Abstract

The method of integrating remote sensing, Geographic Information System (GIS) and field survey was employed. Assessment of the rate and intensity of sand dune encroachment using multi-temporal Landsat images (Landsat.TM, 1986, Landsat.ETM, 2000 and Landsat.OLI, TIR, 2016) and GIS. The satellite images were processed by converting raw Digital Number (DNs) values to radiance images which were converted into reflectance images used for spectral analysis.The satellite images were processed accordingly for evaluating six (6) spectral indices; Crust Index (CI, Grain Size Index, Bare soil Index (BSI), Normalized Difference Sand Dune Index (NDSDI), Normalized Difference Sand Index (NDSI), Normalised Difference Soil Index (NDSLI). An aggregate index of each of the six (6) selected indices was evaluated and the long term geometric mean was determined and used for image differencing with the baseline date image. A combination of MEDALUS.ESA and Image Differencing was adopted for change detection technique. Sandy landscapes were mapped into four (4) natural classes using natural jenks classifier of the ArcGIS analytical tool based on pre-field field determination and post verification. The description of the four (4) sandy landscape classes is as follows: Active, Semi-active, Semi-fixed and Fixed sand dune/sheets. Results of overall sandy desertification based on Aggregate Sandification Index indicates that active and semi-active sandy landscapes have progressed steadily at annual rate of expansion of 1.20 and 1.28 km 2 and intensity for the of 0.13 and 0.23% respectively. This has caused a corresponding decrease in the semi-fixed and fixed sandy landscapes of 1.24 and 1.39 km 2 and intensity for the period of 0.17 and 0.47 % respectively. The highest risk of sandy desertification is in the fixed sandy landscape which is be lost an an annual rate of 1.39 km 2 and 0.47% intensity being the highest among other classes. The result of this study indicates that the natural ecology or vegetation, graze lands, irrigated lands, rainfed farmlands, settlements, infrastructure are at high risk of sandy desertification in the semi-arid zone of Nigeria. This study is also a pointer that the shelter belts have not been very effective in controlling wind erosion and thus sandy desertification. Keywords: : desertification. sandification, eco-geomorphic landscape, semi-arid, encroachment DOI: 10.7176/JNSR/12-14-06 Publication date: July 31 st 2021

Highlights

  • Sandy desertification an interesting topical issue in both bioscience and geoscience which has increasingly attracted the attention of the public, researchers, government officials, and international organizations

  • The highest risk of sandy desertification is in the fixed sandy landscape which is be lost an an annual rate of 1.38 km2 and 0.44% intensity being the highest among other classes

  • The highest risk of sandy desertification is in the fixed sandy landscape which is be lost an annual rate of 1.38 km2 and 0.44% intensity being the highest among other classes

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Summary

Introduction

Sandy desertification an interesting topical issue in both bioscience and geoscience which has increasingly attracted the attention of the public, researchers, government officials, and international organizations It is one of the main eco-geomorphological hazard in the world today when viewed from the perspectives of the ecogeomorphological environment and food security (Wang, 2013). Defined Sandy desertification as a form of land degradation characterized by wind erosion that mainly results from the excessive unsustainable human activities in arid, semiarid and part of www.iiste.org sub-humid regions (Sandy desertification comprising sand dune and sand sheet encroachment has become a critical environmental and research issue in the last decades, especially in susceptible drylands areas of the world; in central and south Tunisia, China, Iraq, Iran, Egypt and other areas of the world (Ahmady-Birgani et al, 2017). Sandy desertification determines degradation of land resources, slow recovery of vegetation and decrease of ecosystem productivity, services and livelihood (Ahmady-Birgani et al, 2017; Wang et al.,2014)

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