Analyzing the Changing Pattern of Nigerian Vegetation Zones and Its Ecological and Socio-Economic Implications Using Spot-Vegetation Sensor
Authors: B. L. Gadiga
This study assesses the major ecological zones in Nigeria with the view to understanding the spatial pattern of vegetation zones and the implications on conservation within the period of sixteen (16) years. Satellite images used for this study were acquired from the SPOT-VEGETATION between 1998 and 2013. The annual NDVI images selected for this study were derived from SPOT-4 sensor and were acquired within the same season (November) in order to reduce differences in spectral reflectance due to seasonal variations. The images were sliced into five classes based on literatures and knowledge of the area (i.e. <0.16 Non-Vegetated areas; 0.16-0.22 Sahel Savannah; 0.22-0.40 Sudan Savannah, 0.40-0.47 Guinea Savannah and >0.47 Forest Zone). Classification of the 1998 and 2013 images into forested and non forested areas showed that forested area decrease from 511,691 km2 in 1998 to 478,360 km2 in 2013. Differencing change detection method was performed on 1998 and 2013 NDVI images to identify areas of ecological concern. The result shows that areas undergoing vegetation degradation covers an area of 73,062 km2 while areas witnessing some form restoration cover an area of 86,315 km2. The result also shows that there is a weak correlation between rainfall and the vegetation zones. The non-vegetated areas have a correlation coefficient (r) of 0.0088, Sahel Savannah belt 0.1988, Sudan Savannah belt -0.3343, Guinea Savannah belt 0.0328 and Forest belt 0.2635. The low correlation can be associated with the encroachment of the Sudan Savannah belt into the forest belt of South-eastern part of the country as revealed by the image analysis. The degradation of the forest vegetation is therefore responsible for the serious erosion problems witnessed in the South-east. The study recommends constant monitoring of vegetation and strict enforcement of environmental laws in the country.
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 Greig-Smith, P. (1983). Quantitative plant ecology. Blackwell Scientific, Oxford.
 Orloci, L. (2001) Pattern dynamics: an essay concerning principles, techniques, and applications. Community Ecology, vol. 2 (1). 1-15.
 Sala, O. E., Chapin, F. S., Armesto, J. J., Berlow, E., Bloomfield, J., Dirzo, R., Huber-Sanwald, E., Huenneke, L. F., Jackson, R. B., Kinzig, A., Leemans, R., Lodge, D. M., Mooney, H. A., Oesterheld, M., Poff, N. L., Sykes, M. T., Walker, B. H., Walker, M. and Wall, D. H., (2000). Biodiversity: global biodiversity scenarios for the year 2100. Science, Vol. 287, pp. 1770–1774.
 Reid, R. S., Kruska, R. L., Muthui, N., Taye, A., Wotton, S., Wilson, C. J., and W. Mulatu, (2000). Land-use and land-cover dynamics in response to changes in climatic, biological and socio-political forces: the case of southwestern Ethiopia,
 Alphan, H., (2003). Land use change and urbanization in Adana, Turkey. Land Degradation and Development, 14.
 Muttitanon, W. and Tripathi, N. K., (2005). Land use/cover changes in the coastal zone of Bay Don Bay, Thailand using Landsat 5 TM data. International Journal of Remote sensing, 26(11).
 Evrendilek, F., 2004. An inventory-based carbon budget for forest and woodland ecosystems of Turkey. Journal of Environmental Monitoring, 6(1).
 Houghton, R. A. (2003). Revised estimates of the natural net flux of carbon to the atmosphere from changes in land use and land management 1850-2000. Tellus, 55B.
 Birgit, R., Annette, O., and Rainer W., 2007. Identifying of land-cover change and their attributes in a marginal European landscape. Landscape and Urban Planning 81.
 Barraclough, S. L. and Ghimire, K. B., 1996. Deforestation in Tanzania: beyond simplistic generalizations. The Ecologist 26 3, pp. 104–107. from changes in land use and land management 1850-2000. Tellus, 55B.
 Hatchinson, C. F., Unru, J. D., and C. J. Bahre, 2000. Land use vs. climate as causes of vegetation change: a study in SE Arizona. Global Environmental Change10 (1), pp. 47-55.
 FAO, (2003) Experience of Implementing National Forest Programmes in Nigeria. EC-FAO Partnership Programme (2000-2003) EC Tropical Forestry Budget Line.
 Xie Y., Sha Z., Yu M, (2008), “Remote Sensing Imagery in Vegetation Mapping: A Review”, J. of Plant Ecology 1(1): pp 9 -23.
 Knight J. F, Lunetta R. S, Ediriwickrema J, (2006), “Regional Scale Land Cover Characterization Using MODIS NDVI 250m Multi-Temporal Imagery: A Phenology-Based Approach”. GIScience Remote Sens 43: pp 1 23.
 Leak S. M. and Venugopal G. (1990), “Thematic Mapper Thermal Infrared Data in Discriminating Selected Urban Features”, Int. J. Remote Sensing 11(5): pp 841 857.
 Anyamba, A. and C. J. Tucker (2005). Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981–2003. Journal of Arid Environments, 63: 596–614.
 Fabricante, I., Oesterheld, M. and J. M. Paruelo (2009) Annual and seasonal variation of NDVI explained by current and previous precipitation across Northern Patagonia. Journal of Arid Environments, 73: 745–753.
 Gadiga, B. L. and Yonnana, E. (2012) Vegetation Dynamics in a Semi-Arid Part of Yobe State, Nigeria. Adamawa State University Journal of Scientific Research. 2 (2).
 De Bie, C. A. J. M., Khan M. R., Smakhtin V. U., Venus V., Weir, M. J. C. and E. M. A. Smaling (2011): Analysis of multi-temporal SPOT NDVI images for small-scale land-use. International Journal of Remote Sensing, DOI:10.1080/01431161.2010.512939.
 Jwan Al-doski, Shattri. B Mansor, and Helmi Zulhaidi Mohd Shafri (2013) NDVI Differencing and Post-classification to Detect Vegetation Changes in Halabja City, Iraq. IOSR Journal of Applied Geology and Geophysics, Vol. 1(2): 1-10.
 Edoardo Simonetti, Dario Simonetti and Damiano Preatoni, (2014) Phenology-based land cover classification using Landsat 8 time series. Technical Report by the Joint Research Centre of the European Commission, DOI: 10.2788/15561 22. NAP, 2007.
 Combating Desertification and Mitigating the Effects of Drought in Nigeria. Report Submitted to UNCCD by Federal Ministry of Environment, Nigeria.
 Ishaku H. T. and M. Rafee Majid (2010) X-Raying Rainfall Pattern and Variability in Northeastern Nigeria: Impacts on Access to Water Supply. J. Water Resource and Protection, Vol. 2, 952-959.
 Geomatics International (1998). Vegetation and land use changes in Nigeria. A report submitted to Forestry Management, Evaluation and Coordinating Unit (FORMECU), Federal Ministry of Environment.
 Lu, D., Mausel, P., Brondízio, E. and Moran, E. 2004 'Change detection techniques'. International Journal of Remote Sensing, 25:(12) pp2365- 2401.
 Pu, R., Gong, P., Tian, Y., Miao, X., Carruthers, R. I. and Anderson, G. L. 2008. Using classification and NDVI differencing methods for monitoring sparse vegetation coverage: a case study of saltcedar in Nevada, USA. International Journal of Remote Sensing. Vol. 29, No. 14, 20 July 2008, 3987–4011.
 Eastman, J. R., 2009. IDRISI Taiga Guide to GIS and Image Processing. Clark Labs, Clark University, Worcenter, MA. USA.
 Gadiga, B. L., Adesina. F. A. & Orimoogunje. I. O. O (2013) Spatial-Temporal Analysis of Vegetation Dynamics in the Semi Arid Belt of Nigeria. Global Journal of Human Social Sciences, Vol. (13) 7: 1-9.