Utilizing Hyperspectral Remote Sensing for Soil Gradation
Soil gradation is an important characteristic for soil mechanics. Traditionally soil gradation is performed by sieve analysis using a sample from the field. In this research, we are interested in the application of hyperspectral remote sensing to characterize soil gradation. The specific objective o...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-10-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/20/3312 |
| _version_ | 1797551274390978560 |
|---|---|
| author | Jordan Ewing Thomas Oommen Paramsothy Jayakumar Russell Alger |
| author_facet | Jordan Ewing Thomas Oommen Paramsothy Jayakumar Russell Alger |
| author_sort | Jordan Ewing |
| collection | DOAJ |
| description | Soil gradation is an important characteristic for soil mechanics. Traditionally soil gradation is performed by sieve analysis using a sample from the field. In this research, we are interested in the application of hyperspectral remote sensing to characterize soil gradation. The specific objective of this work is to explore the application of hyperspectral remote sensing to be used as an alternative to traditional soil gradation estimation. The advantage of such an approach is that it would provide the soil gradation without having to obtain a field sample. This work will examine five different soil types from the Keweenaw Research Center within a laboratory-controlled environment for testing. Our study demonstrates a correlation between hyperspectral data, the percent gravel and sand composition of the soil. Using this correlation, one can predict the percent gravel and sand within a soil and, in turn, calculate the remaining percent of fine particles. This information can be vital to help identify the soil type, soil strength, permeability/hydraulic conductivity, and other properties that are correlated to the gradation of the soil. |
| first_indexed | 2024-03-10T15:42:14Z |
| format | Article |
| id | doaj.art-4803b28d12ef443f9a6794dd4861ca5e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T15:42:14Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4803b28d12ef443f9a6794dd4861ca5e2023-11-20T16:42:49ZengMDPI AGRemote Sensing2072-42922020-10-011220331210.3390/rs12203312Utilizing Hyperspectral Remote Sensing for Soil GradationJordan Ewing0Thomas Oommen1Paramsothy Jayakumar2Russell Alger3Department of Computational Science and Engineering, Michigan Technological University, Houghton, MI 49931, USADepartment of Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, MI 49931, USAU.S. Army CCDC Ground Vehicle Systems Center, Warren, MI 48092, USAThe Institute of Snow Research, Keweenaw Research Center, Calumet, MI 49913, USASoil gradation is an important characteristic for soil mechanics. Traditionally soil gradation is performed by sieve analysis using a sample from the field. In this research, we are interested in the application of hyperspectral remote sensing to characterize soil gradation. The specific objective of this work is to explore the application of hyperspectral remote sensing to be used as an alternative to traditional soil gradation estimation. The advantage of such an approach is that it would provide the soil gradation without having to obtain a field sample. This work will examine five different soil types from the Keweenaw Research Center within a laboratory-controlled environment for testing. Our study demonstrates a correlation between hyperspectral data, the percent gravel and sand composition of the soil. Using this correlation, one can predict the percent gravel and sand within a soil and, in turn, calculate the remaining percent of fine particles. This information can be vital to help identify the soil type, soil strength, permeability/hydraulic conductivity, and other properties that are correlated to the gradation of the soil.https://www.mdpi.com/2072-4292/12/20/3312soil mechanicsspectral analysissoil classification indexUSCSterramechanics |
| spellingShingle | Jordan Ewing Thomas Oommen Paramsothy Jayakumar Russell Alger Utilizing Hyperspectral Remote Sensing for Soil Gradation Remote Sensing soil mechanics spectral analysis soil classification index USCS terramechanics |
| title | Utilizing Hyperspectral Remote Sensing for Soil Gradation |
| title_full | Utilizing Hyperspectral Remote Sensing for Soil Gradation |
| title_fullStr | Utilizing Hyperspectral Remote Sensing for Soil Gradation |
| title_full_unstemmed | Utilizing Hyperspectral Remote Sensing for Soil Gradation |
| title_short | Utilizing Hyperspectral Remote Sensing for Soil Gradation |
| title_sort | utilizing hyperspectral remote sensing for soil gradation |
| topic | soil mechanics spectral analysis soil classification index USCS terramechanics |
| url | https://www.mdpi.com/2072-4292/12/20/3312 |
| work_keys_str_mv | AT jordanewing utilizinghyperspectralremotesensingforsoilgradation AT thomasoommen utilizinghyperspectralremotesensingforsoilgradation AT paramsothyjayakumar utilizinghyperspectralremotesensingforsoilgradation AT russellalger utilizinghyperspectralremotesensingforsoilgradation |