final thesis document for tatenda hove

UNIVERSITY OF ZIMBABWE

FACULTY OF ENGINEERING

DEPARTMENT OF GEOINFORMATICS AND SURVEYING

DEVELOPMENT OF AN APPLICATION FOR MAPPING SOIL

EROSION HOT SPOT AREAS IN THE UPPER RUNDE SUB-

CATCHMENT

TATENDA GWAUYA HOVE

Bsc Honours Degree in Geoinformatics and Surveying

HARARE, MAY 2016

Development of an application for mapping of soil erosion hot spot areas in the URSC Page ii

UNIVERSITY OF ZIMBABWE

FACULTY OF ENGINEERING

DEPARTMENT OF GEOINFORMATICS AND SURVEYING

DEVELOPMENT OF AN APPLICATION FOR MAPPING SOIL

EROSION HOT SPOT AREAS IN THE UPPER RUNDE SUB-

CATCHMENT

By

TATENDA GWAUYA HOVE

Supervisors

Mr. W. GUMINDOGA

Mr. S. TOGAREPI

Mr. L.T BUKA

A thesis submitted in partial fulfillment of the requirements for the degree of Honors in

Geoinformatics and Surveying of the University of Zimbabwe

MAY, 2016

Development of an application for mapping of soil erosion hot spot areas in the URSC Page i

Table of contents

Table of contents ....................................................................................................................... i

List of figures ........................................................................................................................... iv

List of tables.............................................................................................................................. v

DECLARATION..................................................................................................................... vi

Disclaimer ............................................................................................................................... vii

Dedication ............................................................................................................................. viii

Acknowledgement ................................................................................................................... ix

Abbreviations ........................................................................................................................... x

Abstract .................................................................................................................................... xi

1. CHAPTER ONE: INTRODUCTION ......................................................................... 1

1.1 Background .............................................................................................................. 1

1.2 Problem statement ................................................................................................... 2

1.3 Justification .............................................................................................................. 3

1.4 Study objectives ....................................................................................................... 3

1.4.1 Main objective .................................................................................................. 3

1.4.2 Specific objectives............................................................................................ 3

1.5 Research questions .................................................................................................. 4

1.6 Structure of the thesis .............................................................................................. 4

2 CHAPTER TWO: LITERATURE REVIEW ............................................................ 5

2.1 The soil erosion process .......................................................................................... 5

2.1.1 Rainfall ............................................................................................................. 6

2.1.2 Soils .................................................................................................................. 7

2.1.3 Vegetation ........................................................................................................ 7

2.2 Impacts of soil erosion ............................................................................................. 8

2.2.1 Agriculture ....................................................................................................... 8

2.2.2 Infrastructure .................................................................................................... 8

2.2.3 Economy........................................................................................................... 9

2.2.4 Water quality .................................................................................................... 9

Development of an application for mapping of soil erosion hot spot areas in the URSC Page ii

2.3 Soil erosion monitoring ........................................................................................... 9

2.3.1 Laser scanning .................................................................................................. 9

2.3.2 Cut/ Fill volumetric analysis .......................................................................... 10

2.4 Previous researches in soil erosion assessment ..................................................... 11

2.4.1 Global perspective of soil loss estimates ....................................................... 11

2.4.2 Sub-Saharan perspective of soil loss estimates .............................................. 11

2.4.3 Zimbabwean perspective of soil loss estimates.............................................. 11

2.4.4 Upper Runde sub-Catchment perspective of soil loss estimates .................... 11

3 CHAPTER THREE: MATERIALS AND METHODS .......................................... 12

3.1 Description of study area ....................................................................................... 12

3.1.1 Soils and geology ........................................................................................... 12

3.1.2 Landuse activities ........................................................................................... 13

3.1.3 Socio-economic activities .............................................................................. 13

3.1.4 Rainfall and drainage systems ........................................................................ 13

3.2 Methodology for estimating soil loss .................................................................... 13

3.2.1 Estimate of soil loss from bare land (K-factor) .............................................. 14

3.2.2 Methodology for Land cover change analysis ............................................... 15

3.2.3 Topographical factor (X-factor) ..................................................................... 18

3.2.4 Quantification of soil loss estimates............................................................... 20

3.3 Classification of soil loss estimates ....................................................................... 21

3.4 DEVELOPMENT OF THE APPLICATION........................................................ 22

3.5 Comparison of the soil loss volumes ..................................................................... 23

4 CHAPTER FOUR: RESULTS AND DISCUSSION ............................................... 24

4.1 Quantification of soil loss estimates ...................................................................... 24

4.1.1 Spatial variation of the soil loss factor (K-factor) .......................................... 24

4.1.2 Land use change in the URSC........................................................................ 27

4.1.3 Accuracy assessment ...................................................................................... 29

4.1.4 Crop ratio (C-factor)....................................................................................... 30

4.1.5 Topographical factor (X-factor) ..................................................................... 31

4.1.6 Soil loss estimates .......................................................................................... 32

4.2 Soil erosion hot spot areas ..................................................................................... 34

4.3 Validation of the soil loss estimates ...................................................................... 36

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4.3.1 Upper Runde sub-Catchment soil loss estimates ........................................... 36

4.4 Automation of quantification of soil erosion estimates ......................................... 36

4.4.1 Mapping soil erosion hot spots ...................................................................... 36

5 CHAPTER FIVE: CONCLUSIONS AND RECOMMENDATIONS ................... 39

5.1 Conclusions ........................................................................................................... 39

5.2 Recommendations ................................................................................................. 39

6 References .................................................................................................................... 41

Appendix ............................................................................................................................. 45

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List of figures

Figure 3-1: Map of the Upper Runde sub-Catchment ............................................................. 12

Figure 3-2: Soil loss estimates methodology flowchart ........................................................... 14

Figure 3-3: Application flowchart ........................................................................................... 22

Figure 4-1: Spatial distribution of the soils' erodibility (F) ..................................................... 24

Figure 4-2: Spatial and temporal distribution of the mean annual rainfall in the URSC......... 25

Figure 4-3: The spatial and temporal variation of the K-factor ............................................... 26

Figure 4-4: Thematic maps created from land use classes (1984-2015) ................................. 28

Figure 4-5: Spatial distribution of the topographical factor (X-factor) for the years 2011 and

2014.......................................................................................................................................... 31

Figure 4-6: Spatial and temporal representation of the soil erosion hot spot areas ............... 35

Figure 4-7: Application home page ......................................................................................... 37

Figure 4-8: Mapping of erosion hot spots in the URSC .......................................................... 38

Figure 4-9: The application's display of land cover maps ....................................................... 38

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List of tables

Table 3.1: Landsat images used for data interpretation ........................................................... 16

Table 3.2: Landsat mission specific identifiers ........................................................................ 18

Table 3.3: Digital elevation models used in the determination of slope and flow accumulation

.................................................................................................................................................. 19

Table 3.4: The erosion hazard classes used for hot spot area classification ............................ 21

Table 4.1: CHIRPS mean annual rainfall statistics .................................................................. 26

Table 4.2: Statistics for the temporal variation of the K-factor ............................................... 27

Table 4.3: Land use changes in the URSC (1984-2015) ......................................................... 28

Table 4.4: Classification accuracy assessment results ............................................................. 30

Table 4.5: Statistics for the crop ratio ...................................................................................... 30

Table 4.6: Spatial distribution of the topographical factor ...................................................... 32

Table 4.7: Total soil loss for the districts and wards ............................................................... 33

Table 4.8: Soil loss estimates for the land uses in the URSC .................................................. 34

Table 4.9: Distribution of the soil erosion risk ........................................................................ 35

Development of an application for mapping of soil erosion hot spot areas in the URSC Page vi

DECLARATION

I, Tatenda Gwauya Hove, declare that this research report is my own work. It is being

submitted for the degree of Honours in Geoinformatics and Surveying (HSV) of the

University of Zimbabwe. It has not been submitted before for examination for any degree in

any other University.

Date: ________________________

Signature: ____________________

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Disclaimer

This document describes work undertaken as part of the programme of study at the

University of Zimbabwe, Geoinformatics and Surveying Department. All views and opinions

expressed therein remain the sole responsibility of the author, and not necessarily represent

those of the University.

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Dedication

This research is dedicated to my family.

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Acknowledgement

Without any reservations I would like to thank my parents for funding my studies at the

University of Zimbabwe, in the Department of Geoinformatics and Surveying.

I would also want to thank my supervisor Mr. W. Gumindoga from the Department of Civil

Engineering for his assistance during the research work and for his unwavering support;

whole-heartedly I would also want to express my sincere gratitude to my supervisors Mr. S.

Togarepi and Mr L.T. Buka for their expert advice throughout my research.

I would not go without mentioning the general staff, my friends and colleagues from the

Department of Geoinformatics and Surveying for sharing a great deal of knowledge with me

towards achieving my purpose at the University of Zimbabwe.

Finally and above all I want to thank the most high and greatest, Jehovah, for seeing me

through my studies.

Development of an application for mapping of soil erosion hot spot areas in the URSC Page x

Abbreviations

ASTER Advanced Space borne Thermal Emission and Reflection Radiometer

CHIRPS Climatic Hazards Group Infrared Precipitation with Stations

EPA Environmental Protection Agency

ETM+ Enhanced Thematic Mapper Plus

GPS Global Positioning System

ILWIS Integrated Land and Water Information System

IR Infrared

LULC Land Use Land Cover

MSS Multispectral Scanner

NDVI Normalized Difference Vegetation Index

NIR Near Infrared

OLI Operational Land Imager

SLEMSA Soil Loss Estimation Model for Southern Africa

SRTM Shuttle Radar Topography Mission

TM Thematic Mapper

URSC Upper Runde sub-Catchment

VB Visual Basic

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Abstract

The Upper Runde sub-Catchment (URSC), a tributary basin of the Runde Catchment, lies in

one of the driest catchments in Zimbabwe. The economy of the URSC mainly thrives on

agriculture and mining with 67% of the population living in the rural areas; hence it is of

great importance to safeguard the land resource. Soil erosion has a negative impact on the

natural environment and soil quality. This research seeks to demonstrate the applicability of

satellite data and GIS technology to model the temporal and spatial variation of the risk of

soil erosion using the SLEMSA model in the URSC, and also quantifying the amount of soil

lost annually in the URSC. Landsat satellite images, DEMs, satellite rainfall data (CHIRPS)

and the Zimbabwe soil database datasets were analysed and manipulated to determine soil

loss estimates and map soil erosion hot spots for the URSC. This study concluded that within

the URSC, agricultural land use contribute the most to annual soil loss namely the communal

lands. The Mberengwa, Chivi and Zvishavane districts recorded the highest soil loss. The

URSC is under a high risk of erosion hence the rivers are susceptible to a risk of siltation and

sedimentation. The processes of mapping soil erosion hot spot areas, retrieval of soil loss

estimates and land use planning specific to soil erosion were automated. The automation of

the processes would enable the environmental manager to determine the spatial and temporal

variation of the risk of soil erosion without having to go through a complicated series of GIS

operations. The availability of spatial data on soil erosion processes is a step towards

protecting the catchment from accelerated soil erosion rates.

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1. CHAPTER ONE: INTRODUCTION

1.1 Background

Soil erosion is a principal contributor to land degradation (Stocking and Murnaghan, 2003).

In some African countries, soil erosion and soil mineral depletion account for 85 % of the

land‟s degradation (Barungi et al., 2013). From a certain perspective and according to a

relative time scale the extent of land degradation can be considered to be irreversible

(Stocking and Murnaghan, 2003).

Human livelihood is dependent upon agricultural produce. Agriculture is an important

economic factor that has the potential to half poverty levels and contributes to the

achievement of economic growth. However these goals have not been met in Zimbabwe as

statistics reflect that the percentage of people living under the poverty datum stands at 67 %

(Manjengwa et al., 2012) owing to the diminishing agricultural yield, to which land

degradation might be a major contributor. By 1988 the large scale commercial farms were the

major foreign currency earners for Zimbabwe (Whitlow, 1988) to date the agricultural sector

contributes 30 % towards foreign currency earnings and 19 % towards the GDP implying an

estimated drop of -3.6 % from the previous year (Chinamasa, 2016). Hence agriculture has

the potential to be a major contributor in the economic revamp if effective land use

management is rolled out.

Soil erosion has a negative impact on the land‟s agricultural productivity (Stocking and

Murnaghan, 2003). Considering that the majority of the Zimbabwean population is heavily

dependent on agriculture, in the event of a drought the question is posed on how the populace

can obtain livelihood. A number of factors affecting soil erosion in Zimbabwe are given in

literature (Boardman et al., 2003; Nyoni, 2013). The factors which affect soil erosion can be

physical or socio-economic. The aforementioned factors affecting soil erosion are also

prevalent in the URSC which lies in the central south-east region of Zimbabwe. The URSC

has been hit by gold panning as the residents resort to the activities of illegal gold panning to

minimize effects of economic hardship (Mangwende, 2014). These gold panners do not have

the appropriate equipment neither do they have the appropriate methods or the appreciation

of the environment that they operate in. The mining activities are done without

Environmental Impact Assessments (EIA) hence the implementation of an Environmental

Management Plan is overlooked. In a report on mines, energy, environment and tourism it is

alleged that the way mining is done along Boterekwa area is a major concern (Veritas, 2006).


The illegal mining activities have contributed to the acceleration of soil erosion leading to

land degradation. The poor farming techniques have also contributed to accelerated rates of

soil erosion, which has resulted in considerably high levels of land degradation. In addition,

the URSC is home to overpopulated communal lands, Zvishavane, Mberengwa, Shurugwi

and Chivi, the farmers depend mostly on dry cultivation and hence land conservation is a

priority.

The impacts of soil erosion are mentioned in literature (Nkonya et al., 1999). Soil erosion

results in loss of top soil and reduced water holding capacity of the soil, silting of dams,

disruption of lake ecosystems, contamination of drinking water and increased downstream

flooding. All these factors point to stagnation of agricultural productivity, infrastructural

damage, stifling of the economy and a compromise the water quality only to mention a few

impacts. The challenges that environmental managers within the country and in URSC face

are the unavailability of data on the spatial and temporal variation in soil erosion. The lack of

data hinders meaningful decisions to be made on protecting the catchment from accelerated

soil erosion rates. Previous studies in the catchment focus on point based data with few

studies identifying hotspot areas on soil erosion. GIS and remote sensing techniques help us

to quantify inputs into soil erosion models with the advent of GIS software packages with

high processing power and satellites that are capable of acquiring images with high spatial

and temporal resolution.

It is upon this background that this research seeks to implement the remote sensing and GIS

techniques in the interpretation and classification of remotely sensed data to derive various

historical land cover types, study the temporal and spatial variation of soil erosion risk in the

Upper Runde sub-Catchment and quantify soil loss estimates.

1.2 Problem statement

The Upper Runde sub-Catchment is predominantly spanned by the natural region IV

(Mugandani et al., 2012) the URSC also straddles the regions III and V (OCHA, 2009). Since

the Runde catchment is one of the driest, it is more susceptible to soil erosion (Oldeman,

1992) and frequent droughts. The economy of the Upper Runde sub-Catchment largely

thrives on agriculture and mining activities. In some cases the methods and mechanizations

applied in executing these activities fuel soil erosion. Land use activities to which soil erosion

is extremely sensitive are being sited on areas with a high risk of soil erosion.


The environmental manager needs to have knowledge of the temporal and spatial extent of

the risk of erosion. Over the past years the field methods that were being used have faced

abandonment due to their ineffectiveness (Mutambiranwa, 2000). To the best of knowledge,

there is little spatial information on soil erosion hot spot areas within the URSC to aid

decision making that is towards effective land use management. The use of GIS and Remote

Sensing can help the stakeholders in identifying soil erosion hot spot areas and making

decisions on land use planning and policy making with the initiative of achieving effective

soil conservation to curb land degradation, economic drop and poverty.

1.3 Justification

There has been a considerable decrease in the land productivity levels of food grains in

Zimbabwe from the year 1990 to date (Moyo and Nyoni, 2010). With the population of

Zimbabwe estimated to be 14.6 million in 2014 which is 36% growth in population from the

year 1992 and a projected 275% growth by 2100 which implies an increase in pressure onto

the agricultural yield for Zimbabwe. Such a scenario of increased pressure on the land

resource will also be prevalent in the Upper Runde sub-Catchment. To date, 70% of the

population of Zimbabwe lives in the rural areas and are greatly dependent upon the

agricultural productivity of the land for livelihood. In line with the Zim Asset cluster one, on

food security and nutrition, this research seeks to improve on techniques that are applicable

for effective land use management.

Effective land use management will help to conserve the land resource from degradation such

that it continues to sustain the livelihood of the populace of the Upper Runde sub-Catchment

even after the projected population growth. The research is at the interest of the agenda of

Land Reform program in Zimbabwe to achieve effective agricultural produce, and reaffirm

the „bread basket‟ status of the nation.

1.4 Study objectives

1.4.1 Main objective

The main objective is to develop an application for mapping of soil erosion hot spot areas in

the Upper Runde Sub-Catchment area.

1.4.2 Specific objectives

i. To quantify the factors affecting soil erosion in the Upper Runde sub-Catchment area

(URSC).


ii. To map soil erosion hot spot areas using the SLEMSA model.

iii. To develop an application for quantification of soil erosion hot spot areas.

iv. To deploy the model as catchment and environmental management tool in the Upper

Runde sub-catchment.

1.5 Research questions

i. Which factors affect soil erosion and how can these be quantified using remote

sensing?

ii. What are the remote sensing based algorithms that can be used to extract soil loss

estimate?

iii. To what extent do the remote sensing derived soil loss estimates resemble field

observations?

iv. How can the determination of soil loss estimates from remote sensing be automated?

1.6 Structure of the thesis

The study consists of five chapters organized as follows: Chapter One presents the

introduction and general background to study, the problem statement, objectives and

justification of the study.

Chapter Two contains literature review on the process of soil erosion, physical factors

affecting soil erosion, impacts of soil erosion, methods of monitoring soil erosion and

previous studies on monitoring of soil erosion processes. Chapter Three contains a brief

description of the study area and the methodology used for data collection and analysis.

Chapter Four presents the results and discussion on quantification and qualification of soil

erosion factors and LULC change by class. Finally, chapter Five presents the conclusions and

recommendations from the findings of the study.


2 CHAPTER TWO: LITERATURE REVIEW

2.1 The soil erosion process

Soil erosion is the process of detachment and transportation of particles from soil aggregates

by erosive agents (Breetzke et al., 2007) and is considered to be a significant global

environmental problem (Kefi et al., 2012). Soil erosion affects water quality, causes

sedimentation and increases the probability of floods (Ouyang and Bartholic, 2001).

Effectively soil erosion has a detrimental effect on agricultural productivity, water quality

and aquatic ecology (Heng et al., 2010) and hence there is need to monitor and control the

processes of soil erosion to safeguard land resource. Soil erosion can be considered to be a

factor that fuels land degradation if uncontrolled. According to Whitlow (1988) the

description of how vegetation, soil, relief and water have changed for the worse is referred to

as land degradation. Whitlow (1988) further asserts that land degradation is a composite term

and that it has no readily identifiable feature. Soil erosion and soil mineral depletion account

for 85% of the land‟s degradation (Barungi et al., 2013).

Yazidhi (2003) argues that soil erosion is the process which results in soil mineral depletion.

Landi et al. (2011) also suggests that more than 56% of land degradation is accounted for by

water erosion. Soil erosion is a gradual process hence the diurnal change is negligible and

difficult to notice unless in the event of a natural disaster. According to (Heng et al., 2010)

obtaining accurate descriptions of soil surface microtopography is vital to quantify changes to

the soil surface (typically on the sub-centimetre scale) due to erosion processes. Thompson et

al. (2010) defines microtopography as topography consisting of small scale excursions in the

elevation of the land surface on millimetre to centimetre scales. Monitoring of the process of

soil erosion can help in the quantitative and qualitative evaluation of the control techniques

being employed.

According to Mutowo & Chikodzi (2013) soil erosion monitoring methods are basically

divided into three approaches being; field research, surveying and mathematical modelling.

(Heng et al., 2010) assert that project requirements and constraints have to be considered in

selecting a suitable technique. Surveying has normally been used for medium scale projects,

parallel to the other two methods, field research and mathematical modelling. Field research

is suitable for small areas only while mathematical modelling is applicable at any scale

(small, medium and large).


At a catchment level mathematical modelling is the most attractive method of studying soil

erosion considering the spatial extent in question that can be classified as large scale.

The soil erosion mathematical models can be divided into two categories being the statistical

and the empirical based models. Empirical methods are based on knowledge gathered

through field experiments under statistically controlled conditions. Physical methods are

based on the knowledge of physical relationships between different parameters influencing

erosion. The process of quantitative/qualitative monitoring and evaluation of soil erosion can

be achieved through an interactive web based approach that uses mathematical models and

Geographical Information System (GIS) (Ouyang and Bartholic, 2001) to predict soil erosion.

According to Yazidhi (2003) processes of soil erosion are generally influenced by location

factors. These location factors include; climate, soil, relief, vegetation and manmade

conservation measures. The contribution of the aforementioned location factors to processes

of erosion will be explained.

2.1.1 Rainfall

There is relationship between rate of run-off and the rate at which erosion occurs (Mahdi,

2008). The detachment and transportation of soil particles from upland areas is related to

raindrop impact and surface run-off (Julien and Frenette, 1986). Bazigha et al. (2013) asserts

that soil loss is closely related to rainfall in that the striking raindrops may result in the

detachment of soil particles. As mentioned earlier it is also true that 56% of the land

degradation is accounted for by water erosion (Landi et al., 2011). Rosewell (1986) further

clarifies that the close relationship between rainfall and water erosion is due to the following

factors:

a) Impact of raindrops on soil surface in high-intensity storms causes increased

soil particle detachment.

b) Higher rainfall intensity results in higher rates of infiltration excess runoff, and

a much greater transport of suspended sediment load.

Yazidhi (2003) factors in the fact that the duration of the rainfall contributes to the ability of

rainfall to cause erosion (erosivity) as much as the aforementioned characteristics, rain

intensity and energy, do. Erosion is related to two types of rainfall events, the short lived

intense storm where the infiltration capacity of the soil is exceeded thus higher rates of

infiltration excess runoff.


The prolonged storm of low intensity which saturates the soil before run-off begins (Yazidhi,

2003). The raindrop size also has an effect on detachment of soil particles, the bigger the

raindrop the greater the kinetic energy the greater the erosivity.

2.1.2 Soils

The ease with which a certain soil type can be detached and transported by erosive agents can

be described quantitatively by the soil erodibility factor (K-factor) (Kinnell, 1981) for the

USLE soil erosion model. According to Isikwue et al. (2012) the erodibility of soil is a

measure of its resistance to energy sources namely; the effect of raindrops on the soil surface

and the shearing action of runoff between clods in rills or grooves. According to Hjulstrom

(1935) erodibility of soil can be classified depending on soil particle diameter and runoff

velocity. It can be deduced that soil particle size plays an important role in determining the

rate of soil erosion (Hjulstrom, 1935). Complementary to Hjulström‟s work, soil scientists

have long realized that soils react at varying speeds to raindrop attack and structural

degradation. Yazidhi (2003) asserts that larger particles are more resistant to transportation

due to the greater force required to move them, however, in soils whose particles are less than

0.06mm the erodibility is limited by the cohesiveness of the particles.

O‟geen et al. (2005) further classifies the dependence of erodibility on diameter by

mentioning that fine sand and silt are more susceptible to erosion. There are two possible

approaches to improving soil resistance in order to control erosion. Roose (1996)

recommends on solutions that help to minimize the processes of soil erosion the first being;

selecting the most resistant soils in the area for those crops that provide the least cover and

leaving the most fragile soils permanently under plant cover. The second solution was to

control the organic matter in the soil.

2.1.3 Vegetation

Vegetation cover plays a very important role in protecting the soil against erosion thus

reducing soil loss (Roose, 1996). In other words as Megahan and King (2004) puts it across,

the amount of vegetative cover is inversely proportional to the erosion hazard. Thus as

vegetative cover increases the erosion hazard decreases and vice versa. Roose (1996) outlines

how vegetative cover contribute to minimize soil erosion, and among other factors he

mentions; the protection of the soil against falling raindrops, surface run-off is reduced,

vegetative cover binds the soil mechanically.


Vegetative cover also reduces the climatic fluctuation in the soil‟s upper layer, the roughness

of the soil surface is maintained and the chemical, biological and physical properties of the

soil are improved. Other scholars also suggest that vegetative cover reduces the velocity of

surface run-off (Hjulstrom, 1935).The effects of vegetative cover on erosion processes

especially surface erosion are varied by the type of vegetation cover, density of cover, litter

and undergrowth cover. Vegetation cover acts as a protective layer between the atmosphere

and the soil surface (Yazidhi, 2003).

Slope steepness and slope length have a strong relationship to soil loss and therefore both of

them are used in quantitative evaluation of erosion (Qadir, 2014). According to the D‟Souza

and Morgan (1976), the steepness of the slope directly affects the rate of erosion the reason

being that a steeper slope increases the velocity of water flow. Hjulstrom (1935) supports the

fact that as velocity of surface run-off increases so does the rate of erosion. The length of

the slope is very important, because the greater the size of the sloping area, the greater the

concentration of the flooding water.

2.2 Impacts of soil erosion

According to Schultz (2011) the effects of erosion impact two places being onsite effects and

offsite effects. The onsite effects include; loss of top soil, reduced water holding capacity of

the soil. The offsite effect is that; the sediment is carried to distant places and the downstream

effect whereby pollutants and sediments are carried into water ways resulting in silting of

dams, disruption of lake ecosystems, (Smith et al., 2009) and contamination of drinking water

and increased downstream flooding.

Among other things soil erosion can have an impact on; agriculture, economy, infrastructure,

soil quality, water ways, water quality and flood intensity.

2.2.1 Agriculture

The part of the soil profile which is most productive for agricultural purposes is the topsoil.

The topsoil is that which is most vulnerable to soil erosion, the result of eradication of topsoil

is that yields are lowered and production costs are consequently inflated. In some cases the

soil erosion features make cultivation “impossible”.

2.2.2 Infrastructure

If soils with high erodibility are not sufficiently compacted during construction air voids

occur, hence the developed infrastructure becomes vulnerable to soil erosion.


Soil erosion has onsite and offsite impacts on infrastructure being siltation and filling up of

water bodies, clogging of drainage ditches, increased downstream flooding, and damaging of

dams roads and embankments (Nkonya et al., 1999).

In the scenarios outlined in this section where soil erosion has an impact on infrastructure

there is likelihood that the stakeholder will incur cost due damage of infrastructure.

2.2.3 Economy

The compound effect of soil erosion tends to stifle the economy and consequently escalate

the population living under the poverty datum because it basically inflates costs. The

populace that live under the poverty datum do not afford the high production costs but still

need to cultivate to earn a living.

Among other alternatives they resort to is illegal mining activities and selling of firewood

thus aggravating soil erosion and further crippling the economy (Manjengwa et al., 2012).

Since soil erosion results in impairment of water quality, it brings about additional water

treatment costs (Dearmont, D., B. McCarl, 1998). Costs are also increased in repairing

infrastructure damaged by onsite effects of soil erosion, that is, damaged roads, dams,

embankments etc and that damaged by offsite soil erosion for instance flooding. Revenue can

also be lost from the neglect of beneficial activities such as fishing and swimming.

2.2.4 Water quality

Soil erosion has the capability to compromise water quality since the pollutants can be carried

into the water by soil erosion. The pollutants include pesticides, metals, toxins, oil and

grease. Phosphates can also enter waterways and when in high levels can result in algal

blooms and lower the amount of dissolved oxygen decimating aquatic life (Dearmont, D., B.

McCarl, 1998).

2.3 Soil erosion monitoring

2.3.1 Laser scanning

Laser scanning is a surveying observation technique that can be used for geodetic work and

topographical surveys. The observation technique works on the basis that the period of time

that is taken by light to travel from the source of emission to the target surface and back can

be determined. The speed of light is known, hence the distance from the scanner to the target

surface, and both the azimuth and angle of beam can be deduced.


The position of each point where the beam is reflected can be determined (Slob and Hack,

2004). Laser scanning technology has the capability to generate digital elevation models that

accurately represent variations in the landform, offer an important opportunity to measure

and monitor the spatial and temporal morphological change and sediment transfer (Smith et

al., 2009).

Bitelli, Dubbini and Zanutta (2004) assert that laser scanning can be used to obtain digital

terrain models which are fundamental tools to detect classify and monitor landslides.

According to Smith et al (2009) oblique laser scanning can acquire up to 0.01m vertical

resolution. Slob and Hack (2004) suggest that a resolution of up to 0.005m can be acquired

supplemented by a very high point density. Microtopography analysis can be carried out

considering the resolution of the imagery.

However water and vegetation introduce errors in the laser scanning observations of the

terrain as they affect the penetration of laser (Smith et al., 2009).

2.3.2 Cut/ Fill volumetric analysis

The cut/fill tool is an Arc Map three dimensional analysis tool that calculates the area and

volume changes between two surfaces. The process of cutting and filling identifies the areas

and volumes of the surface that has been modified by the addition or removal of surface

material (ESRI, 2012).

Volume calculation:

For calculation of volume for each pixel the equation is given below:

𝑽𝒐𝒍 = (𝒄𝒆𝒍𝒍𝒂𝒓𝒆𝒂) × 𝜟𝒁 2.1

Where:

𝜟𝒁 = 𝒁𝒃𝒆𝒇𝒐𝒓𝒆 − 𝒁𝒂𝒇𝒕𝒆𝒓 2.2

and 𝑍𝑏𝑒𝑓𝑜𝑟𝑒 is the elevation of the surface before modification

𝑍𝑎𝑓𝑡𝑒𝑟 is the elevation of the surface after modification


2.4 Previous researches in soil erosion assessment

2.4.1 Global perspective of soil loss estimates

The first erosion map to be developed in Turkey prepared by TOPRAKSU was based on

aerial photographs in 1981 (Tombus et al., 2012). In previous research it has been

approximated that 75 billion tons of soil are lost per year worldwide, which is however

deemed by other scholars as an under estimation. In research carried out in India and China, it

has been reported that these countries lose 5.5 billion tons of soil per year and 6.6 billion tons

of soil respectively (Pimentel, 2006). Other scholars also suggest that 3 billion tonnes of soil

are lost in the United States per year (Carnel, 2001). An interactive web-based approach to

soil erosion mapping and quantification was developed by Ouyang and Bartholic (2001) that

made use of the RUSLE soil loss estimation model and GIS to predict soil erosion.

2.4.2 Sub-Saharan perspective of soil loss estimates

Soil erosion is higher in the Sub-Saharan Africa than any other place. According to literature

a soil loss of a sheet of one millimetre thick sheet of soil over a hectare accounts for a loss of

15 tons of soil (Pimentel, 2006).

2.4.3 Zimbabwean perspective of soil loss estimates

A soil erosion survey for Zimbabwe derived from photogrammetric survey was done by

Whitlow (1988). The findings in the survey were that there was an underestimation in the soil

loss estimates since sheet wash erosion could only be detected if at an advanced stage. The

spatial occurrence of the degradation showed that four fifths of degraded land was in the

Communal lands. Whitlow (1988) also deduced that the extent of cropland was amongst the

top three variables affecting erosion, with the others being population density and land

tenure.

2.4.4 Upper Runde sub-Catchment perspective of soil loss estimates

GIS has been used to predict soil erosion hazard spatial variations in the Runde sub-

Catchment by Mutowo and Chikodzi (2013). Chikodzi and Mutowo (2013) concluded that

the Runde Catchment was generally at a low risk of soil erosion and also that the rivers were

under low risk of siltation and sedimentation. However it could also be noted that there were

notable areas in which the risk of erosion was high. Chikodzi and Mutowo (2013) allude that

SLEMSA can be used in watershed management.


3 CHAPTER THREE: MATERIALS AND METHODS

3.1 Description of study area

The Upper Runde Sub-catchment (URSC) is located at geographical coordinates 20ᴼS and

30ᴼE. The URSC is a subzone of the Runde catchment. The Runde catchment is one of the

three catchments that lie in the driest parts of Zimbabwe. Since the Runde catchment is one

of the driest it is more susceptible to soil erosion and frequent drought attacks. The districts

which are straddled by the URSC are; Shurugwi, Chivi, Insiza, Mberengwa, the city of

Gweru and the whole of Zvishavane district. The URSC houses the Gwenoro dam, Palawane

dam, Mapongokwe dam and major rivers being; Runde, Muchingwe and Ngezi rivers.

Figure 3-1: Map of the Upper Runde sub-Catchment

3.1.1 Soils and geology

According to the Zimbabwe soil database the URSC is characterized mainly by luvisols and

nitosols. The nitosols have a moderate resilience to land degradation and become very

erodible as the organic carbon content decreases (FAO, 1974). The luvisols are susceptible to

water erosion and fertility loss.

The main parent rock in the URSC is the granite rock which explains the soil textures

predominant in the URSC, that are sand to loamy (Madebwe and Madebwe, 2005).


3.1.2 Landuse activities

Basically the country, Zimbabwe, is divided into seven major land use classes being;

communal areas, resettlement areas, large scale commercial farms, small scale commercial

farms, forest areas, national parks and urban areas (ESS, 2002). These land use classes are

prevalent in the URSC. The communal land is where the communal farmer carries out their

subsistence farming. In regions of low rainfall, the natural region V livestock is the only

viable source of income. All the farmers have the desire to own large herds which results in

overgrazing leading to accelerated soil erosion. Among other land uses are also the wildlife

management systems, that is, private conservations, national parks and CAMPFIRE

programs. Of the aforementioned seven major land uses sub-division into residential,

industrial, mines, roads and other projects have been deemed necessary over the years.

3.1.3 Socio-economic activities

The population of the URSC is estimated to be 749 652 (ZIMSTATS, 2012). The URSC is

home to the overcrowded Mberengwa, Zvishavane, Shurugwi and Chivi communal areas and

67% of the population live in the rural areas. Over 67% of the population lives in the rural

areas and the rural poverty head count ratio is 84.3%. The economy of the URSC mainly

depends on agricultural activities, mining activities and the informal sector.

3.1.4 Rainfall and drainage systems

The URSC straddles the agro-ecological regions III, IV and V (OCHA, 2009) implying that

the sub-Catchment has a three-fold rainfall pattern. The annual rainfall for the natural farming

region III ranges between 650-800 mm and this natural farming region straddles the

Shurugwi and Gweru districts. The annual rainfall for the natural farming region IV ranges

between 450-650mm and this natural farming region is characteristic of the Zvishavane and

Insiza districts. The annual rainfall for the natural farming region V is predominantly less

than 650mm and this natural farming region span the Chivi and Mberengwa districts of the

URSC. The natural farming regions IV and V are prone to severe drought.

The main rivers that drain the URSC are Runde, Muchingwe and Ngezi. The main rivers

drain the URSC from the north-west to the south-east (Madebwe and Madebwe, 2005).

3.2 Methodology for estimating soil loss

The SLEMSA model was used to create the soil erosion risk map using ILWIS GIS software

package. As to meet the input parameter requirements of the SLEMSA model the K-factor,

C-factor and X-factor had to be quantified and the product of these was the soil loss estimate

in tonnes/hectare/year (t/ha/yr) (Bobe, 2004) as shown in equation 3.1.


𝒁 = 𝑲 × 𝑪 × 𝑿 3.1

Where; Z is the estimated mean annual soil loss (t/ha/yr)

K = mean annual loss (t/ha/yr) from a standard bare plot of known erodibility

C = dimensionless crop factor

X = dimensionless combined slope steepness and length

The quantified soil loss estimates were classified into soil erosion hot spot maps for the years

(1984, 1996, 2002, 2008 and 2015). SLEMSA has four physical factors upon which the, K, C

and X factors are underpinned being; rainfall, soil type, vegetation and relief.

Figure 3-2: Soil loss estimates methodology flowchart

3.2.1 Estimate of soil loss from bare land (K-factor)

The K-factor is a sub-model developed from the quantities describing two of the

aforementioned broad physical factors being rainfall and soil type. The rainfall factor is

described by the seasonal rainfall energy (E) variable and the soil factor is described by the

soil erodibility (F) variable. The K-factor was calculated using the equation 3.2 (Bobe, 2004).


𝑲 = 𝒆[(𝟎.𝟒𝟔𝟖𝟏+𝟎.𝟕𝟔𝟔𝟑𝑭) 𝐥𝐧𝑬+𝟐.𝟖𝟖𝟒−𝟖.𝟏𝟐𝟎𝟗𝑭] 3.2

The soil erodibility factor (F) was determined based on data acquired from Zimbabwe soil

database and literature. The literature aided the researcher to determine the soil‟s textural

class and organic matter content so as to determine the soil erodibility from the look up table.

The soil erodibility values were populated in the attribute table of the Zimbabwe soil database

using ILWIS. A raster attribute map for the soil erodibility factor was then created using

ILWIS.

The seasonal rainfall energy was calculated using equation 3.3 from literature (Mutowo and

Chikodzi, 2013).

𝑬 = 𝟏𝟖. 𝟖𝟒𝟔𝒑 3.3

Where; p, is the mean annual rainfall and E is the seasonal rainfall energy.

The rainfall data was acquired from Climatic Hazards Group Infrared Precipitation with

Stations (CHIRPS), that is, satellite rainfall data. The data acquired from CHIRPS

represented mean monthly rainfall data for the months January to December for the years

(1984, 1996, 2002, 2008 and 2015). CHIRPS data is representative of a raster dataset that

represents the spatial and temporal variation in rainfall data. To determine the mean annual

rainfall data (p), the mean monthly rainfall data were added and divided by the number of

months in a year (12 months) to obtain the mean annual rainfall. The same process was done

for the years 1984, 1996, 2002, 2008 and 2015 using ILWIS Map calculator to determine

mean annual rainfall. The CHIRPS data was then projected onto the study area‟s

georeference for all the datasets (1984, 1996, 2002, 2008 and 2015). To obtain the seasonal

rainfall energy (E) for the years 1984, 1996, 2002, 2008 and 2015 the projected mean annual

rainfall maps were multiplied by a constant (18.846) according to equation [3.3] using the

Map Calculator in the ILWIS GIS software package.

After determining the E and F variables, these variables were the input parameters into

equation [3.2] to get the predicted soil loss from a bare standard plot (K-factor).

3.2.2 Methodology for Land cover change analysis

Data acquisition

In this study, Landsat 8 OLI, ETM, TM and MSS images characteristic of less than 10%

cloud cover were acquired from the Earth Explorer website (http://earthexplorer.usgs.gov/)

for the years 1984, 1996, 2002, 2008 and 2015.

(http:/earthexplorer.usgs.gov/)


Dry season images were acquired from either of the months August, September or October

for each year. Table 3.1 shows the details and specifications of the data used in the land use

classification for URSC.

Table 3.1: Landsat images used for data interpretation

Landsat sensor Path/Row Image ID

Landsat 8(OLI) 170/73 LC81700732015291LGN00

Landsat 8(OLI) 170/74 LC81700742015275LGN00

Landsat 7 ETM+ 170/73 LT51700732008240JSA00

Landsat 7 ETM+ 170/74 LT51700742008240JSA00

Landsat 7 ETM+ 170/73 LE71700732002231JSA00

Landsat 7 ETM+ 170/73 LE71700742002231JSA00

Landsat 5 TM 170/74 LT51700731996319JSA00

Landsat 5 TM 170/73 LT51700741996335JSA00

Landsat 5 TM 170/74 LM51700741984302AAA03

Landsat 5 TM 170/73 LM51700731984302AAA03

Image pre-processing and classification

The Integrated Land and Water Information System (ILWIS) GIS software package was used

to analyze the satellite data. Pre-processing of satellite images included steps such as image

importing, stretching, gluing and creation of a map list from the imported image bands.

The map list was created using three bands being, 5, 4 and 3 for Landsat TM, and MSS and 6,

5 and 4 for Landsat 8 images. The bands were assigned to the red, green and blue colours in

the ILWIS GIS software package respectively. After creating a map list the bands were

opened as colour composites that enabled the analyst to differentiate features.

A sample set was created comprising of six land use classes being; bare land, cultivation,

forest, grassland, settlements and water & marsh. Supervised classification was used to

classify the images using the ILWIS GIS software package based on the six classes generated

using the sample set to demonstrate the patterns in land cover and land use change within the

URSC.


Classification was done using the maximum likelihood classifier algorithm. The maximum

likelihood classification algorithm assumes that spectral values of the training pixels are

statistically distributed according to a multivariate normal probability density function (Dube

et al., 2014).

Validation of classification output

A total of 500 points were determined from Google earth. The error matrix was used to

quantify the level of error from the correct or actual measurements on the ground. The

Confusion matrix which is an inbuilt function in ILWIS was used to determine the accuracy

of the classification and to identify where misclassification occurs.

The confusion matrix identifies the nature of the classification errors, as well as their

quantities.

The confusion matrix is derived by crossing the classified image map and the point map of

ground control points in ILWIS. The accuracy assessment procedure was done following the

steps below:

i. A classified raster map for URSC was created from maximum likelihood

classification algorithm.

ii. A test map was created from 500 points generated from Google earth images.

iii. The two maps were assigned the same domain and georeference

iv. A Cross operation was performed with ground truth map and the classified image to

obtain a cross table and a confusion matrix.

Crop ratio (C- factor)

The cover factor (C-factor) for this task was determined using the equation 3.4 (van der

Knijff et al., 2000):

𝑪 = 𝒆 −∝

𝑵𝑫𝑽𝑰

𝜷−𝑵𝑫𝑽𝑰 3.4

Where; α = 2, β = 1 and NDVI is the Normalized Difference Vegetation Index

Therefore the initial step for determining the C-factor was to compute the NDVI (Normalized

Difference Vegetation Index) for the satellite images from the years 1984, 1996, 2002, 2008

and 2015. The datasets used are those in table 3.1, equation 3.5 was used to calculate the

NDVI values (Tucker, 1979)

𝝆𝑵𝑰𝑹−𝝆𝒓𝒆𝒅

𝝆𝑵𝑰𝑹+𝝆𝒓𝒆𝒅 3.5


During determination of NDVI values it was noted that for different satellite missions the

specific band identifiers for the near infrared and the red regions of the electromagnetic

spectrum are different. The specific band identifiers corresponding to the near infrared and

red visible region for the different Landsat missions were used as listed in Table 3.2. The

satellite missions from which data was acquired for this task include Landsat 1-3 (1984

dataset), Landsat 4-5 (1996 dataset), Landsat 7 (2002 and 2008 datasets) and Landsat 8 (2015

dataset).

Table 3.2: Landsat mission specific identifiers

MISSION NIR IDENTIFIER RED IDENTIFIER

Landsat 4-5 Band 3 & 4 Band 2

Landsat 5 Band 4 Band 3



Adapted from http://landsat.usgs.gov/best_spectral_bands_to_use.php

The predefined NDVI function in ILWIS was used to determine the NDVI values for the

years 1984, 1996, 2002, 2008 and 2015. The red identifier and near infrared identifiers were

determined according to table 3.2

The NDVI values determined where input into equation 3.3 and the C-factor determined for

the years 1984, 1996, 2002, 2008 and 2015.

3.2.3 Topographical factor (X-factor)

The topographical factor (X-factor) is a typical combination of the slope length factor and

slope degree factor that is a function of both slope and length of land (Benzer, 2010).

According to Benzer (2010) the X-factor can be determined from equation 3.6.

𝒇𝒍𝒐𝒘𝒂𝒄𝒄×𝒓𝒆𝒔𝒐𝒍𝒖𝒕𝒊𝒐𝒏

𝟐𝟐.𝟏 𝟎.𝟔

× 𝒔𝒊𝒏 𝒔𝒍𝒐𝒑𝒆

𝟎.𝟎𝟗 𝟏.𝟑

3.6

This then translates to;

𝑭𝒍𝒐𝒘𝑨𝒄𝒄𝒖𝒎𝒖𝒍𝒂𝒕𝒊𝒐𝒏 𝑭𝒍𝒐𝒘𝑫𝒊𝒓𝒆𝒄𝒕𝒊𝒐𝒏 𝒆𝒍𝒆𝒗𝒂𝒕𝒊𝒐𝒏 3.7


Where; flowacc is the flow accumulation as determined from the DEM, slope is the slope of

the digital elevation model, and the resolution denotes the digital elevation model‟s pixel

size.

The slope and flow accumulation for this task were determined from the digital elevation

models acquired from Earth Explorer website (http://earthexplorer.usgs.gov/) as listed in

Table 3.3.

Table 3.3: Digital elevation models used in the determination of slope and flow accumulation

The study area straddled over four tiles of the Advanced Space borne Thermal Emission and

Reflection Radiometer (ASTER) DEM and the Shuttle Radar Topography Mission (SRTM)

DEM for each dataset. After acquisition these tiles had to be pre-processed to create an

aggregate tile. The digital elevation models were imported into ILWIS via the geo-gateway a

mosaic of the tiles produced one aggregate tile. After mosaicing the tiles, it was a pre-

requisite within the ILWIS environment to undergo an operation of filling the sinks which is

referred to as cleaning up the DEM (ESRI, 2012). Prior to determining flow accumulation an

intermediate step of determining flow direction had to be done.

The flow accumulation operation in GIS creates a raster of accumulate flow in each cell

hence there should not be any erroneous flow direction in the flow direction raster (ESRI,

2011). To avoid the creation of an erroneous flow direction raster a DEM without depressions

is created by filling in the sinks. The Fill Sinks operator was located on the ILWIS operation

list, the software then prompted the user to input a DEM for the process to execute and the

input DEM for the task was the aggregate tile produced from mosaicing the tiles.

The process of filling sinks took a few minutes to execute. To determine the flow direction,

the Flow Direction operator on the ILWIS operation list was selected. Flow direction should

be determined in a sink free DEM (Bitelli et al., 2004) hence the input DEM was the output

raster map of the Fill Sinks operation. The method used was the steepest slope method.

Digital elevation model Publication/ Acquisition data Resolution

SRTM 1 Arc-Second Global 23 September 2014 1-ARC (30 meter)

ASTER Global 17 October 2011 1-ARC (30 meter)

http://earthexplorer.usgs.gov/


Flow accumulation

After the flow direction was determined, the flow accumulation could therefore be calculated

using the Flow Accumulation operator in ILWIS with the input raster data being; the flow

direction map as specified by the ILWIS GIS software package.

Slope

To determine the slope, the output of the fill operation was used. To calculate the height

differences the Filter operation in the ILWIS GIS software package was used. The linear

functions dfdx and dfdy were used to calculate height differences in the X-direction and Y-

direction respectively. The output map for the former was named dx and dy for the latter.

After obtaining dx and dy the Map Calculator function on the operator list was used to

determine slope. Initially the slope map was calculated in percentages, the output raster map

being slopepct (slope as a percentage), with the maps, dx and dy, being the input parameters

of the equation 3.8.

𝒔𝒍𝒐𝒑𝒆𝒑𝒄𝒕 = 𝟏𝟎𝟎 ∗ 𝑯𝒀𝑷(𝒅𝒚, 𝒅𝒙)/𝑷𝑰𝑿𝑺𝑰𝒁𝑬(𝑫𝑬𝑴) 3.8

Where; HYP is an internal Mapcalc/Tabcalc function and

PIXSIZE (DEM) returns the pixel size of a raster map

To convert the slope in percentages to degrees with an ultimate goal of ending up with the

angles in radians the following map calculation operation was carried out on the slopepct

raster map:

𝒔𝒍𝒐𝒑𝒆𝒅𝒆𝒈 = 𝑹𝑨𝑫𝑫𝑬𝑮(𝑨𝑻𝑨𝑵 𝒔𝒍𝒐𝒑𝒆𝒑𝒄𝒕

𝟏𝟎𝟎 ) 3.9

The slopedeg output raster map was multiplied by 0.01745 to obtain the slope map in radians.

To determine the X-factor the flow accumulation map and the slope map were substituted

into equation 3.6.

3.2.4 Quantification of soil loss estimates

After the K-factor, C-factor and X-factor were determined, they were input into equation 3.1

(Bobe, 2004) to quantify the soil loss estimates.

The Map Calculation operator in the ILWIS GIS software package was used to obtain the soil

loss estimates for the years under study, being 1984, 1996, 2002, 2008 and 2015. The

quantification of the soil loss estimates was determined pixel-wise in (t/ha/yr).


For the year 2015 the values of the soil loss estimates in (t/ha/yr) were multiplied by the area

(ha) which they straddle to determine the soil loss in t/yr for the various districts, wards, land

uses and the URSC as a whole. Raster layers defining the spatial extent of the various wards,

districts and land use classes were used to define their spatial extent on the soil loss estimate

value map using the expression 3.10.

𝒐𝒖𝒕𝒑𝒖𝒕𝒎𝒂𝒑 = 𝑰𝑭𝑵𝑶𝑻𝑼𝑵𝑫𝑬𝑭(𝒆𝒙, 𝒛, ? ) 3.10

The expression was entered into the command line of ILWIS.

Where; ex is the raster dataset defining the spatial extent of the ward, district or land use, z is

the soil loss value map for the URSC and IFNOTUNDEF is an internal Mapcalc/Tabcalc

function.

3.3 Classification of soil loss estimates

The quantified soil loss estimates were classified into soil erosion risk classes as illustrated in

table 3.4 (Mutowo and Chikodzi, 2013). Using the slice operation in ILWIS the soil loss

estimate raster data was reclassified into erosion hazard classes.

Table 3.4: The erosion hazard classes used for hot spot area classification

A new group domain was created for the classification of soil loss estimates into thematic

maps that represent the temporal and spatial distribution of the risk of erosion within the

URSC. The slicing procedure was carried out for the 1984, 1996, 2002, 2008 and 2015

datasets.

Soil loss in tonnes per hectare per year (t/ha/yr) Erosion Hazard Class

0 - 10 Negligible

10.1 – 50 Low

50.1 – 100 Moderate

100.1 – 250 Moderately High

251.0 – 500 High

501.0 – 1000 Very High

>1000 Extremely High


3.4 DEVELOPMENT OF THE APPLICATION

The application‟s objective is to automate soil erosion hot spot mapping in the ILWIS GIS

software package and provide the data analyst a simplified interface to operate from.

Figure 3-3: Application flowchart

This was achieved using vb.net programming language in a Microsoft visual studio

environment. The visual basic interface developed has the capability to invoke the scripts in

ILWIS to enable execution of operations required to map soil erosion risk areas at the click of

a button.

The „Process.Start’ function in vb.net programming language was used to allow the user

interface to invoke soil erosion hot spot mapping processes in the ILWIS software package.


The „SendKeys function’ in vb.net was used to send commands from the user interface to the

ILWIS software package. A communication was established between ILWIS and the

application to allow the retrieval of the soil erosion risk maps by including a command within

the script that would instruct the ILWIS GIS software package to export the processed

erosion maps to a local folder from which the application would fetch the erosion hot spot

maps and display them in the map window. The map window was created from the

„MapWindow‟ GIS plug-in in visual studio.

The functionality of the application to map erosion hot spots is illustrated in the flow diagram

in Figure 3-3. The application was wired to invoke the ILWIS software to map soil erosion

hot spots, make land use decisions specific to soil conservation and retrieve estimate soil loss

quantities in tonnes per year.

3.5 Comparison of the soil loss volumes

Two digital elevation models from two different periods were used (Refer to table 3.4),

ASTER and SRTM. Using the cut and fill tool in ArcGIS the two datasets were compared to

come up with a change in the volumes of soil between the two datasets. The volumes

obtained in the cut / fill process were compared to the SLEMSA estimates to find out if there

was any agreement. In the resultant attribute table of the raster data in regions where material

was eroded the value of parameters in the volume field will be positive. To allow comparison

between the values obtained in the two independent processes of volume calculation, the

SLEMSA quantities which were in t/ha/yr were multiplied by the area in which they straddle

to get the quantities in tonnes per year. The volumes of soil calculated from the cut/fill

method in ArcGIS software package were multiplied by the average bulk density of soil and

divided by the number of years over which change should have taken place which was four in

this case (2011-2014).


4 CHAPTER FOUR: RESULTS AND DISCUSSION

4.1 Quantification of soil loss estimates

4.1.1 Spatial variation of the soil loss factor (K-factor)

Figure 4-1 shows a thematic map representing the spatial distribution of the soil erodibility

factor (F) within the URSC which together with the rainfall energy (E) are the input

parameters for calculating the K-factor. The F values for URSC range between 0.11 and 0.63.

The URSC is predominantly characteristic of soil types with resistance to erosion that have F

values ranging between 0.11 and 0.21. However the URSC is also constituent of highly

erodible soils namely; the extensive belt which partially spans the Shurugwi, Zvishavane,

Insiza districts and the city of Gweru. The southern part of the Chivi district that is spanned

by the URSC, is characterized by chunks of land characterized with soil types that are highly

erodible and also the central part of the Zvishavane district. According to the Zimbabwe soil

database the URSC is characteristic of nitosols, luvisols and lithosols. The nitosols become

very erodible when their organic carbon content decreases. The luvisols are greatly affected

by water erosion (FAO, 1974).

Figure 4-1: Spatial distribution of the soils' erodibility (F)


Figure 4-2 shows the temporal and spatial variations in the CHIRPS rainfall data for the years

1984, 1996, 2002, 2008 and 2015 and Table 4.1 illustrates the statistics for the mean annual

rainfall data. The highest mean annual rainfall was recorded in 1996 with a mean of 61.65

mm for the URSC; a maximum of 86.17mm and a minimum of 37.13mm. The second highest

mean annual rainfall was recorded in 1984 with a mean of 57.3mm for the URSC and a

minimum of 34.9mm and a maximum of 79.7. The year 2002 recorded the least mean annual

rainfall with a mean of 25.1mm for the sub-Catchment. The years 2008 and 2015 each

recorded a mean annual rainfall of 49.6mm and 59.1mm for the URSC.

Figure 4-2: Spatial and temporal distribution of the mean annual rainfall in the URSC

The URSC spans the natural farming regions III, IV and V (OCHA, 2009) receiving mean

annual rainfall in the ranges (54 – 67mm), (38 – 54mm) and (below 38mm) for the natural

farming regions respectively. The climate of the natural farming regions explains the rainfall

trends in the URSC over the study years. The northern part of the URSC is mainly


characterised by the natural farming region III which explains why it usually receives the

most rainfall as illustrated by Fig 4-2.

Table 4.1: CHIRPS mean annual rainfall statistics

Statistic 1984 1996 2002 2008 2015

Min 34.9 37.13 15.3 21.3 42.5

Max 79.7 86.17 35.0 77.9 75.7

Mean 57.3 61.65 25.1 49.6 59.1

Figure 4-3 shows the spatial and temporal distribution of the K-factor in the URSC for the

years 1984, 1996, 2002, 2008 and 2015. The K-factor was classified into seven classes of soil

loss risk being; Negligible, Low, Moderate, Moderately high, High, Very high and Extremely

high, according to Table 3.4. Table 4.2 shows the statistics for the spatial and temporal

variation of the K-factor.

Figure 4-3: The spatial and temporal variation of the K-factor


The K-factor represents a high risk soil loss from bare land for the years 1984, 1996 and 2015

with; 70.13%, 72.51% and 73% of the URSC being under high risk of soil loss from bare

land for the years respectively.

For the year 1984; 10.78% of the URSC is under moderately high risk of soil erosion from

bare land and 19.09% of the URSC is under moderate risk of erosion from bare land. For the

year 1996; 14.57% of the sub-Catchment is under moderately high risk of soil loss from bare

land and 12.92% of the sub-Catchment is under moderate risk of erosion from bare land.

Table 4.2: Statistics for the temporal variation of the K-factor

Erosion Hazard

Class

1984 1996 2002 2008 2015

Area

(ha)

Area

(%)

Area

(ha)

Area

(%)

Area

(ha)

Area

%

Area

(ha)

Area

%

Area

(ha)

Area

%

Negligible

Low

moderate

Moderately high

High

Very High

Extremely high

0.00

0.00

206620.89

116648.94

758982.37

0.00

0.00

0.00

0.00

19.09

10.78

70.13

0.00

0.00

0.00

0.00

139862.65

157639.00

784750.55

0.00

0.00

0.00

0.00

12.92

14.57

72.51

0.00

0.00

0.00

233994.91

44249.02

803891.70

116.56

0.00

0.00

0.00

21.62

4.09

74.28

0.01

0.00

0.00

0.00

51787.65

153368.61

601879.92

275216.03

0.00

0.00

0

4.79

14.17

55.61

25.43

0.00

0.00

0.00

0.00

220604.34

71564.54

790083.32

0.00

0.00

0.00

0.00

20.38

6.61

73.00

0.00

0.00

For the year 2015; 6.61% of the URSC is under moderately high risk of soil loss from bare

land and 20.38% of the sub-Catchment is under moderate risk of erosion. In the years 2002

and 2008 the risk of soil loss for bare land is mostly categorized under moderately high. The

K-factor is sensitive to the amount of rainfall received. The year 2002 had low rainfall hence

the K-factor was moderately high.

4.1.2 Land use change in the URSC

Figure 4-4 shows thematic maps for land use changes and Table 4-3 shows the statistics of

land use change by area. From the digital supervised imagery classification of 1984, 1996,

2002, 2008 and 2015, the following land use classes were obtained; bare soil, cultivation

fields, water& marshes, settlements and forest and shrubs.

Figure 4-4 shows how different LULC have been changing between the period of 1984 and

2015. On investigating the trends it can be noted that, in between the years 1984 and 1996,

there is a steady increase in the percentage of the land which is bare (bare soil). The steady

increase has been estimated at 4%, of the area covered by the sub-catchment.


However the steady increase is followed by a sharp increase, estimated at 29% of the area

covered by the sub-catchment, in the percentage of land which is bare between the years 1996

and 2002.

Figure

4-4: Thematic maps created from land use classes (1984-2015)

Table 4.3: Land use changes in the URSC (1984-2015)

LAND USE Area Area

(km2) %

1984 1984

Area Area

(km2) %

1996 1996

Area Area

(km2) %

2002 2002

Area Area

(km2) %

2008 2008

Area Area

(km2) %

2015 2015

BARE SOIL

CULTIVATION

FOREST & SHRUB

GRASSLAND

SETTLEMENT

WATER&MARSHY

454.61 4.32

3184.99 30.29

1617.87 15.38

4870.14 46.31

363.78 3.46

24.72 0.24

1009.93 9.61

5224.02 49.68

1513.53 14.39

1477.55 14.05

1217.39 11.58

71.94 0.68

4072.28 38.73

670.64 6.38

943.49 8.97

1536.81 14.62

3276.36 31.16

14.93 0.14

4811.77 45.76

2086.39 19.84

989.80 9.41

1333.74 12.68

1110.88 10.56

181.87 1.73

4210.80 40.05

1996.56 18.99

1032.57 9.82

1831.16 17.42

1250.29 11.89

193.38 1.84


The spread of bare land, land cover characteristic, furthermore aggravates by a relatively

small percentage, of 7% of the area covered by the sub-catchment, between 2002 and 2008. A

5% decrease in bare soil areal cover of the catchment can be noted between the years 2008

and 2015.

In the period in between the years 1984 and 2015 the trends represents a steady increase in

percentage land cover of settlements from 3% to 11%. However the year 2002 seems to have

an outlying percentage land cover of settlements, estimated at 31.16%. The trends of forests,

shrub and grassland are more or less the same where we have a diminishing land cover

percentage of these classes between 1984 and 2008 followed by an increase between 2008

and 2015. The percentage land cover of water and marshes is relatively small compared to

other land use classes, but however there is a noticeable increase in the percentage land cover

of water and marsh over the years 1984-2015. Within the period between 1984 and 2015

there has been a decrease in the percentage land cover of forest & shrub, an increase in the

percentage land cover of settlement and bare land.

According to literature the process of deforestation in Zimbabwe can be attributed to the

expansion of arable land, demand for fuel in form of firewood, urban expansion and

construction poles (Chipika and Kowero, 2000) these condition are also prevalent in the

URSC. Within the period between 1990 and 2010 Zimbabwe has lost 29.5% of the forests

(Buttler, 2006). The process of deforestation in the URSC results in the increase of

percentage the land which is bare.

4.1.3 Accuracy assessment

Table 4.4 shows the results of accuracy assessment. The confusion matrix is a function in

ILWIS was used to conduct accuracy assessment.

In a confusion matrix, classification results are compared to additional ground truth

information.

The verification of the accuracy of the derived land use maps was performed for 2015 image.

The overall average accuracy of the classified land use/ land cover map for 2015 was 70 %.


Table 4.4: Classification accuracy assessment results

Classification results

Bare soil Cultivation Forest & Shrub Grassland Settlement Water & Marshy ACC

Bare soil 108 46 0 1 6 0 0.68

Cultivation 37 59 0 0 4 0 0.59

Forest & shrub 1 4 36 4 0 0 0.80

Grassland 1 4 11 44 19 0 0.56

Settlement 8 4 0 0 35 0 0.74

Water & marshy 0 0 10 0 0 58 0.85

RELIABILITY 0.70 0.50 0.63 0.90 0.55 1.00

Average accuracy 70 %

Average reliabilty 71 %

4.1.4 Crop ratio (C-factor)

Table 4.5 shows the statistics of the C-factor for the years 1984, 1996, 2002, 2008 and 2015.

The crop ratio lies in the range between 0 and 2.07. The crop ratio is highest in the year 2015

and lowest in the year 2002. In the year 2015 the mean crop ratio for the catchment exceeds 1

and is 1.09. The mean crop ratio of the URSC was 1.09 for the year 2015 and 0.57 for the

year 2002. The mean crop ratio values for the URSC in the years 1984, 1996 and 2008 were;

0.69, 0.94 and 1.0 respectively.

Table 4.5: Statistics for the crop ratio

Statistic 1984 1996 2002 2008 2015

Min 0.32 0.00 0.30 0.10 0.12

Max 1.05 2.05 1.00 1.80 2.070

Mean 0.69 0.94 0.57 1.0 1.09

StD 0.22 0.63 0.17 0.50 0.614


The C-factor ranges from 0 for well protected soils to 1 for bare soil (Karaburun, 2010) hence

the increase in the mean of the C-factor over the years is evidence of the decrease in land

cover determined in section 4.1.2 from the supervised classification. In some cases the values

of the C-factor is exceedingly high and this represents woodlands and grassland. There is also

a limitation in using the NDVI value to determine the C-factor because it is only sensitive to

photosynthetically active and healthy vegetation (van der Knijff et al., 2000) whereas the

health of vegetation is unimportant in determining its protective property against soil erosion.

4.1.5 Topographical factor (X-factor)

Figure 4-6 shows thematic maps for the topographical factor and Table 4.6 shows the

statistics of the areal distribution of the topographic factor for the datasets used. From the

hydroprocessing of the ASTER and SRTM digital elevation models (DEM) (refer to table

3.4) the following X-factor classes were obtained; (0-0.25), (0.25-0.50), (0.50-1.00) (1.00-

2.00) (2.00-4.00) (4.00-5.00) (5.00-10.00) (10.00-20.00) and that class comprising of all

topographical factors whose magnitude is greater than 20 (>20).

Figure 4-5: Spatial distribution of the topographical factor (X-factor) for the years 2011 and 2014

There is a decrease in the percentage of the URSC that is within the >20 class of the

topographical factor for the ASTER and SRTM dataset. For ASTER 5.54% is characteristic

of the >20 class of the topographical factor and 3.67% of the URSC‟s area lies within that

class for SRTM. 42.54% of the URSC is characteristic of 1-4 topographical factor value

considering the ASTER dataset and considering the STRM dataset; 33.76% of the sub-

Catchment lies within that same interval.


The X-factor explains the effect of slope length and slope steepness on soil erosion (Ouyang

and Bartholic, 2001). The X-factor is equal to one for a standard plot of 22 m and 9%

steepness. That explains why other values are less than one and others greater. The terrain in

the URSC is generally gentle with 64% of the sub-Catchment lying in the range between 0-1

of the topographical factor.

Table 4.6: Spatial distribution of the topographical factor

class interval

(X-factor)

ASTER (2011) SRTM (2014)

Area(ha) Area (%) Area (ha) Area (%)

0 - 0.25

0.25 - 0.50

0.50 - 1.00

1.00 - 2.00

2.00 - 4.00

4.00 - 5.00

5.00 - 10.00

10.00 - 20.00

>20

73599.20

74853.10

153303.40

226045.30

221299.20

55464.40

121605.80

67194.10

58220.20

7.00

7.12

14.58

21.50

21.04

5.27

11.56

6.39

5.54

150568.3

128861.2

210214

208795.2

151380.9

35542.8

88828.2

53437.7

39099.6

14.11

12.08

19.71

19.57

14.19

3.33

8.33

5.01

3.67

4.1.6 Soil loss estimates

Tables 4.7 and 4.8 represent the quantities of the estimated soil loss for the year 2015. The

soil loss estimates were calculated for different wards, districts and land uses.

Soil loss estimates for the districts and wards

Table 4.7 represents the soil loss estimates for the wards and districts that are straddled by the

URSC. The Mberengwa district has the highest mean soil loss of 2469.04 t/ha/yr with the

second highest being Zvishavane, 1763 t/ha/yr, followed by Chivi, 1035.62 t/ha/yr. The city

of Gweru has the lowest mean soil loss of 765.55 t/ha/yr.

Soil loss estimates for the land uses

Table 4.8 shows the statistics for soil loss estimates for the different land uses in the URSC.

The communal lands have been estimated to have the highest estimate for soil loss, that is,

1543.14t/ha/yr, followed by the resettlement areas whose soil loss estimates are

1274.77t/ha/yr.


Other land uses than communal lands, large scale commercial farms, and resettlement areas

and small scale commercial farms have the least soil loss estimate, of 391t/ha/yr. The small

scale commercial farms have an estimated soil loss of 1035.71t/ha/yr.

The parts of the Mberengwa, Zvishavane, Shurugwi and Chivi districts straddled by the

URSC are home to over-crowded communal lands. Whitlow (1988) deduced that 80 % of the

land that was degraded was in the communal areas, where population density is high. The

predominant land use activity in the communal lands is agriculture; conservative farming

methods should be practised to minimize soil erosion.

Table 4.7: Total soil loss for the districts and wards

Districts Wards Soil loss estimate (t/ha/yr) Total soil loss (district)

Chivi Bachi

Badza-tiritose

Batanai

Chemuzangari

Chigwikwi

Chitenderano

Kuvhirimara

Madamombe

Madzivadondo

Matsveru

Munaka

Zvamapere

561.76

910.78

1437.26

1437.26

785.59

389.23

1142.95

636.56

718.73

839.40

1443.28

581.89

1035.62

Gweru _ _ 765.55

Insiza Gwatemba 590.7 812.11

Mberengwa Mataruse_bI

Mataruse_bII

584.76

995.91

2469.04

Shurugwi Donga

Gundura

Hanke

Mazivisa

Ndanga

1079.43

836.44

598.68

1166.97

727.06

886.51


Pisira

Shamba

Tinhira

Tongogara

814.18

1283.48

784.06

677.32

Zvishavane Chenhunguru

Chiwonekano

Dayadaya

Hombe

Indava

Mapirimira

Mototi

Murowa

Mutambi

Ngomayebani

Runde

Shavahuru

Shauke

Vukusvo

989.79

1127.13

1072.83

1381.82

1733.88

732.15

2027.22

2874.14

1543.16

1283.32

707.09

2300.41

3321.82

874.84

1763.09

Table 4.8: Soil loss estimates for the land uses in the URSC

Landuse Area (ha) Estimate soil loss (t/ha/yr)

Communal lands

Large scale commercial farms

Resettlement areas

Other Land

Small scale commercial farms

355975.7

575741.2

96657.1

346.9

20952.1

1543.14

1077.73

1274.77

391.96

1035.71

4.2 Soil erosion hot spot areas

Figure 4-7 shows thematic maps for the multi-temporal variation of the distribution of soil

loss risk and Table 4.7 shows the statistics of the areal distribution of soil erosion risk, as

determined by the SLEMSA model. There are fluctuations in the area of the sub-Catchment

that is under extremely high risk of erosion between the years 1984 and 2015 in the URSC.

The year 1996 has the least area that is under extremely high risk of erosion.


From the year 1996 there is a steady increase in the percentage of the sub-Catchment that is

under extremely high risk of erosion. There is however a decrease in the area under very high

risk of soil erosion for the years 1996 and 2015 followed by an increase in the successive

years after 1996.

The river banks are under extremely high risk of erosion hence the rivers are susceptible to

siltation. Basically the area of the URSC that is at high risk of erosion has increased over the

years (1984-2015).

Figure 4-6: Spatial and temporal representation of the soil erosion hot spot areas

Table 4.9: Distribution of the soil erosion risk

Soil loss risk Area (ha)

1984 1996 2002 2008 2015

Negligible

Low

Moderate

Moderately high

High

Very high

Extremely high

37512.38

119282.25

146629.27

289073.11

208542.04

130200.62

119276.76

32252.40

98151.50

119200.40

263642.50

223637.60

162088.50

151135.10

51755.60

176007.61

174153.70

292356.19

173793.57

95310.21

86737.02

29238.74

84264.20

104877.73

246887.37

226789.77

176257.39

181795.02

51747.59

84623.51

144209.01

283289.48

200681.54

135783.89

149438.54


4.3 Validation of the soil loss estimates

4.3.1 Upper Runde sub-Catchment soil loss estimates

The soil loss volumes estimated by the SLEMSA model are less than those calculated by the

cut/fill process. The difference can be accounted by the reasons discussed in the paragraphs

which follow.

The mean accuracy of the ASTER and SRTM DEMs used were 15.27 meters and 18.52

meters respectively (Tighe and Chamberlain, 2009). According to literature a soil loss of a

sheet of one millimetre thick sheet of soil over a hectare accounts for a loss of 15 tonnes of

soil (Pimentel, 2006). The SLEMSA model is mainly designed to account for the processes of

soil loss and does not account for deposition of soil into the hydrological systems or

depressions (Bobe, 2004). The SLEMSA model only accounts for the removal of soil,

whereas the cut and fill processing of DEMs accounts for all the modifications on the surface

(ESRI, 2012). It was hardly possible to acquire empirical data to validate the study, because

the data is not available for the URSC (Mutowo and Chikodzi, 2013)

4.4 Automation of quantification of soil erosion estimates

Automation of the process of mapping soil erosion hot spot areas was successfully achieved

by invoking ILWIS processes from the user interface. The automation was achieved such that

erosion hot spot maps could be determined and displayed for different districts, wards and the

entire sub-catchment from the interface developed. The area of the URSC under ‘Negligible’

to ’Extremely high’ risk of erosion was retrieved.

The application also gave the user the capability to simulate the effect of proposed land uses

and cropping systems on the risk of erosion helping the land use planner to make informed

decisions. The application also had the capability of executing land use classification based

on the NDVI value reclassification.

4.4.1 Mapping soil erosion hot spots

The user interface developed aides the officer to identify soil erosion hot spots by

manipulation of satellite imagery, digital elevation model, soil database, rainfall data and

vegetation cover all at the click of a button. The mapping of soil erosion hot spot areas was

achieved with minimum human interference saving the operator from a multitude of steps

they would follow given the ILWIS software alone.


The user interface provides a platform for non-remote sensing specialist to determine soil

erosion hot spots using remotely sensed imagery.

i. Soil erosion hot spots

Under the Erosion hot spots drop down menu the user can select an option that allows them

to map hot spot areas for the URSC, districts or wards within the URSC. On selecting the

spatial extent of the entire sub-Catchment and upon clicking the map button the ILWIS

software begins to process the data and produce a soil loss risk map for the entire sub-

Catchment. After the application would have finished processing the user would click the

view map button and the output map and statistics appear in the application‟s window (Refer

to figure 4-8). Figure 4-7 shows the application‟s home page.

Figure 4-7: Application home page

The application was capable of mapping and displaying soil erosion hot spot areas; maps and

statistics in the URSC at the click of a button as illustrated by Figure 4-8.


Figure 4-8: Mapping of erosion hot spots in the URSC

ii. Land cover maps

The application was also capable of producing land cover maps from Landsat images based

on the NDVI values. Under the land use planning menu tab the user can also simulate the

resultant soil erosion risk from a proposed land use at ward level by selecting the ward and

the proposed land use and coming up with a soil erosion risk map.

Figure 4-9: The application's display of land cover maps


5 CHAPTER FIVE: CONCLUSIONS AND

RECOMMENDATIONS

5.1 Conclusions

From this study four conclusions can be drawn

1. The factors affecting soil erosion can be quantified using GIS and Remote Sensing

techniques. The soil erosion estimates acquired from this study were to an extent an over-

estimation. Nevertheless it is possible to make some comments, the agricultural land uses

have the greatest contribution towards soil erosion; being the communal lands, resettlement

areas, large scale commercial farms and small scale commercial farms in order of decreasing

contribution towards soil erosion. The Zvishavane and Mberengwa districts have the highest

contribution to soil erosion and these districts are homes to over-crowded communal areas.

Soil conservation methods have to be prioritized in communal lands. The environmental

manager has to emphasize the need for soil conservation so as to protect the rivers from

sedimentation. The drainage network is susceptible to sedimentation and siltation, as other

rivers have filled up over the period between 2011 and 2014 see Figure 1 in appendix.

2. The mapping of soil erosion hot spot areas was done for the URSC for the years 1984,

1996, 2002, 2008 and 2015. The trend observed shows that there is an increase in the risk of

erosion over the years with the decrease in land cover and variations in the mean annual

rainfall in the URSC for the years under study. The URSC is mostly under moderately high

risk of erosion.

3. The automation of mapping of soil erosion hot spots within ILWIS was successfully

achieved. The output data (maps and quantities) determined in ILWIS could be retrieved

from within the user friendly interface without the user having to go through a series of

complicated processes in ILWIS but, at the click of a button.

4. The application developed had a user-friendly interface and the training of a Remote

Sensing and GIS novice to retrieve soil erosion hot spot areas data from the application will

not take the estimated time required to train them to do the same procedure in ILWIS, instead

it would take shorter to train them to use the application developed in this study.

5.2 Recommendations

1. For future research the use of high resolution satellite images and DEMs of better

accuracy will help in achieving better results.


2. An application of improved processing speed that is independent of ILWIS should be

developed as a tool for catchment management.

3. A modelling approach that is suited to the readily available datasets should be used in

estimating soil loss.


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final thesis document for tatenda hove

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