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6 results for “vegetation hyperspectral”
Vegetation and invertebrate communities in 500 plots in the Duplin and Dean Creek watersheds: ground truth data for matching hyperspectral imagery
We measured characteristics of vegetation (Aster tenuifolius, Batis maritima, Borrichia frutescens, Distichlis spicata, Iva frutescens, Juncus roemerianus, Limonium carolinianum, Salicornia biglovii, Salicornia virginica, Spartina alterniflora, Spartina patens, Sporobolus virginicus), soil (salinity, proportion organic and proportion water) and densities of common gastropods and bivalves in 500 plots in the Duplin and Dean Creek watersheds on Sapelo Island on June 20-26, 2006. Plot locations were determined using a high precision hand-held GPS. These data were used to help ground-truth hyperspectral aerial images collected at the same time by Dr. John Schalles.
2006 AISA hyperspectral imagery of the GCE domain for vegetation
Airborne Imaging Spectrometer for Applications (AISA) Eagle hyperspectral imagery were acquired on June 20-21, 2006, by the Center for Advanced Land Management Information Technologies (CALMIT). This included ten flight lines flown for the examination of salt marsh and upland vegetation spectral properties near Blackbeard Creek, the Duplin River, Dean Creek, and the Altamaha River. Imagery was acquired for 63 bands from 400-980 nm at a 1 m spatial resolution. The bandwidths were preselected by investigators with CALMIT to to capture the vegetative red reflectance feature, leaf water content related NIR reflectance, and carotenoid and chlorophyll driven absorption behaviors. These data were acquired for the following purposes: 1) calculate suites of remote sensing vegetation indices, (2) produce algorithms for predicting plant and phytoplankton chlorophyll and accessory pigments, vegetation biomass, 3) assess vegetative health, and (4) perform atmospheric corrections.
Maximum likelihood classification of 2006 AISA hyperspectral imagery of the GCE domain for vegetation
Airborne Imaging Spectrometer for Applications (AISA) Eagle hyperspectral imagery were acquired on June 20-21, 2006, by the Center for Advanced Land Management Information Technologies (CALMIT). This included four flight lines flown for the examination of vegetation for the Duplin River salt marshes. Imagery was acquired for 63 bands from 400-980 nm at a 1 m spatial resolution. Imagery were classified using the maximum likelihood classifier (MLC) and a post-classification decision tree to achieve an overall classification accuracy of 90%. Classification training and validation data were obtained from the 2006 Hyperspectral ground survey. See Hladik (2012) and Hladik, Alber, and Schalles (2013) and Schalles, et. al. (2013) for additional details.
NDVI images derived from the 2006 AISA hyperspectral imagery of the GCE domain for vegetation
Airborne Imaging Spectrometer for Applications (AISA) Eagle hyperspectral imagery were acquired on June 20-21, 2006, by the Center for Advanced Land Management Information Technologies (CALMIT). This included ten flight lines flown for the examination of salt marsh and upland vegetation and water for spectral properties at 1 m spatial resolution. For all vegetation images, the Normalized difference vegetation index (NDVI) was calculated. NDVI uses the ratio of reflectance in the red and NIR wavelengths (NDVI = (NIR799 - RED675)/ (NIR799 + RED675)) to derive an index of plant vigor (Rouse et al., 1974). The subscript values are the wavelength band centers used to calculate NDVI. Values indicate the amount of green vegetation present in the pixel—higher NDVI values indicate more green vegetation. Vallid results fall between -1 and +1.
Detection of Ore-forming Elements Migrated from Rock and Soil to Vegetation Leaves using Hyperspectral Data
<p>In this paper we present the detection of ore-forming elements migrated from soil and rock to vegetation leaves using hyperspectral data. The rock, soil (C, B, A layer) and vegetation (root, stem, leaf) samples with the measurements of spectra and element concentrations in the vertical section of the abandoned pits were collected. The wavelet approach is applied to analyze the correlations between spectral features and element concentrations. The results show that the significant correlations are found at a lower order mother wavelets such as Haar. Moreover, the significant correlations are negative between Cu and Mo elements and the wavelet energy vector of the soil and rock to vegetation leaves spectrum. The results also show that ore-forming elements migrated from rock and soil to vegetation leaves can be detected using hyperspectral data in the study area.</p>
G-LiHT Hyperspectral Vegetative Indices V001
Goddard’s LiDAR, Hyperspectral, and Thermal Imager ([G-LiHT](https://gliht.gsfc.nasa.gov/)) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.The purpose of G-LiHT’s Hyperspectral Vegetative Indices data product (GLHYVI) is to provide vegetative, stress, and other index data in 44 science dataset layers. Included in the product are vegetative indices such as Normalized Difference Vegetation Index (NDVI), Triangular Vegetation Index (TVI), Renormalized Difference Vegetation Index (RDVI), Modified Triangular Vegetation Index (MTVI), and Difference Vegetation Index (DVI). Stress indices include, but are not limited to, Carter Stress, Gitelson and Merzlyac Stress, Maccioni Stress, and Vogelmann Stress.GLHYVI data are processed as a raster data product (GeoTIFF) at 1 meter spatial resolution over locally defined areas. A browse image displaying NDVI is also included.
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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)
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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.