POTENTIALS AND CHARACTERISTICS OF LANDSAT IMAGERY IN RELATION TO LAND USE /COVER IN OKITIPUPA METROPOLIS, ONDO STATE, NIGERIA

  • A. O. TOBORE Department of Soil Science and Land Management, Federal University of Agriculture, Abeokuta, Nigeria
  • G. OYERINDE Department of Soil Science, Faculty of Agriculture, University of Abuja
  • B. A. SENJOBI Department of Soil Science and Land Management, Federal University of Agriculture, Abeokuta, Nigeria
  • T. O. OGUNDIYI Department of Soil Science and Land Management, Federal University of Agriculture, Abeokuta, Nigeria
Keywords: LandSAT Imagery, land use/cover, Normalized Difference Vegetation Index, Supervised Classification

Abstract

Landsat satellite imagery plays a crucial role in providing information on land use/cover modifications on local, regional, and global scales, especially where aerial photographs are missing. Monitoring land-use changes from past to present tends to be time-consuming especially when dealing with ground-truth information. Determining the past and current land-use change on Earth's surface using Landsat imagery tends to be effective and efficient when high-resolution imagery is unavailable. This study employed the use of Landsat satellite imagery to assess the past and present land use/cover using supervised classification and Normalized Difference Vegetation Index (NDVI). The result of the supervised classification land use/cover showed that forest cover and woodland undergo rapid loss, while farmland, wetland, built-up, and waterbodies tend to experience gradual loss. The NDVI demonstrated that farmland and forest cover was the most affected land use/cover. Hence, land use/cover of the study area is affected by human activities, such as intensive farming, population size, and deforestation.

 

References

Ajayi, R., M.O. Afolabi, E.F. Ogunbodede, A.G. Sunday, 2010. Modeling Rainfall As A Constraining Factor For Cocoa Yield In Ondo State. Am. J. Sci Ind. Res., 1: 127-134.

Anderson, J. R. 1976. A Land Use and Land Cover Classification System for Use with Remote Sensor Data. Geological Survey Professional.

Biro, K., Pradhan, B., Buchroithner, M., and Makeschin, F. Land use/Land cover change analysis and its impact on soil properties in the northern part of Gadarif region, Sudan, Land Degrad.Dev., 24, 90–102. https://doi.org/10.1002/ldr.1116, 2013.

Congalton, R.G., Green, 1999. Assessing The Accuracy of Remotely Sensed Data: Principals And Practices (Boca Raton, FL: Lewis).

Ebenezer, T., 2015. Drought, Desertification and The Nigerian Environment : A Review 7, 196–209. Https://Doi.Org/10.5897/JENE2015.

Gilabert M. A., González-Piqueras J, García-Haro F. J., Meliá J. 2002. “A generalized soil-adjusted vegetation index,” Remote Sens. Environ. 82, 303-310.

Ibrahim, Y.Z., Balzter, J, Kaduk., C.J Tucker, 2015. Land Degradation Assessment Using Residual Trend Analysis of GIMMS NDVI 3g, Soil Moisture And Rainfall In Sub-Saharan West Africa From 1982 To 2012 5471–5494. Https://Doi.Org/10.3390/Rs70505471
Matthiesa W., N, Karimov 2014. Global Food Price Volatility And Spikes: An Overview Of Cost, Causes And Solution Oyerinde, G.T., F.C.C. Hountondji, D Wisser, B, Diekkrüger, A.E, Lawin, A.J, Odofin, A, Afouda, 2015. Hydro-Climatic Changes In The Niger Basin and Consistency of Local Perceptions. Reg. Environ. Chang. 15, 1627–1637. Https://Doi.Org/10.1007/S10113-014-0716-7

Oyinloye, R.O. 2010. A Geoinformation-based model for assessing and monitoring forest reserves in southwestern Nigeria. Unpublished Ph.D. Thesis, Department of Geography, Obafemi Awolowo University, Ile-Ife, Nigeria, pp: 337.

Story, M., R.G. Congalton 1986. Accuracy Assessment: A User’s Perspective. Photogrammetric Engineering And Remote Sensing, 52, 397–399.

Wilson, E.H., S.A, Sader, 2002. Detection of Forest Harvest Type Using Multiple Dates of Landsat TM Imagery. Remote Sensing of Environment, 80, 385–396.
Published
2020-10-05
Section
Original Manuscript