ABSTRACT
Surface runoff is water flow that occurs when the soil is infiltrated to full capacity and excess
water from rain and other sources flow over land. It is a major component of the water cycle and
a primary agent of soil erosion, water pollution and flooding.
Globally, the area of land estimated to be suffering from some form of degradation stands at just
below two billion hectares of the world’s land mass, making it a serious global concern
challenging the well-being of mankind. Water erosion accounts for about 56% of the total land
area estimated to be degraded, making it a major factor contributing to the process of land
degradation.
Runoff is a dynamic process that is dependent on factors that vary both spatially and temporally.
In order to evaluate the rate at which it is generated and how different factors in a catchment
affect it, a modeling design in a GIS provides an ideal environment. Such an approach allows the
storage, integration, analysis and maintenance of large environmental datasets. It provides an
efficient, cost effective technique that offers possibilities to investigate factors that influence the
rate of runoff over a large area.
The information attained can be used to simulate the effects of certain decisions on catchment
management practices to prevent, for instance, excessive runoff that may lead to a number of
problems.
Remote Sensing technology has been employed as an effective tool for data collection and
preparation. The data is captured, processed and analyzed in a GIS environment and a database
for surface runoff is created. The database can be customized to simulate runoff under different
land-use and land cover types within the lake basin for better farming management practices.
The results from the data analysis have indicated that there has been increased human activity
around the lake basin in terms of settlement, agricultural practices and deforestation of the Mau
Escarpment.
The database can be used to monitor change in land use and its effect on surface runoff.
This study therefore considers, a modeling approach in a GIS environment as the most effective
method to assess the rate at which runoff occurs over the study are and the factors that influence
it.
i
DEDICATION
To my family for their enduring love and support.
ii
ACKNOWLEDGEMENT
This project would not have been successful without the Lord’s grace and mercy. For that, I
remain very grateful to the Almighty.
I wish to extend my most sincere appreciation to the entire teaching staff of the Department of
Geospatial and Space Technology for the knowledge they have inculcated in me. I am
particularly grateful to Dr. Ing F.N. Karanja for her guidance throughout the project.
My gratitude further goes to all my classmates, who were kind enough to assist me in this
project.
Finally, I am forever indebted to my family, to whom this project is dedicated, for their enduring
love and support.
iii
TABLE OF CONTENTS
Abstract……………………………………………………………………………………………………………………………………………………i
Dedication……………………………………………………………………………………………………………………………………………….ii
Acknowledgement………………………………………………………………………………………………………………………………….iii
Table of Contents…………………….…………..…………………………………………………………………………………………………iv
List of Tables………….……………………………………………………………………………………………………………………………….vi
List of Figures………………..……………………………………………………………………………………………………………………….vii
List of Abbreviations……………………………………………………………………………………………………………..………………viii
CHAPTER ONE: INTRODUCTION .................................................................................................................... 1
1.1 Background Information ..................................................................................................................... 1
1.2 Problem Statement ............................................................................................................................. 2
1.3 Research Hypothesis ........................................................................................................................... 2
1.4 Objectives............................................................................................................................................ 2
1.5 Research Questions ............................................................................................................................ 2
1.6 Organization of the report .................................................................................................................. 3
CHAPTER TWO: LITERATURE REVIEW ........................................................................................................... 4
2.1. Runoff................................................................................................................................................. 4
2.1.1. Surface Runoff in a Catchment ................................................................................................... 4
2.1.2. Soil Properties Influencing Infiltration ........................................................................................ 4
2.1.3. Land-use and Land-Cover Changes in relation to Surface Runoff .............................................. 5
2.1.4. Surface Runoff in Relation to Soil Erosion .................................................................................. 6
2.2. Runoff Modeling ................................................................................................................................ 6
2.2.1. Runoff Model Classification ........................................................................................................ 7
2.2.2. The U.S SCS Hydrological Model…………………………………………………………………………………………….8
CHATER THREE: MATERIALS AND METHODS ................................................................................................ 9
3.1 Area of Study....................................................................................................................................... 9
3.2 Data Sources and Tools ..................................................................................................................... 10
3.2.1 Data Sources .............................................................................................................................. 10
3.2.2 Tools ........................................................................................................................................... 11
3.3 Application of Remote Sensing in Runoff Modeling ......................................................................... 11
iv
3.3.1 Data Collection ........................................................................................................................... 11
3.3.2 Data Preparation ........................................................................................................................ 12
3.3.3 Database Development.............................................................................................................. 16
3.4 Application of GIS in Runoff Modeling.............................................................................................. 20
3.4.1 Derivation of Soil Data ............................................................................................................... 20
3.4.2 Land Use/Cover Map ................................................................................................................. 21
3.4.3 Land-Soil Map Database Development...................................................................................... 22
3.4.4 Modeling using ArcCN ................................................................................................................ 23
3.4.5 Generation of Runoff Maps ....................................................................................................... 24
CHAPTER FOUR: RESULTS AND DISCUSSIONS…………………………………………………………………………………………25
4.1 Land Use/Cover Mapping…………………………………………………………………………………………………………….25
4.1.1 Classification Results…………………………………………………………………………………………………………….25
4.1.2 Classification Accuracy Reports ................................................................................................. 28
4.2 Spatial Database for Runoff .............................................................................................................. 30
4.3 Surface Runoff Maps ......................................................................................................................... 31
4.4 Changes in Land Use Area Relative to Change in Runoff .................................................................. 34
4.5 Discussion of Results ......................................................................................................................... 35
CHAPTER FIVE: CONCLUSIONS AND RECOMMENDATIONS ........................................................................ 36
5.1 Conclusions ....................................................................................................................................... 36
5.2 Recommendations ............................................................................................................................ 37
REFERENCES ................................................................................................................................................ 38
APPENDIX ....................................................................................................... Error! Bookmark not defined.
v
LIST OF TABLES
PAGE
Table 3.1
Data sources and the characteristics ....................................................................... 11
Table 4.1
Accuracy Assessment Report from year 1988 image classification ......................... 28
Table 4.2
Accuracy Assessment Report from year 2000 image classification ......................... 28
Table 4.3
Accuracy Assessment Report from year 2010 image classification ......................... 29
Table 4.4
Land Use Change vs. Runoff Change between the years 1988 and 2000 ................ 34
Table 4.5
Land Use Change vs. Runoff Change between the years 2000 and 2010 ................ 34
vi
LIST OF FIGURES
PAGE
Figure 3. 1
Area of Study .......................................................................................................................... 9
Figure 3.2
Area of Interest in two images, different paths ................................................................... 13
Figure 3.3
MosaicPro in Erdas Imagine ................................................................................................. 13
Figure 3.4
Mosaicked Images .............................................................................................................. 144
Figure 3.5a
Image Subset in Erdas Imagine ............................................................................................. 15
Figure 3.5b
AOI images (a) 1988, (b) 2000, (c) 2010 ............................................................................... 15
Figure 3.6
Signature Editor .................................................................................................................... 17
Figure 3.7
Supervised Classification ...................................................................................................... 18
Figure 3.8
Accuracy Assessment............................................................................................................ 19
Figure3.9
Soil Data Clipping in ArcGIS 10.1 .......................................................................................... 21
Figure3.10
Digitization of Land Use/Cover Map .................................................................................... 22
Figure 3.11
Intersection of Soil and Land Use Attributes........................................................................ 23
Figure 3.12
Modeling Runoff using ArcCN Tool ...................................................................................... 24
Figure 4.1
Status of Lake Basin in 1988 ................................................................................................. 25
Figure 4.2
Status of the Lake Basin in 2000 ............................................................................................ 26
Figure 4.3
Status of the Lake Basin in 2010 ............................................................................................ 27
Figure 4.4
Runoff Spatial Database for the year 2010 ........................................................................... 30
Figure 4.5
Runoff Map 1988 ................................................................................................................... 31
Figure 4.6
Runoff Map 2000 ................................................................................................................... 32
Figure 4.7
Runoff Map 2010 ................................................................................................................... 33
vii
LIST OF ABBREVIATIONS
RCMRD
Regional Centre for Mapping of Resources for Development
SoK
Survey of Kenya
USGS
United States Geological Survey
GIS
Geographic Information Systems
CN
Curve Number
TIFF
Tagged Image File Format
JPEG
Joint Photographic Expert Group
AOI
Area of Interest
SCS
Soil Conservation Service
NRCS
Natural Resources Conservation Service
CSRS
Canadian Spatial Reference System
WGS
World Geodetic System
DLL
Dynamic-Link Library
viii
CHAPTER ONE: INTRODUCTION
1.1 Background Information
Surface runoff is water flow that occurs when the soil is infiltrated to full capacity and excess
water from rain and other sources flow over land. It is a major component of the water cycle and
a primary agent of soil erosion, water pollution and flooding. A land area which produces runoff
that drains to a common point is called a watershed.
The area of land estimated to be suffering from some form of degradation stands at just below
two billion hectares of the world’s land mass, making it a serious global concern challenging the
well-being of mankind (Barrow, 1994; Scherr et al., 1996). The pressures of an increasing world
population, combined with insufficient agricultural production, have resulted in measures that
have further accelerated the process of land degradation. Poor agricultural management practices
and use of marginal lands for cultivation purposes are notable examples of measures being
implemented (Eswaran et al., 2001; UNEP 2000).
Water erosion accounts for about 56% of the total land area estimated to be degraded, making it
a major factor contributing to the process of land degradation (Swai, 2001). The problems
instigated by water erosion extend to low lying areas where transported soil particles are
deposited. The result is damage of soil structure, leading to decline in infiltration capacity,
surface sealing and crusting, which can cause severe floods (Schwab et al., 1981; White, 1997).
Erosion is a natural process but only when it takes place without pressures of land use or land
cover changes which hasten the rate at which it occurs. Alterations in land use/ cover particularly
affect the physical properties of soil, which are closely related to infiltration characteristics. This
in turn influences the runoff pattern. The consequences of changes in land use on the rate of
erosion can be assessed indirectly by studying runoff in an area. In order to achieve this, it is
necessary to quantify the runoff, through modeling, taking these changes into account (DeRoo et
al., 2000; Dingman, 2002).
The integration of remote sensing and GIS has been applied widely and has been recognized as a
powerful and effective tool in land use and land cover change. Remote sensing is used to collect
multispectral, multiresolution and multitemporal data and turns them into information that is
valuable for understanding and monitoring land use change processes.
GIS technology, on the other hand, provides a flexible environment for entering, analyzing and
displaying digital data from various sources necessary for change detection and database
development.
In hydrologic and watershed modeling, remotely sensed data are found to be valuable for
providing cost-effective data input and for estimating model parameters (Weng, Q, 2011).
1
1.2 Problem Statement
Problems of land degradation have intensified over the last decade as a result of population
pressures. Marginal areas are continuously being encroached for expansion of agricultural fields
without the implementation of proper conservation measures. Additionally, poor land
management practices, including farming on the marginal lands have further aggravated the
problem. Erosion and flooding have become the two main land degradation problems affecting
the economy of the country as well as the lives of many (Shrestha, 2003).
Lake Naivasha basin has seen extensive land use changes through human intervention in the past
two decades. The poor farming techniques and depletion of forests, not only in the watershed but
also the flood plain, contributes to the frequent problems of flooding around the lake. A case in
point is the- flood event of October, 2012, that brought extensive damage to property.
One implication of these problems could be that the soil in the watershed is degraded as a result
of farming management practices resulting in increased rate of runoff.
The study of the influence of different land use types on surface runoff generation has therefore
become a topic of consequence.
1.3 Research Hypothesis



A modeling approach is a suitable technique to assess the impact of land use on surface
runoff
Land use and land cover types can result in reduced rates of infiltration due to changes in
the hydrological properties of soil in the watershed
Changes in land use and land cover characteristics can influence surface runoff
1.4 Objectives
The overall objective of this study is to model the effects of land use change on surface runoff.
To achieve this objective, the following specific objectives were addressed:




Investigate the use of Geospatial techniques on the impact of surface runoff as a result of
land use/ cover types
Identify factors that contribute to surface runoff
Design a spatial database for surface runoff
Model runoff using Geospatial techniques
1.5 Research Questions


Which land use and land cover types result in generation of high surface runoff?
Which land use and land cover types result in generation of low surface runoff?
2

What are the impacts of different land use and land cover types on soil physical
properties related to surface runoff generation?
1.6 Organization of the report
This report has been organized into five chapters. Chapter one defines surface runoff, gives its
importance and effects. In addition, it defines the objectives of this research. Chapter two
contains a literature review of the factors affecting surface runoff and using geospatial
techniques to model surface runoff. Chapter three describes the methodology applied to achieve
the objectives. It also defines the study area. In chapter four, the results from the study and their
discussions are displayed. Chapter five gives the conclusions and recommendations from the
study.
3
CHAPTER TWO: LITERATURE REVIEW
2.1. Runoff
Runoff in a catchment is generated by the portion of rainfall that remains after satisfying both
surface and subsurface losses. Once these demands have been met, the remaining rainwater
follows a number of flow paths to enter a stream channel. The course it follows depends on
several factors including soil characteristics, climatic, topographic and geological conditions of a
catchment (Ward and Robinson, 1990). Overland flow or subsurface runoff is the main flow path
of runoff that can largely be influenced by human activities through catchment management
practices. It is also the flow path of rainwater that triggers the process of soil erosion (Morgan,
1995).
2.1.1. Surface Runoff in a Catchment
Surface runoff refers to the portion of rainwater that is not lost to interception, infiltration,
evapo-transpiration or surface storage and flows over the surface of land to a stream channel
(Morgan, 1995). During a rainstorm, a certain portion of rainfall is intercepted by vegetation
canopy. What is left over falls directly onto the soil as through fall (White, 1997). Intercepted
rainwater either evaporates or in cases of heavy continuous events, when canopy storage capacity
is exceeded, intercepted rainwater falls to the ground as leaf drainage or stem flow (Morgan,
1995). The amount of rainwater that is lost due to interception depends on the vegetation cover
and rainfall pattern (Dingman, 2002). Rainwater retained in vegetation canopy that ultimately
evaporates is referred to as interception loss (White, 1997).
Rainfall that is not lost to interception and reaches the soil surface either infiltrates into the soil,
is stored in surface depressions or evapo-transpires. The remaining excess rainwater travels over
land as surface runoff. Surface runoff occurs either when the soil is saturated from above or from
below. If the rate at which rain falls to the ground is higher than the rate at which it infiltrates
into the soil and surface storage is full then, the excess water at the surface flows along its
gravitational gradient as surface runoff. This is referred to as saturation from above or Hortonian
overland flow. On the other hand, if the soil is already saturated due to previous rainstorm event
and the infiltration capacity of the soil is zero, saturation from below occurs. In this case, most of
the rain that reaches the ground is converted to overland flow after satisfying surface storage and
no or very little water infiltrates into the soil (Dingman, 2002).
2.1.2. Soil Properties Influencing Infiltration
Infiltration rate refers to the rate at which water enters the soil during or after a rainstorm (White,
1997). Infiltration plays a key role in controlling the amount of water that will be available for
surface runoff after a rainfall event (Morgan, 1995). It involves several processes acting together
including gravitational forces pulling water down, attractive forces between soil and water
molecules and the physical nature of soil particles and their aggregates (Morgan, 1995).
4
A number of environmental factors govern the rate at which water infiltrates into the soil. These
include; the rate of rainfall, soil properties (including texture, soil porosity, organic matter
content, structure of soil aggregates, and soil depth and moisture carrying capacity of the soil),
topography (slope), vegetation cover, and the type of land use (Schwab et al., 1981).
The most important soil properties that influence the rate of infiltration, as mentioned above, are
the physical properties of the soil. The size of the particles that make up the soil, the extent of
soil particle aggregation and the way in which the aggregates are arranged, are the properties of
the soil that make it a porous permeable medium through which water can flow (Schwab et al.,
1981). These properties vary extensively both spatially and temporally, and are a consequence of
the geology and geomorphology of an area. They can also be influenced through catchment
management practices (White 1997).
2.1.3. Land-use and Land-Cover Changes in relation to Surface Runoff
As previously stated, in the conversion of rainfall, there are a number of stages in the
hydrological cycle that rainwater goes through before runoff is generated in a catchment. These
different stages result in different losses from the total rain, reducing the amount of water that
will be available for overland flow. Through catchment management practices, man has had
considerable influence over certain aspects of these stages. This has brought notable differences
in the rate at which surface runoff is generated. An increase in the rate of runoff consequently
increases the amount of soil that is eroded resulting in problems of land degradation (Morgan,
1995).
Soil compaction brought about as a result of changes in land use and land cover practices in a
catchment disrupts the natural arrangement of soil particles and their aggregates. This disruption
causes soil particles to be more closely packed which reduces porosity, increases soil bulk
density and destabilizes soil aggregates (Schwab et al., 1981).
Vegetation serves as a protective layer over soil surface and also increases its infiltration
capacity. Removal of vegetation due to changes in land use/ cover exposes the bare soil. When
rain drops to the ground and hits the soil directly, soil particle detachment takes place. Freely
flowing water then may cause fine particles to clog pore spaces provided by soil aggregates. This
results in the formation of a thin compacted layer on the soil surface, referred to as surface
crusting, which prevent water from passing into the soil (White 1997). The formation of a
surface crust at the surface greatly reduces the infiltration capacity of the soil, which ultimately
increases the amount of water available for surface runoff.
Vegetation cover is also important in serving as a barrier to reduce flow velocity of surface
water. It also has an important role in maintaining the structure of the soil. Plant roots increase
the pore spaces along the lines occupied by the roots and dead leaves provide litter that keeps up
the organic content of the soil which strengthens soil aggregates and maintain good soil
5
structure. Clearing of vegetation reduces the effect of roots and input of organic matter into the
soil contributing to a reduction in the infiltration capacity of the soil (Schwab et al., 1981).
2.1.4. Surface Runoff in Relation to Soil Erosion
Runoff is not in itself a form of land degradation but it is one of the major causes of land
degradation problems, of which the main ones are erosion and flooding. Furthermore, the rate at
which runoff is generated can be increased because of land degradation problems. Runoff on the
one hand is an essential process in that it maintains water level in lakes and rivers preventing
them from drying out and providing fresh water on which many living beings including humans
largely depend. If however the rate of runoff is increased as a result of catchment management
practices, it can result in severe land degradation problems (Schwab et al., 1981).
Areas having shallow and compacted soils ensuing from a combination of poor farming
techniques, exploitation of marginal lands, deforestation and excessive erosion are susceptible to
higher rates of runoff. Higher runoff rate leads to an increase in soil erosion by running water.
On the other hand, areas with deeper, more porous soil structures that are densely vegetated
contribute to a reduction in the amount of water available for runoff which results in reduced
rates of erosion (White 1997). Land use/ cover changes that increase runoff rates therefore
ultimately influence the rate at which soil loss occurs. Soil loss brings about problems of soil
degradation which in turn further aggravates problems of runoff.
2.2. Runoff Modeling
There are several approaches to quantify the volume of runoff. One would be to gauge a stream
channel and measure its discharge pattern. However, this will not give adequate information on
the effects of changes in management practice (such as soil, land-use and land-cover changes) on
the rate at which runoff is generated from different areas in a catchment. Due to spatial and
temporal heterogeneity of the factors involved in runoff at catchment scale, a modeling approach
to simulate the physical processes of runoff would be ideal to study the effects of changes in a
catchment on its generation (Dingman, 2002).
The mathematical description of all runoff models are simplifications of the actual process of
runoff in nature. Some models are more simplified than others but at the base of each model,
there is a mathematical description that simplifies the factors that are being considered and that
enables the model to make quantitative predictions (Rientjes, 2004). Factors involved in the
process of runoff, such as soil characteristics, vary extensively over small distances. It is
impossible to account for each variation in space in a mathematical model. For this reason,
average values are taken for sets of variables that share similarities. All models whether
empirical, physical or combinations of the two are therefore based on many assumptions (Beven,
2000).
6
Increasing the complexity of a model by emphasizing more on the physical basis of
environmental processes does not necessarily improve the performance of the model. Making a
certain model more physical based implies that the input parameters are also increased and are
more complicated to attain. Most parameters are obtained through measurements or observations
and are rarely free of error. This error introduced into the model when parameters are processed
contributes to the overall inaccuracy of a model (Duerson, 1995). On the other hand, the use of
simpler empirical models also has its own drawbacks. Simple models tend to generalize details
of environmental processes. This may result in loss of both spatial and temporal information
(Beven, 2000; Duerson, 1995). As a result, there is no perfect model currently in existence that
can fully explain all details involved in runoff and simulate the process as it occurs in nature.
However, there are many types of runoff models available today and through the application of a
suitable modeling approach, useful information on if and where there are problems can be
identified (Beven, 2000; Duerson, 1995). Although a challenging task, once a suitable model for
a particular catchment has been identified and surface runoff is simulated, taking into account
changes in land use/ cover, predictions of the model can be very useful. It can help in identifying
areas that contribute to higher rates of surface runoff. This information can be used to execute
changes in catchment management practices in an effort at reducing the rate of surface runoff, in
the long run diminishing problems of land degradation. The selection of a suitable model should
therefore depend very much on the objectives of a study but additional factors such as
availability of data, money and time should also be taken into account (DeRoo et al., 2000).
2.2.1. Runoff Model Classification
Runoff models can be classified into two: lumped or distributed; and deterministic or stochastic
(Beven, 2000) models.
Lumped modeling approaches consider a catchment to be one unit and single average value
representing the entire catchment is used for the variables in the model. The predictions obtained
from such models are single values (Beven, 2000). In the distributed modeling approach, models
make predictions that are spatially distributed. The spatial variability of model variables is
simulated by grid elements (grid cells), that can either be uniform or non-uniform (Rientjes,
2004). Not only one average value over the entire catchment area is considered but values of
parameters that vary spatially are locally averaged within each grid elements. Model equations
are solved for each element or grid square and depending on the spatial variability of a certain
parameter different values are used for each grid elements (Rientjes, 2004). Such types of models
make predictions that are distributed in space allowing the assessment of effects of changes in a
catchment (such as land use/cover) on the rate at which runoff is generated (Beven, 2000).
Stochastic models (also known as probabilistic models) consider the chance of a hydrological
variable occurring (Ward and Robinson, 1990). Both the input and output variables of stochastic
runoff models are expressed in terms of a probability density distribution. In a stochastic
7
modeling approach, uncertainty or randomness in the possible outcome of the models permitted
because of the uncertainty that is introduced by the input variables of the model (Beven, 2000;
Rientjes, 2004).
Deterministic models on the other hand focus on the simulation of the physical processes
involved in the in the transition from precipitation to runoff. Most runoff models use this
approach in simulating the process of runoff. In this case, only one set of values per variable are
input into the model and the outcome is also one set of values. At any time step, the expected
outcomes from deterministic modeling approach are single values (Beven, 2000; Rientjes, 2004;
Ward and Robinson, 1990).
2.2.2 The U.S Soil Conservation Service Hydrological Model
The U.S. SCS studied a number of watersheds varying in size; land use and land cover and
developed the following relationships (U.S. Soil Conservation Service, 1972):
𝑄=
(𝑃−𝐼)2
. ………………………. (1)
(𝑃−𝐼)+𝑆
Where:
Q = the direct runoff (mm),
P = the depth of rainfall (mm),
I = the initial subtraction (mm), and
S = the retention parameter (mm).
The initial abstraction, I, mainly includes interception, infiltration, and surface storage, which
occurs before runoff begins.
By using rainfall and runoff data from small experimental watersheds, the following relationship
was developed (US Soil Conservation Service, 1972):
𝐼 = 0.2 𝑆 …………………………………………………………………. (2)
Substituting Equation 2 in Equation 1 yields the following relationship:
(𝑃 − 0.2𝑆)2
𝑄=
𝑃 + 0.8𝑆
A runoff curve number (CN), which is defined in term of land cover, land use and hydrologic
25400
soil type and condition, is used as a transformation of S as: CN=
𝑆+254
8
CHATER THREE: MATERIALS AND METHODS
3.1 Area of Study
a) Location
Lake Naivasha, located within the eastern branch of Great Rift Valley and about 100km northwest of Nairobi, occupies a basin area of about 3200 km2. It lies approximately between latitude
0o 10’S to 1o 00’S and longitude 360 10’E to 360 45’E. Basin altitude varies from about 1900 m
at the bottom of the valley to 3200 m in the Mau Escarpment found on the western boundary of
the basin.
The basin is surrounded to the west by the Mau Escarpment, to the south and south-east by the
Olkaria and Longonot volcanic mountains, to the east by the Kinangop Plateau and to north
north-east by the Aberdare Mountain Range, and finally to the north-west by the Eburu volcanic
pile.
Due to the altitudinal differences, there are diverse climatic conditions found in the basin. The
climate of the area is a typical equatorial tropical climate with two rainy seasons followed by a
dry season. The relief controls the precipitation pattern with much more rain in higher altitudes.
The Rift valley floor experiences an average annual rainfall about 640mm while the wettest
slopes of the mountains receive about 1525mm.
Figure 3.1 Area of Study
9
b) Land use and Land Cover
River Gilgil is one of the two main perennial rives flowing into the lake. The Gilgil basin is
about 527km2 contributing about 20% of the discharge into the lake.
According to the soil map obtained from Soil Science Division ITC, the soils in the upper basin
area consist of clay loam to clay. These soils are deep (80-120 cm) and well drained. In the lower
plains, soils are mainly sandy clay loam to sandy clay, and are deep and well drained. On the
mountains, the soils are shallow (<50 cm) to moderately deep and consist of a complex of loam,
clay loam and clay.
The land cover of the Naivasha basin can be broadly categorized into four groups, namely
Agriculture, Grass, Bush land and Forest. In the humid region, predominant land cover classes
are forest and crops. The main crops consist of maize, potatoes and wheat. In addition to that,
there are many other vegetables grown by smallholder farmers in the middle part of the basin. In
the semi-arid region, there are extensive areas of grassland and bush land, which are used for
livestock grazing. Also, intensive horticulture farming under irrigation is common around the
lake. The North-Eastern part of the lake is predominantly occupied by large-scale vegetable and
dairy farms. However, the detailed land use in different parts of the basin is subjected to high
heterogeneity. The natural vegetation surrounding the lake is mainly papyrus swamp vegetation
while; cactus, acacia, bamboo, shrub and coniferous trees are mainly further away from the lake.
The smallholder farmers occupy the upper basin areas.
3.2 Data Sources and Tools
3.2.1 Data Sources
The primary data for land use/cover mapping and change detection was Landsat satellite imagery
captured by Landsat Thematic Mapper 7 sensor. Some of these satellite images were obtained
from Regional Centre for Mapping and Resources Development (RCMRD) and others
downloaded from the United States Geological Survey website (http://www.usgs.gov).
Topographic map sheet covering the area of study was used to provide base information and for
georeferencing the satellite images.
A soil map detailing the characteristics of soil found in the area was downloaded from the
Faculty of Geo-Information Science and Earth Observation. The dataset was in shapefile format,
which was imported to ArcGIS for further processing and analysis.
A summary of the types of data, their characteristics and their sources is shown in Table 3.1
below.
10
Table 3.1 Data sources and the characteristics
Data
Landsat Imagery
(1988, 2000 and 2010)
Characteristics
Resolution: 15m Panchromatic
Source
RCMRD, USGS
30m Multispectral
Topographic Map sheet
Scale, 1:50,000; In softcopy
SoK
Kenya Soil Map
Shapefile
Soil
Science
Division, ITC
3.2.2 Tools
The datasets collected were in different coordinate systems. Erdas IMAGINE version13 and
ArcGIS 10 software were used to project the datasets to UTM Zone 36o S, datum WGS 84
coordinate system.
Erdas IMAGINE version13 software was also used to classify images for production of land use
and land cover maps for the different epochs.
Detailed mapping, database production and map layout creation was done in ArcMap GIS 10.
Runoff map was produced using ArcCN extension.
Global Mapper Version 10 software was used to georeference the topographic map sheet to the
common coordinate system.
Accuracy assessment statistics were analyzed in Erdas IMAGINE.
3.3 Application of Remote Sensing in Runoff Modeling
3.3.1 Data Collection
For change detection, time epochs between the years 1988, 2000 and 2010 were chosen. These
were believed to be well distributed epochs where substantial changes in land use and land cover
could be identified and modeled relative to surface runoff. The Landsat images were captured
between the months of May and June in all the years to ensure consistency in land cover
mapping and to reduce the effects of variations caused by seasonal changes.
The Landsat images were obtained in compressed format in order to reduce their storage space
requirement, enabling easier transfer and download. The images were extracted by unzipping the
compressed files. In addition to the Landsat images, the files contained metadata which showed
the date of acquisition, sensor used, area covered (in terms of row and path), and the bands
included therein.
11
The extracted data files were converted to GeoTiff format which is compatible with Erdas
IMAGINE software.
As for the topographic map, the only conversion required was from JPEG to TIFF. This was
done during the georefencing procedure in Global Mapper software. The topographic map
formed a good source of base information for accuracy assessment of the classified images.
The soil map was downloaded in shapefile format. The only processing procedure carried out for
this data was to project it into the common coordinate system used for this study.
3.3.2 Data Preparation
3.3.2.1 Image Mosaicking
Part of the study area was found to lie along one path of the satellite image and the other part, on
the succeeding path as shown in Fig. 3.2. Image mosaicking and color corrections were carried
out using the MosaicPro option in Erdas IMAGINE as shown in Fig 3.3. The color correction
was done using the histogram matching option. Histogram matching option was preferred to
other options because the two satellite images were acquired at the same local illumination and
over the same location, but different atmospheric conditions and global illumination. The image
mosaic procedure produced one seamless image for each epoch as shown in Fig 3.4.
12
Figure 3.2 Area of Interest in two images, different paths
Figure 3.3 MosaicPro in Erdas Imagine
13
Figure 3.4 Mosaicked Images
3.3.2.2 Image Subset
The mosaicked Landsat images obtained, covered an extensive area of approximately
66,445km2. This was a wide area compared to the study area of about 1500km2. Image subset
was therefore necessary. Image subset required that an area of interest be cropped and separated
from the main large image. For this operation, the mosaicked images were imported to Erdas
IMAGINE; an AOI (Area of Interest) was defined using the AOI drawing tools, and then Image
Subset operation was performed around the created AOI.
Figure 3.5a shows the process of image subset to get the Areas of Interest shown in Fig 3.5b.
14
AOI
Figure 3.5a Image Subset in Erdas Imagine
Figure 3.5b AOI images (a) 1988, (b) 2000, (c) 2010
15
3.3.2.3 Image Enhancement
Image enhancement was performed whereby; false color composites were created by combining
three spectral bands, band 4 (red), band 3 (green) and band 2 (blue) in that order. The false color
composite was preferred since in the visible part of the spectrum, plants reflect mostly green
light (hence they appear green) but their infrared reflection is even higher hence the reddish tint.
False color composite sacrifices natural color rendition in order to ease the detection of features
that are not readily discernible otherwise.
3.3.3 Database Development
3.3.3.1 Information Classes
To extract information on the various land use and land cover types, information level of
classification was employed. This was done because there exists several and complex land cover
types in the River Gilgil basin.
Training sites were created and supervised classification with maximum likelihood algorithm
performed on the spatially enhanced images. Classification was performed by creating small
polygons (training sites) on every land cover type so as to associate each land cover type to a
class value and class name. Six classes were used to classify all land cover types identified as
dominant in the area of interest. The classes included:
1.
2.
3.
4.
5.
6.
Water Body
Forest
Agriculture
Bare Soil
Cleared Areas
Settlement
Lake Naivasha (water body), was the common drainage point of the water flowing within the
river basin. The forested areas consisted mainly of the coniferous forests. This was the catchment
area for the river basin. Agriculture included intensive horticultural farming under irrigation and
small-scale farming within the river basin. The forested areas had been deforested over a period
of time. The deforested areas, classified as cleared areas, were used to analyze the effect of
deforestation on surface runoff. Bare soils had a different curve number compared to other land
cover, particularly the cleared areas, indicating that it experienced different runoff. Settlement
was added to analyze the effect of urbanization on surface runoff.
16
Figure 3.6 Signature Editor
Fig 3.6 shows the use of AOI drawing tools to create training sites for the different land cover
and land use classes for generation of the signature editor.
The process of supervised classification is shown in Fig. 3.7
17
Figure 3.7 Supervised Classification
3.3.3.2 Accuracy Assessment
Accuracy assessment is a measure of how accurate the land cover types have been assigned to
classes where they belong and hence the level of reliability of the data. The original unclassified
image was used for base information and a set of ground coordinate points from each of the land
cover types used as reference classes. These reference classes were compared with the classified
images and used to compute the error matrix and the kappa index for any misclassification. The
process is as shown in Fig. 3.8.
18
Figure 3.8 Accuracy Assessment
In the 1988 imagery, three points had wrongly been classified and after computing the error
matrix, an overall Classification accuracy of 75.00% was achieved, translating to a Kappa of
0.6505. The misclassification can be attributed to the fact that, during that time, land cover
identified as agriculture was not well pronounced, and bare soil occupied a small percentage of
land cover.
In the 2000 imagery, two points were wrongly classified and after computing the error matrix, an
overall Classification accuracy of 83.33% was achieved translating to a Kappa of 0.7692.
In the 2010 imagery, two points were wrongly classified and after computing the error matrix, an
overall Classification accuracy of 83.33% was achieved, translating to a Kappa of 0.7966. This
was the best Kappa index in the classification and showed that the 2010 image was the most
accurately classified image.
With all the Kappa indices greater than 0.6, data could be used comfortably for modeling runoff.
19
3.4 Application of GIS in Runoff Modeling
3.4.1 Derivation of Soil Data
In hydrology a curve number (CN) is used to deter mine how much rainfall infiltrates into soil
and how much rainfall becomes surface runoff. A high curve number means high runoff and low
infiltration (urban areas), whereas a low curve number means low runoff and high infiltration
(dry soil). The curve number is a function of land use and hydrologic soil group.
There are seven major landscape units in Naivasha area: (a) lacustrine plain, (b) volcanic plain,
(c) hill land, (d) highland plateau (e) low plateau (f) step-faulted plateau (g) volcanic lava-flow
plateau. The details for the soil found in these landscapes are given in chapter two of this report.
The soils in the area were classified in four groups, i.e. A, B, C and D. From the NRCS
standards, soil group A has the highest curve number, indicating highest runoff while D has the
lowest curve number hence lowest runoff. Sandy soils around the lake, for instance would
largely be classified as hydrologic soil group D.
The derivation of soil data involved projection of the Kenya Soil Map form NAD 1983 CSRS to
UTM Zone 36o S, datum WGS 84 coordinate system.
The area of interest was then overlaid on the projected soil data and subset of the soil data carried
out using the clipping tool in ArcGIS. The process of clipping in ArcGIS is as shown in Fig 3.9.
20
Figure 3.9 Soil Data Clipping in ArcGIS 10.1
3.4.2 Land Use/Cover Map
One of the inputs of ArcCN is the land use and land cover data as a shapefile. The classified
images were imported to ArcGIS software, and the different land cover classes digitized. The
classes were divided into six thematic areas, including; water body, agriculture, bare soil, forest,
cleared areas and settlement.
Fig. 3.10 shows part of the digitization process.
21
Figure 3.10 Digitization of Land Use/Cover Map
3.4.3 Land-Soil Map Database Development
To reduce processing time, the soil and land use layers were dissolved before intersection based
on attributes ‘hydrogroups’ in the soil data and ‘covername’ in the land use data. For this study,
the polygons for soil group were reduced from 24 to 4 (A, B, C and D). Soil and land use layers
were intersected to generate new and smaller polygons associated with soil hydrgroup and land
use covername.
The intersection process kept all details of the spatial variation of soil and landuse and hence
could be considered more accurate than using raster grid to calculate runoff.
Fig 3.11 shows the intersection of soil and land use attributes to produce a land-soil shapefile.
22
Figure 3.11 Intersection of Soil and Land Use Attributes
3.4.4 Modeling using ArcCN
Modeling using curve numbers is a simple empirical method for estimating the amount of
rainwater available for runoff in a catchment. The initial abstraction values, determined by the
curve numbers, were developed for different soil types and land-use practices by the USGS. The
advantage of using this method is that it is not data demanding and it’s very simple to use. Its
predictions can be useful in identifying whether a problem exists.
Modeling using ArcCN was carried out in two parts, i.e. loading the ArcCN-Runoff tool in .dll
file format into ArcMap and running the tool using the attributes from the land-soil data. The
ArcCN-Runoff tool was downloaded from Esri support website (http://www.arcsript.esri.com). It
was loaded into ArcMap as an extension.
23
Figure 3.12 Modeling Runoff using ArcCN Tool
The inputs to the Runoff tool were; the land-soil data, index database and the precipitation value.
From the land-soil data, the land cover and the hydrologic condition of the soil were generated.
The index table is a general database that was also downloaded from the Esri website. This table
contains all the land use and land cover types and their corresponding curve numbers in the
different hydrological classes (A, B, C and D). The index table is used to match the land soil
database to the general database. Precipitation value was entered as 1000mm for the purposes of
computing the runoff. Fig 3.12 shows the process of modeling.
3.4.5 Generation of Runoff Maps
The ArcCN Runoff tool automatically computed the CN and runoff values for the different land
cover and land use. For visualization, the symbology of the land-soil map was changed to show
categories of runoff using unique values. Runoff maps for the years 1988, 2000 and 2010 are
shown in Figures 4.4, 4.5 and 4.6 respectively.
24
CHAPTER FOUR: RESULTS AND DISCUSSIONS
4.1 Land Use/Cover Mapping
4.1.1 Classification Results
Figure 4.1 Status of Lake Basin in 1988
Forest occupied a large extent of the basin, while bare soils and agricultural practices were
minimal. Settlements within the lake basin could not be identified from the classification map as
can be seen in Fig 4.1.
25
Figure 4.2 Status of the Lake Basin in 2000
From Fig 4.2, intense agricultural activities took place around the lake. The cleared areas
increased, indicating high rates of deforestation. Also, settlements around the lake could be
observed. This could be attributed to increased population around the area and need for increased
food production.
26
Figure 4.3 Status of the Lake Basin in 2010
From Fig 4.3, forest cover increased particularly when compared to the status of the lake basin as
it existed in the year 2000. This could be attributed to land reclamation efforts by the
Government of Kenya and by Non-Governmental Organizations.
27
4.1.2 Classification Accuracy Reports
Table 4.1 Accuracy Assessment Report from year 1988 image classification
Class Name
Reference
Classified
Number
Producer
User
Totals
Totals
Correct
Accuracy
Accuracy
Unclassified
0
0
0
---
---
Forest
5
5
5
100.00%
100.00%
Water Body
2
2
2
100.00%
100.00%
Agriculture
2
0
0
---
---
Cleared Areas 3
4
2
66.67%
50.00%
Bare Soil
0
1
0
---
---
Totals
12
12
9
Overall Accuracy= 75%
Table 4.2 Accuracy Assessment Report from year 2000 image classification
Class Name
Reference
Classified
Number
Producer
User
Totals
Totals
Correct
Accuracy
Accuracy
Unclassified
0
0
0
---
---
Forest
1
2
1
100.00%
50.00%
Water Body
2
2
2
100/00%
100.00%
Agriculture
6
5
5
83.33%
100.00%
Bare Soil
2
1
1
50.00%
100.00%
Cleared Areas 1
2
1
100.00%
50.00%
Settlement
0
0
0
---
---
Totals
12
12
10
Overall Accuracy= 83.33%
28
Table 4.3 Accuracy Assessment Report from year 2010 image classification
Class Name
Reference
Classified
Number
Producer
User
Totals
Totals
Correct
Accuracy
Accuracy
Unclassified
0
0
0
---
---
Forest
1
2
1
100.00%
50.00%
Water Body
2
2
2
100.00%
100.00%
Agriculture
4
3
3
75.00%
100.00%
Bare Soil
2
2
2
100.00%
100.00%
Cleared Areas 2
1
1
50.00%
100.00%
Settlement
1
2
1
100.00%
50.00%
Totals
12
12
10
Overall Accuracy= 83.33%
See Appendices 1, 2 and 3 for the full error matrices and Kappa statistics.
From the above summary of data and classification, it can be established that the classification
was accurate enough to be used for runoff modeling and analysis.
The overall classification for the 1988 imagery was 75% and the Kappa index was 0.6505. A
total of three land cover classes were misclassified. These included agriculture and bare soil. The
reason for misclassification could have been the limited extent of these cover-classes as shown in
Fig 4.1.
The overall classification accuracy for the 2000 imagery was 83.33% and the Kappa index was
0.7692. A total of two cover classes were misclassified. These included cleared areas and forests.
The forests could easily be misclassified due to the vast deforestation as shown in Fig 4.2.
The overall classification accuracy for the 2010 imagery was 83.33% and the Kappa index was
0.7966. A total of two cover classes were misclassified. These included settlement and cleared
areas.
The 2010 imagery was the best classified image because of the relatively higher Kappa index.
This could be attributed to the clarity of the image which enabled easy recognition of features.
29
4.2 Spatial Database for Runoff
Figure 4.4 Runoff Spatial Database for the year 2010
The runoff spatial database consisted mainly of the land-cover name and the hydrologic group of
the soil as the input variables. Form the input, the model computed the curve number (CN),
runoff and runoff volume based on the area of the particular land use.
30
4.3 Surface Runoff Maps
Figure 4.5 Runoff Map 1988
The legend gives the values of runoff as computed from an area weighted average curve number
for the different compartments. It is the amount of runoff that would be experienced in a storm
event of about 1200mm.
Forests A and B as shown in Fig 4.5 had different runoff values. The difference could be
attributed to the difference in hydrologic conditions of the soils.
The values of runoff ranged from 0 to 999.23 mm/m2 for a precipitation value of 1200mm.
31
Figure 4.6 Runoff Map 2000
From the runoff map in Fig 4.6, C and D show the same area, with the same soil characteristics
experiencing different runoff. This difference could only be brought about by the difference in
land use and cover.
In this case, the land cover type with highest surface runoff was agriculture, whereas the land
cover type with lower runoff value was forest cover.
The value of runoff ranged from 0 to 1199.23 mm/m2 from a precipitation value of 1200mm.
32
Figure 4.7 Runoff Map 2010
From Fig 4.7, points E and F experienced different runoff, with the runoff at E being relatively
lower than that in F.
The land cover types with highest surface runoff in this case, were agriculture and settlement.
The land cover types with lower runoff values were forests and bare soils.
The runoff value ranged from 0 to 1099.23 mm/m2 for a precipitation value of 1200mm.
33
4.4 Changes in Land Use Area Relative to Change in Runoff
Table 4.4 Land Use Change vs. Runoff Change between the years 1988 and 2000
CLASS
CHANGE IN AREA (m2)
CHANGE
IN
RUNOFF
IN
RUNOFF
(mm/m2)
Forest
-61 105.05
+198.20
Agriculture
+39 683.34
+193.00
Bare Soil
+2 000.84
+56.00
Cleared Areas
+22 705.29
+203.89
Table 4.4 illustrates the effect of deforestation on surface runoff.
Table 4.5 Land Use Change vs. Runoff Change between the years 2000 and 2010
CLASS
CHANGE IN AREA (m2)
CHANGE
(mm/m2)
Forest
+26391.69
-113.09
Agriculture
-2458.35
-100.00
Bare Soil
-476.98
-109.21
Cleared Areas
-35473.83
-99.97
Settlement
+4893.66
+197.19
Table 4.5 illustrates the effect of urbanization on surface runoff.
34
4.5 Discussion of Results
The Results of supervised classification showed satisfactory values of kappa which meant that
the land use and cover types were correctly put in their correct classes. With overall
classification accuracies of 75% for 1988 image and 83.33% for both 2000 and 2010 images, the
images could be comfortably used to depict runoff in the three epochs.
The change in land use, which was attributed to increase in human settlement around the basin
and increases agricultural activities, led to land degradation with the forest being the most
affected land cover.
From the runoff map, the forest cover was found to have lower runoff compared to other nonwater cover types. Lower curve number translated to lower runoff experienced after a storm. Of
particular interest is the forest marked A, in Fig4.4. It experienced higher runoff compared to
other forest covers, for example the marked B. Such differences in runoff could be identified
from this model and better decisions can be made with regard to its conservation or reclamation.
Generally, forest covers are the only non-water cover types that consistently showed lower
runoff in the watershed.
On the other hand, agriculture and settlements cover types showed higher rates of surface runoff
in the watershed.
Form Table 4.4, it was could be seen that, relatively smaller changes in area of cleared areas and
agriculture produced higher surface runoff than the forest cover class. This showed the effect of
deforestation on surface runoff.
From Table 4.5, of particular interest was the settlement change and runoff produced. There was
rapid increase in settlement in the lake basin region between the years 2000 and 2010. This
produced high rates of surface runoff, showing the effects of urbanization on surface runoff.
From the runoff maps, Figures 4.5, 4.6 and 4.7, it could be seen that with a precipitation value of
1200mm, runoff was highest in the year 2000 with a value of 1199.23mm and lowest in the year
1988 with a value of 999.23mm.
35
CHAPTER FIVE: CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusions
A Soil Conservation Science model was applied to generate runoff in Lake Naivasha basin and
assess the effects of different land use and land cover types on its generation.
The approach was simple, required very little data and its application in a spatially distributed
environment allowed for estimates of runoff at different locations in the catchment.
The model took into consideration the effects of different land use and land cover types and soil
characteristics of the basin in predicting runoff. Results showed that areas of agricultural practice
had higher excess precipitation than the non-agricultural areas.
From the study, areas of agricultural practice could be the concern of soil degradation problems
that resulted in increased surface runoff in the watershed. It was observed that in areas of
agricultural practice, soil characteristics were negatively influenced due to land use and land
cover being implemented in the different areas. The consequences of this were seen in the high
rates of surface runoff changes in land use to agriculture practice.
The study showed that SCS model is a valuable tool to assess the impact of different land use
and land cover types on surface runoff generation. Its flexibility, in terms of manipulation to
simulate a scenario, makes it an effective means to assess impacts of land use and land cover
types on environmental problems such as surface runoff. It enabled the integration and analysis
of large data sets that vary both spatially and temporally, which emphasized the practicality of
employing remote sensing and GIS tools for assessing dynamic environmental problems.
36
5.2 Recommendations
The results indicated that there is a trend in surface runoff pattern of the watershed on different
land use and land cover types. Areas used for agriculture exhibited higher surface runoff than
areas of non-agriculture. This being the case, further investigations should be conducted in the
catchment areas. Perhaps through the collection of adequate model validation data and making
field measurements of the parameters required by the model more accurate predictions can be
made.
Because of its empirical nature, the model can then be implemented in other catchments in the
area. With this information, suitable farming management practices can be formulated and
implemented in an effort at reducing the rate of surface runoff ultimately reducing soil erosion
by water, water pollution and the flooding hazard in the low lying areas.
37
REFERENCES
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DeRoo, A.P.J., 2000. Physically based river basin modeling within a GIS: the LISFLOOD
model. Hydrological processes, 14(11-12): 1981-1992.
Deursen, W.V., 1995. Geographic Information
http://pcraster.geog.uu.nl/thesisWvanDeursen.pdf.
Systems
and
Dynamic
Models
Dingman, S.L., 2002. Physical Hydrology. Prentice Hall, Upper Saddle River, 646 pp.
Eswaran, H., Lal, R., 2001. Land Degradation: An overview, World Soil Resources:
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Morgan, R.P.C. et al., 1995. The European Soil Erosion Model (EUROSEM): documentation
and user guide. Cranfield University, Silsoe, Bedford.
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Schwab, G.O. and K.K Barnes, 1981. Soil and water conservation engineering. Wiley & Sons,
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Shrestha, D.P., 2003.LAND DEGRADATION STUDIES WITH SPECIAL REFERENCE TO
THAILAND, International Institute of Geo-Information Science and Earth Observation (ITC),
Enschebe.
Swai, E.Y., 2001. Soil water erosion modeling in selected watersheds in Southern Spain,
International Institute of Geo-Information Science and Earth Observation (ITC), Enschebe.
UNEP, 2000. Global Environment Outlook, United Nations Environment Programme.
http://www.unep.org/geo2000/english/index.htm
Ward, R.C., and Robinson, M., 1990. Principles of Hydrology. McGraw Hill, London, 365 pp.
Weng, Q., 2011. Remote Sensing and GIS Integration: Theories and Applications. McGraw Hill,
New York, 248 pp.
38
White, R.E., 1997. Principles and Practice of soil science; the soil as a natural resource. Blacwell
Science, Oxford, 348 pp.
39
Appendix 1: Accuracy Report-1988 Classification
CLASSIFICATION ACCURACY ASSESSMENT REPORT
----------------------------------------Image File : d:/output/supervised_class1988.img
User Name : TEDDY-PC
Date
: Tue Apr 16 18:22:50 2013
ERROR MATRIX
------------Classified Data
--------------Unclassified
Forest
Waterbody
Agriculture
Cleared Areas
Bare Soil
Column Total
Classified Data
--------------Unclassified
Forest
Waterbody
Agriculture
Cleared Areas
Bare Soil
Column Total
Reference Data
-------------Unclassifi
Forest Waterbody Agricultur
---------- ---------- ---------- ---------0
0
0
0
0
5
0
0
0
0
2
0
0
0
0
0
0
0
0
2
0
0
0
0
0
5
2
2
Reference Data
-------------Cleared Ar Bare Soil Row Total
---------- ---------- ---------0
0
0
0
0
5
0
0
2
0
0
0
2
0
4
1
0
1
3
0
12
----- End of Error Matrix ----ACCURACY TOTALS
---------------Class
Reference Classified Number
Name
Totals
Totals Correct
---------- ---------- ---------- ------Unclassified
0
0
0
Forest
5
5
5
Waterbody
2
2
2
Agriculture
2
0
0
Cleared Areas
3
4
2
Bare Soil
0
1
0
Totals
12
Overall Classification Accuracy =
Producers
Accuracy
----------100.00%
100.00%
--66.67%
---
Users
Accuracy
------100.00%
100.00%
--50.00%
---
12
9
75.00%
----- End of Accuracy Totals -----
40
KAPPA (K^) STATISTICS
--------------------Overall Kappa Statistics = 0.6505
Conditional Kappa for each Category.
-----------------------------------Class Name
---------Unclassified
Forest
Waterbody
Agriculture
Cleared Areas
Bare Soil
Kappa
----0.0000
1.0000
1.0000
0.0000
0.3333
0.0000
----- End of Kappa Statistics -----
41
Appendix 2: Accuracy Report-2000 Classification
CLASSIFICATION ACCURACY ASSESSMENT REPORT
----------------------------------------Image File : d:/output/superviced_class2000.img
User Name : TEDDY-PC
Date
: Tue Apr 16 18:30:58 2013
ERROR MATRIX
------------Classified Data
--------------Unclassified
Forest
Water Body
Agriculture
Bare Soil
Cleared Areas
Settlement
Column Total
Reference Data
-------------Unclassifi
Forest Water Body Agricultur
---------- ---------- ---------- ---------0
0
0
0
0
1
0
1
0
0
2
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
1
2
6
Classified Data
--------------Unclassified
Forest
Water Body
Agriculture
Bare Soil
Cleared Areas
Settlement
Column Total
Reference Data
-------------Bare Soil Cleared Ar Settlement Row Total
---------- ---------- ---------- ---------0
0
0
0
0
0
0
2
0
0
0
2
0
0
0
5
1
0
0
1
1
1
0
2
0
0
0
0
2
1
0
12
----- End of Error Matrix ----ACCURACY TOTALS
---------------Class
Reference Classified Number
Name
Totals
Totals Correct
---------- ---------- ---------- ------Unclassified
0
0
0
Forest
1
2
1
Water Body
2
2
2
Agriculture
6
5
5
Bare Soil
2
1
1
Cleared Areas
1
2
1
Settlement
0
0
0
Totals
12
12
10
Overall Classification Accuracy =
83.33%
Producers
Accuracy
----------100.00%
100.00%
83.33%
50.00%
100.00%
---
Users
Accuracy
------50.00%
100.00%
100.00%
100.00%
50.00%
---
----- End of Accuracy Totals -----
42
KAPPA (K^) STATISTICS
--------------------Overall Kappa Statistics = 0.7692
Conditional Kappa for each Category.
-----------------------------------Class Name
---------Unclassified
Forest
Water Body
Agriculture
Bare Soil
Cleared Areas
Settlement
Kappa
----0.0000
0.4545
1.0000
1.0000
1.0000
0.4545
0.0000
----- End of Kappa Statistics -----
43
Appendix 3: Accuracy Report-2010 Classification
CLASSIFICATION ACCURACY ASSESSMENT REPORT
----------------------------------------Image File : d:/output/supervised_class2010.img
User Name : TEDDY-PC
Date
: Tue Apr 16 18:33:48 2013
ERROR MATRIX
------------Classified Data
--------------Unclassified
Forest
Water Body
Ariculture
Bare Soil
Cleared Areas
Settlement
Column Total
Reference Data
-------------Unclassifi
Forest Water Body Ariculture
---------- ---------- ---------- ---------0
0
0
0
0
1
0
1
0
0
2
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
1
2
4
Reference Data
-------------Classified Data
Bare Soil Cleared Ar Settlement Row Total
--------------- ---------- ---------- ---------- ---------Unclassified
0
0
0
0
Forest
0
0
0
2
Water Body
0
0
0
2
Ariculture
0
0
0
3
Bare Soil
2
0
0
2
Cleared Areas
0
1
0
1
Settlement
0
1
1
2
Column Total
2
2
1
12
----- End of Error Matrix ----ACCURACY TOTALS
---------------Class
Name
---------Unclassified
Forest
Water Body
Ariculture
Bare Soil
Cleared Areas
Settlement
Totals
Reference Classified Number
Totals
Totals Correct
---------- ---------- ------0
0
0
1
2
1
2
2
2
4
3
3
2
2
2
2
1
1
1
2
1
12
12
10
Producers
Accuracy
----------100.00%
100.00%
75.00%
100.00%
50.00%
100.00%
Users
Accuracy
------50.00%
100.00%
100.00%
100.00%
100.00%
50.00%
Overall Classification Accuracy =
83.33%
----- End of Accuracy Totals -----
44
KAPPA (K^) STATISTICS
--------------------Overall Kappa Statistics = 0.7966
Conditional Kappa for each Category.
-----------------------------------Class Name
---------Unclassified
Forest
Water Body
Ariculture
Bare Soil
Cleared Areas
Settlement
Kappa
----0.0000
0.4545
1.0000
1.0000
1.0000
1.0000
0.4545
----- End of Kappa Statistics -----
45