Remote Sensing, a Tool for Erosion Study; a Case Study of Nekede and Its Environs

REMOTE SENSING, A TOOL FOR EROSION STUDY; A CASE STUDY OF NEKEDE AND ITS ENVIRONS

BY EQUERE, UBONG IMEH (REG NO. 20081597233) [email protected] +2347066875750

Submitted in partial fulfilment of the requirements for the Degree of Bachelor of Engineering (B.Eng.) in Agricultural Engineering (Soil and Water Engineering Technology)

School of Engineering and Engineering Technology Department of Agricultural Engineering Federal University of Technology, Owerri

February, 2014

DEDICATION

This Project work is dedicated to my parents Dr and Mrs Imeh Equere, for their unremitting and resolute support in my education.

ACKNOWLEDGEMENT

Thank you Dear Lord for life, wisdom and strength to press on through this study in all circumstances.

My deep gratitude is to my Project Supervisor, Engr. E. U. Ujah for his patience, advice and support throughout this project.

I am also grateful to my sister Mfonobong, my brother Imeh and my Cousins

Samuel and Donald who in one way or another contributed to my project.

I am also indebted to my friends and Chioma and course mates who all stood by me through it all, Thank you.

ABSTRACT

This study adopts Remote Sensing as a tool to identify and study erosion especially gully erosion in the study area, Old Nekede Road and its environs in

Owerri, Imo State, South Eastern Nigeria which is located between latitude

5.18° – 5.39° N and longitude 6.51° - 7.08° E. Remotely sensed data consisting of the Landsat Thermatic Mapper (TM) imagery of NigeriaSat-2 2012 satellite and Digital elevation model of study area were studied with the objective of identifying the drainage and structures associated with the area and to infer their influence on gully erosion initiation and propagation. The Landsat TM data was analysed and processed using ILWIS 3.3 Academic, Multispec 3.3 and

Landserf 2.3. Results obtained from the structural analysis revealed numerous lineaments at several parts of the satellite image. On the whole, this study has demonstrated the usefulness of Satellite (Remote Sensing) technology in studying erosion.

Keywords: Erosion, Nekede, Remote Sensing, Landsat, DEM

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TABLE OF CONTENTS

Title ………………………………………………………………………………………………………... i

Dedication ………………………………………………………………………………………………. ii

Acknowledgement ………………………………………………………………………………….. iii

Abstract ………………………………………………………………………………………………….. iv

Table of Contents ……………………………………………………………………………………. v

List of Figures ………………………………………………………………………………………….. viii

List of Tables …………………………………………………………………………………………… ix

List of Plates ……………………………………………………………………………………………. x

List of Acronyms …………………………………………………………………………….……….. xi

Chapter One: Introduction

1.0 Background ……………………………………………………………………………………… 1

1.1 Statement of problem ……………………………………………………………………… 2

1.2 Aims & Objectives ……………………………………………………………………………. 3

1.3 Justification ……………………………………………………………………………………… 4

1.4 Scope and limitation ………………………………………………………………………… 4

Chapter Two: Literature Review

2.0 Remote Sensing ……………………………………………………………………………….. 6

2.0.1 Classification of Remote Sensing ………….…………………………………. 8

2.0.2 Remote Sensing Data sets …………………….………………………………… 9

2.0.3 Data Processing …………………………………………………………………….. 11

2.0.4 Image Interpretation …………………………………………………………….. 11

2.0.5 Remote Sensing Computer Softwares ……………………………………. 12

2.1 Erosion ……………………………………………………………………………………………. 13

2.1.1 Classification of Soil Erosion ………………………………………………….. 14

2.1.2 Factors Influencing Erosion ……………………………………………………. 15

2.2 Review of Soil Erosion in the World …………………………………………………. 17

2.3 Review of Soil Erosion in Nigeria ……………………………………………………… 17

2.4 Review of Soil Erosion in South East Nigeria …………………………………….. 19

Chapter Three: Materials and Methods

3.0 Reconnaissance Survey ……………………………………………………………………. 21

3.1 Geology of the Study Area ……………………………………………………………….. 21

3.2 Photos of the Site …………………………………………………………………………….. 23

3.3 Methodology ……………………………………………………………………………………. 24

3.4 Data Type and Acquisition ………………………………………………………………… 24

3.4.1 Landsat 7 ETM+ Image …………………………………………………………… 25

3.4.2 SRTM 90m DEM ……………………………………………………………………… 25

3.5 Software Used ……………………………………………………...... 26 3.6 Pre-Processing of DEM and Landsat TM images ……………………………….. 27 3.6.1 Image Clipping ………………….……………………………………………………. 27 3.6.2 Geo-Referencing ……………………………………………………………………. 27 3.6.3 Image Enhancement ………………………………………………………………. 27

3.7 SRTM 90m DEM processing …………………………………………………………….. 28

3.7.1 Visualisation of surface models of terrain ……………...... 28

3.7.2 Model Transformation ………………………………………………………….. 29

3.7.3 Slope, Aspect and Channels Maps …………………………………………. 29

3.8 Landsat Images Processing ………………………………………………………………. 30 3.8.1 NDVI (Normalized Difference Vegetation Index) ……………………. 30

3.8.2 False-colour images ………………………………………………………………. 31 Chapter Four: Result and Discussion

4.0 NDVI Map ………………………………………………………………………………………. 33 4.1 Colour Composite Images ………………………………………………………. 34 4.1.1 RGB 432: Standard False Colour Composite …………………………… 34 4.1.2 RGB 321: True (Natural) Colour Image …………………………………… 35 4.1.3 RGB 453 ………………………………………………………………………………… 35 4.2 Digital Elevation Model ……………………………………………………………………. 36 4.2.1 Morphometric Analysis ………………………………………………………….. 36 4.2.2 Relief ……………………………………………………………………………………… 37 4.2.3 Frequency Distribution …………………………………………………………… 38 4.2.4 Slope ……………………………………………………………………………………… 38 4.2.5 Terrain Classification ……………………………………………………………… 39 4.2.6 Drainage Pattern ……………………………………………………………………. 40 4.2.7 DEM Transformation to Contour Map ……………………………………. 40 4.2.8 Surface Profiles ………………………………………………………………………. 41 4.3 3D Perspective Representation of the Study Area (Virtual Reality) …… 42 4.4 Google Earth ……………………………………………………………………………………. 43 Chapter Five: Conclusion and Recommendations

5.0 Conclusions ……………………………………………………………………………………… 54

5.1 Recommendation …………………………………………………………………………… 55

References ……………………………………………………………………………………………..... 56

LIST OF FIGURES

FIG. 3.1 Methodology Flow Diagram …………………………………………………… 24

FIG. 4.1: Surface Profile as we move from point A to B ………………………… 41

FIG. 4.2: Elevation Frequency Distribution of the DEM ……………………….. 49

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LIST OF TABLES

TABLE 2.1: Comparison between Human and Computer Information

Extraction ……………………………………………………………………………… 10

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LIST OF PLATES

PLATE 3.1 Map of Nigeria showing Imo State and Study Area ……….……….. 22

PLATE 3.2 Gully Erosion threatening to collapse a home ………………………... 23

PLATE 3.3 Steep v shaped gully by old Nekede road, Umumbazu ……………. 23

PLATE 4.1 Study Area overlay displayed in Google Earth ……………………….. 43

PLATE 4.2 NDVI map of study area………………...... 44

PLATE 4.3 RGB 432 (Standard False Colour Composite) …….…...... 45

PLATE 4.4 RGB 321 (True Natural Colour Image) ………...... 46

PLATE 4.5 RGB 453 ………………………………………………………………………………… 47

PLATE 4.6 Digital Elevation Model (DEM) of study area showing relief ……. 48

PLATE 4.7 Slope Map ……………………………………………………………………………... 50

PLATE 4.8 Terrain Classification of study Area ……………………………………….. 51

PLATE 4.9 Contour Map generated from the DEM of the Study Area …….. 52

LIST OF ACRONYMS

ASTER Advanced Space-borne Thermal Emission and Reflection Radiometer

DEM Digital Elevation Model

DN Digital Number

ETM+ Enhanced Thematic Mapper Plus

FCC False Colour Composites

GIS Geographic Information Systems

GPS Global Positioning System

LANDSAT Land Satellite

MODIS Moderate Resolution Imaging Spectrometer

NASA National Aeronautics and Space Administration

NASRDA National Space Research and Development Agency

NDVI Normalized Difference Vegetation Index

NGA National Geospatial-Intelligence Agency

NIR Near Infra-red

SRTM Shuttle Radar Topography Mission

TM Thematic Mapper

UTM Universal Transverse Mercator

CHAPTER 1 INTRODUCTION

1.0 BACK GROUND

Gully erosion is the most obvious form of soil erosion in south eastern Nigeria mainly because of the remarkable impressions the gullies make which are also visible manifestations of the physical loss of land due to erosion resulting in land degradation and lowering of agricultural productivity. However, human activities like land- clearing, and deforestation, overgrazing, as well as the creations of firewood tracks, accelerate the natural rates of these processes.

The task of managing natural resources of the earth is daily growing in complexities. This is due partly to in-creasing uncertainties in the natural- physical systems, as well as increasing interference of man with these systems.

Natural resources development and management is of tremendous concern to mankind. The utility derivable from resource use and the deleterious effects and consequences of resource abuse are important for continued existence of man and survival of the natural ecosystems. Degradation sets in when the capacity of a natural eco-system to renew itself is constrained by frequent disturbance and/or perturbations and this is a big threat to human survival and livelihood. Maps and measurements of degraded land can be derived directly from remotely sensed data by a variety of analytical procedures, including statistical methods and human interpretation.

Conventional maps are categorical, dividing land into categories of land use and land cover (thematic mapping; land classification), while recent techniques allow the mapping of land degradation and other properties of land as continuous variables or as fraction of the land by different land use-land cover categories, such as tree canopy, herbaceous vegetation, and barren

(continuous fields mapping). These types of datasets may be compared between time periods using Geographic Information Systems (GIS) to map and measure their extent and change at local, regional, and global scales.

South eastern Nigeria is a typical erosion region in the country. The presence of gully sites is one of the hazardous features that characterize Imo State and several other eastern states adjoining it (Okereke et al, 2012).

1.1 STATEMENT OF PROBLEM

From the Reconnaissance survey carried out on the 16th of March 2013, the old

Nekede road was seen to be completely cut off and divided by a huge 1 sided u shaped gully and other gully developments along the side of the road thereby inhibiting movement through the road and preventing the growth and development of businesses and homes in that area. It is a nightmare to pedestrians who use the dilapidated pedestrian bridge to cross over this gully daily. The runoff through the gully is causing sediment deposition both at the downstream end and the discharge outlet (Otamiri River). The impact of rain

drops still detaches soil particles from the exposed gully sides. The overland flow is further eating deep in to the surrounding area of the gully thereby causing further erosion.

The use of old methods and techniques to study erosion is time consuming and tedious.

1.2 AIMS & OBJECTIVES

It is primarily intended that this study will further demonstrate the usefulness and enhance appreciation for the techniques of Remote Sensing in land degradation assessment.

Specifically, the study has the following objectives:-

1. To highlight different tools for erosion appreciation with a bid to identifying

the beauty in the use of remote sensing.

2. To identify the distribution, causes and hazard of erosion in the study area.

3. Proffer possible control measures to tackle the erosion problem and

recommend the use of the remote sensing technique in monitoring against

development of erosion process in general and gullies in particular.

4. To provide useful spatial information of the study area for future

references.

1.3 JUSTIFICATION

Assessing erosion sites and mapping land erosion through the old traditional method of field surveying can be tedious and time consuming. However, with the introduction of Remote Sensing and Geographic Information System technologies, mapping land erosion becomes easy, less time consuming and gives room for regular updating and projection with a view to effectively and efficient management of land resources. Also, the use of remote sensing and

GIS techniques has been shown to have potential for erosion assessment on local and regional scales, including identification of eroded surfaces, estimation of factors that control erosion , investigation of soil and vegetation characteristics and monitoring the advance of erosion over time. (Alatorre and

Begueria, 2009)

1.4 SCOPE AND LIMITATIONS

 This project will be able to map out erosion risk zones for the study area

using remote sensing. A more comprehensive erosion risk analysis would

however involve more information about the physical and geological

characteristics of the study area. This is often not possible because of lack

of appropriate data such as drainage density, precipitation information,

rainfall distribution within the study area, past flood extent maps of the

area and land cover map of the area for some years. The reasons for

unavailability of these information are usually due to lack of technical

expertise in different government agencies responsible for developing and

recording of these information.

 Another significant limitation of the digital Elevation Models and processing

carried out in this project is the scale of analysis implied by the DEM

resolution since each cell in the DEM is 90m x 90m. Most of the analytical

functions used so far work by comparing one cell with its 8 adjacent

neighbours, the results of that analysis are likely to be at the resolution of

approximately 3 times the grid cell size. Unfortunately, the DEM cannot

provide us with direct information at a finer scale than its resolution (90m),

but we can use it to perform analysis at a broader scale.

 Low spatial and spectral resolution of the Landsat scenes also present a

limitation since high resolution imagery are not available for most regions

of the world and the available high resolution images like SPOT and IKONOS

imagery is of relatively high cost.

CHAPTER 2 LITERATURE REVIEW

2.0 REMOTE SENSING:

Remote Sensing is the acquisition of information about an object of phenomenon without making physical contact with the object. In modern usage, the term generally refers to the use of aerial sensor technologies to detect and classify objects on earth (both in the surface, and in the atmosphere and oceans) by means of propagated signals (e.g. electromagnetic radiation emitted from aircraft or Satellites. (Remote Sensing, 2013)

Remote sensing makes it possible to collect data on dangerous or inaccessible areas. Remote Sensing applications include monitoring deforestation in areas such as amazon basin, glacial features in Arctic regions and depth sounding of coastal and ocean depths.

In most cases remote sensing techniques have been applied simply to identify the characteristics (or the absence) of the vegetation cover, largely because of limited visibility of the soil surface in humid and sub-humid environments.

Other studies have demonstrated the usefulness of remote sensing techniques in determining temporal and spatial erosion patterns. Calculation of the percentage of bare ground has also been used to estimate erosion risk. Other methodologies applied to inventories and monitoring of erosion processes

include band ratios vegetation indices, combinations of reflective and microwave data, and combinations of remote sensing data and other ancillary data (Vrieling, 2006).

Remote sensing has been used for geologic interpretations with remarkable success. Remote sensing techniques are used because of their cost effectiveness, their ability to access areas that are difficult to access and because the data can be collected frequently and rapidly on a large scale.

Remote sensing also replaces costly and slow data collection on the ground, ensuring in the processes that areas or objects are not disturbed. These data sets allow earth-based phenomena such as land use and land cover characteristics to be rapidly mapped, if needed repetitively and at relatively low costs. With increasing capacity to rapidly generate maps of large areas, planners in the rural and urban areas are getting more empowered to address issues associated with land use analysis. The process of remote sensing is also helpful for archaeological investigations and geomorphological surveying.

Remotely sensed data, such as satellite images, are measurements of reflected solar radiation, energy emitted by the earth itself or energy emitted by Radar systems that is reflected by the earth. An image consists of an array of pixels

(picture elements) or grid cells, which are ordered, in rows and columns. Each pixel has a Digital Number (DN) that represents the intensity of the received signal reflected or emitted by a given area of the earth surface. The size of the

area belonging to a pixel is called the spatial resolution. The DN is produced in a sensor-system dependent range; the radiometric values. An image may consist of many layers or bands. Each band is created by the sensor that collects energy in specific wavelengths of the electro-magnetic spectrum.

It is important to note that the information provided by remote sensing is limited to the surface characteristics, although some statistical relationships are established between the surface and depth properties (Vrieling, 2006).

2.0.1 CLASSIFICATION OF REMOTE SENSING

The output of a remote sensing system is usually an image representing the scene being observed. A further step of image analysis and interpretation is required to extract useful information from the image. Depending on the scope, remote sensing may be broken down into:

(1) Satellite remote sensing (when satellite platforms are used)

(2) Photography and photogrammetry (when photographs are used to capture visible light)

(3) Thermal remote sensing (when the thermal infrared portion of the spectrum is used)

(4) Radar remote sensing (when microwave wavelengths are used), and

(5) LIDAR remote sensing (when laser pulses are transmitted toward the ground and the distance between the sensor and the ground is measured based on the return time of each pulse) (Ojo and Adesina, 2007).

2.0.2 REMOTE SENSING DATA SETS

Lately, several remote sensing data types are now available for geological and environmental studies. The variety has increased as many nations including some African countries invest in satellite remote sensing. However, each data type has its own peculiar features that may limit or enhance its relevance to capture data for specific range of information.

Some of the most commonly used remote sensing data sets for mapping land use and land cover are those from Landsat, ASTER (Advanced Space-borne

Thermal Emission and Reflection Radiometer), MODIS (Moderate Resolution

Imaging Spectrometer), NigeriaSat-1 and recently, NigeriaSat-2 satellites. The

Landsat data have greater spectral resolution (Gastellu-Etchegorry, 2000) and a longer time series, while SPOT provides better spatial resolution but with shorter historical records. Newer satellite imaging systems are commonly equipped with enhanced instruments to generate additional data that permit more accurate mapping and analysis. Landuse/land cover analyses usually proceed from classification of the area of study. The classified units can be

further analyzed in terms of their characteristics particularly size. Factors that may influence classification accuracy include a sensor’s spatial, radiometry and spectral resolutions. Spatial resolution describes the size each pixel represents in the real world (Cushnie, 1999). For example, a satellite with 30 meter resolution produces pixels that measure a 30x30 meter area on the ground.

Radiometric resolution, on the other hand, is the smallest difference in brightness that a sensor can detect. A sensor with high radiometric resolution would therefore have very low “noise”. The “noise” is described as any unwanted or contaminating signal competing with the desired signal. Spectral resolution is the number of different wavelengths that a sensor can detect. A sensor that produces a panchromatic image alone has a very low spectral resolution, while one that can distinguish many shades of each colour has a high spectral resolution (Jensen, 2007).

TABLE 2.1: COMPARISON BETWEEN HUMAN AND COMPUTER INFORMATION EXTRACTION. Method Merit Demerit Human (image 1. Interpreters knowledge 1. Time consuming interpretation) are available 2. Excellent in spatial 2. Individual difference information extraction Computer (Image 1. Short Processing time 1. Human knowledge is Processing) unavailable 2. Reproductivity 2. Spatial information extraction is poor 3. Extraction of physical quantities or indices is possible

2.0.3 DATA PROCESSING

Generally speaking, remote sensing works on the principle of the inverse problem. While the object or phenomenon of interest (the state) may not be directly measured, there exists some other variable that can be detected and measured (the observation), which may be related to the object of interest through the use of a data-derived computer model. The common analogy given to describe this is trying to determine the type of animal from its footprints. For example, while it is impossible to directly measure temperatures in the upper atmosphere, it is possible to measure the spectral emissions from a known chemical species (such as carbon dioxide) in that region. The frequency of the emission may then be related to the temperature in that region via various thermodynamic relations. The quality of remote sensing data consists of its spatial, spectral, radiometric and temporal resolutions.

2.0.4 IMAGE INTERPRETATION

The features that our brains use when we interpret an image can be grouped into six main types, summarised below:

1. Tone: variations in relative brightness or colour.

2. Texture: areas of an image with varying degrees of smoothness or

roughness.

3. Pattern: the arrangement of different tones and textures; may

indicate certain types of geology or land use.

4. Shape: distinct patterns may be due to natural landforms or human

shaping of the land.

5. Size: recognition of familiar objects allows size estimation of other

features; size is an important aspect of association: for instance, a 20

km-wide circular surface depression is unlikely to be a sinkhole, but

might be a volcanic caldera.

6. Association: the context of features in an image, e.g. a drainage

pattern.

2.0.5 REMOTE SENSING COMPUTER SOFTWARES

Remote Sensing data is processed and analyzed with computer software, known as a remote sensing application. A large number of proprietary and open source applications exist to process remote sensing data. Remote Sensing

Software packages include: ilwis 3.3 Academic, opticks, ERDAS Imagine, ArcGIS from ESRI, TNTmips from MicroImages, PCI Geomatica made by PCI Geomatics,

IDRISI from Clark Labs, Image Analyst from Intergraph, RemoteView made by

Overwatch Textron Systems etc.

2.1 EROSION

Soil erosion is a natural geomorphic process, taking place persistently over the earth’s surface. Soil erosion is one of the most significant environmental problems in the world today, as it seriously threatens agriculture, natural resources and the environment.

Soil erosion is the physical removal of materials (soil particles) from one place to another. It is an accelerated process under which soil is bodily displaced and transported away faster than it can be formed. Soil erosion is caused by the action of water and wind. Rain striking the ground helps to break soil particles loose and then runoff carry away loosened soil. Soil erosion agents can also be anthropogenic factors. Erosion physically removes materials (soil) in place after weathering (breakdown of rock or mineral materials) have broken them down into smaller pieces which are movable. Soil erosion starts with rainfall droplets dislodging particles of soil, removing them and eventually depositing them at a new location different from the original site. The erosion problems of an area is subjected to certain factors which include the geology, land use act, geomorphology, climate, soil texture, nature and bio diversity of the area. It constitutes the major ecological problems in the south eastern states of

Nigeria. Imo State has the fifth highest concentration of active gully sites in

Nigeria. Gully erosion has remained the most prominent feature in the landscape of Imo State and every community in the State has a tale of woe as a result of ever increasing gullies that affects soil productivity, restricts land use and can threaten roads, fences and buildings. (Okereke et al, 2012)

2.1.1 CLASSIFICATION OF SOIL EROSION

The classification of soil erosion is based on its causative factors. Hence, we have wind, water and anthropogenic (man-made) erosions.

The process of soil erosion could be slow and continues unnoticed (natural), or it may occur at an alarming rate causing serious loss of top soil (man-made). As such, it could be classified based on level and degree of formation. These classification include sheet, rill, channel and gully erosion.

 Sheet Erosion: begins with slow and progressive removal of a thin but fairly

uniform layer of topsoil from an area by flood or run-off.

 Rill Erosion: occurs when run-off water laden with soil particles and debris

erodes an area of land surface more than others (OMAFRA Staff, 2003).

 Channel Erosion: Repeated rill erosion along a run-off path that creates a

vertical bank not deeper than three metres produces channel erosion.

 Gully Erosion: occurs when deep and large channel assuming great depths

are created by run-off water (Abegunde and Peter, 2003). This type of soil

erosion is common in Southern-eastern Nigeria.

2.1.2 FACTORS INFLUENCING EROSION

The rate and magnitude of soil erosion by water is controlled by the following factors as illustrated by (OMAFRA Staff, 2003).

 Rainfall Intensity and Runoff: Both rainfall and runoff factors must be

considered in assessing a water erosion problem. The impact of raindrops

on the soil surface can break down soil aggregates and disperse the

aggregate material.

 Soil Erodibility: Soil erodibility is an estimate of the ability of soils to resist

erosion, based on the physical characteristics of each soil. Generally, soils

with faster infiltration rates, higher levels of organic matter and improved

soil structure have a greater resistance to erosion.

 Tillage and cropping practices: Cropping practices which lower soil organic

matter levels, cause poor soil structure and contribute to increases in soil

erodibility.

 Slope Gradient and Length: Naturally, the steeper the slope of a field, the

greater the amount of soil loss from erosion by water. Soil erosion by water

also increases as the slope length increases due to the greater accumulation

of runoff.

 Vegetation: Soil erosion potential is increased if the soil has no or very little

vegetative cover of plants and/or crop residues. Plant and residue cover

protect the soil from raindrop impact and splash, tends to slow down the

movement of surface runoff and allows excess surface water to infiltrate.

 Topography: Hudson (2009) observed that in simplest terms steep land is

more vulnerable to water erosion than flat land for reasons that erosive

forces, splash, scour and transport, all have greater effect on steep slopes.

Soil erosion generally is a function of slope attributes.

 Climate: The rainfall of southern Nigeria generally is heavy and aggressive.

Rainfall intensities are high and often above 50mm/h with short interval

intensities in excess of 100 mm/h. Rainfall often come between the month

of March and last till October. In some years the rainy period is unduly

prolonged while in other years their onset may be delayed for a few weeks.

The present global climate change has not helped issues in this regard.

 Anthropogenic Influence: Misuse of land and poor farming systems

encourage accelerated runoff and soil loss due to erosion. While

uncontrollable grazing caused by the nomads has resulted in deforestation

of the landscape while indiscriminate foot paths created on the landscape

has helped in formation of incipient channels on the landscape. These

channels eventually metamorphose to gullies especially when they are not

checked at the inception. Road constructions including uncontrolled

infrastructural developments have contributed significantly in gully

developments.

2.2 REVIEW OF SOIL EROSION IN THE WORLD

Soil erosion remains the world’s biggest environmental problem, threatening sustainability of both plant and animal in the world. Over 65% of the soil on earth is said to have been displaced by degradation phenomena as a result of soil erosion, salinity and desertification (Okin,2002).

United Nations (UN) Convention to combat land Degradation (CCD) opines that soil erosion automatically results in reduction or loss of the biological and economic productivity and complexity of terrestrial ecosystems, including soil nutrients, vegetation, other biota, and the ecological processes that operate therein (Claassen, 2004).

China faces one of the most serious soil erosion problems in the world. The latest remote sensing survey of the area shows that the country has some 3.56 million square kilometres of soil erosion areas. This accounts for about 38% of

China total territory (Beijing Time, 2002).

2.3 REVIEW OF SOIL EROSION IN NIGERIA

Gully initiation is the result of localized erosion by surface runoff, associated with rainfall events of high intensity. Erosion is frequently focused, where the forest cover has been removed for agricultural purposes and also at the sites of uneven compaction of surface soils by foot (human and livestock) and wheeled traffic, in off-road locations. It also takes place, where soils and sediments abut against artificial materials, notably at poorly designed road culverts and roadside gutters.

Gullies also occur, where springs issue from permeable sands, at contacts with less permeable deposits beneath. In general, the propagation of gullies is by sapping, caving and sliding at the gully head and sliding along the sides, accompanied by the down-slope transportation of gully-floor debris by storm runoff. (Famous, 2011)

Gully erosion has had and will continue to have destructive impacts in Nigeria in the absence of immediate corrective and preventive measures. The government and the world cannot afford to remain silent in the face of this ecological calamity that may wipe out millions of people.

Erosion in Nigeria has led to increasing concern on climate policy for the entire country. Areas in Nigeria have been subject to damages caused by increasing erosion for over 50 years. This issue has grown over the past years, and

responsibility has been pointed at lack of policy. However, locals recognize that they too contribute to the problem of erosion through our attitude to waste disposal as we dump garbage in the drains.

2.4 REVIEW OF SOIL EROSION IN SOUTH EAST NIGERIA

In spite of technological advancement, erosion menace still remains a major problem in Nigeria (especially in South Eastern Nigeria). The yearly heavy rainfall has very adverse impacts altering existing landscape and forms.

Albert, (2006) stated that soil erosion in the South-eastern part of Nigeria has been identified as the most threatening environmental hazards in the country

Gully erosion has impacted the south eastern region of Nigeria adversely more than any other part of the country (Ikenna, 2006).

Boniface (2011) said that it will be a historical success story if one can deal deathblows on the ecological disasters ravaging the different towns and communities in Igbo Land. The Igbo States of Abia, Anambra, Enugu, Ebonyi and Imo that form the Southeast zone of Nigeria have been suffering from these horrendous ecological hazards. These require the most serious control programmes.

It is estimated that about N100.5 billion (one hundred and a half billion naira) would be required to tackle effectively the ecological problems of floods, soil and gully erosion and landslides in the southeast at first instance for a year.

One is most amazed that despite all efforts made by Ndigbo on their ecological problems to the Federal Governments of Nigeria over the years, none of them has taken the ecological problems as a serious matter that requires major funding and actions. Worse still, the Federal officials tend to spend heavy amounts of funds tackling less threatening and less dangerous ecological problems in parts of the north and west while doing nothing in the southeast where damaging ecological problems abound! The entire scenario is tantamount to a serious denial of fundamental human rights of existence, good life, ownership to lands and property and safety of the people in the various communities. All concerned must do everything possible to avert the impending and ominous ecological Armageddon or geo-anthropocide that now threatens the southeast and beyond. Gully erosion has had and will continue to have destructive impact in and around the southeast of Nigeria in the absence of immediate corrective and preventive measures (Boniface, 2011).

Several studies have estimated erosion in the south eastern Nigeria using remote sensing at regional and catchment scales. These studies have revealed that Anambra, Abia, Imo, Enugu and Ebonyi States have over 750, 650, 500,

400 and 250 major erosion sites respectively. This gully census is conservative and incomplete since smaller and younger gullies were not enumerated. These younger gullies shall ultimately mature within a few years and pose as serious a hazard as older ones. They must also be included in any control programmes

(Boniface, 2011)

CHAPTER THREE

MATERIALS AND METHODS

3.0 RECONNAISSANCE SURVEY

The reconnaissance survey of the selected gully erosion site was carried out on

16th of March, 2013. This survey involved physically checking out the gully sites, making observations, visual analysis of outcrops, topography, slope, gullies and vegetation cover. The Global Positioning System (GPS) device was used to measure the coordinates of the study area to give a range of latitude 5.18° –

5.39° N and longitude 6.51° - 7.08° E

3.1 GEOLOGY OF STUDY AREA:

The study area is situated in Owerri west Local Government Area, Imo State, south-eastern Nigeria, and is located approximately between latitude 5.18° –

5.39° North of the Equator and longitude 6.51° - 7.08° East of the Greenwich

with an average altitude of about 300m and above covering about 1,200 square km and has a humid tropical climate, having a mean annual rainfall varying from 1,500mm to 2,200mm (60 to 80 inches) and a mean annual temperature range of 270-280C. Orographic rainfall is common in the area. The

Otamiri river is a major tributary of the Imo river traversing the site.

PLATE 3.1: MAP OF NIGERIA SHOWING IMO STATE AND STUDY AREA.

3.2 PHOTOS FROM THE SITE

PLATE 3.2: GULLY EROSION THREATENING TO COLLAPSE A HOME

PLATE 3.3: ONE SIDED STEEP V SHAPED GULLY BY OLD NEKEDE ROAD, UMUMBAZU

3.3 METHODOLOGY

DATA ACQUISITION

Landsat 7 ETM+ (2013) SRTM 90m DEM

PRE-PROCESSING IMAGE CLIPPING

GEO-REFERENCING CONTRAST ENHANCEMENT IMAGE ENHANCEMENT SPATIAL ENHANCEMENT

NDVI SLOPE & ASPECT

FALSE COLOUR IMAGES RELIEF

DRAINAGE

TERRAIN

CONTOUR

FIGURE 3.1 METHODOLOGY FLOW DIAGRAM

3.4 DATA TYPE AND ACQUISITION

Digital images have some major advantages over paper or film (analogue) images: they take up less storage space, perfect copies can be created time and time again, they can be reduced or enlarged at the push of a button, cartographic errors can easily be removed, and most important of all, digital images can be processed using statistics, to enhance, analyse and classify their features. Images acquired include:

3.4.1 LANDSAT 7 ETM+ IMAGE

The NigeriaSat-2 Earth observation satellite provides the Nigerian National

Space Research and Development Agency (NASRDA) with very high-resolution imaging capability. The landsat data used were the NigeriaSat-2 images acquired in May 2013 from the National Space Research and Development

Agency (NASRDA). The images were obtained using landsat ETM sensor with a resolution of 30m. Landsat TM and ETM data acquired had cloud cover of less than 20%. The images were Geo-referenced to a universal transverse Mercator

(UTM) grid using the softwares to allow compatibility and comparison with other data sets. (Ibeneme, 2013).

3.4.2 SRTM 90m DEM

For regional-scale studies, free 1:250,000 DEM data are available from the

Shuttle Radar Topography Mission (SRTM). SRTM was launched on February

11, 2000 and was a joint project between National Geospatial-Intelligence

Agency (NGA) and the National Aeronautics and Space Administration (NASA)

(USGS, 2008). The mission’s objective was to collect and produce high resolution digital elevation data for almost all of Earth’s land surface (80 percent) between 60°N and 56°S (USGS, 2008). Edited data became available by 2004 at a spatial resolution of 1 arc second for the United States

(approximately 30m) and at 3 arc seconds for the remaining parts of the world

(approximately 90m). SRTM 90m DEM of the study area was downloaded from the CIAT-CSI SRTM website (http://srtm.csi.cgiar.org) and was in datum WGS84 on the 30th April 2013. The data was projected to the UTM coordinate system and clipped to the extent of the study area.

The raster consists of 349 columns and 442 rows with a resolution of 90m per

DEM cell. This means the landscape is approximately 10km by 14km in extent.

We also know from the information that the range of heights within this region is approximately 20m to 181m - a vertical range of about 161m.

3.5 SOFTWARE USED

The software used in data processing and analysis are listed below. These softwares have the capacities of carrying out various data enhancement techniques such as linear enhancement, statistical analysis, principal component analysis and normalized difference vegetation index.

1. Purdue Research Foundation’s Multispec 3.3: for Landsat NDVI processing.

2. 52North’s Ilwis 3.31 Academic: used for image enhancement and False

Color Composite image generation of Landsat data.

3. Landserf 2.3: Used for image clipping and geo-referencing of Landsat data

and generation of contour, drainage, terrain, slope and relief maps of DEM.

3.7 PRE-PROCESSING OF DEM AND LANDSAT TM IMAGES

3.7.1 Image Clipping

Each of the images was clipped to the extent of the study area which is between latitude 5.18° – 5.39° N and longitude 6.51° - 7.08° E.

3.7.2 Geo-Referencing

When an image is created, either by a satellite, the image is stored in row and column geometry in raster format. There is no relationship between the rows/columns and real world coordinates yet. In a process called geo- referencing, the relationship between row and column number and real world coordinates can be established. The images were Geo-referenced to a

Universal Transverse Mercator (UTM) grid using the software.

3.7.3 Image Enhancement

The objective is to create new images from the original image data, in order to increase the amount of information that can be visually interpreted and number of features that can be extracted. Image enhancement deals with the procedures of making a raw image better interpretable for a particular application and improve the visual impact of the raw remotely sensed data for the human eye.

Image enhancement techniques carried out on the images in ilwis 3.3 academic include:

1. Linear Stretching (Contrast enhancement): To transforms the raw

data using the statistics computed over the whole data set.

2. High pass filtering. (spatial enhancement): Sometimes abrupt

changes from an area of uniform DNs to an area with other DNs can

be observed. This is represented by a steep gradient in DN values.

Boundaries of this kind are known as edges. They occupy only a small

area and are thus high-frequency features. High pass filters are

designed to emphasize high frequencies and to suppress low-

frequencies.

3.7 SRTM 90m DEM PROCESSING

3.7.1 VISUALISATION OF SURFACE MODELS OF TERRAIN.

The visual representation of landscape form has long history dating at least as far back as the images of mountains scratched onto earthenware in

Mesopotamia over 4000 years ago (Imhoff, 1982).

The subsequent development of cartographic terrain representation can be seen as a struggle to symbolise multiple perspectives of 3-dimensional surface form in a (usually) static 2-dimensional medium

Solutions have included the use of hachuring to symbolise lines of steepest decent, the widespread use of contour lines to represent slope normals, and

the more recent use of automated shaded relief calculation from Digital

Elevation Models. By stating the scale (ratio) of a map, the reader is able to infer the implied level of detail.

The DEMs were visually assessed to observe their conformance to the field knowledge of the terrain shape and their consistency in representing the prominent geomorphic features like drainage networks and ridges.

One of the most important factors affecting soil erosion by water is topography. Digital Elevation Models (DEMs) have been commonly used in a

Geographic Information System (GIS) for representing topography and for extracting topographical and hydrological features for various applications, including soil erosion studies (Zhang 2008)

3.7.2 MODEL TRANSFORMATION

A contour representation of a DEM is obtained with user defined contour interval and the elevation of the lowest contour.

3.7.3 SLOPE, ASPECT AND CHANNELS MAPS

The output slope map represents the degrees of inclination from the horizontal. The output aspect map indicates the direction of slope gradients and the aspect categories represent the number of degrees of east increasing in the counter clockwise direction.

3.8 LANDSAT IMAGES PROCESSING

3.8 .1 NDVI (Normalized Difference Vegetation Index)

By taking the ratio of red and near infra-red bands from a remotely-sensed image, an index of vegetation “greenness” can be defined. The Normalized

Difference Vegetation Index (NDVI) is probably the most common of these ratio indices for vegetation.

Mathematically, NDVI calculation With Landsat TM or ETM+ is given as:

Where NIR = near infra-red band value for a cell

RED = red band value for the cell

Vegetation cover has been widely studied with remote sensing (Shoshany,

2000), due to its distinct signature in the visible and near-infrared part of the electromagnetic spectrum. The most commonly used imagery is provided by

Landsat TM and SPOT HRV. These are used as indicators for spatial and temporal changes in soil fertility (Julien and Sobrino, 2009). In his study, (Park et al. 2004) used vegetation indices to estimate the impacts of hydrologic properties. He showed that values for NDVI are related to soil runoff potential.

Considering that soil is classified in hydrologic soil groups, based on runoff potential and soil physical conditions, it is suggested that physical degradation

can influence NDVI (Omuto and Shrestha, 2007), (Park et al., 2004). NDVI is correlated with many ecosystem attributes that are of interest to researchers and managers and makes it possible to compare images overtime to look for ecologically significant charges.

(Drainage and FCC) Normalised Difference Vegetation Index (NDVI): responds to green biomass, chlorophyll content and leaf water stress. The use of NDVI in regions with <30% vegetation cover should be treated with caution, due to large amounts of bare soil and rock that will influence the reflectance values.

The output of NDVI is a measure of vegetation richness of an area. Values of

NDVI can range from -1.0 to 1.0, but values less than zero typically do not have any ecological meaning. Low NDVI values mean there is little difference between the red and near infra-red (NIR) signals. This happens when there is little photosynthetic activity, or when there is just very little NIR light reflectance (that is, water reflects very little NIR light from the NDVI of + 0.30 to – 0.38) which shows unhealthy vegetation.

3.8.2 FALSE-COLOUR IMAGES

False-colour images are the most widely used product of image processing. By allocating three of the bands (i.e. wavelengths) from a scanned image to the blue, green and red colour-guns of a computer screen, a false-colour image is

produced. Creating ‘false colour’ images is very useful, as it allows us to view images captured in parts of the spectrum that would otherwise be invisible to our eyes, such as infra-red or ultra-violet. . The reflection of colour tones of different materials on the earth helps in distinguishing surface materials and their boundaries. In this study, there are three colour composite images with

RGB, R=Red, G=Green, B=Blue bands of landsat TM multispectral image respectively.

CHAPTER FOUR

RESULTS AND DISCUSSION

4.0 NDVI MAP

It is clearly shown on the NDVI map that the discrimination between the 3 land cover types is greatly enhanced by the creation of a vegetation index. Green vegetation yields high values for the index. In contrast, water yield negative values and bare soil gives indices near zero.

As we can see in Plate 4.2, The Central region can be visibly spotted as areas represented by the blue colour with NDVI of -1.00 – 0.00. This colour can also be noticed in a large part of the south west area indicating built up areas. The area were much earlier in time protected by dense forest cover which the inhabitants removed in the process of urbanization and agricultural activities leading to an exposure of the fragile soil to the heavy downpour and concentrated runoff of the area. The high speed of the surface runoff culminates in rapid washing away of the soil surface and weakening the soil strata which can cause gullies in the area. This fact is attested to by the findings of Igbokwe (2003). The regions represented by the colour green have the highest vegetation with an NDVI of 0.35 up to 1.00.

4.1 COLOUR COMPOSITE IMAGES

The composite image provides a naturalistic and earth view of the study area and the images reveal the drainage pattern of the study area to be dendritic.

According to (Ofomata, 2001) Soil erosion (gully) are more intensive on soil on which former growth has been disturbed to make way for infrastructure, agriculture and other related landuse activities. Colour composites can help us explore all the different areas on the map as in Plates 4.3, 4.4 and 4.5.

4.1.1 RGB 432: Standard False Colour Composite

This is a very popular band combination and is useful for vegetation studies, monitoring drainage and soil patterns and various stages of crop growth.

Vegetation appears in shades of red, urban areas are cyan blue, and soils vary from dark to light browns. Clouds are white or light cyan. Coniferous trees will appear darker red than hardwoods. Generally, deep red hues indicate broad leaf and/or healthier vegetation while lighter reds signify grasslands or sparsely vegetated areas. Densely populated urban areas are shown in light blue and blue represents water bodies in Plate 4.3.

4.1.2 RGB 321: True (Natural) Colour Image

Plate 4.4 is the "natural colour" band combination because the visible bands are used in this combination, ground features appear in colours similar to their appearance to the human visual system, healthy vegetation is green, recently cleared fields are very light, unhealthy vegetation is brown and yellow, roads are grey, and shorelines are white. Light blue regions show the urban areas and bare soil, while the shades of blue represent the drainage channels. This band combination provides the most water penetration information. It is also used for urban studies. Cleared and sparsely vegetated areas are not as easily detected here as in RGB 432. Clouds appear white and are difficult to distinguish. Also note that vegetation types are not as easily distinguished as the RGB 451. The RGB 321 combination does not distinguish shallow water from soil as well as RGB 753 does.

4.1.3 RGB 453

This combination offers added definition of land-water boundaries and highlights subtle details not readily apparent in the visible bands alone as seen in Plate 4.5. Inland lakes and streams can be located with greater precision.

Vegetation type and condition show as variations of hues (browns, greens and oranges) as well as in tone. This combination demonstrates moisture differences and is useful for analysis of soil and vegetation conditions.

Generally, the wetter the soil, the darker it appears, because of the infrared absorption capabilities of water.

Other useful RGB colour composites include

 RGB 531: Displays topographical textures

 RGB 731: Display differences in rock types

 RGB 745: For geological studies

 RGB 543: For vegetation studies

 RGB 753: Monitoring forest fires.

4.2 DIGITAL ELEVATION MODEL

4.2.1 Morphometric Analysis.

The colour of any point gives an indication of elevation, and the rate of change in colour over the image gives some idea of slope. We already gained some impression of the landscape represented by the DEM (Plate 4.6) simply by visualising it.

We might infer from the DEM image map (Plate 4.6) that the slope surrounding the hills towards the top right of the image is somewhat steeper than that of the (green) south west of the map.

We also know from the landcover information (Plate 4.2) that large parts of the landscape are occupied by vegetation with some patches of grasslands and also sparsely vegetated areas. Such description however has obvious limitations because it is subjective and rather vague.

We thereby use LandSerf 2.3 to provide us with a more systematic and objective description of the landscape (Plate 4.6). The basis for most of the analytical functions in LandSerf 2.3 is the process of quadratic approximation.

4.2.2 Relief

Relief represents the elevation of an area from the mean sea level. As far as the study area is concerned the relief ranges from 20m to 181m from the mean sea level. Using Landserf 2.3, the relief map (Plate 4.6) was prepared for the study area. From the Figure, The South-West region records a low elevations and large coverage with low lands ranging from 48 - 90 meters above sea level. As we travel North-east we notice higher elevations ranging from 90 – 140 meters trending North-east and the highest elevations are found at the North-east corner with a peak of 140 - 180 meters. While the South-

West region has the lowest elevation records, the elevation of the area is increasing as you move from the south-western part to the north-eastern part of the study area and it is characterized by low hills with steep slopes which

when correlated with the intense rainfall can be a causative factor for gully erosion in the area.

4.2.3 Frequency Distribution

The vertical range identified from the DEM only gives us a broad summary of how elevation changes over the surface. We can get a more detailed view by examining the frequency distribution of elevation values (Fig. 4.2).

The most obvious feature of the distribution is the peak in the histogram at about 68m. This corresponds to the area that dominates the south-western half of the DEM.

4.2.4 Slope

We can get a better idea of the ’roughness’ of the terrain by calculating a slope map (Plate 4.7) using quadratic regression. Slope represents plane of tangent to the surface.

Slope is coloured from white (horizontal) through yellow to red (steepest slope). This new image shows the steepest slope at the north of the map.

Steep slopes can also be observed in areas bordering otamiri river and its tributaries.

Slope is an important controlling factor for development and formation of soil erosion. Some of the best transport equations are based on stream power, which is the product of slope and discharge (Hessel and Jetten, 2007). Since discharge itself is also determined by slope, the relation between erosion and slope is a power function and therefore it is very sensitive.

Slope represents one of the four surface parameters that are often used to characterise surface behaviour. The other three are aspect which represents the direction to which the terrain slopes, profile curvature, which describes the rate of change of slope in profile, and plan curvature, which describes the rate of change of aspect in plan. Maps can be generated for each of these parameters.

4.2.5 Terrain Classification

Finally, we shall examine one further characterisation of the surface. We classify the terrain into surface features by grouping all points on a terrain into one of the following: channels, passes, ridges, peaks and planar regions.

On the Terrain Classification map (Plate 4.8), we see a predominantly grey, blue and yellow image appear. The grey areas represent planar regions, blue areas represent channels and yellow ridges and looking carefully, we can also

see occasional red cells that represent local peaks and green cells that represent passes in the landscape.

Feature classifications like this are useful for several reasons. The pattern of channels appears somewhat similar to a drainage network, thus giving us an idea of where water would flow over the surface. Perhaps less obviously the pattern of yellow cells gives us an equivalent ridge network, identifying portions of the landscape where water is likely to flow from.

4.2.6 Drainage Pattern

The patterns produced by drainage networks are a useful guide to underlying soils and geology. Dendritic drainage patterns are typical of relatively uniform, moderately well-drained soils and rocks and forms in easily-erodible silt deposits. (Okereke et al, 2012). The dendritic drainage pattern observed in the study area in terrain classification map (Plate 4.8) is associated with trench branching tributaries joining the main stream at acute angle and this pattern shows up on homogeneous, uniform soil and rock materials mostly in soft sedimentary rocks and old dissected coastal plains (Howard, 1967).

4.2.7 DEM Transformation to Contour Map

A contour representation of a DEM is obtained (Plate 4.9) using contour interval of 8m and the elevation of the lowest contour as 20m and grid width of 4m. All these variables can be varied as desired.

4.2.8 Surface Profiles

Cross-sectional linear profiles can be displayed as we move from one location to another in. The labels along the X-axis give the distance from the first point in the profile in ground units and the Y-axis gives the elevation. Fig. 4.1 below demonstrates this by showing the land profile/shape as we move from point A to B. This profile can help us in assuming the speed and direction of runoff, level of slope and land shape and thereby predicting erosion prone areas.

FIG. 4.1: SURFACE PROFILE AS WE MOVE FROM POINT A TO B.

4.3 3D PERSPECTIVE REPRESENTATION OF THE STUDY AREA (VIRTUAL

REALITY)

Machines with graphics cards capable of accelerating 3D graphics through

OpenGL can take advantage of 3D perspective rendering view option available in LandSerf 2.3. The 3D representation tool is a useful tool for morphological exploration of active erosion areas and erosion risk areas.

The images show an interactive 'fly-through' over the surface where the viewer is immersed within the viewing space itself. Here it is possible to gain both a detailed 'large-scale' view of the surface in the foreground simultaneously with a generalised 'small-scale' view of the background. By allowing the user to control the imaginary camera position interactively, the relationship between cell-by-cell measures (as indicated by the coloured surface) and the more regional morphometry of the surface may be investigated. It offers advantages over static perspective views in that multiple viewpoints can be explored with ease by rotating viewing direction, different

parts of the surface may be viewed in such a manner. This helps to perceive the terrain in different angles.

4.4 GOOGLE EARTH

High-r esolution images available on Google Earth are increasingly being consulted in geographic studies. However, most studies limit themselves to visualizations or on-screen measurements. Google Earth allows users to zoom in and out, make comparison between google earth imagery and satellite imagery. This can help us interpret the satellite imagery better and also to understand why the image appears the way it does.

The DEM map can be exported as KML files for display and overlay in

GoogleEarth. It offers advantages over static perspective views in that multiple viewpoints can be explored with ease by rotating viewing direction.

PLATE 4.1: STUDY AREA OVERLAY DISPLAYED IN GOOGLE EARTH

PLATE 4.2: NDVI MAP OF STUDY AREA

PLATE 4.3: RGB 432 (STANDARD FALSE COLOUR COMPOSITE)

PLATE 4.4: RGB 321 (TRUE NATURAL COLOUR IMAGE)

PLATE 4.5: RGB 453

PLATE 4.6: DIGITAL ELEVATION MODEL (DEM) OF STUDY AREA SHOWING RELIEF

Frequency

Elevation

FIG 4.2: ELEVATION FREQUENCY DISTRIBUTION OF THE DEM

PLATE 4.7: SLOPE MAP

PLATE 4.8: TERRAIN CLASSIFICATION OF STUDY AREA

PLATE 4.9: CONTOUR MAP GENERATED FROM THE DEM OF THE STUDY AREA

CHAPTER FIVE

CONCLUSION AND RECOMMENDATIONS

5.0 CONCLUSION

1. This Study has demonstrated the beauty and utility of remote sensing data

in erosion study at a local scale.

2. All the parameters associated with soil erosion estimated from imagery

including assessments of vegetation cover, calculation of vegetation index,

changes in topography as outlined by Alatorre and Begueria (2009) were all

explored and it was also determine that inherit susceptibility of the study

area to gully erosion is derived from the effects of activities on the geologic

formations of the area which is characterized by poor geotechnical

properties based on previous studies in south eastern Nigeria as a whole.

(Nwajide and Hogue, 1999) (Egboka and Okpoka, 1994) (Ehirim and

Ebeniro, 2006).

3. The Landsat Images of the study area obtained is cost effective and easy to

edit for various scenarios, while the Applications used to analyse them are

user friendly and provide spatial analysis of multiple data layers, technical

professionals can reap the benefits of GIS without having to be a proficient

GIS specialist.

4. Remote sensing techniques were applied in the study area for erosion study

with desirable levels of accuracy and effective and accurate assessment of

soil erosion factors in considerable short time.

5.1 RECOMMENDATIONS

1. The government should releases fund each year to reduce and combat the

challenges resulting from effects of the gully erosions.

2. Application of measures such as channelization of floodwater, tree planting

and erection of concrete breakers etc. in protecting and preserving the

environment and making available more land for agriculture and other

human activities and at the same time create a functional, attractive,

liveable and beautiful environment.

3. To achieve the above, the following landscape elements are required; trees,

shrubs, grasses, walls, buffers, rocks, and gravels. Economic and non-

economic tree should be used, which can be hewn and replaced at

intervals. For shrubs, approved seeds and fast growing leguminous grasses

that can restore worn out soil nutrients as a result of erosion should be

used. Structural and non-structural landscaping measures are

recommended as good control and management techniques to check

continuous gully erosion problems and its impacts. A more practical

approach at the local level, with respect to control of farming practice,

enhanced afforestation, prevention of bush burning and overgrazing would go a long way in reducing the problems and consequences of erosion in The study area, Imo State and Nigeria in general.

REFERENCES

Abegunde, A. A. and Peter, O. O. (2003). “The impact of Erosion on Rural Economy: The Case of Nanka in Anambra State of Nigeria”. In Urban Finance and Infrastructure Development in Nigeria. YomiFawehinmi (ed.) Atlantis Books. PP 227-243.

Alatorre, L. C. and Begueria, S. (2009). Identification of eroded areas using remote sensing in a badlands landscape on marls in the central Spanish Pyrenees CATENA, 76(3):182-190.

Albert, A. A., Samson A. A., Peter O. O. and Olufunmilayo A. O. (2006). An Assessment of the Socio Economic Impacts of Soil Erosion in South- Eastern Nigeria.

Beijing Time (Peoples daily) (2002) “Soil Erosion, Biggest Global Environmental Problem”.Retrieved on Tuesday, 11th April, 2013 from http://english.people daily.com.cn.

Boniface, E. (2011). “Gully Erosion in Alaigbo”.Online edition vol. 4 – Washington DC, USA.

Claassen, R. (2004). Have conservation Compliance Incentives Reduced Soil Erosion? Amber Waves. The Economic of Food, Farming, Natural Resources and Rural America, June 2004. (http://evs.usda.gov/Amwberwave/june04/features/haveconservatio n.htm.

Cushnie, J. L. (1999). The interactive effect of spatial resolution and degree of internal variability within land-cover types on classification accuracies. Photogrammetric Engr. and Remote Sensing 8(1): 15-29.

Egboka, B. C. and Okpoka, J. C. (1994). “Gully Erosion in the Agulu-Nanka Region of Anambra State, Nigeria” International Association of Hydrological Sciences. Publ. No. 144:335-347.

Ehirim, C. N. and Ebeniro, J. O. (2006). “Tectonic Trends Delineated from Drainage Lineament Analysis and Azimuthal Resistivity Survey: A case study of South Eastern Nigeria Gully Erosion Belt”. Global Journal of Pure and Applied Sciences 4(1): 21-28.

Famous, I. (2011).Devastating Effects Of Gully Erosion In Edo State. Retrieved from http://nigerianobservernews.com/10072011/Sundayobserver/ features/features6.html

Gastellu-Etchegorry, J. P. (2000). An assessment of SPOT X and Landsat MSS data for digital classification of near-urban land cover, International Journal of Remote Sensing 11(2): 225-235.

Hessel, R. and Jetten, V. (2007) Suitability of transport equations in modelling soil erosion for a small Loess plateau catchment. Engineering Geology, 91(1):56–71.

Hudson, N. W. (2009) Erosion Control Research – progress Reports on Experiments in Henderson Research Station 1999-2009, Rhodesia Agric Journal. 54 (4): 297-323.

Ibeneme, S. I. (2013). Personal discussion.

Imhoff, E. (1982) Cartographic Relief Presentation. De Gruyter, New York.

Igbokwe, J. I., Ojiako, J. C. and Nnodu, V. C. (2003). Monitoring Characterization and Controlling of Flood water Erosions using Remote Sensing techniques. Proceeding of the Technical Session of 38th Annual conference of Nigerian Institution of Surveyors, Lokoja, Nigeria, Pp. 73-79.

Ikenna, E. E. (2006). Gully Erosion: A Renewed Hope? Retrieved from http://www.gamji.com/article5000/NEWS5627.htm

Jensen, J. R. (2007). Remote sensing of the environment: An Earth resource perspective, 2nd ed. Prentice Hall.

Julien, Y. and Sobrino, J. A. The yearly land cover dynamics (ylcd) method: An analysis of global vegetation from NDVI and LST parameters. Remote Sensing of Environment, 113(2):329–334, 2009.

Nwajide, S.C. and Hoque M. (1999). Gully processes in Southeastern Nigeria. The Nigerian Field Journal pp. 44(2), 64-74.

Ofomata, G. E., Ologe K. O., and Sagau V. O. and Enabor E. E. (2001) Ecological Disasters in Nigeria: Soil Erosion ( Federal Minisry of Science and Technology, Lagos) pp. vii-xv.

OMAFRA Staff (2003). “Soil Erosion, Causes and Effects”. Ridge Town and College of Agricultural Technology, Ontario Institute of Pedelogy. http://www.search.gov.on.ca.8002/compass?view-template=simplr.

Ojo, A. G. and Adesina, F. A. (2007). An assessment of the efficiency of landsat, NigeriaSat-1 and spot images for landuse/landcover analyses in Ekiti west area of Nigeria. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 38, Part II

Okereke, C. N., Onu, N. N., Akaolisa, C. Z., Ikoro, D. O., Ibeneme, S. I., Ubechu, B.(Mrs), Chinemelu, E. S. (Mrs) & Amadikwa, L. O. (2012). “Mapping Gully Erosion Using Remote Sensing Technique: A Case Study Of Okigwe Area, South eastern Nigeria” International Journal of Engineering Research and Applications. Vol. 2, Issue 3, May-Jun 2012, pp.1955-1967

Okin, G. S. (2002). “Toward a Unified View of Biophysical Land Degradation Processess in Arid and Semi-arid Lands”. In Global Desertification: Do Humans Cause Deserts? Edited by J.F. Reynolds and D.M. Stafford Smith. Dahlem University Press.Pp95-97.

Omuto, C. T. and Shrestha, D. P. (2007). Remote sensing techniques for rapid detection of soil physical degradation. Int. J. Remote Sens., 28(21):4785–4805.

Park S., Feddema, J. J. and Egbert, S. L. (2004). Impacts of hydrologic soil properties on drought detection with MODIS thermal data. Remote Sensing of Environment, 89(1):53–62.

Remote sensing (2013). In Wikipedia. Retrieved April 04, (2013), from http://en.wikipedia.org/wiki/Remote_sensing

Shoshany, M. (2000) Satellite remote sensing of natural mediterranean vegetation: A review within an ecological context. Progress in Physical Geography, 24(2):153–178.

United States Geological Survey (USGS) (2008), Shuttle Radar Topography Mission, available at http://srtm.usgs.gov/mission.php, last assessed November 4, 2011.

Vrieling, O. E. (2006) Satellite remote sensing for water erosion assessment: A review. CATENA, 65(1):2–18.

Zhang, J. X., Chang, K. T., Wu, J. Q. (2008). Effects of DEM resolution and source on soil erosion modelling: A case study using the WEPP model. Int. J. Geogr. Inf. Sci., 22, 925-942.