CRANFIELD UNIVERSITY
Usman Muhammad Buhari
Sugar Cane Modelling Using GIS and Remote Sensing Techniques
School of Applied Sciences
Geographical Information Management
MSc
Academic Year: 2013 - 2014
Supervisor: Dr. Stephen Hallett; Dr. Toby Waine
September 2014
CRANFIELD UNIVERSITY
School of Applied Sciences
Geographical Information Management
MSc
Academic Year 2013 - 2014
Usman Muhammad Buhari
Sugar Cane Modelling Using GIS and Remote Sensing Techniques
Supervisor: Dr. Stephen Hallett; Dr. Toby Waine
September 2014
This thesis is submitted in partial fulfilment of the requirements for
the degree of MSc Geographical Information Management
© Cranfield University 2014. All rights reserved. No part of this
publication may be reproduced without the written permission of the
copyright owner.
ABSTRACT
This study addresses land evaluation for sugar cane suitability, and
demonstrates the usefulness of integrating both legacy cartographic and
contemporary data to help solve assessment problems. Land evaluation
techniques have proved useful for supporting rational management of land
resources and sustainable development across many sectors. A Geographical
Information System (GIS) and Remote Sensing (RS) were used to identify
suitable lands for growing sugar cane at 2 sites in North-East Nigeria. The basic
FAO land evaluation framework was adopted, using readily available data
including terrain and soil. Satellite data were utilised to derive several thematic
maps to help identify areas with the required potentials. A GIS-based suitability
analysis was conducted using the ESRI ArcGIS software, and the input
datasets reclassified to assign categories that could be integrated in one model.
A weighted overlay method was used, along with a traditional boolean raster
method, to allow comparison of results from each method. The weighted
overlay method areas demarked more land as ‘suitable’ than did the traditional
boolean method. This could derive from the assignment of differing weightings
in the weighted overlay, making it a more flexible operation when compared to
the strict “true or false” assessment of the boolean method. Across the selected
study area, an estimated 75% of the land was classified as being ‘moderately
suitable’ for sugar cane. One future means to fully differentiate these areas
would be the introduction of precision farming techniques to enable continuous
management of the crop and to obtain improved yield production.
Keywords:
Land suitability analysis, weighted overlay, sugar cane, legacy data, WOSSAC
i
ACKNOWLEDGEMENTS
I will like to acknowledge God Almighty for keeping me alive to witness the
successful completion of this research.
A sincere gratitude goes to my humble supervisor Dr. Stephen Hallett for
guiding and encouraging me through this research, my appreciation also goes
to Dr. Toby Waine my co-supervisor for his advice.
I want to use this opportunity to thank the WOSSAC crew: Dr. Ian Baillie and
Brian Kerr for their endless support and readiness to help when needed. You
really were awesome and I truly appreciate your kind gesture.
I will also like to thank Dr. Samantha Lavender of Plymouth University for her
advice during this research and Joanna Zawadzka for her assistance during my
research.
Finally and most importantly, I will like to thank my parents for believing in me
and giving me the chance to become who I am.
iii
TABLE OF CONTENTS
ABSTRACT ......................................................................................................... i
ACKNOWLEDGEMENTS................................................................................... iii
LIST OF FIGURES ............................................................................................ vii
LIST OF TABLES ............................................................................................. viii
LIST OF EQUATIONS ........................................................................................ ix
LIST OF ABBREVIATIONS ................................................................................ x
1 Introduction...................................................................................................... 2
2 Literature review .............................................................................................. 6
2.1 Introduction ............................................................................................... 6
2.2 Land suitability and evaluation .................................................................. 8
2.3 Sugar cane modelling ............................................................................... 9
2.3.1 Sugar cane and irrigation ................................................................. 12
2.4 Role of GIS in suitability modelling.......................................................... 13
2.5 Conclusion .............................................................................................. 14
3 Materials and methods .................................................................................. 15
3.1 Study area .............................................................................................. 15
3.1.1 The Lau Tau study area ................................................................... 15
3.1.2 The Hadeija study area .................................................................... 16
3.2 Suitability modelling technique ................................................................ 17
3.3 Data sourcing .......................................................................................... 17
3.4 Data preparation and analyses ............................................................... 18
3.4.1 Creating a primary database ............................................................ 19
3.4.2 Preparing the soil map ..................................................................... 19
3.4.3 Preparing the NDVI map .................................................................. 20
3.4.4 Preparing the landforms map ........................................................... 22
3.4.5 Preparing the slope map .................................................................. 23
3.5 Crop suitability model implementation .................................................... 25
3.5.1 Reclassifying the datasets ................................................................ 26
3.5.2 Weighting the datasets ..................................................................... 26
3.6 Conclusion .............................................................................................. 27
4 Results and discussion .................................................................................. 29
4.1 Model outputs ......................................................................................... 29
4.2 Associated challenges ............................................................................ 31
4.2.1 Collecting soil data ........................................................................... 31
4.2.2 Collecting digital elevation model ..................................................... 32
4.2.3 Deriving landforms from the DEM .................................................... 32
4.2.4 Soil moisture data............................................................................. 35
4.2.5 Collecting rainfall data ...................................................................... 35
4.3 Methods adopted .................................................................................... 36
4.3.1 Solar irradiance map ........................................................................ 36
v
4.3.2 NDVI vs. EVI .................................................................................... 37
4.4 Implications ............................................................................................. 38
5 Recommendations......................................................................................... 40
6 Conclusion..................................................................................................... 42
REFERENCES ................................................................................................. 43
APPENDICES .................................................................................................. 48
Appendix A ................................................................................................... 48
Appendix B ................................................................................................... 52
vi
LIST OF FIGURES
Fig. 1. Delineation of the Lau Tau study area ................................................... 16
Fig. 2. Delineation of the Hadeija study area .................................................... 17
Fig. 3. Showing Lau Tau soil map .................................................................... 20
Fig. 4. NDVI maps for both Hadeija and Lau Tau study areas ......................... 22
Fig. 5. Landforms for both Hadeija and Lau Tau study areas using the “SOTERlike” method (see appendix for full legend) ................................................ 23
Fig. 6. Slope maps for both Hadeija and Lau Tau study areas ......................... 24
Fig. 7. Flowchart of the methodology used for this project ............................... 25
Fig. 8. Showing models outputs using the traditional boolean method (fig. a) and
fig. (b-f) showing results derived from the weighted overlay model ........... 30
Figure 9: Showing area distribution of Lau Tau study area based on the
suitability classes ....................................................................................... 31
Fig. 10. Hadeija and Lau Tau’s Digital Elevation Model over a hillshade model
.................................................................................................................. 32
Figure 11: Delineated landforms types of Lau Tau study area ......................... 34
Figure 12: Showing study areas falling into one pixel of the soil moisture data 36
Figure 13: Solar irradiance maps showing daily sun hours received for the study
areas.......................................................................................................... 37
Figure 14: NDVI and EVI maps of Lau Tau study area .................................... 38
vii
LIST OF TABLES
Table 1: Study sites description showing the selected ones for this project in
highlight ....................................................................................................... 7
Table 2: Showing a summary of different approaches considered in the search
for suitable land for growing sugar cane. ................................................... 11
Table 3: Criteria for assessing sugar cane requirements ................................. 27
Table 4: Showing weighting assigned to the 5 map outputs ............................. 27
viii
LIST OF EQUATIONS
NDVI = (NIR_BAND – RED_BAND) / (NIR_BAND + RED_BAND) (1)............ 21
𝑳𝝀 = 𝑳𝑴𝑨𝑿𝝀 − 𝑳𝑴𝑰𝑵𝝀𝑸𝒄𝒂𝒍𝒎𝒂𝒙 − 𝑸𝒄𝒂𝒍𝒎𝒊𝒏𝑸𝒄𝒂𝒍 − 𝑸𝒄𝒂𝒍𝒎𝒊𝒏 + 𝑳𝑴𝑰𝑵𝝀 (2) 21
𝝆𝝀 = 𝝅. 𝑳𝝀. 𝒅𝟐𝑬𝑺𝑼𝑵𝝀. 𝒄𝒐𝒔𝜽𝒔 (3) .................................................................... 21
ix
LIST OF ABBREVIATIONS
GIS
Geographical Information Systems
NDVI
Normalised Difference Vegetation Index
EVI
Enhanced Vegetation Index
SOTER
Soil and Terrain Analyses
WOSSAC
World Soil Survey Archive and Catalogue
This thesis has been prepared in the format used for scientific
papers appearing in the journal Computers and Electronics in
Agriculture with other details in the Appendices. This paper
includes an extended literature review.
x
Sugar cane modelling using GIS and remote sensing techniques
Usman Muhammad Buharia
a
School of Applied Sciences, Cranfield University, Cranfield Bedfordshire
MK43 0AL, UK.
ABSTRACT
This study addresses land evaluation for sugar cane suitability, and
demonstrates the usefulness of integrating both legacy cartographic and
contemporary data to help solve assessment problems. Land evaluation
techniques have proved useful for supporting rational management of land
resources and sustainable development across many sectors. A Geographical
Information System (GIS) and Remote Sensing (RS) were used to identify
suitable lands for growing sugar cane at two sites in North-East Nigeria. The
basic FAO land evaluation framework was adopted, using readily available data
including terrain and soil. Satellite data were utilised to derive several thematic
maps to help identify areas with the required potentials. A GIS-based suitability
analysis was conducted using the ESRI ArcGIS software, and the input
datasets reclassified to assign categories that could be integrated in one model.
A weighted overlay method was used, along with a traditional boolean raster
method, allowing comparison of results from each method. The weighted
overlay method areas demarked more land as ‘suitable’ than did the traditional
boolean method. This outcome was seen to derive from the assignment of
differing weightings in the weighted overlay, making it a more flexible operation
when compared to the strict “true or false” assessment of the boolean method.
Across the selected study area, an estimated 75% of the land was classified as
being ‘moderately suitable’ for sugar cane. One future means to fully
differentiate these areas would be the introduction of precision farming
techniques to enable continuous management of the crop and to obtain
improved yield production.
Keywords:
Land suitability analysis, weighted overlay, sugar cane, legacy data, WOSSAC
1
1 Introduction
With a current and fast growing population of some 170 million, Nigeria has a
critical need to secure access to increased agricultural produce. Currently, Nigeria
is a major food importer, having an annual food import bill of about $11 billion (DI,
2013). Fertile land for crop cultivation is widely available in the country, but little
effort has been made in recent years to help both local and commercial farmers in
determining soil potential which will in turn increase the quantity and quality of
agricultural production to meet the growing population.
Land allocation has also been poorly addressed as its distribution across several
land uses are causing detriment to potential agricultural land. Rational land
planning and management is therefore required for ensuring optimal use (Collins
et al., 2001). Due to inadequate land assessment before commencing
development activities, many lands are wrongly used, thereby causing negative
impacts on forest reserves, agricultural land and urbanisation.
Sugar is consumed in significant quantities in Nigeria about 1.43 trillion metric
tonnes yearly (Zaggi, 2013). Mostly this sugar is imported from foreign countries
thereby making sugar very expensive for the people. Furthermore, imports are not
alone able to meet the growing demand. The two major sugar companies in
Nigeria are unable to meet consumer need and so better planning is needed as to
how this commodity should be produced and processed not just locally but on a
massive scale. Current governmental policy in Nigeria is to encourage local
national production through the adoption of import tariffs.
One of the dominant Nigerian sugar enterprises is Dangote Sugar. Currently,
Dangote mostly import sugar from Brazil and refine it in Nigeria to sell on into the
national market. Increased tariffs on imported sugar have recently risen from 5%
to 60%, presenting the challenge to the company to develop opportunities to grow
sugar cane locally, thereby addressing issues of availability and high cost.
In 2013, it was reported in the news that Dangote Sugar would invest some $1.5
million to ensure sugar cane was grown locally and, as the company’s managing
2
director Abdullahi Sule stated, “this is as a response to the government’s drive to
reduce its reliance on the oil industry” (Alphonsus, 2013).
In turn this policy shift has led to debate as to the best locations for growing sugar
cane, and on capacity building for the expertise required. Dangote Sugar have
sought an efficient and reliable means to determine suitable geographical
locations for farming sugar cane and therefore multi-criteria assessment methods
for land suitability assessment have been investigated.
To begin this enterprise, several locations were identified, based initially on certain
obvious characteristics such as distance to river and availability of land. Further
analyses were then required to determine suitable locations for significant sugar
cane plantations.
Five different sites in both Central and Northern part of Nigeria were identified by
Dangote Sugar, narrowing the search for suitable land for sugar cane production
to these selected areas. Meanwhile, the Northern region, which contains five of
the proposed sites, is mostly comprised of a range of semi-arid to arid zones with
an estimated annual rainfall of about 1,500mm – 1,700mm at maximum.
Because of the capability issues attributed to some of these sites, irrigation
farming was preferred in order to supplement the missing potentials needed for a
successful sugar cane yield. Therefore the availability of a long lasting, proximal
water source represented major criteria for this assessment.
The purpose of this research is to propose robust methods for land evaluation and
suitability assessment, drawing upon data from different sources. This was
enabled through the adoption of a GIS approach, being well suited for this form of
analysis (Sellamuthu et al., 2000). Spatial data for different criteria was
incorporated into ArcGIS workspace and further analyses were made to produce
suitability maps for each criteria used. These were finally combined to produce a
suitability map for growing sugar cane.
In order to conduct this land evaluation, GIS tools were adopted to help in
combining all the necessary datasets and perform a multi criteria land suitability
analyses. The FAO land evaluation framework (1976) was used in this project and
3
the analyses was undertaken by utilizing the weighted overlay method along with
a vector based analyses for comparison. The ranking of suitability classes are
outlined as follows:

Highly suitable: Areas within this class do not have any major limitations as
to the specific crop production and if there is any limitation, it would not
hinder the production;

Moderately suitable: Has limitations that in the aggregate are moderately
severe for sustained application to a given use and may reduce the
productivity marginally. These lands have slight limitations with no more
than three moderate limitations;

Marginally suitable: Land with limitations that in the aggregate are severe
for sustained application to a given use and as such reduce productivity
significantly but is still marginally economical. These lands have more than
three moderate limitations and/or more than one severe limitation that,
however, does not preclude their use for the specified purposes and;

Not Suitable: No suitability detected at all.
ESRI ArcGIS and ERDAS Imagine software suites were used for the data
preparation and analyses processes. A GIS geodatabase was created to hold the
large amount of spatial data arising from different sources and formats which were
processed and classified based on threshold values used to meet the requirement
for sugar cane farming. The approaches adopted are ultimately transferable to
other circumstances than solely sugar-cane suitability assessment. Indeed, it was
seen as important that the land evaluation approaches adopted be applicable for
employment not just in an agricultural context but in other sectors where
assessments are made before development activities commence in order to
protect land resources from misuse (Malczewski, 2006).
During the course of this research, data were collected from different sources.
Important in this process was recording the quality of the data and how this data
could be utilized within the GIS environment for the proposed analyses. The
following are the data sources used for this research:
4

Historic legacy data for soil was retrieved (scanned and digitised) from the
World Soil Archive and catalogue (WOSSAC) available at Cranfield
University www.wossac.com;

The Shuttle Radar Topography Mission 90m Digital Elevation Model;

Landsat 8 satellite imageries by the United States Geological Survey
covering the study areas and;

Soil properties extracted from the Harmonised World Soil Database.
It was considered of great importance to integrate the process of land evaluation
such that the approaches be applicable for any given purpose, delineating soil
constraints, severity and similarity of soil as a means to assist land managers and
farmers to plan for better agricultural production (Sellamuthu et al., 2000).
Aim
To investigation into the development of a crop suitability geodatabase and
modelling system for Sugar Cane in Nigeria, drawing on both contemporary
environmental data and legacy thematic information.
Objectives
1. Adoption of an applied case study-based approach identifying suitability for
Sugar Cane at two land sites in Nigeria;
2. Compilation for selected study sites of sources of contemporary
environmental data, including satellite imagery, together with appropriate
legacy, historical cartographic and report-based information from previous
survey activities;
3. Development of landuse suitability modelling framework for sugar cane
drawing on these available data;
4. Application of model to selected case study areas and review of
appropriateness of approach and;
5. Discussion concerning the adoption of the techniques used to guide further
analyses like this and field surveys in the future.
5
2 Literature review
2.1 Introduction
The process of establishing suitable sites for differing purposes varying from
ecology, urban planning, agricultural development and many more can be
regarded as an effective form of resource management. The method, popularly
known as land suitability analyses, has been widely reported for various purposes.
For the purpose of this study, critical analyses will be carried out to determine
suitable locations for planting sugar cane at some study sites in north-east Nigeria
in Taraba and Jigawa states.
Five potential study sites were identified for this project and an evaluation of land
suitability analyses was required to determine their potential for sugar cane
cultivation. These locations were selected due to their close proximity to rivers,
water availability being an important factor for sugar cane (Carr et al., 2010). In
this part of Nigeria, historical irrigation used to be gravity-fed. However, nowadays
pumping houses are used to lift up the water so areas adjacent to the rivers were
considered acceptable for accessing sufficient water resources. Due to the limited
time frame for this research project, only two of study sites were examined.
(Table 1) describes the characteristics of the sites.
6
Table 1: Study sites description showing the selected ones for this project in
highlight
No. Study sites
Climatic condition
Area (Km2)
Elevation
Region
(Meters)
1
Lau (Taraba
Wetland area with
state)
about 1058mm –
438
120-255
North East
212
355-380
North East
233
170-805
North East
278
135-188
North East
492
49-205
Central Nigeria
Over 1300mm
annual rainfall
2
Hadeija (Jigawa
Semi-arid region
state)
with about 600mm
– 1000mm annual
rainfall
3
Guyuk
Tropical area with
(Adamawa state) about 759mm –
1051mm annual
rainfall
4
Giwano
Tropical area with
(Adamawa state) about 759mm –
1051mm annual
rainfall
5
Ageni (Kogi
Flood plain area
state)
with about 1100mm
– 1300mm annual
rainfall
Several methods reported for suitability analyses and land evaluations have been
assessed to guide the selection of an appropriate method for this study. The
sections below consider the following themes: Land suitability and analyses;
Sugar cane modelling, and; the Role of GIS in suitability analyses.
7
2.2 Land suitability and evaluation
The requirement for determining where and how optimal sites should be identified
for establishing a range of socio-economic activities has led to several methods
being considered. Land suitability evaluation represents the process of identifying
the potential of land for several uses and planning (Rasheed et al., 2009).
Different variables have been considered for the purpose of determining land
suitability for specific uses. Kumar et al. (2010) stated that, although land quality
may vary from place to place approaches for assessing land suitability are based
in the main on some combination of climate, soil topography and water availability.
Dent et al. (1981) describe land evaluation as the estimation of land potential for
alternative uses, including arable farming, livestock production and forestry.
Assessing land potential is necessary for any land management operations to be
successful. It is therefore necessary to acquire and analyse the qualities of a land
before putting it to use.
The need for land suitability evaluation is paramount as many other factors like
urban expansion can exert negative impacts on the ecosystem. Coskun et al.
(2008) describe urban growth as major driver in land use change, exerting a
significant impact on both hydro-geomorphology and vegetation. Therefore,
rational approaches should adopt a multi-criteria assessment on the land.
The FAO (1976) outlined a number of fundamental tenets in the approach and
methods used for land evaluation:

Land suitability can be assessed and classified based on different uses;

A multidisciplinary approach should be considered, and;

Suitability assessment involves comparison for multiple land uses.
Land suitability assessment has been used for several purposes. Abdel Kawy et
al. (2013) identify that assessment of the suitability of land enables optimum crop
development and increases productivity. The fitness of soils for land use cannot
be determined without considering a range of other related environmental factors
(FAO, 1976).
8
In order to identify potential land for crop production, many attribute variables
(such as soil, climate, rainfall, proximity to places of interest etc.) are considered.
These variables have been used in differing studies based on site location,
availability of resources and preference.
Assessing appropriate use of land for specific purposes can be seen an efficient
way of directing optimal land use, considering land potential before establishing
any activity on it so as to achieve the maximum benefit from the land. The use of
land is thus not solely dependent on what the owner seeks to do with it, but also
on land capability, which has several attributes including geology, soil,
topography, hydrology (Bizuwerk et al., 2005).
Abdel Kawy et al. (2013) identified soil suitability and availability of water to
represent the main criteria for any crop cultivation from an economic standpoint.
Other land qualities like erosion resistance, moisture availability, and accessibility
should be taken into account during a decision making (FAO, 1976). Rasheed et
al. (2009) suggested that the topography of an area (such as slope and drainage),
as well as
prevailing climatic conditions should be taken into account for
evaluating land potential in growing sugar cane.
2.3 Sugar cane modelling
Currently, the availability of domestic agricultural produce is unable to meet the
demand of the fast growing population in Nigeria. The demand for sugar is not an
exception and thus a critical land evaluation is required for adequate production.
Due to lack of proper management, many agricultural activities do not yield up to
the land’s full potential (Yinka et al., 2013), therefore careful considerations as to
sugar cane requirements should be prioritized in identifying sites for growing
sugar cane (Saccharum sp.).
Sugar cane is widely cultivated around the world within tropical climate and humid
regions thereby optimising photosynthesis (Lapola et al., 2009). It is cultivated on
about 13 – 15 million hectares of land globally (Delgado et al, 2001). Sugarcane is
highly efficient at converting sunlight into sugars. Brazil, India and China are its
major producers. Cane is mainly seen as solely a source of sugar but it is also an
9
important source of biofuel in addition, leading to a constant increase in its global
demand. Lapola et al. (2009) claim that besides producing sugar for human
consumption, India produces 11 dm3 of ethanol produced from sugar cane, with
the Indian Government’s aim to achieve 75 dm3 by 2015.
With global demand for sugar, it is important that land potential for sugar cane be
properly explored. Nigeria is not an exception and therefore aims to increase its
sugar production to about 1.7 million tonnes by 2018 which will eventually cut
down the annual $11 billion food import bill (Reuters, 2013).
Sugar cane grows within a long period of time ranging across multiple seasons. It
is therefore cultivated around the world from warm to humid regions (Carr et al.,
2010). In order to model land suitability for sugar cane. Rasheed et al. (2009)
stated that it is important to evaluate the soil in a given area for particular crop
production under specific management system.
Urban sprawl without planning has posed a great threat to developing the true
potential of agricultural lands and therefore, rational land management is required.
Different methods have been proposed to identify land suitability (Khoram et al.,
2014). These have included, by example, the use of linear modelling, GIS,
Remote Sensing, and SWAT models. (Table 2) summarises the different methods
from the literature concerning sugar cane modelling.
10
Table 2: Showing a summary of different approaches considered in the search for
suitable land for growing sugar cane.
No.
1.
Author
Carr et al., 2010
Factors considered for Techniques/Methods
Sugar cane modelling
Used
Plant water relation; crop
CANEGRO Model
water requirements; water
productivity of land, and;
irrigation systems.
2.
Kumar et al., 2010
Soil texture; slope; soil
Geographic
moisture content; depth of
Information Systems
water table, and; soluble
(GIS)
salt content.
3.
4.
Abdel Kawy et al.,
Climate; geomorphology;
Automated Land
2012
geology; water resources,
Evaluation System
and; natural vegetation.
(ALES)
Slopes; water availability;
Remote Sensing and
soil temperature, and;
Geographic
rainfall.
Information Systems
Rasheed et al., 2009
(GIS)
5.
6.
El Hajj et al., 2009
Santhi et al., 2005
Normalized Difference
Remote Sensing;
Vegetation Index (NDVI),
Decision Support
derived from satellite
System and; Fuzzy
images.
Inference System.
Crop growth; Irrigation
Soil and Water
operations, and; soil
Assessment Tool
properties.
(SWAT)
11
Critical land evaluation should precede agricultural development in identifying
tracts of land that meet crop-specific requirements. Where rainfall is a limitation,
irrigation techniques are usually needed in farms to compensate for water
inadequacy at some point during the growing period. This is especially the case
for crops with a high demand for water like sugar cane.
2.3.1 Sugar cane and irrigation
Specific irrigation planning is beyond the scope of this study. However, it is
important to take note of the water requirements of sugar cane, acknowledging
how this affects land suitability identification.
The need for water is increasing and groundwater levels have been decreasing
due to the immense sourcing of water for domestic and agricultural use in Nigeria
where most people rely on digging pumps and wells for water, a scarcity of
groundwater is fast developing and therefore making irrigated farming more
difficult.
A land suitability evaluation for irrigation can be complex and so needs an
understanding of both the underlying geology and topographic nature of the land.
Dent et al. (1981) noted infiltration rate, pH, carbonate and gypsum, among other
factors, as comprising the basic soil characteristics to be considered for irrigation
cultivation.
For ease of irrigation scheduling and maintenance, computer software is
employed to make the process quicker and more reliable. Programmes like
IRRICANE (Singels et al., 1998) and CANEGRO (Carr et al., 2010) are very
popular and have been widely used.
Some of the major types of irrigation techniques are as follows:

Surface irrigation;

Sub surface irrigation;

Drip irrigation;

Furrow irrigation and;

Sprinkler irrigation.
12
Among the five sites listed, Lau (Taraba state) and Hadeija (Jigawa state) were
considered for this project due to their close proximity to rivers and distinct
topographic nature (wet and dry land respectively). These contrasting distinctions
allow for comparison of the results and an understanding as to what criteria play
more significant roles in determining suitable land for sugar cane.
2.4 Role of GIS in suitability modelling
Researchers have increasingly adopted geographical data in playing a more vital
role rather than solely statistical parameters in suitability analyses (Rozenstein et
al., 2011). In recent time this has permitted the development of sophisticated GIS
analyses.
Geographical Information Systems (GIS) comprise a computer-based program
capable of acquiring, analysing, managing geographical data and giving visual
representation of the real world as output maps. Its ability to combine data from
different sources with spatial reference has made it convenient for use (Masser,
1998).
GIS in suitability analyses was rooted from the early 20th century by American
landscape architects using hand drawn overlay techniques (Steinitz et al., 1976)
which preceded using computer software to generate digital maps presenting
results from suitability modelling. Land use suitability modelling is one of the most
important functions in GIS (Malczewski, 2004).
GIS has played a major role in planning and management with its ability to
manage substantive amounts of data (ESRI, 2012) and one of its most useful
applications is suitability mapping of a given scenario (McHarg, 1969). Ecologists
have mapped suitable locations for many habitats. Suitable land for agriculture
has also been identified using GIS-aided suitability analyses (Paiboonsak et al.,
2007). The ability of GIS to reclassify and overlay data to meet multiple
requirements is very powerful and this has been applied to many fields like
agriculture, urban planning, ecology and many more.
With several GIS classification models such as fuzzy modelling (Nisar et al, 2000),
it is possible to evaluate appropriately the suitability of farms for silage corn
13
production considering soil and climatic factors when put into the GIS software for
analyses (Houshyar et al., 2014). Kumar et al. (2009) noted that there has been
an increase in GIS approach for crop-specific modelling, integrating both soil and
climatic data.
The use of GIS in suitability analyses is on an increase and highly demandable
(McHarg, 1969). A comparison between old methods of suitability classification
and contemporary GIS, clearly showed GIS to be time saving technique that
produces data with higher quality with possibilities of locating newer potential sites
(Liengsakul et al., 1993).
GIS allows for a multi-criteria technique to be used to create suitability maps for
specific uses. Malczewski (2006) utilized this approach with both boolean overlay
and weighted linear combination in order to determine land use potentials.
Though most GIS-based land suitability analyses are expressed in the form of
boolean overlay, Malczewski (2006) notes this approach lacks a properly defined
mechanism for incorporating decision maker’s priorities into the analyses. Thus
this study seeks to address the issue by employing both a multi-criteria with
hierarchical classification and a vector based analyses for comparison.
2.5 Conclusion
To correctly allocate and manage land, a multi-criteria approach should be
adopted, and several processes - both computer-aided and in-field data collection
- should be undertaken based on the planning purpose. The literature reveals a
number of previous studies implementing methods to analyse soil and land
suitability for sugar cane cultivation. This study builds on these approaches by not
only seeking to identify suitable sites for growing sugar cane based on
contemporary data but also:

Drawing on legacy data and integrating this with contemporary data using
GIS/RS techniques to help understand the temporal changes within the
study area thereby, assisting in making better decision.
14
3 Materials and methods
In order to accomplish the objectives for this project, it was necessary to source all
relevant and available data, and then establish how best to incorporate these
within a GIS environment for the analyses. This section outlines the data
collection and methodology used to determine land suitability for sugar cane in the
study areas. (Fig. 7.) describes the procedure adopted for this research. The
sequences of tasks undertaken to achieve the goal of this project are outlined
below:

Identification of study area;

Assessment of suitability modelling technique;

Data sourcing;

Data preparation and analyses, and;

Crop suitability model implementation.
3.1 Study area
Two study areas were selected for this research so as to provide the basis for a
comparative, critical assessment determining the best possible locations to grow
sugar cane. The study sites are selected due to their proximity to riverine water
supplies, as well as the potential availability of the land for acquisition. A
description of the two study areas, Lau Tau, and Hadeija are provided.
3.1.1 The Lau Tau study area
The delineated study area is situated at coordinates 9° 4’ 0” North and 11° 6’ 0”
East in a small town called Lau Tau which is in the north-eastern part of Nigeria,
occupying about 438 Km2. Its elevation lies between 120m – 251m above sea
level with most of the land surface being considerably flat (Fig. 1.). It lies just to
the south of the Benue River, having a predominant clay-rich soil and an
underlying geology of shale, marine facies, mudstone and limestone. The area is
just to the north of the Taraba state capital and its inhabitants are hausa-fulani by
tribe with farming and cattle trading as their major source of income. This area,
15
and its neighbouring states, has been identified by the Nigerian Sugar
Development Council as the sugar cane belt.
Fig. 1. Delineation of the Lau Tau study area
3.1.2 The Hadeija study area
This study area is located at 12° 29’ 0” North and 9° 44’ 0” in the north-eastern
part of Nigeria with an elevation raging between 357m – 378m above sea level.
The soil type is mostly loamy sand with geology mostly classified as sandstone
and a little amount of clay, its inhabitant’s major activities are farming and fishing.
This area occupies about 212 Km2, having a relatively flat topographic nature (Fig.
2.). The study area is north of the Hadeija River, which serves as a major source
of water for the local people.
16
Fig. 2. Delineation of the Hadeija study area
3.2 Suitability modelling technique
Various methods for land suitability have been trialled, each having its own flaws.
An appropriate suitability method was adopted based on what data is available
and the area of interest. Some techniques were identified during this research
(Table 2) after which the weighted overlay method was chosen for this study as
this was readily available and allows for a multi criteria assessment, accepts data
in different resolutions and analyses these thematic layers based on a user
defined weighting which can be useful to determine the importance of each
parameter used (Raid et al., 2011). The weighted overlay approach was assessed
along with the traditional boolean method for comparison.
3.3 Data sourcing
Land suitability analyses in this research seek to use GIS and remote sensing to
perform a multi criteria analyses, requiring several data inputs. Due to the nature
17
of this research, only freely available data were used. Below are brief descriptions
of the key data used for this project:

Historic cartographic maps and report based information;
The WOSSAC archive (www.wossac.com) at Cranfield University represents a
good source of data containing both paper maps and reports written by field
surveyors for one of the selected study areas. These hold data about the geology
of the area and the soil classification used; they were collected from the field
between 1967 – 1969 by (Klinkenberg, 1967).

SRTM Digital Elevation Model (90m resolution);
This is a high resolution global scale radar satellite derived elevation model
provided by NASA showing the height of places above sea level. The SRTM data
proved helpful in describing the topographic character of the study area.

FAO Harmonised World Soil Database (HWSD);
The FAO Harmonised World Soil Database (HWSD) (De Witte et al., 2013) is
freely accessible online provided at a scale of 1:1million a coarse resolution
dataset not suitable for fine localized assessments. However, the attribute
database attached to the soil data was accessed and used to complete some data
gaps required for the land suitability analyses.

Landsat 8 satellite images (30m resolution).
Landsat 8 data was sourced from the USGS where long-term temporal data for
the whole world is available online. The study areas in this project covered Path:
188, Row: 51 and Path: 186, Row: 54 of the world referencing system.
3.4 Data preparation and analyses
In order to analyse the available data for sugar cane requirements, it was
necessary to first assemble the data and organize them in a geospatial database
for proper management. The data were derived and classified to meet the
suggested requirements for sugar cane as outlined below. The datasets
assembled for the selected classification are:
18
I.
Soil data;
II.
Normalised Difference Vegetation Index (NDVI);
III.
Landforms;
IV.
Slope.
3.4.1 Creating a primary database
Two spatial databases were created in ArcGIS for each study area, using the
spatial reference UTM Zone 32N projection. All the datasets were prepared and
populated within the database. The vector data were stored in a feature dataset
within the database to retain a standard of data management and easy
identification.
The purpose of this geodatabase is to permit the whole work flow to be tracked,
as well as for the efficient management of the data. The database can store both
spatial data and non-spatial database tables to support the modelling procedures
in producing final outputs.
3.4.2 Preparing the soil map
For the Lau Tau study area, a legacy cartographic soil map obtained from the
WOSSAC archive (www.wossac.com) was produced from a field survey by
Klinkenberg, (1964 - 1968), alongside a comprehensive accompanying report,
also located in the WOSSAC collection. This map was raster scanned into a
digital form and was then georeferenced to match its real position on ground. The
map was then subsequently vector digitised, using the topology tool to undertake
an error check to ensure sure the digitising undertaken was accurate.
After the map was digitised and georeferenced, both the map legend and
accompanying report were also read to extract the relevant information (e.g.
geology, soil texture, soil characteristics) which were transcribed into the layers
attribute table. However, it proved difficult to undertake this process to extract the
information, as some soil units were omitted from the map legend, requiring
consultation with soil survey experts (pers comm. Dr. Ian Baillie; Mr. Brian Kerr)
who had on the ground experience from this region, being able to help determine
what the missing map units could be. The missing soil unit in the legend was later
19
identified as being relatively capable of growing sugar cane as there was a
capability rating table with this information in Klinkenberg’s report. The missing
unit was labelled for identification purpose. (Fig. 3.) shows the soil map of Lau Tau
after being digitised and georeferenced.
Fig. 3. Showing Lau Tau soil map
3.4.3 Preparing the NDVI map
The NDVI maps for both study areas were derived from the Landsat 8 data, where
a cloud free image was downloaded from the online USGS archive. Images taken
from vegetation peak period (August 2013) were sought initially to prepare the
scene target. However, it was established that this period did not have a cloud
free image and thus only the months of September and November were available
cloud free.
NDVI is a common vegetative index highlighting the combination of the red and
near infrared bands in help determine the ‘greenness’ (e.g. vigour) of vegetation in
a given area. NDVI is used to help discriminate healthy and non-healthy
20
vegetation - useful for land evaluation (Yunhao et al., 2006). The formula for
calculating the NDVI is:
NDVI = (NIR_BAND – RED_BAND) / (NIR_BAND + RED_BAND) (1)
In order to calculate the NDVI the raw digital numbers collected from the satellite
sensors were converted to radiance, then to ‘Top of Atmosphere’ reflectance, this
conversion was undertaken to correct for any atmospheric distortion and so
effectively to quantify the amount of reflectance from the earth to the sensor. (Fig.
4.) shows the NDVI maps for both study areas as well as the area equations
executed to derive both spectral radiance and reflectance.
DN to at sensor spectral radiance:
𝑳𝑴𝑨𝑿𝝀 −𝑳𝑴𝑰𝑵𝝀
𝑳𝝀 = (𝑸
𝒄𝒂𝒍𝒎𝒂𝒙 −𝑸𝒄𝒂𝒍𝒎𝒊𝒏
) (𝑸𝒄𝒂𝒍 − 𝑸𝒄𝒂𝒍𝒎𝒊𝒏 ) + 𝑳𝑴𝑰𝑵𝝀 (2)
Spectral radiance to TOA reflectance:
𝝆𝝀 =
𝝅.𝑳𝝀 .𝒅𝟐
𝑬𝑺𝑼𝑵𝝀 .𝒄𝒐𝒔𝜽𝒔
(3)
Where
𝐿𝜆 = Spectral radiance at the sensor's aperture [W/(m2 sr μm)]
𝑄𝑐𝑎𝑙 = Quantized calibrated pixel value [DN]
𝑄𝑐𝑎𝑙𝑚𝑖𝑛 = Minimum quantized calibrated pixel value corresponding to LMINλ [DN]
𝑄𝑐𝑎𝑙𝑚𝑎𝑥 = Maximum quantized calibrated pixel value corresponding to LMAXλ [DN]
𝐿𝑀𝐼𝑁𝜆 = Spectral at-sensor radiance that is scaled to Qcalmin [W/(m2 sr μm)]
𝐿𝑀𝐴𝑋𝜆 = Spectral at-sensor radiance that is scaled to Qcalmax [W/(m2 sr μm)]
𝜌𝜆 = Planetary TOA reflectance [unitless]
𝜋= Mathematical constant equal to ~3.14159 [unitless]
21
𝑑= Earth–Sun distance [astronomical units]
𝐸𝑆𝑈𝑁𝜆 = Mean exoatmospheric solar irradiance [W/(m2 μm)]
𝜃𝑠 = Solar zenith angle [degrees]
Fig. 4. NDVI maps for both Hadeija and Lau Tau study areas
3.4.4 Preparing the landforms map
The available data at this stage was limited and therefore efforts were made to
extract more information where possible from the available resources at hand.
Therefore a “SOTER like” methodology (Pourabdolloh et al., 2012) was adopted
to derive the landforms of the study areas taking into account the slope, relief
intensity and elevation. Landforms can produce accurate and timely information
which will help in decision making and planning (SOTER). The soil and terrain
methodology (SOTER) had its ideology from Russia and Germany with an aim of
creating a digital database for the World’s soil and terrain which should help in
determining the landscape. The predefined thresholds for running this procedure
have been altered as SOTER is designed for continental and national scales and
22
this research is only focusing on small areas, see (Fig. 5.) showing the landforms
for both study areas.
Fig. 5. Landforms for both Hadeija and Lau Tau study areas using the “SOTER-like”
method (see appendix for full legend)
3.4.5 Preparing the slope map
To better evaluate the land and its characteristics it is essential to determine slope
(Rasheed et al., 2009). The topography of the land is a key factor in determining
what crop can be grown on what slope level. This research therefore incorporates
the slope map into the overlay analyses to determine sugar cane suitability sites.
To derive the slope grid map, the digital elevation model was hydrologically
corrected using the fill tool in ArcGIS and then the slope tool was executed and
calibrated to produce a slope map with the percentage rise of the area slope. This
was then reclassified to meet its part of the sugar cane requirement. (Fig. 6.)
shows the slope maps derived for both study areas.
23
Fig. 6. Slope maps for both Hadeija and Lau Tau study areas
24
INPUT DATA
LEGACY
CARTOGRAPHY
MAP
LANDSAT 8 MULTISPECTRAL
IMAGERY
SRTM DIGITAL
ELEVATION MODEL
PROJECTION TO UTM 32N
GIS DATABASE
SOIL
PARAMETER
NDVI PARAMETER
LANDFORMS
PARAMETER
SLOPE
PARAMETER
RECLASSIFICATION TO MEET
SUGAR CANE REQUIREMENTS
RECLASSIFIED
PARAMETERS FOR
ANALYSIS
1. Soil texture;
2. NDVI values;
3. Landforms main
soil and;
4. Slope
percentage.
GIS WEIGHTED OVERLAY
ANALYSIS
SUGAR CANE SUITABILITY MAP
BOOLEAN LOGIC
OVERLAY
Fig. 7. Flowchart of the methodology used for this project
3.5 Crop suitability model implementation
Once the available data required for the analyses was assembled, an overlay
method was used to determine sugar cane suitability sites. To execute this
25
process the ArcGIS weighted overlay tool was used and each parameter was
given a weighting percentage. This approach was adopted in order to assign
some parameters a priority over others. Following this, the boolean method was
executed for comparison and checks made between both methods. The FAO crop
suitability classification standard (FAO 1976) was adopted to help classify the
suitability of land in a hierarchical order. The following is the rating technique used
in the assessment:
S1 – Not suitable
S2 – Marginally suitable
S3 – Moderately suitable
S4 – Highly suitable
3.5.1 Reclassifying the datasets
To combine several datasets with differing ranges and values, a reclassification
process was taken in order to get all the data into similar categorical classes to be
passed into the land suitability model. The datasets were classified to meet the
requirements for optimal sugar cane growth highlighted in (Table 3). This drew
upon the reported literature as well as personal contact and advice from experts in
this field (pers comm. Dr. Ian Baillie; Mr. Brian Kerr).
3.5.2 Weighting the datasets
The reclassified datasets used in the weighted overlay model play a vital role in
determining suitable sites for growing sugar cane. It was therefore necessary to
assign each thematic layer a percentage of influence in the analyses (Long et al.,
2006). This is not a straightforward process, as in a multi criteria analyses such as
this variables can be used differently therefore a personal consultation with an
expert in sugar cane modelling was undertaken (pers comm. Dr. Ian Baillie) with
advice on running several models with different weightings seen in (Table 4).
26
Table 3: Criteria for assessing sugar cane requirements
NB: The thresholds are subject to particular study areas
PARAMETERS
Highly
Moderately
Marginally
Not suitable
suitable
suitable
suitable
Slope
0 – 2%
2 – 3%
3 – 5%
>5%
Soil texture
C,
LS-SCL-L
S-LS-SL,
C-LS-S,
SL-SCL,
“OK”
L-LS
LS-C
Vertisols
-
Rocky soils
> 0.5
> 0.3
<= 0.3
CL-C
Landforms
Ferruginous
main soil
tropical soils
(FTS)
NDVI
> 0.6
Table 4: Showing weighting assigned to the 5 map outputs
Map No.
Soil Texture
Landform
Slope
NDVI
Main Soils
1
25%
25%
25%
25%
2
30%
30%
30%
10%
3
40%
30%
10%
20%
4
25%
40%
25%
10%
5
30%
10%
30%
30%
3.6 Conclusion
This approach has proved promising and being advantageous over the traditional
boolean method. A combination of local knowledge expertise and the semi-
27
automated process of weighted overlay method makes it a flexible procedure in
terms of querying the different datasets and assigning priority to the layers for
comparison and validity check of the work done.
28
4 Results and discussion
4.1 Model outputs
The weighted overlay and boolean “true or false” methods were undertaken to
help assess the study areas. This produced an output of thematic layers showing
the suitability classes arising from the interaction of the parameters used in the
modelling process. The results from the weighted overlay shows for the most part,
the area as being moderately suitable while no portion of the land is actually
classified as unsuitable - based on the datasets used for this project. Conversely,
the boolean method identifies most of the land as being marginally suitable. This
is assumed to be as a result of the rigid form of assessment (true or false)
inherent in the traditional boolean method.
Due to the flexibility of the weighted overlay, several suitability maps were derived
using different percentage weighting on the input parameters utilized. This was
able to help in prioritizing some data themes over others. However, regardless of
the weightings, it was understood that most of the area still ranges between
marginally to moderately suitable with little to no highly suitable areas for growing
sugar cane. (Fig. 8.) Shows the different outputs from the weighted overlay and
boolean method and (Figure 9) shows the area distribution of the suitability
classes from both methods used.
29
Fig. 8. Showing models outputs using the traditional boolean method (fig. a) and fig. (b-f) showing results derived from the weighted
overlay model
30
Lau Tau Suitability Classes Quantified in Hectares
40
Thousands (Hectares)
35
30
25
20
Boolean logic
15
Weighted Overlay
10
5
0
Not
Suitable
Marginally Moderately
Suitable
Suitable
Highly
Suitable
Figure 9: Showing area distribution of Lau Tau study area based on the suitability
classes
4.2 Associated challenges
This research was conducted as a rapid ‘desk-based’ assessment with all data
being remotely acquired, with only the soil data being is a historic map collected
from a field survey (Klinkenberg, 1967). Therefore some challenges were
encountered during this research which added to knowledge. The difficulties
experienced during this project are outlined below:
4.2.1 Collecting soil data
The WOSSAC archive at Cranfield University (www.wossac.com) holds a vast
amount of global historic data which can be very useful to integrate in a
contemporary analyses like this however, some challenges also comes with such
data as this has been collected a very long time ago with probably no access to
the original author (Hallett et al., 2011; 2006). The problems at this point were the
soils are mapped as associations and not series with a scale of 1:100,000
31
therefore having a broad information as to the soil texture and other
characteristics within the landforms though personal contacts with soil experts
(pers comm. Dr. Ian Baillie; Mr. Brian Kerr) were made to help identify missing
information in the historic data and this was made possible by the aid of visual
interpretation from aerial photographs of the area and their field experiences.
4.2.2 Collecting digital elevation model
The digital elevation model proved highly applicable for this project in helping
distinguish between landforms and to characterize the topography (Fig. 10.). The
data’s resolution is expressed on a 96x96m grid and mapping detailed information
was not ideal, although it was found useful as a first step in guiding future surveys
within other farm sites.
Fig. 10. Hadeija and Lau Tau’s Digital Elevation Model over a hillshade model
4.2.3 Deriving landforms from the DEM
Data availability for key land characteristics was limited for the study sites. To
better evaluate the land, expert’s advice was sought as a means of establishing a
32
way of understanding the morphology of the land and what the land
characteristics might be and its formation. As a result, the Soil and Terrain
Database (SOTER) method was adopted (ISRIC, 2014) to help determine the
geomorphology of the study areas thereby delineating between the derived
landforms (e.g. river plains, highlands etc.). To do this, the DEM was manipulated
in GIS to derive 4 thematic layers (Slope, Relief intensity, Hypsometry and
Potential drainage density) using a “SOTER-like” methodology as the full SOTER
method could not be adopted in the study area to discriminate features as it is
designed for a global scale (1:1million) while these study areas are 30 kilometres
across. The “SOTER like” method used for this project was found very helpful in
discriminating between landforms and this, with the aid of visual interpretation
from Google earth and Landsat imageries, was combined with expert knowledge
(pers comm. Dr. Ian Baillie) to determine the major soil types of the land could be
(e.g. FTS or Vertisols), as seen in (Figure 11.). This was included in the model to
help determine potential sugar cane plantations.
It should be borne in mind that this methodology requires some local knowledge of
the study area in terms of the labelling of outputs as the topographic
characteristics might not mean the same thing in different places. This was
experienced in whereby a “River plain” in Lau Tau area was not replicated in the
second site in Hadeija, being a much drier area. The methodology therefore can
be described as a semi-automated ‘guided’ approach – however, this approach is
pragmatic where substantive local datasets are not available, this often being the
case in African studies.
33
Figure 11: Delineated landforms types of Lau Tau study area
34
4.2.4 Soil moisture data
Soil moisture can be useful for land evaluation. One source of this data is from
microwave remote sensing. Appropriate data was obtained freely at (www.esasoilmoisyture-cci.org). The global soil moisture data was downloaded in the
NetCDF format which was converted to a raster grid file using the BEAM
application provided by ESA. Although this data was intended to serve as one
of the parameters in the land evaluation analyses, due to the coarse resolution
at which this data was derived in (global scale at 27km2 grid size), it was
deemed inadmissible for the purpose of this research as the study areas are
covered in just one pixel as seen in (Figure 12) and thereby having one value
across the study area.
4.2.5 Collecting rainfall data
One of sugar cane’s major requirements is adequate water supply. The project
therefore sought to source rainfall data to help in the assessment.
Unfortunately, most of these data also do not have a suitable spatial resolution
for the study sites in this project (about 27km2 grid sizes) as the whole or half of
the areas are covered in just one pixel thereby having just one value of rainfall
which cannot help in discriminating rainfall distribution. The study areas are
small and therefore would have same amount of rainfall across. Generalised
rainfall data was therefore considered as insufficient.
35
Figure 12: Showing study areas falling into one pixel of the soil moisture data
4.3 Methods adopted
During this research a range of GIS and Remote techniques, outlined below,
were attempted to help evaluate the study areas, some of these proved useful.
Mostly issues arose due to the limited area of the study sites. The following
section outlines the analyses that were conducted but that were ultimately
excluded in the final assessment.
4.3.1 Solar irradiance map
Solar irradiance was initially intended to form part of the analyses for
determining suitable lands. However, after the results were derived it was
realised that the sun hour duration per day was broadly similar across the study
area (with just few minutes between the highest and lowest areas) as shown in
(Figure 13.). This was deemed insufficient for discriminating between suitable
lands. It is however a requirement for sugar cane and this method can very well
be adopted for larger geographical areas which will have variations is the daily
amount of sun hours and so further analyses can be made.
Solar irradiance was created from the digital elevation model using the area
solar radiation tool in ArcGIS. This was calibrated for the local sun angle over
36
one year to produce an accurate figure for solar radiation which was given in
wh/m2.
In order to convert this to represent duration of sun hours per day, conversions
were made to the derived solar radiation. The standard unit conversion adopted
was 1kwh/m2 being equal to 1 peak hour of sun (www.pveducation.org). Since
the result were in wh/m2, it was divided by 1,000 to get kwh/m2 and then divided
by 365 days which then gives a daily sun hours received by the whole study
area per square meter.
Figure 13: Solar irradiance maps showing daily sun hours received for the study
areas
4.3.2 NDVI vs. EVI
Some vegetation indices were derived from Landsat data which helped in
differentiating between the greenness of vegetation and un-vegetated areas
(bare soil or built up areas), both indices were calculated and had a minimal
difference in the index values (Figure 14.), during this research it was noted that
37
NDVI can easily become saturated in its reflectance and therefore cannot easily
distinguish patches of bare soils between vegetation. By contrast, the EVI
technique tends to discriminate changes in vegetation growth and soil
contamination but this was not very significant in the study areas as most of the
land is flat and therefore little topographic variation, these indices are however
just flagging green areas and not crop specific potentials which could mean high
index values are just canopy cover of trees and not really suitable for planting
sugar cane or any other crop.
Figure 14: NDVI and EVI maps of Lau Tau study area
4.4 Implications
This research has been able to identify the importance of bringing legacy data
from previous surveys into present assessments, highlighting how such data
can be translated. The incorporation of historical with contemporary data has
proved useful for segmenting the areas of interest based on the available data –
however, there is further work that can be undertaken to develop this approach,
but it does provide a useful commencement point for land suitability assessment
38
screening approaches used to guide a full ground survey. Also the use of
existing legacy data can be seen as allowing for an accurate and cost effective
approach. The methodology finally selected has determined the possibility of
blending historic data, contemporary data and experiential advice to provide a
rational basis for land assessment and a basis for future soil and other field
survey activities.
39
5 Recommendations
The methods and approach used for this project have proved useful for the
purpose of segmenting lands to discover their potentials in which case has been
applied to specific study sites for the purpose of this research and can be
utilised for further investigations.
It is however recommended that the outlined actions be adopted which in turn
be of great benefit to the growing of sugar cane for sustainable development.

The introduction of GIS and Remote Sensing for monitoring and
managing the sugar cane farms is highly recommended for further farm
management and precision agriculture, this technique will enable for
easy data collection, storage and analyses to help manage the farming
activities which can be cost effective and convenient for monitoring the
crop growing cycle.

Collection of temporal and real time data for climate, rainfall and
vegetation healthiness will prove very useful for further analyses within
the farms as this will be used for present and future planning for optimal
crop production.

Sugar cane has a long growing season and therefore, it is recommended
that irrigation systems are planned for to compensate water loss in the
soil for optimal crop growth which will hopefully result to high amount of
yield.

Most of the soils within the study area are presumed to be vertisols,
which are often characterised as heavy clay like soils and can be difficult
to manage for irrigation purposes. Therefore a close monitoring and
precise irrigation system is required to avoid over or under water
applications.

Implementation of the techniques used in this project to guide further
surveys, it is also important that specialists in this area are involved to
easily use the remote sensing software for image processing of the farms
to identify areas needing more fertilizer or water which again has proved
cost effective to farmers all over the world.
40

Below is a list of data that should be acquired in order to increase the
opportunity of high yield at the end of every growing season:
I.
High resolution soil data for all farms;
II.
Extend this method to assess other study sites;
III.
Integrate local knowledge with this semi-automated process to yield
better results;
IV.
Temporal climatic data for farm sites (e.g. rainfall) as this can be useful
for yield prediction;
V.
High resolution digital terrain model for detailed topographic analysis;
VI.
Temporal satellite images which are freely available from Landsat though
higher resolution images may be required for precision;
VII.
Software to keep and manipulate all field related data collected for farm
management (e.g. ArcGIS, Quantum GIS, Erdas Imagine, Idrisi etc.);
VIII.
Adopt this method for initial research which will guide towards more
robust outcomes and;
IX.
Groundwater status, this can be collected using instruments like HERON
which is installed on the ground to continuously produce groundwater
levels to keep track of the water availability for precise farm
management.
41
6 Conclusion
This project has sought to analyse one of the approaches in the literature within
the variety of land evaluation techniques, with the numerous methods
applicable to land suitability analyses it was however possible to imitate a
feasible method within the given time of this research.
Data assembling was possible using GIS to build a spatial database holding
several datasets including soil, contemporary and historic data with attribute
tables in order to identify potential sugar cane sites for sustainable production.
The FAO land evaluation framework was adopted for this project which was
integrated with the above mentioned datasets acquired and it was found useful
for this project, it was observed that a lot can be achieved by combining both
legacy cartographic data with contemporary techniques to help in land suitability
analyses.
A GIS based traditional boolean and weighted overlay method was applied to
the produced thematic layers which helped in the process of segmenting the
land based on suitability classes for sugar cane.
In this research, a GIS weighted overlay method proved more advantageous
over the traditional boolean method in combining several data to help in a multicriteria decision analyses with potential of it being extended to other areas. This
project therefore hopes to serve as an initial approach to land suitability
analyses and guide towards field survey activities in order to effectively make
decisions and how further land management can be made.
42
REFERENCES
Abdel Kawy, W. A. M. and Abou El-Magd, I. H. (2013), "Use of satellite data and GIS for
assessing the agricultural potentiality of the soils South Farafra Oasis, Western Desert,
Egypt", Arabian Journal of Geosciences, vol. 6, no. 7, pp. 2299-2311.
Akinci, H., Özalp, A. Y. and Turgut, B. (2013), "Agricultural land use suitability analyses
using GIS and AHP technique", Computers and Electronics in Agriculture, vol. 97, pp.
71-82.
Alphonsus, E. (2013), Nigeria targets increase in sugar production, available at:
http://www.brandpowerng.com/nigeria-targets-increase-sugar-production/ (accessed
May, 30th).
Bizuwerk, A., Peden, D., Taddese, G. and Getahun, Y. (2005), "GIS Application for analyses
of Land Suitability and Determination of Grazing Pressure in Upland of the Awash River
Basin, Ethiopia. Addis Ababa, Ethiopia".
Carr, M. K. V. and Knox, J. W. (2010), "The water relations and irrigation requirements of
sugar cane (Saccharum Officinarum) : A review", pp. 1-25.
Chartres, C. J. (1981), "Land resources assessment for sugar-cane cultivation in Papua New
Guinea", Applied Geography, vol. 1, no. 4, pp. 259-271.
Collins, G. M., Steiner, R. F. and Rushman, J. M. (2001), "Land-Use Suitability Analyses in
the United States: Historical Development and promising Technological Achievements",
vol. 28, no. 5, pp. 611-621.
Coskun, H. G., Alganci, U. and Usta, G. (2008), "Analyses of land use change and
urbanization in the Kucukcekmece Water basin (Istanbul, Turkey) with temporal
satellite data using remote sensing and GIS", Sensors, vol. 8, no. 11, pp. 7213-7223.
Delgado, A. and Casanova, C. (2001), Sugar processing and by-products of the sugar
indutry. Illustrated ed, Food & Agriculture Org, Rome.
Dent, D. and Young, A. (1981), "Soils Survey and Land Evaluation", in London, pp. 115-127230-243.
Doygun, H. (2009), "Effects of urban sprawl on agricultural land: a case study of
Kahramanmaras, Turkey." Vol. 158, no. 1-4, pp. 471.
Dzieszko, M., Dzieszko, P., Królewicz, S. and Cierniewskski, J. (2012), "Digital aerial images
land cover classification based on vegetation indices", Quaestiones Geographicae, vol.
31, no. 3, pp. 5-23.
El Hajj, M., Bégué, A., Guillaume, S. and Martiné, J. (2009), "Integrating SPOT-5 time series,
crop growth modelling and expert knowledge for monitoring agricultural practices — The
case of sugarcane harvest on Reunion Island", Remote Sensing of Environment, vol.
113, no. 10, pp. 2052-2061.
El-Nahry, A. H. and Abdel Kawy, W. A. M. (2013), "Sustainable landuse management on the
coastal zone of the Nile Delta, Egypt", Journal of Land Use Science, vol. 8, no. 1, pp.
85-103.
ESRI. (2012), What is GIS? ESRI, USA.
43
Fagerholm, N., Käyhkö, N. and Van Eetvelde, V. (2013), "Landscape characterization
integrating expert and local spatial knowledge of land and forest resources",
Environmental management, vol. 52, no. 3, pp. 660-682.
FAO (1976), "A Frame Work for Land Evaluation.” no. Soils Bulletin No. 32.
Food and Agriculture Organisation of the United Nations (1995), Global And National Soils
And Terrain Digital Database (SOTER), 76, FAO, United Nations.
Ganapuram, S., Kumar, G. T. V., Krishna, I. V. M., Kahya, E. and Demirel, M. C. (2009),
"Mapping of groundwater potential zones in the Musi basin using remote sensing data
and GIS", Advances in Engineering Software, vol. 40, no. 7, pp. 506-518.
Hadeel, A. S., Jabbar, M. T. and Chen, X. (2009), "Application of remote sensing and GIS to
the study of land use/cover change and urbanization expansion in Basrah province,
Southern Iraq", Geo-Spatial Information Science, vol. 12, no. 2, pp. 135-141.
Hallett, S.H., Baillie, I.C., Kerr, B. and Truckell, I.G. (2011) Development of the
World Soil Survey Archive and Catalogue (WOSSAC) Commission on the
History, Philosophy and Sociology of Soil Science, 18, pp14-17.
Hallett, S.H., Bullock, P., Baillie, I., 2006. Towards a World Soil Survey Archive and
Catalogue. Soil Use and Management 22, 227-228.
Houshyar, E., Sheikhdavoodi, M. J., Almassi, M., Bahrami, H., Azadi, H., Omidi, M., Sayyad,
G. and Witlox, F. (2014), "Silage corn production in conventional and conservation
tillage systems. Part I: Sustainability analyses using combination of GIS/AHP and multifuzzy modelling", Ecological Indicators, vol. 39, pp. 102-114.
Ikiel, C., Ustaoglu, B., Dutucu, A. A. and Kilic, D. E. (2013), "Remote sensing and GIS-based
integrated analyses of land cover change in Duzce plain and its surroundings (north
western Turkey)", Environmental monitoring and assessment, vol. 185, no. 2, pp. 16991709.
Iverson, L. R., Dale, M. E., Scott, C. T. and Prasad, A. (1997), "A GIS-derived integrated
moisture index to predict forest composition and productivity of Ohio forests (U.S.A.)",
Landscape Ecology, vol. 12, no. 5, pp. 331-348.
Khoram, M. R. and Asgari, A. (2014), "Site selection for urban planning by means of GIS; a
case study", Advances in Environmental Biology, vol. 8, no. 1, pp. 70-74.
Kihoro, J., Bosco, N. J. and Murage, H. (2013), "Suitability analyses for rice growing sites
using a multicriteria evaluation and GIS approach in great Mwea region, Kenya",
SpringerPlus, vol. 2, no. 1, pp. 1-9.
Kim, Y., Park, W., Eo, Y. and Kim, Y. (2010), "Land cover classification of a non-accessible
area using multi-sensor images and GIS data", Journal of the Korean Society of
Surveying Geodesy Photogrammetry and Cartography, vol. 28, no. 5, pp. 493-504.
Klinkenberg, K. (1967), The soils Of The Lau-Kaltungo Area, 36, Institute For Agricultural
Research, Samaru Ahmadu Bello University, Nigeria.
Kumar, J. (2011), "Mapping and analyses of land-use/land cover of Kanpur city using remote
sensing and GIS technique, 2006", Transactions of the Institute of Indian Geographers,
vol. 33, no. 1, pp. 43-54.
44
Kumar, R., Mehra, P. K., Singh, B., Jassal, H. S. and Sharma, B. D. (2010), "Geostatistical
and visualization analyses of crop suitability for diversification in sub-mountain area of
Punjab, North-West India", Journal of the Indian Society of Remote Sensing, vol. 38,
no. 2, pp. 211-226.
Lapola, D. M., Priess, J. A. and Bondeau, A. (2009), "Modelling the land requirements and
potential productivity of sugarcane and jatropha in Brazil and India using the LPJmL
dynamic global vegetation model", Biomass and Bioenergy, vol. 33, no. 8, pp. 10871095.
Liengsakul, M., Mekpaiboonwatana, S., Pramojanee, P., Bronsveld, K. and Huizing, H.
(1993), "Use of GIS and remote sensing for soil mapping and for locating new sites for
permanent cropland — A case study in the ‘highlands’ of northern Thailand.
Geoderma ", pp. 293–307.
Long, J. M. and Fisher, W. L. (2006), "Analyses of environmental variation in a Great Plains
reservoir using principal components analyses and geographic information systems",
Lake and Reservoir Management, vol. 22, no. 2, pp. 132-140.
Lv, L. -., Zheng, X. -., Zhao, L. and Hu, Y. -. (2013), "GIS-based weight of evidence
modelling of basic farmland protection planning for basic farmland suitability mapping",
Journal of Food, Agriculture and Environment, vol. 11, no. 2, pp. 1087-1092.
Malczewski, J. (2004), "GIS-based land-use suitability analyses: a critical overview", pp. 365.
Malczewski, J. (2006), "Ordered weighted averaging with fuzzy quantifiers: GIS-based
multicriteria evaluation for land-use suitability analyses", International Journal of
Applied Earth Observation and Geoinformation, vol. 8, no. 4, pp. 270-277.
Masser, I. ( 1998), Governments and geographic information, Taylor and Francis.
McHarg, I. (2000), "Environmentalism: Ideas and Methods in Context", in Michel, C. (ed.)
Environmentalism in Landscape Architecture, , pp. 98-114.
Mendas, A. and Delali, A. (2012), "Integration of MultiCriteria Decision Analyses in GIS to
develop land suitability for agriculture: Application to durum wheat cultivation in the
region of Mleta in Algeria", Computers and Electronics in Agriculture, vol. 83, pp. 117126.
Mundia, C. N. and Murayama, Y. (2009), "Analyses of land use/cover changes and animal
population dynamics in a wildlife sanctuary in East Africa", Remote Sensing, vol. 1, no.
4, pp. 952-970.
Nisar Ahamed, T. R., Gopal Rao, K. and Murthy, J. S. R. (2000), "GIS-based fuzzy
membership model for crop-land suitability analyses", Agricultural Systems, vol. 63, no.
2, pp. 75-95.
Nobre, R. C. M. and Nobre, M. M. M. (2009), "Assessing groundwater vulnerability to nitrate:
Implications of biofuels production in Brazil", In Situ and On-Site Bioremediation-2009:
Proceedings of the 10th International In Situ and On-Site Bioremediation Symposium.
Obiefuna, J. N., Nwilo, P. C., Atagbaza, A. O. and Okolie, C. J. (2013), "Land cover dynamics
associated with the spatial changes in the Wetlands of Lagos/Lekki Lagoon system of
Lagos, Nigeria", Journal of Coastal Research, vol. 29, no. 3, pp. 671-679.
45
Paiboonsak, S. and Mongkolsawat, C. (2007), "Evaluating land suitability for industrial
sugarcane with GIS modelling", 28th Asian Conference on Remote Sensing 2007, ACRS
2007, Vol. 2, pp. 1319.
Rahman, A., Kumar, S., Fazal, S. and Siddiqui, M. A. (2012), "Assessment of Land use/land
cover Change in the North-West District of Delhi Using Remote Sensing and GIS
Techniques", Journal of the Indian Society of Remote Sensing, vol. 40, no. 4, pp. 689697.
Rasheed, S. and Venugopal, K. (2009), "Land suitability assessment for selected crops in
Vellore district based on agro-ecological characterisation", Journal of the Indian Society
of Remote Sensing, vol. 37, no. 4, pp. 615-629.
Rawat, J. S., Biswas, V. and Kumar, M. (2013), "Changes in land use/cover using geospatial
techniques: A case study of Ramnagar town area, district Nainital, Uttarakhand, India",
Egyptian Journal of Remote Sensing and Space Science, vol. 16, no. 1, pp. 111-117.
Reuters. (2013), Nigeria targets rapid expansion in sugar production., available at:
http://www.reuters.com/article/2013/12/04/nigeria-sugar-idUSL5N0JJ2YY20131204
(accessed Dec, 4th).
Riad, P. H. S., Billib, M., Hassan, A. A., Salam, M. A. and El Din, M. N. (2011), "Application of
the overlay weighted model and boolean logic to determine the best locations for
artificial recharge of groundwater", Journal of Urban and Environmental Engineering,
vol. 5, no. 2, pp. 57-66.
Rozenstein, O. and Karnieli, A. (2011), "Comparison of methods for land-use classification
incorporating remote sensing and GIS inputs", Applied Geography, vol. 31, no. 2, pp.
533-544.
Santhi, C., Muttiah, R. S., Arnold, J. G. and Srinivasan, R. (2005), "A GIS-based regional
planning tool for irrigation demand assessment and savings using SWAT", Transactions
of the American Society of Agricultural Engineers, vol. 48, no. 1, pp. 137-147.
Sarkar, B. C., Deota, B. S., Raju, P. L. N. and Jugran, D. K. (2001), "A geographic
information system approach to evaluation of groundwater potentiality of shamri microwatershed in the Shimla Taluk, Himachal Pradesh", Journal of the Indian Society of
Remote Sensing, vol. 29, no. 3, pp. 151-164.
Sellamuthu, K. M., Natarajan, R., Sivasamy, R. and Mani, S. (2000), "Geogarphical
Information System for Delineating Soil Related Constraints in Sugarcane Growing
Areas.", vol. 2, no. 3, pp. 30-33.
Shalaby, A. and Tateishi, R. (2007), "Remote sensing and GIS for mapping and monitoring
land cover and land-use changes in the Northwestern coastal zone of Egypt", Applied
Geography, vol. 27, no. 1, pp. 28-41.
Singels, A., Kennedy, A. and Bezuidenhout, C. (1998), "IRRICANE: A simple computerised
irrigation scheduling method for sugarcane", vol. 72, pp. 117-122.
Spanò, A. and Pellegrino, M. (2013), "Craft data mapping and spatial analyses for historical
landscape modelling", Journal of Cultural Heritage, vol. 14, no. 3 SUPPL, pp. S6-S13.
Steinitz, C., Parker, P. and Jordan, L. (1976), "Hand drawn overlays: their history and
prospective uses. Landscape ", vol. Architecture 9, pp. 444–455.
46
Stewart, L. K., Charlesworth, P. B., Bristow, K. L. and Thorburn, P. J. (2006), "Estimating
deep drainage and nitrate leaching from the root zone under sugarcane using APSIMSWIM", Agricultural Water Management, vol. 81, no. 3, pp. 315-334.
Suharyanto, A., Suhartanto, E. and Pudyono (2013), "The use of satellite remote sensing
data and geographic information systems on critical land analyses", Agrivita, vol. 35,
no. 2, pp. 119-126.
Sui, D. Z. (1992), "A fuzzy GIS modelling approach for Urban land evaluation", Computers,
Environment and Urban Systems, vol. 16, no. 2, pp. 101-115.
Takara, K. and Kojima, T. (1996), "GIS-aided land cover classification assessment based on
remote sensing images with different spatial resolutions", Application of geographic
information systems in hydrology and water resources management.Proc.HydroGIS'96
conference, Vienna, 1996, , no. 235, pp. 659-665.
Taşdemiroǧlu, E. and Ecevit, A. (1985), "Comparisons of the hourly and daily global
irradiances of Turkey on non-horizontal surfaces", Energy Conversion and Management,
vol. 25, no. 1, pp. 119-126.
Theilen-Willige, B. (2010), "Detection of local site conditions influencing earthquake shaking
and secondary effects in Southwest-Haiti using remote sensing and GIS-methods",
Natural Hazards and Earth System Science, vol. 10, no. 6, pp. 1183-1196.
Thompson, M. (1996), "A standard land-cover classification scheme for remote-sensing
applications in South Africa", South African Journal of Science, vol. 92, no. 1, pp. 34-42.
Utset, A. and Lopez, G. (2001), "Regional mechanistic estimations of sugar-cane water use",
IAHS-AISH Publication, , no. 270, pp. 35-40.
Warwade, P., Hardaha, M. K., Kumar, D. and Chandniha, S. K. (2014), "Estimation of soil
erosion and crop suitability for a watershed through remote sensing and GIS approach",
Indian Journal of Agricultural Sciences, vol. 84, no. 1, pp. 18-23.
Xu, F. -., Tao, S., Dawson, R. W. and Li, B. -. (2001), "A GIS-based method of lake
eutrophication assessment", Ecological Modelling, vol. 144, no. 2-3, pp. 231-244.
Yinka, A., Ononse, B., Oluwabanke, F., Enifome, O., Jimi, A. and Olusola, M. (2013),
"Framework model for a Soil Suitability Decision Support System for Crop Production in
Nigeria.", vol. 02, no. 06, pp. 09.
Yuan, F. (2008), "Land-cover change and environmental impact analyses in the Greater
Mankato area of Minnesota using remote sensing and GIS modelling", International
Journal of Remote Sensing, vol. 29, no. 4, pp. 1169-1184.
Yunhao, C., Peijun, S., Xiaobing, L., Jin, C. and Jing, L. (2006), "A combined approach for
estimating vegetation cover in urban/suburban environments from remotely sensed
data", Computers and Geosciences, vol. 32, no. 9, pp. 1299-1309.
Zaggi, H. (2013), Sugarcane: Unexplored gold mine, available at:
http://dailyindependentnig.com/2013/09/sugarcane-unexplored-gold-mine/ (accessed
May 30th, 2014).
47
APPENDICES
Appendix A
This sections holds other relevant information used during this research but not
included in the main thesis.
A.1 : GIS python coding for landforms
During the process of deriving the landforms thematic layers for the study
areas, it was necessary to use a semi-automated technique to facilitate the
renaming of landform classe. Below is the code written in notepad++ and then
inserted to the ArcGIS field calculator:
Recode( !Slope!, !Relief_intensity!, !Elevation! )
def Recode(A,B,D):
text = ""
# Hypsometry
if (D == 1):
text = text + "River plain "
elif (D == 2):
text = text + "Lowlands "
elif (D == 3):
text = text + "Valley floor "
elif (D == 4):
text = text + "Midslopes "
elif (D == 5):
text = text + "Mid plateau "
elif (D >= 6 and D <= 9):
text = text + "High slopes "
elif (D == 10):
text = text + "High plateau "
# Relief Intensity
if (B == 1):
text = text + "on flat land "
elif (B == 2):
text = text + "on gentle land "
elif (B == 3):
text = text + "on rough land "
elif (B == 4):
text = text + "on hillpeak land "
# Slope
if (A >= 1 and A <=3):
text = text + "with gentle slopes"
elif (A >= 4 and A <=5):
text = text + "with mid slopes"
elif (A > 5):
text = text + "with steep slopes"
return text
A
1-3 Gentle slope
4-5 Mid slope
6-7 Steep slope
B
1
2
3
4
Flatland
Gentle
Rough
Peak
D
1 River plain
2 Lowlands
3 Valley floor
4 Midslopes
5 Mid plateau
6-9 High slopes
10 High plateau
48
A.2 Landforms legend for both study areas
The figure below shows the resultant legend from the use of above python
code.
49
Hadeija Landform Units
High slopes on flat land with gentle slopes
High slopes on flat land with mid slopes
High slopes on gentle land with gentle slopes
High slopes on gentle land with mid slopes
High slopes on gentle land with steep slopes
High slopes on hillpeak land with gentle slopes
High slopes on hillpeak land with mid slopes
High slopes on hillpeak land with steep slopes
High slopes on rough land with gentle slopes
High slopes on rough land with mid slopes
High slopes on rough land with steep slopes
Lowlands on flat land with gentle slopes
Lowlands on flat land with mid slopes
Lowlands on flat land with steep slopes
Lowlands on gentle land with gentle slopes
Lowlands on gentle land with mid slopes
Lowlands on gentle land with steep slopes
Lowlands on hillpeak land with gentle slopes
Lowlands on hillpeak land with mid slopes
Lowlands on hillpeak land with steep slopes
Lowlands on rough land with gentle slopes
Lowlands on rough land with mid slopes
Lowlands on rough land with steep slopes
Mid plateau on flat land with gentle slopes
Mid plateau on flat land with mid slopes
Mid plateau on flat land with steep slopes
Mid plateau on gentle land with gentle slopes
Mid plateau on gentle land with mid slopes
Mid plateau on gentle land with steep slopes
Mid plateau on hillpeak land with gentle slopes
Mid plateau on hillpeak land with mid slopes
Mid plateau on hillpeak land with steep slopes
Mid plateau on rough land with gentle slopes
Mid plateau on rough land with mid slopes
Mid plateau on rough land with steep slopes
Midslopes on flat land with gentle slopes
Midslopes on flat land with mid slopes
Midslopes on flat land with steep slopes
Midslopes on gentle land with gentle slopes
Midslopes on gentle land with mid slopes
Midslopes on gentle land with steep slopes
Midslopes on hillpeak land with gentle slopes
Midslopes on hillpeak land with mid slopes
Midslopes on hillpeak land with steep slopes
Midslopes on rough land with gentle slopes
Midslopes on rough land with mid slopes
Midslopes on rough land with steep slopes
River plain on flat land with gentle slopes
River plain on flat land with mid slopes
River plain on flat land with steep slopes
River plain on gentle land with gentle slopes
River plain on gentle land with mid slopes
River plain on gentle land with steep slopes
River plain on hillpeak land with mid slopes
River plain on hillpeak land with steep slopes
River plain on rough land with gentle slopes
River plain on rough land with mid slopes
River plain on rough land with steep slopes
Valley floor on flat land with gentle slopes
Valley floor on flat land with mid slopes
Valley floor on flat land with steep slopes
Valley floor on gentle land with gentle slopes
Valley floor on gentle land with mid slopes
Valley floor on gentle land with steep slopes
Valley floor on hillpeak land with gentle slopes
Valley floor on hillpeak land with mid slopes
Valley floor on hillpeak land with steep slopes
Valley floor on rough land with gentle slopes
Valley floor on rough land with mid slopes
Valley floor on rough land with steep slopes
50
Lau Tau Landform Units
High plateau on hillpeak land with gentle slopes
High plateau on hillpeak land with mid slopes
High plateau on hillpeak land with steep slopes
High slopes on hillpeak land with gentle slopes
High slopes on hillpeak land with mid slopes
High slopes on hillpeak land with steep slopes
High slopes on rough land with gentle slopes
High slopes on rough land with mid slopes
High slopes on rough land with steep slopes
Lowlands on flat land with gentle slopes
Lowlands on flat land with mid slopes
Lowlands on flat land with steep slopes
Lowlands on gentle land with gentle slopes
Lowlands on gentle land with mid slopes
Lowlands on gentle land with steep slopes
Mid plateau on gentle land with gentle slopes
Mid plateau on gentle land with mid slopes
Mid plateau on gentle land with steep slopes
Mid plateau on rough land with gentle slopes
Mid plateau on rough land with mid slopes
Mid plateau on rough land with steep slopes
Midslopes on gentle land with gentle slopes
Midslopes on gentle land with mid slopes
Midslopes on gentle land with steep slopes
Midslopes on rough land with gentle slopes
Midslopes on rough land with mid slopes
Midslopes on rough land with steep slopes
River plain on flat land with gentle slopes
River plain on flat land with mid slopes
River plain on flat land with steep slopes
Valley floor on gentle land with gentle slopes
Valley floor on gentle land with mid slopes
Valley floor on gentle land with steep slopes
51
Appendix B
B.1 Modelling in ArcGIS
Below are the model graphics derived for the steps taken, the model builder
was not only useful for the purpose of modelling the case study but also helped
to keep track and automate all the processes again when needed.
52
Modelling sugar cane suitability using ArcGIS model builder (Traditional boolean and weighted overlay method)
53
Deriving hydrologic features like slope, streams, flow accumulation, flow direction) using ArcGIS model builder
54
Modelling NDVI and EVI using ArcGIS model builder
55
Modelling landforms based on the SOTER ‘like’ method adopted for this project (layers were labelled in the standard
SOTER naming convention for consistency)
56
B.2 Legacy soil cartography data (WOSSAC)
The legacy data used for this project was both a paper map and a report which explained the
map and how it has been derived. The scanned paper map is shown below:
Scanned legacy map before being digitised and georeferenced in ArcGIS
57