| _version_ | 1866902128125018112 |
|---|---|
| author | Parente, Leandro Hunter, Maria Ho, Yufeng Bonannella, Carmelo Ferreira, Laerte Morton, Douglas Consoli, Davide Sloat, Lindsey |
| author_facet | Parente, Leandro Hunter, Maria Ho, Yufeng Bonannella, Carmelo Ferreira, Laerte Morton, Douglas Consoli, Davide Sloat, Lindsey |
| contents | <h2>Sub-dataset: Median vegetation height, 2006—2007</h2> <h2>Description</h2> <p>Global annual maps of median vegetation height for 2000—2022 produced within the scope of the <a href="https://landcarbonlab.org/about-global-pasture-watch/">Global Pasture Wath initiative</a>, integrating multi-sensor remote sensing data (LIDAR ICESat-2, Landsat, MODIS and DTM) and based on spatiotemporal Machine Learning (Gradient boosting trees). While the primary focus is on improving monitoring in short vegetation ecosystems, the dataset provides wall-to-wall coverage to all terrestrial ecosystems and is organized in 70 global mosaics in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 (STAC and GEE) and 0.00075 degrees (Zenodo), including:</p> <ul> <li><strong>Median vegetation height</strong><strong>: </strong> <ul> <li>Mean predicted value: 23 annual maps with values range from 0–20 (<em>embedded scale factor 0.1</em>)</li> <li>Percentile 5th value (upper prediction boundary): 23 annual maps with values range from 0–20 (<em>embedded scale factor 0.1</em>)</li> <li>Percentile 95th value (lower prediction boundary): 23 annual maps with values range from 0–20 (<em>embedded scale factor 0.1</em>)</li> </ul> </li> <li><strong>Trend analysis (2000—2022)</strong>: <ul> <li>Linear regression intercept (filtered p-value < 0.05, Mann-Kendall test): 1 long-term map with values range from -0.92–0.7 (<em>embedded scale factor 0.0001</em>)</li> </ul> </li> </ul> <p>All raster files are in <code>16-bit integer format</code> and use <code>-3200</code> as no-data value (pixels covering ocean waters, lakes and water bodie), following an specific naming convention:</p> <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: Short vegetation height (<code>short.veg.height</code>)</li> <li>Procedure combination: Ensemble Gradient Boosting Trees (<code>egbt</code>), trend/intercept derived per-pixel via linear regression (<code>trend</code>), Mann-Kendall test (<code>mk</code>).</li> <li>Variable type: mean predicted value (<code>m</code>), 5th (<code>p.05</code>) and 95th percentiles (<code>p.95</code>)</li> <li>Spatial resolution: <code>30m</code></li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20220101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20221231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: <code>v1</code></li> </ol> <h3>Related resources</h3> <ul> <li><strong>Maps of median vegetation height:</strong><br><a href="https://doi.org/10.5281/zenodo.15198654">2000-2001</a> <a href="https://doi.org/10.5281/zenodo.15198656">2002-2003</a> <a href="https://doi.org/10.5281/zenodo.15198658">2004-2005</a> <a href="https://doi.org/10.5281/zenodo.15198660">2006-2007</a> <a href="https://doi.org/10.5281/zenodo.15198662">2008-2009</a> <a href="https://doi.org/10.5281/zenodo.15198664">2010-2011</a> <a href="https://doi.org/10.5281/zenodo.15198668">2012-2013</a> <a href="https://doi.org/10.5281/zenodo.15198670">2014-2015</a> <a href="https://doi.org/10.5281/zenodo.15198672">2016-2017</a> <a href="https://doi.org/10.5281/zenodo.15198674">2018-2019</a> <a href="https://doi.org/10.5281/zenodo.15198676">2020-2021</a> <a href="https://doi.org/10.5281/zenodo.15198700">2022</a></li> <li><strong>Trend analysis map:</strong><br><a href="https://doi.org/10.5281/zenodo.15198700">2000—2022</a></li> <li><strong>Global reference samples and machine learning models:</strong><br><a href="https://doi.org/10.5281/zenodo.15194973">Parquet and joblib python files</a></li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15198660 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022) Parente, Leandro Hunter, Maria Ho, Yufeng Bonannella, Carmelo Ferreira, Laerte Morton, Douglas Consoli, Davide Sloat, Lindsey <h2>Sub-dataset: Median vegetation height, 2006—2007</h2> <h2>Description</h2> <p>Global annual maps of median vegetation height for 2000—2022 produced within the scope of the <a href="https://landcarbonlab.org/about-global-pasture-watch/">Global Pasture Wath initiative</a>, integrating multi-sensor remote sensing data (LIDAR ICESat-2, Landsat, MODIS and DTM) and based on spatiotemporal Machine Learning (Gradient boosting trees). While the primary focus is on improving monitoring in short vegetation ecosystems, the dataset provides wall-to-wall coverage to all terrestrial ecosystems and is organized in 70 global mosaics in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 (STAC and GEE) and 0.00075 degrees (Zenodo), including:</p> <ul> <li><strong>Median vegetation height</strong><strong>: </strong> <ul> <li>Mean predicted value: 23 annual maps with values range from 0–20 (<em>embedded scale factor 0.1</em>)</li> <li>Percentile 5th value (upper prediction boundary): 23 annual maps with values range from 0–20 (<em>embedded scale factor 0.1</em>)</li> <li>Percentile 95th value (lower prediction boundary): 23 annual maps with values range from 0–20 (<em>embedded scale factor 0.1</em>)</li> </ul> </li> <li><strong>Trend analysis (2000—2022)</strong>: <ul> <li>Linear regression intercept (filtered p-value < 0.05, Mann-Kendall test): 1 long-term map with values range from -0.92–0.7 (<em>embedded scale factor 0.0001</em>)</li> </ul> </li> </ul> <p>All raster files are in <code>16-bit integer format</code> and use <code>-3200</code> as no-data value (pixels covering ocean waters, lakes and water bodie), following an specific naming convention:</p> <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: Short vegetation height (<code>short.veg.height</code>)</li> <li>Procedure combination: Ensemble Gradient Boosting Trees (<code>egbt</code>), trend/intercept derived per-pixel via linear regression (<code>trend</code>), Mann-Kendall test (<code>mk</code>).</li> <li>Variable type: mean predicted value (<code>m</code>), 5th (<code>p.05</code>) and 95th percentiles (<code>p.95</code>)</li> <li>Spatial resolution: <code>30m</code></li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20220101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20221231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: <code>v1</code></li> </ol> <h3>Related resources</h3> <ul> <li><strong>Maps of median vegetation height:</strong><br><a href="https://doi.org/10.5281/zenodo.15198654">2000-2001</a> <a href="https://doi.org/10.5281/zenodo.15198656">2002-2003</a> <a href="https://doi.org/10.5281/zenodo.15198658">2004-2005</a> <a href="https://doi.org/10.5281/zenodo.15198660">2006-2007</a> <a href="https://doi.org/10.5281/zenodo.15198662">2008-2009</a> <a href="https://doi.org/10.5281/zenodo.15198664">2010-2011</a> <a href="https://doi.org/10.5281/zenodo.15198668">2012-2013</a> <a href="https://doi.org/10.5281/zenodo.15198670">2014-2015</a> <a href="https://doi.org/10.5281/zenodo.15198672">2016-2017</a> <a href="https://doi.org/10.5281/zenodo.15198674">2018-2019</a> <a href="https://doi.org/10.5281/zenodo.15198676">2020-2021</a> <a href="https://doi.org/10.5281/zenodo.15198700">2022</a></li> <li><strong>Trend analysis map:</strong><br><a href="https://doi.org/10.5281/zenodo.15198700">2000—2022</a></li> <li><strong>Global reference samples and machine learning models:</strong><br><a href="https://doi.org/10.5281/zenodo.15194973">Parquet and joblib python files</a></li> </ul> |
| title | Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022) |
| url | https://doi.org/10.5281/zenodo.15198660 |