Hyperspectral Remote Sensing in Characterizing Soil Salinity Severity using SVM Technique - A Case Study of Alluvial Plains
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Hyperspectral Remote Sensing in Characterizing Soil Salinity Severity using SVM Technique - A Case Study of Alluvial Plains |
| 2. | Creator | Author's name, affiliation, country | Justin George K.; Agriculture and Soils Department, Indian Institute of Remote Sensing, ISRO, Dehradun, Uttarakhand, India |
| 2. | Creator | Author's name, affiliation, country | Suresh Kumar; Agriculture and Soils Department, Indian Institute of Remote Sensing, ISRO, Dehradun, Uttarakhand, India |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | Hyperion Hyperspectral Indices; Indo-Gangetic Plains; Salt Salinity Severity; Support Vector Machine (SVM) |
| 3. | Subject | Subject classification | Soil salinity |
| 4. | Description | Abstract |
Hyperspectral remote sensing is widely used for analyzing and estimating the severity of soil salinity in arid and semi-arid regions, throughout the world. The present study is an attempt to map the various soil salinity severity classes using different hyperspectral indices generated using EO-1 Hyperion data and Support Vector Machine (SVM) method, in the Mathura region of Indo-Gangetic plain of India. Various hyperspectral indices such as Soil Adjusted Vegetation Index (SAVI), Desertification Soil Index (DSI), Salinity Index (SI) and Normalized Difference Water Index (NDWI) were chosen, generated and effectively used for characterizing and mapping soil salinity severity. Salt infestation in the study area was categorized into four classes of normal, slight, moderate, high soil salinity. Hyperspectral indices helped in identification of various features like vegetation, waterlogged area and soil areas under various classes of soil salinity. The salinity index and desertification soil indices were found to respond well to varying degrees of soil salinity. The SVM technique generated soil salinity map with overall classification accuracy of 78.13 percent, with a kappa statistic of 0.71. The results indicated highest accuracy in high soil salinity class in comparison to other classes, attaining producers and users accuracies of 85.71% and 90.0% respectively. Slight saline class showed poor producers and users accuracy. The result showed high accuracy for mapping soil salinity severity with machine learning classifier like SVM using various indices generated from hyperspectral remote sensing data. These generated images can be effectively used in planning of various management practices and effective reclamation measures of salt affected soils. |
| 5. | Publisher | Organizing agency, location | |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2015-10-23 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | http://technical.cloud-journals.com/index.php/IJARSG/article/view/Tech-461 |
| 11. | Source | Journal/conference title; vol., no. (year) | International Journal of Advanced Remote Sensing and GIS; Volume 4 (Year 2015) |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
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