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.

i

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

iii

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.

v

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

vi

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

vii

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 “SOTER-like” 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

viii

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

ix

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

x

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.

1

Sugar cane modelling using GIS and remote sensing techniques

Usman Muhammad Buharia

aSchool 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

2

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

3

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

4

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:

5

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.

6

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.

7

Table 1: Study sites description showing the selected ones for this project in

highlight

No. Study sites Climatic condition Area (Km2) Elevation

(Meters)

Region

1 Lau (Taraba

state)

Wetland area with

about 1058mm –

Over 1300mm

annual rainfall

438 120-255 North East

2 Hadeija (Jigawa

state)

Semi-arid region

with about 600mm

– 1000mm annual

rainfall

212 355-380 North East

3 Guyuk

(Adamawa state)

Tropical area with

about 759mm –

1051mm annual

rainfall

233 170-805 North East

4 Giwano

(Adamawa state)

Tropical area with

about 759mm –

1051mm annual

rainfall

278 135-188 North East

5 Ageni (Kogi

state)

Flood plain area

with about 1100mm

– 1300mm annual

rainfall

492 49-205 Central Nigeria

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.

8

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).

9

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

10

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.

11

Table 2: Showing a summary of different approaches considered in the search for

suitable land for growing sugar cane.

No. Author Factors considered for

Sugar cane modelling

Techniques/Methods

Used

1. Carr et al., 2010 Plant water relation; crop

water requirements; water

productivity of land, and;

irrigation systems.

CANEGRO Model

2. Kumar et al., 2010 Soil texture; slope; soil

moisture content; depth of

water table, and; soluble

salt content.

Geographic

Information Systems

(GIS)

3. Abdel Kawy et al.,

2012

Climate; geomorphology;

geology; water resources,

and; natural vegetation.

Automated Land

Evaluation System

(ALES)

4. Rasheed et al., 2009 Slopes; water availability;

soil temperature, and;

rainfall.

Remote Sensing and

Geographic

Information Systems

(GIS)

5. El Hajj et al., 2009 Normalized Difference

Vegetation Index (NDVI),

derived from satellite

images.

Remote Sensing;

Decision Support

System and; Fuzzy

Inference System.

6. Santhi et al., 2005 Crop growth; Irrigation

operations, and; soil

properties.

Soil and Water

Assessment Tool

(SWAT)

12

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.

13

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

14

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.

15

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,

16

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.

17

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

18

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:

19

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

20

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

21

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:

𝑳𝝀 = (𝑳𝑴𝑨𝑿𝝀−𝑳𝑴𝑰𝑵𝝀

𝑸𝒄𝒂𝒍𝒎𝒂𝒙−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]

22

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

23

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.

24

Fig. 6. Slope maps for both Hadeija and Lau Tau study areas

25

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

SLOPE

PARAMETER

LANDFORMS

PARAMETER

SOIL

PARAMETER

NDVI PARAMETER

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

INPUT DATA

LEGACY

CARTOGRAPHY

MAP

LANDSAT 8 MULTISPECTRAL

IMAGERY

SRTM DIGITAL

ELEVATION MODEL

PROJECTION TO UTM 32N

GIS DATABASE

RECLASSIFICATION TO MEET

SUGAR CANE REQUIREMENTS

BOOLEAN LOGIC

OVERLAY

26

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).

27

Table 3: Criteria for assessing sugar cane requirements

NB: The thresholds are subject to particular study areas

PARAMETERS Highly

suitable

Moderately

suitable

Marginally

suitable

Not suitable

Slope 0 – 2% 2 – 3% 3 – 5% >5%

Soil texture C,

SL-SCL,

CL-C

LS-SCL-L

“OK”

S-LS-SL,

L-LS

C-LS-S,

LS-C

Landforms

main soil

Ferruginous

tropical soils

(FTS)

Vertisols - Rocky soils

NDVI > 0.6 > 0.5 > 0.3 <= 0.3

Table 4: Showing weighting assigned to the 5 map outputs

Map No. Soil Texture Landform

Main Soils

Slope NDVI

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-

28

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.

29

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.

30

Fig. 8. Showing models outputs using the traditional boolean method (fig. a) and fig. (b-f) showing results derived from the weighted

overlay model

31

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

0

5

10

15

20

25

30

35

40

NotSuitable

MarginallySuitable

ModeratelySuitable

HighlySuitable

Tho

usa

nd

s (H

ecta

res)

Lau Tau Suitability Classes Quantified in Hectares

Boolean logic

Weighted Overlay

32

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

33

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.

34

Figure 11: Delineated landforms types of Lau Tau study area

35

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.esa-

soilmoisyture-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.

36

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

37

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

38

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

39

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.

40

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.

41

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.

42

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 multi-

criteria 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.

43

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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 Flatland

2 Gentle

3 Rough

4 Peak

D

1 River plain

2 Lowlands

3 Valley floor

4 Midslopes

5 Mid plateau

6-9 High slopes

10 High plateau

49

A.2 Landforms legend for both study areas

The figure below shows the resultant legend from the use of above python

code.

50

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

51

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

52

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.

53

Modelling sugar cane suitability using ArcGIS model builder (Traditional boolean and weighted overlay method)

54

Deriving hydrologic features like slope, streams, flow accumulation, flow direction) using ArcGIS model builder

55

Modelling NDVI and EVI using ArcGIS model builder

56

Modelling landforms based on the SOTER ‘like’ method adopted for this project (layers were labelled in the standard

SOTER naming convention for consistency)

57

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