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Eppanapelli, Lavan Kumar, DrORCID iD iconorcid.org/0000-0002-5943-1476
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Publications (5 of 5) Show all publications
Eppanapelli, L. K., Lintzen, N., Casselgren, J. & Wåhlin, J. (2018). Estimation of Liquid Water Content of Snow Surface by Spectral Reflectance. Journal of cold regions engineering, 32(1), Article ID 05018001.
Open this publication in new window or tab >>Estimation of Liquid Water Content of Snow Surface by Spectral Reflectance
2018 (English)In: Journal of cold regions engineering, ISSN 0887-381X, E-ISSN 1943-5495, Vol. 32, no 1, article id 05018001Article in journal (Refereed) Published
Abstract [en]

This study measures the spectral reflectance from snow with known liquid water content (LWC) in a climate chamber using two optical sensors, a near-infrared (NIR) spectrometer and a Road eye sensor. The spectrometer measures the backscattered radiation in the wavelength range of 920–1,650 nm. The Road eye sensor was developed to monitor and classify winter roads based on reflected intensity measurements at wavelengths of 980, 1,310, and 1,550 nm. Results of the study suggest that the spectral reflectance from snow is inversely proportional to the LWC in snow. Based on the effect of LWC on the spectral reflectance, three optimum wavelength bands are selected in which snow with different LWCs is clearly distinguishable. A widely used remote sensing index known as the normalized difference water index (NDWI) is used to develop a method to estimate the surface LWC for a given snow pack. The derived NDWI values with respect to the known LWC in snow show that the NDWI is sensitive to the LWC in snow and that the NDWI and LWC are directly proportional. Based on this information, the NDWI is used to estimate the surface LWC in snow from measurements on a ski track using the Road eye sensor. The findings suggest that the presented method can be applied to estimate the surface LWC in order to classify snow conditions potentially for ski track and piste applications.

Place, publisher, year, edition, pages
American Society of Civil Engineers (ASCE), 2018
National Category
Oceanography, Hydrology and Water Resources Applied Mechanics Geotechnical Engineering
Research subject
Experimental Mechanics; Soil Mechanics
Identifiers
urn:nbn:se:ltu:diva-65754 (URN)10.1061/(ASCE)CR.1943-5495.0000158 (DOI)000428257200013 ()2-s2.0-85040254798 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-02-05 (rokbeg)

Available from: 2017-09-21 Created: 2017-09-21 Last updated: 2018-04-25Bibliographically approved
Eppanapelli, L. K. (2018). Experimental investigation of snow metamorphism at near-surface layers. (Doctoral dissertation). Luleå: Luleå University of Technology
Open this publication in new window or tab >>Experimental investigation of snow metamorphism at near-surface layers
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Experimentell undersökning avsnömetamorfismen vid nära ytskikt
Abstract [en]

Snow metamorphism is a direct objective in many snow research areas, and its charac-terisation is a major challenge in areas including winter road maintenance, detection of icing on wind turbine blades, and snow quality mapping for skiing. A common effect of snow metamorphism is compaction, which can be investigated from the associated vari-ations in physical properties of snow. While the relation between snow metamorphism and physical properties of snow is fairly well-known, a method to quantify this relationis not extensively researched. This experimental based thesis focuses on the relationship between the physical properties of snow and its degree of metamorphism. The link isestablished and investigated by quantifying near-infrared (NIR) reflectance measure-ments and analysing the microtomographic data. Three experimental approaches are developed to record the NIR reflectance measurements and to understand the influence of compaction at near-surface layers of a snowpack. In addition, an X-ray microtomogra-phy (micro-CT) system is used to visualise the behaviour of snow microstructure during compaction. In this thesis, snow experienced compaction via aging, the melting-freezing process, uniaxial loading, settling and infiltration of liquid water.

A numerical tool based on the well-established Discrete Ordinates Radiative Trans-fer (DISORT) method is used to solve the radiative transfer equation (RTE) for aplane-parallel and semi-infinite snowpack. The numerical solver takes the reflectance measurements as input and returns the coefficients of a first order Legendre phase function of an investigated snowpack at a given wavelength of light. The results from the solver show consistency and strong correlation between the Legendre coefficient sand the physical properties of snow. Furthermore, the physical properties of snow such as specific surface area (SSA) and liquid water content (LWC) were estimated via parameterisation where the reflectance data is used as input. The results suggest that the parameterisation of LWC can provide a qualitative estimate of the LWC in a snowpack, while the parameterisation of SSA provides a quantitative estimate of the snow SSA. As a next step, the influence of compaction on snow microstructure is investigated from three-dimensional (3D) images obtained using the micro-CT system. In this case, compaction is initiated by applying uniaxial load on a snow sample and the effect of compaction is analysed based on digital volume correlation (DVC) and porosity distribution. The micro-CT observations further emphasise that near-surface layers of a snowpack experience a higher degree of impact during compaction.

In summary, this thesis presents experimental methods to quantify the link between snow compaction at near-surface layers, and the physical properties of snow. The mode observations show that the estimated Legendre coefficients can provide qualitative descriptions of snow grain distribution and surface texture. The parameterisation methods can provide the details about the LWC and the SSA of a snowpack. Further, the observations from the micro-CT study suggest that grains breakage and recrystallisation are the prevailing effects of snow compaction. All observations in this thesis are helpful in understanding the metamorphism in a snowpack for relevant research areas.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2018
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords
Snow metamorphism, NIR reflectance, Snow and Ice, Radiative transfer equation, Paramterization, Snow properties, Tomography, Snow microstructure
National Category
Other Materials Engineering Applied Mechanics
Research subject
Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-68394 (URN)978-91-7790-116-7 (ISBN)978-91-7790-117-4 (ISBN)
Public defence
2018-06-18, E 231, Luleå University of Technology, Luleå, 14:00 (English)
Opponent
Supervisors
Available from: 2018-04-18 Created: 2018-04-18 Last updated: 2019-09-11Bibliographically approved
Eppanapelli, L. K., Casselgren, J., Wåhlin, J. & Sjödahl, M. (2017). Investigation of snow single scattering properties of snow based on first order Legendre phase function. Optics and lasers in engineering, 91, 151-159
Open this publication in new window or tab >>Investigation of snow single scattering properties of snow based on first order Legendre phase function
2017 (English)In: Optics and lasers in engineering, ISSN 0143-8166, E-ISSN 1873-0302, Vol. 91, p. 151-159Article in journal (Refereed) Published
Abstract [en]

Angularly resolved bidirectional reflectance measurements were modelled by ap- proximating a first order Legendre expanded phase function to retrieve single scattering properties of snow. The measurements from 10 different snow types with known density and specific surface area (SSA) were investigated. A near infrared (NIR) spectrometer was used to measure reflected light above the snow surface over the hemisphere in the wavelength region 900 nm to 1650 nm. A solver based on discrete ordinate radiative transfer (DISORT) model was used to retrieve the estimated Legendre coefficients of the phase function and a cor- relation between the coefficients and physical properties of different snow types is investigated. Results of this study suggest that the first two coefficients of the first order Legendre phase function provide sufficient information about the physical properties of snow where the latter captures the anisotropic behaviour of snow and the former provides a relative estimate of the single scattering albedo of snow. The coefficients of the first order phase function were com- pared with the experimental data and observed that both the coefficients are in good agreement with the experimental data. These findings suggest that our approach can be applied as a qualitative tool to investigate physical properties of snow and also to classify different snow types.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
Radiative transfer model, Scattering phase function, Snow, SSA, Reflectance spectrum, NIR spectrometer
National Category
Applied Mechanics
Research subject
Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-60532 (URN)10.1016/j.optlaseng.2016.11.013 (DOI)000393264300016 ()2-s2.0-84998694302 (Scopus ID)
Note

Validerad; 2017; Nivå 2; 2016-12-19 (andbra)

Available from: 2016-11-18 Created: 2016-11-18 Last updated: 2018-09-13Bibliographically approved
Eppanapelli, L. K. (2016). Classification of different types of snow using spectral and angular imaging. (Licentiate dissertation). Luleå: Luleå University of Technology
Open this publication in new window or tab >>Classification of different types of snow using spectral and angular imaging
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
Alternative title[sv]
Klassificering av snö med hjälp av spektral och vinkel avbildning
Abstract [en]

The current thesis work details a non-contact detection approach concerningclassification of snow with different physical properties such as grain size, densityand specific surface area (SSA). In this approach, reflected light from snowsurfaces is measured as a function of wavelength and viewing geometry. Essentiallya detector (either a near-infrared (NIR) camera or a spectrometer) and anillumination source are needed to measure the spectrally and angularly resolvedbidirectional reflectance from snow. Classification of snow types is performedbased on the absorption and scattering properties of a respective snow type. Itis furthermore known that snow properties can be modelled using a numericalsolver where the radiative transfer equation (RTE) for snow is solved and ascattering phase function is estimated by expanding into a series of Legendrecoefficients. It is therefore expected to be a connection between snow characteristicsand the Legendre coefficients of the scattering phase function.

Results suggest that different snow types can be classified using two wavelengths(980 nm, 1310 nm) from the high reflectance region and one wavelength(1550 nm) from the high absorption region. It is also observed that thebidirectional reflectance for snow tends to increase in specular direction (antiilluminationdirection) as snow density increases. Results from the numericalmethod suggest that the first coefficient of the Legendre phase function is arelative estimate of the single scattering albedo rather than an absolute estimateand that the second coefficient estimates the anisotropy of a respectivesnow type. Investigations in this thesis suggest that the presented approachcan be used as a tool to classify different snow types in various applicationssuch as icing on wind turbine blades, winter roads maintenance and ski tracksmaintenance.v

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2016
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
National Category
Applied Mechanics
Research subject
Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-59817 (URN)978-91-7583-729-1 (ISBN)978-91-7583-730-7 (ISBN)
Presentation
2016-12-15, E243, Luleå University of Technology, Luleå, 10:00
Available from: 2016-10-18 Created: 2016-10-18 Last updated: 2017-11-24Bibliographically approved
Eppanapelli, L. K., Friberg, B., Casselgren, J. & Sjödahl, M. (2016). Estimation of a low-order Legendre expanded phase function of snow (ed.). Paper presented at . Optics and lasers in engineering, 78, 174-181
Open this publication in new window or tab >>Estimation of a low-order Legendre expanded phase function of snow
2016 (English)In: Optics and lasers in engineering, ISSN 0143-8166, E-ISSN 1873-0302, Vol. 78, p. 174-181Article in journal (Refereed) Published
Abstract [en]

The purpose of this paper is to estimate the scattering phase function of snow from angularly resolved measurements of light intensity in the plane of incidence. A solver is implemented that solves the scattering function for a semi-infinite geometry based on the radiative transfer equation (RTE). Two types of phase functions are considered. The first type is the general phase function based on a low-order series expansion of Legendre polynomials and the other type is the Henyey-Greenstein (HG) phase function. The measurements were performed at a wavelength of 1310 nm and six different snow samples were analysed. It was found that a first order expansion provides sufficient approximation to the measurements. The fit from the first order phase function outperforms that of the HG phase function in terms of accuracy, ease of implementation and computation time. Furthermore, a correlation between the magnitude of the first order component and the age of the snow was found. We believe that these findings may complement present non-contact detection techniques used to determine snow properties.

National Category
Applied Mechanics
Research subject
Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-4108 (URN)10.1016/j.optlaseng.2015.10.013 (DOI)000366769100021 ()2-s2.0-84947210191 (Scopus ID)1fb86369-88ce-4293-89e3-fae4460cfe1f (Local ID)1fb86369-88ce-4293-89e3-fae4460cfe1f (Archive number)1fb86369-88ce-4293-89e3-fae4460cfe1f (OAI)
Note
Validerad; 2015; Nivå 2; 20151028 (lavepp)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-5943-1476

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