Browsing by Author "Shaw, Joseph A."

Now showing 1 - 20 of 31

Agile collaboration: Citizen science as a transdisciplinary approach to heliophysics
(Frontiers Media SA, 2023-04) Ledvina, Vincent; Brandt, Laura; MacDonald, Elizabeth; Frissell, Nathaniel; Anderson, Justin; Chen, Thomas Y.; French, Ryan J.; Mare, Francesca Di; Grover, Andrea; Sigsbee, Kristine; Gallardo-Lacourt, Bea; Lach, Donna; Shaw, Joseph A.; Hunnekuhl, Michael; Kosar, Burcu; Barkhouse, Wayne; Young, Tim; Kedhambadi, Chandresh; Ozturk, Dogacan S.; Claudepierre, Seth G.; Dong, Chuanfei; Witteman, Andy; Kuzub, Jeremy; Sinha, Gunjan
Citizen science connects scientists with the public to enable discovery, engaging broad audiences across the world. There are many attributes that make citizen science an asset to the field of heliophysics, including agile collaboration. Agility is the extent to which a person, group of people, technology, or project can work efficiently, pivot, and adapt to adversity. Citizen scientists are agile; they are adaptable and responsive. Citizen science projects and their underlying technology platforms are also agile in the software development sense, by utilizing beta testing and short timeframes to pivot in response to community needs. As they capture scientifically valuable data, citizen scientists can bring expertise from other fields to scientific teams. The impact of citizen science projects and communities means citizen scientists are a bridge between scientists and the public, facilitating the exchange of information. These attributes of citizen scientists form the framework of agile collaboration. In this paper, we contextualize agile collaboration primarily for aurora chasers, a group of citizen scientists actively engaged in projects and independent data gathering. Nevertheless, these insights scale across other domains and projects. Citizen science is an emerging yet proven way of enhancing the current research landscape. To tackle the next-generation’s biggest research problems, agile collaboration with citizen scientists will become necessary.
Airborne lidar detection of an underwater thermal vent
(2017-08) Roddewig, Michael R.; Churnside, James H.; Shaw, Joseph A.
We report the lidar detection of an underwater feature that appears to be a thermal vent in Yellowstone Lake, Yellowstone National Park, USA, with the Montana State University Fish Lidar. The location of the detected vent was 30 m from the closest vent identified in a United States Geological Survey of Yellowstone Lake in 2008. A second possible vent is also presented, and the appearance of both vents in the lidar data is compared to descriptions of underwater thermal vents in Yellowstone Lake from the geological literature. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
All-sky polarization imaging of cloud thermodynamic phase
(2019-02) Eshelman, Laura M.; Tauc, Martin J.; Shaw, Joseph A.
Knowing the cloud thermodynamic phase (if a cloud is composed of ice crystals or liquid droplets) is crucial for many cloud remote sensing measurements. Further, this knowledge can help in simulating and interpreting cloud radiation measurements to better understand the role of clouds in climate, weather, and optical propagation. Knobelspiesse et al. [Atmos. Meas. Tech. 8, 1537 (2015)] showed that, for simulated zenith observations, the algebraic sign of the S1 Stokes parameter (related to the difference between perpendicular and parallel linear polarization in the scattering plane) can be used to detect cloud thermodynamic phase when observed with a ground-based passive polarimeter. In this paper, we describe the use of our all-sky imaging polarimeter to experimentally test this proposed method of detecting cloud thermodynamic phase in the entire sky dome. The zenith cloud phase was validated with a dual-polarization lidar instrument.
Benthic river algae mapping using hyperspectral imagery from unoccupied aerial vehicles
(SPIE-Intl Soc Optical Eng, 2024-06) Logan, Riley D.; Shaw, Joseph A.
The increasing prevalence of nuisance benthic algal blooms in freshwater systems has led to water quality monitoring programs based on the presence and abundance of algae. Large blooms of the nuisance filamentous algae, Cladophora glomerata, have become common in the waters of the Upper Clark Fork River in western Montana. To aid in the understanding of algal growth dynamics, unoccupied aerial vehicle (UAV)-based hyperspectral images were gathered at three field sites along the length of the river throughout the growing season of 2021. Select regions within images covering the spectral range of 400 to 850 nm were labeled based on a combination of professional judgment and spectral profiles and used to train a random forest classifier to identify benthic algal growth across several classes, including benthic growth dominated by Cladophora (Clado), benthic growth dominated by growth forms other than Cladophora (non-Clado), and areas below a visually detectable threshold of benthic growth (bare substrate). After classification, images were stitched together to produce spatial distribution maps of each river reach while also calculating the average percent cover for each reach, achieving an accuracy of approximately 99% relative to manually labeled images. Results of this analysis showed strong variability across each reach, both temporally (up to 40%) and spatially (up to 46%), indicating that UAV-based imaging with high-spatial resolution could augment and therefore improve traditional measurement techniques that are spatially limited, such as spot sampling.
Cobleigh hall weather station data, 2005-present [dataset]
(2015-11) Shaw, Joseph A.
This record links to data collected by a weather station operated by the Optical Remote Sensor Laboratory at Montana State University, under the direction of Dr. Joseph Shaw. The weather station is on top of Cobleigh Hall on the campus of Montana State University in Bozeman. The latitude is 45.67° N and the longitude is 111.05° W. The elevation on the roof is 5000ft (1524m).
Colors of the Yellowstone thermal pools for teaching optics
(2015-06) Shaw, Joseph A.; Nugent, Paul W.; Vollmer, M.
Nature provides many beautiful optical phenomena that can be used to teach optical principles. Here we describe an interdisciplinary education project based on a simple computer model of the colors observed in the famous thermal pools of Yellowstone National Park in the northwestern United States. The primary wavelength-dependent parameters that determine the widely varying pool colors are the reflectance of the rocks or the microbial mats growing on the rocks beneath the water (the microbial mat color depends on water temperature) and optical absorption and scattering in the water. This paper introduces a teaching module based on a one-dimensional computer model that starts with measured reflectance spectra of the microbial mats and modifies the spectra with depth-dependent absorption and scattering in the water. This module is designed to be incorporated into a graduate course on remote sensing systems, in a section covering the propagation of light through air and water, although it could be adapted to a general university optics course. The module presents the basic 1-D radiative transfer equation relevant to this problem, and allows them to build their own simple model. Students can then simulate the colors that would be observed for different variations of the microbial mat reflectance spectrum, skylight spectrum, and water depth.
Comparison of Supervised Learning and Changepoint Detection for Insect Detection in Lidar Data
(MDPI AG, 2023-12) Vannoy, Trevor C.; Sweeney, Nathaniel B.; Shaw, Joseph A.; Whitaker, Bradley M.
Concerns about decreases in insect population and biodiversity, in addition to the need for monitoring insects in agriculture and disease control, have led to an increased need for automated, non-invasive monitoring techniques. To this end, entomological lidar systems have been developed and successfully used for detecting and classifying insects. However, the data produced by these lidar systems create several problems from a data analysis standpoint: the data can contain millions of observations, very few observations contain insects, and the background environment is non-stationary. This study compares the insect-detection performance of various supervised machine learning and unsupervised changepoint detection algorithms and provides commentary on the relative strengths of each method. We found that the supervised methods generally perform better than the changepoint detection methods, at the cost of needing labeled data. The supervised learning method with the highest Matthew’s Correlation Coefficient score on the testing set correctly identified 99.5% of the insect-containing images and 83.7% of the non-insect images; similarly, the best changepoint detection method correctly identified 83.2% of the insect-containing images and 84.2% of the non-insect images. Our results show that both types of methods can reduce the need for manual data analysis.
Correcting for focal-plane-array temperature dependence in microbolometer infrared cameras lacking thermal stabilization
(2013-01) Nugent, Paul W.; Shaw, Joseph A.; Pust, Nathan J.
Advances in microbolometer detectors have led to the development of infrared cameras that operate without active temperature stabilization. The response of these cameras varies with the temperature of the camera’s focal plane array (FPA). This paper describes a method for stabilizing the camera’s response through software processing. This stabilization is based on the difference between the camera’s response at a measured temperature and at a reference temperature. This paper presents the mathematical basis for such a correction and demonstrates the resulting accuracy when applied to a commercially available long-wave infrared camera. The stabilized camera was then radiometrically calibrated so that the digital response from the camera could be related to the radiance or temperature of objects in the scene. For FPA temperature deviations within ±7.2°C changing by 0.5°C/min, this method produced a camera calibration with spatial-temporal rms variability of 0.21°C, yielding a total calibration uncertainty of 0.38°C limited primarily by the 0.32°C uncertainty in the blackbody source emissivity and temperature.
Detection of polarization neutral points in observations of the combined corona and sky during the 21 August 2017 total solar eclipse
(2020-07) Snik, Frans; Bos, Steven P.; Brackenhoff, Stefanie A.; Doelman, David S.; Por, Emiel H.; Bettonvil, Felix; Rodenhuis, Michiel; Vorobiev, Dmitry; Eshelman, Laura M.; Shaw, Joseph A.
We report the results of polarimetric observations of the total solar eclipse of 21 August 2017 from Rexburg, Idaho (USA). We use three synchronized DSLR cameras with polarization filters oriented at 0°, 60°, and 120° to provide high-dynamic-range RGB polarization images of the corona and surrounding sky. We measure tangential coronal polarization and vertical sky polarization, both as expected. These observations provide detailed detections of polarization neutral points above and below the eclipsed Sun where the coronal polarization is canceled by the sky polarization. We name these special polarization neutral points after Minnaert and Van de Hulst.
Digital all-sky polarization imaging of the total solar eclipse on 21 August 2017 in Rexburg, Idaho, USA
(2020-07) Eshelman, Laura M.; Tauc, Martin Jan; Hashimoto, Taiga; Gillis, Kendra; Weiss, William; Stanley, Bryan; Hooser, Preston; Shaw, Glenn E.; Shaw, Joseph A.
All-sky polarization images were measured from sunrise to sunset and during a cloud-free totality on 21 August 2017 in Rexburg, Idaho using two digital three-camera all-sky polarimeters and a time-sequential liquid-crystal-based all-sky polarimeter. Twenty-five polarimetric images were recorded during totality, revealing a highly dynamic evolution of the distribution of skylight polarization, with the degree of linear polarization becoming nearly zenith-symmetric by the end of totality. The surrounding environment was characterized with an infrared cloud imager that confirmed the complete absence of clouds during totality, an AERONET solar radiometer that measured aerosol properties, a portable weather station, and a hand-held spectrometer with satellite images that measured surface reflectance at and near the observation site. These observations confirm that previously observed totality patterns are general and not unique to those specific eclipses. The high temporal image resolution revealed a transition of a neutral point from the zenith in totality to the normal Babinet point just above the Sun after third contact, providing the first indication that the transition between totality and normal daytime polarization patterns occurs over of a time period of approximately 13 s.
Discrimination of herbicide-resistant kochia with hyperspectral imaging
(2018-03) Nugent, Paul W.; Shaw, Joseph A.; Jha, Prashant; Scherrer, Bryan; Donelick, Andrew; Kumar, Vipan
A hyperspectral imager was used to differentiate herbicide-resistant versus herbicide-susceptible biotypes of the agronomic weed kochia, in different crops in the field at the Southern Agricultural Research Center in Huntley, Montana. Controlled greenhouse experiments showed that enough information was captured by the imager to classify plants as either a crop, herbicidesusceptible or herbicide-resistant kochia. The current analysis is developing an algorithm that will work in more uncontrolled outdoor situations. In overcast conditions, the algorithm correctly identified dicamba-resistant kochia, glyphosate-resistant kochia, and glyphosate-and dicamba-susceptible kochia with 67%, 76%, and 80% success rates, respectively. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.
Experimental observation of signature changes in bulk soil electrical conductivity in response to engineered surface CO2 leakage
(2012-03) Zhou, Xiaobing; Lakkaraju, V. R.; Apple, Martha E.; Dobeck, Laura M.; Gullickson, K.; Shaw, Joseph A.; Cunningham, Alfred B.; Wielopolski, Lucian; Spangler, Lee H.
Experimental observations of signature changes of bulk soil electrical conductivity (EC) due to CO2 leakage were carried out at a field site at Bozeman, Montana, to investigate the change of soil geophysical properties in response to possible leakage of geologically sequestered CO2. The dynamic evolution of bulk soil EC was measured during an engineered surface leakage of CO2 through in situ continuous monitoring of bulk soil EC, soil moisture, soil temperature, rainfall rate, and soil CO2 concentration to investigate the response of soil bulk EC signature to CO2 leakage. Observations show that: (1) high soil CO2 concentration due to CO2 leakage enhances the dependence of bulk soil EC on soil moisture. The bulk soil EC is a linear multivariate function of soil moisture and soil temperature, the coefficient for soil moisture increased from 2.111 dS for the non-leaking phase to 4.589 dS for the CO2 leaking phase; and the coefficient for temperature increased from 0.003 dS/Â°C for the non-leaking phase to 0.008 dS/Â°C for the CO2 leaking phase. The dependence of bulk soil EC on soil temperature is generally weak, but leaked CO2 enhances the dependence,(2)after the CO2 release, the relationship between soil bulk EC and soil CO2 concentration observes three distinct CO2 decay modes. Rainfall events result in sudden changes of soil moisture and are believed to be the driving forcing for these decay modes, and (3) within each mode, increasing soil CO2 concentration results in higher bulk soil EC. Comparing the first 2 decay modes, it is found that the dependence of soil EC on soil CO2 concentration is weaker for the first decay mode than the second decay mode.
Generalized Nighttime Radiative Deficits
(Elsevier BV, 2021-10) Howell, John C.; Yizhaq, Tomer; Drechsler, Nadav; Zamir, Yuval; Beysens, Daniel; Shaw, Joseph A.
We derive a general, tilt-dependent, nighttime, radiative deficit model with an eye towards improved dew collection. The model incorporates atmospheric/environmental incoming radiation, a linear precipitable water vapor transmittance function dependent on local meteo data and the influence of near-horizon obstacles. A brief discussion of cloud cover is given. The model is then used more specifically to predict radiative deficits for an ideal blackbody emitter in an environment with an isotropic temperature. Knowing the tilt angle, near-horizon obstacles and local meteo-data, it is then possible to estimate the radiative deficit of a given emitter. We consider errors resulting from the assumption that the ground and obstacles are at the same temperature as the air. We also analyze the errors arising from the linear precipitable water vapor transmittance function by comparing the results against high-resolution, full-spectrum Modtran® data [1]. We show that for typical tilt angles, the isotropic temperature model is a reasonable approximation as long as the above-horizon environmental heating is small. We believe these results will be broadly valuable for the field of radiative cooling where a general radiative treatment has yet to be made and in particular the field of dew water harvesting.
Gray Spectralon polarized reflectance deviations from Lambertian
(SPIE, 2022-06) Field, Nathaniel J.; Brown, Jarrod P.; Card, Darrel B.; Welsh, Chad M.; Van Rynbach, Andre J.; Shaw, Joseph A.
While Spectralon panels are largely assumed to be ideal Lambertian surfaces, their actual polarized reflective responses deviate from the ideal by at least a small amount at illumination and viewing angles off surface normal. The Mueller matrix response of four different panels between 10% and 99% reflectance were measured and the radiometric response from two distinct monostatic or nearmonostatic polarimeter systems are compared, one at Montana State University and one at the Air Force Research Lab. The deviations from an assumed ideal Lambertian surface are reported.
Hyperspectral Band Selection for Multispectral Image Classification with Convolutional Networks
(2021) Morales, Giorgio; Sheppard, John W.; Logan, Riley D.; Shaw, Joseph A.
In recent years, Hyperspectral Imaging (HSI) has become a powerful source for reliable data in applications such as remote sensing, agriculture, and biomedicine. However, hyperspectral images are highly data-dense and often benefit from methods to reduce the number of spectral bands while retaining the most useful information for a specific application. We propose a novel band selection method to select a reduced set of wavelengths, obtained from an HSI system in the context of image classification. Our approach consists of two main steps: the first utilizes a filter-based approach to find relevant spectral bands based on a collinearity analysis between a band and its neighbors. This analysis helps to remove redundant bands and dramatically reduces the search space. The second step applies a wrapper-based approach to select bands from the reduced set based on their information entropy values, and trains a compact Convolutional Neural Network (CNN) to evaluate the performance of the current selection. We present classification results obtained from our method and compare them to other feature selection methods on two hyperspectral image datasets. Additionally, we use the original hyperspectral data cube to simulate the process of using actual filters in a multispectral imager. We show that our method produces more suitable results for a multispectral sensor design.
Hyperspectral Dimensionality Reduction Based on Inter-Band Redundancy Analysis and Greedy Spectral Selection
(2021-09) Morales, Giorgio; Sheppard, John W.; Logan, Riley D.; Shaw, Joseph A.
Hyperspectral imaging systems are becoming widely used due to their increasing accessibility and their ability to provide detailed spectral responses based on hundreds of spectral bands. However, the resulting hyperspectral images (HSIs) come at the cost of increased storage requirements, increased computational time to process, and highly redundant data. Thus, dimensionality reduction techniques are necessary to decrease the number of spectral bands while retaining the most useful information. Our contribution is two-fold: First, we propose a filter-based method called interband redundancy analysis (IBRA) based on a collinearity analysis between a band and its neighbors. This analysis helps to remove redundant bands and dramatically reduces the search space. Second, we apply a wrapper-based approach called greedy spectral selection (GSS) to the results of IBRA to select bands based on their information entropy values and train a compact convolutional neural network to evaluate the performance of the current selection. We also propose a feature extraction framework that consists of two main steps: first, it reduces the total number of bands using IBRA; then, it can use any feature extraction method to obtain the desired number of feature channels. We present classification results obtained from our methods and compare them to other dimensionality reduction methods on three hyperspectral image datasets. Additionally, we used the original hyperspectral data cube to simulate the process of using actual filters in a multispectral imager.
Hyperspectral imaging and machine learning for monitoring produce ripeness
(2020-04) Logan, Riley D.; Scherrer, Bryan; Senecal, Jacob; Walton, Neil S.; Peerlinck, Amy; Sheppard, John W.; Shaw, Joseph A.
Hyperspectral imaging is a powerful remote sensing tool capable of capturing rich spectral and spatial information. Although the origins of hyperspectral imaging are in terrestrial remote sensing, new applications are emerging rapidly. Owing to its non-destructive nature, hyperspectral imaging has become a useful tool for monitoring produce ripeness. This paper describes the process that uses a visible near-infrared (VNIR) hyperspectral imager from Resonon, Inc., coupled with machine learning algorithms to assess the ripeness of various pieces of produce. The images were converted to reflectance across a spectral range of 387.12 nm to 1023.5 nm, with a spectral resolution of 2.12 nm. A convolutional neural network was used to perform age classification for potatoes, bananas, and green peppers. Additionally, a genetic algorithm was used to determine the wavelengths carrying the most useful information for age classification. Experiments were run using RGB images, full spectrum hyperspectral images, and the genetic algorithm feature selection method. Results showed that the genetic algorithm-based feature selection method outperforms RGB images for all tested produce, outperforms hyperspectral imagery for bananas, and matches hyperspectral imagery performance for green peppers. This feature selection method is being used to develop a low-cost multi-spectral imager for use in monitoring produce in grocery stores.
Infrared cloud imager development for atmospheric optical communication characterization, and measurements at the JPL Table Mountain Facility
(2013-02) Nugent, Paul W.; Shaw, Joseph A.; Piazzolla, S.
The continuous demand for high data return in deep space and near-Earth satellite missions has led NASA and international institutions to consider alternative technologies for high-data-rate communications. One solution is the establishment of widebandwidth Earth–space optical communication links, which require (among other things) a nearly obstruction-free atmospheric path. Considering the atmospheric channel, the most common and most apparent impairments on Earth–space optical communication paths arise from clouds. Therefore, the characterization of the statistical behavior of cloud coverage for optical communication ground station candidate sites is of vital importance. In this article, we describe the development and deployment of a ground-based, long-wavelength infrared cloud imaging system able to monitor and characterize the cloud coverage. This system is based on a commercially available camera with a 62-deg diagonal field of view. A novel internal-shutter-based calibration technique allows radiometric calibration of the camera, which operates without a thermoelectric cooler. This cloud imaging system provides continuous day–night cloud detection with constant sensitivity. The cloud imaging system also includes data-processing algorithms that calculate and remove atmospheric emission to isolate cloud signatures, and enable classification of clouds according to their optical attenuation. Measurements of long-wavelength infrared cloud radiance are used to retrieve the optical attenuation (cloud optical depth due to absorption and scattering) in the wavelength range of interest from visible to near-infrared, where the cloud attenuation is quite constant. This article addresses the specifics of the operation, calibration, and data processing of the imaging system that was deployed at the NASA/JPL Table Mountain Facility (TMF) in California. Data are reported from July 2008 to July 2010. These data describe seasonal variability in cloud cover at the TMF site, with cloud amount (percentage of cloudy pixels) peaking at just over 51 percent during February, of which more than 60 percent had optical attenuation exceeding 12 dB at wavelengths in the range from the visible to the near-infrared. The lowest cloud amount was found during August, averaging 19.6 percent, and these clouds were mostly optically thin, with low attenuation.
Lidar measurements of the diffuse attenuation coefficient in Yellowstone Lake
(2020-03) Roddewig, Michael R.; Churnside, James H.; Shaw, Joseph A.
Airborne lidar study of lake ecosystems is still a relatively unexplored field. In this paper we present measurements of the diffuse attenuation coefficient of downwelling irradiance (𝐾𝑑) obtained using a 532 nm airborne lidar in flights during 2004 and 2016 over Yellowstone Lake, Yellowstone National Park, Wyoming, USA. We compare the lidar measurements with MODIS 𝐾𝑑 data, discuss the impact that local weather and river inflows/outflows may have had on the data, compare to previous models of the diffuse attenuation coefficient, and examine several published relationships converting 𝐾𝑑 to Secchi disk depth.
Lidar remote sensing of the aquatic environment: invited
(2020) Churnside, James H.; Shaw, Joseph A.
This paper is a review of lidar remote sensing of the aquatic environment. The optical properties of seawater relevant to lidar remote sensing are described. The three main theoretical approaches to understanding the performance of lidar are considered (the time-dependent radiative transfer equation, Monte Carlo simulations, and the quasi-single-scattering assumption). Basic lidar instrument design considerations are presented, and examples of lidar studies from surface vessels, aircraft, and satellites are given.