Midwave Infrared Imaging Fourier Transform Spectrometry of Combustion Plumes

A midwave infrared (MWIR) imaging Fourier transform spectrometer (IFTS) was used to successfully capture and analyze hyperspectral imagery of combustion plumes. Jet engine exhaust data from a small turbojet engine burning diesel fuel at a flow rate of 300 cm3/min was collected at 1 cm−1 resolution from a side-plume vantage point on a 200x64 pixel window at a range of 11.2 meters. Spectral features of water, CO, and CO2 were present, and showed spatial variability within the plume structure. An array of thermocouple probes was positioned within the plume to aid in temperature analysis. A single-temperature plume model was implemented to obtain spatiallyvarying temperatures and plume concentrations. Model-fitted temperatures of 811 ± 1.5 K and 543 ± 1.6 K were obtained from plume regions in close proximity to thermocouple probes measuring temperatures of 719 K and 522 K, respectively. Industrial smokestack plume data from a coal-burning stack collected at 0.25 cm−1 resolution at a range of 600 meters featured strong emission from NO, CO, CO2, SO2, and HCl in the spectral region 1800-3000 cm−1. A simplified radiative transfer model was employed to derive temperature and concentrations for clustered regions of the 128x64 pixel scene, with corresponding statistical error bounds. The hottest region (closest to stack centerline) was 401 ± 0.36 K, compared to an in-stack measurement of 406 K, and model-derived concentration values of NO, CO2, and SO2 were 140 ± 1 ppmV, 110,400 ± 950 ppmV, and 382 ± 4 ppmV compared to in-stack measurements of 120 ppmV (NOχ), 94,000 ppmV, and 382 ppmV, respectively. In-stack measurements of CO and HCl were not provided by the stack operator, but model-derived values of 19 ± 0.2 ppmV and 111 ± 1 ppmV are reported near stack centerline. A deployment to Dugway Proving Grounds, UT to collect hyperspectral imagery of chemical and biological threat agent simulants resulted in weak spectral signatures from several species. Plume detection of methyl salicilate was achieved from both a stack release and explosive detonation, although spectral identification was not accomplished due to weak signal strength

Airborne methane remote measurements reveal heavy-tail flux distribution in Four Corners region

Methane (CH_4) impacts climate as the second strongest anthropogenic greenhouse gas and air quality by influencing tropospheric ozone levels. Space-based observations have identified the Four Corners region in the Southwest United States as an area of large CH_4 enhancements. We conducted an airborne campaign in Four Corners during April 2015 with the next-generation Airborne Visible/Infrared Imaging Spectrometer (near-infrared) and Hyperspectral Thermal Emission Spectrometer (thermal infrared) imaging spectrometers to better understand the source of methane by measuring methane plumes at 1- to 3-m spatial resolution. Our analysis detected more than 250 individual methane plumes from fossil fuel harvesting, processing, and distributing infrastructures, spanning an emission range from the detection limit ∼2 kg/h to 5 kg/h through ∼5,000 kg/h. Observed sources include gas processing facilities, storage tanks, pipeline leaks, and well pads, as well as a coal mine venting shaft. Overall, plume enhancements and inferred fluxes follow a lognormal distribution, with the top 10% emitters contributing 49 to 66% to the inferred total point source flux of 0.23 Tg/y to 0.39 Tg/y. With the observed confirmation of a lognormal emission distribution, this airborne observing strategy and its ability to locate previously unknown point sources in real time provides an efficient and effective method to identify and mitigate major emissions contributors over a wide geographic area. With improved instrumentation, this capability scales to spaceborne applications [Thompson DR, et al. (2016) Geophys Res Lett 43(12):6571–6578]. Further illustration of this potential is demonstrated with two detected, confirmed, and repaired pipeline leaks during the campaign

Gas Plume Species Identification in LWIR Hyperspectral Imagery by Regression Analyses

The goal of this research was to develop an algorithm for identifying the constituent gases in stack releases. At the heart of the algorithm is a stepwise linear regression technique that only includes a basis vector in the model if it contributes significantly to the fit. This significance is calculated by an F-statistic. Issues such as atmospheric compensation, gas absorption and emission, background modeling, and fitting a linear regression to a non-linear radiance model were addressed in order to generate the matrix of basis vectors. Synthetic imagery generated by the DIRISG model were used as test cases. Results show that the ability to correctly identify a gas diminishes as a function of decreasing concentration path-length of the plume. Results drawn from pixels near the stack are more likely to give an accurate identification of the gas present in the plume

Detection and identification of effluent gases using invariant hyperspectral algorithms

The ability to detect and identify effluent gases is a problem that has been pursued with limited success. An algorithm to do this would not only aid in the regulation of pollutants but also in treaty enforcement. Considering these applications, finding a way to remotely investigate a gaseous emission is highly desirable. This research utilizes hyperspectral imagery in the infrared region of the electromagnetic spectrum to evaluate invariant methods of detecting and identifying gases within a scene. The image is evaluated on a pixel-by-pixel basis and is also studied at the subpixel level. A library of target gas spectra is generated using a simple radiance model. This results in a more robust representation of the gas spectra which are representative of real-world observations. This library is the subspace utilized by the detection and identification algorithm. An evaluation was carried out to determine the subset of basis vectors that best span the subspace. Two basis vector selection methods are used to determine the subset of basis vectors; Singular Value Decomposition (SVD) and the Maximum Distance Method (MaxD). The Generalized Likelihood Ratio Test (GLRT) was used to determine whether the pixel is more like the target or the background. The target can be either a single species or a combination of gases, however, this study only looks for one gas at a time. Synthetically generated hyperspectral scenes in the longwave infrared (LWIR) region of the electromagnetic spectrum are used for this research. The test scenarios used in this study represented strong and weak plumes with single or multiple gas releases. In this work, strong and weak plumes refer to the release, which is on the order of tens of grams per second and tenths of grams per second, respectively. This work demonstrates the effectiveness of these invariant algorithms for the gas detection and identification problem

An Examination of Environmental Applications for Uncooled Thermal Infrared Remote Sensing Instruments

Advancements in system design for thermal instruments require assessment of potential environmental applications and appropriate data processing techniques. A novel multi-band thermal imaging system was proposed by DRS Leonardo for the National Aeronautics and Space Administration Earth Science Technology Office Instrument Incubator Program, for which these criteria were assessed. The Multi-Band Uncooled Radiometer Imager (MURI) is a six spectral channel instrument designed to collect images in the thermal infrared, specifically in the range of 7.5 to 12.5 μm. The work detailed in this thesis characterizes the ability of a thermal imager with an uncooled microbolometer focal plane array to provide valuable data for environmental science applications. Here, a pair of studies using simulated data demonstrates the ability of a multispectral instrument such as MURI to detect enhanced levels of atmospheric methane using a novel approach that performs similarly to a state of the art algorithm when applied to MURI data. The novel method is evaluated using a controlled concentration simulated dataset to determine the extent of its detection capabilities and its dependence on atmospheric conditions. The methane investigations reveal the system is capable of detecting a 20 m thick CH4 plume of 10-20 ppm above background levels when column water vapor is low using both the NDMI and matched filter approaches. Additionally, land surface temperature and emissivity retrieval techniques were applied to experimental MURI data recorded during initial test flights to assess their accuracy with MURI data. Utilizing split window and Temperature Emissivity Separation make this examination distinct as this establishes that proven methods can be applied to uncooled multiband imager data. Application of these methods to MURI data demonstrated the system is capable of temperature retrievals with Root Mean Square Errors of less than 1 K to measured reference values and surface emissivity retrievals within 2% of reference database values. The definition and application of the Normalized Differential Methane Index in this thesis demonstrates a novel approach for detection of enhanced plumes of methane utilizing a multispectral system with only a single band allocated to methane absorption features

Matched Filter Stochastic Background Characterization for Hyperspectral Target Detection

Algorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters can be used to locate spectral targets by modeling scene background as either structured (geometric) with a set of endmembers (basis vectors) or as unstructured (stochastic) with a covariance or correlation matrix. These matrices are often calculated using all available pixels in a data set. In unstructured background research, various techniques for improving upon scene-wide methods have been developed, each involving either the removal of target signatures from the background model or the segmentation of image data into spatial or spectral subsets. Each of these methods increase the detection signal-to-background ratio (SBR) and the multivariate normality (MVN) of the data from which background statistics are calculated, thus increasing separation between target and non-target species in the detection statistic and ultimately improving thresholded target detection results. Such techniques for improved background characterization are widely practiced but not well documented or compared. This paper provides a review and comparison of methods in target exclusion, spatial subsetting and spectral pre-clustering, and introduces a new technique which combines these methods. The analysis provides insight into the merit of employing unstructured background characterization techniques, as well as limitations for their practical application

Fast and Accurate Retrieval of Methane Concentration From Imaging Spectrometer Data Using Sparsity Prior

The strong radiative forcing by atmospheric methane has stimulated interest in identifying natural and anthropogenic sources of this potent greenhouse gas. Point sources are important targets for quantification, and anthropogenic targets have the potential for emissions reduction. Methane point-source plume detection and concentration retrieval have been previously demonstrated using data from the Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG). Current quantitative methods have tradeoffs between computational requirements and retrieval accuracy, creating obstacles for processing real-time data or large data sets from flight campaigns. We present a new computationally efficient algorithm that applies sparsity and an albedo correction to matched the filter retrieval of trace gas concentration path length. The new algorithm was tested using the AVIRIS-NG data acquired over several point-source plumes in Ahmedabad, India. The algorithm was validated using the simulated AVIRIS-NG data, including synthetic plumes of known methane concentration. Sparsity and albedo correction together reduced the root-mean-squared error of retrieved methane concentration-path length enhancement by 60.7% compared with a previous robust matched filter method. Background noise was reduced by a factor of 2.64. The new algorithm was able to process the entire 300 flight line 2016 AVIRIS-NG India campaign in just over 8 h on a desktop computer with GPU acceleration