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691 results for “vegetation data”
The benefits of coastal adaptation through conservation of foreshore vegetation Supplementary data
<p>This data consists of the first global scale assessment of the benefits of conserving foreshore vegetation as a means of adaptation to future projections of change in coastal flood risk. In doing so, we extend the current knowledge on the economic feasibility of implementing global scale Nature-based Solutions. We show that globally foreshore vegetation can contribute to a large decrease in both absolute and relative flood risk (13% of present-day, and 8.5% of future conditions in 2080 of global flood risk). Although the analysis of the results give a first proxy of the flood risk reduction benefits of conserving foreshore vegetation at the global scale, it shows promising results for including nature-based and hybrid adaptation measures in coastal adaptation schemes.</p>
Data from: High spatiotemporal variability of methane concentrations challenges estimates of emissions across vegetated coastal ecosystems
<div> <p><span>Coastal </span><span>methane (CH<sub>4</sub>) emissions dominate the global ocean CH<sub>4</sub> budget and can offset the "blue carbon" storage capacity of vegetated coastal ecosystems. However, current estimates lack systematic, high-resolution, and long-term data from these intrinsically heterogeneous environments, making coastal budgets </span><span>sensitive to statistical assumptions and uncertainties</span><span>. Using continuous CH<sub>4</sub> concentrations, δ<sup>13</sup>C-CH<sub>4</sub> values, and CH<sub>4</sub> sea-air fluxes across four seasons in three globally pervasive coastal habitats, we show that the CH<sub>4 </sub>distribution is spatially patchy over meter-scales and highly variable in time. Areas with mixed vegetation, macroalgae, and their surrounding sediments exhibited a spatiotemporal variability of surface water CH<sub>4 </sub>concentrations ranging two orders of magnitude (i.e., 6 – 460 nM CH<sub>4</sub>) with habitat-specific seasonal and diurnal patterns. We observed (1) δ<sup>13</sup>C-CH<sub>4</sub> signatures that revealed habitat-specific CH<sub>4</sub> production and consumption pathways, (2) daily peak concentration events that could change >100% within hours across all habitats, and (3) a high thermal sensitivity of the CH<sub>4 </sub>distribution signified by apparent activation energies of </span><span>∼</span><span>1 eV that drove seasonal changes. </span><span>Bootstrapping simulations show that scaling the CH<sub>4</sub> distribution from few samples involves large errors,</span><span> and that </span><span>∼</span><span>50 concentration samples per day are needed to resolve the scale and drivers of the natural variability and improve the certainty of flux calculations by up to 70%. Finally, we identify northern temperate coastal habitats with mixed vegetation and macroalgae as understudied but seasonally relevant atmospheric CH<sub>4</sub> sources (i.e., releasing ≥100 μmol CH<sub>4</sub> m<sup>−2</sup> day<sup>−1</sup> in summer). Due to the large spatial and temporal heterogeneity of coastal environments, high-resolution measurements will improve the reliability of CH<sub>4</sub> estimates and confine the habitat-specific contribution to regional and global CH<sub>4</sub> budgets.</span></p> </div>
Data from: Species-area relationships in continuous vegetation: evidence from Palaearctic grasslands
Aim: Species-area relationships (SARs) are fundamental scaling laws in ecology although their shape is still disputed. At larger areas power laws best represent SARs. Yet, it remains unclear whether SARs follow other shapes at finer spatial grains in continuous vegetation. We asked which function describes SARs best at small grains and explored how sampling methodology or the environment influence SAR shape. Location: Palaearctic grasslands and other non-forested habitats. Taxa: Vascular plants, bryophytes and lichens. Methods: We used the GrassPlot database, containing standardised vegetation-plot data from vascular plants, bryophytes, and lichens spanning a wide range of grassland types throughout the Palaearctic and including 2057 nested-plot series with at least seven grain sizes ranging from 1 cm2 to 1024 m². Using non-linear regression, we assessed the appropriateness of different SAR functions (power, power quadratic, power breakpoint, logarithmic, Michaelis-Menten). Based on AICc, we tested whether the ranking of functions differed among taxa, methodological settings, biomes or vegetation types. Results: The power function was the most suitable function across the studied taxonomic groups. The superiority of this function increased from lichens to bryophytes to vascular plants to all three taxonomic groups together. The sampling method was highly influential as rooted-presence sampling decreased the performance of the power function. By contrast, biome and vegetation type had practically no influence on the superiority of the power law. Main conclusions: We conclude that SARs of sessile organisms at smaller spatial grains are best approximated by a power function. This coincides with several other comprehensive studies of SARs at different grain sizes and for different taxa, thus supporting the general appropriateness of the power function for modelling species diversity over a wide range of grain sizes. The poor performance of the Michaelis-Menten function demonstrates that richness within plant communities generally does not approach any saturation, thus calling into question the concept of minimal area.
Remaining bands of Back scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America
<p><strong>Title: Remaining bands of Back scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America</strong></p> <p>Ricardo Dalagnol (ricds@hotmail.com)</p> <p> </p> <p><strong>This dataset is associated with the dataset found in the Zenodo repository below and a paper under review. Feel free to use this dataset, but please cite the repository below (while the paper is under review).</strong></p> <p>Dalagnol, Ricardo; Galvão, Lênio Soares; Wagner, Fabien Hubert; Moura, Yhasmin Mendes; Gonçalves, Nathan; Wang, Yujie; Lyapustin, Alexei; Yang, Yan; Saatchi, Sassan; Aragão, Luiz Eduardo Oliveira e Cruz. (2022). "AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America". (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3878879</p>
Back scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America
<p><strong>Title: Back scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America</strong></p> <p>Ricardo Dalagnol (ricds@hotmail.com)</p> <p> </p> <p><strong>This dataset is associated with the dataset found in the Zenodo repository below and a paper under review. Feel free to use this dataset, but please cite the repository below (while the paper is under review).</strong></p> <p>Dalagnol, Ricardo; Galvão, Lênio Soares; Wagner, Fabien Hubert; Moura, Yhasmin Mendes; Gonçalves, Nathan; Wang, Yujie; Lyapustin, Alexei; Yang, Yan; Saatchi, Sassan; Aragão, Luiz Eduardo Oliveira e Cruz. (2022). "AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America". (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3878879</p>
Remaining bands of Forward scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America
<p><strong>Title: Remaining bands of Forward scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America</strong></p> <p>Ricardo Dalagnol (ricds@hotmail.com)</p> <p> </p> <p><strong>This dataset is associated with the dataset found in the Zenodo repository below and a paper under review. Feel free to use this dataset, but please cite the repository below (while the paper is under review).</strong></p> <p>Dalagnol, Ricardo; Galvão, Lênio Soares; Wagner, Fabien Hubert; Moura, Yhasmin Mendes; Gonçalves, Nathan; Wang, Yujie; Lyapustin, Alexei; Yang, Yan; Saatchi, Sassan; Aragão, Luiz Eduardo Oliveira e Cruz. (2022). "AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America". (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3878879</p>
Forward scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America
<p><strong>Title: Forward scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America</strong></p> <p>Ricardo Dalagnol (ricds@hotmail.com)</p> <p> </p> <p><strong>This dataset is associated with the dataset found in the Zenodo repository below and a paper under review. Feel free to use this dataset, but please cite the repository below (while the paper is under review).</strong></p> <p>Dalagnol, Ricardo; Galvão, Lênio Soares; Wagner, Fabien Hubert; Moura, Yhasmin Mendes; Gonçalves, Nathan; Wang, Yujie; Lyapustin, Alexei; Yang, Yan; Saatchi, Sassan; Aragão, Luiz Eduardo Oliveira e Cruz. (2022). "AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America". (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3878879</p>
Data for: The formation of "mega‐flocks" depends on vegetation structure in montane coniferous forests of Taiwan
<p>A mixed-species bird flock is a social assemblage where two or more bird species are moving together while foraging and might benefit from increased foraging efficiency and antipredator vigilance. A "mega-flock," which includes flocking species from different vegetation layers, often exhibits high species diversity. Mechanisms for the formation of mega-flocks have not yet been explored. In this study, we evaluated the influence of vegetation structure and bird species diversity/richness in driving the occurrence of mega-flocks. We investigated the composition of mixed-species flocks, local bird communities, and vegetation structure in five vegetation types of two high-elevation sites in central Taiwan. (For more details, please see the paper which has been published in Ecology and Evolution, entitled "The formation of "mega-flocks" depends on vegetation structure in montane coniferous forests of Taiwan." Doi: https://doi.org/10.1002/ece3.8608)</p>
Data from: Defensible-space treatment of <114,000 ha 40 m from high-risk buildings near wildland vegetation could reduce loss in WUI wildfire disasters across Colorado's 27 million ha
<p><strong>Context </strong> </p> <p>WUI wildfire disasters are increasing, as fires are pushed by strong winds and drier fuels across landscapes and into communities. Possible disasters make maintaining and restoring landscape-scale fire in fire-adapted ecosystems difficult. Rapid action is needed to reduce building loss in WUI wildfire disasters. </p> <p><strong>Objectives </strong> </p> <p>In a Colorado case study, I used distance-based empirical modeling to refine potential risk of building loss in WUI wildfire disasters to focus risk-reduction efforts.</p> <p><strong>Methods</strong> </p> <p>New empirical modeling showed 95% of USA building loss in WUI wildfire disasters was within 100 m of wildland vegetation. I used modeling to estimate and map potential relative risk of a WUI wildfire disaster for each of 2,185,953 buildings in Colorado.</p> <p><strong>Results </strong> </p> <p>High-risk buildings were 241,375 or 11% of total buildings. However, the 20-40 m essential defensible space around these buildings covered only 46,767- 114,084 ha. Area within 100 m of wildland vegetation, containing these buildings, covered 475,840 ha or 1.8% of Colorado's 27 million ha. About 95% of at-risk land within 100 m of wildland vegetation is not federally owned, and WUI wildfire disasters are mostly from fires started on private land.</p> <p><strong>Conclusions</strong> </p> <p>Treating ≤114,084 ha of defensible space could leave the 27 million ha of Colorado with lower WUI wildfire disaster-risk to buildings. High risk of building loss is rarely a federal land-management problem. If the goal is rapid reduction of building loss in WUI wildfire disasters, focus resources on defensible space 20-40 m from WUI buildings within 100 m of wildland vegetation.</p>
Data from: Ornithogenic vegetation: How significant has the seabird influence been on the Aleutian Island vegetation during the Holocene?
<p>In the Aleutian Islands during the Holocene, terrestrial predators were actually absent; as a result, large seabird colonies thrived along the coasts or across entire islands. Bird guano enriches the soil with nitrogen, which can lead to the formation of highly modified ornithogenic (bird-formed) ecosystems. For a more detailed investigation of avian influence, we reconstructed more than 10,000-year-old vegetation dynamics of the coast of Shemya Island (Near Islands) by pollen analysis. At the initial stages of vegetation development (10,000-4,600 cal yr BP), sedge-heather tundra grew in the studied area. A seabird colony existed on Shemya from 4,600 to 2,400 cal yr BP according to stable isotope analysis. During a period of at least 2,200-years nitrogen enrichment led to the development of ornithogenic herb meadows with a high presence of Apiaceae. A long-term increase in δ15N above 9-10‰ led to radical shifts in vegetation. Noticeable reduction of seabird colonies due to human hunting led to grass-meadows spreading. After a prolonged decrease δ15N below 9-10‰ (2,400 cal yr BP to present), there was a shift towards less productive sedge-tundra communities. However, the significant enrichment of guano affected only the coastal vegetation and did not alter the inland Shemya Island.</p>
Data used in paper "The sensitivity of tidal channel systems under the influences of initial bed conditions, vegetation, and tidal asymmetry".
<p>Data used in paper "The sensitivity of tidal channel systems under the influences of initial bed conditions, vegetation, and tidal asymmetry".</p>
Data from: Unveiling the landscape predictors of resilient vegetation in coastal wetlands to inform conservation in the face of climate extremes
<div> <p>Unveiling spatial variation in vegetation resilience to climate extremes can inform effective conservation planning under climate change. Although many conservation efforts are implemented on landscape scales, they often remain blind to landscape variation in vegetation resilience. We explored the distribution of drought-resilient vegetation (i.e., vegetation that could withstand and quickly recover from drought) and its predictors across a heterogeneous coastal landscape under long-term wetland conversion, through a series of high-resolution satellite image interpretations, spatial analyses, and nonlinear modelling. We found that vegetation varied greatly in drought resilience across the coastal wetland landscape and that drought-resilient vegetation could be predicted with distances to coastline and tidal channel. Specifically, drought-resilient vegetation exhibited a nearly bimodal distribution and had a seaward optimum at ~2 km from coastline (corresponding to an inundation frequency of ~30%), a pattern particularly pronounced in areas further away from tidal channels. Furthermore, we found that areas with drought-resilient vegetation were more likely to be eliminated by wetland conversion. Even in protected areas where wetland conversion was slowed, drought-resilient vegetation was increasingly lost to wetland conversion at its landward optimum in combination with rapid plant invasions at its seaward optimum. Our study highlights that the distribution of drought-resilient vegetation can be predicted using landscape features but without incorporating this predictive understanding, conservation efforts may risk failing in the face of climate extremes.</p> <p> </p> <p>This ZIP file contains the following datasets and code for the above paper:</p> <p>1. data_cheng_et_al_GCB_2024.zip This file contain a total of three files.</p> <p>(1) analys_grid_50_m.shp This file is a shapefile for the 50-by-50 m grid cells analyzed in the focal study area and their IDs.</p> <p>(2) veg_change.shp This file contains the spatial distribution of vegetation before and after the 2011 drought. </p> <p> Variable list:</p> <p> change: Changes of vegetation after the 2011 drought.</p> <p> vegBefore: Distribution of vegetation before the drought.</p> <p> vegAfter: Distribution of vegetation after the drought.</p> <p>(3) tidalchannel.shp This file contains manually digitized tidal channels (and water surfaces).</p> <p>2. gridfeatures.xlsx This file contains data on the landscape features of each analysis grid.</p> <p>3. analyses.R This file contains the R code used for data analysis.</p> </div>
New parameterization scheme for modeling ozone-caused damage to vegetation in process-based models: data
<p>gmd-2024-6: Quantifying the role of ozone-caused damage to vegetation in the Earth system: A new parameterization scheme for photosynthetic and stomatal responses (Fang Li et al., 2024); https://gmd.copernicus.org/preprints/gmd-2024-6/. It includes three directory: input (O3 concentration), observations (collected PODY-An and PODY-gs), and simulations.</p> <p>It is co-supported by the National Natural Science Foundation of China (41875137) and Guangdong Major Project of Basic and Applied Basic Research (2021B0301030007).</p>
Data for "Climate outweighs human effects on vegetation properties during the early-to-mid Holocene"
<p>Data for analyses used in the PastHumanImpact project published in a manuscript "Climate outweighs human effects on vegetation properties during the early-to-mid Holocene". <br><br>All R code and workflows to reproduce the data analyses and figures are available at <a href="https://github.com/HOPE-UIB-BIO/PastHumanImpact" target="_blank" rel="noopener">HOPE-UIB-BIO/PastHumanImpact</a>.<br><br><strong>Usage:</strong><br>Each zip file should be extracted and placed into the `Data` folder, following the README file in the mentioned GitHub repo.<br><br>❗The data is under a CC BY-NC-ND licence, which requires approval from data owners before use (read <a href="https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode" target="_blank" rel="noopener">here </a>for more details). Specifically, please contact Xianyong Cao (<a href="mailto:xcao@itpcas.ac.cn" target="_blank" rel="noopener">xcao@itpcas.ac.cn</a>) to share the Asian fossil pollen data.❗<br><br><strong>Citation</strong>:<br>If you want to use the data, please cite the mentioned paper "Felde et al. Climate outweighs human effects on vegetation properties during the early-to-mid Holocene". DOI: <a title="Climate outweighs human effects on vegetation properties during the early-to-mid Holocene" href="https://doi.org/10.21203/rs.3.rs-4692574/v1" target="_blank" rel="noopener">10.21203/rs.3.rs-4692574/v1</a></p>
In situ observations used in GMD Manuscript "A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0"
<p>Observations taken by</p> <p>Mika Aurela, Tarek S. El-Madany, Marika Honkanen, Anna Kontu, Juha Lemmetyinen, and Susan C. Steele-Dunne</p> <p>and used in the GMD Manuscript "A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0"</p>
Data and Code for the manuscript "Confounding effects of leaf angle dynamics on vegetation indices - implications for monitoring vegetation from space"
<p>This repository includes the data and code to reproduce the results for the study "<em>Confounding effects of leaf angle dynamics on vegetation indices - implications for monitoring vegetation from space</em>". The study shows that leaf angle dynamics systematically confound widely applied vegetation indices. Moreover, it is demonstrated that these effects are not random but tightly linked to abiotic environmental conditions. These findings demonstrate both challenges and opportunities for using VIs for vegetation monitoring.</p>
Data from: Landscape variability of vegetation change across the forest to tundra transition of central Canada
<p><strong>Paper Abstract: </strong></p> <p>Widespread increases in the productivity of tundra ecosystems and static trends – or even declines – in boreal ecosystems have been detected since the early 1980s using coarse-scale remote sensing. However, intermediate-scale Landsat studies have shown that these changes are heterogeneous and may be related to landscape and regional variability in climate, land cover, topography and moisture availability. In this study, a Landsat Normalized Difference Vegetation Index (NDVI) time-series (1984–2016) was examined for an area spanning the transition from sub-Arctic boreal forest to Low Arctic tundra in central Canada. This was supplemented by analyses of relationships with a suite of environmental variables and in situ measurements of bulk vegetation volume. Results show that NDVI trends were generally positive (i.e. increasing) across the study area but were smallest in the forest zone and largest in the northern tundra zone. More than one-quarter (27%) of un-masked pixels exhibited a significant (p < 0.05) trend and virtually all (99.3%) of those pixels exhibited an increasing, or “greening”, trend. Greening pixels were most common in the northern tundra zone and the southern ecotone zone. Random Forest modeling of the relationship between NDVI and environmental variables indicated that the magnitude and direction of trends varied across the forest to tundra transition. Areas that experienced larger increases in NDVI include: (i) areas where summer temperatures increased; (ii) areas exhibiting predominantly shrub and forest cover; and (iii) locations closer to major drainage systems, further from major lakes, and at lower elevations. Ground validation in the central portion of the study area reveals a strong relationship (R<sup>2</sup> = 0.79) between vegetation volume and NDVI for non-tree functional groups and that alder (<em>Alnus crispa</em>) shrublands and open spruce (<em>Picea mariana</em> and <em>P. glauca</em>) woodland with shrubby understories were most likely to exhibit greening. These findings indicate that the largest positive and more significant NDVI trends were associated with increased productivity in shrub-dominated environments, especially at, and north of the treeline in localities with favorable growing conditions. Smaller and less significant NDVI trends in boreal forest environments south of the treeline were likely associated with long-term successional change following disturbance rather than the variables analyzed here.</p> <p> </p> <p><strong>Data details:<br></strong></p> <p>See paper: <a href="https://www.sciencedirect.com/science/article/pii/S0034425718303675?via%3Dihub">Landscape variability of vegetation change across the forest to tundra transition of central Canada - ScienceDirect</a> </p> <p> </p> <p><strong>If you use these data, please reference:</strong></p> <p>Bonney, M.T., Danby, R.K., Treitz, P.M., 2018. Landscape variability of vegetation change across the forest to tundra transition of central Canada. Remote Sens. Environ. 217 <a href="https://doi.org/10.1016/j.rse.2018.08.002" rel="nofollow">https://doi.org/10.1016/j.rse.2018.08.002</a>.</p> <p>See code on GitHub: <a href="https://github.com/ZZMitch/CentralCanadaShrubification_1984to2016">ZZMitch/CentralCanadaShrubification_1984to2016: Code from "Landscape variability of vegetation change across the forest to tundra transition of central Canada" (RSE, 2018) (github.com)</a></p>
Data for 'Post-disturbance recovery drives 21st century vegetation shifts in the Boreal forest in a dynamic vegetation model'
<p>This is the database containing all the LPJ-GUESS output data used in the paper. For LPJ-GUESS model code, refer to https://zenodo.org/record/8065737. For data processing refer to https://github.com/lucialayr/borealRecovery </p> <p> </p> <p>If you are interested in the raw data, please contact me. </p>
Data accompanying "Pre-fire Vegetation Conditions and Topography Shape Burn Mosaics of Siberian Tundra Fire Scars"
<p>This dataset contains the data used in the burned area classificaiton and statistical analysis in:</p> <p><em>Rietze et al. (<strong>in prep.</strong>) - Pre-fire Vegetation Conditions and Topography Shape Burn Mosaics of Siberian Tundra Fire Scars</em></p> <p>All code to preprocess, analyze and visualize this data can be found under <a href="https://github.com/nrietze/SiberiaFires" target="_blank" rel="noopener">https://github.com/nrietze/SiberiaFires</a>.</p> <p><strong>Folder structure:</strong></p> <p>This dataset contains three major components:</p> <ol> <li>The feature layers like training polygons used for the burned area classification and areas of interest.</li> <li>The burned area maps produced from 3 m PlanetScope imagery.</li> <li>The raster layers used in the statistical analysis, e.g., pre-processed digital elevation models and burned area products.</li> </ol> <pre><code>└───geodata ├───feature_layers │ ├───aoi_wv │ ├───burn_polygons │ │ └───planet │ └───training_polygons └───raster ├───arcticDEM ├───burned_area │ └───planet ├───landsat ├───predictors └───water_area └───planet</code></pre> <p><strong>Detailed description of content:</strong></p> <p> </p> <p><em><strong>Feature layers</strong></em></p> <table> <tbody> <tr> <td>planet_masks.shp</td> <td>Manually delineated polygons to mask out undetected clouds in the Berelech and Lapcha sites.</td> </tr> <tr> <td>aoi_wv/aois_analysis.geojson</td> <td>Square areas of interest for data cropping and selection.</td> </tr> <tr> <td>burn_polygons/planet/rough_burn_perimeter_{<em>fire scar name</em>}.shp</td> <td>6 shapefiles (1 per fire scar) that contain the fire perimeter generated in ArcGIS.</td> </tr> <tr> <td>training_polygons/training_polygons_burn_area.shp</td> <td>Training polygons for all fire scars used for the burned area classification.</td> </tr> </tbody> </table> <p> </p> <p><strong><em>Raster data</em><br></strong></p> <table> <tbody> <tr> <td>arcticDEM/aoi_{<em>fire scar name</em>}_dem_v3_utm.tif</td> <td>Elevation data (<a href="https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/mosaics/v3.0/2m.json">ArcticDEM v3</a>) reprojected to UTM 55N and cropped to the square areas of interest of each fire scar. </td> </tr> <tr> <td>burned_area/ba_descals_landsat_2020_utm_shifted.tif</td> <td>Landsat-based burned area from <a href="https://doi.org/10.1126/science.abn9768" target="_blank" rel="noopener">Descals et al. (2022)</a>, contains data from "Tile 17" only. Dara was reprojected to UTM 55N align with the Landsat-8 Collection-2 Level-2 grid. (burned class (2019) = 29, burned class (2020) = 30, unburned = 0)</td> </tr> <tr> <td>burned_area/N75E145_burn_class_UTM_55N.tif</td> <td>Landsat-based burned area from <a href="https://vapd.gitlab.io/post/gabam/" target="_blank" rel="noopener">Wei et al. (2022)</a>. Dara was reprojected to UTM 55N align with the Landsat-8 Collection-2 Level-2 grid. (burned class = 255, unburned = 0)</td> </tr> <tr> <td>burned_area/planet/{<em>fire scar name</em>}_burned_area_top5TD.tif</td> <td>Binary PlanetScope-based burned area (this study) for each fire scar based on random forest classifiers using the top 5 predictors ranked by transformed divergence (TD). (burned class = 2, unburned = 1)</td> </tr> <tr> <td>landsat/LC08_L2SP_115010_20200608_20200824_02_T1_{<em>spectral or quality band</em>}.TIF</td> <td>Landsat-8 Collection-2 Level-2 pre-fire image from 8 June 2020. B4 = RED, B5 = NIR, ST_B10 = LST</td> </tr> <tr> <td>landsat/LC08_L2SP_116010_20200615_20200824_02_T1_{<em>spectral or quality band</em>}.TIF</td> <td>Landsat-8 Collection-2 Level-2 pre-fire image from 15 June 2020. B4 = RED, B5 = NIR, ST_B10 = LST</td> </tr> <tr> <td>predictors/{<em>fire scar name</em>}_predictors_30m.tif</td> <td>Raster maps of model predictors & the response <em>burned_fraction</em> for the ZOIB model (exported in "ZOIB_model.R" before running the model). Raster bands are named.</td> </tr> <tr> <td>water_area/{<em>fire scar name</em>}_Landsat_mask.tif</td> <td>Landsat-8 binary (water = 2, clear pixels = 1) water mask for each fire scar based on the Quality assessment rasters.</td> </tr> <tr> <td>water_area/planet/{<em>fire scar name</em>}__water_area_top5TD.tif</td> <td>[deprecated] 3 m resolution water areas classified from PlanetScope imagery. Not used for the analysis.</td> </tr> </tbody> </table> <p> </p> <p><strong>Using this data:</strong></p> <p>Clone the Github repository before downloading this data and insert the contents of this dataset into the empty "data" folder from the Github repo. </p> <p>Important: Please move the entire "geodata" folder into the "data" folder from the code repo.</p> <p>If you use this data, please cite as follows:</p> <p>Rietze, N., Heim, R. J., Troeva, E., Schaepman-Strub, G. & Assmann, J. J. (in prep.). Data accompanying "Pre-fire Vegetation Conditions and Topography Shape Burn Mosaics of Siberian Tundra Fire Scars" [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12650945" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12650945</a></p> <p><strong>Abstract (from manuscript):</strong></p> <p>The fire season of 2020 in Siberia set a precedent for extreme wildfires in the Arctic tundra. Large fires burned in the carbon-rich permafrost landscape, releasing vast amounts of carbon, and changing land surface processes by burning vegetation and organic soils. However, little is known about the mosaics of burned and unburned patches formed by tundra fires and the underlying processes that generate them. In this study, we investigated six fire scars in the northeastern Siberian tundra using high-resolution PlanetScope imagery (3 m) to map burned fraction within the scars. We then used Bayesian mixed models to identify which biotic and abiotic predictors influenced the burned fraction. We observed high spatial variation in burned fraction across all tundra landforms common to the region. Current medium-resolution fire products could not capture this heterogeneity, thereby underestimating the burned area of fire scars by a factor of 1.1 to 4.4. The heterogeneity of the burn mosaic indicates a mix of burned and unburned patches, with median unburned patch sizes being smaller than 180 to 324 m². Pre-fire land surface temperature, vegetation heterogeneity and topography predicted burn fraction in our analysis, matching factors previously shown to influence large-scale fire occurrence in the Arctic. Future studies need to consider the fine-scale heterogeneity within tundra landscapes to improve our understanding and predictions of fire spread, carbon emissions, post-fire recovery and ecosystem functioning.</p> <p><strong>Acknowledgements (from manuscript):</strong></p> <p>N.R. was supported through the TRISHNA Science and Electronics Contribution (T-SEC), ESA PRODEX Trishna T-SEC project (PEA C4000133711). Field work and vegetation sample processing were conducted in the scope of State Assignment of the Ministry of Science and Higher Education of the Russian Federation (Project AAAA-A21-121012190038-0), using the equipment of the Centre for collective use of Federal Research Centre "Yakut Scientific Centre" (grant no. 13.TsKP.21.0016). We would like to thank Planet Labs fo free access to PlanetScope imagery. We would like to thank Tim Gyger for helpful discussions regarding our statistical analysis. The authors declare no competing interests.</p>
Data from: Population genetic structure of the giant cactus Echinopsis terscheckii in northwestern Argentina is shaped by patterns of vegetation cover
Species inhabiting drylands commonly depend on the surrounding vegetation for recruitment under stress, while competition may affect populations in moister environments. Our objective was to analyze how different climates and vegetation affect the fine-scale spatial genetic structure (SGS) of the columnar cactus Echinopsis terscheckii. At four sites we estimated vegetation cover by digitized patches and the normalized difference vegetation index (NDVI). We mapped 30 individuals per population and collected tissue for isozyme electrophoresis using 15 putative loci. Spatial autocorrelation between all possible genotype pairs and the number of genetically homogeneous groups and families were calculated for each population. Greater cover (66%) and average NDVI values were detected in the most humid habitat that consisted of fewer, larger, and more dispersed vegetation patches. All populations were genetically diverse and showed significant SGS. Positive correlations were found between the distance at which maximum autocorrelation and kinship values were reached and vegetation area and patch size. Also higher NDVI values were associated with lower number of patches. Populations exposed to higher precipitation and vegetation cover consisted of sparse individuals that clustered at larger distances whereas vegetation patches in arid climates produced groups of closely related genotypes at small distances. These results support the stress-gradient genetic hypothesis. Under water stress, facilitation promotes establishment underneath patchy vegetation resulting in fine-scale family structure. In moister xerophilous forests competition for resources, i.e. light, results in sparse individuals and thus coarse-scale neighborhoods. This information can guide conservation and/or restoration efforts, such as the spatial scale to be considered in germplasm collection.
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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.