Monitoring Vegetation Change in the Dryland Ecosystem of Sokoto, Northwestern Nigeria using Geoinformatics

Abubakar Magaji Jibrillah(1*), Mokhtar Ja'afar(2), Lam Kuok Choy(3)

(1) Universiti Kebangsaan Malaysia
(*) Corresponding Author


The dryland ecosystem of Sokoto state, in the North-western part of Nigeria has been witnessing gradual loss of vegetation cover in the recent decades caused by natural and human induced drivers of ecosystem change. This negative trend poses great challenges to both the physical environment and the people of the area, particularly due to the fragile nature of the ecosystems in the region and the peoples’ over dependence on it for their livelihoods. This study tries to monitor and assess the rate of change in the spatial distribution of vegetation in the area over the time and identify the drivers responsible for changing the vegetation. This is with a view to providing evidence-based information to the policy makers that would guide them in making informed decisions that would assist in conserving the vegetation and the entire ecosystem of the area. Using multi-temporal MODIS-NDVI satellite data, image processing and GIS techniques, this research work tries to monitor and assess gradual change in vegetation cover in Sokoto state, North-western Nigeria. Correlation analysis was also used to measure the degree of relationship between vegetation change and some drivers of ecosystem change in the area. The findings of the research reveal a gradual but persistent decline in vegetation cover in the area, both during the rainy and dry seasons. This is also show a strong positive relationship with the rainfall distribution and a perfect negative relationship with the population distribution of the area. This indicate that, both climate change and anthropogenic drivers plays a significant role in changing vegetation distribution of the area. Anthropogenic drivers however, play a more significant influence. The degree of relationship is however, stronger during the dry season, making the ecosystem more vulnerable during the dry season due to increasing aridity. Although change in the vegetation cover of the area seems to be gradual and unnoticed, if left unchecked the long-term cumulative impacts could have serious negative impacts on both the structure and functions of the ecosystems of the area. This could in turn, affect the livelihoods and socio-economic development of the area.


Vegetation; Change; Dryland; Ecosystem; Geoinformatics.

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Adegboyega, S. A., Olajuyigbe, A. E., Balogun, I. & Olatoye, O. (2016). Monitoring     Drought and Effects on Vegetation in Sokoto State, Nigeria using Statistical and     Geospatial Techniques. Ethiopean Journal of Environmental Studies and     Management, 9(1), 56 – 69.

Aliyu, M. M. (2013). Assessment of Lost of Agricultural Farmlands Using Remote Sensing     Techniques in Gudu Local Government Area of Sokoto State, Nigeria. Journal of     Educational and Social Research, 3(8), 145 -150.  

Al-Rawashdeh, S. B. (2012). Assessment of Change Detection Methods based on Normalised     Difference Vegetation Index in Environmental Studies. International Journal of     applied Sciences and Engineering, 10(2), 89 – 97.

Borelli, P., Modugno, M., Panagos, P., Marchetti, M., Schutt, B. & Montanarella, L. (2014).     Detection of harvest forest areas in Italy using Landsat imagery. Applied Geography,     48, 102 – 111.

Cao, X., Chen, J., Matsushita, B. & Imura, H. (2010). Developing a MODIS-based index to     discriminate dead fuel from photosynthetic vegetation and soil background in the Asian     steppe area. International Journal of Remote Sensing, 31(6), 1589–1604.

Chen, D., Mi, J., Chu, P., Cheng, J., Zhang, L., Pan, Q., Xie, Y. & Bai, Y. (2014).     Patterns     and drivers of soil microbial communities along a regional precipitation gradient on     the Mongolia plateau. Landscape Ecology, 30(9), 1669 - 1682.

Davis, G. (1982). Rainfall and Temperature. In Abdu, P. S. 1982.  Sokoto State in Maps. An     Atlas of physical and Human Resources. Ibadan, University press.

Didan. K. (2015). MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m     SIN Grid V006. NASA EOSDIS Land Processes DAAC. Retrieved from:

Elmore, A. J., Mustard, F., Manning, S. J. & Lobell, D. B. (2000). Quantifying Vegetation     Change in Semi-arid Environment: Precision and Accuracy of Spectral Mixture     Analysis and Normalised Difference Vegetation Index. Remote Sensing of     Environment, 73, 87 – 102.

Eniolorunda, N. B. & Bello, A. G. (2011). Forest Cover Change Assessment Using Landsat and SPOT Data: A Case Study of Tangaza Forest Reserve, North-west of Sokoto State. Ife Research Publications in Geography, 10(1), 66 – 74.

Intergovernmental Panel on Climate Change (2013). Summary for Policymakers. In     T. Stocker, D. Qin, G. Plattner, M. Tignor, S. Allen, J. Boschung, A. Nauels, Y. Xia,     V. Bex & P. Midgley, (eds)., Climate Change 2013: The Physical Science Basis.     Contribution of Working Group I to the Fifth Assessment Report of the     Intergovernmental Panel on Climate Change. Cambridge     University Press,     Cambridge, UK and New York, NY, USA

Jackson, T. J., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C. & Doriaswamy, P. et     al (2004). Vegetation Water Content Mapping using Landsat Data derived NDWI for     Corn and Soya beans. Remote Sensing of Environment, 92, 475 – 482.

Kaminska, I. A., Oldak, A. & Turski, W. A. (2004). Geographical Information System     (GIS)     as a Tool for Monitoring and Analysis of Pesticide Pollution and its     Impact on Public Health. Annals of Agricultural and Environmental Medicine, 11(2),    181 -184.

Kumar, P., Rani, M., Paudey, P. C., Majumdar, A. & Nathawat, M. S. (2010). Monitoring of    Deforestation and Forest Degradation using Remote Sensing and GIS: A case study     of Ranchi in Jharkhand, India. Reports and Opinions, 2(4) 14 – 20.

Li, Z., Xu, D. & Guo, X. (2014). Remote Sensing of Ecosystem Health: Opportunities,     Challenges, and Future Perspectives. Sensors, 14(11), 21117–21139.     doi:10.3390/s141121117

Millennium Ecosystem Assessment (2005). Ecosystems and human well-being: current state and trends, Volume 1. R. Hassan, R. Scholes, & N. Ash, (Eds.) Washington DC.

Matson, E. & Bart, D. (2013). Interactions among fire legacies, grazing and topography     predict shrub encroachment in post-agricultural pa´ramo. Landscape Ecology, 28(9),     1829–1840.

Marian, V., Andreas, B., Francois, D., Dario, S. & Baudouin, D. (2014). Land over Change     Monitoring using Landsat MS/TM Satellite Image Data over West Africa between     1975- 1990. Remote Sensing, 6, 658 – 676.

Mirzaei, J., Mohamadi, A., Heidarizadi, Z., Norolahi, H. & Omidipour, R. (2015).     Assessment of Land Cover Changes using Remote Sensing and GIS (Case Study:     Zagros Forests, Iran). Journal of Mater, Environ. Sci, 6(9), 2565 – 2572.

Mohammed. N. T. (2015). Desertification in Northern Nigeria: Causes and Implications for     National Food Security. Peak Journal of Social Sciences and Humanities, 3(2), 22-31.

Olagunju, T. E. (2015). Drought, Desertification and the Nigerian Environment: A Review.      Journal of Ecology and Natural Sciences, 7(7), 196 – 209.

Olexa, E. M. & Lawrence, R. L. (2014). Performance effects of land cover type on synthetic     surface reflective data and NDVI estimate for assessment and monitoring of semi-arid     rangeland. International Journal of Applied Earth Observation and Geo-information,    30, 30 – 41

Pettorelli, N., Laurence, W. F., O’Brien, T. G., Wengmann, M., Nagendra, H. & Turner, W.     (2014). Satellite Remote Sensing for Applied Ecologists: Opportunities and     Challenges. Journal of Applied Ecology, 51, 839 – 848.

Pooter, L., Bonger, F., Kouame, F.T.N. & Hawthrone, W.D. (2004). Biodiversity of West     African Forest – An Ecological Atlas of Woody Plant Species. Oxford, CABI     Publishing

Rose, R. a, Byler, D., Eastman, J. R., Fleishman, E., Geller, G., Goetz, S., Guild, L. et al.     (2015). Ten ways remote sensing can contribute to conservation.  Conservation     Biology, 29(2), 350–9. doi:10.1111/cobi.12397

Shalaby, A. & Tateishi R. (2007). Remote Sensing and GIS for Mapping and Monitoring     Land Cover and Land Use Changes in the North-west Coastal one of Egypt. Applied     Geography, 27, 28 – 41.

Tunner, W., Spector, S., Gardiner, N., Fladdad, M., Sterling, E. & Steininger, M. (2003).     Remote Sensing for Biodiversity Science and Conservation. Trends in Ecology and     Evolution, 18(6), 306 – 314.
United States Geological Survey (1997). Desertification. Retrieved from:

Usman, U., Yelwa, S. A. & Gulumbe, S. U. (2012). An Assessment of Vegetation Cover     across Northern Nigeria using Trend Line and Principal Component Analysis.     Journal of Agriculture and Environmental Science, 1(1), 01 -18.

Vogelmann, J. E., Xia, G., Homer, C. & Tolk, B. (2012). Monitoring Gradual Ecosystem     Change Using Landsat Time Series Analysis: Case Studies in Selected Forest     Rangeland Ecosystem. Remote Sensing of Environment, 122, 92 – 105.

Zhao, X., Hu, H., Shen, H., Zhou, D., Zhou, L., Myneni, R. B., & Fang, J. (2014).     Satellite-indicated long-term vegetation changes and drives in the Mongolian     Plateau. Landscape Ecology, 30(9), 1599 -1611.

Zhigila, D. A., Sawa, F. B. J., Abdul, S. D., Abba, H. M. and Tela, M. (2015). Diversity and    Phytogeographic Investigation into the Woody Plants of West Tangaza Forest     Reserve, Sokoto State, Nigeria. International Journal of Plants Research, 5(1), 73 –     79.  

Zhou, D., Zhao X., Hu, H., Shen, H., & Fang, J. (2015). Long-term vegetation changes in     the Four Mega-Sandy Lands in Inner Mongolia. Landscape Ecology, 30(9), 1613 -    1626.


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