University of Gondar
College of Social Science and Humanities
Department of GIS and Remote Sensing
Project Title: Estimation of Land Surface Temperature Using
Landsat 8 in Dabat wereda
Course: Thermal and Microwave Remote Sensing
Prepared by: Jejaw Tsehay
IDNO GuR/00639/15
Submitted to: Agenagnew (PhD)
Gondar, Ethiopia
May 2023
Table of Contents
1. INTRODUCTION ...................................................................................................................................................... 1
1.1Background of the study........................................................................................................................................ 1
1.2 Statement of the problem.................................................................................................................................... 1
1.3 Significance of the study area............................................................................................................................... 1
1.4 Objectives of the study ......................................................................................................................................... 2
1.4.1 General objective ............................................................................................................................................ 2
1.4.2 Specific objectives ........................................................................................................................................... 2
2. MATERIALS AND METHODOLOGY.................................................................................................................... 3
2.1 Description of the study area ................................................................................................................................ 3
2.2 Data type, source and material use ....................................................................................................................... 3
2.3 Methods of data processing and Analysis ............................................................................................................ 3
3. RESULTS ................................................................................................................................................................... 7
3.1 Top of Atmosphere Spectral Radiance map ......................................................................................................... 7
3.4 Land surface temperature (LST)........................................................................................................................... 8
4. CONCLUSIONS AND RECOMMENDATION ..................................................................................................... 10
REFERENCES ............................................................................................................................................................. 11
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Abbreviation
BT=Temperature brightness
LST=Land Surface Temperature
NDVI= normalized difference vegetation index
OLI= Operational Land Imager
PV=Proportion of vegetation
TIRS=Thermal Infrared sensor
TOA=Top of Atmosphere
USGS= United States Geological Survey
UTM=Universal Transverse Mercator
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Abstract
Land surface temperature (LST) indicates hotness of the surface of the Earth at a specific location. Land
surface temperature is useful for Meteorological, Climatological changes, Agriculture, Hydrological
processes at local, regional and global level. Currently many satellite sensor data are available for
calculation of land surface temperature among those satellites Landsat 8 is sued. In this study land
surface temperature in Dabat wereda has been calculated by using Landsat-8 Operational Land Imager
(OLI) and Thermal Infrared sensor (TIRS) satellite data of first march 3/28/ 2013 and second data were
taken on April 04/07/ 2023. Image processing was used by ERDAS Imagine software. Available equations
and formulas used for calculation of LST. Thermal band 10 data numbers were converted to Top of
Atmospheric Spectral Radiance using radiance rescaling factors. To determine the density of green on the
study area, normalized difference vegetation index (NDVI) was calculated by using red and near-infrared
bands. Land surface emissivity (E) was also calculated to determine the efficiency of transmitting thermal
energy across the surface into the atmosphere. The results show that LST in the study area varies from
24.5to 35.980C in 2013 data and from 26,120C to 37.740C in 2023 data.
Keywords:
Normalized difference vegetation index (NDVI); land surface emissivity (E); Land surface temperature
(LST); Temperature brightness (BT); Proportion of vegetation (PV); Landsat 8 Operational Land Imager
(OLI) and Thermal Infrared sensor(TIRS)
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1. INTRODUCTION
1.1Background of the study
Thermal remote sensing is very significant in monitoring land surface temperature (LST) and
assessing the thermal properties of the earth’s surface as well as their relationships (1). Throughout
the world, Urbanization and other such activities have increased alarmingly the greenhouse gases
and redesigned the landscape which has hostile climatic effects beyond all scales(2).
Many studies have estimated the relative warmth of cities by measuring the air temperature, using
land based observation stations. Some studies used measurements of temperature using temperature
sensors mounted on cars, along various routes .This method can be both expensive and time
consuming and lead to problems in spatial interpolation. Remote sensing might be a better
alternative to the Aforesaid methods. The advantages of using remotely sensed data are the
availability of high resolution, consistent and repetitive coverage and capability of measurements
of earth’s surface conditions (3).
The soft computing models used for predicting land surface temperature (LST) changes are very
useful to evaluate and forecast the rapidly changing climate of the world.
LST is the skin temperature of the surface of the earth and provides important information about
the surface physical, bio-physical, climatic, environmental and anthropogenic changes (4). LST
changes with a change in climatic condition and other human activities where the exact prediction
becomes challenging. LST has identified as a significant variable of microclimate and radiation
transfer within the atmosphere. Worldwide urbanization has significantly reshaped the landscape,
which has important climatic implications across all scales due to the simultaneous transformation
of natural land cover and anthropogenic surfaces (5). LST is an important phenomenon in global
climate change (6). The greenhouse gases and its effect increase in the atmosphere, LST also will
increase. Land surface temperature is sensitive to vegetation and soil moisture; hence, it can be
used to detect land use/land cover changes (7). LANDSAT 8 carries two sensors these are the
Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). OLI collects data at a
30m spatial resolution with eight bands located in the visible and near-infrared and the shortwave
infrared regions of the electromagnetic spectrum, and an additional panchromatic band of 15m
spatial resolution (8).TIRS senses the TIR radiance at a spatial resolution of 100m using two bands
located in the atmospheric window between 10 and 12.the technique presented in this study is used
for estimating the LST of a given LANDSAT 8 image with the input of band 4 (0.64– 0.67 μm),
band 5 (0.85–0.88 μm), and band10 (10.60– 11.19 μm) (9.
1.2 Statement of the problem
Population growth has a great contribution in increasing temperature as a worldwide, and as a
country Ethiopia. Dabat wereda , which was selected for this study because of its rapidly change of
population leads to bring deforestation and increasing farm lands as a reason there is a change in
land surface temperature time to time, if the situation is continued in such away it will be difficult
to be alive in a Conducive environment. So by using remote sensing technique there have to be
show the increments of the temperature by compare and contrast of 2013 and 2023 Landsat 8 data.
1.3 Significance of the study area
1
This study is expected to produce the temperature difference between 2013 and 20223 in incremental
index. Such result will help environmental department official, policymaker, initiatives, woreda, e t c can
to prevent or minimize increment of land surface temperature which causes by different reasons.
Preventing and minimizing of increasing temperature can be addressed through environment policy,
creating awareness, use environmental friendly industries and vehicles, and other mechanisms. In addition
this study will help any researchers, environmental experts, policymakers and other stakeholders who want
to study further in relation to land surface temperature in the study area.
1.4 Objectives of the study
1.4.1 General objective
The general objective of the study was to estimate and compare the changes in land surface temperature
within 2013-03-28 and 2023-04-07 Landsat 8 OLI and TIRS images in the study area.
1.4.2 Specific objectives
To convert TIRS band 10 data to Top of Atmosphere (TOA) spectral radiance.
To calculate Atmospheric Brightness Temperature (BT)
To calculate Normalized Difference Vegetation Index (NDVI).
To calculate Proportion of vegetation (PV)
Calculate Land Surface Emissivity (E)
To calculate and explore Land Surface Temperature (LST)
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2. MATERIALS AND METHODOLOGY
2.1 Description of the study area
Dabat is a woreda in Amhara Region, Ethiopia. Part of the central Gondar Zone, Dabat is bordered on
the south by Wegera, on the west by central Armachiho, on the northwest by Tegeda, and on the
northeast by Debarq. This place is situated in North Gonder, Amhara, Ethiopia, its geographical
coordinates are 12° 59' 3" North, 37° 45' 54" East and its original name.
Located on the Semien Mountains along the Gondar-Debarq highway it is in the Semien Gondar
Zone of the Amhara Region.
Fig.1 Study Area Map
2.2 Data type, source and material use
Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) satellite data of Dabat
wereda of two dates 2013-03-28 and 2023-04-07 were downloaded from USGS
(www.earthexplorer.usgs.gov). Arc Map 10.3 was used for image processing, extraction of area of interest,
preparation of vegetation index and estimation of LST. Landsat 8 metadata of band 4, band 5 and band 10
were used for calculation of LST. All the data were projected to Universal Transverse Mercator (UTM)
coordinate system, datum WGS84, zone 37. Thermal infrared sensor (TIRS) band 10 was used to estimate
brightness temperature and band 4 and band 5 were used to generate NDVI.
2.3 Methods of data processing and Analysis
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The stepwise process for calculation of LST is given below. In this study, Top of Atmosphere
(TOA), Brightness Temperature (BT), Normalized Difference Vegetation Index (NDVI),
Proportion of vegetation (PV), Land Surface Emissivity (E), Land Surface Temperature (LST) has
been calculated using methods available by “Raster Calculator” of Arc Map 10.3.
Step 1: Calculation of Top of Atmosphere (TOA) Spectral Radiance Formula (Kumar & Kumar,
2020).
The satellite data products were geometrically corrected data set. The metadata of the satellite image is
given in Table 2. In the first step of the work converted the DN (Digital Number) values of band 10 to at-
sensor spectral radiance using the following equation: Top of Atmosphere (TOA) Radiance (Kumar &
Kumar, 2020).
TOA (Lλ) = ML *Qcal+ AL-Oi ....... Equation-1 (Kumar & Kumar, 2020)
Where,
Lλ=Total spectral radiance
ML =Radiance multiplicative Band (N0)/ (Radiance Multi Band x, where x is the band number).
Qcal = Quantized and calibrated standard product pixel values (DN).
AL =Radiance Add Band x, where x is the band number)
Oi = Correction value for band 10
TOA (Lλ) = 0.0003342 * “Band 10” + 0.1
Step2: TOA to Brightness Temperature (BT) Conversion
The temperature converted from the obtained thermal radiance from the ground surface at the satellite level
is called the brightness temperature. After converting DN values to sensor spectral radiance, the TIRS
band data were converted to brightness temperature (BT). TIRS band data were converted from spectral
radiance to brightness temperature using the thermal constants provided in the metadata by the following
equation:
BT = (K2 / (ln (K1 / L) + 1)) − 273.15
Where,
BT= Top of atmosphere brightness temperature (0C)
Lλ= TOA spectral radiance
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).
To obtain the result in degree Celsius, radiant temperature is adjusted by adding the absolute zero
temperature (approx.-273.15°C).
BT =1321.0789/In((774.8853/ Lλ)+1) -273.15
Step 3: Normalized Difference Vegetation Index (NDVI) calculated using near infrared (NIR) and
Red bands. Calculation of NDVI is required for calculation of proportion of vegetation (PV), further,
proportion of vegetation (PV) is required for calculation of land surface emissivity (LSE) and LSE is
4
required for calculation of LST. NDVI was calculated using the following equation(Kumar & Kumar,
2020):
NDVI = (Band 5 – Band 4) / (Band 5 + Band 4)
Where,
NDVI= Normalized Difference
Vegetation Index NIR= DN
values from near-infrared band (band 5)
RED=DN values from the RED band
(band 4)
Step 4: Proportion of vegetation (PV) Calculation:
Proportional vegetation (PV) was calculated from NDVI values obtained in step 3. This proportional
vegetation gives the estimation of area under each land use/land cover type.
Pv = Square ((NDVI – NDVImin) / (NDVImax – NDVImin))
Where,
PV = Proportion of vegetation
NDVI = DN values from NDVI image
NDVI min = Minimum DN values from NDVI image
NDVI max = Maximum DN values from NDVI image
Step 5: Land Surface Emissivity (E) Calculation
Land surface emissivity (E) is the average emissivity of an element of the surface of the Earth
calculated from NDVI. Calculation of land surface emissivity is required to estimate LST.
E = 0.004 * PV + 0.986……Equation-5 (Kumar & Kumar, 2020)
Where,
E = Land Surface Emissivity
PV =Proportion of Vegetation 0.986 to a correction value of the equation
Step 6: Land Surface Temperature (LST) Calculation:
Land Surface Temperature (LST) is the radiative temperature which was calculated using Top of Atmosphere
brightness temperature, wavelength of emitted radiance and Land Surface Emissivity. LST was calculated
using the following equation
LST =(BT / (1 + (0.00115 * BT / 1.4388) * Ln(ε)))
Where,
LST=Land Surface Temperature (°C)
BT = Top of atmosphere brightness temperature (°C)
λ = Wavelength of emitted radiance of band 10
E = Land Surface Emissivity
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Table1. Landsat 8 band designations/OLI and TIRS/
Bands Wavelength in Resolution in
Landsat 8 (micrometers) (meteres)
Operational Land Band 1-coastal aerosol 0.43-0.45 30
Imeger (OLI) Band 2-Blue 0.45-0.51 30
and Band 3-Green 0.53-0.59 30
Thermal Infrared Band 4-Red 0.64-0.67 30
Sensor (TIRS) Band 5-Near Infrared(NIR) 0.85-0.88 30
Band 6-SWIR 1 1.57-1.65 30
Launched February Band 7-SWIR 2 2.11-2.29 30
11, 2013 Band 8-Panchromatic 0.50-0.68 15
Band 9-Cirrus 1.36-1.38 30
Band 10-Thermal Infrared 10.60-11.19 100
(TIRS) 1
Band 11-Thermal Infrared 11.50-12.51 100
(TIRS) 2
Table2. Metadata of the Landsat 8
Variable Description Value
K1 Thermal constants, Band 10 799.0284
K2 1329.2405
ML Band specific multiplicative rescaling factor from the metadata (Radiance 3.8E-04
Multi Band x, where x is the band number). = 0.00038
Qcal Quantized and calibrated standard
product pixel values (DN).
Oi Correction value for band 10 0.29
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3. RESULTS
3.1 Top of Atmosphere Spectral Radiance map
Top of atmospheric spectral radiance map which shows in 2013 high value 16.0647 and low value 9.057
.whereas the 2023 value shows that high 13.7165 and low value 6.82942.
3.2 Brightness Temperature (BT)
Brightness Temperature (BT) of map of 2013 shows that the temperature ranges were from
38.678°C to 27.437, and brightness temperature (BT) of map of 2023 shows that the temperature
ranges were from 36.499 °C to 5.478 °C from high value to low value. From these results the BT
is increased from higher ranges by 2.179°C and from lower ranges by 21.959°C when compared
2021 with 2013 data.
3.3 Normalized Difference Vegetation Index (NDVI)
NDVI map of 2013 result shows that the NDVI values ranging between -0.018 to 0.481 and NDVI map
of 2023 results also shows that NDVI values falling between -0.0338 to 0.5038 (Fig.6 and 7).
7
Fig 2 NDVI map 2013
FIG 3 NDVI map 2023
Proportion of Vegetation (PV)
The proportions of vegetation (PV) results were -0.076 to 1 for 2013 and -0.134 to 0.933 for 2023. These
results show that the proportion of vegetation in 2013 was better than in 2023. Because pv value of in 2013
was greater than in 20213. Indirectly these results indicated that in 2013 the vegetation cover was better than
in 2023.
Land Surface Emissivity (E)
Land Surface Emissivity (E) map of 2023 results shows that the E values ranging between 0.988 to 0.983
and E map of 2013 results also shows that E values falling from 0.986 to 0.988. These results indicated that
in 2023 the E of the study area was higher than in 2013, because the E value of 2023 was greater than 2013
as the lower result shown
3.4 Land surface temperature (LST)
Land Surface Temperature (LST) has been derived using Brightness Temperature (BT) and Land Surface
Emissivity
(E) In the study area. The calculated LST resulted 2013 was ranging from 27.4432°C to 38.3195°C and in
2023 the result was 5.478°C to 39.52°C.
8
FIG FOR 2013 and 2023
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4. CONCLUSIONS AND RECOMMENDATION
In this project the LST of the 2013 data result was from 8.32°C to 38.52O C and the data result was
from 9.40°C to 39.520C. So the results indicated that the LST was averagely increased by 1.040C
within ten years. This result tells us the temperature is increasing from time to time in the study
area. Therefore, to reduce the increment of temperature in the study area as a local and as a global,
any actions have to takes place which is assumed helping to reduce increasing of temperature can
be done either by initiatives, individuals, scholars, governments or all of them together…!! It can
be by afforestation, planting environmental friend industries, use less carbon emitter vehicles and
creating awareness about causes to increase temperature. Because now a days global warming is a
serious issue in worldwide that is caused by increasing of temperature.
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