To assess and predict land use/land cover (LULC) and land surface temperature (LST) using Landsat imagery for the Padma Bridge construction area, the following steps can be taken: Preprocess the Landsat imagery. This includes correcting for geometric distortions, atmospheric effects, and radiometric calibration. Classify the LULC. This can be done using a variety of supervised and unsupervised classification methods. Calculate the LST. This can be done using a variety of methods, such as the Mono-Window Algorithm and the Normalized Difference Vegetation Index (NDVI). Analyze the LULC and LST data. This can be done using a variety of statistical and geospatial methods to identify trends and patterns. Predict the future LULC and LST. This can be done using a variety of machine learning and time series forecasting methods.

1. ASSESSING AND PREDICTING LAND USE/LAND COVER AND LAND
SURFACE TEMPERATURE USING LANDSAT IMAGERY FOR PADMA
BRIDGE CONSTRUCTION AREA
TANVIR SIDDIKE MOIN
Department of Civil Engineering
BUET

2. LAND SURFACE TEMPERATURE (LST)
LST is the radiative skin temperature (~20μm) of the land
IMPORTANT
• It is an important climate variable derived from solar radiation and is influenced by land/atmosphere
boundary conditions
• It determines the emission of surface-to-atmosphere long-wave radiation
• It exerts control over the partitioning of energy into latent and sensible heat fluxes, and heat flux into the
ground
• It influences land/atmosphere coupling – water and carbon cycling
• It can be retrieved from microwave and IR sensors
Application
Climate change: Urban heat, land/atm. coupling, surface energy balance, carbon cycle
Land cover change: Desertification, change detection
Crop management: Irrigation, drought stress
Water management: Evapotranspiration, soil moisture retrievals
Geological applications: Geothermal anomalies, volcanic activity

3. Land Use and Land Cover?
What is meant by land use and land cover?
• Identification of land cover establishes the baseline from which monitoring activities (change detection)
can be performed and provides the ground cover information for baseline thematic maps.
• Land use refers to the purpose the land serves, for example, recreation, wildlife habitat, or agriculture.
What is the difference between land use and land cover map?
• Land cover indicates the physical land type such as forest or open water whereas land use documents how
people are using the land.
• By comparing land cover data and maps over a period of time, coastal managers can document land use
trends and changes.
Important:
• Assessing nonpoint sources of pollution
• Understanding landscape variables for ecological analyses
• Assessing the behavior of chemicals
• Analyzing the effects of air pollution.

4. The Padma Multipurpose Bridge Project aims to remove the last major
physical barrier in the road connection between Dhaka and the Southwest
and South Central regions of Bangladesh. This project area is located at
about 35 km southwest of Dhaka. The bridge is connected between a site
near the village of Mawa, lying north of the Padma River and Janjira on
the south side.
Components of this work such as approach roads and bridge end facilities
will affect an area of 6 km inland on the Mawa side and 4 km inland on the
Janjira side. The 250 km 2 project area comprises areas located in three
separate administrative districts: Munshiganj district on the Mawa side
(north bank) and Shariatpur and Madaripur districts on the Janjira side
(south bank).
Lauhajong and Sreenagar upazilla (sub-district) lie on the north bank and
Janjira and Shibchar upazilla lie along the south bank. The aim is to show
the changing of land use/land cover and land surface temperature at the
construction of Padma Bridge Area by GIS spatial analysis. Also show the
land development for upcoming economic revolution in the Padma Bridge
site area.
STUDY AREA
Source: Asian Development Bank

5. STUDY AREA
http://www.geo-ref.net/en/bgd.htm
BANGLADESH Padma Bridge Construction Area

6. STUDY AREA
1994 2000

7. STUDY AREA
2002 2006

8. STUDY AREA
2014 2016

9. STUDY AREA
2018
• The biotic and a biotic life are directly
and indirectly attached with river
environment that's why a normal scale
changes starting to modifies local
environmental characteristics.
• The Padma river shifting was found
very high as the maximum left bank
shifting and maximum right bank
shifting had occurred at Mawa and
Janzira Area due to wake island from
1994 to 2018.

10. • GIS spatial analysis on changing the LULC and land surface temperature of Padma
Bridge site area (Mawa and Janjira) before-after the construction of Padma
Multipurpose Bridge.
• To evaluate the trend of air temperature in the two areas, Mawa and Janjira in the
last seven years.
• Assessing the trend of Land Surface Temperatures (LST) and Land Use & Land Cover
(LULC) in the two areas using Landsat data and ArcGIS software.
• To quantify the change in urban land cover in the given time period using GIS.
• Estimating the approximate degree of land use and its changes
• To demonstrate a positive correlation between urban land cover and LST
• To predict future LST and LULC values using data of future construction projects, both
private and public
• To highlight possible implications the resultant temperature rises will have on the areas
and their inhabitants
OBJECTIVE

11. Year
Mawa
Temperature
(Day/Night) in
oC
Janjira
Temperature
(Day/Night) in
oC
2015 33.11/18.92 33.11/18.92
2016 33.36/19.39 33.36/19.39
2017 31.72/18.96 31.72/18.96
2018 31.89/18.65 31.89/18.65
2019 33.33/18.51 33.33/18.51
2020 32.08/18.91 32.08/18.91
2021 33.24/18.17 33.24/18.17
Temperature
• Temperatures were obtained for both Mawa and
Janjira area.
• Average day and night temperatures were
calculated
• The time frame was seven years, from 2015 up
to and including 2021
• The month of January to December was chosen
• For comparison, the LST values were also plotted
DATA-COLLECTION
Day and Night Air Temperatures In Mawa Area
And Janjira Area Over The Seven-year Period
For the period of 7 years from 2015 to 2022, three Multi-spectral
Landsat satellite data were acquired from the United States
Geological Survey (USGS) to measure the LULC change and LST in the
study area. All the satellite images were downloaded for January to
December to avoid the consequence of seasonal variation.

12. PARAMETER YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC ANN
TS 2000 18.15 20.64 26.01 30.35 28.83 28.41 28.38 28.13 27.15 26.82 21.96 17.45 25.19
TS 2001 16.05 21.37 26.9 31.58 29.15 27.96 28.05 28.39 28.02 26.7 22.58 17.3 25.34
TS 2002 17.99 21.2 27.27 30.48 30.63 28.65 28.48 27.73 27.38 25.8 22.47 18.37 25.55
TS 2003 16.39 21.94 25.61 30.4 31.6 28.82 28.5 28.72 27.9 26.61 21.57 18.88 25.59
TS 2004 17.89 21.32 28.12 30.23 31.56 29.59 28.05 28.15 27.22 25.11 20.97 19.08 25.62
TS 2005 17.75 23.12 28.3 29.73 30.3 30.43 28.55 28.53 27.91 26.05 21.08 17.84 25.8
TS 2006 16.76 23.4 28.13 31.08 30.68 28.41 28.48 27.94 27.71 26.29 22.05 18.73 25.81
TS 2007 17.12 21.64 26.15 30.37 30.18 28.47 28.16 28.39 27.45 25.42 22.44 17.15 25.26
TS 2008 17.12 18.98 27.3 31.01 31.51 28.62 28.26 27.96 27.58 26.08 22.03 20.11 25.56
TS 2009 19.14 22 27.36 31.73 30.63 29.26 28.68 28.41 27.83 25.58 22.49 18.12 25.94
TS 2010 17.56 21.44 28.51 32.17 30.22 28.65 28.58 28.44 27.73 26.59 23.49 18.13 25.98
TS 2011 16.51 21.48 27.46 30.1 29.81 28.68 28.51 27.76 27.69 26.72 22.9 19.78 25.63
TS 2012 18.71 21.62 27.85 30.02 30.64 29.83 28.81 28.23 27.7 25.47 21.51 16.85 25.61
TS 2013 16.29 21.73 28.02 31.51 28.99 28.7 28.39 27.92 27.95 26.37 22.09 19.07 25.6
TS 2014 17.95 21.31 27.3 33.01 32.05 29.51 28.68 28.24 27.94 26.36 22.53 18.7 26.15
TS 2015 19 22.4 27.29 28.8 30.08 29.15 28.05 28.14 28.16 26.55 23.08 19.03 25.82
TS 2016 18.11 24.06 28.5 31.3 30.01 28.88 28.23 28.3 28.19 26.96 22.51 19.28 26.19
TS 2017 17.25 21.44 24.62 27.54 28.53 28.31 28.06 28.36 28.15 26.33 21.92 19.36 25
TS 2018 14.7 20.52 26.76 27.95 28.01 28.4 28.56 28.41 28.21 25.69 22.49 18.54 24.87
TS 2019 18.16 21.83 25.77 30.15 30.37 29.08 28.58 28.62 28.18 26.33 23.11 17.72 25.67
TS 2020 16.44 18.65 25.22 28.74 28.75 28.54 28.73 28.5 28.58 27.84 22.56 17.65 25.03
TS 2021 16.92 19.98 28.14 32.02 30.76 28.48 28.76 28.39 27.91 26.84 21.39 18.32 25.69
Reference:https://power.larc.nasa.gov/data-access-viewer/
Raw Data Collection

13. Reference:https:youtube.com+ w3school.com
code for collecting the Satellite Image
1. // Exporting Landsat 8 images
2. // Landsat 8
3. var image = ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA")
4. .filterDate('2017-01-01','2017-12-31')
5. .filterBounds(roi)
6. .sort('CLOUD_COVER')
7. .first();
8. var visPaaramsTrue = {bands: ['B4','B5','B6'], min: 0, max: 3000,gamma: 1.4};
9. Map.addLayer (image. Clip(roi),visPaaramsTrue, 'Landsat 2021');
10. Map.centerObject (roi, 8);
11. // Export to Drive
12. Export.image.toDrive ({
13. image: image,
14. description: 'Landsat 2017 mawa',
15. scale:30,
16. region:roi,
17. maxpixels:1e13
18.
19. })

14. 1.TOA (L) = ML * Qcal + AL
2.BT = (K2 / (ln (K1 / L) + 1)) − 273.15.
3.NDVI = (Band 5 – Band 4) / (Band 5 + Band 4)
4.Pv = Square ((NDVI – NDVImin) / (NDVImax – NDVImin))
5.ε = 0.004 * Pv + 0.986.
6.LST = (BT / (1 + (0.00115 * BT / 1.4388) * Ln(ε)))
HOW TO CALCULATE LAND SURFACE TEMPERATURE WITH
LANDSAT 8 SATELLITE IMAGES
ML = Band-specific multiplicative rescaling factor from the metadata (RADIANCE_MULT_BAND_x, where x is the band
number).
Qcal = corresponds to band 10.
AL = Band-specific additive rescaling factor from the metadata (RADIANCE_ADD_BAND_x, where x is the band number).
TOA = Top of Atmospheric
BT =Brightness Temperature
K1 = Band-specific thermal conversion constant from the metadata (K1_CONSTANT_BAND_x, where x is the thermal band
number).
K2 = Band-specific thermal conversion constant from the metadata (K2_CONSTANT_BAND_x, where x is the thermal band
number).
LST =Land Surface Temperature

15. PADMA BRIDGE CONSTRUCTION AREA

16. PADMA BRIDGE CONSTRUCTION AREA

17. HOW TO CALCULATE LAND SURFACE TEMPERATURE WITH
LANDSAT 8 SATELLITE IMAGES
Reference:Modeling the distribution of land surface
temperature for Bystrytsia river basin using Landsat 8 data
Band 10
Top of
Atmospheric
Spectral Radiance
Radians to
Atsensor
temperature
LST
Band 4
Band 5 NDVI
Proportion of
Vegetation Pv
Ground
Emissivity

18. HOW TO CALCULATE LULC WITH LANDSAT 8 SATELLITE IMAGES
Reference:Modeling the distribution of land surface
temperature for Bystrytsia river basin using Landsat 8 data
Landsat TM Image Geometric Correction Visual Interpretation
Accuracy Assessment Maximum Likelihood Identify Training Site
Land Use Map

19. Year
Mawa
Temperature
(Day/Night) in
oC
Janjira
Temperature
(Day/Night) in
oC
2015 25.87 25.87
2016 26.19 26.19
2017 25.24 25.24
2018 25.06 25.06
2019 25.67 25.67
2020 25.11 25.11
2021 25.69 25.69
25.87
26.19
25.24
25.06
25.67
25.11
25.69
25.87
26.19
25.24
25.06
25.67
25.11
25.69
24.4
24.6
24.8
25
25.2
25.4
25.6
25.8
26
26.2
26.4
2015 2016 2017 2018 2019 2020 2021
Air temperatures in Mawa and Janjira over the seven year period
Mawa Temperature (Day/Night) in oC Zanzira Temperature (Day/Night) in oC
ANNUAL AIR TEMPERATURES IN MAWA AND JANJIRA OVER THE SEVEN-YEAR PERIOD
Source:https://www.timeanddate.com/
Annual Air Temperatures In Mawa Area
And Janjira Area Over The Seven-year
Period

20. 27.037
31.7082
31.5907
28.5093
38.6352
27.2777
33.173233.75934.509
0
5
10
15
20
25
30
35
40
45
2015 2016 2017 2018 2019 2020 2021
LST values in Mawa over the seven
year period
Mawa
LAND SURFACE TEMPARATURE VALUES IN MAWA AND JANJIRA OVER THE SEVEN
YEAR PERIOD
27.037
31.7082 31.5907
28.5093
38.6352
27.2777
33.1732
0
5
10
15
20
25
30
35
40
45
2015 2016 2017 2018 2019 2020 2021
LST values in Janjira over the seven
year period
Janjira

21. LST of MAWA and JANJIRA AREA 2015

22. LST of MAWA and JANJIRA AREA 2016

23. INFRARED IMAGE of MAWA and JANJIRA AREA 2017

24. INFRARED IMAGE of MAWA and JANJIRA AREA 2018

25. INFRARED IMAGE of MAWA and JANJIRA AREA 2019

26. INFRARED IMAGE of MAWA and JANJIRA AREA 2020

27. IFRARED IMAGE of MAWA and JANJIRA AREA 2021

28. Land Cover
• Landsat 8 image data has been obtained for use in estimating land cover
• Again, the time frame is seven years, so seven different images of Mawa area and Janjira
area were obtained
• The bands 1-7 are used as it pertains to land cover
• The images have been classified to include the categories consisting of urban, water, grass,
water cover, and bare land.
Infrared Images
• Again, seven Landsat 8 images were obtained of band 10
• The images consist of raw thermal data ranging from the year 2015 to 2021 for the region
consisting of Mawa and Janjira
• This is having been used to estimate LST values
• The method used is described in Anandababu D. et al3
LAND COVER AND INFRARED IMAGES

29. Land use/Land cover of MAWA and JANJIRA AREA 2015
Row Labels Sum of Area
Percentage
(%)
Bare land 3066.979056 61.90%
Forest 440.6246247 8.89%
Grassland 1065.787719 21.51%
Urban 14.77156424 0.30%
Water 366.760649 7.40%
Grand Total 4954.923614 100.00%

30. Land use/Land cover of MAWA and JANJIRA AREA 2016
Row Labels Sum of Area %
Bare land 1392.582988 28.11%
Forest 215.0594023 4.34%
Grassland 2476.544766 49.98%
Urban 458.7321879 9.26%
Water 411.9261287 8.31%
Grand Total 4954.845473 100.00%

31. Land use/Land cover of MAWA and JANJIRA AREA 2017
Row Labels Sum of Area %
Bareland 1154.375772 23.30%
Forest 1108.288019 22.37%
Grassland 1947.177462 39.30%
Urban 364.5384688 7.36%
Water 380.4571804 7.68%
Grand Total 4954.836902 100.00%

32. Land use/Land cover of MAWA and JANJIRA AREA 2018
Row Labels Sum of Area %
Bare land 1834.262569 37.02%
Forest 638.6090026 12.89%
Grassland 1848.814422 37.31%
Urban 213.1208833 4.30%
Water 420.0101703 8.48%
Grand Total 4954.817047 100.00%

33. Land use/Land cover of MAWA and JANJIRA AREA 2019
Row Labels Sum of Area %
Bare land 1627.731858 32.85%
Forest 518.1159477 10.46%
Grassland 2223.625687 44.88%
Urban 222.6531989 4.49%
Water 362.6828579 7.32%
Grand Total 4954.80955 100.00%

34. Land use/Land cover of MAWA and JANJIRA AREA 2020
Row Labels Sum of Area(km2)
Percentage(
%)
Bare land 344.1281291 6.95%
Forest 2172.313079 43.84%
Grassland 1819.903882 36.73%
Urban 272.1298716 5.49%
Water 346.3552908 6.99%
Grand Total 4954.830252 100.00%

35. Land use/Land cover of MAWA and JANJIRA AREA 2021
Row Labels Sum of Area(km2) Percentage(%)
Bare land 1796.964144 36.27%
Forest 343.3275528 6.93%
Grassland 2342.934805 47.29%
Urban 69.98608739 1.41%
Water 401.6688812 8.11%
Grand Total 4954.88147 100.00%

36. Initial Preparations
• An attempt to make supervised classifications of the raster data will be done where possible, yielding a
mixed classification
• The landsat data obtained is in accordance with the coordinate system UTM(Universal Transverse
Mercator) WGS84(World Geodetic System), therefore, all final data is to be projected to that system
METHODOLOGY
INITIAL PREPARATIONS AND DATA PROCESSING
Data Processing
• There will be four main classifications for analysis, namely urban, forested, grass and water land cover
• Each classification will be assigned a predetermined LULC value
• For each year, the areas of the classifications will be calculated from the landsat data
• Similarly, for each year, using the thermal bands, the spectral radiance will be calculated from the
corresponding digital numbers
• The data will be then used to calculate the LST values of different areas in the given seven years

37. • The trend of LST values is plotted
• Using the aforementioned land use intensity, the trend of land use and LULC
will be also observed
• An analysis will be with the LST values versus LULC
• An analysis will be done with the LST values against the land use intensity
• The ratio of land use to temperature will be evaluated and analyzed
• The corresponding graphs will be listed as follows
Water 1
Bare Land 2
Grassland 3
Forest 4
Urban 5
Fig-Table of the decided land use intensities
ANALYSIS

38. -10.00
0.00
10.00
20.00
30.00
40.00
50.00
60.00
Bare Land Crops Forest Urban Water MEAN LST (oC)
Relationship between Mean LST and LULC Class
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030
Relationship between Mean LST and LULC Class

39. 0
10
20
30
40
50
60
2014 2016 2018 2020 2022 2024 2026 2028 2030 2032
Relationship Among Time Period , Mean LST(oC) ,Crops and Urban Area
MEAN LST (oC) Crops Urban
Relationship Among Time Period , Mean LST(oC) ,Crops and Urban
Area

40. Year Bare Land Crops Forest Urban Water MEAN LST (oC)
2015 5.45 53.43 17.13 8.66 15.33 21.36
2016 4.06 52.50 17.59 10.56 15.30 22.53
2017 5.99 47.42 17.75 13.68 15.17 20.3
2018 4.44 50.50 14.47 15.95 14.64 18.43
2019 4.13 50.85 10.54 20.15 14.33 30.22
2020 4.45 45.02 10.87 23.56 16.09 19.88
2021 4.77 46.65 11.89 22.04 14.65 21.32
2022 4.19 44.18 7.71 29.002 14.24 23.34
2023 4.08 42.83 6.17 32 14.1 23.46
2024 3.97 41.47 4.64 35.014 13.96 23.57
2025 3.87 40.11 3.1 38.02 13.81 23.69
2030 3.34 33.33 -4.55 53.05 13.09 24.27
LULC Classes and Mean LST

41. Correlation Among LULC Classes and Mean LST
r Bare Land Crops Forest Urban Water
Mean LST (0C) -0.4651956 -0.15018926 -0.399728206 0.319567062 -0.495580407

42. LULC Year Accuracy
2015 0.9
2016 0.87
2017 0.9
2018 0.85
2019 0.85
2020 0.88
2021 0.88
Accuracy of LULC Classes

43. • There will be a positive correlation between LST values and the land
use & land cover to temperature ratio is near the value of 1
• A positive correlation will be thus demonstrated between the land use
intensity(A measure of the extent to which a land parcel is developed in
conformity with zoning ordinances) and the LST values
• Future possible LST values may be estimated based on land use plans
• A conclusion regarding the overall impact on temperature urban
development can have can be reached
OUTCOMES

44. • Higher urban temperatures can be a health concern, spanning beyond just
acute discomfort
• Officials may take the rising temperature values into account when devising
new urban projects
• More incentive can be put into developing urban structures that help dissipate
heat
• If implemented on a large scale, it can help combat rising global temperatures
SOCIAL BENEFITS

45. • Accuracy regarding the classification of landsat images could have been
improved by acquiring land-based data
• More elaborate statistical analysis should have been done
• Urban thermal field variance index should have been done
• Temperature data could have been collected from on a more comprehensive
scale
• Possible external factors should have been considered
POSSIBLE IMPROVEMENTS
Padma Bridge area, as seen from satellite images- Google Earth

46. • https://www.worldweatheronline.com
• https://earthexplorer.usgs.gov
• https://power.larc.nasa.gov/data-access-viewer/
• https://www.timeanddate.com/
• https://www.ijariit.com/manuscripts/v4i2/V4I2-1195.pdf
• Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi,
Bangladesh- DOI:https://doi.org/10.1016/j.rsase.2020.100314
• Remote sensing approach to simulate the land use/land cover and seasonal land surface temperature change using
machine learning algorithms in a fastest-growing megacity of Bangladesh -DOI:https://doi.org/10.1016/j.rsase.2020.100463
• Investigating the Impact of Land Use/Land Cover Change on Present and Future Land Surface Temperature (LST) of
Chittagong, Bangladesh-DOI:https://ui.adsabs.harvard.edu/link_gateway/2022ESE.....6..221A/doi:10.1007/s41748-021-00291-w
Reference

47. THANK YOU