SIRM: Open Source Data

We have submitted the original chart files (.csv) and density maps (.mrc) related to the images in the article "Correction of preferred-orientation induced distortion in cryo-electron microscopy maps"

Descriptions

SIRM_Fig1_csv.rar

• Fig1A.csv: The CSV file corresponding to the histogram of Euler angle deviations in particle sets with different degrees of missing wedge under conventional refinement.
• Fig1B.csv: The CSV file corresponding to the histogram of Euler angle deviations in particle sets with different degrees of missing wedge After Cross-Validation process.

SIRM_Fig3_MRC_map.rar

• groundTruth.mrc: The ground-truth density map of Apo-ferritin without missing cone, corresponding to Fig.3A.
• loss70_sameParticleNum.mrc: The MC-35 density map of Apo-ferritin, corresponding to Fig.3D.
• loss70_sameParticleNum_SIRM.mrc: The MC-35 density map of Apo-ferritin after SIRM processing, corresponding to Fig.3G.
• *loss80_samePa...

This dataset was superseded by the State Vegetation Type Map (https://datasets.seed.nsw.gov.au/dataset/nsw-state-vegetation-type-map) on 24.06.2022.\r \r Please note, Upper Hunter v1.0. VIS_ID 4894 web service and zipped dataset will be archived and will no longer be available on line after 31st March 2025.\r \r The NSW Office of Environment and Heritage (OEH) is producing a new map of the State’s native vegetation. This seamless map of NSW’s native vegetation types will enable government, industry and the community to better understand the composition and the relative significance of the native vegetation in their local area.\r The State Vegetation Type Map (SVTM) (http://www.environment.nsw.gov.au/vegetation/state-vegetation-type-map.htm) is constructed from the best available imagery, site survey records, and environmental information.\r \r The primary thematic layer in this dataset is a regional scale map of Plant Community Type (PCT) - "quickview" map. \r \r Where spatially coincident, this map of Upper Hunter (v1.0) supersedes the Greater Hunter Native Vegetation Mapping v4.0. VIS ID 3855 and was generated sourcing the following improvements:\r \r * A comprehensive revision of vegetation plot allocation to Plant Community Types (PCT), superseding GHM v4 Map Units.\r * Addition of 463 vegetation plots.\r * Comprehensive revision of aerial photo interpretation of Vegetation Photo Patterns (VPP) at 1:10,000. A relevant selection of PCT’s were nested and modelled within each VPP.\r * Utilisation of Boosted Regression Tree modelling in place of Generalised Dissimilarity Modelling\r * All manual aerial photo interpretation of VPP’s modelled PCT’s performed using high resolution 50cm ADS-40 aerial imagery in place of SPOT-5 2.5m imagery.\r * Semi-automated line work generated using high resolution 50cm ADS-40 aerial imagery in place of SPOT-5 2.5m imagery.\r * Climatic and topographic rule based envelopes were generated to constrain the maximum spatial envelope for each PCT. Each envelope was further manually edited.\r * Dry Sclerophyll communities further constrained by exposure and landform envelopes.\r * Selective integration of the following pre-existing maps to PCT: VIS1849, VIS3863, VIS3913, VIS4184, VIS4778\r * 312 vegetation communities mapped as PCT’s compared to 185 GHMv4 map units over this region.\r \r - - - \r \r QuickView map fields:\r \r * PCTID – Plant Community Type identifier.\r * PCTName – Plant Community Type common names\r * vegClass – The PCT’s Keith Class\r * vegFormation – The PCT’s Keith Formation\r * mapSource - The source of the polygon’s PCT attribution. \r * MapName – The 100k sheet map name\r \r Note that this is a dissolved surface and does not highlight the fine internal line-work within each map unit. Please refer to the 100k full data sheets for the complete editable internal linework, which are available by request to Data.Broker@environment.nsw.gov.au.\r \r The data are provided in an ArcGIS 10.4 compatible file geodatabase.\r \r - - - \r \r Fields in the undissolved 100k sheet fine scale linework:\r \r * polygonID – Unique map polygon identifier\r * PCTID – Plant Community Type identifier\r * PCTName – Plant Community Type common name\r * vegetationClass – The PCT’s Keith Class\r * vegetationFormation – The PCT’s Keith Formation\r * mapSource - The source of the polygon’s PCT attribution. Possible values are:\r \r * Manual editing\r * Site Survey\r * Spatial Modelling\r * Pre-existing mapping: VIS1849\r * Pre-existing mapping: VIS3863\r * Pre-existing mapping: VIS3913\r * Pre-existing mapping: VIS4184\r * Pre-existing mapping: VIS4778\r * Expert Rules (see note on grassland attribution below)\r \r * PCTIDMod1 - The most likely Plant Community Type identifier as derived from the spatial model.\r * PCTIDMod2 - The second most likely Plant Community Type identifier as derived from the spatial model.\r * PCTIDMod3 - The third most likely Plant Community Type identifier as derived from the spatial model.\r * vegStruct - Vegetation Photo Pattern (VPP) as derived from manual aerial photo interpretation of 50cm ADS40 imagery. \r \r Possible values for vegStruct include direct attribution of some PCT’s where possible in addition to these Vegetation Photo Patterns listed below:\r \r * vegStruct (VPP) Description\r * 0 Non Native\r * 1 Candidate Grasslands\r * 2 Dry Sclerophyll\r * 3 Wet Sclerophyll\r * 5 Floodplain Forest\r * 7 Non Woody Wetlands\r * 8 Grass Open Woodlands\r * 10 Rainforests\r * 11 Riparian Forests\r * 12 Acacia Woodlands\r * 13 Shrublands\r * 15 Mallee\r * 16 Rocky Outcrops\r * 17 Belah\r * 100 Dry Rainforest\r \r * PCTmapAccuracyConfidence - Modelling Confidence for PCTIDMod1 – Note that this reflects the modelling surface (PCTIDMod1) only and may not reflect the confidence of the mapped attribution (PCTID). PCTallocationConfidence can only be accurately applied to the published map surface (PCTID) where mapSource = ‘Spatial Modelling’.\r \r * PCTSiteValidation - Type of field validation used to assess PCT reliability:\r \tPossible Values are:\r \r * Not validated\r * RPD (Rapid)\r * Full floristic validation\r * Unknown\r \r - - -\r \r Full details will be provided in the pending Technical Report. \r \r VIS_ID 4893\r

This dataset contains genotypes for 106 Swiss Large White boars that were used to map a stump tail sperm defect and a VCF file that contains genotypes of candiate variants for 87 boars.

The subdirectories contain plink binaries, corresponding phased haplotypes (in MaCH format), the results of a haplotype-based GWAS, and a VCF file that contains genotypes at candidate causal variants.

The affection status (case=2, control=1) of 106 boars is indicated in the file "pheno".

The R script "haplo_linear.R" performs the haplotype-based association study.

The README file provides instructions on how to run the GWAS as well as on how to create the Manhattan plot.

This data annex contains the supplementary data to the IEA PVPS Task 16 report "Worldwide benchmark of modeled solar irradiance data" from 2023. The dataset includes visualizations and tables of the results as well as information concerning the reference stations. The dataset contains the following type of files: StationList.xlsx: list of all stations, including their coordinates, climate zone, station code, continent, altitude AMSL, data source, number of available test data sets, station type (Tier-1 or Tier-2), and available calibration record. Result tables in folder “ResultTables”: Folders “climate_zones” and “continents” contain the tables described in Section 5.3. The filenames are “Component_metric_in_subgroup.html” with “component” DNI or GHI, “metric” describing the metric (see Table 3), and “subgroup” describing the continent or climate zone. World maps: The folder “Resultmaps” contains world maps of the metrics described in Section 5.2. Either four or three metrics, depending on the map, are included in each pdf. A legend describing the meaning of the point size is also included. Scatter plots of test vs. reference irradiance: The folder “Scatterplots” contains two folders, “DNI” and “GHI”, for the two investigated components. Three subfolders are also contained in these two folders: The subfolders “plotsPerSiteYear” contain plots named “scatOverviewCOMPONENT_SITEYYYY.png”, where “COMPONENT” is either DNI or GHI, SITE is the three-letter site abbreviation, and YYYY is the evaluated year. The png plots include the scatterplots for all test data sets evaluated for the case specified by the filename. The subfolders “plotsPerTestdataProvider” contain plots named “scatOverviewTESTDATASET_COMPONENTYYYY.png”, where “TESTDATASET” describes the test data set, “COMPONENT” is either DNI or GHI, and YYYY is the evaluated year. The png plots include the scatterplots for all sites evaluated for the case specified by the filename. The subfolders “plotsPerTestdataProviderSamePosPerStat” contain the same scatterplots as “plotsPerTestdataProvider”, but using a slightly different visualization method. Here, the position of each scatterplot for a given site within the plot is always the same. Although this yields many empty subplots and small scatterplots, it can be helpful to rapidly browse through the plots if only one or a few stations are of interest.

The GRSM Vital Signs Vegetation and Soils plot layer consist of 662 points generated through a Generalized Random Tessellation Stratified (GRTS) sampling design in the spsurvey package of R. Point locations are for potential plot locations, though a position must be accepted in the field before a vegetation and soils plot can be established. The general design of the vegetation and soils monitoring program is for plots to be on a 5 year rotation, with the year of initial plot installation being denoted in the "Panel" field as 5-1, 5-2, 5-3, 5-4, 5-5. Currently, there are 120 plot locations designated into one of these year classes. Besides these classes, each strata of the GRTS draw have oversample points denoted as "Oversample" in the Panel field, and plots that have already been rejected are given a panel value of "REJ". The remaining plots are in the panel "secondary", denoting that they fall within a secondary Vital Signs watershed, are not not currently part of the monitoring design, though they can be added at a later date. See the GRSM Vital Signs Vegetation and Soils Monitoring Protocol (in Draft) for more information on site selection and plot locations.

Here we present three datasets describing three large European landscapes in France (Bauges Geopark - 89,000 ha), Poland (Milicz forest district - 21,000 ha) and Slovenia (Snežnik forest - 4,700 ha) down to the tree level. Individual trees were generated combining inventory plot data, vegetation maps and Airborne Laser Scanning (ALS) data. Together, these landscapes (hereafter virtual landscapes) cover more than 100,000 ha including about 64,000 ha of forest and consist of more than 42 million trees of 51 different species.

For each virtual landscape we provide a table (in .csv format) with the following columns:
- cellID25: the unique ID of each 25x25 m² cell
- sp: species latin names
- n: number of trees. n is an integer >= 1, meaning that a specific set of species "sp", diameter "dbh" and height "h" can be present multiple times in a cell.
- dbh: tree diameter at breast height (cm)
- h: tree height (m)

We also provide, for each virtual landscape, a raster (in .asc format) with the cell IDs (cellID25) which makes data spatialisation possible. The coordinate reference systems are EPSG: 2154 for the Bauges, EPSG: 2180 for Milicz, and EPSG: 3912 for Sneznik.

The v2.0.0 presents the algorithm in its final state.

Finally, we provide a proof of how our algorithm makes it possible to reach the total BA and the BA proportion of broadleaf trees provided by the ALS mapping using the alpha correction coefficient and how it maintains the Dg ratios observed on the field plots between the different species (see algorithm presented in the associated Open Research Europe article).

Below is an example of R code that opens the datasets and creates a tree density map.

------------------------------------------------------------
# load package

library(terra)

library(dplyr)

# set work directory

setwd() # define path to the I-MAESTRO_data folder

# load tree data

tree <- read.csv2('./sneznik/sneznik_trees.csv', sep = ',')

# load spatial data

cellID <- rast('./sneznik/sneznik_cellID25.asc')

# set coordinate reference system

# Bauges:

# crs(cellID) <- "epsg:2154"

# Milicz:

# crs(cellID) <- "epsg:2180"

# Sneznik:

# crs(cellID) <- "epsg:3912"

# convert raster into dataframe

cellIDdf <- as.data.frame(cellID)

colnames(cellIDdf) <- 'cellID25'

# calculate tree density from tree dataframe

dens <- tree %>% group_by(cellID25) %>% summarise(n = sum(n))

# merge the two dataframes

dens <- left_join(cellIDdf, dens, join_by(cellID25))

# add density to raster

cellID$dens <- dens$n

# plot density map

plot(cellID$dens)

Dominant tree species map of Switzerland We created a tree species map of Switzerland for the dominant tree species in the forested areas. The spatial resolution of the map is 10 m and the coordinate system is ETRS89-extended / LAEA Europe (EPSG 3035). The map comprises Sentinel-2 index time series from the year 2020, a digital elevation model and species reference data from the Swiss National Forest Inventory. The map is available as raster (.tif) or vector dataset (.gpkg). Access will be granted upon request. In total, the following 15 species were mapped: Abies alba, Acer pseudoplatanus, Alnus glutinosa, Alnus incana, Betula pendula, Castanea sativa, Fagus sylvatica, Fraxinus excelsior, Picea abies, Pinus cembra, Pinus mugo arborea, Pinus sylvestris, Quercus petraea, Quercus robur, Sorbus aucuparia.

Approach

Data - Swiss National Forest Inventory Data (stand species with > 60 % dominance in upper canopy; on at least more than 9 plots dominant) - Sentinel-2 time series (2020, Indices: CCI, CIRE, NDMI, EVI, NDVI) - Digital elevation model (DEM) (swissalti3d, 5 m) - Biogeographical regions (Federal Office for the Environment FOEN) - Forest mask 2017 (Approach: Waser et al., 2015)

Modeling approach We identified the most meaningful variables that led to separation of the respective groups by using random forest models with a forward feature selection (Meyer et al., 2018; Ververidis & Kotropoulos, 2005). In this approach, the final random forest model is solely built from the selected meaningful variables. By identifying meaningful variables, we can determine which variables might influence the grouping. Further, to avoid overfitting and overly optimistic results, we applied 10-fold spatial cross-validation and put all pixels from a plot in the same spatial fold. The modeling was realized using the CAST package in R (Meyer et al., 2022), based on the well-known caret package (Kuhn, 2022). We used the ranger package in R (Wright & Ziegler, 2017) to implement the random forest models, due to its short computation time.

Training data for modeling - 295 Sentinel-2, DEM & Biogeographical variables - 10525 tree species pixels

Selected variables for final model 1. EVI of 2020.05.16 2. NDMI of 2020.03.12 3. CIRE of 2020.04.16 4. NDMI of 2020.07.05 5. CCI of 2020.05.11 6. dem 7. CCI of 2020.08.14 8. NDMI of 2020.08.24 9. CCI of 2020.12.22 10. NDMI of 2020.04.21 11. NDMI of 2020.11.17 12. NDMI of 2020.08.09 13. CIRE of 2020.03.22 14. CIRE of 2020.08.09 14. CCI of 2020.11.02 15. CIRE of 2020.06.10


Overall Accuracy of final model - 0.759

Nationwide prediction - Predicted throughout forest mask 2017 (Approach: Waser et al., 2015) - Not applied on incomplete Sentinel-2 time series (own category in final map: incomplete_ts) - Applied the Area of Applicability (Meyer 2022) to sort out pixels outside of the feature space; basically where the model had not the same values for pixels as in the available training data

Be aware that the map is only validated with the training data itself, an independent validation with other data sources remains missing



References - Kuhn, M. (2022). Classification and Regression Training. 6.0-93. - Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., & Nauss, T. (2018). Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling and Software, 101, 1-9. https://doi.org/10.1016/j.envsoft.2017.12.001 - Meyer, H., Milà, C., & Ludwig, M. (2022). CAST: 'caret' Applications for Spatial-Temporal Models. 0.7.0. - Ververidis, D., & Kotropoulos, C. (2005). Sequential forward feature selection with low computational cost. 2005 13th European Signal Processing Conference. - Waser, L., Fischer, C.,Wang, Z., & Ginzler, C. (2015). Wall-to-Wall Forest Mapping Based on Digital Surface Models from Image-Based Point Clouds and a NFI Forest Definition. Forests, 6, 12, 4510–4528. - Wright, M. N., & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, 77(1), 1-17. https://doi.org/doi:10.18637/jss.v077.i01

This is a dataset of the NSW BioNet Flora Survey Data Collection that is maintained via the Flora Survey Module of the NSW BioNet Atlas application. This collection is a central, authoritative database for systematic vegetation survey data in NSW. \r This dataset includes full floristic survey sites that have been allocated to PCTs through the quantitative classification method outlined in A revised classification of plant communities of eastern New South Wales (DPE 2022). \r https://environment.nsw.gov.au/research-and-publications/publications-search/a-revised-classification-of-plant-communities-of-eastern-new-south-wales. As such these sites are reference sites for quantitative PCTs.\r \r Access\r \r The full datasets (sites and species) may be accessed via the BioNetAtlas application http://www.BioNet.nsw.gov.au/. Site-level data (without species) is available in a machine readable form via the BioNet OData Web Service https://data.bionet.nsw.gov.au/. That OData data service is delivered to SEED where it is rendered as a map layer (i.e. Web Map Service). \r You may also query and extract simplified data from SEED Map View – this video explains How to access BioNet systematic flora survey plot data using the SEED portal. https://vimeo.com/455328331.\r Data in BioNet is made available in accordance with DPE's Sensitive Species Data Policy https://www.environment.nsw.gov.au/topics/animals-and-plants/wildlife-management/wildlife-policies-and-guidelines/sensitive-species-data. For species categorised as "sensitive", location information may be withheld depending on the species' status under the policy, and on the access rights of the user. Records in BioNet are not guaranteed to be free from error or omission. \r \r

In this study, we analyzed territory sizes of seven migratory songbirds occupying a 10-hectare plot in the Hubbard Brook Experimental Forest, New Hampshire, USA over a 52-year period (1969-2021). All species varied in abundance over the duration of the study, some dramatically. Changes in territory sizes were inversely related to changes in abundance within the study plot despite differences in habitat preference, supporting the ideal free distribution. Territory sizes varied two-fold within a year across species. Results contribute to understanding how variation in territory size relates to 1) how habitat use changes with bird abundance, 2) the evolution of territory size, and 3) the role of territoriality in population dynamics. This dataset includes data, R code, and spatial files supporting this study. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Associated datasets in the data catalog: Holmes, R.T., N.L. Rodenhouse, and M.T. Hallworth. 2022. Bird Abundances at the Hubbard Brook Experimental Forest (1969-present) and on three replicate plots (1986-2000) in the White Mountain National Forest ver 8. Environmental Data Initiative. https://doi.org/10.6073/pasta/6422a72893616ce9020086de5a5714cd (Accessed 2023-12-17). Zammarelli, M.B. and R.T. Holmes. 2023. Hubbard Brook Experimental Forest: 10-ha bird plot territory maps, 1969 - 2021 ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/df93595ba8df60570d472f6e6f58839e (Accessed 2024-01-11).

Data for Figure 3.38 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.38 shows model evaluation of ENSO teleconnection for 2m-temperature and precipitation in boreal winter (December-January-February). --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005. --------------------------------------------------- Figure subpanels --------------------------------------------------- Data provided for all panels in one single directory --------------------------------------------------- List of data provided --------------------------------------------------- This dataset contains observed global patterns for: - temperature from the Berkeley Earth dataset over land - temperature from ERSSTv5 over ocean - precipitation from GPCC over land (shading, mm day–1) - precipitation from GPCP worldwide (contours, period: 1979-2014) and distributions of regression coefficients in IPCC regions for: - temperature - precipitation --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- maps: - reg_tas_NINO34_BEST_ERSSTv5_1901_2018_DJF.nc (var = 'rc', upper map over land) - reg_sst_NINO34_ERSSTv5_ERSSTv5_1901_2018_DJF.nc (var = 'rc', upper map over ocean) - reg_precip_NINO34_GPCP_ERSST5_1979_2018_DJF.nc (var = 'rc', lower map, contours) - reg_pr_NINO34_GPCC_ERSSTv5_1901_2016_DJF.nc (var = 'rc', lower map, shading) histograms: - tas_enso_regression_pdf_v4_no_cosweight_DJF.nc . upper grey histograms: var = 'region_pdfx_hist' and 'region_pdfy_hist' . MME (black line): var = 'region_ave_hist' . Observations (blue lines): var = 'region_obs' - tas_amip_hist_enso_regression_pdf_v4_no_cosweight_DJF.nc (orange dashed line): var = 'region_ave_amip_hist' => Fields correspond to regions numbers with labels in the plot, namely for temperature: 'EAU/RFE/RAR/NWN/NCA/ENA/NSA/MED/NWS/ESAF' (see variable region_info with attributes making the association between the region index and the acronym/name). - pr_enso_regression_pdf_v4_no_cosweight_DJF.nc . lower grey histograms: var = 'region_pdfx_hist' and 'region_pdfy_hist' . MME (black line): var = 'region_ave_hist' . Observations (blue lines): var = 'region_obs' - pr_amip_hist_enso_regression_pdf_v4_no_cosweight_DJF.nc (orange dahsed line): var = 'region_ave_amip_hist' => Fields correspond to regions numbers with labels in the plot, namely for precipitation: 'EAS/SEA/EAU/WNA/NCA/SES/NSA/ESAF/SEAF/MED' (see variable info_region with attributes making the association between the region index and the acronym/name). ENSO is the El Niño Southern Oscillation. GPCC is the Global Precipitation Climatology Centre. GPCP is the Global Precipitation Climatology Project. --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- Data provided in reg_pr_NINO34_GPCC_ERSSTv5_1901_2016_DJF.nc are in mm/month. Values should be divided by 30 for plotting in mm/day. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the figure on the IPCC AR6 website

Tropical forests are a key component of the global carbon cycle. Yet, there are still high uncertainties in forest carbon stock and flux estimates, notably because of their spatial and temporal variability across the tropics. Several upcoming spaceborne missions have been designed to address this gap. High-quality ground data are essential for accurate calibration/validation so that spaceborne biomass missions can reach their full potential in reducing uncertainties regarding forest carbon stocks and fluxes. The BIOMASS mission, a P-band SAR satellite from the European Space Agency (ESA), aims at improving carbon stock mapping and reducing uncertainty in the carbon fluxes from deforestation, forest degradation, and regrowth. In situ activities in support of the BIOMASS mission were carried out in French Guiana and Gabon during the TropiSAR and AfriSAR campaigns. During these campaigns, airborne P-band SAR, forest inventory, and lidar data were collected over six study sites. This paper describes the methods used for forest inventory and lidar data collection and analysis, and presents resulting plot estimates and aboveground biomass maps. These reference datasets along with intermediate products (e.g., canopy height models) can be accessed through ESA's Forest Observation System and the Dryad data repository and will be useful for BIOMASS but also to other spaceborne biomass missions such as GEDI, NISAR, and Tandem-L for calibration/validation purposes. During data quality control and analysis, prospects for reducing uncertainties have been identified, and this paper finishes with a series of recommendations for future tropical forest field campaigns to better serve the remote sensing community.