the land cover mapping of dong hy district, thai nguyen province using satellite images and gis

THAI NGUYEN UNIVERSITY OF AGRICULTURE AND FORESTRY
PROGRAM ADVANCED EDUCATION



Student name: Nguyen Binh Minh
Student ID: 1053060032
K42 - AEP
THE LAND COVER MAPPING OF DONG HY DISTRICT, THAI NGUYEN
PROVINCE USING SATELLITE IMAGES AND GIS

Supervisor: Msc. Nguyen Van Hieu

Thai Nguyen 15th January, 2015


ABSTRACT
Land use/cover change mapping is one of the basic tasks for environmental monitoring
and management. This research was conducted to analyze the land use and land cover
changes in Dong Hy district, Thai Nguyen province. In recent years, a variety of
change detection techniques have been developed. The data sources used in this study
were Landsat 5 and Landsat 8 images taken in November 2004, and December 2013,
respectively.
By using ArcGIS and ENVI software and remote sensing data, a supervised
classification was performed based on fusion data from a composite image of the
bands. Using this output, available secondary data together with field data in order to
perform a Maximum Likelihood supervised classification. Six classes were classified,
namely forest, water, mineral, residential, traffic, rice – crops and water. With overall
accuracy 96.4092% and kappa = 0.9502 in 2004 , overall accuracy 96.2690% and
kappa coefficient = 0.9529 in 2013.
After conducted, we have:

- Land cover map of Dong Hy district in 2004 and 2013
- Land cover changes map of Dong Hy district in period 2004 – 2013
With the results achieved, we can realize the remote sensing and GIS technology is
effective method for high accuracy, cost savings in the classification and analysis of
land cover changes.


ACKNOWLEDGEMENT

Approved by International Training Center – Thai Nguyen University of Agriculture
and Forestry, with the enthusiastic help of Dong Hy district, the teachers, and peoples
at Center for Foreign Language and Applied Informatics, I have done to implement the
research: “The land cover mapping of Dong Hy district, Thai Nguyen province using
satellite images and GIS”. Through the implementation process I have gained much
useful knowledge as well as certain results.
I would like to express my special thanks of gratitude to my teacher Msc Nguyen Van
Hieu who gave me the golden opportunity to do this wonderful project, which also
helped me in doing a lot of Research and i came to know about so many new things
I am really thankful to them.
Secondly i would also like to thank my parents and friends who helped me a lot in
finishing this project within the limited time.
I would also like to thanks the officers and staffs of the Dong Hy district who
enthusiastically communicated word experience and helped me a lot in the supply of
data for my research to create conditions for I can complete this research. In addition, I
would like to thank family, friends and relatives who were always at my side to
encourage and help me in the learning process as well as during the time I performed
research.

Again, I sincerely thank!
Student

Nguyen Binh Minh
Thai Nguyen, January 8 ,2015


LIST OF ACRONYMS

GIS

Geographic information systems

NDVI

Normalized Difference Vegetation Index

RS

Remote sensing

SPOT

System Pour l'Observation de la Terre

IRS

Indian Remote Sensing

Lidar

Light Detection and Ranging

MOS

Marine Observation Satellite

SeaWiFS

Sea-viewing Wide-Field-of View Sensor

FLIR

Forward Looking InfraRed

RADAR

RAdio Detection And Ranging

USLE

Universal Soil Loss Equation

GDP

Gross Domestic Product

VND

Vietnamese dong

ROI

Region Of Interest


LISTS OF TABLES
Table
Name
Table 1
Land cover system
Table 2
Landsat satellite system
Table 3
Parameters of ETM Landsat ( Landsat 5)
Table 4
Parameters of LDCM Landsat (Landsat 8 ):

Page
5
13
14
15

Table 5

Statistics sample collection

22

Table 6:

Economic development in Dong Hy district period 2011 –

23

2013
Table 7

The norms of income

30

Table 8

The information of Landsat

33

Table 9:

Recognizing the features on images and fields

34, 35

Table 10

Results of the accuracy evaluation in 2004

40

Table 11:

Results of the accuracy evaluation in 2013

41

Table 12

Statistical fluctuations of land cover in the period 2004 -

45

2013


LIST OF FIGURES
I.

INTRODUCTION ................................................................................................ 1

1.1.

Background ....................................................................................................... 1

1.1.

Purposes............................................................................................................. 2

1.2.

Requirements ..................................................................................................... 2

1.3.

Signification ...................................................................................................... 2

II. LITERATURE REVIEW......................................................................................... 3
2.1.

Theoretical basis ................................................................................................ 3

2.1.1.

Definitions of land cover ........................................................................... 3

2.1.1.1. Land cover ................................................................................................... 3
2.1.1.2. Normalized Difference Vegetation Index ................................................... 5
2.1.2. . Geographic information system (GIS) ........................................................ 6
2.1.2.1. Geographic information system (GIS) ........................................................ 6
2.1.2.2. ArcGIS software......................................................................................... 8
2.1.3.

Remote sensing (RS) .................................................................................. 9

2.1.3.1. Remote sensing (RS) ................................................................................... 9
2.1.3.2. The Land sat program .............................................................................. 12
2.2.

Practical basis .................................................................................................. 16

2.2.1.

The research in the world ......................................................................... 16

2.2.2.

The research in Viet Nam ......................................................................... 18

3.1. Object, scope and time of research .................................................................... 21
3.1.1. The objects of research ................................................................................ 21
3.1.2. The scope ..................................................................................................... 21
3.2. Content of research ............................................................................................. 21


3.3. Methodology....................................................................................................... 21
3.3.1. Data collection ............................................................................................. 21
3.3.2. Field trips method ........................................................................................ 22
3.3.3. Building the land cover changes map .......................................................... 23
3.3.4. Normalized difference vegetation index (NDVI) ........................................ 24
3.3.5. Accuracy assessment and image processing after classified. ...................... 24
3.3.6. Building map. .............................................................................................. 25
IV.

RESULT AND DISCUSSION ........................................................................... 26

4.1. Evaluating the natural conditions and socioeconomic in research area ............ 26
4.1.1. Natural conditions. ...................................................................................... 26
4.1.1.1. The geographic location. ........................................................................... 26
4.1.1.2. The topography and geomorphology ........................................................ 28
4.1.1.3. The climate and hydrology ....................................................................... 28
4.1.1.4. Natural resources ....................................................................................... 28
4.1.2.

Socioeconomic conditions. ....................................................................... 29

4.1.2.1. Economic development status. .................................................................. 29
4.1.2.2. The Population, labor and employment. ................................................... 30
4.1.2.3. The culture and society. ............................................................................ 31
4.1.2.4. The infrastructure status. ........................................................................... 31
4.2. The process of current status land cover mapping ............................................. 32
4.2.1. Data preparation ........................................................................................... 32
4.2.1.1. Data collection .......................................................................................... 32
4.2.1.2. Data description ........................................................................................ 33

4.3. Analyze remote sensing image, determine land cover in Dong Hy district ....... 33


4.3.1. Image interpretation ..................................................................................... 33
4.3.2. The process of calculate NDVI .................................................................... 36
4.3.2.3. Remote sensing image classification ........................................................ 38
4.3.2.4. Evaluating the accuracy after classification .............................................. 40
4.3. ArcGIS application, editting current status land cover ..................................... 42
4.3.1. Building current status land cover map........................................................ 42
4.3.2. Building fluctuations map ............................................................................ 43
4.3.3.3. Analysis of fluctuation .............................................................................. 45
V. CONCLUSION AND RECOMMENDATION ..................................................... 47
5.1. Conclusion. ......................................................................................................... 47
5.2. Recommendation ................................................................................................ 48
VI.

REFERENCES ................................................................................................... 49

I.
1.1.

INTRODUCTION

Background

Land cover is all the material composition of natural and artificial cover on the
earth’s surface includes: vegetation, the constructions of human, soil, water, sandy
soil… Land cover show the current status.

Over time, land cover is continuous change under strong impact of disasters,
human – That is the economic – Social development activities. Research mapping
land cover using remote sensing and GIS technology helps to shorten the time
compared to the built maps technologies previously and it is important
contributions in the management of natural resources, assess the current state of
vegetation.

With these pressures, land and land cover are constantly fluctuating with the
development of society. This is a special resource can exploitation and use but can
not increase in quantity. Therefore, the monitoring, research, management and the
use of natural resources is an effective and reasonable.

Remote sensing technology is increasingly widely used in many sectors, fields of
meteorology - hydrology, geology, from environment to agriculture - forestry fisheries, ... including monitoring changes in the types of land cover with high
accuracy, which can help managers have more resources to monitor land-use
change. This is considered as one of the solutions for the posed problems. On the
other hand, this method has not been tested application in the area of Dong Hy
district. So, research " The land cover mapping of Dong Hy district, Thai Nguyen

province using satellite images and GIS " is performed.

1


1.1.

-

Purposes

Research overview of the land cover map, satellite images and geographic
information systems (GIS).

-

Research on remote sensing and GIS technology in mapping land cover work.

-

Research on the spectral properties of natural objects.

-

Develop process mapping land cover by remote sensing and GIS technology.

-

Assess the current state of the land cover in Dong Hy District, Thai Nguyen
Province.

1.2.

Requirements

- Adequate the data of natural condition, socioeconomic and spatial data.
- Classify and handling the data collected.
- The result of evaluating the current state of the land cover
- Proficiency in using GIS software to mapping data and analyze the data

1.3.

Signification

Mapping land cover using remote sensing and GIS technology help to shorten the
time compare to the built map technologies previously

-

The significance in learning and research: to learn the research methods,
evaluating the changes in land cover.

-

The significance in reality: Applying the knowledge on reality combine
with collect and analyzing data. Assessing the changes in land cover,
providing information to community in the research area.

2

II. LITERATURE REVIEW
2.1.

Theoretical basis

2.1.1.

Definitions of land cover

2.1.1.1. Land cover
a) Definitions
Land cover is the physical material at the surface of the earth. Land covers include
grass, asphalt, trees, bare ground, water, etc. Earth cover is the expression used by
ecologist Frederick Edward Clements that has its closest modern equivalent being
vegetation. The expression continues to be used by the Bureau of Land
Management.
b) Land cover classification:

Digital image classification is the process of assigning pixels to class. Usually
each pixel is treated as an individual unit composed of values in several spectral
bands. Unsupervised classification method is used to identify natural groups, or
structure, within multispectral data. Only afterwards information labels are
assigned to the resulting groups. The disadvantage and limitation of these methods
primarily arise from a reliance upon “natural” grouping and difficulties in
matching these groups to the informational categories that are of interest to the
interpreter. In addition, the interpreter limit controls over the menu of classes and
their specific identities.

By contrast, supervised classification method is the process of using samples of

known identity (training area or training field) and extends it to the entire image.
Each primitive image is characterized by n observations (the values in n data
channels). The samples training are vectors in an n-dimensional space (the feature
space). A supervised classifier uses the distribution of the samples training for
each class to estimate density functions in the feature space and to divide the space
into class regions.
3


Table 1: Land cover system
Level 1

Level 2

1. Urban

11 Residential
12 Downtown and services
13 Industrial plants.
14 Traffic
15 Public buildings
16 Welfare buildings
17 Sports Recreation Area
18 Mixture Zone
19 Open land and other land

2. Rice - Crops

21 Crops and grassland
22 Fruit trees

23 Barn
24 Other Agriculture

3. Fallow lands

31 Pastoral land
32 Shrub land
33 Mixed land

4. Forest

41 Green forest
42 Deciduous forest
43 Mixed forest
4


44 Bare forest
45 Forest has burnt out
5. Water

51 Streams and canals
52 Lakes
53 Water collection tank
54 Bays and estuaries
55 Sea water

6. Wetlands

61 Wetlands have plant created forest

62 Wetlands have plant can’t created
forest
63 Wetlands haven’t plant

7. Fallow lands.

71 Lake has dry
72 Beach

(Thach Nguyen Ngoc, 2012) [20]
2.1.1.2. Normalized Difference Vegetation Index
The Normalized Difference Vegetation Index (NDVI) is an index of plant
“Greenness” or photosynthetic activity, and is one of the most commonly used
vegetation indices. Vegetation indices are based on the observation that different
surfaces reflect different types of light differently. Photosynthetically active
vegetation, in particular, absorbs most of the red light that hits it while reflecting much
of the near infrared light. Vegetation that is dead or stressed reflects more red light and
less near infrared light. Likewise, non-vegetated surfaces have a much more even
reflectance across the light spectrum.

5


By taking the ratio of red and near infrared bands from a remotely-sensed
image, an index of vegetation “greenness” can be defined. The Normalized Difference
Vegetation Index (NDVI) is probably the most common of these ratio indices for
vegetation. NDVI is calculated on a per-pixel basis as the normalized difference
between the red and near infrared bands from an image:

NDVI = (NIR-RED)/ (NIR+RED)

By which NIR is the near infrared band value for a cell and RED is the red band
value for the cell. NDVI can be calculated for any image that has a red and a near
infrared band. The biophysical interpretation of NDVI is the fraction of absorbed
photosynthetically active radiation.

2.1.2. . Geographic information system (GIS)
2.1.2.1. Geographic information system (GIS)
a. Definition
A geographic information system (GIS) is a computer system designed to capture,
store, manipulate, analyze, manage, and present all types of spatial or geographical
data. The acronym GIS is sometimes used for geographical information science or
geospatial information studies to refer to the academic discipline or career of working
with geographic information systems and is a large domain within the broader
academic discipline of Geo-informatics. What goes beyond a GIS is a spatial data
infrastructure, a concept that has no such restrictive boundaries.
b, Basic Elements of GIS


Hardware: Hardware is the computer system on which a GIS operates.

Today, GIS software runs on a wide range of hardware types, from centralized
computer servers to desktop computers used in stand-alone or networked
configurations.

6




Software: GIS software provides the functions and tools needed to store,

analyze, and display geographic information. A review of the key GIS software
subsystems is provided above.


Data: Perhaps the most important component of a GIS is the data.

Geographic data and related tabular data can be collected in-house, compiled to
custom specifications and requirements, or occasionally purchased from a commercial
data provider. A GIS can integrate spatial data with other existing data resources, often
stored in a corporate DBMS. The integration of spatial data (often proprietary to the
GIS software), and tabular data stored in a DBMS is a key functionality afforded by
GIS.


People: GIS technology is of limited value without the people who manage

the system and develop plans for applying it to real world problems. GIS users range
from technical specialists who design and maintain the system to those who use it to
help them perform their everyday work. The identification of GIS specialists versus
end users is often critical to the proper implementation of GIS technology.


Methods: A successful GIS operates according to a well-designed

implementation plan and business rules, which are the models and operating practices
unique to each organization.
c) The Functions of GIS
Data Pre-processing and Manipulation



Data editing, checking and correcting.



Structure conversion, eg conversion from vector to raster.



Geometric conversion, eg map registration, scale changes, projection

changes, map transformations, rotation.


Generalisation and classification, eg reclassifying data, aggregation or

disaggregation, co-ordinate thinning.


Integration, eg overlaying, combining map layers or edge matching.



Map enhancement, eg image enhancement, add title, scale, key, map

symbolism, draping overlays.

7



Interpolation, e.g. kriging, spline functions, Thiessen polygons, plus centroid

determination and extrapolation.


Buffer generation, eg calculating and defining corridors.



Data searching and retrieval, eg on points, lines or areas, on user defined

themes or by using Boolean logic. Also browsing, querying and windowing.
Data Analysis


Spatial analysis, eg connectivity, proximity, contiguity, intervisibility, digital

terrain modelling.


Statistical analysis, eg histograms, correlation, measures of dispersion,

frequency analysis.


Measurement, eg line length, area and volume calculations, distance and

directions.
Data Display


Graphical display, eg maps and graphs with symbols, labels or annotations.



Textual display, eg reports, tables.

Database Management


Support and monitoring of multi-user access to the database.



Coping with systems failure.



Communication linkages with other systems.



Editing and up-dating of databases.



Organising the database for efficient storage and retrieval.



Maintenance of database security and integrity.



Provision of a “data independent” view of the database.

2.1.2.2. ArcGIS software
ArcGIS is a software program, used to create, display and analyze geospatial data,
developed by Environmental Systems Research Institute (ESRI) of Redlands,
California. ArcGIS consists of three components: ArcCatalog, ArcMap and
8


ArcToolbox. ArcCatalog is used for browsing for maps and spatial data, exploring
spatial data, viewing and creating metadata, and managing spatial data. ArcMap is
used for visualizing spatial data, performing spatial analysis, and creating maps to
show the results of your work. ArcToolbox is an interface for accessing the data
conversion and analysis function that come with ArcGIS. ArcGIS comes in three
variants: ArcView, ArcEditor, or ArcInfo, which are the low end, middle and fully
configured versions of the software.

Any of these versions can be used for this

exercise.
-

ArcGIS allows:

 Create and edit data integration (integrating spatial data with attribute data)
 Query spatial data from different sources and different ways
 Display, query and analyze spatial data
 Create thematic maps and prints with high quality
- The structure and organization of data in ArcGIS
Data in ArcGIS is divided into 3 parts:
+ Vector: is a set of characteristic classes have the same system of reference
+ Raster: is a simple data file or a compressed data set from the wavelength bands of
the individual spectrum or a list of values
+ TIN: contains a set of continuous triangle precisely of an area have Z values for each
node ...
2.1.3. Remote sensing (RS)
2.1.3.1. Remote sensing (RS)
a. Definition
Remote sensing is the acquisition of information about an object or
phenomenon without making physical contact with the object and thus in contrast to in
situ observation. In modern usage, the term generally refers to the use of aerial sensor
technologies to detect and classify objects on Earth (both on the surface, and in
the atmosphere and oceans) by means of propagated signals (e.g. electromagnetic
radiation). It may be split into active remote sensing (when a signal is first emitted
9


from aircraft or satellites) or passive (e.g. sunlight) when information is merely
recorded.
b) Principle of Operation of Remote sensing

In remote sensing, its operating principle link between electromagnetic waves
from the source and the object of interest.

1. Sources of energy (A) - the first requirement for remote sensing is emitting
energy sources to supply electric energy to the object of interest.
2. Electromagnetic waves and atmospheric (B) - the transfer of energy from the
source to the object, it will go on and interact with the atmosphere it passes
through. This interaction can occur when the 2nd energy transmit from the object
to the sensor.
3. The interaction with the object (C) - when energy meet the object after through
the atmosphere, it interacts with objects. Depending on the characteristics of the
object and the electromagnetic that energy of reflection or radiation by the
different subjects.

Figure 1:Remote sensing system

10


4. The record the energy of sensor (D) - after the energy is scattered or emitted
from objects, a sensor to collect and record the electromagnetic wave.
5. The transmission, receiving and processing (E) - the energy recorded by the
sensor must be transmitted to a receiving station and processing. Energy is
transmitted often in the form of electricity. Receiving station will handle this
energy to create images in the form of hardcopy or a number.
6. The interpretation and analysis (F) - Image is processed in the receiving station
will be interpreted visually or by machine classification to extract information
about the object.
7. Applications (G) - this is the last component in the treatment process of remote
sensing technology. The information is extracted from the image can be used to
understand better the subject, exploring some new information or support for
solving a particular problem. (Tran Thong Nhat, Nguyen Kim Loi, 2009)

c) Some Land Observation Satellites/Sensors
The LandSat;
System Pour l'Observation de la Terre (SPOT);
Indian Remote Sensing (IRS);
Light Detection and Ranging (Lidar);
Marine Observation Satellite (MOS);
Sea-viewing Wide-Field-of View Sensor (SeaWiFS);
Forward Looking InfraRed (FLIR);
RAdio Detection And Ranging (RADAR)

d) Applications of RS data
Agriculture:Crop Type Mapping; Crop Monitoring and Damage Assessment
Forestry: Clear Cut Mapping and Deforestation; Species Identification and
Typing; Burn Mapping
11


Geology: Structural Mapping and Terrain Analysis; Geologic Unit Mapping
Hydrology: Flood delineation and Mapping; Soil Moisture
SeaIce: Ice type and Concentration; Ice Motion
Land Cover – Biomass Mapping: Land Cover and Land Use; Land Use
Change (Rural / Urban)
Mapping: Planimetry; Digital Elevation Model; Topographic and Baseline
Thematic Mapping
Oceans and Coastal Monitoring: Ocean Features: Ocean Colour and
Phytoplankton Concentration; Oil Spill Detection
2.1.3.2. The Land sat program
a) The Land sat program
The Land sat program is the longest running enterprise for acquisition of
satellite imagery of Earth . On Jul y 23, 1972 the Earth Resources Technology Satellite

was launched. This was eventually renamed to Landsat .The most recent , Landsat 8,
was launched on February 11, 2013. The instruments on the Landsat satellites have
acquired millions of images. The images, archived in the United States an data Landsat
receiving stations around the world, are a unique resource for global change research
and applications in agriculture, cartography, geology, forestry, regional planning,
surveillance and education, and can be viewed through the USGS 'Earth Explorer'
website. Landsat 7 data has eight spectral bands with spatial resolutions ranging from
15 to 60 meters; the temporal resolution is 16 days

12


-

Satellite chronology

Table 2:Landsat satellite system

Instrument

Landsat 1

Launched

July 23,

Terminated

January 6, 1978

1972

Landsat 2

Duration

Notes

2 years, 11

Originally named Earth

months and 15

Resources Technology

days

Satellite 1.

January 22,

February 25,

2 years, 10

Nearly identical copy of

1975

1982

months and 17

Landsat 1

days
Landsat 3

March 5,

March 31, 1983

1978
Landsat 4

5 years and 26

Nearly identical copy of

days

Landsat 1 and Landsat 2

July 16,

December 14,

11 years, 4

Nearly identical copy of

1982

1993

months and 28

Landsat 4. Longest Earth-

days

observing satellite
mission in history.

Landsat 5

March 1,

June 5, 2013[7]

1984

29 years, 3

Failed to reach orbit.

months and 4
days

Landsat 6

October 5,

October 5, 1993

0 days

1993

Operating with scan line
corrector disabled since
May 2003

Landsat 7

April 15,

Still active

1999

15 years, 8

Originally named Landsat

months and 22

Data Continuity Mission

days

from launch until May
30, 2013, when NASA
operations were turned
over to USGS.

13


Landsat 8

February 11,

Still active

2013

1 year, 10

Originally named Earth

months and 26

Resources Technology

days

Satellite 1.

b) Land Sat 5
Landsat 5 was a low Earth orbit satellite launched on March 1, 1984 to collect
imagery of the surface of Earth. A continuation of the Landsat Program, Landsat 5
was jointly managed by the U.S. Geological Survey (USGS) and the National
Aeronautics and Space Administration (NASA). Data from Landsat 5 was
collected and distributed from the USGS's Center for Earth Resources Observation
and Science (EROS). Landsat 5 had a maximum transmission bandwidth of 85
Mbit/s. It was deployed at an altitude of 705.3 km (438.3 mi), and it took about 16
days to scan the entire Earth.
Table 3 : Parameters of ETM Landsat ( Landsat 5)

Bands

Wavelength

Types

(micrometers)

Resolution(meters)

Band 1
Band 2

0,45 - 0,52
0,52 - 0,60

Blue
Green

30
30

Band 3

0,63 - 0,69

Red

30

Band 4

0,76 - 0,90

Near Infrared

30

Band 5

1,55 - 1,75

Near Infrared

30

Band 6

10,4 - 12,5

Thermal

120

Band 7

2,09 - 2,35

Mid-Infrared

30

14


b) The Landsat 8
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 satellite 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)
sensor provides two thermal bands
Table 4 : Parameters of LDCM Landsat (Landsat 8 ):
Bands

Wavelength
(micrometers)

Types

Resolution(meters)

Band 1
Band 2

0.433 - 0.453
0.450 – 0.515

Coastal aerosol
Blue

30
30

Band 3

0.525
0.600
0.450 –- 0.515

Green
Blue

30

Band 4

0.630
0.680
0.525 –- 0.600

Red

30

Band 5

0.630 –- 0.680
0.845
0.855

Near Infrared (NIR)

30

Band 6

1.560
1.660
0.845 –- 0.885

SWIR 1

30

Band 7

1.560 –- 1.660
2.100
2.300

SWIR 2

30

Band 8

0.500
2.100 - 0.680
2.300

Panchromatic

15

Band 9

1.360 - 1.390

Cirrus

30

Band 10

10.3 - 11.3

Thermal Infrared (TIR)

100

Band 11

11.5 - 12.5

Thermal Infrared (TIR)

100

Landsat images is applied in many fields of study from researchers to monitor the
current state of volatility and is used the most common, at a lower price. With
15


multispectral channels 2-5, using Landsat 8 materials have been widely used in the
world of mapping the current state of land use and vegetation maps.
2.2.

Practical basis

2.2.1. The research in the world
Land Use / Land Cover Change of Delhi: A Study using Remote Sensing and
GIS Techniques by Singh Bijender. The change analysis was performed by post
classification comparison method, comparing the data of two different sensors (Lands
at TM and LISSIII IRS P-6), at different time periods (years 1992 and 2004). The
growth of Delhi measured between two time periods was based on the above data set.
The results showed that there was rapid change in land cover/land use. It was found

that there was a phenomenal change in the built-up area in watersheds, loss of forest
cover and change in agriculture land. Singh Bijender, 2014, Land Use / Land Cover
Change of Delhi: A Study using Remote Sensing and GIS Techniques, International
Research Journal of Earth Sciences [10].
Land use and land cover changes detection using remote sensing and GIS in
parts of Coibatore and Tiruppur districts, Tamil Nadu, India by Pandian. M. In this
paper an attempt has been made to study the changes in land use and land cover
parts of Coimbatore and Tiruppur districts. The study was carried out through
Remote Sensing and GIS approach using SOI toposheets, LANDSAT imagery of
2000 and IRS-P6-LISS-III 2009. The land use/land cover classification was
performed based on the Survey of India toposheets and Satellite imageries. GIS
software is used to prepare the thematic maps and ground truth observations
were also performed to check the accuracy of the classification. Pandian. M,
2014, Land use and land cover changes detection using remote sensing and GIS in
parts of Coibatore and Tiruppur districts, Tamil Nadu, India, Land use and land cover
changes detection using remote sensing and GIS in parts of Coibatore and Tiruppur
districts, Tamil Nadu, India [7].

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Researching Land Use Mapping Using Remote Sensing & GIS Techniques in
Naina - Gorma Basin, Part of Rewa District, M.P, India of Vimla Singh. For this study
the ERDAS Imagine 8.6 computer software used to develop a land use classification
using IRS 1-C, LISS III, image. Unsupervised classification, ISODATA clustering
method is use to classify the image and visual image interpretation approach
used to delineate the land use classes. The present study is focus on demarcating
boundaries of different land use / land cover units from an analysis, with the help of
SOI to posheet, and Satellite images on 1:50,000 scale, divides the study area into
forest, open scrub, dense scrub, Agriculture (agr. crop area), Rocky/Stony waste

land, Sandy soil / land / patches, Settlement, River, and other water bodies. Vimla
Singh, 2012, Land Use Mapping Using Remote Sensing & GIS Techniques in Naina Gorma Basin, Part of Rewa District, M.P, India, ISSN 2250-2459 [14].
An Analysis on Land Use/Land Cover Using Remote Sensing and GIS – A
Case Study In and Around Vempalli, Kadapa District, Andhra Pradesh, India by G.
Sreenivasulu. This study three thematic maps such as location map, drainage
map and land use / land cover maps were prepared. The land use and land cover
analysis on the study area has been attempted based on thematic mapping of the
area

consisting

of

built-up

land, cultivated land, water bodies, forest and

uncultivated land using the satellite image. The research concludes that there is a
rapid expansion of built-up area. Land use and land cover information, when used
along with information on other natural resources, like water, soil, hydrogeomorphology, etc. will help in the optimal land use planning at the macro and
micro level. G. Sreenivasulu, 2013, Analysis on Land Use/Land Cover Using Remote
Sensing and GIS – A Case Study In and Around Vempalli, Kadapa District, Andhra
Pradesh, India, International Journal of Scientific and Research Publications [1]

Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in
Rize, North-East Turkey by Selçuk Reis. In this

study,

LULC changes

are

investigated by using of Remote Sensing and Geographic Information Systems
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