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TreeGOER: Tree Globally Observed Environmental Ranges
<p><strong>TreeGOER (Tree Globally Observed Environmental Ranges)</strong> is a database that documents the environmental ranges (minimum, maximum, median, mean and 5%, 25%, 75% and 95% quantiles) for 48,129 tree species and for 51 environmental variables, including 38 bioclimatic variables, 8 soil variables and 3 topographic variables. These ranges were calculated after cleaning occurrence records and standardizing species names with the <a href="https://bsapubs.onlinelibrary.wiley.com/doi/10.1002/aps3.11388">WorldFlora</a> R package to <a href="https://onlinelibrary.wiley.com/doi/10.1002/tax.12373">World Flora Online</a> or the <a href="https://www.nature.com/articles/s41597-021-00997-6">World Checklist of Vascular Plants</a> for a global GBIF occurrence download of 44,267,164 occurrences (GBIF.org 2021 <strong>GBIF Occurrence Download</strong> <a href="https://doi.org/10.15468/dl.77gcvq">https://doi.org/10.15468/dl.77gcvq</a>). The 5% and 95% quantiles were calculated separately for two methods of outlier detection and for the full data set. The process of compilation of TreeGOER with 30 arc-seconds global grid layers, two examples of BIOCLIM applications that investigated the effects of climate change on global tree diversity patterns and R scripts to repeat these analyses have been described by Kindt, R. (2023). <strong>TreeGOER: A database with globally observed environmental ranges for 48,129 tree species</strong>. Global Change Biology 29: 6303–6318. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</p> <p><strong>TreeGOER </strong>can be used in combination with the <strong>CitiesGOER</strong> database (<a href="https://doi.org/10.5281/zenodo.8175429">https://doi.org/10.5281/zenodo.8175429</a>) that documents the conditions for the same environmental variables (except elevation) for 52,602 cities with a human population ≥ 5000. An alternative to CitiesGOER is the <strong>ClimateForecasts</strong> database (<a href="../records/10776414">https://zenodo.org/records/10776414</a>), a database documenting the environmental conditions at the locations of 15,504 weather stations that was also integrated in the <strong><a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees database</a></strong> (see <a href="https://doi.org/10.1038/s41598-023-39552-1">Kindt et al. 2023</a>). <strong>TreeGOER</strong> could also be used with the <strong>TreeGOER Global Zones</strong> atlas that can be obtained from <a href="https://doi.org/10.5281/zenodo.8252756">https://doi.org/10.5281/zenodo.8252756</a>. This high-resolution atlas includes sheets with global zones for the Climatic Moisture Index (CMI) and the number of months with average temperature > 10 degrees C (Tmo10); these are zones for which presence of the 48,129 species was documented by TreeGOER.</p> <p>Changes between different versions of the databases are documented in a specific sheet in the metadata file.</p> <p><strong>The distribution of the same 48,129 species across historical and contemporary Holdridge Life Zones is available via this Zenodo archive: <a href="https://zenodo.org/records/14020914">https://zenodo.org/records/14020914</a>. The distribution of the same 48,129 species across 1931-1960, 1961-1990 and 1991-2020 Köppen-Geiger climate zones is available via this Zenodo archive: <a href="https://zenodo.org/records/14211619">https://zenodo.org/records/14211619</a>. The distribution of the same 48,129 species for Whittaker Biome Types are available via this Zenodo archive: <a href="https://zenodo.org/records/14908943">https://zenodo.org/records/14908943</a>. Globally observed environmental ranges as in TreeGOER are available since 14<sup>th</sup> June 2024 for another set of species (including several bamboo and hybrid tree species not included in GlobalTreeSearch) via this Zenodo archive: <a href="../records/11652972">https://zenodo.org/records/11652972</a>. <br></strong></p> <p> </p> <p>The development of TreeGOER was supported by the <strong>Darwin Initiative</strong> to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by Norway’s <strong>International Climate and Forest Initiative</strong> through the Royal Norwegian Embassy in Ethiopia to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, and by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project. When using TreeGOER in your work, cite the publication (Kindt <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">2023</a>) as well as this repository using the DOI (<a href="https://doi.org/10.5281/zenodo.7922927">https://doi.org/10.5281/zenodo.7922927</a>).</p> <p> </p>
Alignments and ML trees of cassava brown streak virus and Ugandana cassava brown streak virus polyprotein nucleotide sequences
<p>Alignments of full and nearly full polyprotein-length nucleotide sequences from GenBank for the two ipomoviruses that cause cassava brown streak disease, in fasta format. Separate alignments for 67 cassava brown streak virus sequences and 81 Ugandan cassava brown streak virus sequences are provided, as well as a combined alignment of 148 sequences. Alignments were created with MUSCLE and then modified by eye in AliView.</p> <p>Also, two tree files (in nexus) format are supplied, resulting from a maximum likelihood analysis with IQTree on each of the two single-species datasets. Support for nodes with aLRT and 100 actual bootstrap replicates are provided (aLRT/bootstrap).</p>
Appendix S3 from: Droissart V, Dauby G, Hardy OJ, Deblauwe V, Harris DJ, Janssens S, Mackinder BA, Blach-Overgaard A, Sonké B, Sosef MSM, Stévart T, Svenning J-C, Wieringa JJ, Couvreur TLP (2018) Beyond trees: biogeographical regionalization of tropical Africa. Journal of Biogeography. DOI:10.1111/jbi.13190
<p>This dataset corresponds to GIS file that were generated in the study published by Droissart, Dauby et al. in <em>Journal of Biogeography</em>:</p> <p>Droissart V, Dauby G, Hardy OJ, Deblauwe V, Harris DJ, Janssens S, Mackinder BA, Blach-Overgaard A, Sonké B, Sosef MSM, Stévart T, Svenning J-C, Wieringa JJ, Couvreur TLP (2018) Beyond trees: biogeographical regionalization of tropical Africa. <em>Journal of Biogeography. </em>DOI:10.1111/jbi.13190</p> <p><em>Please cite the aforementioned article and the dataset herein, when using of any of these files in this dataset.</em></p> <p> </p> <p>The GIS file is referred in the paper as <strong>Appendix S3</strong> and correspond to the map presented in Figure 1. Each polygons of the shapefile correspond to the main floristic bioregions and transition zones of tropical Africa delimited using bipartite network clustering analysis of 24,719 plant species.</p> <p>The coordinate system of the ESRI shapefile is GCS_WGS_1984. Field descriptions for the associate table are:</p> <ul> <li><strong>bionames</strong>: name of the bioregions as given in Table S1.1.</li> <li><strong>bioreg_ID</strong>: identifier of the bioregions as given in Table S1.1 and Fig. 1. T= Transition zones</li> <li><strong>cluster_ID</strong>: identifier of clusters delimited using bipartite network clustering on the 24,719 plant species of the RAINBIO database, as given in Table S1.1 and Fig. S2.1.</li> </ul>
Elevation modulates the phenotypic responses to light of four co-occurring Pyrenean forest tree species
<p>Data on plant water potential for seedlings of four Pyrenean tree species planted along an elevation gradient. The dataset contains three files:</p> <ol> <li><strong>Biomass.txt: </strong>Data on plant biomass per fraction (leaf, stem and roots) 4 years after plantation. Included variables:<br> - Piso (factor): elevational stage at which the seedling was planted. Two levels: montane (M) or subalpine (S)<br> - Luz (factor): whether the seedling was plantes at a gap or in the understory. Two levels: gap (O) or understroy (T)<br> - N (numeric): number of plant in that plot<br> - Sp (factor): species. Four levels: BEPE (Betula pendula) / PISY (Pinus sylvestris) / PIUN (Pinus uncinata) / ABAL (Abies alba)<br> - Planta (factor): code to identify uniquely each plant<br> - File (factor): code to identify uniquely each plant<br> - GLI (num): Global Light Index, the amount of irradiance that receives each seedling<br> - Code (factor): code to identify uniquely each plant<br> - PLB (numeric): total plant biomass (g)<br> - LFB (numeric): leaf biomass (g)<br> - STB (numeric): stem biomass (g)<br> - RTB (numeric): root biomass (g)<br> - LMF (numeric): leaf mass fraction (LFB/PLB)<br> - SMF (numeric): stem mass fraction (STB/PLB)<br> - RMF (numeric): root mass fraction (RTB/PLB)<br> - SLA (numeric): specific leaf area<br> - H (numeric): plant height (mm)<br> - D (numeric): plant diameter at root collar (mm)<br> - PB2 (numeric): total plant biomass without considering leaves (g)<br> - SF2 (numeric): stem mass fraction without considering leaves (STB/PB2)<br> - RF2 (numeric): root mass fraction without considering leaves (RTB/PB2)</li> <li><strong>init_biomass.txt:</strong> for biomass at the moment of plantation<br> - Piso (factor): elevational stage at which the seedling was planted. Two levels: montane (M) or subalpine (S)<br> - N (numeric): number of plant<br> - Sp (factor): species. Four levels: BEPE (Betula pendula) / PISY (Pinus sylvestris) / PIUN (Pinus uncinata) / ABAL (Abies alba)<br> - Planta (factor): code to identify uniquely each plant<br> - File (numeric): code to identify uniquely each plant<br> - PB (numeric): total plant biomass (g)<br> - LB (numeric): leaf biomass (g)<br> - SB (numeric): stem biomass (g)<br> - RB (numeric): root biomass (g)</li> <li><strong>WaterPot.txt</strong>: data on plant water potential for seedlings of four Pyrenean tree species planted along an elevation gradient during a period of intense drought<br> - Piso (factor): elevational stage at which the seedling was planted. Two levels: montane (M) or subalpine (S)<br> - Luz (factor): whether the seedling was plantes at a gap or in the understory. Two levels: gap (O) or understroy (T)<br> - N (numeric): number of plant <br> - Parcela (factor): identifier ofthe plot<br> - Sp (factor): species. Four levels: BEPE (Betula pendula) / PISY (Pinus sylvestris) / PIUN (Pinus uncinata) / ABAL (Abies alba)<br> - Estacion (factor): the moment for the measurement. One level: September<br> - GLI (numeric): global light index, the ration of total irradiance received by the plant at the moment of plantation<br> - WPt (numeric): water potential (bars)</li> </ol>
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>
Geographic variation of tree height of Pinus pinea L. gathered from common gardens in Europe
<p>This dataset collects individual georeferenced tree height data from <em>Pinus pinea </em>L. planted in common gardens in France and Spain, between years 1993 and 1997. The experimental design varies depending on the common garden, from a randomized complete to incomplete block design, RCB or RIB, respectively. The final dimensions of this database is 56,624 individual tree height measurements <em> </em>with 9 common gardens and 55 different provenances. The data can be used to assess genetic variation and phenotypic plasticity with further applications in biogeography and forest management. </p>
Geographic variation of tree height of Pinus nigra Arn. gathered from common gardens in Europe
<p>This dataset collects individual georeferenced tree height data from <em>Pinus nigra</em> Arn. planted in common gardens in France, Germany and Spain, between years 1968 and 2009. The experimental design varies depending on the common garden, from a randomized complete to incomplete block design, RCB or RIB, respectively. The final dimension of the dataset is 194,642 individual tree height data measurements <em> </em>with 15 common gardens and 78 different provenances. The data can be used to assess genetic variation and phenotypic plasticity with further applications in biogeography and forest management. </p> <p> </p>
Geographic variation of tree height of Pinus pinaster Aiton gathered from common gardens in Europe and North-Africa
<p>This dataset collects individual georeferenced tree height data from <em>Pinus pinaster</em> Aiton planted in common gardens in France, Morocco and Spain, between years 1966 and 1992. The experimental design varies depending on the common garden, from a randomized complete to incomplete block design, RCB or RIB, respectively. The final dimension of the dataset is 123,801 individual tree height data measurements <em> </em>with 14 common gardens and 182 different genetic units. The data can be used to assess genetic variation and phenotypic plasticity with further applications in biogeography and forest management. </p>
Data associated with the manuscript: Andean bear tree selectivity for scent-marking in Ecuadorian cloud forests
<p>This is the original version of data used for the manuscript, <em>Andean bear tree selectivity for scent-marking in Ecuadorian cloud forests</em>. The data are in four files following the numerical order and titles of the Results section in the manuscript, i.e. <em>1_PCA.csv</em>, <em>2_Modeling tree selection at the individual-tee level.csv</em>, <em>3_Modeling tree selection on a local spatial scale.csv</em>, <em>4_Modeling formation of marked-tree cluster sites.csv. We used these datasets for our analysis in the program R, the details are provided in the Methods section. </em><span>Our field work was performed in compliance with the Framework Agreement for access to genetic resources called "Biodiversity Study of Ecuador '' made between the Ecuadorian Ministry of Environment and the UTPL. The code for the Agreement is MAE-DNB-CM-2015-0016-M-0002. The research was funded by Bears in Mind, International Association for Bear Research and Management, the Faculty of Environmental Sciences of Czech University of Life Sciences in Prague, National Geographic Society, Nature and Culture International, GIZ Ecuador, and Trailcampro. </span></p>
ForestPaths: European tree genus map
<h2>Abstract</h2> <p>This dataset provides an <strong>early access version</strong> of the European tree genus map at <strong>10 m resolution</strong> for the year 2020, derived from <strong>Sentinel-1 and Sentinel-2 </strong>satellite data. The map distinguishes eight classes (Larix, Picea, Pinus, Fagus, Quercus, other needleleaf, other broadleaf, and no trees) and is distributed as <strong>Cloud Optimized GeoTIFFs </strong>(COGs) over a 100 km grid in <strong>EPSG:3035 (ETRS89 / LAEA Europe)</strong>. </p> <p><br>The map was generated using a <strong>CatBoost model </strong>trained on forest plot inventories, citizen science observations, orthophoto interpretation, and LUCAS data, with additional features from DEM and climate datasets. Labels were filtered and aggregated to genus level to reduce noise. </p> <p> </p> <h2>Early access notice</h2> <p>This release is<strong> </strong>provided as an <strong>early access version</strong>. The map is still undergoing validation and fine-tuning, and a formal publication is planned. Updates and improvements may therefore be made in future releases. </p> <p>We <strong>welcome feedback</strong> and contributions of additional training data to further improve the map. <br> </p> <h2>Dataset description</h2> <ul> <li><strong>Resolution</strong>: 10m</li> <li><strong>Format</strong>: Cloud Optimized GeoTIFFs (COGs)</li> <li><strong>Tiling</strong>: 100km grid</li> <li><strong>Coordinate reference system</strong>: EPSG: 3035 (ETRS89 / LAEA Europe)</li> </ul> <h3>Legend</h3> <p>0 – Larix <br>1 – Picea <br>2 – Pinus <br>3 – Fagus <br>4 – Quercus <br>5 – Other needleleaf <br>6 – Other broadleaf <br>7 – No trees </p> <h2>Methodology summary</h2> <p>The classification was performed using a <strong>CatBoost model</strong> trained on diverse reference sources [1-10]: <br>- National and regional plot inventories <br>- Citizen science observations <br>- Orthophoto interpretation <br>- LUCAS data </p> <p>Training labels were filtered to reduce noise and aggregated to genus level. Predictor variables include annual statistics from Sentinel-1 and Sentinel-2, combined with auxiliary datasets on altitude (DEM) and climate. </p> <h2>Further details on the methodology will be made available in the product publication, which will follow this early access release. <br> <br>Usage Notes </h2> <ul> <li><strong>CRS</strong>: EPSG:3035 (ETRS89 / LAEA Europe). Reprojection may be required for use with other datasets.</li> <li><strong>Tiling scheme</strong>: Provided as 100 km × 100 km COG tiles. Users may mosaic tiles if needed. </li> <li><strong>Classes</strong>: See legend above. Class 7 (“No trees”) includes cropland, grassland, built-up, and other non-tree areas. </li> <li><strong>Early access status</strong>: Not yet fully validated. Regional inconsistencies and misclassifications may be present.</li> </ul> <p><strong>Feedback & contributions</strong>: We invite users to share validation results and contribute additional reference data to improve future releases. </p> <h2> How to cite </h2> <p>If you use this dataset, please cite as: </p> <p><br>De Keersmaecker, W., Zanaga, D., Senf, C., Viana-Soto, A., Klapper, J., Blickensdörfer, L., Govaere, L., Lerink, B., Leyman, A., Schelhaas, M.-J., Teeuwen, S., Verkerk, P. J., & Van De Kerchove, R. (2025). European Tree Genus Map 2020 (Early Access Release) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13341104 </p> <p><strong>BibTeX </strong></p> <p>@dataset{dekeersmaecker2025_treegenus, <br> author = {De Keersmaecker, Wanda and Zanaga, Daniele and Senf, Cornelius <br> and Viana-Soto, Alba and Klapper, Johanna and Blickensdörfer, Lukas <br> and Govaere, Leen and Lerink, Bas and Leyman, Anja <br> and Schelhaas, Mart-Jan and Teeuwen, Sander and Verkerk, Pieter Johannes and Van De Kerchove, Ruben}, <br> title = {European Tree Genus Map 2020 (Early Access Release)}, <br> year = {2025}, <br> publisher = {Zenodo}, <br> version = {early-access}, <br> doi = {10.5281/zenodo.13341104}, <br> url = {https://doi.org/10.5281/zenodo.13341104} <br>} </p> <h2>References</h2> <p>[1] Alberdi, I., Bombín, R. V., González, J. G. Á., Ruiz, S. C., Ferreiro, E. G., García, S. G., Mateo, L. H., Jáuregui, M. M., Pita, F. M., & de Oliveira Rodríguez, N. (2017). The multi-objective Spanish national forest inventory. Forest systems, 26(2), 14. <br> <br>[2] Álvarez-González, J. G., Canellas, I., Alberdi, I., Gadow, K. V., & Ruiz-González, A. (2014). National Forest Inventory and forest observational studies in Spain: Applications to forest modeling. Forest Ecology and Management, 316, 54-64. </p> <p>[3] Finnish Forest Centre (Metsäkeskus). (2025). Forest resource lattice data (Hila-aineisto) [2019–2021]. Retrieved from https://www.metsakeskus.fi.</p> <p>[4] Fridman, J., Holm, S., Nilsson, M., Nilsson, P., Ringvall, A. H., & Ståhl, G. (2014). Adapting National Forest Inventories to changing requirements–the case of the Swedish National Forest Inventory at the turn of the 20th century. Silva Fennica, 48(3). </p> <p>[5] Govaere L. & Leyman A. (2023). Vlaamse bosinventarisatie Agentschap Natuur en Bos (VBI1: 1997-1999; VBI2: 2009-2018; VBI3: 2019-2021, v2023-03-17).</p> <p>[6] Heisig, J., & Hengl, T. (2020). Harmonized Tree Species Occurrence Points for Europe (0.2). https://doi.org/https://doi.org/10.5281/zenodo.5524611 </p> <p>[7] IGN. (2016). BD Forêt Version 2.0. January 2016 </p> <p>[8] Riedel T., Hennig P., Kroiher F., Polley H., Schmitz F., Schwitzgebel F. (2017): Die dritte<br>Bundeswaldinventur (BWI 2012). Inventur- und Auswertemethoden, 124 S.</p> <p>[9] Schelhaas MJ, Teeuwen S, Oldenburger J, Beerkens G, Velema G, Kremers J, Lerink B, Paulo MJ, Schoonderwoerd H, Daamen W, Dolstra F, Lusink M, van Tongeren K, Scholten T, Pruijsten L, Voncken F, Clerkx APPM (2022). Zevende Nederlandse Bosinventarisatie; Methoden en resultaten. Wettelijke Onderzoekstaken Natuur & Milieu, WOt-rapport 142. https://edepot.wur.nl/571720</p> <p>[10] Villaescusa, R. & Díaz, R. (1998) Segundo inventario forestal nacional (1986–1996). Ministerio de Medio Ambiente, ICONA, Madrid.</p> <h2>Acknowledgements</h2> <p>We are very grateful for access to the forest plot inventories. We thank the Ministerio para la Transición Ecológica y Reto Demográfico (MITECO) for open access of the Spanish Forest Inventory (https://www.miteco.gob.es/). Finally, we would like to acknowledge the ForestPaths project (Co-designing Holistic Forest-based Policy Pathways for Climate Change Mitigation), that receives funding from the European Union's Horizon Europe Research and Innovation Programme (ID No 101056755), as well as from the United Kingdom Research and Innovation Council (UKRI).</p> <p> </p>
Nairobi_Street_Trees_Distribution_Diversity
<p>Input data and code to accompany the paper:</p> <p>Alice Gerow, Vivian Kathambi, Dexter Locke, Mark Ashton, Craig Brodersen. Street tree communities reflect socioeconomic inequalities and legacy effects of colonial planning in Nairobi, Kenya. Urban Forestry & Urban Greening. <a href="https://doi.org/10.1016/j.ufug.2024.128530">https://doi.org/10.1016/j.ufug.2024.128530</a></p> <p>The input data consists in street tree observations collected during a field survey conducted between June and August 2023 in Nairobi, Kenya. The code includes descriptive tables and plots, statistical tests, and alpha and beta diversity metrics and visualizations used to compare ecological communities across social groups.</p>
Tree inventory data such as tree identity, position in the plot, height, architecture and biomass on Mt. Kilimanjaro
<p>This dataset describes position and sizes of all trees above 10 cm diameter at breast height in all plots, also fruiting and flowering events and if it is a canopy tree or not in KiLi project. -999999 represents NA in numeric variables. </p> <p>Within each plot, all trees wider than 10 cm diameter at breast height (dbh) were marked with aluminium tags and their dbh and height were measured. The dbh was measured with a diameter tape (Forestry Suppliers, USA) at 1.3 m for normally shaped trees and 20 cm below or above when branches or irregular shapes impeded measurement at that height. The 1.3 m height was measured from the highest ground level around the stem to standardize measurements taken on slopes. For trees which were strongly buttressed or too big to measure by hand, a laser dendrometer (Criterion RD 1000 with TruPulse 200/200, Centennial, USA) was used to measure the tree above the buttresses and at 1.3 m. Lianas above 10 cm in diameter were also marked and their dbh was measured. Tree height was measured using an ultra-sonic hypsometer (Vertex IV Hypsometer, Haglöf, Langsele, Sweden) or a laser rangefinder (TruPulse 200/200). The tree inventories were carried out between December 2010 and March 2013.</p> <p>The KiLi project (2010-2018) is a German Science Foundation (DFG) funded research unit (DFG research unit FOR1246) that focuses on biodiversity and ecosystem processes along altitudinal and disturbance gradients on Mt. Kilimanjaro (Tanzania, Africa), capitalizing on its world-wide unique range of climatic and vegetation zones. The research unit comprises 2 central projects and 7 subprojects from various disciplines. On a total of 60 study sites in both natural and human-disturbed ecosystems biodiversity (e.g. plants, soil arthropods, ants, bees, frogs, lizards, bats, birds), related ecosystem processes (decomposition, seed dispersal, pollination, herbivory, predation), and biogeochemical processes and properties of ecosystems (climate, soil properties and nutrient status, regulation of water and carbon fluxes, trace gas emissions, primary productivity, functional diversity) are analyzed.</p>
Data, Analytical Code, and Model Outputs From: "Green is the New Black: Outcomes of Post-Fire Tree Planting Across the Interior West, USA"
<p>This archive includes data (locations of tree plantings, one-year survival records, remotely sensed canopy cover change), statistical code, and model outputs from Rodman et al. (2024). For more information on specific information, processing methods, and data formats, see "README.md" or "README.html" files associated with this archive</p>
RT-Trees: Evaluation and RGB training images with masks
<p>This is the RT-Trees dataset proposed and used in the paper titled, "Shadowsense: Unsupervised Domain Adaptation and Feature Fusion for Shadow-Agnostic Tree Crown Detection From RGB-Thermal Drone Imagery", published at the <a href="https://openaccess.thecvf.com/content/WACV2024/html/Kapil_ShadowSense_Unsupervised_Domain_Adaptation_and_Feature_Fusion_for_Shadow-Agnostic_Tree_WACV_2024_paper.html">IEEE/CVF WACV 2024</a> conference. Due to the size of the dataset and Zenodo's 50GB limit, the dataset is partitioned into two separate uploads. This upload contains the evaluation splits (test & val), along with the labelled subset of RGB training images used for a supervised training experiment, and the much larger set of unlabelled RGB images used for fully-unsupervised training. </p> <p>The second upload includes the corresponding unlabelled thermal images used for unsupervised training. </p>
Data for paper "Parametric analyses of attack-fault trees"
<p>This is the dataset for paper "Parametric analyses of attack-fault trees" published in the proceedings of the 19th International Conference on Application of Concurrency to System Design (ACSD 2019).</p>
EXPLO. Sovjan 2021. Radiocarbon raw data from tree-rings
<p>Raw radiocarbon measurements from the tree-rings sampled from wooden elements from the archaeological site of Sovjan, Albania, measured in 2020 and presented in "<em>The Early Bronze Age dendrochronology of Sovjan (Albania): A first tree-ring sequence of the 24th – 22nd c. BC for the southwestern Balkans</em>", Maczkowski et al., 2021, DOI: <a href="http://dx.doi.org/10.1016/j.dendro.2021.125811">10.1016/j.dendro.2021.125811</a></p>
Dataset from : Browsing is a strong filter for savanna tree seedlings in their first growing season
<p>1: Newly germinated seedlings are vulnerable to biomass removal but usually have at least six months to grow before they are exposed to dry-season fires, a major disturbance in savannas. In contrast, plants are exposed to browsers from the time they germinate, making browsing potentially a very powerful bottleneck for establishing seedlings. 2: Here we assess the resilience of seedlings of 10 savanna tree species to top-kill during the first 6 months of growth. Newly-germinated seeds from four dominant African genera from across the rainfall gradient were planted in a common garden experiment at the Wits Rural Facility and clipped at 1 cm when they were ~2, 3, 4, and 5 months old. Survival, growth, and key plant traits were monitored for the following 2.5 years. 3: Seedlings from environments with high herbivory pressure survived top-kill at a younger age than those from low-herbivore environments, and more palatable genera had higher herbivore-tolerance. Most individuals that survived were able to recover lost biomass within 12 months, but the clipping treatment affected root mass fraction and branching patterns. 4: Synthesis: The impact of early browsing as a demographic bottleneck can be predicted by integrating information on the probability of being browsed and the probability of surviving a browse event. Establishment limitation through early-browsing is an under-recognised constraint on savanna tree species distributions. Data may be used without requesting permission after the the publication of the paper</p>
UAV outputs and associated field measurement of the herbaceous and tree of the Senegalese savanna of the Dahra Djoloff research center
<p>The dataset contains UAV outputs (mosaic , surface model and terrain) and the associated measurements of vegetation( herbaceous and woody) that were made within the research isra station of Dahra Djoloff.</p> <p>Sites</p> <p>The sites were 38 ha-1 plots across the research station. The UAV were collected on the same site at the same date in October 2018(end of the wet season and maximum of the biomass). The sites were the sites of previous studies (Raynal 1964, Ndiaye et al. 2014, Ndiaye et al. 2015). The plots were chosen based on several studies of vegetation dynamics and these plots were judged to be representative of the diversity of vegetation type within the research station.</p> <p>UAV flight plan</p> <p>We used a low-cost UAV with an RGB (Red Green Blue) captor integrated in the UAV. The plots were mapped using a Dji Spark UAV with the litchi application for the automatic flight. The flight plan was six 100 m transects each separated by 20 m was performed at an altitude of 80 m and at a speed of 5 m.s-1. Images were acquired in autofocus mode (ISO exposure were automatically adjusted) at two-second intervals throughout the flight. The angle of view was 80°. The frontal overlap was about 90% and the side overlap about 80% with 80° angle</p> <p>Field measurement.</p> <p>Herbaceous Biomass.</p> <p>For the Landscape dataset, 10 squares of 1 m² were sampled; All the aboveground biomass was cut and weighted in fresh. A composite sample was made for each site and weighted dry to evaluated the dry matter content and so the dry matter of each sample.</p> <p>The positions of the squared was mark r with a plastic bag on the ground.</p> <p>Tree measurement.</p> <p>For the landscape, we selected 10 trees on the UAV maps. The measurements were made after image analysis in January 2019 and January 2020. The trees were not measured on all the site.</p> <p>The measured variables were the maximum height of the tree (using a clinometer), the diameter of the tree crown in the north-south direction and in the west-east direction. Their tree crown area was calculated assuming that the crown was a circle. The trunk diameters were measured at 0.30 cm in both direction and the circumference were calculated. All woody species were identified at the species and genus levels.</p> <p>Image analysis.</p> <p>The images taken during each flight were processed using a PiX4D mapper (Pix4D SA, Lausanne, Switzerland). 3D mapping is the basic parameter proposed in the software. For each plot, an orthophotograph, a digital surface model, and a digital elevation model were computed and exported in GeoTIFF format.</p> <p>Data organization</p> <p>For each plot, we had</p> <ul> <li>DSM that contains the surface model in tiff</li> <li>DTM that contains the terrain model in tiff</li> <li>Mosaic that the orthomosaic in tiff.</li> </ul> <p>All the different geotiff can directly be download.</p> <p>Data are in a zip file that contains the shapefile with the position and table with the field measurements.</p> <p>The shapefile “Herbaceous.shp” contain the positions of the squared sample but also of squared that contains only soil (squared cut before the flight).</p> <p>The CSV “Herbaceous-landscape.csv” contains the measurement of Aboveground biomass. (FM fresh mass and DM dry mass). Both are in g (g.m-²). The biomass was available for 346 squared.</p> <p>The shapefile “tree.shp” contains the positions of the tree. Here the shapefile contains the positions of all the tree preselected on the map. Only a selection of theses tree was measured on the field.</p> <p>The file “Tree-landscape.csv” contains the tree measurements with the species, the height (in m), the trunk circumference (TC) in cm and the area of the crown(area) in m². The tree measurements were available for 240 trees.</p> <p> </p><p>reference</p> <p></p> <p>Ndiaye, O., A. T. Diop, L. E. Akpo, and M. Diène. 2014. Dynamique de la teneur en carbone et en azote des sols dans les systèmes d’exploitation du Ferlo: cas du CRZ de Dahra. Journal of Applied Biosciences <strong>83</strong>:7554-7569.</p> <p>Ndiaye, O., A. T. Diop, M. Diène, and L. E. Akpo. 2015. Étude comparée de la végétation de 1964 et 2011 en milieu pâturé: Cas du CRZ de Dahra. Journal of Applied Biosciences <strong>88</strong>:8235–8248.</p> <p>Raynal, J. 1964. Etude botanique de pâturages du Centre de Recherches Zootechniques de Dahra-Djoloff (Sénégal).</p> <p> </p>
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