scholarly journals Airborne DOAS retrievals of methane, carbon dioxide, and water vapor concentrations at high spatial resolution: application to AVIRIS-NG

2017 ◽  
Author(s):  
Andrew K. Thorpe ◽  
Christian Frankenberg ◽  
David R. Thompson ◽  
Riley M. Duren ◽  
Andrew D. Aubrey ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Water Vapor ◽  
Spatial Resolution ◽  
Absorption Spectroscopy ◽  
High Spatial Resolution ◽  
Google Earth ◽  
Optical Absorption Spectroscopy ◽  
Processing Plant ◽  
Emission Sources ◽  
Methane Carbon

Abstract. At local scales, emissions of methane and carbon dioxide are highly uncertain. Localized sources of both trace gases can create strong local gradients in its columnar abundance, which can be discerned using absorption spectroscopy at high spatial resolution. In a previous study, more than 250 methane plumes were observed in the San Juan Basin near Four Corners during April 2015 using the next generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) and a linearized matched filter. For the first time, we apply the Iterative Maximum a Posteriori Differential Optical Absorption Spectroscopy (IMAP-DOAS) method to AVIRIS-NG data and generate gas concentration maps for methane, carbon dioxide, and water vapor plumes. This demonstrates a comprehensive greenhouse gas monitoring capability that targets methane and carbon dioxide, the two dominant anthropogenic climate-forcing agents. Water vapor results indicate the ability of these retrievals to distinguish between methane and water vapor despite spectral mixing in the short wave infrared. We focus on selected cases from anthropogenic and natural sources, including emissions from mine ventilation shafts, a gas processing plant, tank, pipeline leak, and natural seep. In addition, carbon dioxide emissions were mapped from the flue-gas stacks of two coal-fired power plants and a water vapor plume was observed from the cooling towers of one power plant. Observed plumes were consistent with known and suspected emission sources verified by the true color AVIRIS-NG scenes and higher resolution Google Earth imagery. Real time detection and geolocation of methane plumes by AVIRIS-NG provided unambiguous identification of individual emission source locations and communication to a ground team for rapid follow up. This permitted verification of a number of methane emission sources using a thermal camera, including a tank and buried natural gas pipeline.

scholarly journals Airborne DOAS retrievals of methane, carbon dioxide, and water vapor concentrations at high spatial resolution: application to AVIRIS-NG

2017 ◽  
Vol 10 (10) ◽  
pp. 3833-3850 ◽  
Author(s):  
Andrew K. Thorpe ◽  
Christian Frankenberg ◽  
David R. Thompson ◽  
Riley M. Duren ◽  
Andrew D. Aubrey ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Water Vapor ◽  
Spatial Resolution ◽  
Absorption Spectroscopy ◽  
High Spatial Resolution ◽  
Google Earth ◽  
Optical Absorption Spectroscopy ◽  
Processing Plant ◽  
Emission Sources ◽  
Methane Carbon

Abstract. At local scales, emissions of methane and carbon dioxide are highly uncertain. Localized sources of both trace gases can create strong local gradients in its columnar abundance, which can be discerned using absorption spectroscopy at high spatial resolution. In a previous study, more than 250 methane plumes were observed in the San Juan Basin near Four Corners during April 2015 using the next-generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) and a linearized matched filter. For the first time, we apply the iterative maximum a posteriori differential optical absorption spectroscopy (IMAP-DOAS) method to AVIRIS-NG data and generate gas concentration maps for methane, carbon dioxide, and water vapor plumes. This demonstrates a comprehensive greenhouse gas monitoring capability that targets methane and carbon dioxide, the two dominant anthropogenic climate-forcing agents. Water vapor results indicate the ability of these retrievals to distinguish between methane and water vapor despite spectral interference in the shortwave infrared. We focus on selected cases from anthropogenic and natural sources, including emissions from mine ventilation shafts, a gas processing plant, tank, pipeline leak, and natural seep. In addition, carbon dioxide emissions were mapped from the flue-gas stacks of two coal-fired power plants and a water vapor plume was observed from the combined sources of cooling towers and cooling ponds. Observed plumes were consistent with known and suspected emission sources verified by the true color AVIRIS-NG scenes and higher-resolution Google Earth imagery. Real-time detection and geolocation of methane plumes by AVIRIS-NG provided unambiguous identification of individual emission source locations and communication to a ground team for rapid follow-up. This permitted verification of a number of methane emission sources using a thermal camera, including a tank and buried natural gas pipeline.

Supplementary material to "Airborne DOAS retrievals of methane, carbon dioxide, and water vapor concentrations at high spatial resolution: application to AVIRIS-NG"

2017 ◽  
Author(s):  
Andrew K. Thorpe ◽  
Christian Frankenberg ◽  
David R. Thompson ◽  
Riley M. Duren ◽  
Andrew D. Aubrey ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Water Vapor ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Supplementary Material ◽  
Methane Carbon

scholarly journals High spatial resolution measurements of NO<sub>2</sub> applying Topographic Target Light scattering-Differential Optical Absorption Spectroscopy (ToTaL-DOAS)

2008 ◽  
Vol 8 (24) ◽  
pp. 7595-7601 ◽  
Author(s):  
E. Frins ◽  
U. Platt ◽  
T. Wagner
Keyword(s):  
Light Scattering ◽  
Optical Absorption ◽  
Spatial Resolution ◽  
Absorption Spectroscopy ◽  
High Spatial Resolution ◽  
Differential Optical Absorption Spectroscopy ◽  
Optical Absorption Spectroscopy ◽  
Air Masses ◽  
Target Light ◽  
Mixing Ratios

Abstract. Topographic Target Light scattering – Differential Optical Absorption Spectroscopy (ToTaL-DOAS), also called Target-DOAS, is a novel experimental procedure to retrieve trace gas concentrations present in the low atmosphere. Scattered sunlight (diffuse or specular) reflected from natural or artificial targets located at different distances are analyzed to retrieve the spatial distribution of the concentration of different trace gases like NO2, SO2 and others. We report high spatial resolution measurements of NO2 mixing ratios in the city of Montevideo (Uruguay) observing three buildings as targets with a Mini-DOAS instrument. Our instrument was 146 m, 196 m, and 280 m apart from three different buildings located along a main Avenue. We obtain temporal variation of NO2 mixing ratios between 30 ppb and 65 ppb from measurements of November 2007 and mixing ratios up to 50 ppb from measurements of August and September 2008. Our measurements demonstrate that ToTaL-DOAS observations can be made over relative short distances. In polluted air masses, the retrieved absorption signal was found to be sufficiently strong to allow measurements over distances in the range of several tens of meters.

scholarly journals High spatial resolution measurements of NO<sub>2</sub> applying Topographic Target Light scattering-Differential Optical Absorption Spectroscopy (ToTaL-DOAS)

2008 ◽  
Vol 8 (3) ◽  
pp. 10257-10273
Author(s):  
E. Frins ◽  
U. Platt ◽  
T. Wagner
Keyword(s):  
Light Scattering ◽  
Optical Absorption ◽  
Spatial Resolution ◽  
Absorption Spectroscopy ◽  
High Spatial Resolution ◽  
Differential Optical Absorption Spectroscopy ◽  
Optical Absorption Spectroscopy ◽  
Air Masses ◽  
Target Light ◽  
Mixing Ratios

Abstract. Tomographic Target Light scattering – Differential Optical Absorption Spectroscopy (ToTaL-DOAS), also called Target-DOAS, is a novel experimental procedure to retrieve trace gas concentrations present in the low atmosphere. Scattered sunlight (partially or totally) reflected from natural or artificial targets of similar albedo located at different distances is analyzed to retrieve the concentration of different trace gases like NO2, SO2 and others. We report high spatial resolution measurements of NO2 mixing ratios in the city of Montevideo (Uruguay) observing three buildings as targets with a Mini-DOAS instrument. Our instrument was 146 m apart from the first building, 196 m from the second and 286 m from the third one. All three buildings are located along a main Avenue. We obtain temporal variation of NO2 mixing ratios between 30 ppb and 65 ppb (±2 ppb). Our measurements demonstrate that ToTaL-DOAS measurements can be made over very short distances. In polluted air masses, the retrieved absorption signal was found to be strong enough to allow measurements over distances in the range of several ten meters, and achieve a spatial resolution of 50 m approximately.

scholarly journals High spatial resolution imaging of methane and other trace gases with the airborne Hyperspectral Thermal Emission Spectrometer (HyTES)

2016 ◽  
Author(s):  
G. C. Hulley ◽  
R. M. Duren ◽  
F. M. Hopkins ◽  
S. J. Hook ◽  
N. Vance ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Thermal Emission ◽  
High Spatial Resolution ◽  
Matched Filter ◽  
Study Data ◽  
High Spectral Resolution ◽  
Emission Sources ◽  
Source Attribution ◽  
Economic Sectors ◽  
Strong Emission

Abstract. Currently large uncertainties exist associated with the attribution and quantification of fugitive emissions of criteria pollutants and greenhouse gases such as methane across large regions and key economic sectors. In this study, data from the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) have been used to develop robust and reliable techniques for the detection and wide-area mapping of emission plumes of methane and other atmospheric trace gas species over challenging and diverse environmental conditions with high spatial resolution that permits direct attribution to sources. HyTES is a pushbroom imaging spectrometer with high spectral resolution (256 bands from 7.5–12 µm), wide swath (1–2 km), and high spatial resolution (~2 m at 1 km altitude) that incorporates new thermal infrared (TIR) remote sensing technologies. In this study we introduce a hybrid Clutter Matched Filter (CMF) and plume dilation algorithm applied to HyTES observations to efficiently detect and characterize the spatial structures of individual plumes of CH4, H2S, NH3, NO2, and SO2 emitters. The sensitivity and field of regard of HyTES allows rapid and frequent airborne surveys of large areas including facilities not readily accessible from the surface. The HyTES CMF algorithm produces plume intensity images of methane and other gases from strong emission sources. The combination of high spatial resolution and multi-species imaging capability provides source attribution in complex environments. The CMF-based detection of strong emission sources over large areas is a fast and powerful tool needed to focus more computationally intensive retrieval algorithms to quantify emissions with error estimates, and is useful for expediting mitigation efforts and addressing critical science questions.

scholarly journals A Comparison of Methods for Determining Forest Composition from High-Spatial-Resolution Remotely Sensed Imagery

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1290
Author(s):  
Benjamin T. Fraser ◽  
Russell G. Congalton
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remotely Sensed ◽  
Local Scale ◽  
Google Earth ◽  
Forest Composition ◽  
Visual Interpretation ◽  
Individual Tree ◽  
Remotely Sensed Imagery ◽  
Composition Classes

Remotely sensed imagery has been used to support forest ecology and management for decades. In modern times, the propagation of high-spatial-resolution image analysis techniques and automated workflows have further strengthened this synergy, leading to the inquiry into more complex, local-scale, ecosystem characteristics. To appropriately inform decisions in forestry ecology and management, the most reliable and efficient methods should be adopted. For this reason, our research compares visual interpretation to digital (automated) processing for forest plot composition and individual tree identification. During this investigation, we qualitatively and quantitatively evaluated the process of classifying species groups within complex, mixed-species forests in New England. This analysis included a comparison of three high-resolution remotely sensed imagery sources: Google Earth, National Agriculture Imagery Program (NAIP) imagery, and unmanned aerial system (UAS) imagery. We discovered that, although the level of detail afforded by the UAS imagery spatial resolution (3.02 cm average pixel size) improved the visual interpretation results (7.87–9.59%), the highest thematic accuracy was still only 54.44% for the generalized composition groups. Our qualitative analysis of the uncertainty for visually interpreting different composition classes revealed the persistence of mislabeled hardwood compositions (including an early successional class) and an inability to consistently differentiate between ‘pure’ and ‘mixed’ stands. The results of digitally classifying the same forest compositions produced a higher level of accuracy for both detecting individual trees (93.9%) and labeling them (59.62–70.48%) using machine learning algorithms including classification and regression trees, random forest, and support vector machines. These results indicate that digital, automated, classification produced an increase in overall accuracy of 16.04% over visual interpretation for generalized forest composition classes. Other studies, which incorporate multitemporal, multispectral, or data fusion approaches provide evidence for further widening this gap. Further refinement of the methods for individual tree detection, delineation, and classification should be developed for structurally and compositionally complex forests to supplement the critical deficiency in local-scale forest information around the world.

High-spatial-resolution detection of new single-photon emission sources

SPIE Newsroom ◽  
2016 ◽  
Author(s):  
Luiz H. G. Tizei ◽  
Sophie Meuret ◽  
Romain Bourrellier ◽  
Anna Tararan ◽  
Odile Stéphan ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Single Photon ◽  
Photon Emission ◽  
Emission Sources ◽  
Single Photon Emission

scholarly journals High Spatial Resolution Topsoil Organic Matter Content Mapping Across Desertified Land in Northern China

2021 ◽  
Vol 9 ◽  
Author(s):  
Yang Junting ◽  
Li Xiaosong ◽  
Wu Bo ◽  
Wu Junjun ◽  
Sun Bin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organic Matter ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Organic Matter Content ◽  
Multiple Linear Regression Model ◽  
Northern China ◽  
Google Earth ◽  
Support Vector ◽  
Combating Desertification

Soil organic matter (SOM) content is an effective indicator of desertification; thus, monitoring its spatial‒temporal changes on a large scale is important for combating desertification. However, mapping SOM content in desertified land is challenging owing to the heterogeneous landscape, relatively low SOM content and vegetation coverage. Here, we modeled the SOM content in topsoil (0–20 cm) of desertified land in northern China by employing a high spatial resolution dataset and machine learning methods, with an emphasis on quarterly green and non-photosynthetic vegetation information, based on the Google Earth Engine (GEE). The results show: 1) the machine learning model performed better than the traditional multiple linear regression model (MLR) for SOM content estimation, and the Random Forest (RF) model was more accurate than the Support Vector Machine (SVM) model; 2) the quarterly information regarding green vegetation and non-photosynthetic were identified as key covariates for estimating the SOM content in desertified land, and an obvious improvement could be observed after simultaneously combining the Dead Fuel Index (DFI) and Normalized Difference Vegetation Index (NDVI) of the four quarters (R2 increased by 0.06, the root mean square error decreased by 0.05, the ratio of prediction deviation increased by 0.2, and the ratio of performance to interquartile distance increased by 0.5). In particular, the effects of the DFI in Q1 (the first quarter) and Q2 (the second quarter) on estimating low SOM content (&lt;1%) were identified; finally, a timely (2019) and high spatial resolution (30 m) SOM content map for the desertified land in northern China was drawn which shows obvious advantages over existing SOM products, thus providing key data support for monitoring and combating desertification.

scholarly journals Measurement of atmospheric water vapor using infrared differential optical absorption spectroscopy

2012 ◽  
Vol 61 (14) ◽  
pp. 140705
Author(s):  
Sun You-Wen ◽  
Liu Wen-Qing ◽  
Xie Pin-Hua ◽  
Chan Ka-Lok ◽  
Zeng Yi ◽  
...  
Keyword(s):  
Water Vapor ◽  
Optical Absorption ◽  
Absorption Spectroscopy ◽  
Differential Optical Absorption Spectroscopy ◽  
Atmospheric Water Vapor ◽  
Optical Absorption Spectroscopy ◽  
Atmospheric Water
Sign in / Sign up

Export Citation Format

Share Document