zenodoopen

Supporting Dataset for "Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests"

This data set is a supplement for: 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. J. Geophys. Res.-Biogeosci., 125 (8), e2020JG005 677, doi:<a href="http://dx.doi.org/10.1029/2020JG005677">10.1029/2020JG005677</a>. This data set contains the following files (which should be all downloaded and uncompressed in the same root directory): <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>   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: <table align="left"> <caption>List of variables and units of data structure census[[1]], rlidar[[1]], and tchdat[[1]].</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> census[[1]], rlidar[[1]], tchdat[[1]] </td> <td> Site identifier: <ul> <li>115: Km 115 of BR-163 highway, PA, BRA</li> <li>ana: Anambé, PA, BRA</li> <li>and: Fazenda Andiroba, PA, BRA</li> <li>bon: Fazenda Bonal, AC, BRA</li> <li>cau: Fazenda Cauaxi, PA, BRA</li> <li>duc: Reserva Ducke, AM, BRA</li> <li>fc2: Feliz Natal (zone C, area 2), MT, BRA</li> <li>fd1: Feliz Natal (zone D, area 1), MT, BRA</li> <li>fd2: Feliz Natal (zone D, area 2), MT, BRA</li> <li>fd3: Feliz Natal (zone D, area 3), MT, BRA</li> <li>fn2: Feliz Natal (long transect 2), MT, BRA</li> <li>fna: Feliz Natal (zone A), MT, BRA</li> <li>fst: Saracá-Taquera National Forest, PA, BRA</li> <li>gf1: Paracou (Guyaflux plots), GUF</li> <li>gf2: Paracou (Logging experiment plots), GUF</li> <li>hum: Fazenda Humaitá, AC, BRA</li> <li>jm2: Jamari National Forest (area 2), RO, BRA</li> <li>jm3: Jamari National Forest (area 3), RO, BRA</li> <li>par: Fazenda Nova Neonita, PA, BRA</li> <li>sbe: </li> </ul> </td> <td> </td> </tr> <tr> <th scope="row">local</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td> Region identifier (used for regional cross-validation): <ul> <li>bte: Belterra, PA, BRA</li> <li>duc: Manaus (Reserva Ducke), AM, BRA</li> <li>fst: Saracá-Taquera National Forest, PA, BRA</li> <li>fzn: Feliz Natal, MT, BRA</li> <li>gyf: Paracou, GUF</li> <li>jam: Jamari National Forest, RO, BRA</li> <li>prg: Paragominas, PA, BRA</li> <li>rib: Rio Branco, AC, BRA</li> <li>sfx: São Félix do Xingu, PA, BRA</li> <li>tan: Tanguro, MT, BRA</li> <li>sbe: Southeastern Belterra, PA, BRA</li> <li>sx1: São Félix do Xingu (area 1), PA, BRA</li> <li>sx2: São Félix do Xingu (area 2), PA, BRA</li> <li>tac: Tomé-Açu, PA, BRA</li> <li>tal: Fazenda Talismã, AC, BRA</li> <li>tn1: Fazenda Tanguro (Sustainable Landscapes transects), MT, BRA</li> <li>tn2: Fazenda Tanguro (fire experiment transects), MT, BRA</li> <li>tp1: Tapajós National Forest, PA, BRA</li> <li>tp2: São Jorge (area 2), PA, BRA</li> <li>tp3: 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> Disturbance flag: <ul> <li>bnm: Burnt multiple times</li> <li>bno: Burnt once</li> <li>cvl: Conventional logging</li> <li>int: Intact (minimally disturbed) forest</li> <li>lbn: Logged and burnt once</li> <li>lth: Logged and thinned</li> <li>ril: Reduced-impact logging</li> <li>sbn: Secondary growth then burnt</li> <li>sec: Secondary growth</li> <li>ukn: 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−2</td> </tr> <tr> <th scope="row">ba.std</th> <td>census[[1]]</td> <td>Basal area of individuals with DBH ≥ 10 cm</td> <td>cm2 m−2</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>m2 m−2</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−2</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>1st 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>5th 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>10th 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>25th 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>50th 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>75th 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>90th 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>95th 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>99th 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>

ShareScore

24/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship: 4
Harmonization: 4
Access: 16
Reuse readiness: 0
Engagement: 0