Detection and analysis of vegetation cover changes in the city of M'Sila, Algeria, between the years 1990-2023 using the NDVI (Normalized Difference Vegetation Index)

Authors

  • Mohamed Soufiane Dogha Institute of Urban Techniques Management, University of M'sila, Laboratory of City, Environment, Hydraulics and Sustainable Development, PO Box 166 Ichebilia, 28000 M’sila, Algeria https://orcid.org/0009-0006-4708-9657
  • Bachir Faid Institute of Urban Techniques Management, University of M'sila, Laboratory of City, Environment, Hydraulics and Sustainable Development, PO Box 166 Ichebilia, 28000 M’sila, Algeria

DOI:

https://doi.org/10.15243/jdmlm.2025.123.7501

Keywords:

change in vegetation cover , M'Sila city , NDVI, remote sensing

Abstract

This study aimed to monitor and evaluate the change in the vegetation cover of the city of M'Sila based on calculating the Normalized Difference Vegetation Index (NDVI) linked to Landsat satellite image data for the years 1990-2023 and measuring the change for approximately every 10 years. The region was classified based on the index values ??into two categories: an area free of vegetation cover and an area containing vegetation cover. The results showed a difference in the area and density of vegetation cover by increasing the area of ??vegetation cover at different rates of change according to the three periods, respectively 45.98%, 6.66%, and 6.87%, which led to an increase in the area classified as vegetation cover according to the three periods 1.89 km2, 0.4 km2, 0.44 km2, with an average annual change estimated at 0.19 km2/year, 0.04 km2/year, 0.03 km2/year, where the percentage of vegetation cover in 1990 was about 8.70% of the total area of ??the city, while in 2023 it increased to record a percentage of 14.48%. This study showed the possibility of using remote sensing techniques and geographic information systems to provide valuable basic spatial information to support monitoring of vegetation cover, identifying areas exposed to environmental risks and threats, and thus developing strategies for adaptation and conservation of the ecosystems and natural resources of the study area.

References

Alawamy, J.S., Balasundram, S.K., Mohd. Hanif, A.H. and Sung, C.T.B. 2020. Detecting and analyzing land use and land cover changes in the region of Al-Jabal Al-Akhdar, Libya, using time-series Landsat data from 1985 to 2017. Sustainability 12(11):4490. https://doi.org/10.3390/su12114490

Awoniran, D.R., Olugbamila, O.B. and Omisore, E.O. 2020. Spatio-temporal analysis of the practice of urban agriculture in Lagos Metropolis and the implications for urban planning. Analele Universit??ii din Oradea, Seria Geografie 30(1):76-87. https://doi.org/10.30892/auog.301109-819

Daramola, A. and Ibem, E.O. 2010. Urban environmental problems in Nigeria: Implications for sustainable development. Journal of Sustainable Development in Africa 12(1):124-145.

Foley, J.A., DeFries, R., Asner, G.P., Barford, C., Bonan, G., Carpenter, S.R., Chapin III, F.S., Coe, M.T., Daily, G.C., Gibbs, H., Helkowski, J.H., Holloway, T., Howard, E., Kucharik, C.J., Monfreda, C., Patz, J., Prentice, I.C., Ramankutty, N. and Snyder, P.K. 2005. Global consequences of land use. Science 309(5734):570-574. https://doi.org/10.1126/science.1111772

Gallego, F.J., Kussul, N., Skakun, S., Kravchenko, O., Shelestov, A. and Kussul, O. 2014. Efficiency assessment of using satellite data for crop area estimation in Ukraine. International Journal of Applied Earth Observation and Geoinformation 29:22-30. https://doi.org/10.1016/j.jag.2013.12.013

Gann, G.D., McDonald, T., Walder, B., Aronson, J., Nelson, C.R., Jonson, J., Hallett, J.G., Eisenberg, C., Guariguata, M.R., Liu, J., Hua, F., Echeverria, C., Gonzales, E., Shaw, N., Decleer, K. and Dixon, K. 2019. International principles and standards for the practice of ecological restoration. Restoration Ecology 27(S1):S1-S46. https://doi.org/10.1111/rec.13035

Gao, L., Wang, X., Johnson, B.A., Tian, Q., Wang, Y., Verrelst, J., Mui, X. and Gu, X. 2020. Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review. ISPRS Journal of Photogrammetry and Remote Sensing 159:364-377. https://doi.org/10.1016/j.isprsjprs.2019.11.018

Garcia-Pardo, K.A., Moreno-Rangel, D., Dominguez-Amarillo, S. and Garcia-Chavez, J.R. 2022. Remote sensing for the assessment of ecosystem services provided by urban vegetation: A review of the methods applied. Urban Forestry & Urban Greening 74:127636. https://doi.org/10.1016/j.ufug.2022.127636

Hailu, F. 2023. Climate change as a trigger for desertification and possible alternatives to reduce biodiversity loss. Journal of the Selva Andina Biosphere 11(1):94-111. https://doi.org/10.36610/j.jsab.2023.110100091

Hashim, H., Latif, Z.A. and Adnan, N.A. 2019. Urban vegetation classification with NDVI threshold value method with very high resolution (VHR) Pleiades imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42:237-240. https://doi.org/10.5194/isprs-archives-XLII-4-W16-237-2019

Hussain, S., Mubeen, M. and Karuppannan, S. 2022. Land use and land cover (LULC) change analysis using TM, ETM+ and OLI Landsat images in district of Okara, Punjab, Pakistan. Physics and Chemistry of the Earth, Parts a/b/c 126:103117. https://doi.org/10.1016/j.pce.2022.103117

Jim, C.Y. 2004. Green-space preservation and allocation for sustainable greening of compact cities. Cities 21(4):311-320. https://doi.org/10.1016/j.cities.2004.04.004

Kadri, D. and Atmani, A. 2023. The contribution of planning and urbanization tools to managing the risk of flooding, the case of the urban expansion area in the city of M'sila, Algeria. Analel Universitatii din Oradea, Seria Geografie 33(2):125-134. https://doi.org/10.30892/auog.332104-914

Kpiebaya, P., Osei-Mensah, K., Ebenezer, A., Abdul-Ganiyu, S., Baatuuwie, B., Avornyo, V., Dekongmen, B.W., Blege, P.W. and Sogfaa, D. 2022. Comparative analysis of classification algorithms in ArcGIS and QGIS: An emerging evidence in groundwater potential assessment. 2nd International Conference on Environment, Technology, and Management, At: Nigde/Turkiye.

Kumar, U. 2006. Comparative evaluation of the algorithms for land cover mapping using hyperspectral data. MSc thesis, ITC, Faculty of Geo-information Science and Earth Observation. University of Twente Student Theses.

Lasaponara, R. and Masini, N. 2011. Satellite remote sensing in archaeology: Past, present and future perspectives. Journal of Archaeological Science 38(9):1995-2002. https://doi.org/10.1016/j.jas.2011.02.002

Lemenkova, P. and Debeir, O. 2022. Satellite image processing by Python and R using Landsat 9 OLI/TIRS and SRTM DEM data on Côte d’Ivoire, West Africa. Journal of Imaging 8(12):317. https://doi.org/10.3390/jimaging8120317

Li, S., Xu, L., Jing, Y., Yin, H., Li, X. and Guan, X. 2021. High-quality vegetation index product generation: A review of NDVI time series reconstruction techniques. International Journal of Applied Earth Observation and Geoinformation 105:102640. https://doi.org/10.1016/j.jag.2021.102640

Lopez-Mosquera, N. and Sanchez, M. 2011. The influence of personal values in the economic-use valuation of peri-urban green spaces: An application of the means-end chain theory. Tourism Management 32(4):875-889. https://doi.org/10.1016/j.tourman.2010.08.003

Lunetta, R.S., Knight, J.F., Ediriwickrema, J., Lyon, J.G. and Worthy, L.D. 2022. Land-cover change detection using multi-temporal MODIS NDVI data. In: Geospatial Information Handbook for Water Resources and Watershed Management, Volume II (pp. 65-88). CRC Press. https://doi.org/10.1201/9781003175025-5

Merem, E.C., Twumasi, Y., Wesley, J., Alsarari, M., Fageir, S., Crisler, M., Romorno, C., Olagbegi, D., Hines, A., Mwakimi, O.S., Nwagboso, E., Leggett, S.S., Foster, D., Purry, V. and Washington, J. 2019. Analyzing land use and change detection in Eastern Nigeria using GIS and remote sensing. American Journal of Geographic Information System 8(2):103-117. https://doi.org/10.5923/j.ajgis.20190802.06

Ramanathan, V. and Feng, Y. 2009. Air pollution, greenhouse gases, and climate change: Global and regional perspectives. Atmospheric Environment 43(1):37-50. https://doi.org/10.1016/j.atmosenv.2008.09.063

Saliha, A. 2024. Detection changes and analysis of urban sprawl in M’sila City (Algeria): Contribution of spatial remote sensing and GIS. International Journal of Innovative Technologies in Social Science 4(44). https://doi.org/10.31435/ijitss.4(44).2024.2947

Singh, R.L. and Singh, P.K. 2017. Global Environmental Problems. Principles and Applications of Environmental Biotechnology for a Sustainable Future, 13-41, Springer Nature. https://doi.org/10.1007/978-981-10-1866-4_2

Thakur, J.K., Singh, S.K. and Ekanthalu, V.S. 2017. Integrating remote sensing, geographic information systems, and global positioning system techniques with hydrological modeling. Applied Water Science 7(4):1595-1608. https://doi.org/10.1007/s13201-016-0384-5

Vogelmann, J.E., Xian, G., Homer, C. and Tolk, B. 2012. Monitoring gradual ecosystem change using Landsat time series analyses: Case studies in selected forest and rangeland ecosystems. Remote Sensing of Environment 122:92-105. https://doi.org/10.1016/j.rse.2011.06.027

Xie, Y., Sha, Z. and Yu, M. 2008. Remote sensing imagery in vegetation mapping: a review. Journal of Plant Ecology 1(1):9-23. https://doi.org/10.1093/jpe/rtm005

Xiong, G., Cao, X., Hamm, N.A., Lin, T., Zhang, G. and Chen, B. 2021. Unbalanced development characteristics and driving mechanisms of regional urban spatial form: a case study of Jiangsu Province, China. Sustainability 13(6):3121. https://doi.org/10.3390/su13063121

Xue, J. and Su, B. 2017. Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors 2017(1):1353691. https://doi.org/10.1155/2017/1353691

Zahoor, Z., Latif, M.I., Khan, I. and Hou, F. 2022. Abundance of natural resources and environmental sustainability: the roles of manufacturing value-added, urbanization, and permanent cropland. Environmental Science and Pollution Research 29(54):82365-82378. https://doi.org/10.1007/s11356-022-21545-8

Zhu, Z. and Woodcock, C.E. 2014. Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment 144:152. https://doi.org/10.1016/j.rse.2014.01.011

Downloads

Submitted

31-12-2024

Accepted

09-02-2025

Published

01-04-2025

How to Cite

Dogha, M. S., & Faid, B. (2025). Detection and analysis of vegetation cover changes in the city of M’Sila, Algeria, between the years 1990-2023 using the NDVI (Normalized Difference Vegetation Index). Journal of Degraded and Mining Lands Management, 12(3), 7501–7508. https://doi.org/10.15243/jdmlm.2025.123.7501

Issue

Vol. 12 No. 3 (2025)

Section

Research Article

License

Copyright (c) 2025 Journal of Degraded and Mining Lands Management

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Submission of a manuscript implies: that the work described has not been published before (except in the form of an abstract or as part of a published lecture, or thesis) that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Scientific Journal by Eko Handayanto is licensed under a Creative Commons Attribution 4.0 International License.
Based on a work at https://ub.ac.id.
Permissions beyond the scope of this license may be available at https://ircmedmind.ub.ac.id/.