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1,235 results for “Brazilian Amazon”
L2C - Canopy height models across the Brazilian Amazon
<p>Canopy height models derived from LiDAR data collected across the Brazilian Amazon. The files are provided in .tiff format in 7 zip folders. A full description of the data set is available here: https://zenodo.org/record/4968706#.YzB693ZKg5s</p> <p>We also provide the summary data used for statistical analysis in the associated publication: </p> <p>Reis and Jackson et al 2022. Forest disturbance and growth processes are reflected in the geographic distribution of large canopy gaps across the Brazilian Amazon. Journal of Ecology.</p> <p>Each transect covered 375 ha (12.5 km × 300 m) by emitting full-waveform laser pulses from a Trimble Harrier 68i airborne sensor (Trimble; Sunnyvale, CA) aboard a Cessna aircraft (model 206). The average point density was set at four returns per square meters, the field of view was equal to 30°, the flying altitude was 600 m, and transect width on the ground was approximately 494 m. Global Navigation Satellite System (GNSS) data were collected on a dual-frequency receiver (L1/L2). The pulse footprint was set to be below 30 cm, based on a divergence angle between 0.1 and 0.3 milliradians. Horizontal and vertical accuracy were controlled to be under 1 m and under 0.5 m, respectively.</p> <p>The data collection was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Brasil (CAPES; Finance Code 001); Conselho Nacional de Desenvolvimento Científico e Tecnológico (Processes 403297/2016-8 and 301661/2019-7); Amazon Fund (grant 14.2.0929.1)</p> <p>The research project was funded by the UK Natural Environment Research Council project number <strong>NE/S010750/1</strong></p>
Data for "Deforestation in the Brazilian Amazon could be halved by scaling up the implementation of zero-deforestation cattle commitments"
<p>The processed data supporting the Global Environmental Change publication "Deforestation in the Brazilian Amazon could be halved by scaling up the implementation of zero-deforestation cattle commitments".</p> <p>These data can be analyzed and visualized with the code at: <a href="https://github.com/sam-a-levy/Levyetal2023_cattlemarketshare">https://github.com/sam-a-levy/Levyetal2023_cattlemarketshare</a></p> <p>For a description of each file & the variables contained, please look to the README file.</p>
L4E - Maximum tree height extracted from LiDAR transects over the Brazilian Amazon
<p>Between 2016 and 2018, the EBA airborne missions (conducted by the Brazilian National Institute for Space Research (INPE) and funded by Amazon Fund) collected airborne lidar transects of 375 ha (12.5 x 0.3 km) each. A majority of the transects were flown over randomly selected locations of old growth and second growth as forests defined by the PRODES and TerraClass databases (PRODES, INPE, 2016; TerraClass, INPE, 2014). PRODES separates forests from non-forest while TerraClass identifies second growth forest and other land covers.</p>
L4A - Biomass map of the Brazilian Amazon and uncertainty
<p>The AGB final map (further referred as EBA - Estimativa de Biomassa para a Amazônia - map) presented a maximum AGB value of 518 Mg ha-1, a mean AGB of 174 Mg ha-1, and a standard deviation of 102 Mg ha-1. The map is provided in TIF format, projected using EPSG 4236. The uncertainty map is provided in TIF format, projected using EPSG 4236. The information is offered in Mg ha-1.</p>
Dataset from paper "Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning"
<p><strong>Data and code from the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p> <p><strong>Link:</strong> <a href="https://doi.org/10.1002/rse2.264">https://doi.org/10.1002/rse2.264</a></p> <p> </p> <p><strong>This repository contains:</strong></p> <p><strong>1) model_train.R:</strong> This is the code to run the U-Net model in R language.</p> <p><strong>2) input.rar:</strong> Dataset of lidar canopy height model (CHM) images and masks (labels) patches of canopy palms obtained from four sites in the Brazilian Amazon. The images/masks have 128 x 128 pixels, where each pixel represents 0.5 m in the terrain. The dataset contains 2,269 images and masks, with close to 7,000 palms manually labelled.</p> <p><strong>3) unet_weights_best.h5:</strong> These are the best weights for the U-Net architecture achieved in the paper.</p> <p><strong>4) palm_stats.RData:</strong> Data frame with the lat/lon coordinates and palm metrics extracted for the 610 lidar sites in the Brazilian Amazon. (i) n_total is the number of palms, (ii) n_ha is the density of palms per hectare, (iii) crown_ metrics are based on the area of palm segments (in square meters), (iv) cover_total is the total area occupied by palms in the forest canopy (in square meters), (v) cover_rel is the relative cover of palms in the forest canopy (in percentage), (vi) height_ metrics are based on the height of palm segments (in meters), (vii) palm_height_dif_mean is the mean difference between palm height and local canopy height, and (viii) palm_height_dif_pvalue is the p-value assessing the statistical difference between the palm and canopy heights where 0 means no difference and -1/+1 means a negative/positive difference.</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p> <p> </p> <p><strong>If you use these data, please cite the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p>
Canopy gap sizes across the Brazilian Amazon
<p>This data frame contains the raw gap data used to study the gap size-frequency distributions across the Brazilian Amazon in:</p> <p>Reis, C., Jackson, T., et al 2022. Forest disturbance and growth processes are reflected in the geographic distribution of large canopy gaps across the Brazilian Amazon. Journal of Ecology. </p> <p>They gaps were extracted from the EBA LiDAR data set https://zenodo.org/record/4968706#.YygSGHZKg5s</p>
Trajetorias dataset: environmental, epidemiological, and economic indicators for the Brazilian Amazon
<p>The Trajetorias dataset is a harmonized set of environmental, epidemiological, and poverty indicators for all municipalities of the Brazilian Legal Amazon (BLA). This dataset is the result of a scientific synthesis research initiative conducted by scientists from several natural and social sciences fields, consolidating multidisciplinary indicators into a coherent dataset for integrated and interdisciplinary studies of the Brazilian Amazon. The Trajetorias dataset is organized in dimensions describing: environmental degradation, land use and land cover, human mobility, climate anomalies, the burden of vector-borne diseases, and poverty indices for rural and urban populations for each of the BLA municipalities. Characterizing the environmental, epidemiological, and socioeconomic profile of the municipalities. These indicators were designed to unveil the specificities of the Amazon region, so that the relationships between these dimensions can be explored regarding past and current enacted policies. The Trajetorias dataset relies on four surveys - the two demographic censuses conducted in 2000 and 2010, and the two agrarian censuses conducted in 2006 and 2017, from which were defined fixed timestamps for analysis. The demographic censuses are the source of data for the multidimensional poverty indices. Environmental data come from satellite images collected by several national and international programs, such as the Amazon Deforestation Monitoring Program (PRODES), DEGRAD, and DETER, accounting for changes in landscape that took place between each demographic census and the subsequent agrarian census. Lastly, disease control data was obtained from the National Disease Notification System and summarized for the 5-year period centered in the agrarian censuses.</p>
Satellite-based precipitation estimates using a dense rain gauge network over the Southwestern Brazilian Amazon: Implication for identifying trends in dry season rainfall
<h1>Satellite-based precipitation estimates using a dense rain gauge network over the Southwestern Brazilian Amazon.</h1>
Fig. 6 in A new species of Paralithoxus (Siluriformes: Loricariidae: Ancistrini) from the highlands of Serra da Mocidade, Roraima State, Brazilian Amazon
Fig. 6. Type-locality of Paralithoxus mocidade in Ajarani River, tributary of the Branco River basin, located immediately downstream of a large waterfall. Photo by Marcos Amend.
Fig. 1 in A new species of Paralithoxus (Siluriformes: Loricariidae: Ancistrini) from the highlands of Serra da Mocidade, Roraima State, Brazilian Amazon
Fig. 1. Paralithoxus mocidade, holotype, INPA 54745, female, 50.5 mm SL, in lateral, dorsal and ventral views.
Data from paper: Large carbon sink potential of Secondary Forests in Brazilian Amazon to mitigate climate change (public)
<p><strong>Title</strong>: Large carbon sink potential of Secondary Forests in the Brazilian Amazon to mitigate climate change</p> <p><strong>Contact:</strong> Viola Heinrich (viola.heinrich@bristol.ac.uk)</p> <p><strong>This repository contains</strong>:</p> <ol> <li>Zipped folder:<strong> Fig1_data_input.zip</strong> - all the files needed to produce Figure 1a-e of the main paper. Set the working directory to folder containing the file and use the script "Fig1a_f_plot.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 1 - these files are in the format "<strong><driver>_assessment_v2.csv</strong>". The columns in the files are: A: age of secondary forest; B: 50th percentile (median) of the modal Aboveground Biomass (AGB) value for the given age (note, units are in biomass not carbon: Mg/ha/yr); C: The bias-corrected AGB value, calculated by subtracting the lowest AGB value in column B such that the AGB data starts at or near 0Mg/ha/yr at age 1. D: the number of secondary forest pixels observed to have the given age, E: "Threshold" : the threshold limits of the given driver e.g. 0 Fires in fire_assessmentv2.csv implies the corresponding secondary forest pixels experienced 0 fires throughout the analysis period. The folder also contains the output regrowth models seen in Figure 1 in the format "<strong>regrowth_model_<driver_threshold>.RData" </strong>where driver_threshold refers to the driving variable name and the associated threshold limit for the given driver.</li> <li>Zipped folder:<strong> Fig2_regions_outline.zip</strong> - contains the boundaries of the 4 regions identified in Figure 2a of the main paper in a shapefile (.shp) format and the corresponding file formats needed to produce and load a shapefile. </li> <li>Zipped folder: <strong>Fig1g_2b_e_variable_importance.zip</strong> - contains the output files of the random forest analysis assessing the variable importance for the whole Amazon ("whole_Amazon" subfolder) and for the different regions identified in Figure2a. Files are given as .RDS files that can be loaded in R and the corresponding figures produced using the script "Fig1g_2b_e_plot.R". Files start with the region of interest e.g. "whole_Amazon" or "NE_sector". Middle part of the filename - importance_conditionalTrue/False - this determines whether the importance was calculated using the conditional permutation (True) or not (False). The end of the file name - seed<NUM> - denotes the number of the random seed that was set to extract the sample data. e.g. whole_Amazon_2500_cforest_important_conditionalTrue_seed200.RDS - shows the conditional permutation importance assessment using a sample size of 2500 when the setseed parameter was set to 200 to extract a random sample representing the whole Amazon. The remaining files are the random forest output - as .RDS file. Please note the code to produce the random forest model and the importance assessment has not been included here - this code takes multiple days to run, so only the input and outputs have been included here. Please contact the corresponding author (see end) for more information on this. </li> <li>Zipped folder: <strong>Fig3_data_input.zip</strong> - all the files needed to produce Figure 3a-d of the main paper. Set the working directory to folder containing the file and use the script "Fig3_plot.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 3 - these files are in the format "<strong><REGION>-Group.csv</strong>". See bullet point 1 for explanations for the columns in the file. Again column E -"threshold" denotes the code used to identify the the 4 subclasses of regrowth seen in the Figure. Where 11 = No disturbance; 12 = Only burning; 21 = Only (multiple) deforestations; 22 = Both burning and multiple deforestations as disturbance. The code takes data in AGB and converts to AGC. The folder also contains the output regrowth models seen in Figure 3 in the format <strong>"regrowth_model_<region_disturbance_type>.RData" </strong>where region_disturbance refers to the region and the type of disturbance experienced. </li> <li> Zipped folder: <strong>Fig4_5_carbon_sink_2017.zip </strong>- Contains two subfolders: a) <strong>Map_aggre_0.1deg</strong> -this folder contains .tiff files (and associated files) of the losses, gains and net change in AGC between 2016 - 2017 in secondary forests in Amazonia - this has been aggregated to 0.1 degree grid cells so each cell contains the total sum of the losses/gains experienced by secondary forests in that 0.1degree grid cell. b) <strong>secondary_forest_by_region_and_disturbance </strong>- this folder contains .tiff files (and associated files) of the secondary forest data at the original resolution (30m) for 2016 and 2017 split up according to the regions identified in Figure 2, and the type of disturbance (if any). The associated files include a .dbf file which includes additional data [read "README.txt" file in folder] - upon loading the data in a GIS software - the age of the secondary forest pixel will be displayed - open the attribute table to see more data associated with that given pixel e.g. modelled associated AGB for a given pixel. Files in this folder can be used to make Figure 4d and Figure 5 - see script "Fig4_Fig5_plot.R" in the code repository (see below). </li> </ol> <p><strong>Code: </strong>The corresponding code mentioned here can be access here: <a href="https://github.com/heinrichTrees/secondary-forest-regrowth-amazon-public">heinrichTrees/secondary-forest-regrowth-amazon-public (github.com)</a></p> <p><strong>Data usage: </strong>When using any code or data in this repository or another related to this study please cite Heinrich et al.2021 and the original paper as well as the DOI of this repository. </p> <p>If you need anything else, please contact the corresponding author: Viola Heinrich (viola.heinrich@bristol.ac.uk)</p>
FIGURE 5. Aphyolebias boticarioi, UFRJ 5987 in Moema apurinan sp. n. and Aphyolebias boticarioi sp. n. (Teleostei: Cyprinodontiformes: Rivulidae): two new annual killifishes from the Rio Purus basin, Brazilian Amazon
FIGURE 5. Aphyolebias boticarioi, UFRJ 5987, female, paratype, 32.4 mm SL (one day after collection; pectoral, caudal and anal fins damaged); Brazil: Acre: Porto do Acre: Rio Purus basin.
FIGURE 4. Aphyolebias boticarioi, UFRJ 5986 in Moema apurinan sp. n. and Aphyolebias boticarioi sp. n. (Teleostei: Cyprinodontiformes: Rivulidae): two new annual killifishes from the Rio Purus basin, Brazilian Amazon
FIGURE 4. Aphyolebias boticarioi, UFRJ 5986, male, holotype, 42.1 mm SL (one day after collection; caudal and anal fins damaged); Brazil: Acre: Porto do Acre: Rio Purus basin.
FIGURE 2. Moema apurinan, UFRJ 5981 in Moema apurinan sp. n. and Aphyolebias boticarioi sp. n. (Teleostei: Cyprinodontiformes: Rivulidae): two new annual killifishes from the Rio Purus basin, Brazilian Amazon
FIGURE 2. Moema apurinan, UFRJ 5981, female, paratype, 44.4 mm SL (one day after collection); Brazil: Acre: Porto do Acre: Rio Purus basin.
FIGURE 1. Moema apurinan, UFRJ 5980 in Moema apurinan sp. n. and Aphyolebias boticarioi sp. n. (Teleostei: Cyprinodontiformes: Rivulidae): two new annual killifishes from the Rio Purus basin, Brazilian Amazon
FIGURE 1. Moema apurinan, UFRJ 5980, male, holotype, 52.5 mm SL (one day after collection); Brazil: Acre: Porto do Acre: Rio Purus basin.
FIGURE 5. Rivulus amanapira, UFRJ 5930 in Rivulus uakti sp. n. and R. amanapira sp. n. (Teleostei: Cyprinodontiformes: Rivulidae): two new species from the upper Rio Negro, Brazilian Amazon
FIGURE 5. Rivulus amanapira, UFRJ 5930, female, paratype, 32.6 mm SL (two days after collection); Brazil: Amazonas: São Gabriel da Cachoeira: upper rio Negro basin.
FIGURE 6 in Rivulus uakti sp. n. and R. amanapira sp. n. (Teleostei: Cyprinodontiformes: Rivulidae): two new species from the upper Rio Negro, Brazilian Amazon
FIGURE 6. Brazil: Amazonas: São Gabriel da Cachoeira; type locality of Rivulus amanapira: upper rio Negro basin.
FIGURE 4. Rivulus amanapira, UFRJ 5929 in Rivulus uakti sp. n. and R. amanapira sp. n. (Teleostei: Cyprinodontiformes: Rivulidae): two new species from the upper Rio Negro, Brazilian Amazon
FIGURE 4. Rivulus amanapira, UFRJ 5929, male, holotype, 38.6 mm SL (one day after collection); Brazil: Amazonas: São Gabriel da Cachoeira: upper rio Negro basin.
FIGURE 2. Rivulus uakti, UFRJ 5926 in Rivulus uakti sp. n. and R. amanapira sp. n. (Teleostei: Cyprinodontiformes: Rivulidae): two new species from the upper Rio Negro, Brazilian Amazon
FIGURE 2. Rivulus uakti, UFRJ 5926, female, paratype, 27.9 mm SL (two days after collection); Brazil: Amazonas: São Gabriel da Cachoeira: upper rio Negro basin.
FIGURE 3 in Rivulus uakti sp. n. and R. amanapira sp. n. (Teleostei: Cyprinodontiformes: Rivulidae): two new species from the upper Rio Negro, Brazilian Amazon
FIGURE 3. Brazil: Amazonas: São Gabriel da Cachoeira: upper rio Negro basin; type locality of Rivulus uakti.
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