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92 results for “tree cover”
Land transformation on multi-decadal timescales reveals expanding croplands and settlements at the expense of tree-covered areas and mangroves in Nigeria
<p>A comparative assessment of the change patterns was conducted for seven categories using multi-decadal timescales in seven agroecological zones during three time-intervals (i.e., 1986 – 2000, 2000 – 2013, and 2013 – 2022). These selected periods cover important epochs in Nigeria’s recent history. To examine how much humans have appropriated natural cover (HANLC) in Nigeria over the last four decades, we differentiated natural covers (e.g., tree-covered areas, grasslands, wetlands, and waterbodies) from human activity-related uses (e.g., cropland, artificial surfaces and otherland). To identify trajectories of changes signifying human appropriation of land cover, we evaluated the drivers and processes underlying these major transitions, 1) Natural regeneration and afforestation, 2) Cropland expansion, and 3) Settlement and infrastructure development. Cropland expansion is Nigeria’s most widespread change process with much loss of croplands related to natural regeneration and settlement expansion. The transition matrix is provided showing the extent of land-cover changes in Nigeria over almost four decades (1986 - 2022). Major land cover transitions in each agroecological zone is presented. Analysis of land cover change in each agroecological zone is over 100% when areas of persistence (i.e., areas of no change) are not considered in the analysis.</p>
Landsat bands (cloud free), tree cover (2000, 2010), bare-ground and surface water occurrence at 250 m based on GlobalForestWatch and USGS
<p>Landsat bands (cloud free) and tree cover (2000) based on Hansen et al. (2013), global surface water occurrence based on Pekel at al. (2016), and tree cover and bare-ground cover (2010) based the USGS land cover mapping projects (University of Maryland, Department of Geographical Sciences and USGS). All layers resampled to spatial resolution 1/480 d.d. (about 250 m) using gdalwarp with "average" resampling. Antarctica is not included. Original layers are available at 30 m resolution.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>lcv = theme: land cover,</li> <li>bareground = variable: occurrence of bareground,</li> <li>landsat.usgs = determination method: Landsat landcover at 30 m resolution project (https://landcover.usgs.gov/glc/),</li> <li>p = probability or fraction,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2010..2010 = time reference: year 2010,</li> <li>v1.0 = version number: 1.0,</li> </ul>
Tree-covered and intact forest landscapes BC1000, 1995, 2000, 2005, 2010, 2013, 2016 at 250 m
<p>Based on the <a href="http://www.unep-wcmc.org/resources-and-data/generalised-original-and-current-forest">UNEP historic forest cover map</a>, ESA land cover time series and <a href="http://www.intactforests.org/data.ifl.html">intact forest landscape (IFL 2000, 2013 and 2016) data</a>. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/LDN">here</a></strong>. Antartica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>ldg = theme: land degradation,</li> <li>forest.cover = variable: forest / tree cover,</li> <li>esacci.ifl = determination method: combination of ESA land cover and IFL maps,</li> <li>c = factor,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>1995 = time reference: year 1995,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Observational constraints of fire, environmental and anthropogenic on pantropical tree cover - Data
<p>Data used for analysis in "Explainable Clustering Applied to the Definition of Terrestrial Biome" - using Decision Tree and Clustering techniques to identify biomes.</p> <p>Land surface properties:</p> <ul> <li><strong>TreeCover </strong>- Vegetation Continuous Fields (VCF) collection 6 fractional tree cover from <sup>1</sup>, regridded as per <sup>2</sup>.</li> <li><strong>urban </strong>cover from the History Database of the Global Environment, Version 3.1 (HYDE) <sup>3,4</sup></li> <li><strong>crop </strong>cover (from HYDE)</li> </ul> <ul> <li><strong>pas - Pasture </strong>Cover (from HYDE)<strong>PopDen </strong>(population density from HYDE)</li> <li><strong>BurntArea_xxxxx </strong>- Burnt area with xxxx denoting different products, provided by fireMIP <sup>5–7</sup>: <ul> <li>GFED_four: Global Fire Emissions Database, Version 4 (GFED4) <sup>8</sup></li> <li>GFED_four_s: Global Fire Emissions Database, Version 4.1, including small fires (GFEDv4.1) <sup>9</sup></li> <li>MCD_forty_five: MCD45 <sup>10</sup></li> <li>Meris: Fire_CCI4.0 <sup>11</sup></li> <li>MODIS: Fire_CCI5.1 <sup>12</sup></li> </ul> </li> </ul> <p>Climate:</p> <ul> <li><strong>MAP_xxx </strong>- Mean annual precipitation where xxx denotes data source: <ul> <li><strong>CMORPH </strong><sup>13,14</sup></li> <li><strong>CRU </strong>from version 4.03 of the Climatic Research Unit Time Series high-resolution gridded dataset (CRU TS v4.01) <sup>15</sup></li> <li><strong>GPCC: </strong><sup>16</sup></li> <li><strong>MSWEP: </strong><sup>17</sup></li> </ul> </li> <li><strong>MAT </strong>- Mean annual temperature from CRU)</li> <li><strong>MConc_xxx </strong>– Mean annual concentration of rainfall as defined by <sup>18</sup>, where xxx denotes precip data source (see “MAP_xxx”)</li> <li><strong>MADD_xxx</strong>- Mean annual fractional dry days from CRU - i.e. seasonality of rainfall), where xxx denotes precip data source (see “MAP_xxx”)</li> <li><strong>MDDM_xxx </strong>– Mean fractional dry days of the driest month.</li> <li><strong>MADM_xxx – </strong>Mean annual precipitation of the driest month<strong>.</strong></li> <li><strong>MTWM </strong>- Mean Maximum Temperature of the warmest month from CRU</li> <li><strong>MTCM </strong>- Mean minimum temperature of the coldest month from CRU</li> <li><strong>SW1 </strong>- direct downwards SW simulated using the SLASH model using CRU cloud cover</li> <li><strong>SW2 </strong>- diffuse downwards SW simulated using the SLASH model using CRU cloud cover</li> <li><strong>MaxWind </strong>(Mean Max Windspeed from CRU-(National Centers for Environmental Prediction <sup>15</sup></li> </ul> <p>‘output_summary’ contains framework output. There are several directories for different experiments, each containing a netcdf file. Along with standard latitude and longitude,each file contains ‘model_level_number’ dimension, with each layer representing the 1, 5, 10, 25, 50, 75, 90, 95 and 99% quantiles of the model posterior. The folder represents the experiment:</p> <ul> <li>Control – standard full model reconstruction</li> <li>noHumans – without human influence (from crop, pasture, population density or urban influence)</li> <li>noMortality – without disturbance stress (burnt area, wind, heat stress, rainfall seasonality</li> <li>noMAP – without mean annual precip influence.</li> <li>noNoneMAT – without mean annual temperature influence.</li> <li>noFire – tree cover without the influence of fire</li> <li>noDrought – without the influence of rainfall distribution</li> <li>noTasMort – without mortality from heat stress</li> <li>noWind – without influence from max. windspeed</li> <li>noPas – without exclusion from pasture</li> <li>noCrop – without exclusion from crop</li> <li>noPop – without reduction from population density</li> <li>noUrban – without exclusion from urban</li> <li>firePlus1pc – tree cover with burnt area was 1% higher.</li> </ul> <p> </p> <p><strong>References</strong></p> <p> </p> <p>1. Dimiceli, C. & Others. MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2015). Preprint at (2015).</p> <p>2. Kelley, D. I. <em>et al.</em> How contemporary bioclimatic and human controls change global fire regimes. <em>Nat. Clim. Chang.</em> <strong>9</strong>, 690–696 (2019).</p> <p>3. Klein Goldewijk, K., Goldewijk, K. K., Beusen, A., Van Drecht, G. & De Vos, M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. <em>Glob. Ecol. Biogeogr.</em> <strong>20</strong>, 73–86 (2010).</p> <p>4. Hurtt, G. C. <em>et al.</em> Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. <em>Climatic Change</em> vol. 109 117–161 Preprint at https://doi.org/10.1007/s10584-011-0153-2 (2011).</p> <p>5. Hantson, S., Arneth, A., Harrison, S. P. & Kelley, D. I. The status and challenge of global fire modelling. (2016).</p> <p>6. Hantson, S. <em>et al.</em> Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project. <em>Geoscientific Model Development</em> vol. 13 3299–3318 Preprint at https://doi.org/10.5194/gmd-13-3299-2020 (2020).</p> <p>7. Rabin, S. S., Melton, J. R. & Lasslop, G. The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols with detailed model descriptions. <em>Geoscientific Model</em> (2017).</p> <p>8. Giglio, L., Randerson, J. T. & van der Werf, G. R. Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4). <em>J. Geophys. Res. Biogeosci.</em> <strong>118</strong>, 317–328 (2013).</p> <p>9. van der Werf, G. R. <em>et al.</em> Global fire emissions estimates during 1997–2016. <em>Earth Syst. Sci. Data</em> <strong>9</strong>, 697–720 (2017).</p> <p>10. Roy, D. P., Boschetti, L., Justice, C. O. & Ju, J. The collection 5 MODIS burned area product — Global evaluation by comparison with the MODIS active fire product. <em>Remote Sensing of Environment</em> vol. 112 3690–3707 Preprint at https://doi.org/10.1016/j.rse.2008.05.013 (2008).</p> <p>11. Alonso-Canas, I. & Chuvieco, E. Global burned area mapping from ENVISAT-MERIS and MODIS active fire data. <em>Remote Sens. Environ.</em> <strong>163</strong>, 140–152 (2015).</p> <p>12. Chuvieco, E. <em>et al.</em> Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. <em>Earth System Science Data</em> vol. 10 2015–2031 Preprint at https://doi.org/10.5194/essd-10-2015-2018 (2018).</p> <p>13. Joyce, R. J., Janowiak, J. E., Arkin, P. A. & Xie, P. CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution. <em>J. Hydrometeorol.</em> <strong>5</strong>, 487–503 (2004).</p> <p>14. Marthews, T. R., Blyth, E. M., Martínez-de la Torre, A. & Veldkamp, T. I. E. A global-scale evaluation of extreme event uncertainty in the eartH2Observe project. <em>Hydrol. Earth Syst. Sci.</em> <strong>24</strong>, 75–92 (2020).</p> <p>15. Harris, I. C. & Jones, P. D. CRU TS4.03: Climatic Research Unit (CRU) Time-Series (TS) version 4.03 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2018). (2019) doi:10.5285/10D3E3640F004C578403419AAC167D82.</p> <p>16. Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A. & Ziese, M. GPCC Full Data Monthly Product Version 2018 at 0.5◦: Monthly Land-Surface Precipitation from Rain-Gauges Built on GTS-Based and Historical Data. <em>Deutscher Wetterdienst: Offenbach am Main, Germany</em> (2018).</p> <p>17. Beck, H. E., Van Dijk, A. & Levizzani, V. MSWEP: 3-hourly 0.25 global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data. <em>Hydrol. Earth Syst. Sci.</em> (2017).</p> <p>18. Kelley, D. I., Harrison, S. P., Wang, H. & Simard, M. A comprehensive benchmarking system for evaluating global vegetation models. (2013).</p>
Tree species, diameter, regeneration, and herbaceous cover from 218 plots in 1985 in Black Rock Forest, NY.
A stand inventory was completed in 1985 in Black Rock Forest, Cornwall, NY across 3112 acres. Trees greater than 2" in diameter at breast height (DBH) were tallied using a 10 basal area factor prism in 218 plots across 71 stands. For each tree, species, DBH, number of eight foot pieces, overall form, crown class, and any special notes were recorded. Regeneration was measured at each location by tallying all trees less than 2" DBH in a 2-m radius plot. Shrub and herbaceous cover at each location were also tallied in a 2-m radius plot.
Tree regeneration after fire: Effects of burn severity, CPCRW vegetative cover analysis
This study examines the effects of burn severity on patterns of post-fire tree establishment in the boreal forest. We collected data from 5 separate field experiments examining seedling establishment across different severity levels in 4 burns in central Yukon Territory, Canada, and interior Alaska, USA. The experimental studies focus on the germination, survival, and growth responses of four common tree species, trembling aspen (Populus tremuloides), lodgepole pine (Pinus contorta), white spruce (Picea glauca) and black spruce (Picea mariana). Data on the soil organic layer (depth, moisture, bulk density, pH) were also collected at each site. This file contains visual cover estimates, by species, for 2000-2002 at the CPCRW site. Sample dates are: 4 August 2000 25 July 2001 16 July 2002. Values are percent cover in a 1x1m quadrat, where T=trace (less than 0.5%), out=outside quadrat but inside experimental plot. An additional line is included which give the full species names associated with the 6-letter codes used as column headers.
Tree regeneration after fire: Effects of burn severity, Delta vegetative cover analysis
This study examines the effects of burn severity on patterns of post-fire tree establishment in the boreal forest. We collected data from 5 separate field experiments examining seedling establishment across different severity levels in 4 burns in central Yukon Territory, Canada, and interior Alaska, USA. The experimental studies focus on the germination, survival, and growth responses of four common tree species, trembling aspen (Populus tremuloides), lodgepole pine (Pinus contorta), white spruce (Picea glauca) and black spruce (Picea mariana). Data on the soil organic layer (depth, moisture, bulk density, pH) were also collected at each site. This file contains visual cover estimates, by species, for 2000-2002 at the Delta site. Data for severity and warming treatments are included. Sample dates are: 31 July 2000 18 July 2001 13 July 2002. Values are percent cover in a 1x1m quadrat, where T=trace (less than 0.5%), out=outside quadrat but inside experimental plot. An additional line is included which give the full species names associated with the 6-letter codes used as column headers.
Tree survey:Effects of Long Term Fertilization and Oak Canopy Cover on Plant Communities and Ecosystem Processes
In 1996 E142 was established in field D on top of the E004 macroplots. E004 was conducted in fields A, B, C and D by Dave Tilman. The purpose of E004 was to see what effect NH4NO3 addition has on large areas over a longer period of time with exposure to naturally-occurring levels of herbivory. The nutrient addition treatments in E004, E142 plots have been applied annually since 1982. These experiments, along with others at Cedar Creek, examine the community and ecosystem consequences of chronic nutrient loading.
1-km forest tree height, cover, plant area index, and foliage height diversity for the CONUS
<p>Consistent and spatially explicit periodic monitoring of forest structure is essential for estimating forest-related carbon emissions, analyzing forest degradation, and supporting sustainable forest management policies. To date, few products are available that allow for continental to global operational monitoring of changes in canopy structure. In this study, we explored the synergy between the NASA’s spaceborne Global Ecosystem Dynamics Investigation (GEDI) waveform LiDAR and the Visible Infrared Imaging Radiometer Suite (VIIRS) data to produce spatially explicit and consistent annual maps of canopy height (CH), percent canopy cover (PCC), plant area index (PAI), and foliage height diversity (FHD) across the conterminous United States (CONUS) at 1-km resolution for 2013-2020. The accuracies of the annual maps were assessed using forest structure attribute derived from airborne laser scanning (ALS) data acquired between 2013 and 2020 for the 48 National Ecological Observatory Network (NEON) field sites distributed across the CONUS. The root mean square error (RMSE) values of the annual canopy height maps as compared with the ALS reference data varied from a minimum of 3.31-m for 2020 to a maximum of 4.19-m for 2017. Similarly, the RMSE values for PCC ranged between 8% (2020) and 11% (all other years). Qualitative evaluations of the annual maps using time series of very high-resolution images further suggested that the VIIRS-derived products could capture both large and “more” subtle changes in forest structure associated with partial harvesting, wind damage, wildfires, and other environmental stresses.</p>
Projection of potential future tree cover persistence for 2029 based on the global model
<p>Tree cover persistence projection results for 2029 based on the global model under a business-as-usual scenario.</p>
Global tree cover extent for 2014
<p>Global tree cover extent for 2014 based on loss between 2001–2014 (defined as any change below the 10% threshold of tree cover) subtracted from 1 km tree cover extent map for 2000.</p>
Projection of potential future tree cover persistence for 2029 based on the six regional models
<p>Tree cover persistence projection results for 2029 based on the six regional models under a business-as-usual scenario.</p>
DeepRainForest Output Data : Simulated daily rainfall output (2001-2020) under observed tree cover and no deforestation scenarios in South America
<p>This dataset deposited contains simulation data related to the analysis of forest-rainfall relationships and the impact of historical deforestation on rainfall patterns in South America. The data includes outputs from a spatiotemporal neural network model, DeepRainForest, developed to simulate rainfall based on vegetation and climate inputs in South America. This dataset is the data necessary to recreate the figures that appear in an accepted (but yet to be published manuscript) in Global Change Biology titled "Assessing the impact of past and ongoing deforestation on rainfall patterns in South America". When the manuscript is accepted then the article will be linked from here.</p> <p><strong><em>DeepRainForest_daily_rainfall_with_observed_treecover.nc:</em></strong> contains simulated daily rainfall data spanning from 2001 to 2020, considering the observed tree cover. </p> <p><em><strong>DeepRainForest_daily_rainfall_with_2000_treecover.nc: </strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 2000 onwards. </p> <p><em><strong>DeepRainForest_daily_rainfall_with_1982_treecover.nc:</strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 1982 onwards.</p>
Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer
As cities warm and the need for climate adaptation strategies increases, a more detailed understanding of the cooling effects of land-cover across a continuum of spatial scales will be necessary to guide management decisions. We asked how tree canopy cover and impervious surface cover interact to influence daytime and nighttime summer air temperature, and how effects vary with the spatial scale at which land-cover data are analyzed (10, 30, 60 and 90-m radii). A bicycle-mounted measurement system was used to sample air temperature every 5 m along 10 transects (about 7 km length, sampled 3-12 times each) spanning a range of impervious and tree canopy cover (0 to 100%, each) in a mid-sized city in the Upper Midwest, USA. Variability in daytime air temperature within the urban landscape averaged 3.5 degreeC (range 1.1 to 5.7 degreeC). Temperature decreased nonlinearly with increasing canopy cover, with the greatest cooling when canopy cover exceeded 40%. The magnitude of daytime cooling also increased with spatial scale, and was greatest at the size of a typical city block (60-90 m). Daytime air temperature increased linearly with increasing impervious cover, but the magnitude of warming was less than the cooling associated with increased canopy cover. Variation in nighttime air temperature averaged 2.1C (range 1.2 to 3.0 degreeC), and temperature increased with impervious surface. Effects of canopy were limited at night; thus, reduction of impervious surfaces remains critical for reducing nighttime urban heat. Results suggest strategies for managing urban land-cover patterns to enhance resilience of cities to climate warming.
Dalton and Nenana study site data including: invasive plant density estimates, invasive plant density, soil data, seedling estimates for dominant tree species and ground cover estimates for sites
This dataset contains invasive plant and stand level data for study sites along the Dalton and Parks highways in interior Alaska in the summer of 2012. Study sites were situated in burned and mature black spruce forests to compare invasive plant colonization patterns. Invasive plant density estimates along the road adjacent to each site are included, as well as invasive plant density within study sites. Other data includes ground cover estimates for dominant ground cover types, estimates of seedling abundance for dominant tree species, soil paramters (mineral soil pH and mineral soil moisture, residual organic layer/ organic layer depths, and active layer depths).
LegacyVegetation: Northern Hemisphere reconstruction of past plant cover and total tree cover from pollen archives of the last 14 ka
Open the record for dataset details and reuse information.
Input data for: Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.
<p>This repository includes input data used in the following article:</p> <p><strong>Vieilledent G., C. Grinand, F. A. Rakotomalala, R. Ranaivosoa, J.-R. Rakotoarijaona, T. F. Allnutt, and F. Achard.</strong> Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.</p> <p>For this article, data have been processed with a R/GRASS script. The development version of this script is available on GitHub at https://github.com/ghislainv/deforestation-maps-Mada. The last release of this script is archived on Zenodo: [DOI: 10.5281/zenodo.1118484].</p>
Output data from: Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.
<p>This repository includes output data from the following article:</p> <p><strong>Vieilledent G., C. Grinand, F. A. Rakotomalala, R. Ranaivosoa, J.-R. Rakotoarijaona, T. F. Allnutt, and F. Achard</strong>. Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.</p> <p>This repository includes Madagascar forest cover (forXXXX.tif), forest density (fordensXXXX.tif), distance to forest edge (dist_edge_XXXX.tif) and forest fragmentation index (fragXXXX.tif) for the years 1953, 1973, 1990, 2000, 2005, 2010 and 2014. Data are available as GeoTIFF raster files at 30m resolution in the UTM 38S projection (EPSG:32738).</p>
Tree cover for the year 2010 of the Metropolitan Region of São Paulo, Brazil
<p><strong>Dataset description</strong></p> <p>The tree cover dataset for the year 2010 of the Metropolitan Region of São Paulo (Brazil) was produced using 71 orthorectified RGB aerial photographs taken in 2010 at 1 m spatial resolution. This aerial photographs were made available by the EMPLASA (Empresa Paulista de Planejamento Metropolitano S.A) and can be visualized on the following Brazilian government website http://datageo.ambiente.sp.gov.br/app/# in the directory /Base Imagem/Portal de imagens - DigitalGlobe/Ortofotos do Estado de São Paulo - 2010 / 2011 (EMPLASA). The map of the tree cover was produced using a state-of-the-art deep learning method for image segmentation called U-net. The U-net method was originally developed by Ronneberger et al. (2015) for medical imagery and first applied to remote sensing of vegetation by Wagner et al. (2019). To train the U-net algorithm to recognize and segment trees, a mask of tree cover was manually delineated in one of the images (image ID: SF-23-Y-C-VI-2-SO) resulting in 4015 polygons. The image for manual sampling was chosen because it presented all different types of São Paulo tree cover, that are, isolated trees, natural forests, natural degraded forests and eucalyptus plantations as well as a high diversity in the background classes, with different urban building types (high rise buildings, individual houses, slums and industrial buildings) and also other important classes for the city of São Paulo such as water reservoir, roads and highways. Clipping the image and the mask in 64 x 64 pixels resulted in a sample of 1608 images and their associated labelled mask to train the model. Among these images, 1296 contained trees or forest and 312 contained only background. 1286 images were used for the training and 322 for independent validation. The algorithm presents an excellent level of accuracy, with an overall accuracy of 96.4 % and a F1-score of 0.941 (precision = 0.945 and recall = 0.937). The main known limitations of the algorithm are (i) that it misses sometimes the segmentation of trees on the image borders, however it is largely corrected by the overlap between tiles and has been corrected since Version 2.0; (ii) due to the shade of some buildings or mountains, the image is too dark for the algorithm to recognize objects; and, (iii) in relatively few occasions, it segments some green vegetation or algae which are not trees but present a similar texture or colour.</p> <p>The dataset is distributed in 71 tiles (EPSG:32723, WGS 84 / UTM zone 23S) that are available in raster and shapefile format in the compressed file. The rasters contain one band with value 1 if the pixel is tree cover and 0 otherwise. The shapefiles contain only polygons and these polygons are the tree cover. The tree cover can represent individual trees, natural forests, natural degraded forests or eucalyptus plantation. The total size of the decompressed archives is 6.40 Go.</p> <p>When using this dataset, please cite the original data descriptor article : <strong><a href="https://doi.org/10.3390/data4040145">https://doi.org/10.3390/data4040145</a></strong></p> <p><strong>References</strong></p> <p>Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, CoRR, abs/1505.04597, http://arxiv.org/abs/1505.04597, 2015.</p> <p>Wagner, F. H., Sanchez, A., Tarabalka, Y., Lotte, R. G., Ferreira, M. P., Aidar, M. P. M., Gloor, E., Phillips, O. L., and Aragão, L. E. O. C.: Using the U-net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images, Remote Sensing in Ecology and Conservation, https://doi.org/10.1002/rse2.111, https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.111, 2019.</p>
CATCD: The First 30m China Annual Tree Cover Dataset from 1985 to 2024 (Part Ⅰ: 1985-1991)
<p><strong>China Annual Tree Cover Dataset (CATCD)</strong> is the first long-term, high spatial resolution annual tree cover dataset for China, generated through the integration of time series of Landsat imagery and ensemble learning techniques based on random forests. The reliability and accuracy of CATCD have been validated using multi-source reference data (correlation: 0.70-0.96, RMSE: 5.6%-25.2%). The current version of CATCD (v 0.0.1) includes 30m resolution annual tree cover datasets for China from 1985 to 2024. Each year's data is stored in a GeoTIFF file with 'TreeCover' and 'Uncertainty' bands. TreeCover data ranges from 0 to 100, representing the percentage of tree cover in each pixel, while Uncertainty represents the uncertainty estimation of tree cover derived through 5-fold cross-validation, expressed as the standard deviation of predictions from five models. In addition to providing annual tree cover information for China, users can extract forest dynamic ranges by setting tree cover thresholds using CATCD. </p> <p><strong>In addition to Zenodo, users can access, download, and reanalyze CATCD on the </strong><strong>Google Earth Engine</strong> <strong>platform via the following link:</strong> <a href="https://code.earthengine.google.com/a28dcf064bba1975f5a6c28400c41edb">https://code.earthengine.google.com/a28dcf064bba1975f5a6c28400c41edb</a>. CATCD is expected to be updated annually at the end of each year on both the Zenodo and GEE platform. Please note that while Zenodo contains annual tree cover products, the uncertainty estimates pertaining to tree cover are not available within its repository. However, these estimates have been furnished within the Google Earth Engine (GEE) platform.</p> <p><a target="_blank"><strong>The dataset has now been updated to include data up to 2024.</strong></a></p> <p><strong>Data citation:</strong> Yaotong Cai, Xiaocong Xu, Sheng Nie, Cheng Wang, Peng Zhu, Yujiu Xiong, and Xiaoping Liu (2024). Unveiling Spatiotemporal Tree Cover Patterns in China: The First 30m Annual Tree Cover Mapping from 1985 to 2023. <em>ISPRS Journal of Photogrammetry and Remote Sensing</em>, 216: 240-258. DOI:10.1016/j.isprsjprs.2024.08.001.</p> <p>For data-related inquiries, please contact Dr. Yaotong Cai (<strong><a target="_new">caiyt33@mail2.sysu.edu.cn</a></strong>).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.