Land Cover Change Detection Using Landsat Data in Giao Thuy District, Nam Dinh Province, Vietnam.

THESIS

LAND COVER CHANGE DETECTION USING

LANDSAT DATA IN GIAO THUY DISTRICT,

NAM DINH PROVINCE, VIETNAM

NGUYEN TUYET LAN NIM 1491261019

MASTER DEGREE PROGRAM

STUDY PROGRAM OF ENVIRONMENTAL SCIENCE

POSTGRADUATE PROGRAM

UDAYANA UNIVERSITY

DENPASAR

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LAND COVER CHANGE DETECTION USING

LANDSAT DATA IN GIAO THUY DISTRICT,

NAM DINH PROVINCE, VIETNAM

Thesis to Get Master Degree

At Master Program on Environmental Science Postgraduate Program Udayana University

NGUYEN TUYET LAN 1491261019

MASTER DEGREE PROGRAM

STUDY PROGRAM OF ENVIRONMENTAL SCIENCE

POSTGRADUATE PROGRAM

UDAYANA UNIVERSITY

DENPASAR

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AGREEMENT SHEET

THIS THESIS HAS BEEN AGREED On June 21st, 2016

First Supervisor Second Supervisor

Ass. Prof. Dr. Takahiro Osawa Prof. Dr. Ir. I Wayan Nuarsa, M.Si NIP. 196805111993031003

Knowing, Head of Master Program of

Environmental Science Postgraduate Program

Udayana University

Prof. Dr. Ir. I Wayan Nuarsa, M.Si NIP. 196805111993031003

Director of Postgraduate Program

Udayana University

Prof. Dr. dr. A.A. Raka Sudewi, Sp.S(K) NIP. 195902151985102001

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This Thesis Has Been Examined By Examiner Committee at

Postgraduate Program Udayana University on June 13th, 2016

Based on the Decree Letter of Director of Udayana University Number: 2626/UN.14.4/HK/2016

Date: June 7th, 2016

Examiner Committee of Thesis Research Proposal Examiner as follows:

Head of Examiner: Ass. Prof. Dr. Takahiro Osawa

Member:

1. Prof. Dr. Ir. I Wayan Nuarsa, M.Si 2. Prof. Dr. Ir. I Wayan Sandi Adnyana, MS 3. I Wayan Gede Astawa Karang, S.Si, M.Si, PhD

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STATEMENT FREE FROM PLAGIARISM

The undersigned below: Name

NIM

Date of Born Adress

Thesis Title

: Nguyen Tuyet Lan : 1491261019

: June 8th 1985, Hanoi, Vietnam

: 1bP4 Nguyen An Ninh stress, Hoang Mai district, Hanoi city, Vietnam

: Land cover change detection using Landsat data in Giao Thuy district, Nam Dinh province, Vietnam

Hereby declare that the scientific work is plagiarism free. If in the future prove to have plagiarism in scientific work, and then I am willing to accept sanctions in accordance with the regulation of the Ministry of Republic number 17 in 2010 and regulation applicable in the Republic of Indonesia.

Denpasar, June 20th ₂₀₁₆

Respectfully,

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ACKNOWLEDGEMENT

Over two years, studying master program at the Udayana University, taught the enthusiasm of the Indonesian and Japanese teachers in Study Program of Environmental Science and South East Asia Remove sensing education, I have basic knowledge of the theory and practice. After a period of study and research, I have

already completed this thesis what entitled “Land cover change detection using Landsat data in Giao Thuy district, Nam Dinh province, Vietnam”.

I would like to thank:

1. Rector of Udayana University as supreme leader who has given me the opportunity to do a study at the University of Udayana. As a president of Udayana University, Prof. Dr. dr. Ketut Suastika, Sp.PD.KEMD has given me the facility to complete my research and study at the Udayana University.

2. Director of Postgraduate Program Udayana University, Prof. Dr. dr. A.A Raka Sudewi, Sp.S(K) who has been providing various facilities in finishing my study in Postgraduate Program of Environment Science.

3. Ministry of Education and Culture Indonesia who supported the author for given a

“Beasiswa Unggulan” Scholarship to study in Postgraduate Program of

Environment and Environmental Remote Sensing concentration, University of Udayana.

4. Ass. Prof. Dr. Takahiro Osawa, my first supervisor, the Head of Center of Remote Sensing and Oceanography (CReSOS), has given many information about image

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processing, method and literatures, suggestions, supports and spend time for correction this thesis until it could be finished.

5. Prof. Dr. Ir. I Wayan Nuarsa, M.Si, my second supervisor, the vice Head of Master Program of Environmental Science, also has given many informations about image data and method, suggestions, supports and spend time for correction this thesis until it could be finished.

6. Ass. Prof. Dr. Ngo The An, the Head of Agro-Ecology Deparment in Vietnam National University of Agriculture, has given ideas and suggestions on proposal. 7. Prof. Dr. Ir. I Wayan Sandi Adnyana, MS and I Wayan Gede Astawa Karang,

S.Si, M.Si, PhD, my examniners, have given some suggestions and corrections for my thesis.

8. Special thanks to the officers and the people in Giao Thuy district have facilitated helping during field trip.

9. Special thanks to all of my friends who had helping, support, advised and gave many information about how to image processing and what necessary procedures are to complete the thesis.

10.Finally, special thanks to my family, especially my husband spent time taking care of my son and I have time to participate and complete this program.

Denpasar, June 17th 2016

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ABSTRAK

DETEKSI PERUBAHAN TUTUPAN LATTAN MENGGUNAKAN DATA LANDSAT DI KECAMATAN GIAO THUY, PROVINSI NAM DINH,

VIETNAM

Giao Thuy merupakan daerah pesisir yang memiliki karakteristik yang dipengaruhi oleh delta Sungai Mearh. Penelitian ini diadakan untuk mencari perubahan tutupan lahan dari tahun 2000 sampai 2015 dan penyebab perubahannya. Data Landsat digunakan untuk mengklasifkasikan penggunaan lahan dan dikombinasikan dengan interview. Hasil penelitian menunjukka bahwa penutupan lahan yang paling dominan adalah sawah namun terjadi perubahan di kecamatan Giao Thuy tahun 2000-2015 sebagai berikut: Daerah bervegetasi (persawahan menurun (39.2-29.4%), hutan meningkat (5.6-7.2%)), Air (kolam budidayan meningkat (8.9-10.2%), air permukaan menurun (25.1-17.9%)) dan Lahan datar yang tidak terpakai meningkat (5.2-11%). Karena kebijakan pemerintah, pasar, aktivitas ekonomi, intrusi air laut, perubahan iklim dan penumpukan alluvial. Kegiatan ekonomi dan pasar adalah yang paling utama dari faktor yang mempengaruhi konversi dari penggunaan lahan oleh para petani.

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ABSTRACT

LAND COVER CHANGE DETECTION USING LANDSAT DATA IN GIAO THUY DISTRICT, NAM DINH PROVINCE, VIETNAM

Giao Thuy is a coastal area, has fully the characteristics of microclimate Red River delta. This study was conducted to detect the land cover changes from 2000 to 2015 and the causes of land cover change. Landsat data was employed to classification land use combined with interviews. These results showed that most portion of the land cover class was crops land and driving force of land cover change in Giao Thuy district period 2000 – 2015 are presented as follows: Vegetation area (crops land decreased (39.2–29.4%), forest land rised (5.6–7.2%)), Surface water area (aquaculture ponds increased (8.9–10.2%), surface water reduced (25.1–17.9%)) and Unuse flat land area increased (5.2–11%). Because of policy issuses, market, economic, salinity intrusion, climate change and deposits alluvial. Economic and market what are the most important factors affected to decide the conversion of land use purpose of famers.

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SUMMARY

Nguyen Tuyet Lan. Land cover change detection using Landsat data in Giao Thuy district, Nam Dinh province, Vietnam. First supervisor: Ass. Prof. Dr. Takahiro Osawa and second supervisor: Prof. Dr. Ir. I Wayan Nuarsa, M.Si.

Giao Thuy district located on the southern of the Red River delta is 26,500 ha area. Giao Thuy has fully the characteristics of microclimate Red River delta, is a tropical area, monsoon, hot, humid and rainy, with four seasons. Annually, Giao Thuy often affected by typhoons or tropical depressions, averaging from 4 – 6 episodes/year. The extreme weather phenomena: temperature increase; rainfall change; increased frequency and severity of cold weather damage, etc combined with sea level rise, salinity intrusion is difficult for agricultural production (An and Bang, 2014). According to the guidelines of the State, Giao Thuy also directed the communes implemented the new rural construction, combined with land consolidation embellishment field, transforming from low productivity agricultural production to aquaculture ponds, move economic restructuring to enhance economic efficiency, increase income for laborers (Giao Thuy district People's Committee, 2013).

This research conducted to analyzing land cover change and the causes of land cover change from 2000 – 2015 in Giao Thuy district. Aim of this study provided more information about land cover changes and how much area did land cover change in each class. This research was about the process of land cover changes, includes vegetation areas (crops land, forests land), surface water areas (aquaculture ponds, surface water – river, channel and sea water) and unused flat land areas (barren land, unused land and mudflats) from 2000 to 2015 using Landsat data in 2000, 2005, 2010 and 2015.

Results of classification were land cover map in Giao Thuy district in 2000, 2005, 2010 and 2015 with seven categories including crops land, built-up land, river channel, sea surface water, aquaculture ponds, forest land and unused flat land. The percentages of land cover for 2000 are crops land (36.2%), forest land (5.7%), aquaculture ponds (7.9%), surface water (25.1%), and unused flat land (6.1%). There are crops land (30.6%), forest land (5.9%), aquaculture ponds (8.9%), the remaining 25.3% is surface water and unused flat land (5.2%) in percent coverage of 2005. Crops land, forest land, aquaculture ponds, surface water, and un use flat land amounted to about 29.4%, 6.4%, 9.8%, 24.5% and 5.8% in 2010. Finally, for 2015 crops land and forest land accounting for 29.4% and 7.2%, while aquaculture ponds, surface water and unused flat land amounted to about 10.2%, 17.9% and 11.0%. Most portion of the land cover class was crops land during this period.

During 2000 – 2015 in Giao Thuy district, crops land decreased due to conversion into built-up land and aquaculture ponds; which amounted built-up land (1,220 ha – (12.7 %)) and aquaculture ponds (430 ha – (4.5%)). Built-up land category has

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increased 1.27 times (from 5,047 ha in 2000 to 6,436 ha in 2015) due to crops land was converted. Strong relationship between surface water and unused flat land and 27.3% (1,820 ha) of surface water is converted to unused flat land. Aquaculture ponds category has increased at the expense of crops land and surface water which accounts to 16% and 12.4% respectively. 5.9% aquaculture ponds area (123 ha) and 1.1% crop land area (103 ha) in 2000 were converted into forest land area in 2015 to make increase forest land area.

Crops land has tended to decrease during the fifteen years because built-up land and aquaculture ponds were converted to it because population growth to make increasing the demand for shelter, policy about encouraging land use conversion from rice land into aquaculture ponds since 2002 and the salinity intrusion and storm, flood made its low productivity. Moreover, it was also influenced by the needs of the market and the economy. Forest land has tended to rising from 2000 to 2015. Due to the local government had noticed the role of protection forests against wind, sand and storms and mangroves to the effects of climate change. Giao Thuy also has Xuan Thuy National Park located in the core zone Biosphere Reserve south of the Red River delta was organized by UNESCO. Therefore, they usually maintain, protect existing forest area and plant more in some new areas. Unused flat land in there in 2015 increased and surface water decreased opposite significantly because many new mudflats appeared by the deposits alluvial annual.

The conclusion of this research: Driving force of land cover change in Giao Thuy district from 2000 to 2015 was presented as follows: vegetation areas (crops land decreased (39.2–29.4%), forest land rose (5.6–7.2%)), surface water areas (aquaculture ponds increased (8.9–10.2%), surface water reduced (25.1–17.9%)) and unused flat land area increased (5.2–11%). Most portion of the land cover class was crops land during this period. Due to policy issuses, market, economic, salinity intrusion, climate change and deposits alluvial. Economic and market what were the most important factors affected to decide the conversion of land use purpose of famers.

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LIST OF CONTENT

Page

INSIDE COVER PAGE……….. PREREQUISITE PAGE………. AGREEMENT SHEET……….. APPROVAL PAGE OF EXAMINER COMMITTEE……… STATEMENT FROM PLAGIARISM……….. ACKNOWLEDGEMENT………..

ABSTRAK………

ABSTRACT……….

SUMMARY………..

LIST OF CONTENT………..…. LIST OF TABLE………... LIST OF FIGURE………... LIST OF APPENDIX……….. CHAPTER I INTRODUCTION………..

1.1 Background………..

1.2 Formula of Problems………

1.3 Aim of Research………...

1.4 Benefits of Research……… CHAPER II LITERATURE REVIEW………...………

2.1 Land Cover………...………

2.1.1 Denifition……… 2.1.2 Land Cover Classification System……….

2.2 Remote Sensing……….... 2.3 Landsat……….……

2.3.1 Landsat 7 ETM+……….

2.3.2 Landsat 8 OLI/TIRS………

CHAPTER III FRAMEWORK OF RESEARCH……….. i ii iii iv v vi viii ix x xii xv xvi xviii 1 1 3 3 3 4 4 4 4 8 11 12 17 20

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CHAPTER IV RESEARCH METHODS……….

4.1 Research Location……….

4.1.1 Geography……… 4.1.2 Climate………. 4.1.3 Hydrological……… 4.1.4 Land Resource………. 4.1.5 Water Resource……… 4.1.6 Forest Resource……… 4.1.7 Marine Resource………..

4.2 Research Scope………...

4.3 Data Source……… 4.4 Research Instruments……… 4.5 Research Procedure………... 4.5.1 Image Preprocessing……….

4.5.1.1 Gap - Fill Procedure ………. 4.5.1.2 Geometric correction………. 4.5.1.3 Radiometric correction………. 4.5.1.4 Colour Composite Images……….

4.5.2 Image Classification with Maximum Likelihood Method…………...

4.5.3 Accuracy Assessment………...

4.5.4 Detection Land Cover Change……….

CHAPTER V RESULTS……….

5.1 Image Preprocessing ……… 5.1.1 Gap - Fill Procedure ……… 5.1.2 Cropping Images……….. 5.1.3 Colour Composite Images……… 5.2 Classification Image……….. 5.3 Accuracy Assessment……… 5.4 Land Cover Change Detection……….

CHAPTER VI DISSCUSIONS……….………

22 22 23 23 24 24 25 25 26 26 27 29 29 29 29 30 31 31 32 34 36 37 37 38 39 40 41 48 50 58

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6.1 Classification Image……….. 6.2 Accuracy Assessment………

6.3 Land Cover Change Detection……….. 6.4 The Cause of Driving Force Of Land Cover Change………

CHAPTER VII CONCLUSIONS AND SUGGESTIONS………

7.1 Conclusions………... 7.2 Suggestions………

REFERENCES………..………..………

APPENDIX……….. 58 59 60 62 66 66 67 68 71

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LIST OF TABLE

Table

Table 2.1 Land use and land cover classification system for use with remote sensor data……….. Table 2.2 Land cover classification system……… Table 2.3 Landsat 7 ETM + Satellite Characteristics………. Table 2.4 Bands on Landsat 7 ETM +……… Table 2.5 Bands on Landsat 8 OLI/TIRS………... Table 4.1 The data source used………... Table 4.2 The confusion matrix……….. Table 5.1 Confusion matrix in 2015………... Table 5.2Assessment classification results in 2015………... Table 5.3 Land cover classes, area and percentcoverage in Giao Thuy district

from 2000 – 2015………. Table 5.4 Change detection matrix in Giao Thuy district periods 2000 – 2015 Table 6.1 The cause of driving force of land cover change during 2000 –

2015 in Giao Thuy district………..

Page

6 7 13 14 19 27 35 49 49

55 57

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LIST OF FIGURE

Figure

Figure 2.1 Principles of Remote Sensing……….. Figure 2.2 Strength of reflection and radiation of electromagnetic waves from

plants, earth and water in each wavelength……… Figure 2.3 Landsat Thematic Mapper (TM) image……… Figure 2.4 LANDSAT 8 Satellite Sensor……….. Figure 3.1 Framework of Research……… Figure 4.1 Map of Research Location……… Figure 4.2 Landuse map of Giao Thuy district in 2010………... Figure 5.1 Landsat images in 2000, 2005, 2010 and 2015 (Path 126, Row 46)…. Figure 5.2 Gapfill processing 7 ETM+ SLC-Off………... Figure 5.3 Before and after cropping image in Landsat 8 image in 2015 of

Landsat ………. Figure 5.4 Composite Image at November 16th 2000 of combination 453 (RGB) Figure 5.5 Composite Image at July 10th 2015 of combination 564 (RGB) …….. Figure 5.6 Classification with Landsat 8 image at July 10th 2015 of 564 (RGB)

combination... Figure 5.7 Classification with Landsat 7 image at November 16th 2000 of 453

(RGB) combination... Figure 5.8 Land cover classification map in Giao Thuy district in 2000 (a), 2005

(b), 2010 (c) and 2015 (d)……….

Figure 5.9 Aquaculture ponds area in Giao Thuy district in 2000, 2005, 2010 and 2015... Figure 5.10 Crops land area in Giao Thuy district in 2000, 2005, 2010 and 2015 Figure 5.11 Forest land area in Giao Thuy district in 2000, 2005, 2010 and 2015 Figure 5.12 Surface-water area in Giao Thuy district in 2000, 2005, 2010 and

2015………. Page 9 10 12 17 21 22 28 37 39 40 41 41 42 43 45 48 50 51 52 53

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Figure 5.13 Un used flat land area in Giao Thuy district in 2000, 2005, 2010 and 2015………... Figure 5.14 Land cover change detection in Giao Thuy district from 2000 –

2015……….

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LIST OF APPENDIX

Appendix

Appendix 1 Structure questionnaire interview………... Appendix 2 Points GPS data in 2015………. Appendix 3 Weighted scoring factors afecting the conversion of land use

purpose……… Appendix 4 Pictures of interview householder………..……… Appendix 5 Pictures of crops land……….………… Appendix 6 Pictures of forest land………. Appendix 7 Pictures of aquaculture ponds………. Appendix 8 Fluctuation of the average sea level in the period 1960 – 2002 in

Nam Dinh province……….

Appendix 9 Evolution of salt concentration in the sluice gates along the Red

River for recent years………..

Page 71 73

77 78 79 80 81

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CHAPTER I INTRODUCTION

1.1 Background

Giao Thuy district located on the southern of the Red River delta, 45 km northwest of Nam Dinh City has 26,499.6 ha area. Population is 193.306 people and population density is 811people/km². There are 2 towns and 20 communes, with 32 km coastline and dense river system created by the Red River and the tributaries of the Red River (So River, Con Nhat River). The topography varied both plains and semi – mountain hills, but quite flat. Giao Thuy has Xuan Thuy National Park located in the core zone Biosphere Reserve south of the Red River delta was organized by UNESCO. The natural conditions and location in there have made good conditions for the economic – society development, especially agricultural, aquaculture and salt production. Giao Thuy has fully the characteristics of microclimate Red River delta, is a tropical area, monsoon, hot, humid and rainy, with four seasons (spring, summer, autumn and winter). Annually, Giao Thuy often affected by typhoons or tropical depressions, averaging from 4 – 6 episodes/year. The extreme weather phenomena: temperature increase; rainfall change; increased frequency and severity of cold weather damage, etc combined with sea level rise, salinity intrusion is difficult for agricultural production, water resources, environmental hygiene; threatening the food security of the district. The storm caused heavy flooding eroded river dike, mudflats lead to loss of arable land, threatening the lives of people along the dike. Sea level rise combined typhoon caused to landslide sea dike, mudflats that affecting farming activities fishing, salt production; damage to people and property of the people, especially the coastal areas of the district (An and Bang, 2014).

According to the guidelines of the State, Giao Thuy also directed the communes implemented the new rural construction, combined with land consolidation embellishment field created favorable conditions for economic development, transforming from high productivity rice production land to goods land, and low productivity agricultural production to aquaculture ponds, the cultivation of crops as well as models move economic restructuring opens up many prospects for developing agricultural commodity to enhance economic efficiency, increase income for laborers (Giao Thuy district People's Committee, 2013).

These characteristics result that land cover recent 15 years in Giao Thuy district has changed. The present situation requires detailed assessment of the process of changing land cover, statistical analysis - the area of land cover change and change in land cover classes through stages in Giao Thuy district. Therefore, the research is “Land cover change detection using

landsat data in GiaoThuy district, Nam Dinh provine, Vietnam” during 2000 – 2015 be implications for the decisions related to land cover, land use in local in the future.

1.2 Formula of Problems

Based on this background, the problem is formulated as follows:

1. How to make land cover map using Landsat data in 2000, 2005, 2010 and 2015 and asses the accuracy of land cover classification in 2015 in Giao Thuy district?

2. How to determine land cover change area, analyze changes in land cover classes and the cause of driving force/ trends of land cover change from 2000 – 2015 in Giao Thuy district?

1.3 Aim of Research

1. To classify the land use using Landsat data in 2000, 2005, 2010 and 2015 and asses the accuracy of land cover classification in 2015 in Giao Thuy district.

2. To determine the area of land cover change, analyze changes in land cover classes and the cause of driving force/ trends of land cover change from 2000 – 2015 in Giao Thuy district.

1.4 Benefits of Research

This study is expected to provide information about what kind of land cover changes and how much area did land cover change in each class. This study is also useful for not only land use policy and planning but also sustainable agro-ecosystems development adapt to climate change in local in the future.

CHAPTER II LITERATURE REVIEW

2.1 Land cover

2.1.1 Denifition

Land cover is the observed (bio) physical cover on the earth's surface (Antonio and Louisa, 1976). When considering land cover in a very pure and strict sense it should be confined to describe vegetation and man-made features. Consequently, areas where the surface consists of bare rock or bare soil are describing land itself rather than land cover. Also, it is disputable whether water surfaces are real land cover.

Land cover is the physical material at the surface of the earth. Earth cover is the expression used by ecologists that has its closest modern equivalent being vegetation. Land cover data documents how much of a region is covered by forests, wetlands, impervious surfaces, agriculture and other land and water types, etc. So when referring to land cover change is said to a change in the coverage of forests, wetlands, impervious surfaces, agriculture and other land and water types through the years.

There are two primary methods for capturing information on land cover: field survey and analysis of remote sensing imagery. Using satellite imageries is the importance of techniques and methods as data sources have been developed and successfully applied for land cover classification and change detection in various environments including rural, urban, and urban fringes. Satellite-based remote sensing technology cannot yet be used to monitor land cover at the level of accuracy required by developers, engineers and planners interests. Therefore, the use of remote sensing satellite data for land use land cover change detection and monitoring is widely applying through out the world with the aid of technological improvement that provides

high resolution images. Three satellite imagery systems which could supply the required data were the Landsat Multi-Spectral Scanner (MSS); Landsat Thematic Mapper and SPOT MSS. Land cover maps provide information to help managers best understand the current landscape. To see change over time, land cover maps for several different years are needed. With this information, managers can evaluate past management decisions as well as gain insight into the possible effects of their current decisions before they are implemented.

2.1.2 Land cover classification system

The types of land cover categorization developed in the classification system can be related to systems for classifying land capability, vulnerability to certain management practices, and potential for any particular activity or land value, either intrinsic or speculative. Anderson (1976) mentioned about a land cover classification system for use with remote sensor data. Heclassified land coverclassification system what is shown in table 2.1.

Table 2.1

Land use and land cover classification system for use with remote sensor data

Level I Level II

1. Urban or Built-up Land 1.1. Residential

1.2. Commercial and Services 1.3. Industrial

1.4. Transportation, Communications, and Utilities 1.5. Industrial and Commercial Complexes

1.6. Mixed Urban or Built-up Land 1.7. Other Urban or Built-up Land 2. Agricultural Land 2.1. Cropland and Pasture

2.2. Orchards, Groves, Vineyards, Nurseries, and Ornamental Horticultural Areas

2.3. Confined Feeding Operations 2.4. Other Agricultural Land 3. Rangeland 3.1. Herbaceous Rangeland

3.2. Shrub and Brush Rangeland 3.3. Mixed Rangeland

4. Forest Land 4.1. Deciduous Forest Land 4.2. Evergreen Forest Land 4.3. Mixed Forest Land 5. Water 5.1. Streams and Canals

5.2. Lakes 5.3. Reservoirs

5.4. Bays and Estuaries 6. Wetland 6.1. Forested Wetland

6.2. Nonforested Wetland 7. Barren Land 7.1. Dry Salt Flats.

7.2. Beaches

7.3. Sandy Areas other than Beaches 7.4. Bare Exposed Rock

7.5. Strip Mines Quarries, and Gravel Pits 7.6. Transitional Areas

7.7. Mixed Barren Land 8. Tundra 8.1. Shrub and Brush Tundra

8.2. Herbaceous Tundra 8.3. Bare Ground Tundra 8.4. Wet Tundra

8.5. Mixed Tundra 9. Perennial Snow or Ice 9.1. Perennial Snowfields

9.2. Glaciers Source: Anderson, 1976.

National Land Cover Dataset 1992 (NLCD1992) is a 21 class land cover classification scheme that has been applied consistently across the lower 48 United States at a spatial resolution of 30 meters. NLCD92 is based primarily on the unsupervised classification of Landsat Thematic Mapper (TM) circa 1990's satellite data. Other ancillary data sources used to generate these data included topography, census, and agricultural statistics, soil characteristics, and other types of land cover and wetland maps. The classification system used for NLCD 92 is modified from the Anderson land use and land cover classification system is shown in Table 2.2.

Table 2.2

Land cover classification system

Level I Level II

1. Water 1.1. Open Water

1.2. Perennial Ice/Snow 2. Developed 2.1. Low Intensity Residential

2.2. High Intensity Residential

2.3. Commercial/Industrial/Transportation 3. Barren 3.1. Bare Rock/Sand/Clay

3.2. Quarries/Strip Mines/Gravel Pits 3.3. Transitional

4. Forested Upland 4.1. Deciduous Forest 4.2. Evergreen Forest 4.3. Mixed Forest 5. Shrubland 5.1. Shrubland

6. Non – Natural Woody 6.1. Orchards/Vineyards/Other 7. Herbaceous Upland Natural/Semi

Natural Vegetation

7.1. Grasslands/Herbaceous 8. Herbaceous Planted/Cultivated 8.1. Pasture/Hay

8.2. Row Crops 8.3. Small Grains 8.4. Fallow

8.5. Urban/Recreational Grasses 9. Wetlands 9.1. Woody Wetlands

9.2. Emergent Herbaceous Wetlands Source: http://landcover.usgs.gov

A land cover classification system which can effectively employ orbital and high altitude remote sensor data should meet the following criteria (Anderson, 1971):

1. The minimum level of interpretation accuracy in the identification of land use and land cover categories from remote sensor data should be at least 85 percent.

2. The accuracy of interpretation for the several categories should be about equal.

3. Repeatable or repetitive results should be obtainable from one interpreter to another and from one time of sensing to another.

4. The classification system should be applicable over extensive areas.

5. The categorization should permit vegetation and other types of land cover to be used as surrogates for activity.

6. The classification system should be suitable for use with remote sensor data obtained at different times of the year.

7. Effective use of subcategories that can be obtained from ground surveys or from the use of larger scale or enhanced remote sensor data should be possible.

8. Aggregation of categories must be possible.

9. Comparison with future land use data should be possible. 10. Multiple uses of land should be recognized when possible.

2.2 Remote sensing

Remote Sensing is the science of acquiring, processing and interpreting images that record the interaction between electromagnetic energy and matter (Sabins, 1996).

Remote Sensing is the science and art of obtaining information about an object, area or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area or phenomenon under investigation (Lillesand and Kiefer, 1994).

Figure 2.1

Principles of Remote Sensing Source: http://www.crisp.nus.edu.sg

Remote sensing is based on measuring electro magnetic (EM) energy. The reflected or radiated EM waves are received by sensors aboard platform. The characteristics of reflected or radiated EM waves depend on the type or condition of the objects. By understanding characteristics of EM response and comparing observed information, we can know the size, shape and character of the objects.

Objects’ information is obtained from analysis of data collected by the sensors remotely

mounted on a vehicle in the form of aircraft, satellites, space shuttles or other vehicle.

Sensors are used to obtain visibility data of the earth through EM enery that is the result of transmission and reflection from the object. Remote sensing has using EM waves from the sun natural energy source. In additional to the sensitivity of each sensor is different, in recording the smallest objects that can still be recognizable and distinguishable against other objects or to the

surrounding environment. The ability of sensors to present the picture of the smallest object is called spatial resolution which is an indication of the quality of the sensor (Sutanto, 1986).

Interaction energy with the object in accordance with the principle of conservation of energy, then there are three interactions, namely reflected, absorbed and transmitted/forwarded. The amount of energy reflected, absorbed, transmitted would differ in each land cover. This implies that if the value of the reflected power at a place similar to other place where it can be assumed to have characteristics similar land cover.

Figure 2.2

Strength of reflection and radiation of electromagnetic waves from plants, earth and water in each wavelength.

Source: http://www.eorc.jaxa.jp

Remote sensing systems offer four basic components to measure and record data about an area from a distance. These components include a target, an energy source, a transmission path and a sensor. The target is the object or material that is being studied. The components in the system work together to measure and record information about the target without actually coming into physical contact with it. There must also be an energy source which illuminates or provides electromagnetic energy to the target. The energy interacts with the target, depending on

the properties of the target and the radiation, and will act as a medium for transmitting information from the target to the sensor. The sensor is a remote device that collects and records the electromagnetic radiation. Sensors can be used to measure energy that is given off (or emitted) by the target, reflected off of the target, or transmitted through the target (Lillesand and Kiefer, 1994).

2.3 Landsat

Landsat (Land Satellite) is a satellite series that uses remote sensing to observing the earth and taking images of the landscape of the world. The radiometers instruments aboard satellite measuring then processing the electromagnetic energy reflected and emitted from the earth’s surface and transmitted through the atmosphere to produce satellite images (Moraniec, 2011). The Landsat series is one of the major forces leading to the development of the global earth systems science concept (Williams et al_{., 2006).}

Since four decades (1972), Landsat program has provided calibrated high resolution multispectral data of the earth surface on global basis. Because its images are perfect quality, Landsat imagery provides a unique resource for researchers and common users in agricultural evaluation and monitoring, geological survey, global change detection, archaeology, mapping, water management and regional planning.

Figure 2.3

Landsat Thematic Mapper (TM) image Source: www.ga.gov.au

2.3.1 Landsat 7 ETM +

Landsat 7 was launched on 15 April 1999 to bring ETM + Scanner, have an orbit aligned with the sun (sun synchronous), crossing the equator at 10:00 loacal time, and covered the same area (repeat covearge interval) every 16 days with a swath width of each coverage is 185 km, more details about the characteristics of Landsat 7 ETM + can be seen in Table 2.3.

Table 2.3

Landsat 7 ETM + Satellite Characteristics

System Landsat 7 ETM + Swath width 185 km Repeat coverage interval 16 days (233 orbits)

Altitude 705 km Quantization Best 8 of 9 orbits On board data storage 375 Gb (solid state)

Inclination Sun synchronous, 98.2 degree Equatorial crossing Descending node; 10:00 am +/- 15min

Launch vehicle Delta II Launch date April 1999 Source: http://landsathandbook.gsfc.nasa.gov

Landsat 7 ETM + are composed of seven different bands plus one panchromatic band, with a different ground resolution range as seen on the table below. Each band represents a different portion of the spectral. Shorter range of wave length is better than the ability to differentiated orbit on the earth. Base on Lillesand and Kiefer (1994) statement, the best light penetration in pure water is in the band channel that has a range of wavelengths from 0.48 – 0.6 um. In the Landsat 7 ETM + satellite image, the wavelength range between 0.48 – 0.6 um contained in the band 1 (blue) and the band 2 (green).

Landsat Enhanced Thematic Mapper Plus (ETM+) images consist of eight spectral bands with a spatial resolution of 30 meters for Bands 1 to 7. The resolution for Band 8 (panchromatic) is 15 meters. All bands can collect one of two gain settings (high or low) for increased radiometric sensitivity and dynamic range, while Band 6 collects both high and low gain for all scenes. Approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi).

Table 2.4

Enhanced Thematic Mapper Plus (ETM+)

Landsat 7 Wavelength (micrometers)

Resolution (meters) Band 1 0.45-0.52 30 Band 2 0.52-0.60 30 Band 3 0.63-0.69 30 Band 4 0.77-0.90 30 Band 5 1.55-1.75 30 Band 6 10.40-12.50 60 * (30) Band 7 2.09-2.35 30 Band 8 .52-.90 15

Source: http://landsat.usgs.gov

* ETM+ Band 6 is acquired at 60-meter resolution. Products processed after February 25, 2010 are resampled to 30-meter pixels.

The Landsat 7 system insured continuity of Thematic Mapper type data into the next century. These data will be made available to all users through Eros Data Centre at the cost of fulfilling user request. Browse data (a lower resolution image for determining image location, quality and information content) and metadata (descriptive information on the image) will be available, on line, to users within 24 hours of acquisition of the image by the primary ground station. Eros Data Centre will process all Landsat 7 data received to “level 0R” (i.e. corrected for scan direction and band alignment but without radiometric or geometric correction) and archive the data in that format. A systematically corrected product (level 1G) generated and distributed to users on request. The user have the option of performing further processing on the data on user operated digital processing equipment or by a commercial, value added firm.

On 31 May 2003, the Scan Line Corrector (SLC) on Landsat 7 ETM + has a failure. SLC is a technology on landsat 7 ETM + is designed to fill the gaps in satellite images of Landsat 7 ETM +, which is caused by the forward motion of the satellite during orbit. As a result of this failure, it is estimated about 22% of the image produced on a path/row have a losing information (Scramuzza, et al. 2004). This failure was known as the Landsat 7 ETM + SLC – OFF.

One technique to fill the information gaps occurred, developed alghoritm Localized linear Histrogram Match (LLHM). Pixels on the image which gap fill filled by another image that does not lose information, while minimizing the area that experienced gapfill image pixels in the same area both the primary image and the fill image. It is using moving windows. The illustrated of moving windows represented, where the white colour represented gaps in the Landsat 7 ETM + and mix colours represented the area with no gaps. The red square represented limited of sampling pixel for moving windows.

After the calculation, alghoritm LLHM employed the value of the gain and bias. Alghoritm results obtained for using to calculate the value of the missing pixels in the primary image pixel base of the fill image pixel. Equation (2.1) showed the equation for LLHM:

MergedDN = FillDN * Gain + Bias (2.1)

Where: FillDN – Digital number fill image

Gain and Bias values calculated with the equation follow

�� =�_�� �

(2.2)

��� = ��− ��∗ �� (2.3)

�� = _{� ∑}1 � � � ��� �

�=

�� = _{� − 1 ∑}1 � � � ��� − �� �

�=

�� = _{� ∑}1 � �� �

�=

�� = _{� − 1 ∑}1 � � � ��� − �� �

�=

2.3.2 Landsat 8 OLI/TIRS

Landsat 8 satellite sensor is part of the Landsat Data Continuity Mission was successfully launched on February 11, 2013 from Space Launch Complex – 3, Vandenberg Air Force Base in California and will join Landsat 7 satellite in orbit.

Figure 2.4

LANDSAT 8 Satellite Sensor Source: http://landsat.gsfc.nasa.gov

The Landsat 8 satellite images the entire earth every 16 days in an 8-day offset from Landsat 7. Data collected by the instruments onboard the satellites are available to download at no charge from GloVis, EarthExplorer, or via the LandsatLook Viewer within 24 hours of reception.

Landsat 8 carries two instruments: The Operational Land Imager (OLI) sensor includes refined heritage bands, along with three new bands: a deep blue band for coastal/aerosol studies, a shortwave infrared band for cirrus detection, and a Quality Assessment band. The Thermal Infrared Sensor (TIRS) provides two thermal bands. These sensors both provide improved signal-to-noise (SNR) radiometric performance quantized over a 12-bit dynamic range. (This

translates into 4096 potential grey levels in an image compared with only 256 grey levels in previous 8-bit instruments.) Improved signal to noise performance enable better characterization of land cover state and condition. Products are delivered as 16-bit images (scaled to 55,000 grey levels).

Landsat 8 images have a large file size, at approximately 1 GB compressed. Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) images consist of nine spectral bands with a spatial resolution of 30 meters for Bands 1 to 7 and 9. New band 1 (ultra-blue) is useful for coastal and aerosol studies. New band 9 is useful for cirrus cloud detection. The resolution for Band 8 (panchromatic) is 15 meters. Thermal bands 10 and 11 are useful in providing more accurate surface temperatures and are collected at 100 meters. Approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi) (http://landsat.usgs.gov). Table 2.5 showed bands on Landsat 8 OLI/TIRS with wavelength and resolution.

Table 2.5

Bands on Landsat 8 OLI/TIRS Landsat 8

Operational Land Imager (OLI)

and Thermal Infrared Sensor (TIRS) Launched

February 11, 2013

Bands Wavelength (micrometers)

Resolution (meters) Band 1 - Coastal aerosol 0.43 - 0.45 30

Band 2 - Blue 0.45 - 0.51 30 Band 3 - Green 0.53 - 0.59 30 Band 4 - Red 0.64 - 0.67 30 Band 5 - Near Infrared (NIR) 0.85 - 0.88 30 Band 6 - SWIR 1 1.57 - 1.65 30 Band 7 - SWIR 2 2.11 - 2.29 30 Band 8 - Panchromatic 0.50 - 0.68 15 Band 9 - Cirrus 1.36 - 1.38 30 Band 10 - Thermal Infrared

(TIRS) 1

10.60 - 11.19 100 * (30)

Band 11 - Thermal Infrared (TIRS) 2

11.50 - 12.51 100 * (30)

Source: http://landsat.usgs.gov

* TIRS bands are acquired at 100 meter resolution, but are resampled to 30 meter in delivered data product.

On 31 May 2003, the Scan Line Corrector (SLC) on Landsat 7 ETM + has a failure. SLC is a technology on landsat 7 ETM + is designed to fill the gaps in satellite images of Landsat 7 ETM +, which is caused by the forward motion of the satellite during orbit. As a result of this failure, it is estimated about 22% of the image produced on a path/row have a losing information (Scramuzza, et al. 2004). This failure was known as the Landsat 7 ETM + SLC – OFF.

One technique to fill the information gaps occurred, developed alghoritm Localized linear Histrogram Match (LLHM). Pixels on the image which gap fill filled by another image that does not lose information, while minimizing the area that experienced gapfill image pixels in the same area both the primary image and the fill image. It is using moving windows. The illustrated of moving windows represented, where the white colour represented gaps in the Landsat 7 ETM + and mix colours represented the area with no gaps. The red square represented limited of sampling pixel for moving windows.

After the calculation, alghoritm LLHM employed the value of the gain and bias. Alghoritm results obtained for using to calculate the value of the missing pixels in the primary image pixel base of the fill image pixel. Equation (2.1) showed the equation for LLHM:

MergedDN = FillDN * Gain + Bias (2.1)

Where: FillDN – Digital number fill image

Gain and Bias values calculated with the equation follow �� =�_��

�

(2.2)

��� = ��− ��∗ �� (2.3)

�� = _{� ∑}1 � � � ��� �

�=

�� = _{� − 1 ∑}1 � � � ��� − �� �

�=

�� = _{� ∑}1 � �� �

�=

�� = _{� − 1 ∑}1 � � � ��� − �� �

�=

2.3.2 Landsat 8 OLI/TIRS

Landsat 8 satellite sensor is part of the Landsat Data Continuity Mission was successfully launched on February 11, 2013 from Space Launch Complex – 3, Vandenberg Air Force Base in California and will join Landsat 7 satellite in orbit.

Figure 2.4

LANDSAT 8 Satellite Sensor Source: http://landsat.gsfc.nasa.gov

The Landsat 8 satellite images the entire earth every 16 days in an 8-day offset from Landsat 7. Data collected by the instruments onboard the satellites are available to download at no charge from GloVis, EarthExplorer, or via the LandsatLook Viewer within 24 hours of reception.

Landsat 8 carries two instruments: The Operational Land Imager (OLI) sensor includes refined heritage bands, along with three new bands: a deep blue band for coastal/aerosol studies, a shortwave infrared band for cirrus detection, and a Quality Assessment band. The Thermal Infrared Sensor (TIRS) provides two thermal bands. These sensors both provide improved signal-to-noise (SNR) radiometric performance quantized over a 12-bit dynamic range. (This

translates into 4096 potential grey levels in an image compared with only 256 grey levels in previous 8-bit instruments.) Improved signal to noise performance enable better characterization of land cover state and condition. Products are delivered as 16-bit images (scaled to 55,000 grey levels).

Landsat 8 images have a large file size, at approximately 1 GB compressed. Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) images consist of nine spectral bands with a spatial resolution of 30 meters for Bands 1 to 7 and 9. New band 1 (ultra-blue) is useful for coastal and aerosol studies. New band 9 is useful for cirrus cloud detection. The resolution for Band 8 (panchromatic) is 15 meters. Thermal bands 10 and 11 are useful in providing more accurate surface temperatures and are collected at 100 meters. Approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi) (http://landsat.usgs.gov). Table 2.5 showed bands on Landsat 8 OLI/TIRS with wavelength and resolution.

Table 2.5

Bands on Landsat 8 OLI/TIRS Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) Launched

February 11, 2013

Bands Wavelength

(micrometers)

Resolution (meters) Band 1 - Coastal aerosol 0.43 - 0.45 30

Band 2 - Blue 0.45 - 0.51 30

Band 3 - Green 0.53 - 0.59 30

Band 4 - Red 0.64 - 0.67 30

Band 5 - Near Infrared (NIR) 0.85 - 0.88 30

Band 6 - SWIR 1 1.57 - 1.65 30

Band 7 - SWIR 2 2.11 - 2.29 30

Band 8 - Panchromatic 0.50 - 0.68 15

Band 9 - Cirrus 1.36 - 1.38 30

Band 10 - Thermal Infrared (TIRS) 1

10.60 - 11.19 100 * (30)

Band 11 - Thermal Infrared (TIRS) 2

11.50 - 12.51 100 * (30)

Source: http://landsat.usgs.gov

* TIRS bands are acquired at 100 meter resolution, but are resampled to 30 meter in delivered data product.