Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)

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Improved Vegetation Cover Classification Using Remote Sensing Images and Spectral Indices: Case Study of Mecheria in Southwestern Algeria

Authors
Nezha Farhi1, 2, *, Li Shuai2, Sarah Kreri1
1Agence Spatiale Algérienne, Centre des Techniques Spatiales, Arzew, Algeria
2Beihang University, Beijing University of Aeronautics and Astronautics), Beijing, China
*Corresponding author. Email: farhinezha@gmail.com
Corresponding Author
Nezha Farhi
Available Online 31 August 2024.
DOI
10.2991/978-94-6463-496-9_7How to use a DOI?
Keywords
K-Harmonic Means; Vegetation Indices; Automatic Classification; Landsat Images; Indices Correlation
Abstract

Environmental issues like deforestation are major challenges in the context of dry regions. To characterize this topic, we propose a new algorithm based on the unsupervised K-Harmonic Means classification algorithm and vegetation indices (VIs). The purpose is to optimize vegetation cover information mapping using Landsat images. The region of Mecheria in South-Western Algeria, classified as a semi-arid to arid area, is selected for experimentations. Moreover, two dates 1987 and 2019 are considered for a better assessment of the results.

The proposed methodology integrates multiple vegetation indices regarding their ability to extract vegetation covers in dry climate conditions. The classes presenting the highest correlation ratio are then combined in a quick yet ingenious way creating the final vegetation area.

The new combination technique, inspired from clustering ensembles algorithms, shows an average improvement in accuracy of 16.55% and 29.15% respectively for 1987 and 2019 classification results. These values were computed using confusion matrices. An additional assessment is conducted comparing the proposed methodology with established combination techniques using multiple criteria.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
Series
Advances in Intelligent Systems Research
Publication Date
31 August 2024
ISBN
978-94-6463-496-9
ISSN
1951-6851
DOI
10.2991/978-94-6463-496-9_7How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Nezha Farhi
AU  - Li Shuai
AU  - Sarah Kreri
PY  - 2024
DA  - 2024/08/31
TI  - Improved Vegetation Cover Classification Using Remote Sensing Images and Spectral Indices: Case Study of Mecheria in Southwestern Algeria
BT  - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
PB  - Atlantis Press
SP  - 73
EP  - 88
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-496-9_7
DO  - 10.2991/978-94-6463-496-9_7
ID  - Farhi2024
ER  -