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3,105 results for “Vegetation =”
Supplementary material to: Dengler, J., Jansen., F., … & Gillet, F. (2023) Ecological Indicator Values for Europe (EIVE) 1.0. Vegetation Classification and Survey.
<p>The newly developed Ecological Indicator Values for Europe (EIVE) 1.0, together with all source systems in a flexible, harmonised open access database.</p> <p><br> Supplementary material 2: The analysed 31 EIV systems with original and harmonised plant nomenclature and original and rescaled indicator values for M, N, R, L and T (*.xlsx).</p> <p>Supplementary material 3: Documentation of additions to and modifications of the taxonomic backbone from Euro+Med (2022) in EIVE 1.0 (*.xslx).</p> <p>Supplementary material 8: EIVE 1.0 indicator values for niche position and niche width of M, N, R, L and T (*.xlsx).</p>
HeadwaterstreamSNevada: data on riparian vegetation and water parameters of headwater streams in Sierra Nevada, Spain
<p>Providing historical data on riparian plant biodiversity and physico-chemical parameters of stream water in Mediterranean mountains helps to assess the effects of climate change and other human stressors on these sensitive and critical ecosystems. This database collects data from the main natural headwater streams of the Sierra Nevada (southeastern Spain), a high mountain (up to 3,479 meters above sea level, m.a.s.l.) recognized as a biodiversity super hotspot (Arroyo et al., 2022) in the Mediterranean Basin. On this mountain, rivers and landscapes depend on snowmelt water, representing an excellent scenario for evaluating global change's impacts. This dataset covers first- to third-order headwater streams at 41 sites from 832 to 1,997 m.a.s.l., collected from December 2006 to July 2007. Our goal is to supply information on the vegetation associated with streambanks, the essential physico-chemical parameters of stream water, and the physiographic features of the subwatersheds. Riparian vegetation data correspond to six plots sampled at each site, including total canopy, individual number, height and DBH (diameter at breast height) in woody species, and cover percentage for herbs. Physico-chemical parameters were measured in situ (electric conductivity, pH, dissolved O2 concentration, stream discharge) and determined in the laboratory [alkalinity, soluble reactive phosphate-phosphorus (SRP), total phosphorus (TP), nitrate-nitrogen (NO3-–N), ammonium-nitrogen (NH4+–N), total nitrogen (TN)]. Watershed physiographic variables comprise drainage area, minimum altitude, maximum altitude, mean slope, orientation, stream order, stream length, and land cover surface percentage. We recorded 197 plant taxa (67 species, 28 subspecies and 2 hybrids), representing 8.4% of the Sierra Nevada vascular flora. Due to the botanical nomenclature used, the database can be linked to FloraSNevada database (Lorite et al., 2020), contributing to Sierra Nevada (Spain) as a laboratory of global processes.</p> <p>For more information about the data, see the metadata document (Metadata_HeadwaterstreamSNevada.docx).</p>
VegAnn: Vegetation Annotation of a large multi-crop RGB Dataset acquired under diverse conditions for image segmentation
<p> VegAnn - Vegetation Annotation - dataset, a collection of 3795 multi-crop RGB images acquired for different phenological stages using different systems and platforms in diverse illumination conditions. </p>
Data from: Flying high: Sampling savanna vegetation with UAV-lidar
<p>The flexibility of UAV-lidar remote sensing offers a myriad of new opportunities for savanna ecology, enabling researchers to measure vegetation structure at a variety of temporal and spatial scales. However, this flexibility also increases the number of customizable variables, such as flight altitude, pattern, and sensor parameters, that, when adjusted, can impact data quality as well as the applicability of a dataset to a specific research interest. <br>To better understand the impacts that UAV flight patterns and sensor parameters have on vegetation metrics, we compared 7 lidar point clouds collected with a Riegl VUX-1LR over a 300 x 300 m area in the Kruger National Park, South Africa. We varied the altitude (60 m above ground, 100 m, 180 m, and 300 m) and sampling pattern (slowing the flight speed, increasing the overlap between flightlines, and flying a crosshatch pattern), and compared a variety of vertical vegetation metrics related to height and fractional cover. <br>Comparing vegetation metrics from acquisitions with different flight patterns and sensor parameters, we found that both flight altitude and pattern had significant impacts on derived structure metrics, with variation in altitude causing the largest impacts. Flying higher resulted in lower point cloud heights, leading to a consistent downward trend in percentile height metrics and fractional cover. The magnitude and direction of these trends also varied depending on the vegetation type sampled (trees, shrubs, or grasses), showing that the structure and composition of savanna vegetation can interact with the lidar signal and alter derived metrics. While there were statistically significant differences in metrics among acquisitions, the average differences were often on the order of a few centimeters or less, which shows great promise for future comparison studies.<br>We discuss how these results apply in practice, explaining the potential trade-offs of flying at higher altitudes and alternating flight pattern. We highlight how flight and sensor parameters can be geared toward specific ecological applications and vegetation types, and we explore future opportunities for optimizing UAV-lidar sampling designs in savannas.</p>
Data from: Vegetation management shapes arthropod and bird communities in an African savanna
<p>Habitat heterogeneity is a key driver of the diversity and distribution of species. African savannas are experiencing changes to their vegetation structure causing shifts towards increased woody plant cover, which results in vegetation structure homogenization. Given the impact that increasing woody plant cover has on patterns of animal use, resource managers across Africa are implementing habitat management practices that are intended to reduce woody plant cover. To understand the ecological implications of various habitat management practices on arthropod and bird communities, we leveraged large-scale tree clearing and subsequent mowing in an African savanna to understand how changes to both the herbaceous layer and woody plant cover (i.e., structural heterogeneity) may shape arthropod and bird communities at the local scale. We focused on four replicated treatments: 1) annual summer mow, 2) annual winter mow, 3) >5 years since last mow (rest), and 4) an adjacent unmanipulated savanna to act as a control. We found that the mowing treatments significantly influenced vegetation structure both with respect to tree density and herbaceous layer. Both arthropod and bird community composition varied across treatments. Grass biomass was the best predictor of arthropod richness and abundance, with arthropods selecting for areas with high biomass. Insectivorous bird richness and abundance was driven by tree density (i.e., perching locations) and not arthropod abundance. Our results suggest that vegetation management practices contribute to habitat heterogeneity at the landscape scale and increase bird species richness through species turnover. However, we caution that if a single vegetation management practice dominates the landscape, it is plausible that it could lead to the simplification of the avian community.</p>
Tetiaroa Vegetation Sampling
<p>Measurement of seedling recruitment for four native woody species, the shade cover of four herbaceous plant species, and coconuts on the Motu Ahuroa in Tetiaroa were conducted by students with the UC Berkeley Island Sustainability Program. The four woody species we observed were Pisonia, Pandanus, Guettarda, and Ipomoea. For the herbaceous species, we observed Asplenium, Boerhavia, Microsorum, and Portulaca. Once we completed our data collection in each transect, we compiled our data to see the effect of rat eradication programs on plant growth.</p>
FIG. 5 in The marine vegetation of the Kerguelen Islands: history of scientific campaigns, inventory of the flora and first analysis of its biogeographical affinities
FIG. 5. — Graph of the similarity network between regions (coloured nodes) based on the Kerguelen macroalgae (black nodes) co-occurrence dataset (using ForceAtlas2 layout algorithm). Colour codes are the same as in Figure 1. Nodes size is relative to the number of species co-occurring in the Kerguelen Islands. 51 species of doubtful taxonomic status were not considered. Sites: AMS, Amsterdam I.; SPA, Saint-Paul I.; MAR, Marion I.; PED, Prince Edwards Is.; CRO, Crozet Is.; HEA, Heard and McDonald Is.; MAQ, Macquarie I.; AUK, Auckland Is.; CAM, Campbell Is.; FUE, Fuegia; FAL, Falkland Is.; SGI, South Georgia Is.; SSI, South Shetland Is.; SOI, South Orkney Is.; APE, Antarctic Peninsula; END, Enderby L.; McR, MacRobertson L.; QMA, Queen Mary L.; WIL, Wilkes L.; ADE, Adelie L.; VIC, Victoria L.
FIG. 4 in The marine vegetation of the Kerguelen Islands: history of scientific campaigns, inventory of the flora and first analysis of its biogeographical affinities
FIG. 4. — Number of marine macroalgae species reported in the Kerguelen Islands since the Ross expedition (1840), in each phylum (Delépine 1996 is a personnal communication to J.-P. Féral).
FIG. 3. — A in The marine vegetation of the Kerguelen Islands: history of scientific campaigns, inventory of the flora and first analysis of its biogeographical affinities
FIG. 3. — A, Number of nominal species per family of marine macroalgae of the Kerguelen Islands; B, taxonomic status (accepted, uncertain, co-occurrence in the Northern Hemisphere) of the species for the three considered phyla (Chlorophyta, Ochrophyta-Phaeophycae and Rhodophyta).
FIG. 2 in The marine vegetation of the Kerguelen Islands: history of scientific campaigns, inventory of the flora and first analysis of its biogeographical affinities
FIG. 2. — Location of historical collections of macroalgae in the Kerguelen Islands. Precise locations are not given by all reports and should be partly inferred. The map shows the sites visited during 1, the Ross expedition (Hooker 1844-1847); 2, the Challenger expedition (Dickie 1876a, b, c, e); 3, the US (Farlow 1876); 4, English (Dickie 1876d, f); and 5, German (Askenasy 1889); 6, Venus transit expeditions, as well as the German south polar expedition (Foslie 1908, Reinbold 1908). 7, The Hopeful Bay (Zinova 1973) was added.
FIG. 1 in The marine vegetation of the Kerguelen Islands: history of scientific campaigns, inventory of the flora and first analysis of its biogeographical affinities
FIG. 1. — Sub-Antarctic and Antarctic regions considered in the biogeographical analysis of the Kerguelen Islands marine macroalgae also occurring elsewhere in the Southern Ocean. Colours: orange, temperate Southern Africa; blue, sub-Antarctic Islands; light blue, sub-Antarctic New Zealand; light green, Magellanic region; green, Scotia Sea; violet, continental High Antarctic (after Spalding et al. 2007). Geographical coordinates are given in the inset.
Herbaceous vegetation responses to experimental fire in savannas and forests depend on biome and climate
<p>Fire-vegetation feedbacks potentially maintain global savanna and forest distributions. Accordingly, vegetation in savanna and forest ecosystems should have differential responses to fire, but fire response data for herbaceous vegetation has yet to be synthesized across biomes. Here, we examined herbaceous vegetation responses to experimental fire at 30 sites spanning four continents. Across a variety of metrics, herbaceous vegetation increased in abundance where fire was applied, with larger responses to fire in wetter and in cooler and/or less seasonal systems. Compared to forests, savannas were associated with a 4.8 (±0.4) times larger difference in burned versus unburned herbaceous vegetation abundance. In particular, grass cover decreased with fire exclusion in savannas, largely via decreases in C<sub>4</sub> grass cover, whereas changes in fire frequency had a relatively weak effect on grass cover in forests. These differential responses underscore the importance of fire for maintaining the vegetation structure of savannas and forests.</p>
Hurricane Disturbance Vegetation Anomaly (HDVA) [Data set for Turner et al.]
<p>The Hurricane Disturbance Vegetation Anomaly (HDVA) is a rapid assessment approach to understand the severity of ecological damage from a high intensity storm event on an otherwise healthy, mature mangrove forest. Data archived here focuses on Cuba, where Hurricane Irma (Category 5) hit the northern coast in September 2017 and caused wide-spread damage to mangroves and coastal forests. Local scientists were not able to assess the full extent or severity of damages through field work due to limited infrastructure and resources, so they turned to remote sensing analysis. We developed a multitemporal, multiresolution approach to assess the damage 1) extent and 2) relative severity using changes from the typical green-leaf phenology represented by the Enhanced Vegetation Index with MODIS and Sentinel-2 data. All data was processed in Google Earth Engine API to access and utilize large amounts of historical data, as well as compare sensors’ spatial resolution impacts on results. This data set includes the HDVA products and categorization of data by quartile of damage (catastrophic, severe, moderate, mild, no loss). </p>
Dataset used in "Comment on "Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam" by Kim-Anh Nguyen, Yuei-An Liou, Ha-Phuong Tran, Phi-Phung Hoang and Thanh-Hung Nguyen"
<p>The Excel file provides all the data included in Tab.4 of Nguyen et al. 2020 plus reflectances extracted from the Landsat 8 OLI image acquired on 14 February 2017 and downloaded from the USGS Earth Explorer website. Observations on the number of pixels falling of water, land and mixed water/land surfaces are provided as well as water percentage cover estimated using regular spaced points. </p> <p>The dataset includes vector files (kml format) of the grids corresponding to the selected L8 pixels as well as the regularly spaced points generated within the selected pixels. These files can be imported in QGIS, Google Earth Pro and other free GIS software.</p>
Figs 3, 4 in The megadrile fauna (Annelida: Oligochaeta) of Queen Elizabeth Park, South Africa: species composition and distribution within different vegetation types
Figs 3, 4. Tritogenia howickiana: (3) anterior part of the body, showing papillae and tubercula pubertatis ventrally; (4) dorsal dissection showing thickened septa. Abbreviations: Cl – clitellum, DB – double blood vessel, P – papillae of 15–17, S – septa 4/5–6/7, Tp – tubercula pubertatis,.
FIG. 3. — TWINSPAN classification for the 23 in The Epiphytic Bryophyte Vegetation of Buxus sempervirens L. forests in the Fırtına Valley (Rize, Turkey)
FIG. 3. — TWINSPAN classification for the 23 relevés carried out in the studied area and 31 taxa found.
FIG. 1 in The Epiphytic Bryophyte Vegetation of Buxus sempervirens L. forests in the Fırtına Valley (Rize, Turkey)
FIG. 1. — Location of the study area. The red point indicates the Fırtına Valley. The black rectangle indicates the Rize Province, and the black points indicate the Rize districts. The numbers indicate the nine localities sampled (see Table 1).
Average monthly backward moisture footprints for 40 Ramsar wetland basins under potential and current vegetation scenarios (2008 - 2017)
<p>The dataset contains the backward moisture footprints of the basins of 40 selected Ramsar wetlands for a base run with ERA5 reanalysis evaporation and precipitation, and two additional runs based on evaporation and precipitation from a potential vegetation and a current land use scenario. The dataset can be used to study the upwind moisture sources of the 40 included wetland basins under 'normal' conditions (ERA5 reanalysis), and under a potential vegetation scenario and a current land used scenario.<br>The data was generated to study the impact of upwind land use changes and hydroclimatic changes on selected wetland basins (Fahrländer et al. (2024) using the UTrack atmospheric moisture tracking database by Tuinenburg et al. (2020) and data inputs from the ERA5 reanalysis dataset (Hersbach et al. 2020) and from Wang-Erlandsson et al. (2018) (see References section).</p> <p>The moisture footprints are stored in individual NetCDF format files for each wetland basin and in separate folders for each run. The files are marked with the according Ramsar Convention ID for each respective wetland. The footprints are saved in a spatial resolution of 0.5° and contain monthly average evaporation flows for the period 2008 - 2017. The backward footprints contain the moisture sources for the precipitation in the wetland basins, whereas the forward footprint contain the locations where the evaporation from the basins rains down again.</p> <p>In addition, the dataset contains the delineated basins of the 40 Ramsar wetlands, which are provided in shapefile format and marked with the individual wetland ID of the Ramsar Convention.</p> <p> </p> <p>References:</p> <p>Fahrländer, S. F., Wang‐Erlandsson, L., Pranindita, A., & Jaramillo, F. (2024). Hydroclimatic Vulnerability of Wetlands to Upwind Land Use Changes. <em>Earth’s Future</em>, <em>12</em>(3). <a href="https://doi.org/10.1029/2023EF003837">https://doi.org/10.1029/2023EF003837</a></p> <p>Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., et al. (2020). The ERA5 global reanalysis. <em>Quarterly Journal of the Royal Meteorological Society</em>, <em>146</em>(730), 1999–2049. <a href="https://doi.org/10.1002/qj.3803">https://doi.org/10.1002/qj.3803</a></p> <p>Tuinenburg, O. A., Theeuwen, J. J. E., & Staal, A. (2020). High-resolution global atmospheric moisture connections from evaporation to precipitation. <em>Earth System Science Data</em>, <em>12</em>(4), 3177–3188. <a href="https://doi.org/10.5194/essd-12-3177-2020">https://doi.org/10.5194/essd-12-3177-2020</a></p> <p>Wang-Erlandsson, L., Fetzer, I., Keys, P., van der Ent, R. J., Savenije, H. H. G., & Gordon, L. J. (2018). Remote land use impacts on river flows through atmospheric teleconnections. <em>Hydrology and Earth System Sciences</em>, <em>22</em>(8), 4311–4328. <a href="https://doi.org/10.5194/hess-22-4311-2018">https://doi.org/10.5194/hess-22-4311-2018</a></p>
STEAM evaporation and precipitation for potential vegetation and current land use scenarios
<p>This dataset contains global evaporation and precipitation data generated and described in the following research article:</p> <p><strong>Wang-Erlandsson, L., Fetzer, I., Keys, P. W., van der Ent, R. J., Savenije, H. H. G., and Gordon, L. J.: Remote land use impacts on river flows through atmospheric teleconnections, Hydrol. Earth Syst. Sci., 22, 4311–4328, https://doi.org/10.5194/hess-22-4311-2018, 2018.</strong></p> <p>The dataset includes evaporation and precipitation for a potential vegetation (pv) and a current land use scenario (cur) and comes in monthly resolution and a spatial grid of 1.5° over the period 2000 - 2013. The files are saved in MAT file format.</p> <p>In addition, it includes the data in NetCDF files regridded using cdo (remapnn) to 0.5° spatial resolution.</p>
Durch Aufstauung neu geschaffenes Libellengewässer. Der mit Torf überdeckte Damm, auf dem die Teilnehmenden das Gebiet besuchten, wird nach und nach wieder von der typischen Vegetation besiedelt. (Foto Daniel Küry) in Exkursionsbericht Schweizerische Arbeitsgemeinschaft für Libellenschutz (SAGLS)
Durch Aufstauung neu geschaffenes Libellengewässer. Der mit Torf überdeckte Damm, auf dem die Teilnehmenden das Gebiet besuchten, wird nach und nach wieder von der typischen Vegetation besiedelt. (Foto Daniel Küry)
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Allen Brain Atlas
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OpenNeuro
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