Full Length Article

Assessment of vegetation cover changes and the contributing factors in the Al-Ahsa Oasis using Normalized Difference Vegetation Index (NDVI)

  • Walid CHOUARI
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  • aDepartment of Geography, College of Arts, King Faisal University, Al-Ahsa, 31982, Saudi Arabia
    bLaboratory Syfacte, Faculty of Arts and Humanities of Sfax, University of Sfax, Sfax, 1168-3000, Tunisia
*E-mail address: wchouari@kfu.edu.sa (Walid CHOUARI).

Received date: 2023-03-18

  Accepted date: 2024-02-28

  Online published: 2024-04-30

Abstract

The abandonment of date palm grove of the former Al-Ahsa Oasis in the eastern region of Saudi Arabia has resulted in the conversion of delicate agricultural area into urban area. The current state of the oasis is influenced by both expansion and degradation factors. Therefore, it is important to study the spatiotemporal variation of vegetation cover for the sustainable management of oasis resources. This study used Landsat satellite images in 1987, 2002, and 2021 to monitor the spatiotemporal variation of vegetation cover in the Al-Ahsa Oasis, applied multi-temporal Normalized Difference Vegetation Index (NDVI) data spanning from 1987 to 2021 to assess environmental and spatiotemporal variations that have occurred in the Al-Ahsa Oasis, and investigated the factors influencing these variation. This study reveals that there is a significant improvement in the ecological environment of the oasis during 1987-2021, with increase of NDVI values being higher than 0.10. In 2021, the highest NDVI value is generally above 0.70, while the lowest value remains largely unchanged. However, there is a remarkable increase in NDVI values between 0.20 and 0.30. The area of low NDVI values (0.00-0.20) has remained almost stable, but the region with high NDVI values (above 0.70) expands during 1987-2021. Furthermore, this study finds that in 1987-2002, the increase of vegetation cover is most notable in the northern region of the study area, whereas from 2002 to 2021, the increase of vegetation cover is mainly concentrated in the northern and southern regions of the study area. From 1987 to 2021, NDVI values exhibit the most pronounced variation, with a significant increase in the “green” zone (characterized by NDVI values exceeding 0.40), indicating a substantial enhancement in the ecological environment of the oasis. The NDVI classification is validated through 50 ground validation points in the study area, demonstrating a mean accuracy of 92.00% in the detection of vegetation cover. In general, both the user’s and producer’s accuracies of NDVI classification are extremely high in 1987, 2002, and 2021. Finally, this study suggests that environmental authorities should strengthen their overall forestry project arrangements to combat sand encroachment and enhance the ecological environment of the Al-Ahsa Oasis.

Cite this article

Walid CHOUARI . Assessment of vegetation cover changes and the contributing factors in the Al-Ahsa Oasis using Normalized Difference Vegetation Index (NDVI)[J]. Regional Sustainability, 2024 , 5(1) : 100111 . DOI: 10.1016/j.regsus.2024.03.005

References

[1] Abdelatti, H., Elhadary, Y., Babiker, A.A., 2017. Nature and trend of urban growth in Saudi Arabia: The case of Al-Ahsa Province-eastern region. Resources and Environment. 7(3), 69-80.
[2] Abderrahman, W., 1988. Water management plan for the Al-Hassa irrigation and drainage project in Saudi Arabia. Agric. Water Manage. 13(2-4), 185-194.
[3] Abolkhair, Y., 1981. Sand encroachment by wind in Al-Hasa of Saudi Arabia. PhD Dissertation. Bloomington: Indiana University, 415.
[4] Adams, J.B., Gillespie, A.R., 2006. Remote Sensing of Landscapes with Spectral Images:A Physical Modeling Approach. Cambridge: Cambridge University Press, 362.
[5] Allbed, A., Kumar, L., Sinha, P., 2017. Soil salinity and vegetation cover change detection from multi-temporal remotely sensed imagery in Al Hassa Oasis in Saudi Arabia. Geocarto Int. 33(8), 830-846.
[6] Almadini, A.M., Hassaballa, A.A., 2019. Depicting changes in land surface cover at Al-Hassa oasis of Saudi Arabia using remote sensing and GIS techniques. PLoS One. 14(11), e0221115, doi: 10.1371/journal.pone.0221115.
[7] Alqurashi, A.F., Kumar, L., 2014. Land use and land cover change detection in the Saudi Arabian desert cities of Makkah and Al-Taif using Satellite Data. Advances in Remote Sensing. 3, 106-119.
[8] Alqurashi, A.F., Kumar, L., Sinha, P., 2016. Urban land cover change modelling using time-series satellite images: A case study of urban growth in five cities of Saudi Arabia. Remote Sens. 8, 838, doi: 10.3390/rs8100838.
[9] Alqurashi, A.F., Kumar, L., 2019. An assessment of the impact of urbanization and land use changes in the fast-growing cities of Saudi Arabia. Geocarto Int. 34(1), 78-97.
[10] Biro Turk, K.H., Aljughaiman, A.S., 2020. Land use/land cover assessment as related to soil and irrigation water salinity over an oasis in arid environment. Open Geosci. 12(1), 220-231.
[11] Bruzzone, L., Prieto, D.F., 2000. Automatic analysis of the difference image for unsupervised change detection. IEEE Trans. Geosci. Remote Sensing. 38(3), 1171-1182.
[12] Chen, J., Gong, P., He, C.Y., et al., 2003. Land-use/land-cover change detection using improved change-vector analysis. Photogramm. Eng. Remote Sens. 69(4), 369-379.
[13] Chouari, W., 2021. Wetland land cover change detection using multitemporal Landsat data: A case study of the Al-Asfar wetland, Kingdom of Saudi Arabia. Arab. J. Geosci. 14(6), 523, doi: 10.1007/s12517-021-06815-y.
[14] Chouari, W., 2023. Spatiotemporal analysis of land cover changes in Al-Hubail Wetland (Kingdom of Saudi Arabia). J. Indian Soc. Remote Sens. 5(1), 585-599.
[15] Coppin, P., Jonckheere, I., Nackaerts, K., et al., 2004. Digital change detection methods in ecosystem monitoring: A review. Int. J. Remote Sens. 25, 1565-1596.
[16] Foody, G.M., 2003. Remote sensing of tropical forest environments: Towards the monitoring of environmental resources for sustainable development, Int. J. Remote Sens. 24(20), 4035-4046.
[17] Foody, G.M., 2007. Editorial: Ecological applications of remote sensing and GIS. Ecol. Inform. 2, 71-72.
[18] Gandhi, G.M., Parthiban, S., Thummalu, N., et al., 2015. NDVI: Vegetation change detection using remote sensing and GIS-a case study of Vellore District. Procedia Computer Science. 57, 1199-1210.
[19] Girardin, P., Bockstaller, C., van der Werf, H., 2000. Assessment of potential impacts of agricultural practices on the environment: the AGRO*ECO method. Environ. Impact Assess. Rev. 20(2), 227-239.
[20] Giri, C.P., 2012. Remote Sensing of Land Use and Land Cover:Principles and Applications Remote Sensing Applications Series. Boca Raton: CRC Press, 477.
[21] Hayes, D.J., Sader, S.A., 2001. Comparison of change-detection techniques for monitoring tropical forest clearing and vegetation regrowth in a time series. Photogrammetric Eng. Remote Sens. 67(9), 1067-1075.
[22] Hua, W.J., Chen, H.S., Zhou, L.M., et al., 2017. Observational quantification of climatic and human influences on vegetation greening in China. Remote Sens. 9(5), 425, doi: 10.3390/rs9050425.
[23] Jensen, J.R., 1996. Introductory Digital Image Processing: A Remote Sensing Perspective (2nd edition). New Jersey: Prentice Hall, 526.
[24] Jiang, L.G., Liu, Y., Wu, S., et al., 2021. Analyzing ecological environment change and associated driving factors in China based on NDVI time series data. Ecol. Indic. 129, 107933, doi: 10.1016/j.ecolind.2021.107933.
[25] Ju, J., Roy, D.P., 2008. The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally. Remote Sens. Environ. 112(3), 1196-1211.
[26] Kleynhans, W., Olivier, J.C., Wessels, K.J., et al., 2011. Detecting land cover change using an extended Kalman Filter on MODIS NDVI time-series data. IEEE Geosci. Remote Sens. Lett. 8(3), 507-511.
[27] Mancino, G., Nolè, A., Ripullone, F., et al., 2014. Landsat TM imagery and NDVI differencing to detect vegetation change: Assessing natural forest expansion in Basilicata, southern Italy. iForest. 7(2), 75-84.
[28] Martinez, B., Gilabert, M.A., 2009. Vegetation dynamics from NDVI time series analysis using the wavelet transform. Remote Sens. Environ. 113(9), 1823-1842.
[29] Mas, J.F., 1999. Monitoring land-cover changes: A comparison of change detection techniques. Int. J. Remote Sens. 20(1), 139-152.
[30] Mueller, T., Dressler, G., Tucker, C.J., et al., 2014. Human land-use practices lead to global long-term increases in photosynthetic capacity. Remote Sens. 6(6), 5717-5731.
[31] Patel, N., Kaushal, B., 2010. Improvement of user’s accuracy through classification of principal component images and stacked temporal images. Geo-Spat. Inf. Sci. 13(4), 243-248.
[32] Peters, A.J., Walter-Shea, E.A., Ji, L., et al., 2002. Drought monitoring with NDVI-based standardized vegetation index. Photogramm. Eng. Remote Sens. 68(1), 71-75.
[33] Podeh, S.S., Oladi, J., Pormajidian, M.R., et al., 2009. Forest change detection in the north of Iran using TM/ETM+ imagery. Asian Journal of Applied Sciences. 2(6), 464-474.
[34] Pribadi, D.O., Pauleit, S., 2015. The dynamics of peri-urban agriculture during rapid urbanization of Jabodetabek Metropolitan Area. Land Use Pol. 48, 13-24.
[35] Saleh, M.B., Jaya, I.N., Santi, N.A., et al., 2019. Algorithm for detecting deforestation and forest degradation using vegetation indices. Telecommunication Computing Electronics and Control. 17(5), 2335-2345.
[36] Salih, A., 2018. Classification and mapping of land cover types and attributes in Al-Ahsaa Oasis, Eastern Region, Saudi Arabia Using Landsat-7 Data. Journal of Remote Sensing & GIS. 7(1), 228-234.
[37] Sinha, P., Kumar, L., 2012. Binary images in seasonal land-cover change identification: A comparative study in parts of New South Wales, Australia. Int. J. Remote Sens. 34(6), 2162-2186.
[38] Sun, J.Y., Wang, X.H., Chen, A.P., et al., 2011. NDVI indicated characteristics of vegetation cover change in China’s metropolises over the last three decades. Environ. Monit. Assess. 179, 1-14.
[39] Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ. 8(2), 127-150.
[40] Viana, C.M., Gir?o, I., Rocha, J., 2019. Long-term satellite image time-series for land use/land cover change detection using refined open source data in a rural region. Remote Sens. 11(9), 1104, doi: 10.3390/rs11091104.
[41] Wang, F.Y., Xu, Y.J., 2010. Comparison of remote sensing change detection techniques for assessing hurricane damage to forests. Environ. Monit. Assess. 162, 311-326.
[42] Woodcock, C.E., Allen, E., Anderson, M., et al., 2008. Free access to Landsat imagery. Science. 320(5879), doi: 10.1126/science.320.5879.1011a.
[43] Xu, X.L., 2018. Spatial Distribution Data Set of China Annual Vegetation Index (NDVI). Data Center for Resources and Environmental Sciences of Chinese Academy of Sciences. [2023-01-15]. https://doi:10.12078/2018060601.
[44] Yi, L., Chen, J.S., Jin, Z.F., et al., 2018. Impacts of human activities on coastal ecological environment during the rapid urbanization process in Shenzhen, China. Ocean Coastal Manage. 154, 121-132.
[45] Zhang, D.J., Jia, Q.Q., Xu, X., et al., 2018. Contribution of ecological policies to vegetation restoration: A case study from Wuqi County in Shaanxi Province, China. Land Use Pol. 73, 400-411.
[46] Zhu, Z., Woodcock, C.E., Olofsson, P., 2012. Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sens. Environ. 122, 75-91.
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