MONITORING THE HEALTH OF DRYLAND ECOSYSTEM ACROSS NORTH-WESTERN NIGERIA USING MULTI-TEMPORAL MODIS-NDVI REMOTE SENSING DATA
Abstract
The study aimed at monitoring the health of the dryland ecosystem of Northwestern Nigeria using geospatial techniques. The need for constant monitoring of the ecosystem in order to keep track of its condition viz a viz its ability to produce qualitative and adequate good and service necessary for human survival and economic development cannot be over emphasized. This is particularly so with fragile dryland ecosystem of Northwestern Nigeria due to the constant disturbances it suffers from both natural and anthropogenic drivers, which could negatively affect the structure and functions of the ecosystem. In this study,
MODIS – NDVI remote sensing data was used to monitor the health of the dryland ecosystem of North-western Nigeria. Findings of the study indicate a steady decline in the ecosystem health as indicated by the declining trend of mean NDVI values from 0.56 in 2000 to 0.47 in 2016, which is positively correlated (r = 0.85) with the annual rainfall distributions of the area during the same years. Similarly, the spatial extent of the vegetation cover declined from 97% in 2000 to 84% in 2015. This has a very serious implications on the livelihood of the inhabitants of the area as it negatively affects the supply of ecosystem goods and services, threatening the livelihoods in multiple ways. Collaborations amongst different stake holders, sustainable land management practices, environmental protection policies, as well as Ecosystem-based Adaptation (EbA) are recommended as means of addressing this negative development.
References
Abdul Aziz, A., Phina, S., Dargusch, P., Omar, H. & Arjasakusuma, S. (2015). Assessing the potential of Landsat archive in the ecological monitoring and management of a production mangrove forest in Malaysia. Wetland Ecology and Management, 23(6), 1049 – 1066.
Abdullahi, S. A., Muhammad, M. M., Adeogun, B. K. & Muhammed, I. U. (2014). Assessment of Water Availability in the Sokoto Rima River Basin. Resource & Environment, 4(5), 220 – 233.
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.
Ahmed, N. (2016). Application of NDVI in Vegetation Monitoring using GIS and Remote Sensing in Northern Ethiopian Highlands. Abyssinia Journal of Science and Technology, 1(1), 12 – 17.
Beck, P. S. A., Jonsson, P., Hogda, K. A., Karlsen, S. R., Eklundh, L. & Skidmore, A. K. (2007), A Ground-validated NDVI Dataset for Monitoring Vegetation Dynamics and Mapping Phenology in Fennoscandia and the Kola Peninsula. International Journal of Remote Sensing, 28, 4311 - 4330
Borrelli, 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.
Chen, Z. H. & Wang, J. (2005). Establishing Ecosystem Health Model in Arid and Semi-arid Area by using Remote Sensing Data. Proceedings of 2005 IEEE International Geoscience and Remote Sensing Symposium, Seoul, Korea, 25–29 July 2005, pp. 2953–2956.
Chen, Z. H., Yin, Q., Li, L. & Xu, H. (2010). Ecosystem Health Assessment by using Remote Sensing derived Data: A case study of Terrestrial Region along the Coast in Zhejiang Province. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA, 25–30 July 2010; pp. 4526–4529.
Clark, J. & Bobbe, T. (2006). Using Remote Sensing to Map and Monitor Fire damage in Forest Ecosystems. In Wulda, M. A. & Franklin, S. E. (Eds). Understanding Forest Disturbances and Spatial Pattern Boca Raton. Florida: CRC Press.
Convention on Biological Diversity (CBD) 2009. Year in Review 2009. Online at: http://www.cbd.int/doc/legal/cbd-un-en.pdf. Retrieved 15/15/2017
Coppin, P., Johnckeere, I., Nackaerts, K., Muys, B. & Lambin, E. (2004). Digital Change Detection Methods in Ecosystem Monitoring: A review. International Journal of Remote Sensing, 25, 1565 -1596.
Cramer, W., Kicklighter, D., Bondeau, A., I, B. M.. Churkina, G., Nemry, B., Ruimy, A. & Schloss, A. (1999). Comparing Global Models of Terrestrial Net Primary Productivity (NPP): Overview and Key Results. Glob. Chang. Biol. 5, 1–15.
Cui, X., Gibbes, C., Southworth, J. & Waylen, P. (2013). Using Remote Sensing to Quantify Vegetation Change and Ecological Resilience in a Semi-Arid System. Land, 2, 108 – 130.
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. Online at: https://doi.org/10.5067/MODIS/MOD13Q1.006. Retrieved 16/01/2017.
Fuller, D. O. & Ottke, C. (2002). Land cover, Rainfall and Land-surface Albedo in West Africa. Climate Change, 54, 181 – 204.
Hansen, M. C., Roy, D. P., Lindquist, E., Adusei, B., Justice, C. O. & Alstatt, A. (2008). A Method for Integrating MODIS and Landsat Data for ystematic Monitoring of Vegetation Cover and Change in Congo Basin. Remote Sensing of Environment, 112, 2495 – 2513.
Intergovernmental Panel on Climate Change (IPCC) (2007). Summary for Policy Makers in: Climate Change 2007: The Physical Science Basis. The contribution of the Working Group I to the Forth Association of the ICPC, Solomom, S., Quin, D., Karing, M., Chan, Z., Marquins, M., Avery, K. B., Tignnor, M. and Miller, H. L. (eds) Cambridge University Press, Cambridge, UK.
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
Jibrillah, M. A. (2012). The Impact of Climate Change on Education in Sokoto State in Iliya, M. A. and Fada A. G (eds) The Impacts of Climate Change in Sokoto State, Nigeria Evidence and Challenges
UNDP/Sokoto State Government, Nigeria. Kennedy, R. E., Yang, Z. & Cohen, W. (2010). Detecting Trend in Forest Disturbance and Recovery using Yearly Landsat Time series: 1. Landtrendr- Temporal Segmentation Algorithm. Remote Sensing of Environment, 114, 2897 – 2910.
Lord, D., Desjardins, R. L. & Dube, P. A. (1985). Influence of Wind on Crop Canopy Reflectance Measurement. Remote Sensing of Environment, 18, 113 – 123.
Millennium Ecosystem Assessment (2005). Ecosystems and Human Well-being: Biodiversity Synthesis. World Resources Institute, Washington, DC.
National Population Commission (NPC) (1993). The 1991 Census Result. Census News. NPC. Quarterly Publication. NPC, Nigeria
National Population Commission (NPC) (2014). Census News. NPC. Quarterly Publication. NPC, Nigeria
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 Geoinformation, 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.
Rapport, D. J., Costanza, R. & McMichael. J. (1998).Assessing Ecosystem Health. Three. 13(10), 397 – 402.
Rapport, D. J. (1999) Gaining respectability: Development of quantitative methods in ecosystemhealth. Ecosyst. Health. 5, 1–2.
Rio Declaration on Environment and Development (1992). The Earth Summit: the United Nations Conference on Environment and Development (Johnson, S., ed.), Graham and Troutman/Martinus Nijhoff, London
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.
Suo, A. N., Xiong, Y. C., Wang, T. M., Yue, D. X. & Ge, J. P. (2008). Ecosystem Health Assessment of the Jinghe River Watershed on the Huangtu Plateau. Ecosyt. Health. 5, 127–136.
Copyright (c) 2023 FUDMA JOURNAL OF SCIENCES
This work is licensed under a Creative Commons Attribution 4.0 International License.
FUDMA Journal of Sciences
