Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) has been collecting global night
light imaging data for more than 40 years. With the launch of Suomi-NPP satellite in 2011, the Day/Night Band (DNB)
of the Visible Infrared Imaging Radiometer Suite (VIIRS) represents a major advancement in night time imaging
capabilities because it surpasses DMSP-OLS in having broader radiometric measurement range, more accurate
radiometric calibration, finer spatial resolution, and better geometric quality. DMSP-OLS sensor does not have on-board
calibration and data is recorded as digital number (DN). Therefore, VIIRS-DNB provides opportunities to perform
quantitative radiometric calibration of DMSP-OLS sensor. In this paper, vicarious radiometric calibration of DMSP-OLS
at night under lunar illumination is performed. Events were selected when satellite flies above Dome C in Antarctic at
night and the moon illuminates the site with lunar phase being more than quarter moon. Additional event selection
criteria to limit solar and lunar zenith angle range have been applied to ensure no influence of stray light effects and
adequate lunar illumination. The data from DMSP-OLS and VIIRS-DNB were analyzed to derive the characteristic
radiance or DN for the region of interest. The scaling coefficient for converting DMSP-OLS DN values into radiance is
determined to optimally merge the observation of DMSP-OLS into VIIRS-DNB radiance data as a function of lunar
phases. Calibrating the nighttime light data collected by the DMSP-OLS sensors into radiance unit can enable
applications of using both sensor data and advance the applications of night time imagery data.
This paper examines the feasibility and potential benefits of enhancing the spatial resolution of the VIIRS DNB channel for the JPSS-2 mission and beyond, by modifying the on-chip pixel binning recipe used in the DNB CCD to aggregate detector area within the scan swath. Presently the DNB delivers 16 cross-scan samples similar in size to the detectors in the VIIRS Moderate Resolution Bands. The relatively low-cost enhancement proposed would instead bin the pixels of the existing DNB CCD into 32 cross-scan samples, each half as large in track and scan, effectively doubling the spatial resolution of DNB in each dimension to match the VIIRS Imaging Bands at nadir. Other potential benefits include, for unresolved point sources, improved detection sensitivity and SNR. Smaller DNB image pixels would be less spatiallymixed, and therefore better suited to many quantitative analyses. DNB geolocation accuracy would also benefit from the increased spatial resolution, but less so for regions approaching edge-of-scan. To implement the maximum DNB spatial resolution enhancement over the full cross-track scan swath would require design and hardware modifications to the focal plane electronics (FPIE) to increase certain CCD clock rates. This paper explores the performance of this option, alongside other options that (if constrained to use the existing FPIE design with minimal changes) would not provide the enhanced DNB spatial resolution all the way out to the edges of the cross-track scan. These would instead offer other benefits, such as better maintaining SNR to edge-of-scan and, if away from nadir DNB sampling matched the I-bands, better enabling future environmental products merging the enhanced DNB data with existing VIIRS spectral band data.
"ProSpecTIR" Imaging spectrometer (hyperspectral imagery or "HSI") data were collected for the city of Las Vegas,
Nevada, USA at 10:55 PM July 28, 2009 for the purposes of identification, characterization, and mapping of urban
lighting based on spectral emission lines unique to specific lighting types. The ProSpecTIR sensor measures the
spectrum in 360 spectral bands between 0.4 and 2.5 micrometers at approximately 5nm spectral resolution, and for this
flight, at 1.2m spatial resolution. Spectral features were extracted from the data and compared to a spectral library of
known lighting measurements. Specific lighting types identified based on spectral signatures using the ProSpecTIR data
included blue and red neon, high pressure sodium, and metal halide lights. A binary encoding method was used to map
the spatial distribution of lighting types based on simplified spectral signatures. Results were overlain on a Quickbird
panchromatic 0.6m spatial resolution image. The observed locations of specific light types were compared to a 3-D Las
Vegas building model, and airborne signatures validated against spectral library measurements. The ProSpecTIR data
successfully identified and mapped different lighting types and distributions, allowing determination of the nature and
spatial associations of specific lights. Results illustrate the potential for using imaging spectrometer data to characterize
urban development.
This paper proposes an experimental methodology toward describing and quantifying coral reef bleaching using very high spatial resolution optical satellite imagery. Sea surface temperature-based bleaching alerts issued by NOAA's Coral Reef Watch triggered image acquisition and served as an indication for high bleaching probability. Images of suspected coral reef bleaching events and reference images of the same reefs during previous unbleached conditions were coregistered and radiometrically normalized for change detection. An experimental methodology was developed to describe the severity and extent of the bleaching. The methodology hinges on the creation of the Coral Bleaching Index (CBI), constructed from change detected in the green, blue, and red wavelength bands. Results are provided in the form of colorized difference images showing areas of observed bleaching in gold, as well as CBI images, visualizing varying bleaching intensities. Comparison of the CBI with available field validation data yielded a correlation, however additional reference data would be needed for more detailed quality assessment. This technique is seen as a step toward the routine detection and long-term monitoring of coral reef bleaching from space and serves as a proposed tool for detecting bleaching in remote areas where observers cannot be deployed.
A concept has been developed for a satellite sensor system capable of global observation of the location, extent and
brightness of night-time lights at a spatial resolution suitable for the delineation of primary features within human
settlements. Nightsat should be capable of producing a complete cloud-free global map of lights on an annual basis. We
have used a combination field spectra of outdoor lighting, moderate resolution color photography of cities at night from
the International Space Station, and high-resolution airborne camera imagery acquired at night to define a range of
spatial, spectral, and detection limit options for a future Nightsat mission. Primary findings of our study are that
Nightsat should collect data from a near-synchronous orbit in the mid-evening with 50 to 100 m spatial resolution,
detection limits in the range of 10-8 watts/cm2/sr/um, and a capacity for in-flight radiometric calibration. Although
panchromatic low-light imaging data would be useful, multispectral low-light imaging data would provide valuable
information on the type or character of lighting; potentially stronger predictors of variables, such as ambient population
density and economic activity. The Nightsat mission concept is unique in its focus on observing a human activity, in
contrast to traditional Earth observing systems that focus on natural systems.
In this paper, we report an automatic land cover tracking system which is based on a neural network classifier to extract the land cover from multi-temporal satellite images. The neural network classifier has a three-layer feedforward structure. The input layer has several input units for each of the preprocessed spectral bands of the LANDSAT multispectral scanner, one unit for the digital elevation model, and several units for texture features obtained from a 5 by 5 moving window. The output layer has a neuron for each of the land-cover classes. A pixel is classified with the label of the output layer neuron with the largest activation. The proposed approach provides a quick assessment on the land cover transformation for multitemporal satellite images.
Derivative spectroscopy has been widely used in chemistry and physics for signal analysis. This technique was applied to the analysis of high spectral-resolution Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data from series of plots containing varying quantities of green vegetation. A derivative- based green vegetation index for high spectral-resolution AVIRIS data, DGVI (Derivative Green Vegetation Index), has been developed. The DGVI has proven to be effective in the estimation of green vegetation cover in areas having discontinuous plant canopies.
The reflectance of six vegetated areas was tracked from spring through early summer and early fall using three dates of groundreflectance calibrated AVIRIS data sets acquired in 1989. Annual vegetation types exhibit green vegetation spectral features in the spring. The annual plants are largely senesced by early summer and have decreased chlorophyll pigment and leaf water absorption. With the loss of leaf water lignin-cellulose absorptions emerge at 2. 09 and 2. 27 jim. AVIRIS spectra from two forest types show a slight increase in the magnitude ofthe chlorophyll red edge in early summer when compared to the spring data. In the early fall data there is a major decline in the magnitude of the chlorophyll red edge and leafwater absorptions at 0. 95 and 1. 15 jim in tree and shrub dominated areas in drought induced dormancy or undergoing senescence. 1.
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