The characteristic coefficient of vertical leaf nitrogen (N) profile is a canopy parameter that indicates the attenuation steepness of leaf N from the top of canopy downward. It is an important and useful parameter in plant sciences and agronomy. This dataset includes the characteristic coefficient of vertical leaf N profile on a mass basis within winter wheat canopies. The canopy reflectance spectra and a figure of study site and experimental fields are also included.
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The SPYSTUF hyperspectral data contains high spatial and spectral resolution Aisa Eagle II (visible to near infrared, 400-900 nm) airborne imaging spectrometer above a Hyytiälä forest research station hosting the SMEAR II (Station for Measuring Ecosystem-Atmosphere Relations, 61°50' N, 24°17' E) on 3 July 2015. The spectral resolution of the data is 4.6 nm, and the spatial resolution 0.6 m.
SPYSTUF hyperspectral data
Authors:
Matti Mõttus, Vincent Markiet, Rocío Hernández-Clemente, Viljami Perheentupa, Titta Majasalmi
The SPYSTUF hyperspectral data contains high spatial and spectral resolution Aisa Eagle II (visible to near infrared, 400-900 nm) airborne imaging spectrometer above a Hyytiälä forest research station hosting the SMEAR II (Station for Measuring Ecosystem-Atmosphere Relations, 61°50' N, 24°17' E) on 3 July 2015. The spectral resolution of the data is 4.6 nm, and the spatial resolution 0.6 m. The data are partly multiangular with the sensor tilted 30° off-nadir for two flight lines, resulting in measurements with the angle between the directions to the sensor and the sun of 19° (closest to hotspot), 55° (nadir) and 76° (dark spot). The data are processed to top-of-canopy geolocated reflectance factors and mosaicked. All mosaicked data were collected with the sensor pointing approximately nadir. The hyperspectral imagery is accompanied by data on basic forest variables and optical LAI from 20 plots in the image area, determined within approx. one week around the airborne acquisition.
The data were obtained between 10:44 and 12:20 (GMT+3) at approximately 1 km altitude above the ground with flight lines consecutively in the northwestern and southeastern directions to minimize BRF effects. The Aisa Eagle II sensor had a field of view (FOV) of 37.5° divided between 1024 pixels. The average solar zenith angle was 48°, the photosynthetic photon flux density ranged from 1285 to 1493 μmol/(m^2*s) with a mean value of 1408 μmol/(m^2*s) (SMEAR II measurement data above the forest). The weather conditions were optimal for an airborne hyperspectral acquisition with a clear blue sky.
The collection and processing of the dataset was largely funded by the Academy of Finland (project SPYSTUF, PI M. Mõttus, grants 266152, 272989 and 303633). All authors were affiliated with the University of Helsinki, Finland, during the data acquisition. Data processing was mostly performed by Vincent Markiet and Matti Mõttus at VTT Technical Research Centre of Finland.
The multiangular data are described in detail in the publication (open access) by Markiet et al. (2017)
The mosaic is described in the publication (open access) by Markiet & Mõttus (2020)
Additional data on the imaged forests are available from external sources, e.g.
* SMEAR II weather and flux data are available via the SmartSMEAR system: https://smear.avaa.csc.fi
* USGS provides EO-1 Hyperion imagery coincident with the airborne data, centered on SMEAR II: https://earthexplorer.usgs.gov
REQUEST_ID = "1890172015184_20001"
ACQUISITION_DATE = 2015-07-03
START_TIME = 2015 184 08:26:46
END_TIME = 2015 184 08:31:05
* Dataset of tree canopy structure and understory composition obtained two years earlier, https://data.mendeley.com/datasets/dyt4nkp583/1
Majasalmi, T., & Rautiainen, M. (2020). Dataset of tree canopy structure and variation in understory composition
in a boreal forest site. Data in Brief, 30, [105573]. https://doi.org/10.1016/j.dib.2020.105573
Data identification number: 10.17632/dyt4nkp583.1
Files in the project:
20150703_mosaic: The hyperspectral mosaic data (BSQ format, 16778 samples, 16255 lines, 128 bands, 16-bit signed integer), reflectance factor mutiplied by 10,000
20150703_mosaic.hdr: ENVI header file for 20150703_mosaic
forestdata.txt: forest plot data, see below for detailed contents
line01_20150703atm06mFnnGeo: off-nadir image (one flight line), in the darkspot direction (angle between sensor and sun directions 76°), reflectance factor mutiplied by 10,000
line01_20150703atm06mFnnGeo.hdr: ENVI header for line01_20150703atm06mFnnGeo
line02_20150703atm06mFnnGeo: off-nadir image (one flight line), close to the hotspot direction (angle between sensor and sun directions 19°), reflectance factor mutiplied by 10,000
line02_20150703atm06mFnnGeo.hdr
markiet2017.pdf: the paper by Markiet et al. (2017) describing the multiangular data
markiet2020.pdf: the paper by Markiet and Mõttus (2020) describing the image mosaic
README.txt: this file
SPYSTUF_hyperspectral_preview.pgw: geographic information for SPYSTUF_hyperspectral_preview.png
SPYSTUF_hyperspectral_preview.png: PNG preview of the image with forest plots
Geographic projections:
All data are projected to UTM35N. See the ENVI header files for details.
Forest data in the columns of forestdata.txt:
ID: plot ID (string)
Easting_UTM35N: Easting in UTM35N projected coordinate system [units: m]
Northing_UTM35N: Northing in UTM35N projected coordinate system [m]
LAI_effective: the effective (optical) LAI of the plot as determined with LAI-2000 using a modified "VALERI cross" sampling design: eight measurement points in each cardinal direction at four and eight meters distance from the plot center point. See Majasalmi & Rautiainen (2020) for details.
LAI2000_gaps1: the mean "gaps" value for ring 1 (zenith) of LAI-2000
LAI2000_gaps2: the mean "gaps" value for ring 2 of LAI-2000
LAI2000_gaps3: the mean "gaps" value for ring 3 of LAI-2000
LAI2000_gaps4: the mean "gaps" value for ring 4 of LAI-2000
LAI2000_gaps5: the mean "gaps" value for ring 5 of LAI-2000
BA_pine: basal area of Scots pine (Pinus sylvestris) [m^2/ha]
BA_spruce: basal area of Norway spruce (Picea abies) [m^2/ha]
BA_birch: basal area of silver birch (Betula pendula) and other broadleaf species [m^2/ha]
dbh: mean diameter at breast height (1.3 m) [cm]
treeheight: mean tree height [m]
crownbase: mean height to bottom of crown (crown base) [m]
References:
Majasalmi, Titta; Rautiainen, Miina. 2020. "Dataset of tree canopy structure and variation in understory composition
in a boreal forest site" Data in Brief, 30: 105573. https://doi.org/10.1016/j.dib.2020.105573
Markiet, Vincent; Mõttus, Matti. 2020. "Estimation of boreal forest floor reflectance from airborne hyperspectral data of coniferous forests" Remote Sensing of Environment 249: 112018, DOI:10.1016/j.rse.2020.112018, https://www.sciencedirect.com/science/article/pii/S0034425720303886
Markiet, Vincent; Hernández-Clemente, Rocío; Mõttus, Matti. 2017. "Spectral Similarity and PRI Variations for a Boreal Forest Stand Using Multi-angular Airborne Imagery" Remote Sens. 9, no. 10: 1005, DOI:10.3390/rs9101005, https://www.mdpi.com/2072-4292/9/10/1005
Data license: Creative Commons Attribution 4.0 International (CC BY 4.0)
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WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.
A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images
Email the authors at ushasi@iitb.ac.in for any query.
Classes in this dataset:
Airplane
Baseball Diamond
Buildings
Freeway
Golf Course
Harbor
Intersection
Mobile home park
Overpass
Parking lot
River
Runway
Storage tank
Tennis court
Paper
The paper is also available on ArXiv: A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images
Feel free to cite the author, if the work is any help to you:
``` @InProceedings{Chaudhuri_2020_EoC, author = {Chaudhuri, Ushasi and Banerjee, Biplab and Bhattacharya, Avik and Datcu, Mihai}, title = {A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images}, booktitle = {http://arxiv.org/abs/2008.05225}, month = {Aug}, year = {2020} }
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Data are collected on a 5m×10msized test bed, which is set up at Kadir Has University,Istanbul. Wireless access points are located around the corners of the testbed and markers are placed at every 45 cm. RSSI measurements done on the grid shown in Figure are stored via NetSurveyor program running on a Lenovo IdeapadFLEX 4 laptop, which has an Intel Dual Band Wireless-AC8260 Wi-Fi adaptor.At each measurement point, RSSI data are collected for1 min with a sampling interval of 250 ms.
Data are collected on a 5m×10msized test bed, which is set up at Kadir Has University,Istanbul. Wireless access points are located around the cornersof the test bed and markers are placed at every 45 cm.RSSI measurements done on the grid shown in Figure 2 arestored via NetSurveyor program running on a Lenovo IdeapadFLEX 4 laptop,
which has an Intel Dual Band Wireless-AC8260 Wi-Fi adaptor.At each measurement point, RSSI data are collected for1 min with a sampling interval of 250 ms. XML file is read with MATLAB for data of full area and applied trajectory.
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The datasets in the compressed file were used in the case study of the article entitled Automated Machine Learning Pipeline for Geochemical Analysis by Germán H. Alférez, et al. Our approach was evaluated with a compositional dataset from 6 fault-separated blocks in the Peninsular Ranges Province and Transverse Ranges Province. The Peninsular Ranges are a group of mountain ranges, stretching from Southern California to Southern Baja California, Mexico. North of the Peninsular Ranges Province is the east-west Transverse Ranges Province.
The Cinco.csv file contains the original dataset with 514 samples. The CincoTrain.csv file contains the dataset used to train and evaluate the models. The CincoUnknown.csv file contains the dataset used to predict the unknown samples.
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Segmentation of TC clouds in 2016. The segmentation task was accomplished by an algorithm which takes a time series of brightness temperature images of TCs and uses image processing techniques to acquire segmentation for each image in a semi-supervised manner.
2016 TC cloud segmentation animation
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As part of the 2018 IEEE GRSS Data Fusion Contest, the Hyperspectral Image Analysis Laboratory and the National Center for Airborne Laser Mapping (NCALM) at the University of Houston are pleased to release a unique multi-sensor optical geospatial representing challenging urban land-cover land-use classification task. The data were acquired by NCALM over the University of Houston campus and its neighborhood on February 16, 2017 between 16:31 and 18:18 GMT.
Data files, as well as training and testing ground truth are provided in the enclosed zip file.
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BTH Trucks in Aerial Images Dataset contains videos of 17 flights across two industrial harbors' parking spaces over two years.
If you use these provided data in a publication or a scientific paper, please cite the dataset accordingly.
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