Statistical Correlation between Land Surface Temperature (LST) and Vegetation Index (NDVI) using Multi-Temporal Landsat TM Data
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Statistical Correlation between Land Surface Temperature (LST) and Vegetation Index (NDVI) using Multi-Temporal Landsat TM Data |
| 2. | Creator | Author's name, affiliation, country | Anbazhagan S.; Centre for Geoinformatics & Planetary Studies, Dept. of Geology, Periyar University, Salem, Tamilnadu, India |
| 2. | Creator | Author's name, affiliation, country | Paramasivam C.R.; Centre for Geoinformatics & Planetary Studies, Dept. of Geology, Periyar University, Salem, Tamilnadu, India |
| 3. | Subject | Discipline(s) | Geological Remote Sensing |
| 3. | Subject | Keyword(s) | LST; NDVI; Remote Sensing; Image Processing; Regression |
| 3. | Subject | Subject classification | LST and NDVI |
| 4. | Description | Abstract |
Remote sensing TIR as a part of the electromagnetic spectrum is one of the best observations of Land surface temperature (LST). Our earth contains heterogeneous land feature and it is composed of a variety of materials. Now-a-days there are many remote sensing satellites that provide thermal data. In the present study, the Landsat TM data with multi temporal periods such as 1992, 2001 and 2010 were utilized to study LST in the mining area. The aim of the study is to prepare LST mapping for estimating land surface temperature from multi temporal Landsat TM thermal bands and compare with the associated phenomenal condition. The Landsat 5 TM data of 11-03-1992, 09-02-2010 and Landsat 7 data of 15-05-2001 were used and the land surface emissivity’s for these particular periods were estimated. The Normalized Difference Vegetation Index (NDVI) was calculated in the same period. The image processing was adopted using the ENVI 4.7 software. The results of emissivity and vegetation index interpreted for each period indicated that when the emissivity increases vegetation index shows negative anomaly. The standardization of error coefficient was founded with the aid of statistical software SPSS, which strengthened the approach of the study. The statistical regression analysis of NDVI and LST were shown in Standardized regression coefficient (B) value as -0.209, -0.143 and -0.190 in the years of 1992, 2001 and 2010 respectively. Comparison of LST and its associated constraint will precisely indicate that these variables are mutually important. Remote sensing multi temporal satellite data when coupled with an image processing technique will support for estimate land surface temperature, normalized vegetation index and preferably utilized for an empirical data report from the Indian Meteorological Department (IMD) ground based observation data. |
| 5. | Publisher | Organizing agency, location | |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2016-04-12 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | remote Sensing Multi temporal Satellite Data |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | http://scientific.cloud-journals.com/index.php/IJAESE/article/view/Sci-409 |
| 11. | Source | Journal/conference title; vol., no. (year) | International Journal of Advanced Earth Science and Engineering; Volume 5 (Year 2016) |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | magnesite mining region located in parts of Salem, India, Land Surface Temperature,Vegetation |
| 15. | Rights | Copyright and permissions |
Copyright Terms & Conditions Authors who publish with this journal agree to the following terms: a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work Cloud Publications reserves the right to amend/change the copyright policy; with/without notice.
|