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239 results for “remote sensing data”
Figure 7 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 7 - Species photographs. A Cryptoblepharus boutonii ater, Grand Comoro B Cryptoblepharus boutonii mohelicus, ZSM 1680/2008, Mohéli C Cryptoblepharus boutonii mayottensis, ZSM 1703/2008, Mayotte D Cryptoblepharus boutonii degrijsii, ZSM 63/2010, Anjouan E Amphiglossus johannae, ZSM 54/2010, Anjouan F Phelsuma laticauda, Anjouan G Phelsuma dubia, Mayotte H Phelsuma robertmertensi, Mayotte.
Figure 17 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 17 - Distribution maps, and distribution over habitat and altitude classes, for Phelsuma nigristriata, Phelsuma pasteuri and Phelsuma robertmertensi.
Figure 18 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 18 - Distribution maps, and distribution over habitat and altitude classes, for Phelsuma v-nigra, Ramphotyphlops braminus and Trachylepis comorensis.
Figure 13 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 13 - Distribution maps, and distribution over habitat and altitude classes, for Hemidactylus frenatus, Hemidactylus mercatorius and Hemidactylus parvimaculatus.
High-resolution residual dry matter (RDM) map for a California oak savanna/annual grassland derived from drone multispectral remote sensing imagery and in-situ grass biomass data
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Data from: Bioclimatic variables derived from remote sensing: assessment and application for species distribution modeling
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Data from: Towards a framework for agent-based image analysis of remote-sensing data
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CAMP2Ex P-3 Remotely Sensed Aerosol Data
CAMP2Ex_Aerosol_AircraftRemoteSensing_P3_Data are remotely sensed aerosol measurements conducted onboard the P-3 aircraft during the Clouds, Aerosol and Monsoon Processes-Philippines Experiment (CAMP2Ex) NASA field study. Data collection for this product is complete.CAMP2Ex was a NASA field study, with three main science objectives: aerosol effect on cloud microphysical and optical properties, aerosol and cloud influence on radiation as well as radiative feedback, and meteorology effect on aerosol distribution and aerosol-cloud interactions. Research on these three main objectives requires a comprehensive characterization of aerosol, cloud, and precipitation properties, as well as the associated meteorological and radiative parameters. Trace gas tracers are also needed for airmass type analysis to characterize the role of anthropogenic and natural aerosols. To deliver these observations, CAMP2Ex utilized a combination of remote sensing and in-situ measurements. NASA’s P-3B aircraft was equipped with a suite of in-situ instruments to conduct measurements of aerosol and cloud properties, trace gases, meteorological parameters, and radiative fluxes. The P-3B was also equipped passive remote sensors (i.e. lidar, polarimeter, radar, and radiometers). A second aircraft, the SPEC Learjet 35A, was primarily dedicated to measuring detailed cloud microphysical properties. The sampling strategy designed for CAMP2Ex coordinated flight plans for both aircraft to maximize the science return. The P-3B was used primarily to conduct remote sensing measurements of cloud and precipitation structure and aerosol layers and vertical profiles of atmospheric state variable, while the Learjet flew below the P-3B to obtain the detailed cloud microphysical properties. During the 2019 field deployment in the vicinity of the Philippines, completed from August 20-October 10, the P-3B conducted 19 science flights and the SPEC Learjet conducted 11 flights. Ground-based aerosol observations were also recorded in 2018 and 2019. CAMP2Ex was completed in partnership with Philippine research and operational weather communities. Measurements completed during CAMP2EX provide a 4-D observational view of the environment of the Philippines and its neighboring waters in terms of microphysical, hydrological, dynamical, thermodynamical and radiative properties of the environment, targeting the environment of shallow cumulus and cumulus congestus clouds.
SMEX02 European Remote Sensing Satellite (ERS-2) AMI Data, Iowa, Version 1
This data set consists of browse images acquired by the C-band Active Microwave Instrument (AMI) onboard the European Remote Sensing Satellite 2 (ERS-2) and provides only a general quality assessment of the ERS-2 AMI data.
Remote Sensing Data Before and After California Rim and King Forest Fires, 2010-2015
This data set provides high-resolution surface reflectance, thermal imagery, burn severity metrics, and LiDAR-derived structural measures of forested areas in the Sierra Nevada Mountains, California, USA, collected before and after the August 2013 Rim and September 2014 King mega forest fires. Pre-fire data were paired with post-fire collections to assess pre- and post-fire landscape characteristics and fire severity. Field estimates of fire severity were collected to compare with derived remote sensing indices. Reflectance measurements for the spectroscopic AVIRIS and MASTER sensors are distributed as multi-band geotiffs for each megafire and acquisition date. Derived operational metric products for each sensor are provided in individual GeoTIFFs. GeoTIFFs produced from LiDAR point data depict first order topographic indices and summary statistics of vertical vegetation structure.
ARISE 2014 C-130 Remotely Sensed Aerosol and Trace Gas Data
ARISE_Cloud_AircraftInSitu_C130_Data_1 is the Arctic Radiation - IceBridge Sea & Ice Experiment (ARISE) 2014 in-situ cloud data product. This product is a result of a joint effort of the Radiation Sciences, Cryospheric Sciences and Airborne Sciences programs of the Earth Science Division in NASA's Science Mission Directorate in Washington. Data were collected via the Spectrometers for Sky-scanning, Sun-Tracking Atmospheric Research (4STAR) instrument. Data collection is complete.ARISE was NASA's first Arctic airborne campaign designed to take simultaneous measurements of ice, clouds and the levels of incoming and outgoing radiation, the balance of which determined the degree of climate warming. Over the past few decades, an increase in global temperatures led to decreased Arctic summer sea ice. Typically, Arctic sea ice reflects sunlight from the Earth. However, a loss of sea ice means there is more open water to absorb heat from the sun, enhancing warming in the region. More open water can also cause the release of more moisture into the atmosphere. This additional moisture could affect cloud formation and the exchange of heat from Earth’s surface to space. Conducted during the peak of summer ice melt (August 28, 2014-October 1, 2014), ARISE was designed to study and collect data on thinning sea ice, measure cloud and atmospheric properties in the Arctic, and to address questions about the relationship between retreating sea ice and the Arctic climate. During the campaign, instruments on NASA’s C-130 aircraft conducted measurements of spectral and broadband radiative flux profiles, quantified surface characteristics, cloud properties, and atmospheric state parameters under a variety of Arctic atmospheric and surface conditions (e.g. open water, sea ice, and land ice). When possible, C-130 flights were coordinated to fly under satellite overpasses. The primary aerial focus of ARISE was over Arctic sea ice and open water, with minor coverage over Greenland land ice. Through these efforts, the ARISE field campaign helped improve cloud and sea ice computer modeling in the Arctic.
DC3 Remotely Sensed DC-8 Differential Absorption Lidar (DIAL) Data
DC3_AircraftRemoteSensing_DIAL_DC8_Data are remotely sensed data collected by the Differential Absorption Lidar (DIAL) onboard the DC-8 aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.The Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.DC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.In addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.
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: Measuring β‐diversity by remote sensing: a challenge for biodiversity monitoring
Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer bio- diversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, especially when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this context, airborne or satellite remote sensing allow information to be gathered over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (β-diversity) might add crucial information related to relative abundance of dif- ferent species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript we propose novel techniques to measure β-diversity from airborne or satellite remote sensing, mainly based on: i) multivariate statistical analysis, ii) the spectral species concept, iii) self-organizing feature maps, iv) multi- dimensional distance matrices, and the v) Rao's Q diversity. Each of these measures addresses one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating β-diversity from remotely sensed imagery and potentially relating them to species diversity in the field.
Figure 20 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 20 - Proposed areas of conservational importance for the herpetofauna on the Comoro islands.
Figure 2 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 2 - Land cover, localities and number of recorded species
Figure 4 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 4 - Localities and number of species in relation to altitude.
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
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Data from: Measuring β‐diversity by remote sensing: a challenge for biodiversity monitoring
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SOLVE I DC-8 Aircraft Remotely Sensed Lidar Data
SOLVE1_AircraftRemoteSensing_DC8_Lidar_Data is the remotely sensed lidar trace gas data for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment (SOLVE). Data were collected by Differential Absorption Lidar (DIAL) and the Airborne Raman Ozone, Temperature, and Aerosol Lidar (AROTAL). Data collection for this product is complete.The SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA’s Earth Science Enterprise (ESE). SOLVE’s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.SOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center’s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.
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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.