Find research datasets worth reusing

Indicative distribution map for Ecosystem Functional Group TF1.2 Subtropical/temperate forested wetlands

This archive contains indicative distribution maps and profiles for TF1.2 Subtropical/temperate forested wetlands, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith et al. (2020) for details. The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

Indicative distribution map for Ecosystem Functional Group TF1.1 Tropical flooded forests and peat forests

This archive contains indicative distribution maps and profiles for TF1.1 Tropical flooded forests and peat forests, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith et al. (2020) for details. The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

Indicative distribution map for Ecosystem Functional Group MFT1.2 Intertidal forests and shrublands

This archive contains indicative distribution maps and profiles for MFT1.2 Intertidal forests and shrublands, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith et al. (2020) for details. The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

Indicative distribution map for Ecosystem Functional Group T2.2 Deciduous temperate forests

This archive contains indicative distribution maps and profiles for T2.2 Deciduous temperate forests, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith et al. (2020) for details. The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

Indicative distribution map for Ecosystem Functional Group T2.4 Warm temperate laurophyll forests

This archive contains indicative distribution maps and profiles for T2.4 Warm temperate laurophyll forests, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith et al. (2020) for details. The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

opencc-by-sa-4.0Oct 2018View details →

Predicted soil organic carbon stock at 30 m in t/ha for 0-100 cm depth global / update of the map of mangrove forest soil carbon

This is the 2nd update of maps produced by <a href="https://doi.org/10.1088/1748-9326/aabe1c">Sanderman et al (2018)</a>. The improvements to the <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-prediction-of-soil-organic-carbon.html">3D spatial predictions</a> include: <ul> <li> new updated global mangrove coverage map (contact Thomas Worthington), </li> <li> spatiotemporal predictions to account for differences in spectral reflectance at the time of field work, </li> <li> additional SOC points <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41558-018-0162-5/MediaObjects/41558_2018_162_MOESM2_ESM.xlsx">published in Rovai et al. (2018)</a> used in model training (see gpkg file). </li> </ul> To open map in QGIS or similar, drag and drop the *.tif files. You can than add also the gpkg file contain the training points. Production steps (ensemble predictions using SuperLearner) are explained in detail at:  <ul> <li>R code: <a href="https://github.com/whrc/Mangrove-Soil-Carbon/">https://github.com/whrc/Mangrove-Soil-Carbon/</a> (see "R_code/GMW_mangroves_SOC_30m.R")</li> <li>Tutorial: <a href="https://envirometrix.github.io/PredictiveSoilMapping/soilmapping-using-mla.html#ensemble-predictions-using-superlearner-package">"Predictive Soil Mapping with R"</a></li> </ul> Produced for the purpose of Mangrove Restoration Potential Map funded by The Nature Conservancy and IUCN. Contact TNC: Emily Landis <<a href="mailto:elandis@TNC.ORG">elandis@TNC.ORG</a>>. Contact IUCN / University of Cambridge: Thomas Worthington <<a href="mailto:taw52@cam.ac.uk">taw52@cam.ac.uk</a>>. <ul> <li>The mangrove restoration potential map is available at: <a href="https://www.researchgate.net/deref/http%3A%2F%2Fmaps.oceanwealth.org%2Fmangrove-restoration%2F">http://maps.oceanwealth.org/mangrove-restoration/</a></li> </ul>

opencc-by-4.0Mar 2023View details →

FRUC multiple sensor forest dataset including absolute, map-referenced localization

FRUC Datasets (Forest environment dataset) This dataset was collected as part of the work conducted by the Forestry Robotics @ University of Coimbra team (<a href="https://www.youtube.com/@forestryroboticsuc">https://www.youtube.com/@forestryroboticsuc</a>; part of the Institute of Systems and Robotics, <a href="https://www.isr.uc.pt/">https://www.isr.uc.pt/</a>) within the scope of the Safety, Exploration and Maintenance of Forests with Ecological Robotics (SEMFIRE, ref. CENTRO-01-0247-FEDER-03269; <a href="http://semfire.ingeniarius.pt/">http://semfire.ingeniarius.pt/</a>) and the Semi-Autonomous Robotic System for Forest Cleaning and Fire Prevention (SafeForest, CENTRO-01-0247-FEDER-045931) research projects. Its purpose is to allow researchers in forestry robotics to have an in-depth analysis of a florests environment; obtain an a priori map for robot operations (e.g. path plannning, landscaping, etc…) and to train segmentation algorithms;   The dataset in question includes data from multiple sensors and absolute, map-referenced localization which can be used to register the sensor data to a fixed coordinate system. It was collected at the <a href="https://www.google.com/maps/place/Mata+Nacional+do+Choupal/@40.2208522,-8.4429989,842m/data=!3m1!1e3!4m6!3m5!1s0xd22f91d7cec3b95:0xb02aedc4d8380d48!8m2!3d40.2222536!4d-8.4438944!16s%2Fm%2F026jw89?hl=pt-PT">Choupal National Woods, Coimbra, Portugal</a> (40◦13′13.3′′N;8◦26′38.1′′W). The dataset was collected during a partly clouded day in a forest environment by performing two circular loop laps amounting to a total distance of approximately 800m, with a total duration of 14 minutes and 22 seconds. The scenario is rich in features relevant to forestry robotics applications, including trees, bushes, tree trunks, etc. To better handle the multimodal nature of the acquired data, the dataset is bundled into <a href="http://wiki.ros.org/rosbag">rosbags</a>, a file format used by the <a href="http://wiki.ros.org/">ROS (Robot Operating System)</a> to record and play back data. More specifically, the datasets include: <ul> <li>RGB Images from an Intel Realsense D435i</li> <li>Aligned Depth Images from an Intel Realsense D435i</li> <li>Left and Right Mono Images from a Mynt Eye s1030</li> <li>Point Clouds from a Livox Mid-70 LiDAR</li> <li>Unfiltered acceleration, gyroscopic and magnetic data from a Xsens MTi IMU</li> <li>Unfiltered acceleration, gyroscopic data from an Intel Realsense D435i</li> <li>GNSS Fix data from a Xiaomi Mi Mix 3 device</li> </ul> Description of files: <ol> <li>The dataset is contain in choupal.bag.</li> <li>The rosbag_info.txt contains the information of each rosbag;</li> <li>The sensor_box.urdf contains all the required transforms;</li> <li>The sensor_box.stl contains the 3D model of the apparatus;</li> <li>The choupal.launch publishes the sensor transforms and plays the dataset;</li> <li>The localization.bag contains the final graph of poses extracted with Cartographer republished as nav_msgs/odom at 4.98Hz.</li> <li>The localization_15Hz.bag contains a map-referenced localization extracted with Cartographer at a higher frequency, but the poses are interpolated. If you don't require a high frame rate, please use the localization.bag instead.</li> </ol> Usage: <ol> <li>Extract the fruc_dataset_choupal_launch.zip into a catkin workspace</li> <li>Install the necessary dependencies of the package: <ol> <li> <pre><code class="language-bash">cd [/path/to/catkin_ws]</code></pre>   </li> <li> <pre><code class="language-bash">rosdep install --from-paths src --ignore-src -y -r</code></pre> </li> </ol> </li> <li>Copy the rosbags into the fruc_dataset_choupal_launch/rosbag/</li> <li>Edit the fruc_dataset_choupal_launch/launch/choupal.launch file to your use case: <ol> <li>Change the file_path argument if the rosbags are not in the default location;</li> <li>Set localization_file to  the path of the desired localization bag, leave it empty to run the dataset without localization.</li> </ol> </li> <li>Compile the package and source the environment: <ol> <li> <pre><code class="language-bash">catkin_make [/your_catkin_workspace/]</code></pre>   </li> <li> <pre><code class="language-bash">source [/your_catkin_workspace/devel/setup.bash]</code></pre>   </li> </ol> </li> <li>Launch the files: <pre><code class="language-bash">roslaunch fruc_dataset_choupal_launch choupal.launch</code></pre> </li> </ol>

Indicative distribution map for Ecosystem Functional Group T1.4 Tropical heath forests

This archive contains indicative distribution maps and profiles for T1.4 Tropical heath forests, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith et al. (2020) for details. The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

openCC (other)May 2023View details →

Long-term (1993-2019) dynamics of tree populations on a mapped 3-ha permanent plot in old-growth northern hardwood forest, Huron Mts., Marquette Co., MI, USA

This data-set includes multiple remeasurements, over 25 years, of all woody stems >2 cm diameter (total of 2125 stems) on a 2.72-ha stem-mapped plot in old-growth northern hardwood forest in the Huron Mountains region of northern Marquette County, MI. The plot and surrounding forest is dominated by sugar maple (Acer saccharum) and eastern hemlock (Tsuga canadensis). Among secondary species, yellow birch (Betula alleghaniensis) and basswood (Tilia americana) are most common. Soils (identified as Kalkaska series) are developed on deep sandy glacial outwash. The plot is within a much larger region of old-growth forest, protected since ca. 1880, with only minimal disturbance associated with access tracks and trails. Numerous other forest community and dendrochronological studies support the interpretation that the area around the study plot has not experienced stand-initiating disturbance for at least 400 years. Initial mapping and measurements (1993-1995 for 2.52 ha; an additional 0.2 ha added in 1999) used a 20x20 m grid established in a near-level area of uniform substrate. All stems were identified to species, mapped on polar coordinates from the center of each grid cell (including, at first measurement, identifiable dead trees, standing and down), and diameter at breast height (dbh) measured to nearest 0.1 cm. All stems were remeasured on a five-year cycle 1999-2019, and new mortality was recorded at each remeasurement. New recruits > 2 cm dbh were added at each remeasurement.

openCC (other)Nov 2023View details →

Long-term (1993-2019) tree population measurements from a mapped 2.9-ha permanent plot in old-growth northern hardwood forest, Dukes Research Natural Area, Marquette Co., MI, USA

The Dukes Research Natural Area (Hiawatha National Forest, Marquette Co., MI) amounts to ca. 100 ha of minimally disturbed original forests, including a mix of mesic 'hemlock-northern hardwood' types and peaty wetlands dominated by several species of swamp conifers and black ash (Fraxinus nigra). The RNA hosts a regular grid of 250 permanent monitoring plots (data to be provided in a separate package). In 1993-95, a macroplot of 2.91 ha was established in a mixed mesic upland forest area within the RNA, in which all woody stems >2 cm diameter at breast height (DBH) were identified, measured, and mapped. In 1999 and again every five years subsequently through 2019, the macroplot was recensused; all stems were remeasured, stems newly recruited (>2 cm DBH) were measured and mapped, and any mortality since previous census was noted and described. A severe storm in 2002 resulted in extensive mortality throughout the RNA, particularly in the area in and around the macroplot.

openCC (other)Nov 2025View details →

Aspen Forest Stem Map and Tree Census at the University of Michigan Biological Station, Pellston, MI 1974-2018

In 1974, a one hectare plot was established at the University of Michigan Biological Station to further understand successional trajectories of birch and aspen forests in northern Michigan. Trees with a DBH greater than 5 cm were inventoried and later, the location of the tree within the plot was documented by the UMBS resident biologist. Plots were remeasured 5 additional times by different groups at the station.

openCC0Jul 2021View details →

Stem maps of eight 1 ha forest plots distributed around Ann Arbor, MI and around the University of Michigan Biological Station (UMBS)

In this project we established a network of forest inventory plots to gather the data needed to forecast future forest performance under global change. Data collected from forest inventory plots, i.e., size and location of individual trees from all ages and species, have been shown to be particularly useful to link tree species demographic rates (survival, growth, age at maturity, fecundity) with community characteristics (assemblages and species turnovers), and are also widely used to estimate biomass removal (logging) and biomass production (carbon sequestration).

openCustomJan 2020View details →

May to July 2018 ground control points GPS coordinates of tidal marsh and tidal forest plant species to be used as ground control points in habitat mapping.

We collected field data from sites distributed in habitats along the salinity axis of the Altamaha River estuary and the Duplin River to be used as ground control points (GCP) and ground reference data for habitat mapping. GCPs for tidal marsh (salt, brackish, tidal fresh) and tidal fresh forest vegetation species were acquired. A real time kinematic (RTK) GPS survey of GPS coordinates and ground elevations for tidal marsh vegetation was carried out in May of June of 2018. A handheld GPS was used to collect GPS coordinates for tidal forest plant species in July of 2018. A total of 101 GCPs were collected in tidal habitats, with 26 in salt, 28 in brackish and 29 in tidal fresh marsh, and another 18 in tidal fresh forest. These observations will be used to create habitat maps from aerial photographs of the Altamaha River estuary, GA taken following Hurricane Irma to better understand how the storm surge affected tidal vegetation and to examine any shifts in vegetation type.

openCC (other)Dec 2023View details →

Hubbard Brook Experimental Forest: 10-ha bird plot territory maps, 1969 - 2021

Maps showing the estimated territorial boundaries of all bird species occupying the 10-ha bird plot in the Hubbard Brook Experimental Forest, 1969-2021. These data were used in estimating the abundance of bird populations during this period (e.g., Holmes and Sturges 1975, Holmes et al. 1986, Holmes and Sherry 1988, 2001, Holmes 2011). 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. Papers associated with this dataset: Holmes, R. T., & Sturges, F. W. (1975). Bird Community Dynamics and Energetics in a Northern Hardwoods Ecosystem. Journal of Animal Ecology, 44(1), 175–200. https://doi.org/10.2307/3857 Sherry, T. W. (1979). Competitive interactions and adaptive strategies of American Redstarts and Least Flycatchers in a northern hardwoods forest. The Auk, 96(2), 265-283. Holmes, R. T., Bonney, R. E., & Pacala, S. W. (1979). Guild Structure of the Hubbard Brook Bird Community: A Multivariate Approach. Ecology, 60(3), 512–520. https://doi.org/10.2307/1936071 Holmes, R. T., Sherry, T. W., & Sturges, F. W. (1986). Bird Community Dynamics in a Temperate Deciduous Forest: Long-Term Trends at Hubbard Brook. Ecological Monographs, 56(3), 201–220. https://doi.org/10.2307/2937074 Holmes, R. T., & Robinson, S. K. (1988). Spatial patterns, foraging tactics, and diets of ground-foraging birds in a northern hardwoods forest. The Wilson Bulletin, 377-394. Holmes, R. T., & Sherry, T. W. (1988). Assessing population trends of New Hampshire forest birds: local vs. regional patterns. The Auk, 105(4), 756-768. 10.2307/4087390 Holmes, R. T., & Sherry, T. W. (2001). Thirty-year bird population trends in an unfragmented temperate deciduous forest: importance of habitat change. The Auk, 118(3), 589-609. https://doi.org/10.1093/auk/118.3.589 Holmes, R. T. (2011). Avian population and community process

openCC (other)Nov 2023View details →

Tree Map for Census at the Luquillo Forest Dynamics Plot (LFDP), Puerto Rico

This data set shows the Tag number, Quadrat location, Species code, diameter and XY coordinates of stems >=10 cm D130 present at the time of Hurricane Hugo and in the first census. The data set is composed of two files both with the same file structure. In LFDP_C1treemap.txt the diameters (Fdiam) are as recorded in the field data. In LFDP_C1TREEMAPa.txt the stem diameters (Fdiam) were calculated to allocate "missed" stems (stems >=10 cm D130) that were found in survey 2, 3 or Census 2 to Census 1 survey 1. We calculated the diameter the stem would have had, if it had been recorded at the same time the quadrat it was located in was assessed, in the appropriate survey for that stem size. To extrapolate the stem size back in time, we used the actual growth rate of that individual stem if more than one measurement was available. If only one diameter measurement was available we used the median growth rate for that species in the appropriate size class stems >=10, <30 cm D130). In our publications we will combine data sets LFDP_C1treemap.txt and LFDP_C1TREEMAPa.txt to make Census 1 and to reconstruct the forest for stems >= 10 cm D130 at the time of Hurricane Hugo. We have divided the data into two separate files to ensure that when stem diameters are compared to future censuses the diameter data in LFDP_C1TREEMAPa.txt are not used to calculate growth rates. The last corrections to the Census 1 data were made in May 2001. The National Science Foundation requires that data from projects it funds are posted on the web two years after any data set has been organized and "cleaned". The data from each census of the LFDP will be updated at intervals as each survey of the LFDP shows errors in the previous data collection. After posting on the web, researchers who are not part of the project are then welcome to use the data. Given the enormous amount of time, effort and resources required to manage the LFDP, obtain these data, and ensure data accuracy, LFDP Princi

opencc-by-4.0May 2024View details →

DigiMedFor Forest Management Map Database

Geodatabase of Forest Management Map created in DigiMedFor project.

opencc-by-4.0Nov 2021View details →

Dataset from paper "Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning"

Data and code from the paper: 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 Link: <a href="https://doi.org/10.1002/rse2.264">https://doi.org/10.1002/rse2.264</a>   This repository contains: 1) model_train.R: This is the code to run the U-Net model in R language. 2) input.rar: 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. 3) unet_weights_best.h5: These are the best weights for the U-Net architecture achieved in the paper. 4) palm_stats.RData: 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.   If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).   If you use these data, please cite the paper: 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

opencc-by-4.0Jun 2018View details →

CLAMATO2017: IGM Lyman-alpha Forest Tomography Survey Public Data Release of Spectra and Maps

CLAMATO 2017 Data Release 1  Public release: 2017 October 9 Uploaded to Zenodo on 2018 June 19th after acceptance for publication in ApJS By Khee-Gan Lee (kglee@lbl.gov) and collaborators Supporting paper: https://arxiv.org/abs/1710.02894 These are data products associated with the first data release of the COSMOS Lyman-Alpha Mapping And Tomography Observations (CLAMATO) survey with the Keck-I telescope, which mapped 3D Lyman-alpha forest absorption at 2.05<z<2.55 within the COSMOS field. The following is the summary of the main products: - Source catalog (CL2017_VALUEADDED_RELEASE_20171009.TXT) - Reduced spectra, in /spec_v0/ (blue) and /spec_v0_red (red) sub-directories - Continuum-fitted 2.05<z<2.55 Lyman-alpha forest pixel data (pixel_data.bin) - Wiener-reconstructed 3D absorption map (map_2017_v3.bin) Versions: v0 (not public): Initial rough extraction for 2.15<z<2.55  v1 (not public): Extended redshift range to 2.05<z<2.55  v2 (not public): Caught bug that caused wrong [RA,Dec] for ~4-5 objects v3 (released 2017 Oct 9): Fixed bug that caused wrong aspect ratio in output map v4 (released 2018 Mar 29): Fixed bug that caused negative continua in some spectra Redshift Catalog and Spectra  We provide our redshift catalog and reduced spectra obtained with Keck-I/LRIS The source catalog is provided in the ASCII file CL2017_VALUEADDED_RELEASE_20171009.TXT, with the following columns: - BLUE_SPEC: Blue spectrum filename (in /spec_v0/ sub-directory) - TOMO_ID: CLAMATO ID number - GMAG: g-magnitude (AB) per Capak et al 2007 photometric catalog - CONF: Redshift confidence grade: see https://arxiv.org/abs/1710.02894 - ZSPEC: Spectroscopic redshift as determined from CLAMATO spectrum - QSO: QSO flag (1 if QSO, 0 if non-QSO) - RA: R.A. in degrees (J2000) - DEC: Dec in degrees (J2000) - S/N_1: Estimated Lya-forest S/N at 2.05<z<2.15, -9.0 denotes no estimate - S/N_2: Estimated Lya-forest S/N at 2.15<z<2.35, -9.0 denotes no estimate - S/N_2: Estimated Lya-forest S/N at 2.35<z<2.55, -9.0 denotes no estimate - S/N_RED: Estimated S/N over restframe 1250 ang < lambda < 1350 ang, -9.0 denotes no estimate - TOMOFLAG: Flag on whether sightline was used in tomographic map (0 for no, 1 for yes) - EXPTIME: Exposure time on the spectrum, in seconds (aggregate) - RED_SPEC: Red spectrum filename (in /spec_v0_red/ sub-directory), 'NA' if doesn't exist The tarballs spec_v0.tar.gz and spec_v0_red.tar.gz include all the reduced spectra from LRIS-Blue and LRIS-Red, respectively. The individual LRIS spectra are provided in FITS format, with the following HDU Extensions: - HDU0: Object spectral flux density, in units of 10^{-17} ergs/s/cm^2/angstrom - HDU1: Noise standard deviation - HDU2: Pixel Wavelengths in angstroms Pixel Data   The binary file PIXEL_DATA_v4.BIN stores the concatenated Lyman-alpha forest pixels at 2.05<z<2.55 that have been extracted from the 1D spectra and continuum-fitted.  The first value in the binary is a 32-bit integer specifying the number of pixels (64332), followed by 5 double-precision floating point (64-bit) vectors storing the x, y, z, sigma_f, and delta_f of the pixels. An example python script to read pixel_data is as follows: import numpy as np with open('CLAMATO2017_public/pixel_data_v4.bin','r') as f:     npix = np.fromfile(f, dtype=np.int32, count=1)     f.seek(4)     pixel_data = np.fromfile(f,dtype=np.float64).reshape((npix,5)) LIST_TOMO_INPUT_2017.TXT is a summary file of corresponding to PIXEL_DATA.BIN, listing the [x,y,z] position of the sightlines that contributed to the file as well as, in the final two columns, the index range that can be used to grab the relevant pixels from the concatenated pixel list. Tomographic Map The Wiener-reconstructed map of the 2.05<z<2.55 IGM within the CLAMATO field is the result of applying the dachshund algorithm (http://github.com/caseywstark/dachshund) to PIXEL_DATA.BIN, with the configuration file INPUT.CFG . (Caveat: the version of PIXEL_DATA.BIN here is not actually the right version to directly input into the dachshund code: the first integer in this file should not be present for input to dachshund).  The reconstructed map is MAP_2017_V4.BIN, which is a 60x48x876 = 2552880 pixel double-precision binary with. The dimension that changes fastest is the z-dimension (876 pixels per dimension), followed by the y-dimension (48 pixels per dimension) and x-dimension (60 per dimension). Each map pixel represents a 0.5Mpc/h comoving voxel of the Ly-alpha forest absorption. See the Appendix of https://arxiv.org/abs/1710.02894 for the conversion factors to assume to switch between pixel/voxel and [RA, Dec, redshift]. The file MAP_2017_V4_SM2.0.BIN is the same map, but smoothed with a R=2Mpc/h Gaussian kernel.

opencc-by-4.0May 2023View details →

Dataset: Country-wide mapping of harvest areas and post-harvest forest recovery using Landsat time series data in Japan

Description: This version of the repository holds an updated dataset of the annual forest disturbance agents/stable land cover maps across Japan using Landsat time series data. This version covers the period for 1985-2023 (1985-2019 in the original dataset). Harvest, Conversion, Thinning, and Other disturbances are mapped as different disturbance agents. The maps cover the entire country of Japan, excluding several isolated islands. The maps are available in compressed GeoTIFF format in Albers Equal Area Conic projection. The maps can be browsed in <a href="https://dulvrq3317.users.earthengine.app/view/japan-harvest-year-1985-2023">Google Earth Engine Apps</a>. Citation: Shimizu, K. and Saito, H. (2021) Country-wide mapping of harvest areas and post-harvest forest recovery using Landsat time series data in Japan. International Journal of Applied Earth Observation and Geoinformation 104: 102555. <a href="https://doi.org/10.1016/j.jag.2021.102555">https://doi.org/10.1016/j.jag.2021.102555</a> Period: 1985 to 2023 annually Spatial resolution: 30m Projection: Albers Equal Area Conic Projection (Two standard parallels: 33N and 44N, Central Meridian: 135E)