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691 results for “vegetation data”
ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2018
This dataset provides vegetation community characteristics and biophysical data collected in 2018 from areas that were burned by wildfire in 2014 and 2015, and from nine unburned validation sites in the Northwest Territories, Canada. The data include vegetation inventories, ground cover, regrowth, tree diameter and height, and woody seedling/sprouting data at burned sites, and similar vegetation community characterization at unburned validation sites. Additional measurements included soil moisture, collected for validation of the UAVSAR airborne collection, and depth to frozen ground at the nine unburned sites. This 2018 fieldwork completes four years of field sampling at the wildfire areas.
LiDAR Data, DEM, and Maximum Vegetation Height Product from Southern Idaho, 2014
This dataset provides the point cloud data derived from small footprint waveform LiDAR data collected in August 2014 over Reynolds Creek Experimental Watershed and Hollister in southern Idaho. The LiDAR data have been georeferenced, noise-filtered, and corrected for misalignment for overlapping flight lines and are provided in 1 km tiles. High resolution digital elevation models and maps of maximum vegetation height derived from the LiDAR data are provided for each site.
SMEX03 Vegetation Data: Alabama, Version 1
This data set includes vegetation data collected over the Soil Moisture Experiment 2003 (SMEX03) area of northern Alabama, USA.
SMEX03 Vegetation Data: Oklahoma, Version 1
This data set contains various vegetation parameters for several locations from Oklahoma North (ON) and Oklahoma South (OS).
SMEX02 Regional Vegetation Sampling Data, Iowa, Version 1
This data set contains the vegetation parameters plant height, row spacing, stand density, and leaf area index (LAI) as part of the Soil Moisture Experiment 2002 (SMEX02).
Vegetation Species Data (FIFE)
The Vegetation Species and Cover Abundance Data Set documents the species present at the FIFE staff data measurement sites. Percent cover is estimated for each species at approximately the time of the IFC's. Disturbances occur over a variety of spatial and temporal scales in North American grasslands, and interactions of these different disturbances affect community structure. Two types of disturbance commonly occur over large spatial scales in grasslands, namely, fire and grazing. Analysis of percent cover of dominant species indicated that composition and heterogeneity was significantly affected by grazing intensity and burning. The effects of disturbances on community structure are not additive, and may not be extrapolated from studies of single factors. The interpretation of patterns in natural communities is clearly scale dependent, and processes may act differently when viewed from different spatial or temporal scales. The effects of scale may not always be predictable; therefore, an understanding of pattern and process at one hierarchical level may not provide useful information about pattern and process at a different hierarchical level.
Resilience of patterned vegetation in the Sahel - network centrality data
Input data for a time-series analysis of the connectedness of vegetation from several locations within the Sahel. The analysis uses NDVI images from the Sentinel 2 satellite, from ESA's Copernicus program, downloaded from Google Earth Engine.
Data from: Vegetation type controls root turnover in global grasslands
Abstract: Aim: Root turnover is an important process determining carbon and nutrient cycling in terrestrial ecosystems. It is an established fact that root turnover is jointly regulated by climatic, edaphic, and biotic factors. However, the relative importance of these forces in determining the global patterns of root turnover time is far from clear. Location: Global. Time period: 1946–2017. Major taxa studied: Grasslands. Methods: We compiled a database of 141 sites with 433 observations on root turnover time and applied structural equation modelling (SEM) to investigate the relative contribution of climate, soil properties, and vegetation type to the observed variations in root turnover time. Results: Root turnover time was 3.1 years on average across the global grasslands and differed significantly among grassland types (tropical grassland & savanna, temperate grassland & meadow, alpine grassland & meadow, tundra, and desert). It decreased with mean annual temperature, mean annual precipitation, and Palmer Drought Severity Index but increased with soil organic carbon content, total nitrogen content, and carbon: nitrogen ratio. Soil bulk density and soil texture also significantly affected root turnover time, with clay content negatively correlating to root turnover time and explaining more variations than bulk density and sand content. The SEM showed that climatic factors had dominant effects on root turnover time when vegetation type was not considered. Vegetation type became the primary driver when it was included in the SEM. Main conclusions: Our results highlight that the influences of climatic and edaphic factors on root turnover time are predominantly manifested through vegetation type. The critical role of precipitation as revealed for the first time in this study challenges our current understanding of climate impacts on root turnover time. The findings necessitate accurate representation of vegetation type in Earth system models to predict root function dynamics under global change.
Data from: Exploring the perceptions of physicians, caregivers and families towards artificial nutrition and hydration for people in permanent vegetative state: how can a photo-elicitation method help?
The question of withdrawing artificial nutrition and hydration from people in a permanent vegetative state sparks considerable ethical and legal debate. Therefore, understanding the elements that influence such a decision is crucial. However, exploring perceptions of artificial nutrition and hydration is methodologically challenging for several reasons. First, because of the emotional state of the professionals and family members, who are facing an extremely distressing situation; second, because this question mirrors representations linked to a deep-rooted fear of dying of hunger and thirst; and third, because of taboos surrounding death. We sought to determine the best method to explore such complex situations in depth. This article aims to assess the relevance of the photo-elicitation interview method to analyze the perceptions and attitudes of health professionals and families of people in a permanent vegetative state regarding artificial nutrition and hydration. The photo-elicitation interview method consists in inserting one or more photographs into a research interview. An original set of 60 photos was built using Google Images and participants were asked to choose photos (10 maximum) and talk about them. The situations of 32 patients were explored in 23 dedicated centers for people in permanent vegetative state across France. In total, 138 interviews were conducted with health professionals and family members. We found that the photo-elicitation interview method 1) was well accepted by the participants and allowed them to express their emotions constructively, 2) fostered narration, reflexivity and introspection, 3) offered a sufficient "unusual angle" to allow participants to go beyond stereotypes and habits of thinking, and 4) can be replicated in other research areas. The use of visual methods currently constitutes an expanding area of research and this study stressed that this is of special interest to enhance research among populations facing end-of-life and ethical issues.
Data from: Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system
To examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m−2 s−1. The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China.
Data from: Species richness and phylogenetic diversity of seed plants across vegetation zones of Mount Kenya, East Africa
Mount Kenya is of ecological importance in tropical east Africa due to the dramatic gradient in vegetation types that can be observed from low to high elevation zones. However, species richness and phylogenetic diversity of this mountain have not been well studied. Here, we surveyed distribution patterns for a total of 1,335 seed plants of this mountain and calculated species richness and phylogenetic diversity across seven vegetation zones. We also measured phylogenetic structure using the net relatedness index (NRI) and the nearest species index (NTI). Our results show that lower montane wet forest has the highest level of species richness, density, and phylogenetic diversity of woody plants, while lower montane dry forest has the highest level of species richness, density, and phylogenetic diversity in herbaceous plants. In total plants, NRI and NTI of four forest zones were smaller than three alpine zones. In woody plants, lower montane wet forest and upper montane forest have overdispersed phylogenetic structures. In herbaceous plants, NRI of Afro‐alpine zone and nival zone are smaller than those of bamboo zone, upper montane forest, and heath zone. We suggest that compared to open dry forest, humid forest has fewer herbaceous plants because of the closed canopy of woody plants. Woody plants may have climate‐dominated niches, whereas herbaceous plants may have edaphic and microhabitat‐dominated niches. We also proposed lower and upper montane forests with high species richness or overdispersed phylogenetic structures as the priority areas in conservation of Mount Kenya and other high mountains in the Eastern Afro‐montane biodiversity hotspot regions.
Data from: Consumer control as a common driver of coastal vegetation worldwide
Rapid, global anthropogenic alteration of food webs in ecosystems necessitates a better understanding of how consumers regulate natural communities. We provide a global synthesis of consumer control of vegetation in coastal wetlands, where the domineering role of physical factors such as nutrient and salinity, rather than consumers, has been emphasized for decades. Using a dataset of 1748 measures of consumer effects reported in 443 experiments/observations on all continents except the Antarctica, we examine the generality of consumer control in salt marshes and mangroves globally. Our analyses show that salt marsh herbivores, including insects, snails, crabs, waterfowl, small mammals, and livestock, generally and often strongly suppress plant survival, aboveground biomass, and height, while their effects on plant density, belowground biomass, reproduction, and cover are more variable. These effects occur in forbs, grasses, and shrubs, and in both seedlings and adult plants. Herbivores additionally affect plant nutrient stoichiometry, and mediate plant interactions, though their effects on plant diversity are less consistent. Higher trophic levels also affect plants, as predators facilitate plant growth through trophic cascades that suppress grazer abundance and grazing rate. In mangroves, there are also signs of consumer control, though the relatively few studies available have often focused on mangrove propagules and seedlings rather than adults. Our analyses further reveal that the strength of consumer control is regulated by many physical factors. Nutrient, disturbance, and flooding, for example, amplify the negative effects of herbivores. Along latitudinal gradients, increased temperature enhances the negative effects of ectothermic herbivores, but has no effect on those of endothermic herbivores. Consumer control of coastal plants is also apparent across study methodologies: in field and laboratory settings, in observational studies, in consumer exclusion and addition experiments, in natural and transplanted plants, and in experiments of various durations. The role of consumer control in coastal vegetation worldwide highlights the need to better recognize and theoretically conceptualize both top-down and bottom-up forcing and their interactions in coastal wetlands. Improved understanding and conservation of coastal wetlands will only occur if we incorporate what the science has revealed: trophic feedbacks are an important and pervasive determinant of coastal plant communities.
Data from: Mapping and exploring variation in post-fire vegetation recovery following mixed severity wildfire using airborne LiDAR
There is a public perception that large high severity wildfires decrease biodiversity and increase fire hazard by homogenising vegetation composition and increasing the cover of mid-story vegetation. But a growing literature suggests that vegetation responses are nuanced. LiDAR technology provides a promising remote sensing tool to test hypotheses about post-fire vegetation regrowth because vegetation cover can be quantified within different height strata at fine-scales over large areas. We assess the usefulness of airborne LiDAR data for measuring post-fire mid-story vegetation regrowth over a range of spatial resolutions (10x10m, 30x30m, 50x50m, 100x100m cell size) and investigate the effect of fire severity on regrowth amount and spatial pattern following a mixed severity wildfire in Warrumbungle National Park, Australia. We predicted that recovery would be more vigorous in areas of high fire severity, because park managers observed dense post-fire regrowth in these areas. Moderate to strong positive associations were observed between LiDAR and field surveys of mid-story vegetation cover between 0.5–3m. Thus our LiDAR survey was an apt representation of on-ground vegetation cover. LiDAR-derived mid-story vegetation cover was 22–40% higher in areas of low and moderate than high fire severity. Linear mixed-effects models showed that fire severity was among the strongest biophysical predictors of mid-story vegetation cover irrespective of spatial resolution. However much of the variance associated with these models was unexplained, presumably because soil seedbanks varied at finer-scales than our LiDAR maps. Dense patches of mid-story vegetation regrowth were small (median size 0.01ha) and evenly distributed between areas of low, moderate and high fire severity, demonstrating that high severity fires do not homogenise vegetation cover. Our results are relevant for ecosystem conservation and fire management because they: indicate that native vegetation are responsive and resilient to high severity fire, and show the usefulness of remote sensing tools such as LiDAR to monitor post-fire vegetation recovery over large area in situ.
Data and code for 'Global disparity in synergy of solar power and vegetation growth'
<p>The 'stepwisefit' requires Statistics and Machine Learning Toolbox installed in the MATLAB to run the code. The import data are attached.</p>
Vegetation phenology data based on GIMMS4g NDVI from 1982 to 2020
<p>We used five phenological extraction methods, which include three threshold-based methods (i.e., Gaussian midpoint, spline midpoint, and TIMESAT-SG methods) and two change-rate-based methods (i.e., the HANTS maximum and polyfit maximum methods), following previous studies, to retrieve spring (start of growing season, SOS) and autumn (end of growing season, EOS) phenological events . Phenological extraction based on multiple methods consists of three steps: (1) smoothing and interpolating the NDVI date to obtain the smooth and continuous NDVI daily time series, (2) using the threshold value (0.2 and 0.5) or the maximum rate of change to extract the vegetation phenology from each single method, and (3) averaging the phenological results obtained by different extraction methods to reduce uncertainties associated with a single method (due to the different fitting methods, interpolation methods, and threshold settings of different extraction methods).</p>
Vegetation data of Lauson and Lavassey proglacial forelands in the Gran Paradiso National Park (northwestern Italy)
<p>This dataset contains the vegetation data collected in two alpine glacier forelands (Lauson and Lavassey) of the Gran Paradiso National Park, northwestern Italy.</p> <p>Data were collected using the grid-point intercept method on a total of 36 plots in the years 2016, 2017, 2020 and 2021.</p>
Data and code for "Soil as a Reservoir of Multi Annual Rainfall Determining Semiarid Vegetation Productivity"
<p><strong>Soil as a Reservoir of Multi Annual Rainfall Determining Semiarid Vegetation Productivity (Fux, Svoray & Bell, 2024)</strong></p> <p><strong>Abstract</strong><br>Climate change and natural variability alter rainfall characteristics. In dry environments, these changes <br>affect vegetation productivity, however, studies of remotely sensed data in high resolution temporal are scarce. <br>We quantified daily scale rainfall-productivity relationships by analyzing daily time series of rainfall and NDVI <br>from Landsat and Sentinel imagery from Israel, New Mexico, and SW Australia. Methodologically wise, we found that <br>the representative area size varied from 0.02--0.14 \(Km^2\), depending on the uniformity of vegetation formations. <br>More substantive, herbaceous vegetation showed maximal response to 3--4 months cumulative rainfall, while woody <br>vegetation response is dominated by rainfall accumulated over a year and even longer periods. These results emphasize <br>the water reservoirs' significance upon intermittent rainfall events and may have implications for ecohydrological <br>modeling, conservation biology, and ecosystem management.</p> <p><strong>Plain Language Summary</strong><br>Dry environments are characterized by high rainfall variability and water scarcity sets it as a limiting factor for <br>vegetation growth. Yet, vegetation response to rainstorms in semiarid regions is not fully understood. Specifically, <br>it is unclear what dominant statistical characteristic of rainstorms dictates vegetation growth. It has been long <br>debated whether daily, seasonal or annual rainfall characteristics are crucial for triggering vegetation’s response. <br>Here, we used high-resolution satellite images and daily rainfall records, from three locations worldwide, to <br>investigate the rainfall-productivity relationship. We found that for most vegetation formations, maximal correlation <br>is obtained for no lag and seasonal, or longer, rainfall accumulation periods. These results suggest that vegetation <br>does not show a significant response to rainfall pulses but rather to accumulated rainfall over longer periods (even <br>years). Thus, accumulated rainfall is likely stored in soil water reservoirs and remains accessible to ecosystems. <br>These results are more pronounced in woody vegetation and should be considered for environmental management policies.</p> <p><strong>Description</strong><br>The compressed file contains two folders, one for the characteristic unit size test and one for the rainfall-NDVI <br>cross correlations.<br>The unit size folder contains NDVI images for the dates used to calculate the characteristic unit size, the shape <br>files of the polygons used to delineate the different vegetation types areas and the R code.<br>The rainfall-NDVI cross correlations folder contain the NDVI images for each vegetation type in each region, <br>processed from the original data, and the code for creating the detrended and summed rainfall time series, the <br>detrended NDVI time series and the cross-correlation analyses.<br>The rainfall and the original satellite data are available from the resources listed in the article.</p>
Data and Codes for: Segmentation uncertainty of vegetated porous media propagates during X-ray CT image-based analysis
<p>Phase segmentation is a crucial step in X-ray computed tomography (CT) for image-based analysis (CT-IBA) to derive soil and root information. How segmentation uncertainty (SU) affects CT-IBA of vegetated soil has never been explored. The enclosed data and codes are used to assist the analysis of SU quantification and propagation in the journal paper published in Plant & Soil. The title of the paper is Segmentation uncertainty of vegetated porous media propagates during X-ray CT image-based analysis. </p>
Data from: Discrepancies in vegetation phenology trends and shift patterns in different climatic zones in middle and eastern Eurasia between 1982 and 2015
Changes in vegetation phenology directly reflect the response of vegetation growth to climate change. In this study, using the Normalized Difference Vegetation Index (NDVI) dataset from 1982 to 2015, we extracted start date of vegetation growing season (SOS), end date of vegetation growing season (EOS) and length of vegetation growing season (LOS) in the middle and eastern Eurasia region and evaluated linear trends in SOS, EOS and LOS for the entire study area, as well as for four climatic zones. The results show that the LOS has significantly increased by 0.27 d/yr (days per year), mostly due to a significantly advanced SOS (-0.20 d/yr) and a slightly delayed EOS (0.07 d/yr) over the entire study area from 1982 to 2015. The vegetation phenology trends in the four climatic zones are not continuous throughout the 34 year period. Furthermore, discrepancies in the shifting patterns of vegetation phenology trend existed among different climatic zones. Turning points (TP) of SOS trends in the Cold zone, Temperate zone and Tibetan Plateau zone occurred in the mid or late 1990s. The advanced trends of SOS in the Cold zone, Temperate zone and Tibetan Plateau zone exhibited accelerated, stalled and reversed patterns after the corresponding TP, respectively. The TP did not occurred in Cold-Temperate zone, where the SOS showed a consistent and continuous advance. TPs of EOS trends in the Cold zone, Cold-Temperate zone, Temperate zone and Tibetan Plateau zone occurred in the late 1980s or mid-1990s. The EOS in the Cold zone, Cold-Temperate zone, Temperate zone and Tibetan Plateau zone showed weak advanced or delayed trends after the corresponding TP, which were comparable with the delayed trends before the corresponding TP. The shift patterns of LOS trends were primarily influenced by the shift patterns of SOS trends and were also heterogeneous within climatic zones.
Data from: Use of normalized difference vegetation index to assess N status and predict grain yield in rice
Fine tuning N recommendations requires an understanding of crop N status and yield potential early enough in the growing season when changes to N management can influence yields. Recent studies have demonstrated the ability of Normalized Difference Vegetation Index (NDVI) to assess crop N status and predict yield in wheat (Tricticum aestivum L.) and maize (Zea mays L.); however, there has been relatively little such research on rice (Oryza sativa L.). The objectives of this study were to determine how well NDVI measured at the panicle initiation (PI) rice growth stage assesses crop N status and predicts final grain yield. Nitrogen response trials were established over a 4-yr period (10 site-years) at various locations throughout the Sacramento Valley rice growing region of California. Additionally, the relationship between NDVI and crop N status was characterized across 28 on-farm plots representing a range of environmental conditions and management practices. The NDVI at PI was best correlated with total N uptake (NUP, r2 = 0.66), followed by N concentration (NCONC, r2 = 0.54), and aboveground biomass (AGB, r2 = 0.51). The utility of NDVI was greatest at lower values of crop N status, whereas at higher values, NDVI saturated. The NDVI at PI was positively correlated with final grain yield (r2 = 0.58) indicating utility for developing in-season yield predictions. While NDVI is a potentially useful tool to improve N fertilizer management and develop in-season yield predictions in rice, alternative indices that do not saturate would likely provide a basis for a better tool.
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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.