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69 results for “Degraded Forest”
Data from: Critical analysis of forest degradation in the southern Eastern Ghats of India: comparison of satellite imagery and soil quality index
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Riparian forests can mitigate warming and ecological degradation of agricultural headwater streams
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Changes in leaf litter decomposition of primary Korean pine forests after degradation succession into secondary broad-leaved forests
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Data from: Effects of forest degradation on Amazonian ferns in a land-bridge island system as revealed by non-specialist inventories
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Aboveground Biomass, Landcover, and Degradation, Kalimantan Forests, Indonesia, 2014
This dataset provides estimates of aboveground biomass, percent canopy cover, mean canopy height, landcover, and forest degradation index products for forests in Kalimantan, Indonesia (Island of Borneo) representative of conditions in late 2014. Data were combined from several sources including field sampling, airborne lidar, satellite measurements, a forest-type land cover map, and integrated into a random forest algorithm to produce these estimates.
Supporting Dataset for "Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests"
<p>This data set is a supplement for:</p> <p>Longo, M., S. S. Saatchi, M. Keller, K. W. Bowman, A. Ferraz, P. R. Moorcroft, D. Morton, D. Bonal, P. Brando, B. Burban, G. Derroire, M. N. dos-Santos, V. Meyer, S. R. Saleska, S. Trumbore, and G. Vin- cent, 2020: Impacts of degradation on water, energy, and carbon cycling of the Amazon tropical forests.<em> J. Geophys. Res.-Biogeosci</em>., <strong>125</strong> (<strong>8</strong>), e2020JG005 677, doi:<a href="http://dx.doi.org/10.1029/2020JG005677">10.1029/2020JG005677</a>.</p> <p>This data set contains the following files (which should be all downloaded and uncompressed in the same root directory):</p> <ul> <li>00_SiteLidar.zip – R scripts to process forest inventory plots and Airborne LiDAR point clouds. Sub-directories contains a directory Template, which should be copied for each site for which data are to be processed.</li> <li>01_LidarSynthesis.zip – R scripts to fit the statistical models of aggregated properties, and to evaluate both the statistical model and the prediction of Airborne LiDAR profiles to be used to initialize ED-2.2.</li> <li>02_model_eval.zip – R scripts to compare the ED-2.2 model output and evaluate the model against tower observations.</li> <li>03_degrad_mtr – R scripts to visualize the ED-2.2 simulation results.</li> <li>InputData – Miscellaneous data to be used by the scripts.</li> <li>Util – Additional R scripts <ul> <li>Rsc – Mostly R functions, which may be called by other R scripts</li> <li>OutsideLAS – List of plots that were not fully overlapped by the Airborne LiDAR surveys</li> <li>GenMERRA2_ED2 – Utility scripts to process MERRA-2 to generate the met drivers needed by ED-2.2</li> <li>GenMSWEP2_ED2 – Utility scripts to process MSWEP-2.2 to generate the met drivers needed by ED-2.2 </li> </ul> </li> <li>ED2IN_Config – list of ED2IN files used in the runs.</li> </ul> <p> </p> <p>To see the input data used for this analysis, load any of the objects available in 01_LidarSynthesis/01_eval_multivar, and look for the following structures:</p> <table align="left"> <caption>List of variables and units of data structure <strong>census[[1]]</strong>,<strong> rlidar[[1]]</strong>, and <strong>tchdat[[1]]</strong>.</caption> <thead> <tr> <th scope="col">Variable</th> <th scope="col">Structure</th> <th scope="col">Description</th> <th scope="col">Units</th> </tr> </thead> <tbody> <tr> <th scope="row">identifier</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td>Plot identifier. This always has the site identifier (see below), the area within each site, the nominal year of the campaign, the unique sub-plot ID (Pxx_Byy for rectangular plots, and Txx_Pyy for long transects) </td> <td> </td> </tr> <tr> <th scope="row">iata</th> <td> <p>census[[1]], rlidar[[1]], tchdat[[1]]</p> </td> <td> <p>Site identifier:</p> <ul> <li><strong>115:</strong> Km 115 of BR-163 highway, PA, BRA</li> <li><strong>ana:</strong> Anambé, PA, BRA</li> <li><strong>and:</strong> Fazenda Andiroba, PA, BRA</li> <li><strong>bon:</strong> Fazenda Bonal, AC, BRA</li> <li><strong>cau:</strong> Fazenda Cauaxi, PA, BRA</li> <li><strong>duc:</strong> Reserva Ducke, AM, BRA</li> <li><strong>fc2:</strong> Feliz Natal (zone C, area 2), MT, BRA</li> <li><strong>fd1:</strong> Feliz Natal (zone D, area 1), MT, BRA</li> <li><strong>fd2:</strong> Feliz Natal (zone D, area 2), MT, BRA</li> <li><strong>fd3:</strong> Feliz Natal (zone D, area 3), MT, BRA</li> <li><strong>fn2:</strong> Feliz Natal (long transect 2), MT, BRA</li> <li><strong>fna:</strong> Feliz Natal (zone A), MT, BRA</li> <li><strong>fst:</strong> Saracá-Taquera National Forest, PA, BRA</li> <li><strong>gf1:</strong> Paracou (Guyaflux plots), GUF</li> <li><strong>gf2:</strong> Paracou (Logging experiment plots), GUF</li> <li><strong>hum:</strong> Fazenda Humaitá, AC, BRA</li> <li><strong>jm2:</strong> Jamari National Forest (area 2), RO, BRA</li> <li><strong>jm3:</strong> Jamari National Forest (area 3), RO, BRA</li> <li><strong>par:</strong> Fazenda Nova Neonita, PA, BRA</li> <li><strong>sbe:</strong> </li> </ul> </td> <td> </td> </tr> <tr> <th scope="row">local</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td> <p>Region identifier (used for regional cross-validation):</p> <ul> <li><strong>bte:</strong> Belterra, PA, BRA</li> <li><strong>duc:</strong> Manaus (Reserva Ducke), AM, BRA</li> <li><strong>fst:</strong> Saracá-Taquera National Forest, PA, BRA</li> <li><strong>fzn:</strong> Feliz Natal, MT, BRA</li> <li><strong>gyf:</strong> Paracou, GUF</li> <li><strong>jam:</strong> Jamari National Forest, RO, BRA</li> <li><strong>prg:</strong> Paragominas, PA, BRA</li> <li><strong>rib:</strong> Rio Branco, AC, BRA</li> <li><strong>sfx:</strong> São Félix do Xingu, PA, BRA</li> <li><strong>tan:</strong> Tanguro, MT, BRA</li> <li><strong>sbe:</strong> Southeastern Belterra, PA, BRA</li> <li><strong>sx1:</strong> São Félix do Xingu (area 1), PA, BRA</li> <li><strong>sx2:</strong> São Félix do Xingu (area 2), PA, BRA</li> <li><strong>tac:</strong> Tomé-Açu, PA, BRA</li> <li><strong>tal:</strong> Fazenda Talismã, AC, BRA</li> <li><strong>tn1:</strong> Fazenda Tanguro (Sustainable Landscapes transects), MT, BRA</li> <li><strong>tn2:</strong> Fazenda Tanguro (fire experiment transects), MT, BRA</li> <li><strong>tp1:</strong> Tapajós National Forest, PA, BRA</li> <li><strong>tp2:</strong> São Jorge (area 2), PA, BRA</li> <li><strong>tp3:</strong> São Jorge (area 3), PA, BRA</li> </ul> </td> <td> </td> </tr> <tr> <th scope="row">poi</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td>Nominal size of each plot</td> <td> </td> </tr> <tr> <th scope="row">when</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td>Date of measurement</td> <td> </td> </tr> <tr> <th scope="row">col</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td>Colour associated with plot (for plotting only)</td> <td> </td> </tr> <tr> <th scope="row">pch</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td>Symbol associated with plot (for plotting only)</td> <td> </td> </tr> <tr> <th scope="row">dist.key</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td> <p>Disturbance flag:</p> <ul> <li><strong>bnm:</strong> Burnt multiple times</li> <li><strong>bno:</strong> Burnt once</li> <li><strong>cvl:</strong> Conventional logging</li> <li><strong>int:</strong> Intact (minimally disturbed) forest</li> <li><strong>lbn:</strong> Logged and burnt once</li> <li><strong>lth:</strong> Logged and thinned</li> <li><strong>ril:</strong> Reduced-impact logging</li> <li><strong>sbn:</strong> Secondary growth then burnt</li> <li><strong>sec:</strong> Secondary growth</li> <li><strong>ukn:</strong> Unknown/Unclassified</li> </ul> </td> <td> </td> </tr> <tr> <th scope="row">dist.age</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td>Age since last disturbance</td> <td>yr</td> </tr> <tr> <th scope="row">dist.col</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td>Colour associated with disturbance (for plotting only)</td> <td> </td> </tr> <tr> <th scope="row">dist.pch</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td>Symbol associated with disturbance (for plotting only)</td> <td> </td> </tr> <tr> <th scope="row">agb.std</th> <td>census[[1]]</td> <td>Above-ground biomass of individuals with DBH ≥ 10 cm</td> <td>kgC m<sup>−2</sup></td> </tr> <tr> <th scope="row">ba.std</th> <td>census[[1]]</td> <td>Basal area of individuals with DBH ≥ 10 cm</td> <td>cm<sup>2</sup> m<sup>−2</sup></td> </tr> <tr> <th scope="row">lai.std</th> <td>census[[1]]</td> <td>Potential (allometry-based) leaf area index of individuals with DBH ≥ 10 cm</td> <td>m<sup>2</sup> m<sup>−2</sup></td> </tr> <tr> <th scope="row">nplant.std</th> <td>census[[1]]</td> <td>Stem number density of individuals with DBH ≥ 10 cm</td> <td>m<sup>−2</sup></td> </tr> <tr> <th scope="row">elev.mean</th> <td>rlidar[[1]]</td> <td>Mean elevation of point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.sdev</th> <td>rlidar[[1]]</td> <td>Standard deviation of point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.skew</th> <td>rlidar[[1]]</td> <td>Skewness of point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.kurt</th> <td>rlidar[[1]]</td> <td>Kurtosis of point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.p01</th> <td>rlidar[[1]]</td> <td>1<sup>st</sup> percentile of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.p05</th> <td>rlidar[[1]]</td> <td>5<sup>th</sup> percentile of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.p10</th> <td>rlidar[[1]]</td> <td>10<sup>th</sup> percentile of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.p25</th> <td>rlidar[[1]]</td> <td>25<sup>th</sup> percentile of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.p50</th> <td>rlidar[[1]]</td> <td>50<sup>th</sup> percentile (median) of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.p75</th> <td>rlidar[[1]]</td> <td>75<sup>th</sup> percentile of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.p90</th> <td>rlidar[[1]]</td> <td>90<sup>th</sup> percentile of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.p95</th> <td>rlidar[[1]]</td> <td>95<sup>th</sup> percentile of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.p99</th> <td>rlidar[[1]]</td> <td>99<sup>th</sup> percentile of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.iqr</th> <td>rlidar[[1]]</td> <td>Interquartile range of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.max</th> <td>rlidar[[1]]</td> <td>Maximum of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">fcan.elev.1.0.to.2.5.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns between 1.0 and 2.5 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.2.5.to.5.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns between 2.5 and 5.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.5.0.to.7.5.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns between 5.0 and 7.5 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.7.5.to.10.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns between 7.5 and 10.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.10.0.to.15.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns between 10.0 and 15.0 m </td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.15.0.to.20.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns between 15.0 and 20.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.20.0.to.25.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns between 20.0 and 25.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.25.0.to.30.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns between 25.0 and 30.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.above.1.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns above 1.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.above.2.5.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns above 2.5 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.above.5.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns above 5.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.above.7.5.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns above 7.5 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.above.10.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns above 10.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.above.15.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns above 15.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.above.20.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns above 20.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.above.25.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns above 25.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.above.30.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns above 30.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">ztch</th> <td>tchdat[[1]]</td> <td>Mean top canopy height (0.25ha average from 1-m pixels)</td> <td>m</td> </tr> </tbody> </table> <p> </p>
FIGURE 3 in Description of two new species of torrent frog, Amolops Cope (Anura: Ranidae) from a degrading forest in the northeast Indian state of Nagaland
FIGURE 3. Amolops nidorbellus sp. nov. in life: A. dorsal view of holotype (ZSI A 10965), B-F Paratype (BNHS 5276) in life, B. dorsal view, C. ventral view, D. ventral view of hand, E. ventral view of foot, F. posterior thighs and vent showing extensive granulation.
Differential effects of nitrogen addition on soil organic carbon decomposition correlate with changes in microbial C-degradation functional potentials in a Pinus tabulaeformis forest
GEO Series GSE147041. uncultured soil microorganism; Bacteria; Eukaryota; Viruses; Archaea. 16 samples. Type: Other.
Dataset used for "Recruitment credit cannot compensate for extinction debt in a degraded dry Afromontane forest, northern Ethiopia"
<p>The data is part of a large dataset collected by WeForest Ethiopia, a nonprofit organisation engaged in restoring degraded forests in different parts of Ethiopia. This is particularly data from Desa'a forest, a dry Afromontane forest in Tigray. This data presents the identity and number of mature woody plant species (individuals with >1.5 height), their DBH (diameter at breast height, 1.3 m) or DSH and height (diameter at stump height, 0.3 m) measured at 400 m<sup>2</sup> and, the identity and number of regeneration of woody plants, height < 1.5 m, measured in 9m<sup>2</sup> nested within the 400 m<sup>2</sup> plot.</p> <p>These data were used in a manuscript entitled "Recruitment credit cannot compensate for extinction debt in a degraded dry Afromontane forest, northern Ethiopia", submitted to the Journal of Vegetation Sciences and accepted for publication.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.