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Shahfahad, S, Naikoo, MW, Rahman, A, Gagnon, A, Islam, ARMT, Mosavi, A and Talukdar, S (2023) Comparative Evaluation of Operational Land Imager sensor on board Landsat 8 and Landsat 9 for Land use Land Cover Mapping over a Heterogeneous Landscape. Geocarto International (TGEI), 38 (1). ISSN 1010-6049
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Comparative evaluation of operational land imager sensor on board landsat 8 and landsat 9 for land use land cover mapping over a heterogeneous (1).pdf - Published Version Available under License Creative Commons Attribution. Download (9MB) | Preview |
Abstract
Since its advent in 1972, the Landsat satellites have witnessed consistent improvements in sensor characteristics, which have significantly improved accuracy. In this study, a comparison of the accuracy of Landsat OLI and OLI-2 satellites in land use land cover (LULC) mapping has been made. For this, image fusion techniques have been applied to enhance the spatial resolution of both OLI and OLI-2 multispectral images, and then a support vector machine (SVM) classifier has been used for LULC mapping. The results show that LULC classification from OLI-2 has better accuracy (83.4%) than OLI (92.4%). The validation of classified LULC maps shows that the OLI-2 data is more accurate in distinguishing dense and sparse vegetation as well as darker and lighter objects. The relationship between LULC maps and surface biophysical parameters using Local Moran’s I also shows better performance of the OLI-2 sensor in LULC mapping than the OLI sensor.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Landsat datasets; Land use land cover; Surface biophysical parameters; Moran's I; Support vector machine; 0909 Geomatic Engineering; Geological & Geomatics Engineering |
| Subjects: | G Geography. Anthropology. Recreation > GE Environmental Sciences |
| Divisions: | Biological & Environmental Sciences (from Sep 19) |
| Publisher: | Taylor & Francis |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 30 Nov 2022 11:23 |
| Last Modified: | 23 May 2023 09:45 |
| DOI or ID number: | 10.1080/10106049.2022.2152496 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/18227 |
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