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REMOTE SENSING, A TOOL FOR EROSION STUDY;
A CASE STUDY OF NEKEDE AND ITS ENVIRONS
BY
EQUERE, UBONG IMEH
(REG NO. 20081597233)
+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
i
DEDICATION
This Project work is dedicated to my parents Dr and Mrs Imeh Equere, for their
unremitting and resolute support in my education.
ii
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.
iii
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
v
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
vi
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
vii
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
viii
LIST OF TABLES
TABLE 2.1: Comparison between Human and Computer Information
Extraction ……………………………………………………………………………… 10
ix
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
x
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
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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.
2
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
3
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.
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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
5
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.
6
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
7
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
8
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
9
(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
10
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 interpretation)
1. Interpreters knowledge are available
2. Excellent in spatial information extraction
1. Time consuming 2. Individual difference
Computer (Image Processing)
1. Short Processing time
2. Reproductivity 3. Extraction of physical
quantities or indices is possible
1. Human knowledge is unavailable
2. Spatial information extraction is poor
11
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.
12
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.
13
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
14
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.
15
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
16
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
17
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).
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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
19
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.
20
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,
21
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
22
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.
23
PLATE 3.1: MAP OF NIGERIA SHOWING IMO STATE AND STUDY AREA.
3.2 PHOTOS FROM THE SITE
24
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
25
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:
DATA ACQUISITION
Landsat 7 ETM+ (2013) SRTM 90m DEM
PRE-PROCESSING
IMAGE CLIPPING
GEO-REFERENCING
SPATIAL ENHANCEMENT
SLOPE & ASPECT NDVI
CONTRAST ENHANCEMENT
DRAINAGE
TERRAIN
CONTOUR
RELIEF FALSE COLOUR IMAGES
IMAGE ENHANCEMENT
26
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
27
(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.
28
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:
29
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
30
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.
31
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
32
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
33
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.
34
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.
35
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.
36
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.
37
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.
38
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
39
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.
40
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
41
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
42
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.
43
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
44
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.
45
PLATE 4.1: STUDY AREA OVERLAY DISPLAYED IN GOOGLE EARTH
46
PLATE 4.2: NDVI MAP OF STUDY AREA
47
PLATE 4.3: RGB 432 (STANDARD FALSE COLOUR COMPOSITE)
48
PLATE 4.4: RGB 321 (TRUE NATURAL COLOUR IMAGE)
49
PLATE 4.5: RGB 453
50
PLATE 4.6: DIGITAL ELEVATION MODEL (DEM) OF STUDY AREA SHOWING
RELIEF
51
Elevation
FIG 4.2: ELEVATION FREQUENCY DISTRIBUTION OF THE DEM
Freq
uen
cy
52
PLATE 4.7: SLOPE MAP
53
PLATE 4.8: TERRAIN CLASSIFICATION OF STUDY AREA
54
PLATE 4.9: CONTOUR MAP GENERATED FROM THE DEM OF THE STUDY AREA
55
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.
56
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,
57
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.
58
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