Abstract Land Cover Change in a Savanna Environment. A

Total Page:16

File Type:pdf, Size:1020Kb

Abstract Land Cover Change in a Savanna Environment. A ABSTRACT LAND COVER CHANGE IN A SAVANNA ENVIRONMENT. A CASE STUDY OF BAWKU MUNICIPAL by Kwame Adusei Savanna environments currently are undergoing rapid changes. A key debate is whether these observed changes are of anthropogenic activities or simply seasonal or climatic variations. This study mapped and assessed land cover change in the Bawku Municipal through the application of remote sensing and GIS. Multi-date Landsat Thematic Mapper (TM) images acquired in 1989 and 2009 were used to characterize and assess the pattern of land cover change through a post- classification comparison. The results indicated 33% loss of forest cover, while bare fields increased by 68% and settlement increasing by 331%. Human activities like intensive cultivation and harvesting of wooded resources as fuel for both commercial and domestic use were the principal influence of the changes observed. The results also confirm findings of similar and earlier studies in the region indicating efforts to address the problem are not comprehensive enough. LAND COVER CHANGE IN A SAVANNA ENVIRONMENT. A CASE STUDY OF BAWKU MUNICIPAL A Thesis Submitted to the Faculty of Miami University in partial fulfillment of the requirements for the degree of Master of Arts Department of Geography by Kwame Adusei Miami University Oxford, Ohio 2014 Advisor_______________________________ (Dr. Mary C. Henry) Reader________________________________ (Dr. John K. Maingi) Reader________________________________ (Robbyn J. F. Abbitt) Table of Contents ABSTRACT ................................................................................................................................................... i LIST OF TABLES ....................................................................................................................................... iv LIST OF FIGURES ...................................................................................................................................... v ACKNOWLEDGEMENT ........................................................................................................................... vi CHAPTER ONE ......................................................................................................................................... 1 INTRODUCTION ................................................................................................................................... 1 STUDY OBJECTIVES ........................................................................................................................... 2 JUSTIFICATION ................................................................................................................................... 3 CHAPTER TWO ........................................................................................................................................ 5 LITERATURE REVIEW ...................................................................................................................... 5 LAND COVER CHANGE ..................................................................................................................... 5 Causes of Land Cover Change ........................................................................................................... 5 DIGITAL CHANGE DETECTION ...................................................................................................... 6 Data acquisition and preprocessing ................................................................................................... 7 Image Classification ............................................................................................................................ 7 Change Detection Techniques ............................................................................................................ 9 Accuracy Assessment ........................................................................................................................ 10 CHAPTER THREE .................................................................................................................................. 11 METHODS ............................................................................................................................................ 11 STUDY AREA ....................................................................................................................................... 11 DATA ..................................................................................................................................................... 13 Satellite Images .................................................................................................................................. 13 Preparation before Field Work ....................................................................................................... 14 Field Sampling ................................................................................................................................... 14 Field Data Collection ........................................................................................................................ 14 Ancillary Data ................................................................................................................................... 15 Image Enhancement ......................................................................................................................... 15 LAND COVER MAPPING .................................................................................................................. 18 Classification Scheme ....................................................................................................................... 18 Image classification ........................................................................................................................... 20 ii Classification Accuracy Assessment ................................................................................................ 22 Change Detection .............................................................................................................................. 23 CHAPTER FOUR ..................................................................................................................................... 24 RESULTS ANALYSIS AND DISCUSSION ...................................................................................... 24 LAND COVER MAPPING RESULTS AND DISCUSSION ............................................................ 24 1989 Landsat TM Multi-date Image classification ........................................................................ 24 2009 Landsat TM Multi-date Image Classification ....................................................................... 26 Accuracy Assessment Results and Discussion ................................................................................ 29 CHANGE DETECTION RESULTS AND DISCUSSION ................................................................ 30 Post Classification Comparison ....................................................................................................... 30 Land Cover Change Causes ............................................................................................................. 33 Vegetation Loss ................................................................................................................................. 36 CHAPTER FIVE ...................................................................................................................................... 39 CONCLUSION AND RECOMMENDATIONS ................................................................................ 39 SUMMARY OF RESULTS ................................................................................................................. 39 CONCLUSION ..................................................................................................................................... 40 RECOMMENDATIONS ...................................................................................................................... 41 REFERENCES .......................................................................................................................................... 42 iii LIST OF TABLES Table 1: Landsat images used in the study ................................................................................... 13 Table 2: Percentage information contained in the standard principal components produced from the satellite images ........................................................................................................................ 18 Table 3: Land cover classification scheme ................................................................................... 19 Table 4: Area and percentage area covered by each land cover class in 1989 ............................. 26 Table 5: Area and percentage area covered by each land cover class in 2009 ............................. 26 Table 6: 2009 land cover map error matrix .................................................................................. 29 Table 7: 1989 land cover map error matrix .................................................................................. 30 Table 8: Land cover change matrix for 1989 and 2009 land cover maps ..................................... 31 Table 9: Land cover changes between 1989 and 2009 ................................................................. 31 iv LIST OF FIGURES Figure 1: Map of Study Area .....................................................................................................................
Recommended publications
  • Upper East Region
    REGIONAL ANALYTICAL REPORT UPPER EAST REGION Ghana Statistical Service June, 2013 Copyright © 2013 Ghana Statistical Service Prepared by: ZMK Batse Festus Manu John K. Anarfi Edited by: Samuel K. Gaisie Chief Editor: Tom K.B. Kumekpor ii PREFACE AND ACKNOWLEDGEMENT There cannot be any meaningful developmental activity without taking into account the characteristics of the population for whom the activity is targeted. The size of the population and its spatial distribution, growth and change over time, and socio-economic characteristics are all important in development planning. The Kilimanjaro Programme of Action on Population adopted by African countries in 1984 stressed the need for population to be considered as a key factor in the formulation of development strategies and plans. A population census is the most important source of data on the population in a country. It provides information on the size, composition, growth and distribution of the population at the national and sub-national levels. Data from the 2010 Population and Housing Census (PHC) will serve as reference for equitable distribution of resources, government services and the allocation of government funds among various regions and districts for education, health and other social services. The Ghana Statistical Service (GSS) is delighted to provide data users with an analytical report on the 2010 PHC at the regional level to facilitate planning and decision-making. This follows the publication of the National Analytical Report in May, 2013 which contained information on the 2010 PHC at the national level with regional comparisons. Conclusions and recommendations from these reports are expected to serve as a basis for improving the quality of life of Ghanaians through evidence-based policy formulation, planning, monitoring and evaluation of developmental goals and intervention programs.
    [Show full text]
  • Land Cover Austin, TX This Enviroatlas Map Shows Land Cover for the Austin, TX Area at High Spatial Resolution (1-Meter Pixels)
    • Austin, TX • New Haven, CT • Baltimore, MD • New York, NY • Birmingham, AL • Paterson, NJ • Brownsville, TX • Philadelphia, PA • Chicago, IL • Phoenix, AZ • Cleveland, OH • Pittsburgh, PA • Des Moines, IA • Portland, ME • Durham, NC • Portland, OR • Fresno, CA • Salt Lake City, UT • Green Bay, WI • Sonoma County, CA • Los Angeles, CA • St Louis, MO • Memphis, TN • Tampa, FL • Milwaukee, WI • Virginia Beach, VA • Minneapolis, MN • Washington, DC • New Bedford, MA • Woodbine, IA Meter Scale Urban Land Cover Austin, TX This EnviroAtlas map shows land cover for the Austin, TX area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, and Agriculture.1 Why is high resolution land cover important? Land cover data present a “birds-eye” view that can help identify important features, patterns and relationships in the landscape. The National Land Cover Dataset (NLCD)2 provides land cover for the entire contiguous U.S. at 30- meter pixel resolution. However, for some analyses, the density and heterogeneity of an urban landscape requires higher resolution land cover data. Meter Scale Urban Land Cover (MULC) data were developed to fulfill that need. In comparison, there are 900 MULC pixels for every one NLCD pixel. Anticipated users of MULC include city and regional planners, water authorities, wildlife and natural resource Figure 2: MULC for Austin, managers, public health officials, citizens, teachers, and TX. students. Potential applications of these data include the How can I use this information? assessment of wildlife corridors and riparian buffers, This data layer can be used alone or combined visually and stormwater management, urban heat island mitigation, access analytically with other spatial data layers.
    [Show full text]
  • Large Scale High-Resolution Land Cover Mapping with Multi-Resolution Data
    Large Scale High-Resolution Land Cover Mapping with Multi-Resolution Data Caleb Robinson Le Hou Kolya Malkin Georgia Institute of Technology Stony Brook University Yale University Rachel Soobitsky Jacob Czawlytko Bistra Dilkina Chesapeake Conservancy Chesapeake Conservancy University of Southern California Nebojsa Jojic∗ Microsoft Research Abstract uses include informing agricultural best management practices, monitoring forest change over time [10] and measuring urban In this paper we propose multi-resolution data fusion meth- sprawl [31]. However, land cover maps quickly fall out of ods for deep learning-based high-resolution land cover map- date and must be updated as construction, erosion, and other ping from aerial imagery. The land cover mapping problem, at processes act on the landscape. country-level scales, is challenging for common deep learning In this work we identify the challenges in automatic methods due to the scarcity of high-resolution labels, as well large-scale high-resolution land cover mapping and de- as variation in geography and quality of input images. On the velop methods to overcome them. As an application of other hand, multiple satellite imagery and low-resolution ground our methods, we produce the first high-resolution (1m) truth label sources are widely available, and can be used to land cover map of the contiguous United States. We have improve model training efforts. Our methods include: introduc- released code used for training and testing our models at ing low-resolution satellite data to smooth quality differences https://github.com/calebrob6/land-cover. in high-resolution input, exploiting low-resolution labels with a dual loss function, and pairing scarce high-resolution labels Scale and cost of existing data: Manual and semi-manual with inputs from several points in time.
    [Show full text]
  • Prospecting for Groundwater in the Bawku West District of the Upper East Region of Ghana Using the Electromagnetic and Vertical Electrical Sounding Methods C
    PROSPECTING FOR GROUNDWATER IN THE BAWKU WEST DISTRICT OF THE UPPER EAST REGION OF GHANA USING THE ELECTROMAGNETIC AND VERTICAL ELECTRICAL SOUNDING METHODS C. Adu Kyere, R. M. Noye, and Aboagye Menyeh Department of Physics, Geophysics Section, Kwame Nkrumah University of Science and Technology, University Post Office, Kumasi, Ghana ABSTRACT An integrated approach involving the Electromagnetic (EM) and Vertical electrical sounding (VES) survey methods, has been used to locate potential drilling sites to find groundwater for twenty (20) rural communities in the Bawku West District of the Upper East Region of Ghana. The EM method involved the use of the Geonics EM 34-3 equipment to obtain traversing data for the horizontal dipole (HD) and vertical dipole (VD) modes at station intervals of 10 m for a 20 m coil separation. The EM survey provided subsurface information relating to positions of high electrical conductivity anomalies which presumably pointed to locations for groundwater structures. As a follow up, the VES was carried out with respect to the anomalous positions, using the dipole-dipole configuration with the McOhm-EL resistivity meter. The VES enabled the estimation of the depth of the weathered layer to the bed rock. The results of the VES data were interpreted quantitatively by modeling using IX1D V.3 software. The study identified prospective sites for drilling of bore- holes to provide potable water for the communities. Results for the VES suggested that the aquifer zones in the area under study are mainly deeply weathered and fractured. The lithology for some of the recommended drilled points correlated well with the results obtained from the EM and VES investigations.
    [Show full text]
  • Ghana Poverty Mapping Report
    ii Copyright © 2015 Ghana Statistical Service iii PREFACE AND ACKNOWLEDGEMENT The Ghana Statistical Service wishes to acknowledge the contribution of the Government of Ghana, the UK Department for International Development (UK-DFID) and the World Bank through the provision of both technical and financial support towards the successful implementation of the Poverty Mapping Project using the Small Area Estimation Method. The Service also acknowledges the invaluable contributions of Dhiraj Sharma, Vasco Molini and Nobuo Yoshida (all consultants from the World Bank), Baah Wadieh, Anthony Amuzu, Sylvester Gyamfi, Abena Osei-Akoto, Jacqueline Anum, Samilia Mintah, Yaw Misefa, Appiah Kusi-Boateng, Anthony Krakah, Rosalind Quartey, Francis Bright Mensah, Omar Seidu, Ernest Enyan, Augusta Okantey and Hanna Frempong Konadu, all of the Statistical Service who worked tirelessly with the consultants to produce this report under the overall guidance and supervision of Dr. Philomena Nyarko, the Government Statistician. Dr. Philomena Nyarko Government Statistician iv TABLE OF CONTENTS PREFACE AND ACKNOWLEDGEMENT ............................................................................. iv LIST OF TABLES ....................................................................................................................... vi LIST OF FIGURES .................................................................................................................... vii EXECUTIVE SUMMARY ........................................................................................................
    [Show full text]
  • GHA35336 Country: Ghana Date: 9 October 2009
    Refugee Review Tribunal AUSTRALIA RRT RESEARCH RESPONSE Research Response Number: GHA35336 Country: Ghana Date: 9 October 2009 Keywords: Ghana– Bawku – Mamprusi – Kusasi – Conflict – Passports – State protection – Relocation This response was prepared by the Research & Information Services Section of the Refugee Review Tribunal (RRT) after researching publicly accessible information currently available to the RRT within time constraints. This response is not, and does not purport to be, conclusive as to the merit of any particular claim to refugee status or asylum. This research response may not, under any circumstance, be cited in a decision or any other document. Anyone wishing to use this information may only cite the primary source material contained herein. Questions 1. Please provide a brief summary of the relationships between the Mamprusi and Kusasi ethnic groups. Is there evidence of ongoing conflict between these groups? 2. An election was held in late 2008. Please provide details of the results of the election and whether or not conflict between the contesting parties resulted in ethnic clashes leading up to or after the election, particularly in the North East region. 3. Are there any news or other reports of incidents of violence or attacks in Bawku in August 2005? Are there reports of Mamprusi-targeted violence in April 2006? Are there any other reports of violence or ethnic related violence or accidents in November or December 2006? 4. Please provide information on the treatment of NPP supporters currently. 5. What is the process for applying for and obtaining a passport in Ghana? 6. Please provide information on the availability of state protection for minority groups in the event of ethnic clashes.
    [Show full text]
  • Spatio-Temporal Analyses of Impacts of Multiple Climatic Hazards in a Savannah Ecosystem of Ghana ⇑ Gerald A.B
    Climate Risk Management xxx (2016) xxx–xxx Contents lists available at ScienceDirect Climate Risk Management journal homepage: www.elsevier.com/locate/crm Spatio-temporal analyses of impacts of multiple climatic hazards in a savannah ecosystem of Ghana ⇑ Gerald A.B. Yiran PhD Senior Lecturer a, , Lindsay C. Stringer PhD Professor b a Department of Geography and Resource Development, University of Ghana, Legon, Ghana b Sustainability Research Institute, School of Earth and Environment, University of Leeds, UK article info abstract Article history: Ghana’s savannah ecosystem has been subjected to a number of climatic hazards of varying Received 25 January 2016 severity. This paper presents a spatial, time-series analysis of the impacts of multiple haz- Revised 20 June 2016 ards on the ecosystem and human livelihoods over the period 1983–2012, using the Upper Accepted 7 September 2016 East Region of Ghana as a case study. Our aim is to understand the nature of hazards (their Available online xxxx frequency, magnitude and duration) and how they cumulatively affect humans. Primary data were collected using questionnaires, focus group discussions, in-depth interviews and personal observations. Secondary data were collected from documents and reports. Calculations of the standard precipitation index (SPI) and crop failure index used rainfall data from 4 weather stations (Manga, Binduri, Vea and Navrongo) and crop yield data of 5 major crops (maize, sorghum, millet, rice and groundnuts) respectively. Temperature and windstorms were analysed from the observed weather data. We found that tempera- tures were consistently high and increasing. From the SPI, drought frequency varied spa- tially from 9 at Binduri to 13 occurrences at Vea; dry spells occurred at least twice every year and floods occurred about 6 times on average, with slight spatial variations, during 1988–2012, a period with consistent data from all stations.
    [Show full text]
  • Mapping Vegetation Types in a Savanna Ecosystem in Namibia: Concepts for Integrated Land Cover Assessments
    Mapping Vegetation Types in a Savanna Ecosystem in Namibia: Concepts for Integrated Land Cover Assessments Christian H ttich Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Friedrich-Schiller-Universität Jena Mapping Vegetation Types in a Savanna Ecosystem in Namibia: Concepts for Integrated Land Cover Assessments Dissertation zur Erlangung des akademischen Grades doctor rerum naturalium (Dr. rer. nat.) vorgelegt dem Rat der Chemisch-Geowissenschaftlichen Fakult t der Friedrich-Schiller-Universit t Jena von Dipl.-Geogr. Christian H&ttich geboren am 12. Mai 19,9 in Jena 1 2 Gutachter- 1. .rof. Dr. Christiane Schmullius 2. .rof. Dr. Stefan Dech (Universit t /&rzburg) 0ag der 1ffentlichen 2erteidigung- 29.04.2011 3 Acknowledgements I _____________________________________________________________________________________ Ackno ledgements 0his dissertation would not have been possible without the help of a large number of people. First of all I want to thank the scientific committee of this work- .rof. Dr. Christiane Schmullius and .rof. Dr. Stefan Dech for their always present motivation and fruitful discussions. I am e9tremely grateful to my scientific mentor .rof. Dr. Martin Herold, for his never-ending receptiveness to numerous questions from my side, for his clear guidance for the definition of the main research issues, the frequent feedback regarding the structure and content of my research, and for his contagious enthusiasm for remote sensing of the environment. Acknowledgements are given to the former remote sensing team of “BIO0A S&d?- Dr. Ursula Gessner (DFD-DLR), Dr. Rene Colditz (COAABIO, Me9ico), Manfred Beil (DFD-DLR), and Dr. Michael Schmidt (COAABIO, Me9ico) for e9tremely fruitful discussions, criticism, and technical support during all phases of the dissertation.
    [Show full text]
  • Bawku West District
    BAWKU WEST DISTRICT Copyright © 2014 Ghana Statistical Service ii PREFACE AND ACKNOWLEDGEMENT No meaningful developmental activity can be undertaken without taking into account the characteristics of the population for whom the activity is targeted. The size of the population and its spatial distribution, growth and change over time, in addition to its socio-economic characteristics are all important in development planning. A population census is the most important source of data on the size, composition, growth and distribution of a country’s population at the national and sub-national levels. Data from the 2010 Population and Housing Census (PHC) will serve as reference for equitable distribution of national resources and government services, including the allocation of government funds among various regions, districts and other sub-national populations to education, health and other social services. The Ghana Statistical Service (GSS) is delighted to provide data users, especially the Metropolitan, Municipal and District Assemblies, with district-level analytical reports based on the 2010 PHC data to facilitate their planning and decision-making. The District Analytical Report for the Bawku West district is one of the 216 district census reports aimed at making data available to planners and decision makers at the district level. In addition to presenting the district profile, the report discusses the social and economic dimensions of demographic variables and their implications for policy formulation, planning and interventions. The conclusions and recommendations drawn from the district report are expected to serve as a basis for improving the quality of life of Ghanaians through evidence- based decision-making, monitoring and evaluation of developmental goals and intervention programmes.
    [Show full text]
  • Netsforlife® Anglican Diocesan Development and Relief Organization (ADDRO)
    Episcopal Relief & Development (ERD)/ NetsforLife® Anglican Diocesan Development and Relief Organization (ADDRO) Malaria Communities Program: Ghana FINAL REPORT Program Location: Bawku West and Garu-Tempane districts, Upper East Region (UER), Ghana Cooperative Agreement Number: GHS-A-00-09-00006-00 Program Dates: October 1, 2009 – September 20, 2012 Date of Submission: November 14, 2012 Report Prepared by: Abagail Nelson, Senior Vice President for Programs, Episcopal Relief & Development Gifty Tetteh, Strategic Outreach Officer, NetsforLife® , Episcopal Relief & Development Dr. Stephen Dzisi, Technical Director, NetsforLife®, Episcopal Relief & Development Denise DeRoeck, Consultant Writer Table of Contents Introduction ..................................................................................................................... 1 A: Final Year Report: Main Accomplishments........................................................... 2 B. Methodology for the project’s final evaluation..................................................... 16 C. Main accomplishments of the MCP project ........................................................ 16 D. Questions related to MCP objectives.................................................................. 34 1. New USAID partners or networks identified by the project ........................ 34 2. Evidence of an increase in local capacity to undertake community-based malaria prevention and treatment activities during the project period........ 34 3. Evidence or indication that local ownership
    [Show full text]
  • Ecological Land Cover and Natural Resources Inventory for the Kansas City Region
    Ecological Land Cover and Natural Resources Inventory for the Kansas City Region 2. Ecological Land Cover Assessment and Natural Resource Inventory Methods This section describes the general approach and methodology used to complete the ecological classification and inventory for the Kansas City region. Applied Ecological Services, Inc. (AES) used the following tasks to complete the ecological survey and inventory, which are described in detail in the following sub-sections: 1. Data Assembly and Mapping: digital information from several government sources was used to establish baseline information about land cover in the region. 2. Field Reconnaissance: The digital information was validated and/or refined through field inspections and verifications. 3. Ecological Land Cover Classification Development: Using data from the data assembly and subsequent field reconnaissance, AES created an ecological classification representing existing natural resources in the region, a GIS-based information database, and a regional map of ecological land cover. 4. Data Extrapolation and Second Field Verification: The ecological classification involved an iterative process in which initial data were assembled, evaluated in the field, revised, and then re-evaluated in the field a second time. Final data were assembled after the second field reconnaissance, evaluated, and incorporated into the GIS program and the regional land cover map. Details of the methodology of this process are provided in Appendix A. This program was completed between June 2003 and June 2004. 2.1. Data Assembly and Base Mapping The initial phase of the ecological classification and inventory work involved data identification and assembly; the synthesis of data and creation of GIS base maps and graphics; and solicitation of input from local experts on the type and condition of natural resources in the Kansas City metropolitan area.
    [Show full text]
  • Natural Resources Inventory
    Natural Resources Inventory Columbia Metropolitan Planning Area Review Draft (10-1-10) NATURAL RESOURCES INVENTORY Review Draft (10-1-10) City of Columbia, Missouri October 1, 2010 - Blank - Preface for Review Document The NRI area covers the Metropolitan Planning Area defined by the Columbia Area Transportation Study Organization (CATSO), which is the local metropolitan planning organization. The information contained in the Natural Resources Inventory document has been compiled from a host of public sources. The primary data focus of the NRI has been on land cover and tree canopy, which are the product of the classification work completed by the University of Missouri Geographic Resource Center using 2007 imagery acquired for this project by the City of Columbia. The NRI uses the area’s watersheds as the geographic basis for the data inventory. Landscape features cataloged include slopes, streams, soils, and vegetation. The impacts of regulations that manage the landscape and natural resources have been cataloged; including the characteristics of the built environment and the relationship to undeveloped property. Planning Level of Detail NRI data is designed to support planning and policy level analysis. Not all the geographic data created for the Natural Resources Inventory can be used for accurate parcel level mapping. The goal is to produce seamless datasets with a spatial quality to support parcel level mapping to apply NRI data to identify the individual property impacts. There are limitations to the data that need to be made clear to avoid misinterpretations. Stormwater Buffers: The buffer data used in the NRI are estimates based upon the stream centerlines, not the high water mark specified in City and County stormwater regulations.
    [Show full text]