Determining Causes of Gully Erosion and Associated Rates of Change in South-East Nigeria, using a Remote Sensing and GIS Methodology

Abstract

In this work a study of gully erosion in southeast Nigeria is presented. The study of gully development on a regional scale is currently undermined by the inherent costs associated with consistent field monitoring and the lack of historic measurements to perform time series analysis. As a result, there are very few studies which implement long term analyses of gullies in the region as a collective. Consequently, the building of knowledge of the role of environmental changes on the development of gullies is inhibited. Remote sensing methodologies, via the Landsat archive, are used as a low-cost data source to allow analyses of gullies over the time period 1986 to 2015. In conjunction with long term environmental variables, the Landsat data is used to establish landcover changes over the time period, via pixel and object-based classification, to identify its role in gully development. The use of classification for this purpose identifies this study as a first of its kind in Nigeria. Aiming to link environmental characteristics and land cover changes with gully development and erosion rates at multiple current locations. 14 gully sites, identified via field work validation of remote sensing imagery, are monitored in terms of extent and rates of change. Digital Elevation Models (DEM) and remote sensing imagery are used to detect topographical and landscape characteristics and to calculate gully dimensions. The influence of environmental variables on gully development is directly examined using soil, geology, and precipitation. Landscape analysis over the study period reveals a steady increase in Gully/Open Land both locally and regionally; the increasing area of gullies consistently correlating with vegetation clearance, (r= -0.97 (p<0.05)). Analysis of study area topography at 30m resolution reveals 85% of the surveyed gullies develop on concave slopes with high values of 6 plan curvatures and >50 inclines. Results also reveal high association with ferralsols soils. Statistical analysis to determine significance of variables on the proportional yearly gully change in metre squared per square metre were conducted via hierarchical cluster analysis, principle component analysis and multiple linear regression. For this, analysis was restricted to the time periods 2006/7, 2009/10, and 2014/15. None of the approaches reported the existence of one singular driver of erosion across the studied years and multiple sites confirming the complexity of gullies. Regression R2 achieved a maximum of 0.325 in year 2014/15 when using the independent variables Gully Area, Vegetation Loss, Elevation, Gully Stream Order, Slope, and Soil. Cluster analysis showed contrasting results across the 3 studied time periods with yearly gully change in metre squared per square metre clustered most closely with vegetation loss, slope, gully stream order and soil in year 2014/15 and 2009/10 and vegetation loss and elevation for 2006/07 in the respective years. The PCA showed that the level of variance explained in the yearly gully change variable was most similar in PC1 (representing the component with the highest eigenvalue) to Vegetation loss, Vegetation loss and slope in the respective years. Comparison of open source and proprietary approaches to the methodology reported close similarity in results with no statistically significant differences. The study offers a method of monitoring gully development from early stage to maturity and exemplifies the complexity and variability of erosion drivers in the SE Nigeria region. It presents a verified approach to local and regional monitoring of gullies, enacted through use of low budget/computing cost remote sensing and classification technologies, and serves to embolden civilian and governmental efforts to manage the societal and environmental menace of soil erosion.

Date of Award	Aug 2018
Original language	English
Awarding Institution	University of Brighton
Supervisor	Matthew Brolly (Supervisor), David Nash (Supervisor) & Graeme Awcock (Supervisor)