Due to the surge in the development of unmanned aerial vehicles (UAVs) and small spacecraft (CubeSats and SmallSats) in recent years, there has been a push to develop miniaturized instrumentation to be incorporated on such platforms. A compact hyperspectral imager integrated with these vehicles provides a cost-effective platform for a range of environmental sensing applications that include the monitoring of vegetation, urban development, and lightning. We present the snapshot hyperspectral imaging system (SNAP-IMS), requiring no scanning and capable of integration with a UAV. The collected hyperspectral data cube is 350 × 400 × 55 (x , y , λ) and is acquired within a single camera exposure. The system (288 mm × 150 mm × 160 mm) weighs 3.6 kg (7.9 lb), and its power consumption is marginal as there are no scanning components. Experimental testing included several flights over an area covered by diverse types of vegetation and man-made structures. Data cubes are recorded at a 1/100 s integration time, which mitigated motion-related artifacts. The low size, mass, and power consumption of the imager can enable longer and higher flights at smaller drone sizes and allow easy, portable spectral imaging. Imaging results and the system description are presented and discussed.
Due to the growth of miniature unmanned aerial vehicles (UAVs) and small spacecraft (SmallSats) in recent years, there has been a push for the development of miniaturized spectral imagers to be incorporated with them. An efficient, compact hyperspectral imager integrated with these vehicles provides a cost-effective platform for environmental sensing applications that include the monitoring of agriculture, vegetation, geology, and pollutants. We present here the development and integration of a hyperspectral imaging system called the SNAP-IMS, originally used for biomedical detection, with an Octocopter UAV. The entire collected hyperspectral data cube is 350x400x55 (x,y,λ) spatial/spectral samples. The final system enclosure (288 mm x 150 mm x 160 mm) weighs 3.6 kg (7.9 lbs), offering minimal size and weight. The payload’s power consumption is marginal as there are no mechanical scanning components; the existing power requirements are dedicated exclusively to CCD frame acquisition. Experimental testing included several flights on board the Octocopter UAV, acquiring hyperspectral data cubes at 1/100 second. Snapshot mode and short integration times mitigate motion artifacts. The low size, weight, and power consumption can offer longer and higher flights at smaller drone sizes. These improvements augment the potential for additional instrument incorporation (i.e. LiDAR, Multi-spectral IR) in the future. Imaging results and system description are presented and discussed.
The additional heating of the air over the city is the result of the replacement of naturally vegetated surfaces with those
composed of asphalt, concrete, rooftops and other man-made materials. The temperatures of these artificial surfaces can
be 20 to 40 ° C higher than vegetated surfaces. This produces a dome of elevated air temperatures 5 to 8 ° C greater over
the city, compared to the air temperatures over adjacent rural areas. This effect is called the "urban heat island". Urban
landscapes are a complex mixture of vegetated and non-vegetated surfaces. It is difficult to take enough temperature
measurements over a large city area to. The use of remotely sensed data from airborne scanners is ideal to characterize
the complexity of urban albedo and radiant surface temperatures. The National Aeronautics and Space Administration
(NASA) Airborne Thermal and Land Applications Sensor (ATLAS) operates in the visual and IR bands was used in
February 2004 to collect data from San Juan, Puerto Rico with the main objective of investigating the Urban Heat Island
(UHI) in tropical cities. In this presentation we will examine the techniques of analyzing remotely sensed data for
measuring the effect of various urban surfaces on their contribution to the urban heat island effect.
Evaluation of near-surface soil properties via remote sensing (RS) could facilitate soil survey mapping, erosion prediction, fertilization regimes, and allocation of agrochemicals. The objective of this study was to evaluate the relationship between soil spectral signature and near surface soil properties in conventionally managed row crop systems. High-resolution RS data were acquired over bare fields in the Coastal Plain, Appalachian Plateau, and Ridge and Valley provinces of Alabama using the Airborne Terrestrial Applications Sensor (ATLAS) multispectral scanner. Soils ranged from sandy Kandiudults to fine textured Rhodudults. Surface soil samples (0-1 cm) were collected from 163 sampling points for soil water content, soil organic carbon (SOC), particle size distribution (PSD), and citrate dithionite extractable iron (Fed) content. Results showed that covariance among soil properties combined with mixed signatures limited our ability to identify discrete spectral response patterns for near-surface soil attributes. Dry, sandy epipedons at the Coastal Plain site provided ideal conditions and allowed for better discrimination among soil properties. Using ATLAS thermal infrared (TIR) bands, this study provides evidence that thermal spectra are more sensitive to small changes in near -surface mineral, organic and water content.
The portable ground-based atmospheric monitoring system (PGAMS) is a spectroradiometer system that provides a set of in situ solar and hemispherical sky irradiance, path radiance, and surface reflectance measurements. The observations provide input parameters for the calibration of atmospheric algorithms applied to multispectral and hyperspectral images in the visible and near infrared spectrum. Presented in this paper are the results of comparing hyperspectral surface radiances calculated using MODTRAN3 with PGAMS field measurements for a blue tarp and grass surface targets. Good agreement was obtained by constraining MODTRAN3 to only a rural atmospheric model with a calibrated visibility and surface reflectance from PGAMS observations. This was accomplished even though the sky conditions were unsteady as indicated by a varying aerosol extinction. Average absolute differences of 11.3 and 7.4 percent over the wavelength range from 400 to 1000 nm were obtained for the grass and blue tarp surfaces respectively. However, transformation to at-sensor radiances require additional constraints on the single-scattering albedo and scattering phase function so that they exhibit the specific real-time aerosol properties rather than a seasonal average model.
Detecting changes in the Earth's environment using satellite images of ocean and land surfaces must take into account atmospheric effects. As a result, major programs are underway to develop algorithms for image retrieval of atmospheric aerosol properties and atmospheric correction. However, because of the temporal and spatial variability of atmospheric transmittance, it is very difficult to model atmospheric effects and implement models in an operational mode. For this reason, simultaneous in situ ground measurements of atmospheric optical properties are vital to the development of accurate atmospheric correction techniques. Presented in this paper is a spectroradiometer system that provides an optimized set of surface measurements for the calibration and validation of atmospheric correction algorithms. The portable ground-based atmospheric monitoring system (PGAMS) obtains a comprehensive series of in situ irradiance, radiance, and reflectance measurements for the calibration of atmospheric correction algorithms applied to multispectral and hypserspectral images. The observations include: total downwelling irradiance, diffuse sky irradiance, direct solar irradiance, path radiance in the direction of the north celestial poles, path radiance in the direction of the overflying satellite, almucantar scans of path radiance, full sky radiance maps, and surface reflectance. Each of these parameters are recorded over a wavelength range from 350 to 1050 nm in 512 channels. The system is fast, with the potential to acquire the complete set of observations in only 8 to 10 minutes depending on the selected spatial resolution of the sky path radiance measurements.
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