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27
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ShareScore release 0.7.1
Dataset results
27 results for “plant sensing”
Rethinking the fundamental unit of ecological remote sensing: Estimating individual level plant traits at scale
<p>derived data of leaf and plant structural traits for two National Ecological Observatory Network (NEON) Airborne Observatory Platform (AOP) sites. Dataset contains spatial explicit information for 4.5 million trees, and include: Nitrogen (%mass), Phosphorus (%mass), Leaf mass per area (g m<sup>-2</sup>), diameter at breast height (cm), crown area (m2), tree height (m) and other physical topographic variables (Albedo, Elevation, Slope, Aspect). data are associated to the </p>
Dataset: Remotely sensed soil moisture can capture dynamics relevant to plant water uptake
<p><strong>Dataset Description</strong><br> Stable isotope water uptake profiles were consulted across 45 datasets to determine the primary zone of root water uptake ("Uptake Range Top" to "Uptake Range Bottom"), whether the uptake increases in proportion nearer to the surface ("Decay of Water Uptake With Depth"), and whether uptake temporarily switches to shallow soils ("Temporary Uptake of Upper Layers"). More details on the data collection are shared in our Water Resources Research publication (in revision).</p> <p>Correlation length scales, or the effective depth of representation of L-band satellite soil moisture, are estimates in Short Gianotti et al. 2019 using SMAP surface soil moisture and GPM precipitation retrievals.</p> <p><strong>Citations</strong><br> Those that use the stable isotope table are asked to cite our Water Resources Research publication (in revision) as well as the 45 references contributing to the table.<br> Those that use the correlation length scale dataset are asked to cite:<br> Short Gianotti, D.J., Salvucci, G.D., Akbar, R., McColl, K.A., Cuenca, R., Entekhabi, D., 2019. Landscape water storage and subsurface correlation from satellite surface soil moisture and precipitation observations. Water Resour. Res. 9111–9132. https://doi.org/10.1029/2019wr025332</p>
Dataset for manuscript "Plants as inspiration for material‑based sensing and actuation in soft robots and machines"
<p>The dataset includes data for Figure 2 in the article "Plants as inspiration for material-based sensing and actuation in soft robots and machines<em>" MRS Bulletin</em> (2023). https://doi.org/10.1557/s43577-022-00470-8</p>
Dataset for paper "Interpreting the shifts in forest structure, plant community composition, diversity, and functional identity by using remote sensing-derived wildfire severity"
<p>Interpreting the shifts in forest structure, plant community composition, diversity, and functional identity by using remote sensing-derived wildfire severity . New collected data</p>
data_Systematic review and best practices for drone remote sensing of invasive plants
<p>We generated this dataset to compile a review article titled "Systematic Review and Best Practices for Drone Remote Sensing of Invasive Plants." </p>
Parcel level temporal variance of remotely sensed spectral reflectance predicts plant diversity
<p>Over the last two decades, considerable research has built on remote sensing of spectral diversity to assess plant diversity. The spectral variation hypothesis (SVH) proposes that spatial variation in reflectance data of an area is positively associated with plant diversity. While the SVH has exhibited validity in dense forests, it performs poorly in highly fragmented and temporally dynamic agricultural landscapes covered mainly by grasslands. Such underperformance can be attributed to the mosaic-like spatial structure of human-dominated landscapes with fields in varying phenological and management stages. Therefore, we argued for re-evaluating SVH's flawed window-based spatial analysis and underutilized temporal component. In particular, In particular, we captured the spatial and temporal variation in reflectance and assessed the relationships between spatial and temporal components of spectral diversity and plant diversity at the parcel level as a unit that relates to management patterns. Our investigation spanned three grasslands on two continents covering a wide spectrum of agricultural usage intensities. To calculate different components of spectral diversity, we used multi-temporal spaceborne Sentinel-2 data. We showed that plant diversity was negatively associated with the temporal component of spectral diversity across all sites. In contrast, the spatial component of spectral diversity was related to plant diversity in sites with larger parcels. Our findings highlighted that in agricultural landscapes, the temporal component of spectral diversity drives the spectral diversityplant diversity associations. Consequently, our results offer a novel perspective for remote sensing of plant diversity globally.</p>
Parcel level temporal variance of remotely sensed spectral reflectance predicts plant diversity
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Analyzing coastal fog effects on carbon and water fluxes in a California agricultural system using approaches in biometeorology, remote sensing, and plant physiology
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Data from: Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors
Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant–soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are short-term and small-scale due to practical constraints for field-scale quantification of PSF effects, yet field experiments are warranted to assess actual PSF effects under less controlled conditions. Here we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content, and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data were acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE = 5.12 cm, R2 = 0.79), chlorophyll content (RMSE = 0.11 g m−2, R2 = 0.80), N-content (RMSE = 1.94 g m−2, R2 = 0.68), and fresh biomass (RMSE = 0.72 kg m−2, R2 = 0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m−2, respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m−2, respectively), while the lowest values (76 cm, 0.41 g m−2, respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAV-based optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms.
Data from: Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors
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Data from: Plants’ ability to sense and respond to airborne sound is likely to be adaptive: reply to comment by Pyke et al.
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Data from: Magnitude and sign epistasis among deleterious mutations in a positive-sense plant RNA virus
How epistatic interactions between mutations determine the genetic architecture of fitness is of central importance in evolution. The study of epistasis is particularly interesting for RNA viruses because of their genomic compactness, lack of genetic redundancy, and apparent low complexity. Moreover, interactions between mutations in viral genomes determine traits such as resistance to antiviral drugs, virulence and host range. In this study we generated 53 Tobacco etch potyvirus genotypes carrying pairs of single-nucleotide substitutions and measured their separated and combined deleterious fitness effects. We found that up to 38% of pairs had significant epistasis for fitness, including both positive and negative deviations from the null hypothesis of multiplicative effects. Interestingly, the sign of epistasis was correlated with viral protein–protein interactions in a model network, being predominantly positive between linked pairs of proteins and negative between unlinked ones. Furthermore, 55% of significant interactions were cases of reciprocal sign epistasis (RSE), indicating that adaptive landscapes for RNA viruses maybe highly rugged. Finally, we found that the magnitude of epistasis correlated negatively with the average effect of mutations. Overall, our results are in good agreement to those previously reported for other viruses and further consolidate the view that positive epistasis is the norm for small and compact genomes that lack genetic robustness.
Figure 1 from: Garzon-Lopez C, Hattab T, Skowronek S, Aerts R, Ewald M, Feilhauer H, Honnay O, Decocq G, Van De Kerchove R, Somers B, Schmidtlein S, Rocchini D, Lenoir J (2018) The DIARS toolbox: a spatially explicit approach to monitor alien plant invasions through remote sensing. Research Ideas and Outcomes 4: e25301. https://doi.org/10.3897/rio.4.e25301
Figure 1 DIARS toolbox workflow. The green gears correspond to the sections of the toolbox and are accompanied by boxes stating its main goal. The gray gears describe the advantages of the DIARS toolbox.
Figure 5 from: Garzon-Lopez C, Hattab T, Skowronek S, Aerts R, Ewald M, Feilhauer H, Honnay O, Decocq G, Van De Kerchove R, Somers B, Schmidtlein S, Rocchini D, Lenoir J (2018) The DIARS toolbox: a spatially explicit approach to monitor alien plant invasions through remote sensing. Research Ideas and Outcomes 4: e25301. https://doi.org/10.3897/rio.4.e25301
Figure 5 Some examples of reconstructed images: A. Sylt island reconstructed image and plot locations (wavelengths: 170R, 65G, 17B). B. Compiègne Forest reconstructed image and plot locations (wavelengths: 207R, 65G, 10B).
Figure 3 from: Garzon-Lopez C, Hattab T, Skowronek S, Aerts R, Ewald M, Feilhauer H, Honnay O, Decocq G, Van De Kerchove R, Somers B, Schmidtlein S, Rocchini D, Lenoir J (2018) The DIARS toolbox: a spatially explicit approach to monitor alien plant invasions through remote sensing. Research Ideas and Outcomes 4: e25301. https://doi.org/10.3897/rio.4.e25301
Figure 3 Example of the workflow used for the mapping of alien plants. The same approach was used for all the tutorials.
Data from: The impact of high-order epistasis in the within-host fitness of a positive-sense plant RNA virus
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Data from: Magnitude and sign epistasis among deleterious mutations in a positive-sense plant RNA virus
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Data from: Trichobaris weevils distinguish amongst toxic host plants by sensing volatiles that do not affect larval performance
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Tactile active sensing in an insect-plant pollinator
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A bacterial receptor PcrK senses the plant hormone cytokinin to promote adaptation to oxidative stress
GEO Series GSE105769. Xanthomonas campestris pv. campestris. 3 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.