Seagrass canopy height was measured by grasping a handful of seagrass and ignoring the longest 20% (Duarte and Kirkman 2001), four canopy height measurements were taken from each of the three quadrats. Seagrass aboveground biomass was measured in each quadrat using assessments in the field and post-field calibrations following the methods described in Mellors (1991). The effects of time and treatment (and their interaction) on (1) canopy height and (2) seagrass biomass was analysed using a generalised linear model (GLM) with a gamma distribution and log-link in R v3.5.2 (R core team, 2019). Each location was analysed separately. Analysis of deviance was used to determine significance levels of main effects and F statistics are presented for each model. For each model a post-hoc Tukey test was conducted to compare differences between caging treatments at each sampling time using the emmeans package. Residual and q-q plots of normalised results were inspected for heteroscedasticity and non-normality. More information: Duarte, C. M., and Kirkman, H. (2001). “Methods for the measurement of seagrass abundance and depth distribution.,” in Global seagrass research methods, eds. F. T. Short and R. G. Coles (Elsevier, Amsterdam), p 141−153. Mellors, J.E. 1991. An evaluation of a rapid visual technique for estimating seagrass biomass. Aquatic Botany 42 (1): 67–73. https://doi.org/10.1016/0304-3770(91)90106-F. Scott, A. L., York, P. H. and Rasheed, M. A. (2020). Green turtle (Chelonia mydas) grazing plot formation creates structural changes in a multi-species Great Barrier Reef seagrass meadow. Mar. Environ. Res. 162, 105183. doi: https://doi.org/10.1016/j.marenvres.2020.105183. Scott, A. L., York, P. H. and Rasheed, M. A. (2021a). Herbivory Has a Major Influence on Structure and Condition of a Great Barrier Reef Subtropical Seagrass Meadow. Estuaries and Coasts 44, 506–521. doi: https://doi.org/10.1007/s12237-020-00868-0. Limitations of the dataset: During the March 2022 survey of Orman Reefs, damage to one of the cages and the seagrass inside the cage was evident, so this cage was excluded from the analysis for March and April 2022. Canopy height at Orman Reefs was not measured in April 2022 due to tide restrictions, however biomass was recorded. We were unable to sample the site at Mabuyag Island in March 2022 due to COVID restrictions, however both canopy height and aboveground biomass were measured in April 2022. Format of the dataset: One spreadsheet with two tabs, one for biomass (gDWm-2) and one for canopy height (cm). The date and treatment, exclusion cage (cag) or control (con) are listed as well as the site (Mabuyag or Orman). eAtlas Processing: The original data was provided as an excel spreadsheet which were converted to open formats (2 CSV files). These conversion were performed with no modifications to the underlying data. This dataset is filed in the eAtlas enduring data repository at: data\custodian\2021-2022-NESP-MaC-1\1.14_Torres-Strait-seagrass-decline