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63 results for “tree maps”
Tree Ring Data from the Lyford Mapped Tree Plot at Harvard Forest 1861-2014
Is it possible to reconstruct aboveground carbon/biomass from tree rings? If so, how far back in time can researchers go when using tree-ring data in the reconstruction of past biomass? Answers to these questions will have a significant impact on our understanding of dynamics in the terrestrial carbon sink. Long tree-ring records of biomass can reveal intra-annual to annual to multidecadal variations that cannot be resolved by forest census data that is not conducted annually. Additionally, while these dynamics might be resolved using remote sensing, most remotely-sensed products are only two decades or less in length. By having long records of carbon biomass, we can then identify not only the dominant drivers of biomass, but how the importance of these drivers might change during different eras as environmental factors change (e.g., climate, air pollution, disturbance). To test these and other questions, we collected tree-ring records from three 20m radius plots set within the Lyford Plot at the Harvard Forest. The Lyford Plot has been remeasured, on average, decadally since 1969. We can convert these data to biomass using allometric equations and compare tree-ring inferred aboveground biomass to the census data going back in time. Dye et al. (2016) have shown that these data fall within the range of uncertainty of census data sampled in similar plots going back to 1969. Dye, A., Barker Plotkin, A., Bishop, D., Pederson, N., Poulter, B. and Hessl, A., 2016. Comparing tree‐ring and permanent plot estimates of aboveground net primary production in three eastern US forests. Ecosphere, 7(9).
Mapped Trees in CRUI Land Use Project at Harvard Forest 1996-2006
Numerous variables related to land use disturbance and recovery processes influence forest composition, structure, and growth. We measured forest communities in six sites that were formerly plowed, pastured, or continuously forested woodlots in Prospect Hill to test predictions about species composition, stand structure, and productivity in response to agricultural land use legacies. A permanent 30 x 50 m plot gridded in 5 x 5 m sub-plots was established in each of the 6 sites. All trees at least 2.5 cm DBH (diameter at breast height, 1.3 m) were mapped visually in the field, marked with an aluminum tag, and their DBH recorded in summer 1996. Individual boles of multi-stemmed trees were measured separately and a composite single DBH was calculated. Standing dead trees were also mapped and their DBH recorded as well. The 1996 data were used to determine above ground woody biomass using allometric equations for individual trees. The permanent plots were re-surveyed ten years later in fall 2006. The status of each tree mapped in 1996 was recorded (alive, dead standing, dead fallen, forked) and DBH’s were re-measured. Additional trees that grew across the 2.5 cm DBH threshold during the ten-year period were mapped and their DBH’s measured. Composite diameters and above ground woody biomass were determined for forked trees as in 1996. The 2006 re-measurements were used to analyze changes in species composition, stand structure (density, diameter distribution), and mortality patterns among species and size classes, and to calculate net changes in above ground woody biomass across the ten-year period.
Hemlock Mapped Tree Plot at Harvard Forest since 1990
Most of the central New England landscape was cleared for agriculture in the mid-19th century and then naturally reforested into "secondary forests" with the abandonment of agricultural land. Some sites, often poorly drained, remained forested, but were usually subjected to intensive fuelwood cutting or logging and are termed "primary forests." The Hemlock Woodlot was never cleared for agriculture, but has a history of cutting and natural disturbance. The hemlock woodlot is located in the center of Harvard Forest's Prospect Hill Tract, adjacent to a spruce-blackgum swamp. Soils are moist and rocky, with a thick organic layer. Hemlock dominates tree species composition (62% by basal area), with hardwoods and scattered large white pine comprising the remainder. Most of the trees are 100-150 years old, with a few hemlock trees up to 230 years old. While the site was never cleared for agriculture, it was logged several times and chestnut blight removed a chestnut-dominated overstory in the 1910s. The 0.72 ha stem-mapped plot is at the center of a 4-ha hemlock-dominated forest. This plot serves as a major reference site and is part of a network of hemlock forests that are being intensively sampled as the hemlock woolly adelgid arrives.
Lyford Mapped Tree Plot at Harvard Forest since 1969
Permanent forest plots provide an empirical understanding of forest change over time, and are an invaluable part of forestry and ecological research. Walter Lyford began measurements of a 2.88 ha red oak-red maple forest on the Prospect Hill Tract of Harvard Forest in 1969. All trees over 2 inches (5 cm) were mapped on very large-scale (1 inch = 5 feet) hand-drawn maps, and included live and dead trees, stumps, windthrows and other features such as stone walls, boulders, soil moisture and a damage boundary from the 1938 hurricane. All living and dead trees have been re-located and measured (diameter at breast height, canopy class for live trees; condition, decay class, diameter, bole length and stem orientation for fallen dead trees) in 1969, 1975, 1987-1992, 2001, and 2011. In 2001, the original, hand-drawn maps were digitized using ArcView GIS. From 1969 to 2011, red oak (Quercus rubra) increased its dominance of the stand’s total basal area from 52% to 60%; however, red maple (Acer rubrum) has become relatively less abundant, decreasing from 30% to 23%. While red oak and red maple continue to account for the majority of the basal area in the stand, the secondary species experienced a dramatic increase in relative abundance of individuals in the stand; yellow birch (Betula alleghaniensis), black birch (Betula lenta), American chestnut (Castanea dentata), American beech (Fagus grandifolia), witch hazel (Hamamelis virginiana), eastern white pine (Pinus strobus), and eastern hemlock (Tsuga canadensis) have increased from comprising 25% of the individuals in the stand in 1969 to comprising 52% in 2011. The total biomass of living individuals is increasing linearly (R2=0.99, p=0.0002), which implies that the stand has not yet experienced an age-induced decrease in biomass accumulation.
Overstory Mapped Tree Plots at Harvard Forest since 1990
These plots were established and mapped in 1990 for an experiment designed to study the effects of selective overstory tree mortality. The planned manipulation was to kill and leave standing one species in each of four plots, to simulate mortality by a species-specific pathogen. This manipulation was never done, for logistical reasons and because the appearance of the hemlock woolly adelgid provided a more pressing "natural" experiment to study; however, the plots are maintained and have been used in other studies. There are four 50m x 50m plots, located in a mixed hardwood forest (red oak and maple species are major components), north of the experimental hurricane. Tree data from the control plot of the experimental hurricane study could be added to this set for some analyses, since all the plots are in the same general area and forest type, and similar types of measurements were made on all of these trees. Tree diameter and condition were re-surveyed in these plots in Autumns 2003, 2013 and 2023, and saplings growing into the "tree" size class of greater than or equal to 5cm diameter were measured, tagged and mapped.
INTERPNT Software for Mapping Trees Using Distance Measurements
The INTERPNT method can be used to produce accurate maps of trees based solely on tree diameter and tree-to-tree distance measurements. For additional details on the technique please see the published paper (Boose, E. R., E. F. Boose and A. L. Lezberg. 1998. A practical method for mapping trees using distance measurements. Ecology 79: 819-827). Additional information is contained in the documentation that accompanies the program. The Abstract from the paper is reproduced below. "Accurate maps of the locations of trees are useful for many ecological studies but are often difficult to obtain with traditional surveying methods because the trees hinder line of sight measurements. An alternative method, inspired by earlier work of F. Rohlf and J. Archie, is presented. This "Interpoint method" is based solely on tree diameter and tree-to-tree distance measurements. A computer performs the necessary triangulation and detects gross errors. The Interpoint method was used to map trees in seven long-term study plots at the Harvard Forest, ranging from 0.25 ha (200 trees) to 0.80 ha (889 trees). The question of accumulation of error was addressed though a computer simulation designed to model field conditions as closely as possible. The simulation showed that the technique is highly accurate and that errors accumulate quite slowly if measurements are made with reasonable care (e.g., average predicted location errors after 1,000 trees and after 10,000 trees were 9 cm and 15 cm, respectively, for measurement errors comparable to field conditions; similar values were obtained in an independent survey of one of the field plots). The technique requires only measuring tapes, a computer, and two or three field personnel. Previous field experience is not required. The Interpoint method is a good choice for mapping trees where a high level of accuracy is desired, especially where expensive surveying equipment and trained personnel are not available."
Long-term (1993-2019) dynamics of tree populations on a mapped 3-ha permanent plot in old-growth northern hardwood forest, Huron Mts., Marquette Co., MI, USA
This data-set includes multiple remeasurements, over 25 years, of all woody stems >2 cm diameter (total of 2125 stems) on a 2.72-ha stem-mapped plot in old-growth northern hardwood forest in the Huron Mountains region of northern Marquette County, MI. The plot and surrounding forest is dominated by sugar maple (Acer saccharum) and eastern hemlock (Tsuga canadensis). Among secondary species, yellow birch (Betula alleghaniensis) and basswood (Tilia americana) are most common. Soils (identified as Kalkaska series) are developed on deep sandy glacial outwash. The plot is within a much larger region of old-growth forest, protected since ca. 1880, with only minimal disturbance associated with access tracks and trails. Numerous other forest community and dendrochronological studies support the interpretation that the area around the study plot has not experienced stand-initiating disturbance for at least 400 years. Initial mapping and measurements (1993-1995 for 2.52 ha; an additional 0.2 ha added in 1999) used a 20x20 m grid established in a near-level area of uniform substrate. All stems were identified to species, mapped on polar coordinates from the center of each grid cell (including, at first measurement, identifiable dead trees, standing and down), and diameter at breast height (dbh) measured to nearest 0.1 cm. All stems were remeasured on a five-year cycle 1999-2019, and new mortality was recorded at each remeasurement. New recruits > 2 cm dbh were added at each remeasurement.
Long-term (1993-2019) tree population measurements from a mapped 2.9-ha permanent plot in old-growth northern hardwood forest, Dukes Research Natural Area, Marquette Co., MI, USA
The Dukes Research Natural Area (Hiawatha National Forest, Marquette Co., MI) amounts to ca. 100 ha of minimally disturbed original forests, including a mix of mesic 'hemlock-northern hardwood' types and peaty wetlands dominated by several species of swamp conifers and black ash (Fraxinus nigra). The RNA hosts a regular grid of 250 permanent monitoring plots (data to be provided in a separate package). In 1993-95, a macroplot of 2.91 ha was established in a mixed mesic upland forest area within the RNA, in which all woody stems >2 cm diameter at breast height (DBH) were identified, measured, and mapped. In 1999 and again every five years subsequently through 2019, the macroplot was recensused; all stems were remeasured, stems newly recruited (>2 cm DBH) were measured and mapped, and any mortality since previous census was noted and described. A severe storm in 2002 resulted in extensive mortality throughout the RNA, particularly in the area in and around the macroplot.
Aspen Forest Stem Map and Tree Census at the University of Michigan Biological Station, Pellston, MI 1974-2018
In 1974, a one hectare plot was established at the University of Michigan Biological Station to further understand successional trajectories of birch and aspen forests in northern Michigan. Trees with a DBH greater than 5 cm were inventoried and later, the location of the tree within the plot was documented by the UMBS resident biologist. Plots were remeasured 5 additional times by different groups at the station.
Tree Map for Census at the Luquillo Forest Dynamics Plot (LFDP), Puerto Rico
This data set shows the Tag number, Quadrat location, Species code, diameter and XY coordinates of stems >=10 cm D130 present at the time of Hurricane Hugo and in the first census. The data set is composed of two files both with the same file structure. In LFDP_C1treemap.txt the diameters (Fdiam) are as recorded in the field data. In LFDP_C1TREEMAPa.txt the stem diameters (Fdiam) were calculated to allocate "missed" stems (stems >=10 cm D130) that were found in survey 2, 3 or Census 2 to Census 1 survey 1. We calculated the diameter the stem would have had, if it had been recorded at the same time the quadrat it was located in was assessed, in the appropriate survey for that stem size. To extrapolate the stem size back in time, we used the actual growth rate of that individual stem if more than one measurement was available. If only one diameter measurement was available we used the median growth rate for that species in the appropriate size class stems >=10, <30 cm D130). In our publications we will combine data sets LFDP_C1treemap.txt and LFDP_C1TREEMAPa.txt to make Census 1 and to reconstruct the forest for stems >= 10 cm D130 at the time of Hurricane Hugo. We have divided the data into two separate files to ensure that when stem diameters are compared to future censuses the diameter data in LFDP_C1TREEMAPa.txt are not used to calculate growth rates. The last corrections to the Census 1 data were made in May 2001. The National Science Foundation requires that data from projects it funds are posted on the web two years after any data set has been organized and "cleaned". The data from each census of the LFDP will be updated at intervals as each survey of the LFDP shows errors in the previous data collection. After posting on the web, researchers who are not part of the project are then welcome to use the data. Given the enormous amount of time, effort and resources required to manage the LFDP, obtain these data, and ensure data accuracy, LFDP Princi
Adult baobab trees's distribution map across Sahel
<p><span>The baobab tree (<em>Adansonia digitata</em> <em>L.</em>) is an integral part of rural livelihoods throughout the African continent. However, the combined effects of climate change and increasing global demand for baobab products are currently exerting pressure on the sustainable utilization of these resources. Here we employ sub-meter resolution satellite imagery to identify nearly 3</span><span> million baobab trees in the Sahel, a dryland region of 1.5 million km<sup>2</sup>. This achievement is considered an essential step towards improving valuable woody species' management and monitoring system. To prevent mismanagement of this specific tree species, we aggregated every single adult baobab tree map to<span> 5 <span>× </span>5 km grids. We also classified the baobab trees using the tree crown diameters( small: 3-9m; medium 9m-13m; large: >13m). The baobab tree count map is also available for this three different size classes.</span></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>
Fig. 5. Combined tree from Fig. 4 with mapped morphological characters. Numbers above branches represent characters from Table 3. Mapped using WinClada ver. 1.61 in A revision of Scipopus Enderlein including the subgenera Scipopus s. str., Phaeopterina Frey and Parascipopus subgen. nov. (Diptera, Micropezidae, Taeniapterinae)
Fig. 5. Combined tree from Fig. 4 with mapped morphological characters. Numbers above branches represent characters from Table 3. Mapped using WinClada ver. 1.61 (Nixon 1999–2002) with unambiguous characters only.
The Adult Adansonia digitata L. (baobab tree) distribution map derived from very high resolution satelite imagery for 2010s across the Sahel at 1km resolution
<p>The baobab tree (<em>Adansonia digitata</em> <em>L.</em>) is an integral part of rural livelihoods throughout the African continent. However, the combined effects of climate change and increasing global demand for baobab products are currently exerting pressure on the sustainable utilization of these resources. Here we employ sub-meter resolution satellite imagery to identify nearly 3 million baobab trees in the Sahel, a dryland region of 1.5 million km<sup>2</sup>. This achievement is considered an essential step towards improving valuable woody species' management and monitoring system. The map's overall underestimate bias is 0.27. To prevent mismanagement of this specific tree species, we aggregated every single adult baobab tree map to 1 × 1 km grids. We also classified the baobab trees using the tree crown diameters( small: 3-9m; medium 9m-13m; large: >13m). The baobab tree count map is also available for these three different size classes. </p>
Mapping Tree Species Fractions in Temperate Mixed Forests Using Sentinel-2 Time Series and Synthetically Mixed Training Data
<p>This dataset contains the latest version of a selection of result data of the paper "Mapping Tree Species Fractions in Temperate Mixed Forests Using Sentinel-2 Time Series and Synthetically Mixed Training Data" (DOI: https://doi.org/10.1016/j.rse.2025.114740 )</p> <p>The dataset contains:</p> <ol> <li>A geopackage of training points of pure tree species</li> <li>The resulting 12-band tree species fraction map of Rhineland-Palatinate</li> <li>HSV-colored map of dominant tree species. For information which tree species are represented by the different colors, refer to the Supplemental in the original paper.</li> <li>CSV-table of predicted and reference propotion of the tree species in the validation polygon (the original polygon data can not be published due to data privacy regulations) </li> </ol> <p> </p>
Figure 2. Mitotype tree and distribution maps for 98 in Integrative taxonomy reveals cryptic diversity in North American Lasius ants, and an overlooked introduced species
Figure 2. Mitotype tree and distribution maps for 98 DNA-barcodes belonging to 7 mitotypes of the ant Lasius niger (blue, n = 70) and 15 mitotypes of L. ponderosae sp. nov. (red, n = 28). The red dashed line delimits the expected natural range of L. ponderosae sp. nov.53 Maps have been created using the free R-package "ggmap" v3.0.0 (https://github.com/dkahle/ggmap) in R v4.1.1. Map tiles by Stamen Design, under CC BY 3.0.
Maps and R code from: Marginality indices for biodiversity conservation in forest trees
<p>This dataset provides the raster map of marginality indices for eight European tree species as described in the article "Marginality indices for biodiversity conservation in forest tree". It also provided the R code to compute the marginality indices.</p> <p>The eight species are the following:</p> <ul> <li><em>Abies alba</em> Mill. (silver fir)</li> <li><em>Fagus sylvatica</em> L. (European beech)</li> <li><em>Picea abies</em> (L.) H.Karst. (Norway spruce) </li> <li><em>Pinus halepensis</em> Mill. (Aleppo pine)</li> <li><em>Pinus nigra</em> J.F.Arnold (black pine)</li> <li><em>Pinus pinaster</em> Aiton (maritime pine) </li> <li><em>Pinus pinea</em> L. (stone pine)</li> <li><em>Pinus sylvestris</em> L. (Scots pine)</li> </ul> <p>For each species, a 7z archive of a multi-layered image of the raster maps of the marginality indices is given. Each image has 12 layers:</p> <ul> <li>Layer 1: distribution map of the species</li> <li>Layer 2: map of the probability of being marginal according to the Maxent model using eight marginality indices and countries as predictors</li> <li>Layer 3: map of the environmental marginality index, defined as the z-transform of the suitability of each location as predicted by a species distribution model using climatic variables as predictors</li> <li>Layer 4: map of the segmented distribution according to morphological spatial pattern analysis</li> <li>Layer 5: map of the area index</li> <li>Layer 6: map of the gravity index</li> <li>Layer 7: map of the centroid index</li> <li>Layer 8: map of the edge index</li> <li>Layer 9: map of the isolation index</li> <li>Layer 10: map of the second nearest core index</li> <li>Layer 11: map of the north/south index</li> <li>Layer 12: map of the east/west index</li> </ul>
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>
Full Tree QSMs: LIDAR Driven Stemflow Mapping
<p>Quantitative structural models (QSM) of two trees derived using terrestrial lidar scanning in the winter (leafless season) at Secrest Arboretum, Wooster OH, USA (40°46'41.9"N 81°55'06.2"W, 311 m a.s.l.). Trimmed point clouds were imported into CompuTree 5.0 (http://computree.onf.fr/) and analyzed using the SimpleForest plugin (https://gitlab.com/SimpleForest/computree) in order to produce the attached files.</p> <p>File 32-06 represents a specimen of Celtis occidentalis L. (common hackberry) and 27-05 a specimen of Ulmus americana L. (American elm). </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.