GUJARAT TECHNOLOGICAL
UNIVERSITY
CHANDKHEDA, AHMEDABAD
L.D. COLLEGE OF ENGINEERING
A REPORT ON – LAND USE /LAND CHANGE CLASSIFICATION
SUBJECT UNDER
DESIGN ENGINEERING BE SEM – V (CIVIL ENGINEERING)
SR.
NAME OF STUDENT
ENROLMENT NO.
1.
2.
3.
4.
SHRIMALI TUSHAR R.
SUTHAR MEET N.
THATHAGAR PAVAN C.
VADOLIYA KEVAL L.
230283106029
230283106030
230283106032
230283106034
INTERNAL GUIDE:
PROF: - MRS.ANJUM MIRZA
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ABSTRACT
The assessment of land use land cover change is extremely important for
understanding the relationship between humans and nature. The enormous
changes at a regional scale and advancements in technology have encouraged
researchers to gather more information. The remote sensing technology and GIS
tools cooperatively have made it easier to monitor the changes in land use land
cover (LULC) from past to present. This technology has unraveled the changes
at the regional and global level and has also contributed tremendous benefits to
the scientific community. A variety of change detection algorithms have been
used in the history of remote sensing to detect changes at earth's surface and
newer techniques are still in process.
The data from remote sensing satellites are the primary sources that provide
an opportunity to acquire information about LULC change in recent decades,
which extensively use different algorithms according to the research needs. The
selection of appropriate change detection method is highly recommended in every
remote sensing project. This review paper begins with the traditional pre and postclassification change detection techniques related to LULC information at the
regional level. Therefore, this paper evaluated the mostly used change detection
method among all others to find remarkable results.
Thus, the review concludes the post-classification change detection
method using maximum likelihood classifier (MLC) supervised classification is
applicable in all cases. The comparative analysis was also performed in a selected
region having multiple land features during review in which MLC results best in
comparison to others. MLC is the most commonly used technique from the past
till present that has achieved high accuracy in all regions comparatively to other
techniques.
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Acknowledgment
I would like to express my sincere gratitude to Prof. Mrs. Anjum Mirza,
my internal guide at L.D. College of Engineering, for her continuous support,
encouragement, and invaluable guidance throughout the duration of this project.
Her expertise and insightful suggestions have been pivotal in shaping this project
on Land Use/Land Cover Change Detection, helping me to achieve a deeper
understanding of the subject.
I extend my appreciation to Gujarat Technological University for
providing the platform and resources that enabled me to undertake this project as
part of Design Engineering (2A). The knowledge and experience gained from
this project have greatly contributed to my academic growth and practical
understanding of change detection techniques.
I would also like to acknowledge the assistance of my peers and the faculty
members of the Civil Engineering Department, who provided their support and
valuable feedback during various stages of this project. Their inputs played a
crucial role in refining my work.
Special thanks to USGS Earth Explorer and the Survey of India for
providing the data and maps essential for the successful completion of the project.
The tools and resources they offer were critical in analysing and interpreting the
land cover changes in Ahmedabad.
Finally, I am grateful to my family and friends for their unwavering
support, patience, and encouragement during this journey. Their belief in me has
been a constant source of motivation.
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1.1
INTRODUCTION
What is the land use and land change :
Land cover change denotes a change in certain continuous characteristics of the land
such as vegetation type, soil properties, and so on, whereas land-use change consists of an
alteration in the way certain area of land is being used or managed by humans. This involves
the transformation in the natural landscape due to urban growth. It is interesting to note that
this change is responsible for a number of local and global effects, including biodiversity loss
and its associated effects on human health, and the loss of habitat and ecosystem services (Patel
et al., 2019). It is mainly driven by urban growth and is particularly important now for
developing and underdeveloped countries. However, natural causes may result in land cover
change, but land-use change requires human intervention
1.2
Context and background:
Monitoring land use and land cover (LULC) changes has become increasingly essential
in fast-growing urban areas like Ahmedabad. Land use refers to the human utilization of land
for various purposes such as agriculture, residential, industrial, or commercial activities, while
land cover denotes the physical surface of the earth, including forests, water bodies, barren
land, and built-up areas. Tracking LULC changes helps in understanding how human activities
and natural phenomena shape the landscape over time.
As cities like Ahmedabad experience rapid population growth and urbanization, they undergo
significant transformations in their land use. This urban expansion leads to a substantial increase in
developed areas (such as residential and industrial zones) and often results in the reduction of agricultural
lands and natural spaces like water bodies and barren lands. These changes can strain local resources, alter
ecosystems, and affect climate patterns, making it essential to monitor these shifts for effective urban
planning and environmental conservation.
1.3
Study area:
Ahmedabad, located in the western part of India in Gujarat, is a key economic and industrial hub.
It has experienced rapid urban growth, especially in the last few decades. Its geographic location on the
banks of the Sabarmati River and its semi-arid climate influence the natural environment and the city's
land use. Additionally, Ahmedabad’s socio-economic background has seen considerable transformation
due to industrialization, infrastructure development, and real estate expansion, impacting its land use
patterns. The city has expanded beyond its traditional boundaries, absorbing surrounding rural and
agricultural areas, and significantly altering the landscape.
1.4
Problem statement:
The growing urbanization of Ahmedabad has led to drastic changes in land use patterns. As the
city expands, agricultural lands, forests, and water bodies are often converted into urban settlements or
industrial zones. This land conversion brings about a range of environmental and socio-economic
challenges, such as:
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1.4.1 Loss of Agricultural Land: With rapid urban development, agricultural fields are
shrinking. This can negatively impact the food security of the region and lead to a
reduction in green cover.
1.4.2 Depletion of Water Bodies: Ahmedabad has experienced a reduction in water bodies
due to encroachment, pollution, and improper water management. The loss of these
water bodies can exacerbate water scarcity and negatively affect biodiversity.
1.4.3 Urban Heat Island Effect: The conversion of natural land covers into built-up areas
increases the local temperature in urban centers, leading to the urban heat island effect.
This can affect local weather patterns and worsen living conditions.
1.4.4 Environmental Degradation: The replacement of natural ecosystems with urban areas
can lead to habitat destruction, loss of biodiversity, and increased pollution levels.
Scope of the study :
1.5
This study focuses on understanding the changes in land use patterns in the Ahmedabad region
through supervised classification techniques. The primary objective is to classify different land use
types, track their changes over time, and analyze the impact of these changes on the environment and
urban planning.
-
The study area includes various land use categories, such as:
1.5.1 Water Bodies: Lakes, rivers, and reservoirs in and around Ahmedabad, crucial for local water
supply and biodiversity.
1.5.2 Agricultural Areas: Farmlands located around the city, which have been affected by urban
expansion.
1.5.3 Urban/Developed Areas: Residential, industrial, and commercial zones that have expanded
significantly.
1.5.4 Barren Lands: Areas with little to no vegetation, which may include dry lands or unused
spaces.
1.5.5 Glaciers: While glaciers are not relevant to Ahmedabad, any reference may be symbolic of
areas that require attention in cold or higher altitude regions of Gujarat or nearby.
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1.6
Objectives:
1.6.1 Classification of Land Use Types in Ahmedabad:
Using satellite imagery and GIS-based tools, the study aims to classify the land into
distinct categories such as water bodies, agricultural land, developed (urban) areas, and barren
lands.
1.6.2 Detection and Analysis of Land Use Changes Over Time:
Through time-series analysis, the study will monitor and quantify the land use changes
that have occurred over a specified period (e.g., over the last 5 or 10 years). This will provide
insights into the pace and nature of urbanization, deforestation, and agricultural decline.
1.6.3 Accuracy Assessment:
Evaluate the accuracy of the classification through methods like confusion matrix,
kappa coefficient, and ground truthing.
1.6.4 Change Detection:
Identify key transitions in land use, such as agricultural land being converted to urban
land or the reduction of water bodies due to human encroachment.
1.6.5 Significance of the study:
The results of this study will provide valuable insights for urban planning,
environmental policies, and resource management in Ahmedabad. Specifically, the findings
will contribute in the following ways:
1.6.6 Urban Planning:
By identifying areas experiencing rapid urbanization, the study will assist urban
planners in designing infrastructure projects, transportation networks, and residential areas that
accommodate the city's growth while minimizing environmental degradation.
Accurate classification will help planners identify areas for sustainable development
and avoid encroachment on agricultural or environmentally sensitive areas.
1.6.7 Environmental Policies:
The study can help environmental policymakers understand the impact of urbanization
on natural resources and ecosystems. It can guide the creation of regulations and policies aimed
at conserving water bodies, protecting agricultural lands, and promoting green spaces within
urban areas.
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1.6.8 Water Resource Management:
With the depletion of water bodies being a critical concern for Ahmedabad, the results
of the study will inform strategies for water conservation, restoration of lost water bodies, and
the sustainable management of the city's water resources.
1.6.9 Biodiversity and Habitat Conservation:
The classification and change detection will highlight areas where urban expansion
threatens natural habitats, offering insights for the preservation of biodiversity and ecosystem
health.
1.6.10 Climate Change Mitigation:
The project’s findings will contribute to strategies aimed at mitigating the urban heat
island effect and promoting climate resilience in Ahmedabad by identifying green cover loss
and providing areas for reforestation or preservation.
1.6.11 Sustainable Development Goals (sdgs):
This study can support the achievement of several sdgs, including sustainable cities and
communities (SDG 11), climate action (SDG 13), and life on land (SDG 15), by providing data
that aligns development with environmental conservation.
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LITERATURE REVIEW
1. TITLE: -LAND USE & LAND COVER CHANGE DETECTION
USING G.I.S. & REMOTE SENSING
 RESEARCH BY: - Dhaval Dodiya, Sarju Goswami, Dhrohit Chauhan, Mayur
Bhuva, Ruchita Parekh
 International Research Journal of Engineering and Technology
 The literature review highlights the use of GIS and remote sensing to track land use
changes in Vadodara, India, from 1998 to 2008. By analyzing satellite imagery, the
study categorized land into agricultural, forest, built-up areas, water bodies, and
barren land. The methodology emphasized the need for accurate preprocessing of
images. The review notes that urban expansion led to a 9.598% increase in built-up
areas, while agricultural and forest lands declined. It also emphasizes the role of these
technologies in aiding urban planning and environmental management.
2. TITLE: - SATELLITE IMAGE BASED LAND USE LAND
COVER CHANGE ANALYSIS OF RANCHI DISTRICT,
JHARKHAND
 RESEARCH BY: - Rabindar Kumar, Obaidullah Ehrar,Dilip Kumar Mahto
 International Research Journal of Engineering and Technology
 The literature review examines land use and land cover changes in Ranchi, India,
between 1992 and 2017 using satellite imagery. It highlights significant increases in
built-up areas and agricultural land, while forest and vegetation cover declined. The
study emphasizes the role of geospatial technology and supervised classification in
tracking these changes and stresses the importance of sustainable land management
to address the challenges posed by urbanization and population growth.
3.
TITLE: - A LITERATURE REVIEW ON LAND USE LAND
COVER CHANGES
 RESEARCH BY: - Shaghla Parveen , Jasmeen Basheer and Bushra Praveen.
 International Journal of Advanced Research (IJAR)
 The literature review on Land Use and Land Cover (LULC) changes highlights the
continuous alterations in land use due to both natural and anthropogenic factors.
Historically, humans have modified land to meet survival needs, but recent
exploitation rates have resulted in significant ecosystem changes. These changes
impact biodiversity, climate, and pollution, emphasizing the importance of managing
land use. Remote sensing and GIS have become critical tools for monitoring these
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changes, enabling the collection of accurate, up-to-date information on land use
patterns and trends for better decision-making.
4.
TITLE: - ANALYZING AND MODELING LAND USE/LAND
COVER CHANGE IN PHU THO PROVINCE, VIETNAM
 RESEARCH BY: - Bui Bao Thien, Vu Thi Phuong
 JOURNAL OF DEGRADED AND MINING LANDS MANAGEMENT
 The literature review on land use and land cover (LULC) changes in the Mandavi
River Basin, YSR Kadapa District, Andhra Pradesh, highlights significant
transformations in land use over time, observed through remote sensing and GIS
technologies. The study applied supervised classification using the maximum
likelihood algorithm to satellite data from 2006 and 2018. Results show a decline in
agricultural and forested areas, along with reductions in water bodies and river
coverage. Conversely, built-up and fallow lands have expanded. The use of remote
sensing, specifically Landsat imagery, proved essential for detecting these
spatiotemporal changes. This analysis underscores the importance of geospatial tools
for effectively monitoring environmental shifts and managing natural resources in
the region.
5. TITLE: - LCLU OF AHMEDABAD DISTRICT BY SUPERVISED
CLASSIFICATION (IRGET)
 RESEARCH BY: - Rabindar Kumar, Obaidullah Ehrar,Dilip Kumar Mahto
 Analyzing and modeling land use/land cover change in Ahmedabad
 The literature review focuses on the analysis of land use and land cover (LULC)
changes inAhmedabad, over a 30-year period from 1992 to 2022. Using remote
sensing and GIS techniques, five key LULC categories were identified: agricultural
land, barren land, vegetation, built-up areas, and water bodies. Significant changes
were observed, with increases in agricultural land, built-up areas, and water bodies,
while barren land and vegetation decreased. The study also utilized the Normalized
Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index
(NDBI) to assess the spatial changes in vegetation and urbanization. A high level of
classification accuracy was achieved, with Kappa coefficients of 0.835, 0.879, and
0.916 for the years 1992, 2010, and 2022, respectively, highlighting the precision of
the LULC classifications. These findings emphasize the environmental impacts of
urbanization, population growth, and climate change on land use, calling for
sustainable land management practices.
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6. TITLE: - IMPACTS OF LAND COVER CHANGES ON LAND
SURFACE TEMPERATURE USING LANDSAT IMAGERY
WITH THE SUPERVISED CLASSIFICATION METHOD
 RESEARCH BY: - Astrid Damayanti , Farisya Isnaayu Khairunisa , Kintan
Maulidina
 Ache International Journal of science technology
 The study examines how urbanization in Tarogong Kidul, Garut Regency, Indonesia,
has led to changes in land cover, reducing vegetation and increasing built-up areas.
Using Landsat 8 satellite images, the researchers found that this shift significantly
increased land surface temperatures from 2014 to 2020. The study revealed an
84.49% correlation between the decrease in vegetation, measured by NDVI, and
rising temperatures, highlighting the importance of green spaces in regulating surface
heat.
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3
MAP CLASSIFICATION AND DATA SOURCES:
3.1
Land Use Classification: Focuses on how land is utilized by humans (e.g.,
residential, commercial, agricultural).
3.2
Land Cover Classification: Concentrates on the physical material at the surface of
the earth (e.g., forests, water, bare soil).
3.3
IMPORTANCE:
3.3.1 Resource Management:
Helps in managing natural resources effectively by identifying areas that require
conservation, development, or restoration.
Also help in maintaining the buffer stock of the farming yields.it also include the
construction materials like sand ,rock ,etc
3.3.2 Environmental Monitoring:
Enables the tracking of changes in land use and cover over time, which is critical for
assessing environmental impacts and trends (e.g., urbanization, deforestation).
My monitoring the environment changes we find which precautionary step we will take
to maintain the beauty of are and prevent the adverse effect and miss ativities happens at
particular area .
3.3.3 Urban Planning:
Supports urban planners in making informed decisions about land allocation,
infrastructure development, and zoning regulations.
Proper planning and zoning increase the facility and class of the particular location
people can do their activities without disturbing other people for example if the library and
industry place side by side the disturbance to library casued by the industrial activity so proper
planning s necessary this also include circulation within the different zones .
3.3.4 Biodiversity Conservation:
Assists in identifying habitats and ecosystems, guiding conservation efforts to protect
biodiversity.
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3.3.5 Policy Making:
Provides data that inform policymakers about land use trends, helping to shape policies
that promote sustainable development.
In our india very less policies are maintained by various authority to maintain our
ecological system so we make the proper regulatory body and make new rule which are
accepted by all the people and agencies.
3.3.6 Climate Change Studies:
Aids in understanding how land use changes contribute to climate change and can
inform mitigation strategies. This include the characteristic of the land in different climate
season from that we can analyse the amount of the crops develop at particular location , pond
,well, ground water studies , construction activities etc .
3.3.7 Socioeconomic Studies:
Offers insights into the relationship between land use patterns and socioeconomic factors,
facilitating targeted interventions.
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4
HOW REMOTE SENSING AND GIS HELP:
The various sources of remote sensing information include aerial photography (using
traditional aircraft or unmanned aerial vehicles), satellite imagery, and Light Detection and
Ranging (LiDAR). These methods collect data from a distance, allowing GIS professionals to
safely analyze it using an easy-to-use spatial data platform.
Remote sensing increases the capabilities of GIS by providing geospatial information
even in hazardous areas, such as those experiencing natural calamities. It also provides users
with a way to systematically collect data for various applications. Moreover, its unobtrusive
processes allow researchers to map areas and objects without disturbance.
4.1
Software we use :
- Arc gis :
ArcGIS is a comprehensive geospatial platform for professionals and organizations,
and the leading geographic information system (GIS) technology. ArcGIS connects maps,
apps, data, and people in ways that help empower organizations to make data-driven decisions
more efficiently. ArcGIS accomplishes this by making it easy for everyone in an organization
to discover, use, make, and share maps from any device, anywhere, at any time. ArcGIS is
designed to be flexible, offering these capabilities through multiple implementation patterns
and approaches.
1. As a single, multi-purpose GIS system that supports a variety of user needs and workflows
and delivers a wide range of enterprise services.
2. As a collection of GIS systems, each of which delivers a focused set of capabilities to the
enterprise. This may include systems of record for managing different types of data, systems
of insights for empowering data scientists and other users with a variety of analytic capabilities,
and systems of engagement for delivering location services, enterprise applications, and selfservice capabilities.
3. Using ArcGIS to extend and enable existing business systems, such as Enterprise Asset
Management (EAM) or Customer Relationship Management (CRM) systems, with location,
mapping, and spatial analysis capabilities.
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5
METHODOLOGY:
5.1.1 Data collection:
This stage involves gathering relevant datasets, primarily focusing on remote sensing
imagery. Sources may include satellite data from platforms such as Landsat, Sentinel-2, or
commercial satellites like PlanetScope. In addition, historical aerial photographs and ground
truth data collected through field surveys can provide valuable context. Ensure that data spans
multiple time points to facilitate change detection. Consider environmental factors, such as
seasonal variations, when selecting data.
5.1.2 Data analysis:
Once the data is collected, it undergoes preprocessing to enhance its quality. This may
include steps like atmospheric correction to eliminate distortions from the atmosphere,
geometric correction to align images accurately, and cropping to focus on the area of interest.
Following this, various analytical techniques, such as calculating vegetation indices (e.g.,
NDVI, EVI), can help differentiate between land cover types based on their spectral signatures.
5.1.3 Data interpretation:
This phase, the results from the analysis are interpreted to understand the dynamics of
land cover change. This involves comparing images from different time periods to identify
shifts in land use, such as urban expansion, deforestation, or changes in agricultural practices.
The findings are contextualized with socio-economic data, such as population density and land
use policies, to explain the underlying causes of the observed changes.
5.1.4 Acquiring necessary information:
To support the analysis and interpretation, gather supplementary information that may
influence land cover changes. This can include climate data (temperature, rainfall), socioeconomic indicators (income levels, urbanization rates), and land tenure systems. This
contextual information helps to build a comprehensive understanding of the factors driving
land use change in the study area.
.
5.1.5 Training samples:
Carefully select training samples representing each land cover class to train the
classification model. This involves identifying areas within the dataset that accurately reflect
different land cover types, such as urban, forest, water, and agriculture. The training samples
should be diverse and cover the variability within each class to enhance model performance.
Consider using stratified sampling methods to ensure balanced representation of all classes.
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5.1.6 Supervised classification:
Implement supervised classification algorithms, such as Random Forest, Support
Vector Machines (SVM), or Deep Learning methods like Convolutional Neural Networks
(CNN). The selected model is trained using the prepared training samples, allowing it to learn
the distinctive features associated with each land cover class. The model is then applied to
classify the entire dataset, generating a land cover map that illustrates the spatial distribution
of different land use categories.
5.1.7 Change Detection Analysis
Change detection analysis is vital for understanding land use/land cover changes over
time using multiple time periods of imagery. Techniques like image differencing allow for the
subtraction of pixel values between classified images to identify changes, while postclassification comparison directly compares classified maps from different periods to assess
shifts in land cover types. To quantify these changes, metrics such as area change, percentage
change, and transition matrices provide insights into the magnitude and nature of land cover
transformations.
5.1.8 Visualization and Mapping
Visualization and mapping are crucial for effectively communicating change detection
findings. Thematic maps visually represent classified land use and cover categories,
highlighting significant changes clearly. Utilizing GIS tools in ArcGIS enhances this process
by allowing the integration of various data layers. Alongside thematic maps, graphs and charts
summarize key statistics, making the information accessible and engaging for stakeholders.
These visual tools support informed decision-making and raise awareness about the importance
of sustainable land management practices.
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6
SUPERVISED CLASSIFICATION OF STUDY AREA
(AHMEDABAD)
6.1
Why Select Ahmedabad for Land Cover Change Classification
Ahmedabad, one of India's rapidly developing cities, serves as an ideal case study for
land cover change classification due to its significant urbanization, diverse land uses, and
ongoing socio-economic transformations. The city has witnessed substantial growth in recent
decades, resulting in changes to agricultural land, green spaces, and urban areas. Analysing
these changes provides critical insights for urban planning, resource management, and
environmental sustainability, making Ahmedabad a relevant and impactful study area.
6.2
Geographical Coordinates of Ahmedabad

Location: Ahmedabad is situated in the western Indian state of Gujarat.

Coordinates:
o
North Latitude: Approximately 23.0225° N
o
East Longitude: Approximately 72.5714° E
These coordinates allow for effective mapping and analysis of land cover using a North-South
reference system.
STUDY AREA
8087 SQUARE KM
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6.3
Preparation of Classification for Ahmedabad Area
The preparation for classification in the Ahmedabad area involves the following steps:
1. Defining Objectives:

Establish clear goals for the classification, focusing on specific land cover types and the
timeframe for change detection.
2. Selecting Appropriate Data:

Choose satellite images from USGS Earth Explorer that capture seasonal variations and
significant events impacting land cover.

Incorporate Survey of India maps to contextualize and validate the classifications.
3. Creating Training Samples:

Utilize ground truth data to delineate and categorize various land cover classes, ensuring
representative training samples are collected.
4. Implementing Classification Algorithms:

Use ArcGIS tools to perform supervised classification based on the training data, refining
parameters as necessary for accuracy enhancement.
5. Conducting Accuracy Assessment:

Evaluate the results against validation data to ensure reliability, adjusting the model as
needed based on accuracy metrics.
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c
DATASET MAP
Sr.
No.
1.
2.
3.
4.
5.
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Types
Water Land
Developed Area
Barren Land
Forest Area
Planted / Cultivated Area
Area in
sq.km
417.52
683.97
888.4
677.14
4590.14
18
6.4
Change Detection Process
The change detection process for Ahmedabad includes the following key steps:
1. Data Acquisition:
o
Utilize satellite imagery from USGS Earth Explorer, which provides access to various
datasets (e.g., Landsat) covering different time periods for effective change detection.
o
Supplement satellite data with maps downloaded from the Survey of India to gain
additional insights into land use patterns and classifications.
2. Data Preprocessing:
o
Perform radiometric and geometric corrections on the acquired images to ensure accuracy.
o
Conduct image enhancements to improve feature visibility.
3. Supervised Classification:
o
Define land cover classes (e.g., urban, agricultural, water, barren) based on the local
context and the available ground truth data.
o
Collect training samples from various land cover types to train the classification model.
4. Classification Execution:
o
Use classification algorithms (e.g., Maximum Likelihood, Support Vector Machine) in
ArcGIS to classify the imagery based on the training samples.
o
Assess the classification accuracy using validation datasets.
5. Change Detection Analysis:
o
Compare classified maps from different periods to identify areas of change, employing
techniques like image differencing and post-classification comparison.
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REFERENCES
1. USGS Earth Explorer (United States Geological Survey). "Land Use/Land Cover
Classification System (LULC)."
- providing Data for the Project (Downloading data)
Link: https://earthexplorer.usgs.gov/
2. Bhuvan portal
-providing Data for the Project (Downloading data provided by ISRO)
Link: https://bhuvan.nrsc.gov.in/ngmaps
3. ArcGIS
ArcGIS Pro Documentation: Image Classification
-ArcGIS Pro provides a comprehensive guide to supervised classification, which includes
workflows for training samples, choosing classification algorithms, and evaluating accuracy.
Link: ArcGIS Pro Image Classification
4.QGIS Documentation (for Supervising Classification)
- QGIS is a free and open-source Geographic Information System that supports supervised
classification through various plugins such as Semi-Automatic Classification Plugin (SCP).
Link: QGIS Documentation
5.Research Papers
TITLE: -LAND USE & LAND COVER CHANGE DETECTION USING G.I.S. & REMOTE
SENSING
TITLE: - SATELLITE IMAGE BASED LAND USE LAND COVER CHANGE ANALYSIS
OF RANCHI DISTRICT, JHARKHAND
TITLE: - A LITERATURE REVIEW ON LAND USE LAND COVER CHANGES
TITLE: - ANALYZING AND MODELING LAND USE/LAND COVER CHANGE IN PHU
THO PROVINCE, VIETNAM
TITLE: - LCLU OF AHMEDABAD DISTRICT BY SUPERVISED CLASSIFICATION
(IRGET)
TITLE: - IMPACTS OF LAND COVER CHANGES ON LAND SURFACE
TEMPERATURE USING LANDSAT IMAGERY WITH THE SUPERVISED
CLASSIFICATION METHOD
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