Centre for Geo-Information
Thesis Report GIRS-2005-017
LAND USE CHANGES IN MOROBE PROVINCE
1975 – 2000
PAPUA NEW GUINEA
May 2005
Tine Fabian Ningal
Land Use Changes in the Morobe province
1975 – 2000
Papua New Guinea
Tine Fabian Ningal
Registration number 67 04 22 605 060
Supervisors:
Prof. Dr. Ir. Arnold K. Bregt
Dr. Alfred E. Hartemink
John Stuiver
A thesis submitted in partial fulfillment of the degree of Master of Science at
Wageningen University and Research Centre, The Netherlands.
May 2005
Wageningen, The Netherlands
Thesis code number: GRS-80337
Wageningen University and Research Centre
Laboratory of Geo-Information Science and Remote Sensing
Thesis Report: GIRS-2005-017
………....… Dedicated to my late parents for all the
inspiration, dedication and encouragements …..........
Acknowledgement
The work in this thesis would not have been possible without the help and support
from a number of important people and organizations.
First, I would like to thank my three dynamic supervisors, Prof. Dr. Ir. Arnold K.
Bregt, Mr. John Stuiver and Dr. Alfred E. Hartemink. You were all pleasant to work
with, Prof. Bregt for keeping accurate dates for meetings and creating a pleasant
atmosphere in your office during meetings, Dr. Hartemink for suggesting the topic of
this thesis and for the sharp eyes in pointing out anomalies in results. John’s expert
knowledge in ArcInfo and Oracle that streamlined the processing is particularly
acknowledged and applauded. It was a privilege to work with you all as supervisors.
Next, I would like to acknowledge the support of my employer, PNG University of
Technology for releasing me for study. Then, I would like to thank the study advisor
for MGI, Mr. Willy ten Haaf for his worthy efforts in making adjustments to my
course schedule when I had to return to PNG for my mother’s funeral. On this note, I
would also like to express my gratitude to the Dean for International students; Carla
Haenen for encouragement, assistance and friendliness.
Then I would like to extend my appreciation to my course mates for the friendship,
support and collaboration. Your voices and faces will fade but your impressions
would live on. I would like to acknowledge the friendships of Taye Mengesha,
Angela Kros, Jie Li, Jun Wang, Lucie Homolova, Worku Zwedie, Elisa Liras,
Jochem Verrelst, Teshome Esethe, Ernesto Bastidas, Marc Vis, Babs Stuiver and
many more there is no space for all.
I would like to thank the staff of CGI in Alterra for occasional support, the ICT
personal for keeping the computers running and Cartography section for assisting me
in scanning all the topographic maps. Others that I cannot omit are Dr. Michael
Govorov from Guelp University, Ontario, Canada for support and advice, David
Freyne with European Commission in Port Moresby for sending the topographic
maps, Ripa Karo for publications on PNGRIS and Forestry. I particularly thank Dr.
Bryant Allen from Australian National University (ANU) for supplying much needed
data and answering my questions about land use in PNG. I am indebted to you all.
Finally, I would like to acknowledge the silent patience exhibited by my two young
sons, Junior and Edwin during the long separation. I am proud of you two. Last but
not least, many thanks go Marlous for all the support.
Acknowledgement
ii
Abstract
Land use change is an ongoing and dynamic phenomenon as a result of human
interactions with environment and from natural forces. Population, technology and
affluence are regarded as the three main forces that can influence land use change in
any society. Papua New Guinea (PNG) is a developing nation with a growing
population that relies mainly on agriculture. About 97% of the total land is owned
and controlled by the people who have intimate knowledge and relationships with
their environment. More than 85% of the total population lives in the rural areas and
depends on subsistence agriculture. Although there is documented evidence of
agricultural practices in the highlands of PNG dating back thousands of years, studies
on land use and land use change have begun only in the early fifties. Studies on land
use hitherto have revealed weak relationship between land use change and population
growth on a national scale. This draws questions for more detail studies with
appropriate datasets to gain a better understanding on land use changes. Therefore,
this study focuses on Morobe province as a representative with the objective to
determine if there is a significant relationship between population growth and land
use change from 1975 to 2000. To achieve the objective, topographic data of 1975,
Landsat TM images of 1990 and 2000 over Morobe province are used to represent
land use conditions for the respective dates. Population data for the same period are
georeferenced to Resource Mapping Unit (RMU) polygons and are assumed to be
equally distributed. RMU boundary is defined by landform, rock type and altitude,
hence, forms the Minimum Mapping Unit (MMU) in this study. Land use for the
different dates are digitized and geometrically intersected by spatial operations with
RMU, and the areas are updated by computing area statistics per RMU in ArisFlow,
ArcInfo, Oracle RDBMS and ArcGIS environments. Land use change criteria are
defined and by means of relational SQL queries, land use and population change
tables are created in Oracle from which land use type change and population per
RMU are derived. The result showed that agriculture gained significantly from the
loss in forest. Some of the areas gained by agriculture indicate high land use intensity
and high population density. However, there are other areas that show significant
population growth but no corresponding expansion in agriculture. The correlation
between population growth and agriculture change appears to be a weak one as the
growth in population is twice the expansion in agriculture. It can be assumed that the
land is used more intensively and expansions into new areas are governed by
ownership rights, technology and ecological constraints such as slope, altitude,
temperature, rainfall, soil type et cetera. Other classes show little variations with
gains in grassland, plantation and urban while water experienced loss.
Keywords: Land use change, Land use intensity, population growth, Morobe
province, Resource Mapping Unit (RMU), GIS, Remote Sensing, spatial overlay,
processing, SQL, analysis, correlation.
Abstract
iii
Acronyms and abbreviations
Acronyms
AGSYST
AIDAB
Agricultural Systems
Australian International Development Assistance Bureau (now
AusAID)
ANU
Australian National University
AusAID
Australian Agency for International Development (formerly AIDAB)
CSIRO
Commonwealth Scientific and Industrial Research Organization
DAL
Department of Agriculture and Livestock
DSLS
Department of Surveying & Land Studies
FARMSYS Farming Systems
FIMS
Forest Inventory & Mapping Systems
FMU
Forest Mapping Unit
MASP
Mapping Agricultural Systems Project
NMA
National Mapping Agency/Authority
NMB
National Mapping Bureau (NMA in PNG)
NSO
National Statistical Office (PNG)
PNG
Papua New Guinea
PNGRIS
Papua New Guinea Resource Information System
UNITECH
University of Technology
Abbreviations
AGD66
Australian Geodetic Datum 1966
AMG
Australian Map Grid
GIS
Geographic Information System
MMU
Minimum Mapping Unit
RDBMS
Relational Database Management System
TM
Thematic Mapper
UTM
Universal Transverse Mercator
UN
United Nations
WGS84
Wold Geodetic System 1984
Acronyms and Abbreviations
iv
Table of Contents
ACKNOWLEDGEMENT ....................................................................................................................II
ABSTRACT ......................................................................................................................................... III
ACRONYMS AND ABBREVIATIONS............................................................................................ IV
TABLE OF CONTENTS ...................................................................................................................... V
LIST OF FIGURES............................................................................................................................VII
LIST OF TABLES............................................................................................................................ VIII
LIST OF APPENDICES..................................................................................................................... IX
CHAPTER 1
1.1
1.2
1.3
1.4
1.5
BACKGROUND .........................................................................................................................1
PROBLEM DEFINITION ..............................................................................................................2
OBJECTIVES .............................................................................................................................3
STIPULATIVE DEFINITION FOR LAND USE CLASSES ...................................................................3
STRUCTURE OF THESIS ............................................................................................................4
CHAPTER 2
2.1
2.2
2.3
2.4
2.5
2.6
- INTRODUCTION .................................................................................................1
- LITERATURE REVIEW .....................................................................................5
LAND USE CHANGE ..................................................................................................................5
INTERACTIONS BETWEEN CANDIDATE DRIVING FORCES OF LAND USE CHANGE .......................5
METHODS OF DETERMINING LAND USE CHANGE ......................................................................6
REVIEW ON LAND USE CHANGE IN PNG...................................................................................7
COMPARING LAND USE WITH OTHER TROPICAL COUNTRIES.....................................................9
PNG AT A GLIMPSE ................................................................................................................11
CHAPTER 3
- DATASETS OVER STUDY AREA...................................................................15
3.1
TOPOGRAPHIC MAPS ..............................................................................................................15
3.2
SATELLITE - LANDSAT THEMATIC MAPPER IMAGES ..............................................................15
3.3
RESOURCE MAPPING UNIT (RMU) ........................................................................................16
3.4
AUXILIARY DATASETS ...........................................................................................................16
3.4.1 Population ........................................................................................................................16
3.4.2 Agriculture Systems of PNG (AGSYS) ..............................................................................16
3.4.3 Forest Inventory Mapping System (FIMS) .......................................................................17
3.5
DATA PREPARATION ..............................................................................................................17
3.5.1 Topographic maps for T1 Land Use .................................................................................18
3.5.2 Satellite images for T2 and T3 Land Uses........................................................................18
3.5.3 Digitizing land use classes................................................................................................18
3.5.4 Land use class transformations ........................................................................................19
3.5.5 Land use class assignment................................................................................................20
CHAPTER 4
- METHODOLOGY ..............................................................................................22
4.1
STUDY AREA – MOROBE PROVINCE .......................................................................................22
4.1.1 General description ..........................................................................................................22
4.1.2 Climate and Topography ..................................................................................................25
4.1.3 Resources indicators.........................................................................................................26
4.1.4 Land use systems ..............................................................................................................26
4.2
METHOD ................................................................................................................................27
4.2.1 Data processing................................................................................................................30
4.2.2 Analysis.............................................................................................................................34
CHAPTER 5
- RESULTS AND DISCUSSION ..........................................................................35
5.1
RESULTS ................................................................................................................................35
5.1.1 General land use and population changes........................................................................35
5.1.2 Changes by land use type and population ........................................................................36
5.1.3 Correlation Surface between Changes .............................................................................37
List of Figures
v
5.1.4 Changes in hotspots..........................................................................................................39
5.2
DISCUSSION ...........................................................................................................................40
5.2.1 General result ...................................................................................................................40
5.2.2 Correlation between population and land use changes....................................................40
5.2.3 Observed trend .................................................................................................................41
5.2.4 Possible Implications........................................................................................................42
5.2.5 Discussion on the methods used in this study ...................................................................43
CHAPTER 6
- CONCLUSION ....................................................................................................44
REFERENCES .....................................................................................................................................46
APPENDICES ......................................................................................................................................50
List of Figures
vi
List of Figures
Figure 1.1. Location map of PNG. ........................................................................................................1
Figure 2.1. Regions, provinces and towns of PNG .............................................................................11
Figure 2.2. Population growth in PNG by urban and rural sectors .................................................13
Figure 3.1. Data pre-processing...........................................................................................................17
Figure 3.2. Before and after images using 5x5 filters to smooth classified images..........................18
Figure 3.3. Disjointed land use objects in T1 merged into a single object in T2 ..............................21
Figure 3.4. Land use object in T1 disappeared in T2..........................................................................21
Figure 3.5. Emergence of new land use objects in T2 ........................................................................21
Figure 3.6. Example of a new feature (red) intersecting over two land use types...........................21
Figure 4.1. Location of study area.......................................................................................................22
Figure 4.2. Population and area by districts ......................................................................................23
Figure 4.3. General reference map of Morobe...................................................................................24
Figure 4.4. Rainfall and elevation of Morobe....................................................................................26
Figure 4.5. Land use classification schema.........................................................................................27
Figure 4.6. Overview of conceptual methodology..............................................................................29
Figure 4.7. General Land use change Entity Relationship (ER) diagram. ......................................30
Figure 4.8. Union function in spatial overlay (adopted from ESRI ArcGIS manual) ...................31
Figure 4.9. ER diagram for computing statistics ..............................................................................32
Figure 4.10. ER diagram for creating Cartesian product and multi-attribute table.....................32
Figure 4.11. SQL representation of computing change tables in Oracle RDBMS.........................34
Figure 5.1. Change classification for thematic maps, a-f, h (%), (g) ordinal scale .........................37
Figure 5.2. Different change correlations between land use and population ..................................38
Figure 5.3. Areas considered being hotspots for Population and agriculture .................................39
List of Figures
vii
List of Tables
Table 2-1.Natural hazards and environmental issues .......................................................................10
Table 2-2. Comparison of population and land use with 6 countries in the Tropics ......................10
Table 3-1. Appearance of Features on Composite Images. (Adapted and modified from
www.earthsat.com) ......................................................................................................................19
Table 3-2. Land use feature change possibilities................................................................................20
Table 5-1. Summary statistics of land use and population changes .................................................35
Table 5-2. Land use FromÆTo change matrix ..................................................................................36
List of Tables
viii
List of Appendices
Appendix A: Land problems related to population growth.............................................................50
Appendix B: Topographic map sheets of 1975 for Time 1 (T1) .......................................................51
Appendix C: Clipped Landsat image of 1990 for Time 2 (T2) .........................................................52
Appendix D: Clipped Landsat image of 2000 for Time 3 (T3) .........................................................53
Appendix E: The concept of RMU .....................................................................................................54
Appendix F: Resource Mapping Units (RMU) of Morobe...............................................................55
Appendix G: Detailed data processing steps .....................................................................................56
Appendix H: Data flow of input, processing, and output.................................................................57
Appendix I: ArisFlow data action model for computing land use areas ........................................58
Appendix J: ArisFlow data action model for creating and converting tables to Oracle tables ....59
Appendix K: Creating intermediate tables for land use change computation in Oracle ..............60
Appendix L: SQL script for generating change tables.....................................................................61
Appendix M: ArisFlow data action model for converting change tables from Oracle to info
tables .............................................................................................................................................65
Appendix N: Land use change matrix in km2 ....................................................................................65
Appendix O: Land use intensity and villages of Morobe ..................................................................66
Appendix P: Comparison of changes in Population and Agriculture..............................................66
Appendix Q: TIN of Morobe in elevation (meters) ...........................................................................67
Appendix R: Annual rainfall in Morobe ............................................................................................68
Appendix S: Soil types in Morobe.......................................................................................................69
Appendix T: Using forest concession areas to assess loss in forest and gain in agriculture...........70
List of Appendices
ix
Chapter 1 - Introduction
1.1 Background
Papua New Guinea, (PNG) lies 6 degrees south of the Equator and 147 degrees east of
Greenwich Meridian, (Figure 1.1). PNG comprises more than 600 islands and covers
a total area of 474,000 square kilometres. The population in 2000 national census was
over 5 million according to the National Statistical Office (URL 1).
Figure 1.1. Location map of PNG.
PNG’s geographic location and social structure supports a diverse bio-physical and
cultural diversity (Sem, 1995). Forests cover more than 70% of the total land area
(Saunders, 1993) and 97% of the land is owned and used for subsistence agriculture
by tribal groups; 3% is in the hands of the state and private sector (Pat, 2003). Tribal
groups comprise of clans in tribal hierarchies (White, 1965) and differ in languages
and customs. There are more than 800 different languages although Pidgin-English
(unofficial national language) is spoken by most Papua New Guineans. More than
80% of the population lives in rural areas and depends on subsistence agriculture for
food production (URL 2; Pat, 2003).
Studies on land use change in tropical countries emphasis on utilizing satellite remote
sensing (Townshend, 1987; Pahari, Umezaki et al., 2001; and Epema, Botoro, 2000).
Developers of Papua New Guinea Resource Information System (PNGRIS) focused
on developing a database system that links a mapping interface to serve predefined
Introduction
1
maps from custom queries (McAlpine, 1998). Others advocate integrating Remote
Sensing and GIS in monitoring land use change (Venkatachalam, 1991; Vicar, 1991).
Previous studies carried out on land use and land cover change in PNG differed in
scope and objectives (DSLS, 2000) and made little use of the combined potential of
GIS and Remote Sensing. However, the approach used to determine the changes were
GIS oriented where spatial operations were performed on vector layers and tables
computed to determine the magnitude of changes. All studies tend to focus on either
GIS or Remote Sensing with little overlap. The TREES II project carried out by PNG
University of Technology utilized both GIS and Remote Sensing but targeted at major
logging areas. This study will use both GIS and Remote Sensing techniques to
quantify land use changes in Morobe province.
1.2 Problem definition
The population of PNG has increased from 2.3 million in 1975 to 5.2 million in 2000
(URL 1- National Statistical Office of PNG). Since 85% of the total population lives in
rural areas and depends on subsistence agriculture, food production must increase to
meet the population’s needs (Macfarlane, 2000; Pat, 2003). More land is required for
cultivation to maintain food supply.
When PNGRIS database was updated with 1980 and 1990 rural census and land use
information to 1996 using Landsat imagery, the area for food production showed an
increase by 10% (McAlpine et al., 2000). Between 1980 and 1990 the total population
grew by 25% and rural population by almost 20% (Bellamy et al., 1995). Between
1975 and 1996, rural population grew by 40-50%. Land use appears to expand less
significantly. There tends to be intensification and continuous cultivation with little or
no fallow. A study on agricultural systems in PNG by Bourke reported that in the
early 1980s and mid 1990s, there were few major land use changes, although
population grew more than 20% in the same period (Bourke et al., 1998). It was
anticipated there will be continuing changes in agricultural systems, but no major
changes for the next 10-15 years. Therefore, the question is how can food production,
supply, and distribution support the growing population without significant land use
change?
Although PNG’s population is increasing rapidly, population density is low, if not the
lowest compared to other countries of comparable land area (URL 4). Complex and
sensitive land ownership systems inhibit uniform land policy implementations by the
government (URL 2; URL 3; Pat, 2003; Windybank, 2003; Appendix A). PNG differs
from other countries from the assumption that high population density is a proxy for
increased agricultural land use (Kok, 2004). More research effort to asses and
evaluate land use change and relating to population growth has been suggested by
Introduction
2
Sem (1995). An approach combining GIS and Remote Sensing is reported to be on
the agenda of PNGRIS developers for future implementation (B.J. Allen, ANU personal communication, 2004). Therefore, there is a need for research to determine
land use change in relationship to population growth. This research is relevant for
PNG and the results could be useful for planning and support decision-making by
authorities. One of the aims of this study is to utilize GIS and Remote Sensing
techniques to quantify land use change in order to gain specific information on a
provincial level.
1.3 Objectives
The main objective of this research is:
To quantify land use change in the Morobe province of PNG between 1975 and
2000 using GIS and remote sensing techniques.
To achieve the main objective, the following questions are relevant:
1.
2.
3.
4.
Which data are relevant to evaluate and determine land use change?
How can the available data be used to quantify land use change?
What is the land use change in Morobe?
How can the changes be explained?
1.4 Stipulative definition for land use classes
In this research, land use is understood to be a specific piece of land that relates to
human activity or economic function.
•
•
•
Agriculture areas are gardens under cultivation and/or abandoned gardens in
fallow between 5-15 years. Fallow period could be more than 15 years according
to Bourke et al., (1998). Rural populated areas are in this category as they
generally engage in subsistence and cash crop small holder activities and cannot
be distinguished for a separate class.
Areas with sago stands are classed under forests as they could not be discerned on
the images. These areas provide staple food source for populations in wetland
areas, (Bellamy and McAlpine, 1995)
Grassland and savannah are caused by human activities like cultivation followed
by regular burning/hunting or are naturally occurring such as high altitude
plateaus. Grassland areas under fallow or inhabited by man are not included in this
class but come under agriculture class.
Introduction
3
•
•
•
Urban areas have a population of more than 3000 people with infrastructural
facilities and services. Rural sub-district headquarters are classed as urban for
their administrative functions. The minimum number of population in urban
centres is 3000 (URL 7: City Population - PNG).
Plantations are areas planted with food and non-food crops. Non-food crops are
forest plantations while food crops are sugar cane, coconut, cocoa, coffee, and tea.
Forests areas are undisturbed areas. Other less significant areas like quarry,
mining and bare land are grouped into forests as forest tend to dominate at 30
meter resolution.
1.5 Structure of Thesis
This thesis comprises six chapters. Chapter 1 covers the background, research
problem, objective, and land use definitions for this study. Chapter 2 reviews land use
change in general and PNG in particular by elaborating on past works relating to land
use and land cover change. Chapter 3 deals with data collection, description, and
preparation for processing and analysis (research question 1). The methodology
(research question 2) is treated in chapter 4 which covers study area and method used.
Various physical and demographic characteristics like, geography, climate, resources,
demography, and land use systems are discussed under study area. The method
describes the steps carried out to determine land use change phenomena. In chapter 5,
the results (research question 3) are presented with a discussion (research question 4)
based on the results. Chapter 6 concludes the report, followed by references and
appendices.
Introduction
4
Chapter 2 - Literature review
2.1 Land use change
Land use and land cover change is dynamic in nature and caused by various natural
and cultural forces interacting with the environment. While natural forces relate to
naturally occurring events like floods, earthquakes, volcanoes, land slips, tsunamis
etc, human-induced forces can be grouped into six categories: population; level of
affluence; technology; political economy; political structure; and attitudes and values
(URL5). Others broadly classify these forces as human institutions, population size
and distribution, economic development and technology among others (Loveland et
al., 2003).
Environmental factors that pose constraints on land use are: soil characteristics,
climate, topography, and vegetation. Human activities that cause land cover change
result from a wide range of objectives (Turner, 1993). These objectives range from
local to global and individual to collective such as the need for food, living space,
recreation or for large scale development. The amount of land used is determined by
ownership rights and structures of power. Other factors such as population density,
level of economic and social development also place demand on land while
technology influences the intensity of exploitation. These are by no means exhaustive
and depending on the environmental, historical, and social contexts, there can be a
myriad of incentives that motivate different land use approaches. While land use
change is often a driver of environmental and climate changes, a changing climate can
in turn affect land use (Loveland et al., 2003).
2.2 Interactions between candidate driving forces of land use change
The environmental condition and changes observed result from the interaction
between the driving forces coupled with climatic conditions over time. Out of the
possible six human-induced forces driving land use changes, the first three;
population, level of affluence and technology relate to the impact (I) on environment
expressed as I=PAT where (I) is considered to be a function of population (P),
affluence (A) and technology (T) (Commoner, 1972).
It is a common assumption that population growth has a direct relationship to land use
changes, hence a contribution towards environmental damage (Ambio, 1992). This is
because accurate worldwide population data are available for statistical assessment.
Other studies using the same data disagreed that population change is the cause of
environmental changes (Boserup, 1965, 1981; Ehrlich and Ehrlich, 1990, 1988 and
Simon, 1981). This illustrates the complexity in trying to establish the geophysics of
Literature Review
5
climate with the socioeconomic drivers of land use change using a single driver; for
instance: population.
At the regional scale, several studies relate population growth and deforestation in
developing countries in the tropics although the findings and methods have been
questioned (URL5). Other studies on comparative assessments of population and land
use suggest that population growth is positively associated with the expansion of
agricultural land and deforestation but the relationships are weak and dependent on
inclusion or exclusion of statistical outliers (Geores et al., 1991). A study by Zaba
(1991) on Africa concluded that population density was related to agricultural
expansion and intensification everywhere, but only in some regions to deforestation.
Detailed studies of specific regions for example, modelling exercises with Amazonian
data indicate subtle and varying relationships (O'Neill, 1992; Skole, 1992).
The interactions of population, affluence, and technology as causes of environmental
change shows that direct association of affluence or technology with land use change
is not common. This is due to lack of globally comparative data for statistical
assessments. Furthermore, the levels of affluence or technology do not by themselves
govern human-environment relationships and population plays a pivotal role (URL5).
The role of technology as a potential cause of land use change places different
demands for different natural resources.
The other three forces are: political economy, which includes the systems of
exchange, ownership, and control; political structure, involving the institutions and
governing organizations, and attitudes and values of individuals and groups. The
candidate driving forces within these categories have received much less attention
than population growth because they do not encompass clearly defined variables and
causal relationships.
2.3 Methods of determining land use change
In order to determine land change, data collection at different times over the same
area is a prerequisite for comparative analyses. Data collection methods have changed
dramatically as technology advances, from classical field surveying to aerial
photography and Remote Sensing. Remote Sensing provides quick and comparatively
inexpensive information about land cover over large areas (Loveland et al., 2003;
Townshend, 1987).
Most studies carried out to detect and measure changes on land use monitoring focus
on raster based environment and less attention is paid to vector based GIS processing
(Sonneveld et al., 2000). One of the reasons could be that the processing time is long
Literature Review
6
and a substantial understanding of geoprocessing and database skills are required. A
study aimed at reducing noise propagation in vector based GIS land use change
monitoring shows how noise can be reduced (Sonneveld et al., 2000). In this study,
the most recent data was taken as reference from which to test previously known data.
Ontology was defined for land use classes and super-classes and relating them to land
use objects, both spatially and thematically representing T=1 and T=2. The method
developed is called MonGIS and operates in ArcInfo environment where the user has
the option to alter criteria through a menu interface. This could be useful for
calibration purposes.
Studies on raster based land use changes take advantage of built in statistical analyses
programs and various simulation and predictive models (Loveland et al., 2003) that is
lacking in vector GIS environment. A study by Civco and others (Civco et al., 2002)
compared land use change detection methods by testing several existing methods.
These include traditional post-classification cross-tabulation, cross-correlation
analysis, neural networks, knowledge based expert system, and image segmentation
and object oriented classification. The purpose was to compare the results of the
different land use change detection approaches. One of the major outcomes from this
study was that there is merit to each of the change detection methods but much
research remains to be done to improve the results.
2.4 Review on land use change in PNG
Literature relating to land use change in PNG started from the 1950s (Bellamy, 1986).
The CSIRO regional resource survey program was undertaken between 1953 and
1972 to provide rapid and large area information on natural resources over PNG.
Nearly half the country was covered in this program with 15 regional surveys
including major proportions of population, used areas, and major environments. The
methods employed in this survey made use of aerial photographs and rapid ground
missions. Information was provided in two spatial resolutions, descriptive and map.
Descriptive information describes portions of land units which were mapped into land
systems.
Since the regional resource survey program collected and presented information in an
integrated manner, no information was provided for the whole of PNG about specific
criteria like landform, soils or other geographic or resource characteristics. This need
triggered research for the creation of a national agricultural resource database to
determine the nature, variability, and distribution of major natural resources; which
led to the development of PNGRIS.
The development of PNGRIS database made use of resources gathered during the
regional survey program by CSIRO. Several publications on PNG were released
Literature Review
7
around that time which covered vegetation by Paijmans, (1975), geomorphology by
Löffler, (1977), soils (Bleeker, 1983) and climate by McAlpine et al., (1983).
Additional information that became available and included during the PNGRIS
development were: the 1980 national population census, the 1:250,000 geological
series maps and 1:100,000 topographic maps of PNG. The documentation was
published by Bellamy (1986) on the inventory of natural resources, population, and
land use which became PNGRIS handbook. The regional natural resources survey and
PNGRIS implementation became the formal head start relating to land use
documentation covering PNG. Publications on land use and population cite PNGRIS
publications as the primary reference.
Bellamy and McAlpine (1995) explained the derivation, classification, and
presentation of the information in PNGRIS database like natural resources,
demographic and socio-economic. Its specific objective was to determine both the
current use and development potential of the nation’s natural resources for food and
cash crop production taking into account present and future population growth and
distribution. The authors noted that between 1980 and 1990 population increased by
20%.
When PNGRIS was updated from 1975 to 1996 with 1980 and 1990 population data
and manual Landsat imagery interpretations with selective ground missions, the land
area for food production showed an increase by 10% (McAlpine et al., 2000). The
population between 1980 and 1990 increased by 25% and rural population by almost
20%. The estimated rural population growth between 1975 and 1996 was between 4050%; however, the actual increase was 64% according to the population figures of
2000. It appeared that land use was intensified with little or not fallow in cultivation
areas for subsistence agriculture. Climatic conditions and physical limitations such as
extreme slopes, stoniness, swamps, and cultural practises like, ownership rights and
farming systems impede expansion and variations on land use.
PNGRIS data is derived from a scale of 1:500,000 and is suitable for regional analysis
of natural resources and lacks agricultural data at local scale. The text summaries on
Agricultural Systems of PNG by Bourke and others (1998) and the related database
bridges this gap. The spatial mapping unit is based on RMU (Appendix E); however,
discrete agricultural units are defined within the RMUs. By retaining RMUs, the
agricultural information can be related to PNGRIS for querying and mapping. All
provinces of PNG are summarized with data on agricultural systems, population, area
of land used for agriculture and density per agricultural system. Based on past studies
and historical data on agricultural systems and subsystems, it is shown that between
early 1980s and mid 1990s, there were few major changes in PNG. It is anticipated
that there will be continuing changes in farming systems but no major changes in
agriculture are expected for the next 10-15 years.
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8
A recent study by Pahari and others (2001) made use of multi-sensor satellite data to
evaluate Land use dynamics in Tari Basin in the remote Southern Highlands province
of PNG. Data from Landsat MSS (1974), SPOT HRV (1988), and Landsat TM (1994)
were used for broad land use classification using maximum likelihood classification.
The multi-temporal datasets were used to get a synoptic view of environmental and
resource condition of the area. This data would be combined with a detailed study
over a small area using IKONOS images at 4 meter multi-spectral and 1 meter
panchromatic bands and the results are expected to be released in a final report. This
study tends to use various satellite images for a comparative analysis on human
impacts on the environment. The preliminary result concluded that population
pressure resulted in forest areas being cleared for food cultivation and the primary
forests remaining are found on high altitude areas where it is steep or difficult for
cultivations. This finding is in agreement with Land Management Dilemma in Papua
New Guinea by Bryant Allen (URL 9).
2.5 Comparing land use with other Tropical countries
To investigate land use patterns caused by biophysical and socio-economic dynamics
in PNG, a comparison is necessary with countries in the tropics that share similar
climatic conditions and biophysical characteristics (Table 2-2), however, the land use
system area largely dependent on the local socio-economic forces at play, native to
the respective countries.
Land use is grouped into 5 classes: arable, crops, pasture, forests, and others. PNG has
the highest area dominated by forest and Costa Rica the least (Table 2-2). This could
be due to forest areas being converted to pasture after deforestation in Costa Rica.
Pasture is not present in PNG but varies among the other countries. All countries
show cropland below 15% which could imply absence of large scale agricultural
farming. The natural hazards and current environmental issues that have varying
influences on land use changes are summarised in Table 2-1.
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9
Table 2-1.Natural hazards and environmental issues
Country
Brazil
Brunei
Cameroon
Costa Rica
Hazards
droughts; floods and occasional
frost
typhoons,
earthquakes,
and
flooding
Volcanoes, poisonous gases from
Lake Nyos and Lake Monoun
volcanoes
earthquakes, hurricanes along
Atlantic
coast;
flooding,
landslides; volcanoes
Fiji
cyclonic storms, November to
January
volcanoes; situated along the
PNG
Pacific "Ring of Fire"; frequent
and severe earthquakes; mud
slides; tsunamis
typhoon, 5-6 cyclonic storms per
year;
landslides;
volcanoes;
Philippines
earthquakes; tsunamis
Source: United Nations (URL 6)
Issues
deforestation in Amazon Basin, illegal wildlife trade;
land degradation and water pollution from mining
activities; wetland degradation; oil spills
seasonal smoke/haze resulting from forest fires in
Indonesia
water-borne diseases; deforestation; overgrazing;
desertification; poaching; over fishing
deforestation and land use change, as a result of
clearing land for cattle ranching and agriculture; soil
erosion; coastal marine pollution; solid waste
management; air pollution
deforestation; soil erosion
rain forest subject to deforestation as a result of
growing commercial demand for tropical timber;
pollution from mining projects; severe drought
deforestation, soil erosion; air and water pollution,
increasing pollution of coastal mangrove swamps
which are important fish breeding grounds in Manila
Table 2-2. Comparison of population and land use with 6 countries in the Tropics
Population
Country
Brazil
Brunei
Cameroon
Costa
Rica
Fiji
PNG
Philippines
Land use (%)
172,860,370
343,653
15,803,220
Land area
sq km
8,511,965.00
5,770
475,440.00
arable
5
1
13
crops
1
1
2
pastures
22
1
4
forest
58
85
78
others
14
12
3
Density
2000
20
60
33
3,773,057
844,330
5,049,055
82,841,518
51,100.00
18,270.00
462,840.00
300,000.00
6
10
0
19
5
4
1
12
46
10
0
4
31
65
93
46
12
11
6
19
74
46
11
276
Source: United Nations (URL 6)
It must be noted that forest composition (Table 2-2) of 93% for PNG is based on
estimated figures from CIA fact book and is higher than reliable sources like FIMS
and PNGRIS. Recently, the land use categories have been reduced to: (i) arable, (ii)
permanent crops and (iii) others, from the Web site. The information in the above
table shows relative comparison with other countries in the tropical region.
Literature Review
10
2.6 PNG at a glimpse
Land use change and population in PNG is well summarised by Graham Sem (Sem,
1995). The tropical location of PNG with its diverse geomorphologic characteristics
and prehistoric remains of farming systems in the highlands set it apart from other
civilizations. The social, cultural, and biophysical diversities consolidate its unique
nature. PNG is located on a tectonically active area between the northward moving
Australian continental plate and the northwest moving Pacific plate (Sem, 1995). The
main islands are reported to be formed by plate tectonics as block-faulted, folded with
mountainous interiors, especially in the highlands region. Mt. Wilhelm in the Simbu
province is the highest mountain which is 4,509 m above sea level. Major navigable
rivers are Fly River in Western province, Sepik River in East Sepik, Ramu River in
Madang province and Purari River in the Gulf province. The longest road network
starts from Lae in the Morobe province to Wabag in the Enga province. Infrastructural
developments are mostly concentrated in the major cities and towns like Port
Moresby, Lae, Madang, Rabaul, Goroka and Mount Hagen. Except the five Highlands
provinces in the interior, the rest are Maritime Provinces. Western province with the
capital Daru has the largest land area and Manus province with Lorengau capital is the
smallest. PNG is divided into four major regions, Highlands, Islands, Momase and
Southern (Figure 2.1).
Figure 2.1. Regions, provinces and towns of PNG
The coastal provinces supplement staple diets (sago, taro, yam, banana, and cassava)
with both terrestrial and marine resources like wild pigs, kangaroos, deer,
cassowaries, possums, bandicoots, fish, crustaceans, crocodiles, and sharks. Most
Literature Review
11
coastal villages are sparsely populated while the highlands provinces are densely
populated. The main staple food in the highlands is kaukau or sweet potato (Ipomoea
batata) and domesticated livestock like pigs, chicken, cattle, goats, and sheep are
raised for consumption and sale. The highlanders are notorious for tribal warfare that
can stem from disputes relating to land, pigs, or women. The bulk of the population in
the rural areas depends on subsistence agriculture and has a closer spiritual attachment
to the land. Development initiatives by government and companies at national,
provincial or local scales are slow because negotiations with land owners is a
painstaking process and can cause delays and at times lead to cancellations. Logging
and mining companies are major industry players that involve resource owners in
negotiations for resource exploitations.
2.6.1
Land use prior to 1975
Land use observations before independence (1975) were from investigations such as
gold exploration and plantations. The Land Use Research Division of CSIRO carried
out more systematic investigations of the geology and landscape, including land
evaluation investigations. The method used for land-evaluation was based on the land
systems survey (Christian and Stewart, 1953). This system defines land system as a
unique assembly of land features like soils, vegetation, land-forms, rainfall, land use,
and population. The main objective of this survey was to assess land for agricultural
suitability. The data were based on aerial photo interpretation and representative field
checking of each area under investigation. About 50% of the total land area was
covered in this survey and 15 land systems were identified (Bellamy, 1995). Land use
was divided into three broad categories, subsistence cultivation, cash cropping
(indigenous), and plantation. Most people in each land system were involved in
subsistence cultivation for cash cropping, mainly tree crops, such as coconuts, cocoa,
coffee. Some rice was practiced by the indigenous population in both lowland and
highland areas and some pyrethrum (Pyrethrum cinerariifolium), passion fruit, tea,
and livestock were introduced in the highlands (Sem, 1995). Plantations were owned
and managed by the non-indigenous population in both lowland and highland areas.
Based on land-system surveys between 1954 and 1976, it was estimated that 20% of
PNG land was under shifting cultivation (Sem, 1995). This figure may have changed
due to population growth, economic and social demands. Land use intensity was not
clear due to incomplete population figures between 1954 and 1976. Where sweet
potato (Ipomoea batatas) was dominant in densely populated areas of the highlands,
intensive, almost permanent short-fallow cultivation was evident. McAlpine (1970)
suggested that the length of cultivation and fallow cycles differed markedly in the
highland areas and was probably related to land pressure, environment, and
cultivation techniques. Growth of interest in cash cropping among the indigenous
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12
people was stimulated by a variety of land-tenure and marketing schemes when
expatriate owned plantations could not survive after the Second World War because
of shortage of labor, low commodity prices and high shipping costs (Crocombe,
1964). From 1951 agricultural extension efforts concentrated mainly on cash crops
(Bourke, Carrad et al., 1981). Land systems under various uses were divided into
subsistence cultivation, cash cropping, both indigenous and non-indigenous, and
plantations which were mainly owned by expatriates.
Due to increasing malnutrition in rural PNG, the National Food and Nutrition Policy
(NFNP) was formulated in 1975 to increase domestic food production. The need for
cash crop and food production brought much of the land into some form of use (Sem,
1995). As a result, significant changes have occurred in food production during the
last 40 years, which has led to other changes in food production and the nutrition
system in PNG. The current land use patterns could be related to past policies and
events that triggered subsequent changes to land use as population increases.
2.6.2
Population dynamics
A long-term population trend of PNG is difficult to understand because censuses have
been conducted for relatively short periods. Censuses were conducted in 1966, 1971,
1980, 1990 (Sem, 1995) and lately 2000. Prior to 2000 census, only 1980 and 1990
census attempted a complete enumeration of the rural and urban populations (Figure
2.2). The population of PNG has grown from 2.2 million in 1966 to 3.6 million in
1990 and 5.2 million in 2000. This increase represents a growth rate of 2.7% per
annum between 1966 and 1990 and 4.4% per annum between 1990 and 2000.
6 000 000
5 000 000
4 000 000
Urban
3 000 000
Rural
2 000 000
1 000 000
1980
1990
2000
Figure 2.2. Population growth in PNG by urban and rural sectors
Literature Review
13
The population of PNG grew rapidly and almost doubles every 20 years. The land
ownership system is based on traditional system and varies between provinces and
regions, where land is passed from father to son or from mother to daughter. As a
result of different land ownership and inheritance systems by tribes, clans and
families, the land use systems vary between provinces and regions. Where there is
population pressure such as the Highlands region, land is used more intensively
because; there are limited alternatives like fishing, gathering or hunting. These
limitations force people to work on the land for all their needs. Although other regions
and provinces have alternate sources of food, subsistence cultivation is still a common
way of life. The difference lies in the intensity of use on the same piece of land.
Where there is low population density, the fallow periods are longer in between
different plantings as opposed to highly dense areas. With the current situation where
the government has little, if not no control over ownership and distribution on
traditional land, the increase in population is likely to compound the existing
problems relating to land. Moreover, the social structures vary between provinces,
regions and localities and the amount of land used is not necessarily a direct
indication of population growth.
Given the current population growth rate of 4.4%, and the increasing threat of logging
and conversion of primary rainforest, it is likely that more land will be used for
cultivation. The fallow systems are likely to be reduced because; it relies on low
population densities and large areas of undisturbed forest. Shorter fallows will cause
forest and land degradation and environmental stress assuming little or no
technological changes. Farming will not be sustainable in the longer term without soil
fertility maintenance techniques, and introducing new crops and new technology.
The distribution of population shows that the southern and northwestern coastal
regions have low population densities, while the islands and highland regions are
densely populated. The Western, Gulf, and West Sepik provinces are sparsely
populated (Sem, 1995). Over one-third of PNG's population is concentrated in the
highlands region. Although 22 persons/km² is the average density for the highlands
region, reports indicate that the densities exceed 200 persons km² in some parts,
especially fertile highland valleys (Allen, 1984). It is in these areas that reports of
"population pressure" on land have been experienced most frequently.
Literature Review
14
Chapter 3 - Datasets over study area
This chapter attempts to answer the first research question by providing descriptions
and pre-processing on relevant datasets. The timestamps are 1975 as Time 1 (T1),
1990 as Time 2 (T2), and 2000 as Time 3 (T3).
3.1 Topographic maps
Topographic maps at scale 1:100,000 (25 sheets of 1.5 x 1.5 degrees or 5.5 x 5.5
kilometres) compiled between 1974 and 1979 by Australian Army Corps represented
land use conditions at T1 (Appendix B). These maps were compiled from aerial
photogrammetric interpretations based on Transverse Mercator (TM) projection and
referenced to Australian Geodetic Datum of 1966 (AGD66) and Australian Map Grid
Zone 55 (AMG55). Geometric accuracy varies between map sheets up to 40 meters
horizontally and 17 meters vertically. Contour interval is 40 meters and the legend
shows major land use classes, hence T1 was adopted as baseline data. Topographic
maps of PNG are printed and distributed by the National Mapping Bureau (NMB) of
the Lands Department.
3.2 Satellite - Landsat Thematic Mapper images
The satellite data for T2 is from Landsat-5 (Appendix C) and T3 from Landsat-7;
Enhanced Thematic Mapper Plus (ETM+) (Appendix D). T2 and T3 images represent
land use conditions for 1990 and 2000 respectively. The spatial resolution for T2 is
28.5 meters and T3 at 14.25 meters and come in Mr.SID format. The images are
orthorectified and come in tiles or mosaics of 6 degrees in longitude by 5 degrees in
latitude. The spectral bands are preselected to Green (2), Near Infra Red (4) and Short
Wave Infra Red (7) by the supplier. Band 2 is useful for vegetation and cultural
feature identification, while band 4 is useful for determining vegetation types and
band 7 sensitive to mineral and rock discrimination. Moreover, a company contrast
stretch filter, LOCAL, (Locally Optimized Continuously Adjusted Look-up-tables) is
applied by the provider to maximize the information of each tile. The panchromatic
sharpening band in Landsat-7 has a spatial resolution of 15 meters with a broad
spectral response which, when applied across other bands, sharpens the resolution
from 28.5 to 14.25 meters. Each tile is referenced to the World Geodetic System of
1984 (WGS84) and projected in UTM coordinates. The images over the study area are
downloaded
free
from
NASA
Earth
Science
Enterprise;
https://zulu.ssc.nasa.gov/mrsid/. Landsat TM data is chosen because it covers the
entire study area for both 1990 and 2000 with less than 10% cloud cover and have
spatial resolutions considered suitable for this study.
Data
15
3.3 Resource Mapping Unit (RMU)
PNG Resource Information System (PNGRIS) is polygon based natural resources
database in MapInfo tables compiled from 1:500,000 topographic base maps and
aerial photo interpretations (Appendix F). The spatial unit is Resource Mapping Unit
(RMU) which is based on landform, rock type, and altitude; (Appendix E). PNGRIS
contains data on natural resources, agriculture, and basic demographic indicators. The
Department of Agriculture and Livestock (DAL) is the main custodian of PNGRIS
database and other government departments and agencies have access to use PNGRIS.
RMU is the spatial mapping unit in our study from which land use layers are spatially
intersected with to derive land use and population change information.
3.4 Auxiliary datasets
3.4.1 Population
Population figures for 1975, 1980, 1990, and 2000 over the study area formed an
important demographic data. Since the baseline date for this study is 1975 and with no
official population data for 1975, the mean of 1970 and 1980 population figures were
assumed for 1975 as there was no other way to obtain the exact figures for 1975. The
rest of the population data were official census figures. Prior to 2000, various census
units were used to record population data. During 2000 national census, all census
points (villages) were georeferenced with GPS. The previous census units are updated
to conform to 2000 census by matching and geocoding the previous census units. The
geocoding enables census data to be integrated with RMU and other GIS layers for
spatial querying, analyses and mapping. The population in any RMU is assumed to be
evenly distributed. The National Statistical Office (NSO) of PNG is the authority on
census data.
3.4.2 Agriculture Systems of PNG (AGSYS)
AGSYS is agriculture systems data based on RMU in MapInfo polygons and
maintains basic PNGRIS attributes but customized to agriculture. AGSYS contains
information on small holder agriculture at provincial and national levels that relates to
farming systems, crop types, land use intensity, cash crop activity, and other
agricultural aspects. The data were collected between 1990 and 1996 and is deemed to
be valid for the next 5 to 10 years (Bourke et al., 1998). Department of Agriculture
and Livestock (DAL) is the custodian of AGSYS data. AGSYS is used to derive land
use intensity and to verify land use interpretations in this study.
Data
16
3.4.3 Forest Inventory Mapping System (FIMS)
FIMS is polygon based forestry database in MapInfo table compiled from 1:100,000
topographic base maps and contains forest information. The spatial unit is Forest
Mapping Unit (FMU) and based on major forest types and forest zones in PNG. The
PNG Forest Authority (PNGFA) is mandated to manage FIMS. FIMS shows
vegetation classes including land use areas which are used to compare and verify the
digitized land uses.
3.5 Data preparation
The data preparation steps are prerequisites to the final input data for processing and
analysis (Figure 3.1).
Figure 3.1. Data pre-processing
Data
17
3.5.1 Topographic maps for T1 Land Use
The topographic maps are scanned and georeferenced to AGG66 and AMG55 to
conform to source datum and projection (Appendix H). The images are tiled and
clipped to the study area. The scan resolution is 300 dpi to assist clarity when
delineating land use types during digitizing at different zoom levels.
3.5.2 Satellite images for T2 and T3 Land Uses
The T2 and T3 images are first reprojected from UTM coordinate system to AGD66
AMG55 (Appendix H). Subsets of the images are clipped to the study area and saved
into TIFF format. Erdas Imagine software is used to perform unsupervised
classifications on both images and smoothed with a 5x5 majority filter to aid
digitizing (Figure 3.2).The 5x5 majority filter gave a satisfactory result after
comparisons were made with other smoothing filters.
Before filter
After filter
Figure 3.2. Before and after images using 5x5 filters to smooth classified images
3.5.3 Digitizing land use classes
Land use for T1 is captured by screen digitizing using the tiled image as background.
The topographic map legend is used to label the land use classes. The geometry of T1
is checked for errors and attributes updated prior to converting to coverage.
A copy of the digitized T1 land use is made to T2 and using T2 image and T2 classified
image as background, the land use for T2 is adjusted according to changes observed.
The same procedure is repeated for T3 by making a copy of T2 land use to T3 and
using T3 image and T3 classified image as background, land use classes are adjusted to
T3. When uncertainties arise in labelling for T2 and T3 land uses, AGSYS, RMU and
FIMS layers are used in complementary to assign land use labels. T2 and T3 band
combinations and the reflected colour composite on different surface features are
shown in Table 3-1 with other band combinations in true and false colours.
Data
18
Table 3-1. Appearance of Features on Composite Images. (Adapted and modified from
www.earthsat.com)
Trees and
bushes
Crops
Wetland
Vegetation
Water
Urban
areas
Bare soil
True Colour
False Colour
SWIR (GeoCover)
Red:
Band 3
Green: Band 2
Blue: Band 1
Olive Green
Red:
Band 4
Green Band 3
Blue: Band 2
Red
Red:
Band 7
Green: Band 4
Blue: Band 2
Shades of green
Medium to light green
Dark green to black
Pink to red
Dark red
Shades of green
Shades of green
Shades of blue and
green
White to light blue
Shades of blue
Black to dark blue
Blue to gray
Lavender
White to light gray
Blue to gray
Magenta, Lavender, or
pale pink
Examples
3.5.4 Land use class transformations
T1 land use classes are explicitly inherited from the topographic map legends and
need no further definition. However, for T2 and T3, several land use changes are
possible, three being significantly noticeable when digitized T1 land use is overlaid on
T2 image and digitized T2 on T3 image. Changes can occur at feature level where land
use objects in a class changes or at land use level where complete land use classes can
transform into other classes. At feature level, first is the spatial change
(expansion/reduction) of land use objects in T2, then the emergence of new land use
objects in T2 and third, the disappearance of land use object in T2, from T1, see Table
3-2 for all the possible changes. The same applies for T2 on T3 and T1 on T3.
Data
19
Table 3-2. Land use feature change possibilities
From
1975
To
1990/2000
Criteria
Land use emerging
Land use disappeared
No change in area and land use –
remain same
No change in area but change in
land use
Increase in area but land use
remains unchanged
Decrease in area but land use
remains unchanged
Change in both area (increase) and
land use
Change in both area (decrease) and
land use
Change Domain
Spatial &
attribute
Spatial &
attribute
Spatial &
attribute
attribute
spatial
spatial
Spatial &
attribute
Spatial &
attribute
3.5.5 Land use class assignment
Land use class assignments for T2 and T3 are done in the following manner. Where
land use objects for a class in T1 is observed to have corresponding adjacent spatial
changes on T2 image, the change is considered an increase/decrease of the same land
use, hence, the land use label of T1 is retained and the boundary is adjusted in T2. If
disjointed (fragmented) features of the same land use class in T1 are observed to fall
within a single object discerned in T2, these features are merged to one object with the
same land use label from T1 and the boundary is reshaped accordingly (Figure 3.3).
Objects in T1 that are not observed in T2 are considered to have transformed into other
land use, hence, the object is deleted in T2 (Figure 3.4). Objects that are not in T1 but
emerge in T2 are considered new land use objects (Figure 3.5) and a decision is made
to assign land use labels. The Agricultural system dataset is used to assign land use
label to new objects. If the new object intersects with more than one agricultural land
use system, the label of the biggest area of intersection is transferred to the new object
(Figure 3.6).
Data
20
Disjointed
objects
Merged
object
Figure 3.3. Disjointed land use objects in T1 merged into a single object in T2
Object
disappeared
in T2
Object in T1
Figure 3.4. Land use object in T1 disappeared in T2
Forest
New object
Figure 3.5. Emergence of new land use objects in T2
New object gets
agriculture label
based on area of
intersection.
Figure 3.6. Example of a new feature (red)
intersecting over two land use types
Data
21
Chapter 4 - Methodology
4.1 Study Area – Morobe province
Morobe provides an excellent opportunity as study area because it is representative of
a set of conditions that are generally prevalent in much of PNG and has clear links to
land use. In addition, there is comprehensive existence of data available. Some of the
conditions are size of land area, demographic trends, diversity of natural and cultural
phenomena and related historical significance.
Figure 4.1. Location of study area.
4.1.1 General description
Morobe province is located 147° east and 6° south of the equator, approximately in
the centre of PNG (Figure 4.1) with a land area of 33,933 km2 (McAlpine and
Quigley, 1998) and a population of 536,591. Morobe is in the Momase region with
Madang, East Sepik and West Sepik provinces all situated towards the northwest
(Figure 2.1). Morobe shares common land border with Madang, Eastern Highlands,
Gulf, Oro and Central provinces and maritime border with West New Britain.
Morobe is by far the largest in terms of land mass and population, representing over
7% of PNG’s total land area and over 10% of PNG total population in 2000. There are
more than 14 different tribes that speak 171 distinct languages or over 20% of the
total languages spoken in PNG (URL 8: Languages of PNG). The majority of the
Methodology
22
population depend on subsistence agriculture and small holder cash crop activities.
Morobe’s population grew steadily since 1975 and Lae comprises close to a quarter of
the total population for Morobe in 2000. Bulolo has the second highest followed by
Menyamya and Nawae district has the lowest population. The annual population
growth between 1990 and 2000 is 5% compared to 2% between 1980 and 1990
(Figure 4.2).
Population by Districts
30
Population (%)
25
TEWAE/SIASSI
20
FINSCHAFEN
NAWAE
15
HUON
MARKHAM
10
KABWUM
MENYAMYA
5
BULOLO
0
1975
1980
1990
2000
TEWAE/SIASSI
8%
BULOLO
14%
TEWAE/SIASSI
8%
FINSCHAFEN
8%
MENYAMYA
13%
NAWAE
7%
NAWAE
6%
HUON
11%
LAE
23%
FINSCHAFEN
7%
BULOLO
27%
KABWUM
8%
MARKHAM
9%
Population by Districts - 2000
HUON
17%
MENYAMYA
11%
KABWUM
10%
MARKHAM
13%
Land area by Districts
Figure 4.2. Population and area by districts
Methodology
23
Figure 4.3. General reference map of Morobe.
The humid tropical climate, complex landform, and soil types (Appendix S) support
an abundant biodiversity of flora and fauna. Over 60% of the land is canvassed by
tropical forest. Morobe sports a geographically contrasting topography (Figure 4.3)
from the long Markham valley, a fault zone with frequent earthquakes and spans 190
kilometres east–west separating Huon and the Highlands mountain systems. Huon’s
rugged Finisterre and Sarawaget ranges rise steeply from the sea to some of PNG’s
highest mountains, Mount Sarawaget 4,121 m and Mount Bangeta 4,005 m which are
raised coral limestone. The Highlands Mountain Systems southward are not as high
but rich in mineral deposits like gold, silver, nickel, and chromium. Morobe has 93
lakes of which Lake Wanum and Lake Trist are the largest with exotic aquaitic
Methodology
24
species. The Markham River is PNG’s sixth largest swift flowing but unnavigable
river and receives other tributaries as it courses 180 kilometres to meet the Solomon
Sea near Lae city. Markham Bridge is the longest bridge (550 meters) in PNG and is a
crucial link between Lae and the populous sub-districts of Mumeng, Bulolo, Wau,
Aseki, Menyamya and Garaina where most of Morobe’s economic activities like
agriculture, forestry and mining take place. There are 57 islands and Umboi (Siassi); a
volcanic Island is the largest. The Vitiaz Strait between Umboi Islands and
Finschhafen is considered one of the most dangerous waters due to strong winds
along the Vitiaz corridor.
Lae developed from a noble mission station into the second largest city and industrial
capital. Its strategic location serves as the gateway to the Highlands and Islands
provinces. The bulk of the indigenous people of Morobe are subsistence farmers
scattered across the province while Lae city has a high proportion of population that
migrate from other provinces in search of jobs or other opportunities. Lae is the
industrial hub where all vessels berth at Lae Harbour for loading and unloading of
cargoes from agricultural products to merchandise and machinery. The Highlands
provinces and parts of Momase region depend on Lae Harbour. All the equipment for
mining, oil and gas companies in the Highlands region and Madang province pass
through Lae port.
The following sections briefly give a sketch of Morobe such as, soils, rainfall,
demography, and land use.
4.1.2 Climate and Topography
The climatic pattern in Morobe ranges from dry (1,000-1,500 mm) of rainfall per year
generally along the Markham valley to wet (4,000-5,000 mm) around Lae,
Finschhafen, Siassi, Salamua and Aseki (Figure 4.4-A). The variables used to
describe the climate from RMUs are mean annual rainfall, seasonal variability of
monthly rainfall, rainfall deficit; mean maximum and mean minimum temperatures
(Bellamy, 1986). The monthly seasonal variability of rainfall for Finschhafen and
Aseki shows significant variation. Rainfall deficit indicates the probability of drought
to measure water balance and soil moisture content. When compared with annual and
monthly rainfalls, there is significant variation, meaning the loss of water to
transpiration is proportional to altitude (Figure 4.4-B). In PNG, altitude is closely
related to temperature, hence a direct surrogate of temperature and is of importance to
climatic description as well as to landform description (Bellamy, 1986).
Methodology
25
A
B
Annual rainfall (see Appendix R for detail)
Elevation (see Appendix Q for detail)
Figure 4.4. Rainfall and elevation of Morobe
4.1.3 Resources indicators
The combination of landform, soil, and climate provides the condition for various
forms of flora and fauna to flourish. The notable natural resources in Morobe are
minerals, forests, marine resources such as fish and agricultural potential. Wau was
famous in the 1930s when gold was discovered and Salamua Island became one of the
busiest airfields in the world where gold dredges were airlifted to Wau. Forest covers
more than 80% of the province providing significant potential for logging. However,
the physical topographic constraints like high slopes and rugged terrains render these
areas inaccessible.
4.1.4 Land use systems
Agriculture accounts for 35% of the land use activity in Morobe according to AGSYS
dataset. The dominant staple foods in most districts are sweet potato, banana, and
taro. In Finschhafen district Chinese taro and sago are dominant staple foods. Other
staple subdominant crops like Chinese taro, cassava, yam, and coconut vary between
districts. Most staple foods are grown for consumption and excess is sold for cash
income. Plantation comprises 2% while urban is less than 1% and the remaining
occupied by forests (Bourke et al., 1998). Soil type has influence on the type of crop
and number of plantings at which the same area is cultivated. A soil map of Morobe is
on Appendix S.
The land use classes for this study are similar to Bourke’s notes on PNG Agricultural
Systems (Figure 4.5). Sub land use classes in level iii for 1975 are aggregated to
super-classes in level ii in this study.
Methodology
26
Figure 4.5. Land use classification schema
The land use classes in this study are:
U
Ag
P
Gl
F
W
Urban - residential, commercial, industrial areas and settlements on urban
fringes.
Agriculture - gardens with food crops, secondary growths and areas in
fallow after a period of cultivation.
Plantation – planted crops and non-crops covering large areas.
Grassland/savannah – result of farming, hunting, burning or naturally
occurring
Forests – all forests including smaller areas like cemetery, quarry, mining,
and logging.
Water – water bodies.
4.2 Method
The main objective of this study is to determine the land use changes over Morobe
province. The principal datasets used for this study are two temporal land use datasets
of 1975 and 2000. For the same years there is population data based on Resource
Mapping Unit. From this data the aim is to derive changes on land use and population
by RMU as mapping unit. Therefore, in methodology the main objective of the study
is addressed by answering the second research question on how the available data can
be used to determine the land use changes. This approach involves GIS and database
functionalities and comprised two major steps: processing and analysis (Figure 4.6).
The immediate outcome from the processing and analysis would be (i) geometrically
intersected polygon coverage with updated attribute table for land use type per year
per RMU (ii) a multi-attribute table with all possible combinations of land use and
RMU for both years with population changes and (iii) change tables per land use type
and population, see Appendix G for detailed diagram. Based on these outcomes, some
of the expected results to derive are; (a) general land use change pattern (b) specific
changes by land use type, (c) changes in population (d) changes in agriculture and
Methodology
27
corresponding changes in population (d) general relationship between land use and
population changes, (e) cross-tabulation of FromÆTo changes, (f) change correlation
between agriculture and population. The software used in processing and analysis are;
ArcGIS, Oracle RDBMS, ArisFlow and ArcInfo.
The first step undertaken in processing involved a spatial overlay of the input datasets.
Within this process, several sub steps such as computing area statistics by land use
type per year per RMU and creating intermediate tables are accomplished as
illustrated in Figure 4.6. The desired output at the end of the processing are change
tables representing each land use type and population by RMU (Appendix G). The
land use change tables are created from a set of criteria on change conditions for both
1975 and 2000; see the script in Appendix K and Appendix L for details. The change
tables are related to the overlay for mapping the changes in land use classes and
population. The specific steps are described in the method. Figure 4.7 gives a generic
view on the tables and their relationships.
Methodology
28
Figure 4.6. Overview of conceptual methodology
Methodology
29
Figure 4.7. General Land use change Entity Relationship (ER) diagram.
Note:
The ER diagram shows the main tables and their relationships. The lookup tables can be inferred or
found in the appendix and specific ER diagrams. The conversion of Info tables to Oracle and the
change tables are not depicted above.
4.2.1 Data processing
The input data for processing are (i) land use 1975, (ii) land use 2000 and (iii) RMU.
The spatial mapping unit is RMU and population data is integrated into RMU. Since
changes in land use and population are required, spatial overlay becomes necessary to
compute and analyse changes. The land use data for 1990 has been left out in
processing due to volume of processing and analysis required compared to time.
Spatial overlay
Spatial overlay is the first step in processing, however, ESRI user guide and online
resources deal comprehensively on spatial operations, hence descriptions here would
be repetitious. Figure 4.8 is a classical example of union operation used in this step.
More information of spatial operations can be found under spatial over and geometric
intersections in ArcGIS.
Methodology
30
Figure 4.8. Union function in spatial overlay (adopted from ESRI ArcGIS manual)
Computing statistics
The second task involves computing and updating land use type areas per RMU per
year. Three lookup tables; (i) RMU, (ii) RMU/land use 75 and (iii) RMU/land use
2000 are generated from the intermediate result to achieve this task. By means of
relational queries with the intermediate data and the lookup tables, the area statistics
per land use type per RMU for 1975 and 2000 are computed and output to a final
coverage (Appendix I). The common join item for the lookup tables and the
intermediate result is RMU. Land use 1975 and 2000 have a combination of RMU
and land use candidate-keys as unique join item (Figure 4.9).
Methodology
31
Figure 4.9. ER diagram for computing statistics
Creating Multi-attribute table and converting to Oracle
In the third task, the final coverage is used to generate three tables: (i) RMU number,
(ii) land use type and (iii) RMU/land use combinations. The first two are lookup
tables and the third is a combination of all existing RMU and land use. Through
Oracle RDBMS connectivity, a cartesian product is created from RMU number and
land use type lookup tables and then joined with the RMU/land use combination table
to produce a multi attribute table for analysis (Figure 10, Appendix J). Then, the three
tables, RMU number, land use and multi-attribute tables are converted to Oracle for
multi-key attribute querying which is presently lacking in ArcGIS environment.
Figure 4.10. ER diagram for creating Cartesian product and multi-attribute table
Methodology
32
Evaluating land use and population changes in Oracle
The fourth task comprises two major activities. First, 8 intermediate tables are created
representing various land use conditions in an 8-step activity (Appendix K). These
tables are (1) unique land use class, (2) all RMU and selected land use class, (3) all
RMU with selected land use in 1975, (4) all RMU with selected land use in 2000, (5)
selected land use type in both 1975 and 2000, (6) selected land use type appearing
only in 1975, (7) selected land use type appearing only in 2000, (8) selected land use
type that neither exists in 1975 nor in 2000.
Second, from the 8 tables in the first activity, 4 SQL cases are built to create 4 change
tables where case 1 would create a table when land use exists in both years. Case 2
would create a table when land use exists only in 1975, case 3 when land use exists
only in 2000 and case 4, when land use does not exist in both 1975 and 2000. In all
cases, the presence and absence of population are evaluated (Figure 4.11, Appendix
L). The SQL executes iteratively for 6 times with user input of land use type. The
changes in area and population are computed in percentages and saved to output
oracle tables after each run.
Converting change tables from Oracle to Info tables
The fifth task requires the conversion of the change tables from Oracle to Info format.
This is achieved through ArisFlow data action model (Appendix M).The tables are
returned with RMU as unique identifiers and can be related to the coverage for
querying, analysis, and mapping.
Methodology
33
Figure 4.11. SQL representation of computing change tables in Oracle RDBMS
4.2.2 Analysis
The sixth task involves analysis of the multi-attribute table, the land use type change
tables and the final coverage. The multi-attribute table is used to compute crosstabulation of FromÆTo land use transformations while the change tables are joined
with the final coverage to produce choropleth maps representing each land use type
and population between 1975 and 2000. The correlation between population change
and forest to agriculture change is compared with a moving window script in
ArcView. The general correlation of total population change to total land use change
is also compared. The summary changes in population and land use are analysed by
conventional means and presented in tables, graphs, and charts.
Methodology
34
Chapter 5 - Results and Discussion
This chapter attempts to answer the third research question on land use changes in
Morobe Province by providing the results of the processing and analysis.
5.1 Results
5.1.1 General land use and population changes
Table 5-1. Summary statistics of land use and population changes
Land use
Agriculture
Forest
Grassland
Plantation
Urban
Water
Total
1975
sq.km
(%)
5001
15
26463
78
2169
6
237
1
25
0
37
0
33933
100
Population
Annual growth
Per Capita
270701
2% (75-90)
1.85
1990
sq.km
(%)
7266
21
23170
68
2764
8
632
2
64
0
38
0
33933 100
359892
5% (90-2000)
2.02
2000
sq.km
7918
22287
2945
678
67
39
33933
(%)
23
66
9
2
0
0
100
536591
4% (75-2000)
1.5
There is growth in both agriculture and population for Morobe between 1975 and
2000 (Table 5-1). Population increased by 98% or an annual increment of 4% or
about 10,600 persons. Agriculture increased by 58% between 1975 and 2000, an
increment of 2% or 117 km2 annually. The area under forest decreased, indicating
conversion to other forms of land use. There is little change in grassland, plantation
and urban. Between 1975 and 1990, agriculture shows significant increase compared
to 1990 and 2000. The decrease in forest likewise accelerated between 1975 and 1990
compared to 1990 and 2000. The total loss in forests between 1975 and 1990 is 10%
or 3,300 km2 or an annual clearing of almost 220 km2. On the other hand, population
growth between 1975 and 1990 was 2% per annum compared to 5% between 1990
and 2000. The annual population growth between 1975 and 2000 is almost twice the
rate of change in agricultural areas (Appendix P). The population density of Morobe
in 1975 was 8 persons per km2 and doubled in 2000. The density in agricultural area
was 54 in 1975 and 68 in 2000. However, when density was computed using
population within the agricultural areas and the areas under agriculture, the densities
for 1975 and 2000 were respectively 64 and 72, approximately 1% annual increase.
The overall change shows that forest continue to lose to other land use types (Table 52). Annual urban population growth between 1975 and 2000 was 6% compared to
approximately 3% expansion in area for the same period but these figures are too
small to represent in percentages. Grassland shows steady growth between 1975 and
Results and Discussion
35
2000 while water shows loss. Agricultural areas where population densities per RMU
are high show relatively high land use intensities (Appendix O). Agriculture and
plantation increased by 100% while grassland increased by 43% between 1975 and
2000. Forest decreased by 20% in the same period. Urban and water show negligible
changes in percentage although they show changes in absolute values (Appendix N).
The area that remained unchanged between 1975 and 2000 was 75%, see Table 5-2.
Table 5-2. Land use FromÆTo change matrix
2000
Agriculture
Forest
Grassland
Plantation
Urban
Water
Agriculture
8
1
3
1
0
0
Forest
15
62
2
0
0
0
12
80
1975
Grassland Plantation
1
0
1
0
5
0
0
0
0
0
0
0
Urban
0
0
0
0
0
0
Water
0
0
0
0
0
0
0
0
Change
2000
24
64
10
2
0
0
75
Change 75
7
1
100
5.1.2 Changes by land use type and population
The objective to quantify the changes in land use is achieved by evaluating the
changes in areas by land use type between 1975 and 2000. The four land use change
criteria evaluated during the SQL execution tested for the presence and absence of
each land use type in both, either or none of the years (Appendix L) The result of the
SQL execution are six land use change tables with population data which can be
mapped by relating to RMU layer (Figure 5.1). The changes when mapped show that
forest suffered mainly losses with water (Figure 5.2-a,f). Agriculture gained, although
some areas transformed to other land use types (Figure 5.2-b). There is net gain in
grassland (Figure 5.1-c) while plantation and urban gained (Figure5.2-d, e).
Population changes show both positive and negative growths, however, there are
areas with no population in both years (Figure 5.2-g).
Results and Discussion
36
(a) Forest
(b) Agriculture
(c) Grassland
(d) Plantation
(e) Urban
(f) Water
(g) Population growth
(h) Population change
Figure 5.1. Change classification for thematic maps, a-f, h (%), (g) ordinal scale
5.1.3 Correlation Surface between Changes
The spatial correlation surfaces between population change and changes fromÆto
respective land use classes were generated (Figure 5.2) by using custom Avenue
script in ArcView. This script uses moving window to compute surface of correlation
coefficients between two grid themes. The size of the moving window was set to 16
km (156 cells or measurements) and overlapping area was 5 km between windows.
The percentage of maximum no-data for each moving window was 10%. The grid cell
Results and Discussion
37
size was 1x1 km. The moving window size, overlap and the percentage of no data are
user-defined.
The first case tested the local correlation between total land use change and total
population change. The correlation coefficient scale is from -1 to 1. The results were
classified to be represented in three intervals: -1 to -0.5 as strong negative correlation;
-0.5 to 0.5 as no significant correlation and 0.5 to 1 as strong positive correlation.
Figure 5.2-a shows that correlation between total population change and total land
use change is strong and mostly positive across Morobe, especially in the populated
areas along the Markham valley, Bulolo, Wau, Menyamya, Kabwum and parts of
Garaina. There is also correlation between total population change and forest to
agriculture changes and these areas are spreading randomly across the province.
(Figure 5.2-b). The correlation between total population change and forest to
grassland change showed good relationship on smaller regions (Figure 5.2-c).
Correlation surface between total population change and grassland to agriculture
change shows a positive relationship in highly populated areas (Figure 5.2-d). This
could mean that areas under fallow are re-cultivated for subsistence agriculture or the
fallow periods have been shortened.
(a) Total area and population change
(c) Population and forest to grassland
(b) Population and Forest to Agriculture
(d) Population and grassland to agriculture
Figure 5.2. Different change correlations between land use and population
Results and Discussion
38
5.1.4 Changes in hotspots
Certain areas in Morobe characterise varying relationships between population and
agriculture which can be regarded as hotspots. Seven conditions were tested for
positive and negative growths between population and agriculture (Figure 5.3).
Menyamya sub-district showed strong positive correlation between population growth
and agriculture but also experienced negative growth in agriculture while population
increased. Kaiapit sub-district showed a variation of population and agriculture;
however, negative growths both in population and agriculture are observed. Mumeng,
Bulolo, Wau, and Garaina areas in the Bulolo sub-district show contrasting
relationships where agriculture decreased when population increased but some areas
within the same sub-district experienced growth in agriculture while population
decreased. .
Figure 5.3. Areas considered being hotspots for Population and agriculture
Results and Discussion
39
5.2 Discussion
5.2.1 General result
The changes in land use between 1975 and 2000 show positive growths in both
agriculture and population, however, the growth rate in population is greater than
expansion in agriculture. The general trend where population growth is faster than
expansion in agriculture supports the results of past works such as McAlpine (2000).
At RMU level, there can be a number of bio-physical and socio-economic actors
directly or indirectly influencing the outcome. Population has a positive relationship
to agricultural change, but in certain parts of Morobe, there is negative correlation.
Forest lost significantly between 1975 and 1990 and during the same period
agriculture gained markedly. It can be assumed that most of the loss in forest
converted to agriculture. Whether this transformation is from direct clearing of forest
for agricultural purposes or initially cleared for other uses and subsequently
converted to agriculture requires additional data to investigate. From observations
and basing on expert knowledge, it is a common trend to clear forest areas for small
scale subsistence agriculture. If large forest areas are cleared, this could be assumed
to be for reasons other than agriculture, for example; logging. This assumption is
confirmed when the forest concession layer is overlaid on forest types, forest change
layer and agriculture change layer (Appendix T). Most of the gains in agriculture fall
within the forest areas that experienced most loss. This indicates that forest have been
cleared for agricultural purposes. These areas also show high population density and
the villages are clustered together. Only a small fraction of the loss in forest falls
within concession areas and this indicates minimum logging activities. The rate of
loss in forest between 1975 and 1990 was more rapid than 1990 to 2000. This can be
due to a country wide moratorium on logging in the late eighties and early nineties
for illegal practices in the logging industry. Large timber rights areas for commercial
logging have been declared in Morobe; however, little logging activities are taking
place to this date apart from Bulolo forest plantation.
5.2.2 Correlation between population and land use changes
The surface correlations show that population depends on agriculture in most parts of
Morobe province. The relationship between population and forest to grassland is
random and this could mean that clearing of forest areas for cultivation is not
widespread which also reflects the real situation. Interestingly, population and forest
to grassland correlation shows both positive and negative relationships but localized
in certain parts of the province. The locations of the negative correlation when
compared with forest data, (FIMS), show that these areas area dominated mostly by
grassland or vegetation other than forest. The positively correlated areas fall within
Results and Discussion
40
areas of agricultural potential on lower altitudes. There is one exception and that is
Bulolo where there is Forest plantation and the assumption that clearing of forest to
grassland would require further investigation. The forest to grassland areas are also
within or in the vicinity of allocated or planned concession areas (Appendix T). The
correlation of population and grassland to agriculture shows positive relationship and
this is observed to be occurring at currently populated places. In places where there is
negative correlation between population and agriculture but show high population
density, it is assumed that the population has other means of livelihood than entirely
on agriculture. The possible food sources are like relying on store food, buying from
markets, fishing and hunting.
5.2.3 Observed trend
The concentrations of populated places (villages) are more clustered for both periods
indicating no significant relocation (migration) to new areas as a result of population
increase. There can be other factors that limit this from happening such as land
ownership rights, physical constraints such as difficult terrain, isolation from
transportation system and others. The other indicator is that people live together in
communities more than in isolation which is a known pattern in many parts of PNG.
About 75% of the areas remain unchanged which indicates that land use areas
transformed from one land use type to another more than taking new areas from the
forest. This is also indicated by 20% loss in forest between 1975 and 2000.
The urban population showed significant growth for Lae city and other major centres
but the corresponding changes in land area in the last 25 years are comparatively
small. This shows that more people are migrating to towns and cities in search of jobs,
education, or other opportunities. The slow growth in areas for urban expansion is
mostly related to land ownership issues. Customary land owners tend to be reluctant
in giving land away and have tight controls over their land, especially in the urban
fringes. In Morobe, this is a common trend. Plantation gained significantly which
reflects introduction of agricultural crops and reforestation, mostly along the
Markham valley and Bulolo. Sugar plantation was introduced in the early nineties and
other agricultural and forestry efforts are taking place which reflects in plantation
growths. The large scale forest plantation in the Bulolo Wau area is one of the biggest
in the country and is still in active operation. Some forest areas that lost to grassland
are gained by agriculture or first to agriculture, then to grassland. The loss in water
can be due to seasonal variations during the year when lakes and streams shrink
during dry periods.
A direct relationship between land use intensity and distribution of villages is evident
where highly conglomerated locations show high land use intensity. There are areas
Results and Discussion
41
that were not inhabited in both 1975 and 2000. These places were also tested with the
1980 and 1990 population data and appear to remain unpopulated; however, about 8%
of this area comes under agriculture. This could mean that people use the areas for
agricultural purposes but live away in villages. Population growth could be the
possible cause that forces people to cultivate further from villages. When the
agricultural areas for both 1975 and 2000 are overlaid with the elevation data, some
agricultural areas are observed to have expanded into higher altitudes. This can be due
to local restrictions and constraints as mentioned earlier and population growth,
forcing people to cultivate at higher altitudes. This trend is also similar to the findings
of the case study in Tari Basin by Pahari and others (2001). The rugged Sarawaget
Mountains and the Highlands mountainous areas around Garaina show zero
population for both years. The per capita (Table 5-1) showed that between 1975 and
2000, the agriculture area decreased from 1.85 to 1.5 km2 per person indicating
increase in population. The rise in population could be caused by in migration than
high fertility rate of women in Morobe.
5.2.4 Possible Implications
The main observation for land use change in Morobe can be clearly stated that there is
growth in Agricultures. Relating to the literature regarding the possible causes such as
population, affluence and technology, population appears to be the major driving
force. The level of affluence is difficult to assign and is subjective to local conditions
like ownership. Technology could safely be ruled out as a cause of land use change
driver. The other drivers like political economy, political structure; and attitudes and
values do apply in the case of Morobe and in most parts of PNG. Most decisions are
made collectively in groups and leaders have more influence and tend to be in power
and have more wealth.
There are no major known logging operations in Morobe although there are
concessions in the areas where there is positive correlation between population and
forest to grassland. A new trend that is gaining popularity is that forests are cleared by
small scale sawmills operators who are hired to fell trees for the local owners. This is
not significant but if consistently carried out over a period of time, it will have
consequences on land use. At the current population growth of 4.4% per year and
observing the areas that have been converted from forest to agriculture (15%) in the
last 25 years, it can be stated that this trend will continue unless there is a significant
decline in population (unlikely to happen) or a policy in place to control and monitor
land use (also unlikely). Log prices are falling in Asia for some time now and the
chances of large scale logging that would affect land use change in Morobe is
unlikely, at least, in the foreseeable future. This study is based on a province level and
using satellite imagery, however, for specific case studies; smaller areas must be
Results and Discussion
42
selected with accurate and highly detailed data complemented with field missions. To
this end, the methods used in this study would be discussed.
5.2.5 Discussion on the methods used in this study
This section reviews the methods used in quantifying the land use changes in Morobe.
Initially, a number of software and methods were considered such as eCognition,
Erdas and Envi as well as GIS software like ESRI ArcGIS. In the end, ArcGIS,
ArcInfo, ArishFlow and Oracle were used. Based on the results achieved in meeting
the research objective using the above software, the strengths and weakness
encountered would be reported. The use of ArishFlow data modelling software
combined with ArcInfo, any geoprocessing task in vector format can be performed
efficiently. The disadvantage in the ArcInfo-Arisflow combination is the lack of
querying many relational tables as in conventional DBMS. This was the main reason
Oracle was used.
The reason for using a number of different software is due to the nature of the
processing and analysis to output the required change tables by land use type after
evaluating all the criteria defined. No single software above can perform all the
required tasks. Therefore the capability of Oracle was utilized to perform a compound
SQL statement. This SQL statement (Appendix L) executed a series of functions from
evaluating the conditions for land use change criteria in an 8-step process until four
final tables are created. In between, numerous sequences of SQL statements were
carried out. This is where GIS capability is limited. On the other hand, Oracle
RDBMS lacks the fully fledged capabilities of GIS, although there is a spatial
component in Oracle called Oracle spatial. The main user interface in Oracle is
command prompt and requires substantial knowledge of Oracle and database
languages, unlike the user-friendly GUI based GIS environment. The required
functionalities of both systems were used to create the change tables to achieve the
aim of this study. Any development in marrying the two systems or a new system
with the capabilities of both GIS and relational database would meet the current need,
especially in GIS environments.
Results and Discussion
43
Chapter 6 - Conclusion
The land use change in Morobe have been evaluated by using different datasets from
topographic maps of 1975, satellite images of 1990 and 2000 to quantify changes on
RMU level. In data preparation, the land use classes were digitized from the scanned
topographic maps and satellite images. The images were smoothed using a 5x5
majority filter to aid digitizing of 1990 and 2000 land use. It was necessary to
implement a series of geoprocessing steps from data preparation, processing and
analysis. The spatial overlay by union enabled geometric intersection of all data
layers. The land use attributes were computed and updated with land use areas and
population. Through relational database connectivity and SQL executions using
predefined land use change criteria in Oracle RDBMS, the changes in land use and
population were computed by land use type and population per RMU. This made
querying and analysis possible for changes relating to both population and land use.
The result of this study indicated that in the last 25 years, there have been significant
changes in forest and agriculture in regard to land use in the Morobe province. The
rapid growth in population forced people to cultivate more land for agriculture and
this is reflected on the growth in agriculture. Forest areas decreased by 20% and with
no significant commercial logging taking place in Morobe, it can be attested that most
of the loss in forest are gained by agriculture. However, the response to increase food
production can be local and not directly in proportion to population growth
throughout the province. Almost a quarter of the population lives in urban areas and
depends on imported food like rice and meat and supplement the diet with locally
grown foods from markets or stores. The population in the coastal areas and Islands
depend on the sea and swamps for food sources as well which can lead to less
cultivation.
The uninhabited areas are identified as inaccessible or not suitable for agriculture
given the physical and ecological constraints like latitudinal difference, soil type and
others. With other limiting factors relating to bio-physical and socio-economic
aspects, significant agricultural changes are observed in places where there is high
concentration of villages. The land use intensity in these places is high. The areas that
show significant gain in agriculture are also known for suitable conditions in terms of
soil and climate. The Menyamya region is one of the isolated areas with rugged
terrains but show significant gain in agriculture. This is due to people turning to small
scale cash crop like coffee production. The same can be said about Wantoat region,
another area that showed significant gain in agriculture. Plantation and urban gained
in the same period
Conclusion
44
The main conclusion that can be drawn from this study to answer the main research
objective is that there is significant growth in agriculture, although population
escalated twice as high. Whether this growth is in direct response to population
pressure needs more research at the local level because; the significant growths are
happening in areas known for having potential for commercial cash cropping. The
population in these areas are not being the only factor that would drive the changes in
agriculture. Any concrete statement about the future land use in Morobe would be
premature because; land has been a complicated issue involving many tribes with
differing customs, traditions and languages within the province. However, we can
predict basing on this study that agriculture will continue to grow whether it takes
land from forest or grassland as population grows.
Relating the main result to the research objective, research questions and the
hypothesis based on literature search, the following can be stated. The literature cited
were mostly based on studies on national, regional or local scales in PNG. This study
is based on a province. The research objective has been achieved by measuring the
changes in land use and population at RMU level which indicated that population
doubled and agriculture gained significantly in the last 25 years in Morobe province.
Taking into consideration that 25% of the population is based in urban areas, the
growth for rural population was 75% and the 58% growth in Agriculture indicates a
parallel growth in both population and Agriculture. Therefore the hypothesis that
there is no direct correlation between population growth and land use change in PNG
could not be completely supported in the light of the results from this study.
Agriculture, forestry and PNGRIS data were used to verify the digitizing because;
there is no suitable data for validation available. A field validation would be necessary
in relation to this study or any future extension regarding land use in Morobe.
Conclusion
45
References
Allen, B. J. (1984). Agricultural and Nutritional Studies on the Nembi Plateau,
Southern Highlands. Port Moresby, Papua New Guinea, Department of Geography,
University of Papua New Guinea.
Bellamy, J. A. (1986). Papua New Guinea Inventory of Natural Resources, Population
distribution and Land Use Handbook. Canberra, Commonwealth Scientific and
Industrial Research Organization (CSIRO).
Bellamy, J. A. and J. R. McAlpine (1995). Papua New Guinea Inventory of Natural
Resources, Population Distribution and Land use Handbook. Canberra, Australian
Agency for International Development.
Bourke, R. M., B. J. Allen, et al. (1998). Papua New Guinea: Text Summaries.
Canberra, Department of Human Geography, Research School of Pacific and Asian
Studies, The Australian National University. 1: 221.
Bourke, R. M., B. J. Allen, et al. (1998). Papua New Guinea: Text Summaries.
Canberra, Department of Human Geography, Research School of Pacific and Asian
Studies, The Australian National University. 2: 461.
Bourke, R. M., B. Carrad, et al. (1981). "Papua New Guinea's Food Problems: Time
for Action." Department of Primary Industry Research Bulletin 29. Port
Moresby.(29).
Christian, C. S. and G. A. Stewart (1953). General report on survey of KatherineDarwin Region, 1946. . Commonwealth Scientific and Industrial Research
Organisation, Australian Land Research Service 1. Canberra: C.S.I.R.O. Canberra,
Australia.
Civco, D. L., J. D. Hurd, et al. (2002). A comparison of Land Use and Land Cover
Change Detection Methods. ASPRS-ACSM XXII, Washington.
Coiffer, C. (1994). "Of People and Plants - a Botanical Ethnography of Nokopo
Village, Madang and Morobe Provinces, Papua-New-Guinea- Schmid,Ck." Homme
34(3): 197-198.
Commoner, B. (1972). The Environmental Cost of Economic Growth in Population.
Resources and the Enviromnent, Government Printing Office, Washington, DC: 24
(339-63).
Crocombe, R. G. (1964). "Communal Cash-cropping Among the Orokaiva." New
Guinea Research Bulletin, Australian National University, Canberra 4.
DSLS, D. o. S. a. L. S. (2000). Trees II Report. Lae, Papua New Guinea University of
Technology: 17.
References
46
Epema, G. F., M. F. Botoro, et al. (2000). Monitoring Land use in Tigray (Ethiopia).
Remote Sensing for Developing Countries, Ghent, Belgium, European Association of
Remote Sensing Laboratories.
Ferraz, S. F. d. B., C. A. Vettorazzi, et al. (2004). "Landscape dynamics of
Amazonian deforestation between 1984 and 2002 in Central Rondonia, Brazil:
assessment and future scenarios." Elsevier - Science Direct: 17.
Kok, K. (2004). "The role of population in understanding Honduran land use
patterns." Journal of Environmental Management (JEM) - Elsevier(in press): 17.
Loveland, T., G. Gutman, et al. (2003). Land Use / Land Cover Change. Strategic
Plan for the U.S. Climate Change Science Program. Washington DC, US Climate
Change Science Program: 8.
Macfarlane, D. (2000). Country Pasture/Forage Resource Profiles - Papua New
Guinea. Grassland and Pasture Crops. J. M. Suttie and S. G. Reynolds. Queensland,
Kilbirinie: 18.
McAlpine, J. and J. Quigley (1998). Forest Resources of Papua New Guinea Summary Statistics from the Forest Inventory Mapping (FIM) System. C. M. P. Ltd.
Port Moresby, PNG Forest Authority: 13.
McAlpine, J. R. (1970). "Population and land use in the Goroka-Mt Hagen Area. In:
H.A. Hanntjens (ed.), Lands of the Goroka-Mt Hagen Area, Territory of Papua New
Guinea." CSIRO Land Research Series No. 27. CSIRO. Canberra(27).
McAlpine, J. R., D. F. Freyne, et al. (2000). Land Use and Rural Population Change
in PNG, 1975-1996. Food Security for Papua New Guinea, PNG University of
Technology, Lae, Papua New Guinea, ACIAR publications.
Pahari, K., M. Umezaki, et al. (2001). Land use dynamics in Tari basin of Papua New
Guinea using multi sensor satellite data. 22nd Asian Conference on Remote Sensing,
Singapore, Asian Association on Remote Sensing (AARS).
Pat, R. L. K. (2003). Customery Land Tenure in a Changing Context. Port Moresby,
Department of Lands and Physical Planning: 10.
Saunders, J. C. (1993). Forest Resources of Papua New Guinea. Canberra, Australian
International Development Assistance Bureau (AIDAB).
Sem, G. (1995). Land use change and population in Papua New Guinea. Population,
Land Management and Environmental Change, Tokyo, Japan, The United Nations
University (UNU).
Sonneveld, M. P. W., J. A. T. v. d. Brink, et al. (2000). Noise Reduction in GIS-based
Land Use Monitoring. Spatial Data Handling, Beijing, China.
References
47
Townshend, J. R. G. and C. O. Justice (1987). "Selecting the Spatial Resolution of
satellite sensors required for global monitoring of land transformations." International
Journal of Remote Sensing 9(2): 49.
Turner, B. L., R. H. Moss, et al. (1993). Relating land use and global land-cover
change: A poposal for an IGBP core project. Land-Use and Land-Cover Change.
Stockholm, Royal Swedish Accademy of Sciences.
Venkatachalam, P., C. V. S. S. B. R. Murty, et al. (1991). "Integration of Remote
Sensing and Geographic Information System to Study temporal changes in Land
Use." 11 th ACRS - Asian Conference on remote Sensing ii: 3.
Verschuren, P. and H. Doorewaard (1999). Designing a Research Project.
Utrecht, Lemma.
White, O. (1965). Parliament of a Thousand Tribes. Melbourne, Wren Publishing Pty
Ltd.
Windybank, S. and M. Manning (2003). "Papua New Guinea On the Brink." The
Centre for Independent Studies (CIS) 30.
(White 1965; Venkatachalam, Murty et al. 1991; Vicar, Jupp et al. 1991; Verschuren
and Doorewaard 1999; Vanniere, Bossuet et al. 2003; Windybank and
Manning 2003; Watson 2004; Woodock and Ozdogan 2004)
ONLINE REFERENCES
[URL:1]
National Statistical Office of PNG.
http://www.nso.gov.pg/
Accessed 02-Jul-04
[URL:2]
Third World Network Features.
http://www.hartford-hwp.com/archives/24/083.html
Accessed 05-Jul-04
[URL:3]
PNG says no to the “terrible twins.”
http://www.greenleft.org.au/back/1995/197/197p22.htm
Accessed 05-Jul-04
[URL:4]
List of countries by Population Density
http://www.factindex.com/l/li/list_of_countries_by_population_density.html
Accessed 05-Jul-04
[URL:5]
References
Human causes of Land use change
http://www.ciesin.org/docs/002-105/002-105b.html
48
Accessed 20-Jul-04
[URL:6]
Countries of the World
http://www.geographic.org/countries/countries.html
Accessed 01-Mar-05
[URL:7]
City Population – Papua New Guinea
http://www.citypopulation.de/PapuaNewGuinea.html#i1456
Accessed 03-Mar-05
[URL:8]
Languages of Papua New Guinea
http://www.ethnologue.com/show_country.asp?name=Papua+New+Guine
a
Accessed 06-Feb-05
[URL:9]
Land Management: Papua New Guinea’s Dilemma
http://www.ulb.ac.be/soco/apft/GENERAL/PUBLICAT/ARTICLES/ART
ICLE.HTM
Accessed 22-Feb-05
Personal Communication: Dr. Bryant Allan, Department of Human Geography,
Australian National University, Australia
References
49
Appendices
Appendix A: Land problems related to population growth
Appendices
50
Appendix B: Topographic map sheets of 1975 for Time 1 (T1)
Appendices
51
Appendix C: Clipped Landsat image of 1990 for Time 2 (T2)
Appendices
52
Appendix D: Clipped Landsat image of 2000 for Time 3 (T3)
Appendices
53
Appendix E: The concept of RMU
Attribute I (e.g. landform type:
H - hills)
H
a
Attribute II (e.g. rock type:
a – ultrabasics
b –fine grained metamorphics)
b
Attribute III (e.g. altitude:
X – 1200-1800 m)
Y –1800-2100 m)
X
Y
2
1
4
3
RMU
Number
1
2
3
4
RMU map
Attributes
I
II
III
H
H
H
H
a
b
a
b
X
X
Y
Y
Description
Hills on ultrabasics between 1200 and 1800 m
Hills on fine grained metamorphics between 1200 and 1800 m
Hills on ultrabasics between 1800 and 2100 m
Hills on fine grained metamorphics between 1800 and 2100 m
(Adapted from PNGRIS Handbook)
Appendices
54
Appendix F: Resource Mapping Units (RMU) of Morobe
There is a discrepancy of 153 sq. km between RMU
(green) and Administrative (red) boundaries. PNGRIS
developers used slightly different RMU boundary.
Appendices
55
Appendix G: Detailed data processing steps
Appendices
56
Appendix H: Data flow of input, processing, and output
Topographic maps 1975
Landsat TM 1990
Landsat ETM+ 2000
RMU
Population table
Scanned, georef. tiled
Masked and clipped
Masked and clipped
Updated RMU with population data
Digitized 1975 land use
Digitized 1990 land use
Digitized 2000 land use
Updated RMU map
Forest hull
Spatial overlay and tables of land use 1975, land use 2000, updated RMU with population and Forest hull
Population change 75-00
Forest change
Agriculture change
Grassland change
Plantation change
Urban change
Water change
FromÆTo Change
Land use intensity
Correlation map
Appendices
57
Appendix I: ArisFlow data action model for computing land use areas
Intermediate dataset
Final coverage
Appendices
58
Appendix J: ArisFlow data action model for creating and converting
tables to Oracle tables
Final coverage
Multi-attribute table
Appendices
59
Appendix K: Creating intermediate tables for land use change
computation in Oracle
Appendices
60
Appendix L: SQL script for generating change tables
Appendices
61
Appendices
62
Appendices
63
Appendices
64
Appendix M: ArisFlow data action model for converting change tables
from Oracle to info tables
Appendix N: Land use change matrix in km2
2000
Agriculture
Forest
Grassland
Plantation
Urban
Water
Change 75
Appendices
Agriculture
2536
282
962
351
22
1
Forest
5034
20895
817
125
11
9
4154
26890
1975
Grassland
Plantation
322
39
311
16
1574
8
34
167
10
2
1
0
2253
232
Urban
1
0
0
0
22
0
Water
4
6
1
0
0
16
25210
24
26
Change
2000
7938
21509
3363
678
67
27
33581
65
Appendix O: Land use intensity and villages of Morobe
High Land use intensity areas are in Menyamya, Bulolo and parts of Huon sub-districts.Number
of plantings refers to the number of continuous plantings before fallow. Higher number of
planting means high land use intensity.
(Source: Agriculture systems of PNG)
Appendix P: Comparison of changes in Population and Agriculture
Change in Population
1975-2000
annual growth
265890 persons
98%
4%
Change in Agriculture
1975-2000
annual change
2
2917 km
58%
2%
Population Density (persons/km2)
1975
8 – Morobe province
54 – in agriculture areas
64 – in agriculture areas with population
within agricultural areas
Appendices
2000
16 – Morobe province
68 – in agriculture areas
72 – in agriculture areas with population
within agricultural areas
66
Appendix Q: TIN of Morobe in elevation (meters)
Appendices
67
Appendix R: Annual rainfall in Morobe
Appendices
68
Appendix S: Soil types in Morobe
Appendices
69
Appendix T: Using forest concession areas to assess loss in forest and
gain in agriculture
Forest change with concession overlay
Morobe forest and concession types
Agriculture change with concession overlay
Growth in agriculture overlay on forest
change and concessions
When gain in agriculture is overlaid over forest, most of the gains in agriculture fall
within the forest areas that lost more. This indicates the forest have been cleared for
agricultural purposes. Those areas also show high population figures and clustered
villages. Only a small fraction of the loss in forest falls within concession areas.
Appendices
70