(An Autonomous Institute)
Walchand College Of Engineering, Sangli.
(An Autonomous Institute)
Department of Computer Science and Engineering
Mini-Project II (6CS342)
Synopsis on
Land Surface Temperature Analysis for
Sangli City
by
Laxmikant Mahindrakar (23520013)
Shreyash Deotale (22510055)
Rohit Shinde (22510124)
Under the Guidance of
Dr. N. L. Gavankar
2024-25
Semester VI
PROBLEM STATEMENT :
This project aims to analyze and predict changes in Land Surface Temperature (LST) for
Sangli city using Landsat satellite data over the past 20 years. The project will focus on
understanding patterns in LST and their correlation with land use changes, such as the decline in
agricultural land and urbanization. The study will also provide insights into possible environmental
causes and forecast future temperature trends using Machine Learning models.
ABSTRACT :
The primary objective of this project is to develop a Machine Learning and remote sensingbased system to analyze and predict changes in Land Surface Temperature (LST) for Sangli city
using historical Landsat satellite data. The study will involve preprocessing and analyzing satellite
images, extracting LST values, and investigating correlations between LST and land use changes
over the past 20 years.
Key tasks will include data collection, image processing using ERDAS Imagine, feature
extraction, and the development of predictive models to forecast future LST trends. The system will
also visualize LST variations and provide insights into environmental changes contributing to these
trends. This project aims to provide valuable insights to urban planners, policymakers, and
environmentalists to address urban heat islands and environmental degradation.
PROBLEM DOMAIN :
This project focuses on the domains of remote sensing, environmental monitoring, and data
science. It aims to solve the problem of effectively analyzing and predicting LST changes using
satellite imagery and Machine Learning techniques. The study addresses the growing concern of
urban heat islands and climate change by offering a data-driven solution for environmental analysis
and future forecasting.
CUSTOMER IDENTIFICATION, SPONSORSHIP DETAILS :
The primary customers are:



Urban planners and policymakers.
Environmental researchers and analysts.
Local government bodies interested in sustainable urban development.
Potential sponsors could include environmental agencies, research organizations, and academic
institutions focused on climate studies.
Literature Review\Related Work :
Sr.
No.
Title and Author
Journal /Conference Key and Highlights
and Year
1
"Estimating Land Surface
Temperature Using
Landsat 8" - Zhao et al.
Remote Sensing of
Environment (2017)
Techniques for estimating LST using the
thermal bands of Landsat 8.
2
"Impacts of Urbanization
on Land Surface
Temperature" - Li, L., et
al.
Urban Climate
(2020)
Analysis of the impact of urban expansion
on LST and heat island effect.
3
"Machine Learning for
Climate Analysis" Bennington, P., et al.
Environmental
Informatics (2021)
Applications of ML models for
temperature forecasting and
trend analysis.
4
"ERDAS Imagine for
Geospatial
Image Analysis" - Clark, J. Techniques Journal
& Martinez, P.
(2018)
Image processing techniques for analyzing
remote sensing data.
5
"Time-Series Analysis of
Environmental Data" Greenfield, A.
Techniques for analyzing time-series data
to study environmental changes.
International Journal
of Climate Studies
(2022)
OBJECTIVES:





To study satellites and remote sensing for analyzing Land Surface Temperature (LST) and
environmental changes.
To collect historical Landsat satellite data for Sangli city over the past 20 years.
To collect time series data from satellite images to analyze and predict Land Surface
Temperature (LST) trends
To extract Land Surface Temperature (LST) values from the thermal bands of the satellite
images using ERDAS Imagine.
To develop machine learning model that predict future LST trends based on historical data
Flowchart:
UML DIAGRAM:
Use-Case Diagram:
Class Diagram:
METHODOLOGY :
1] Phases of Implementation:
Phase 1: Data Collection and Preprocessing
Phase 2: LST Extraction and Analysis
Phase 3: Model Development and Prediction
Phase 4: Visualization and Insights
2] Algorithms Used:
Data Preprocessing: Image Noise Removal and Correction Techniques.
LST Extraction: Radiative Transfer equation and thermal band analysis.
Prediction Model: Time-series analysis using LSTM (Long Short-Term Memory) networks.
OUTCOMES/ DELIVERABLES:
The final deliverable will include:

Web Portal: A dashboard to visualize LST variations and insights.

Predictive Model : Machine Learning model for forecasting future LST trends
PROJECT POTENTIALS :
This project has significant potential in environmental research and urban planning. By
providing actionable insights and accurate predictions of LST trends, it can help mitigate the effects
of urban heat islands and support sustainable development. The research findings can also contribute
to academic publications and collaborations with environmental agencies.
PROJECT PLAN :
● Schedule :
A Gantt chart will outline the project phases, starting from requirement analysis, through
development, testing, and deployment. The plan will include specific milestones and deadlines
for each phase.
Week
Work
Week 1-2
Idea Discussion
Week 3-4
Data Collection and Preprocessing
Week 5-6
LST Extraction and Analysis
Week 7-8
Week 9-10
Week 11-12
Model Development and Prediction
Visualization and Dashboard Development
Report Submission
● Work Distribution :
The team members will be assigned specific tasks based on their skills and interests. For
example, one member will focus on collecting and preprocessing satellite data, another will
work on extracting LST using remote sensing tools, and a third will develop machine
learning models for predicting future temperature trends.
REFERENCES :
[1] Zhao, W., Liu, C., & Yang, Z. (2017). Estimating Land Surface Temperature Using Landsat 8
Thermal Infrared Imagery. Remote Sensing of Environment, 195, 187-202.
[2] Li, L., Zhang, X., & Yang, H. (2020). Impacts of Urbanization on Land Surface Temperature: A
Comparative Study. Urban Climate, 34, 100681.
[3] Bennington, P., Singh, A., & Miller, S. (2021). Machine Learning Applications for Climate
Change Analysis. Environmental Informatics, 15(4), 34-47.
[4] Clark, J., & Martinez, P. (2018). Advanced Image Processing Using ERDAS Imagine:
Techniques for Remote Sensing. Geospatial Techniques Journal, 7(2), 120-134.
[5] Greenfield, A. (2022). Time-Series Analysis of Environmental Data: Challenges and Methods.
International Journal of Climate Studies, 19(3), 345-360.
[6] Huang, X., Wang, Y., & Lee, D. (2019). Assessment of Land Use Changes on Urban Heat
Islands: A Satellite Data Analysis. Journal of Environmental Monitoring, 12(5), 789-801.
[7] Smith, R., & Patel, D. (2023). Satellite-Based Temperature Analysis for Climate Monitoring.
Advances in Remote Sensing, 14(1), 45-62.
[8] Khan, M., Bhatt, V., & Nanda, R. (2022). Machine Learning Applications in Geospatial Analysis
for Environmental Predictions. GeoSpatial Intelligence Journal, 21(6), 198-210.