| Issue |
E3S Web Conf.
Volume 310, 2021
Annual International Scientific Conference “Spatial Data: Science, Research and Technology 2021”
|
|
|---|---|---|
| Article Number | 05001 | |
| Number of page(s) | 8 | |
| Section | Monitoring of Land, Natural Resources and Emergencies | |
| DOI | https://doi.org/10.1051/e3sconf/202131005001 | |
| Published online | 15 October 2021 | |
A comparison of mono-seasonal and multiseasonal Landsat images for vegetation cover classification in the Mediterranean region: a case study in Latakia, Syria
1
Moscow State University of Geodesy and Cartography, Moscow 105064, Russia
2
Tishreen University, Lattakia, Syria
* Corresponding author: syriaheart@live.com
Providing constantly updated information on vegetation serves as a basis for studies of natural resources and ecological issues. This paper discusses the question related to an appropriate season(s) for classification vegetation cover in the Mediterranean region and detecting its changes using Landsat imagery. Autumn, spring, and multi-seasonal satellite images, captured in 2017, were used to classify vegetation cover in a part of the Lattakia province, Syria. The satellite images were classified using the random forest algorithm, and high spatial resolution satellite images Google Earth Pro were used as reference data. The results indicate better effectiveness of the autumn images over spring ones for vegetation cover classification with 73.6% and 62.4% overall accuracy, respectively. In addition, a comparison of autumn and multi-seasonal Landsat images indicates no significant statistical difference in the accuracy of vegetation cover classification at the significance level of 0.05, which illustrates the effectiveness of using autumn images to classify the vegetation cover of the Mediterranean region. Furthermore, the obtained results show the necessity of using additional features as the spectral channels may not be sufficient for mapping vegetation cover in the Mediterranean region with high accuracy.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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