Research Article Algorithm for Automated Mapping of Land ...(Moncton area) for the areas shown in...

Research ArticleAlgorithm for Automated Mapping of Land Surface TemperatureUsing LANDSAT 8 Satellite Data

Ugur Avdan and Gordana Jovanovska

Research Institute of Earth and Space Sciences Anadolu University Iki Eylul Campus 26555 Eskisehir Turkey

Correspondence should be addressed to Ugur Avdan uavdananadoluedutr

Received 25 November 2015 Revised 21 January 2016 Accepted 4 February 2016

Academic Editor Guiyun Tian

Copyright copy 2016 U Avdan and G JovanovskaThis is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Land surface temperature is an important factor in many areas such as global climate change hydrological geo-biophysical andurban land useland cover As the latest launched satellite from the LANDSAT family LANDSAT 8 has opened new possibilitiesfor understanding the events on the Earth with remote sensingThis study presents an algorithm for the automatic mapping of landsurface temperature from LANDSAT 8 data The tool was developed using the LANDSAT 8 thermal infrared sensor Band 10 dataDifferent methods and formulas were used in the algorithm that successfully retrieves the land surface temperature to help us studythe thermal environment of the ground surface To verify the algorithm the land surface temperature and the near-air temperaturewere compared The results showed that for the first case the standard deviation was 24∘C and for the second case it was 27∘CFor future studies the tool should be refined with in situmeasurements of land surface temperature

1 Introduction

Land surface temperature (LST) is defined as the temperaturefelt when the land surface is touched with the hands orthe skin temperature of the ground [1] As one of the mostimportant aspects of the land surface LST has been a maintopic for developing methodologies to be measured fromspace LST is an important factor in many areas of studiessuch as global climate change hydrological and agriculturalprocesses and urban land useland cover Calculating LSTfrom remote sensed images is needed since it is an importantfactor controlling most physical chemical and biologicalprocesses of the Earth [2] There is a growing awarenessamong environmental scientists that remote sensing can andmust play a role in providing the data needed to assessecosystems conditions and to monitor change at all specialscales [3]The tool developed in this paper is simple and doesnot require any background knowledge so scientists can useit very easy in their researches

The algorithm introduced in this paper has been devel-oped using ERDAS IMAGINE 2014 with the Model Makerallowing us to create a model that will repeat the processautomatically and it is easy to develop a simple tool useful

for doing pixel calculations Without the tool the processof retrieving LST is very long and it is prone to manymistakes The tool also can be developed in any softwaresupporting pixel calculations from a given image followingstep by step this paper Although an LST retrieval method forLANDSAT 8 has been developed [1 4] a tool is needed forthe complicated process of obtaining the LST A similar studyfor retrieving LST in ERDAS IMAGINE has been conductedfor LANDSAT 7 data [5] but not for LANDSAT 8 The toolpresented in this paper is used for calculating the LST ofa given LANDSAT 8 image with the input of the fourth(redwavelengthmicrometres 064ndash067) fifth (near infrared(NIR) wavelengthmicrometres 085ndash088) and tenth (ther-mal infrared sensor (TIRS) wavelengthmicrometres 1060ndash1119) bands Following January 6 2014 recommendations ofUSGS of not using TIRS Band 11 due to its larger calibrationuncertainty only Band 10 was included in the algorithm

2 Data and Methods

The algorithm was created in ERDAS IMAGINE 2014 andit can only be used to process LANDSAT 8 data becauseof the data complexity The LST of any Landsat 8 satellite

Hindawi Publishing CorporationJournal of SensorsVolume 2016 Article ID 1480307 8 pageshttpdxdoiorg10115520161480307

2 Journal of Sensors

Input Band 10 Input Band 4 Input Band 5

Top of atmosphericspectral radiance(see Section 21)

Calculating NDVI(see Section 231)

Determination ofground emissivity(see Section 233)

Calculating LST(see (7))

LST result

Conversions ofradians to at-sensor

temperature(see Section 22)

Calculatingproportion ofvegetation P

(see Section 232)

Figure 1 Flowchart for LST retrieval

image can be retrieved following the steps of Figure 1 Thedata of Landsat 8 is available at the Earth Explorer websitefree of charge In this study the TIR band 10 was used toestimate brightness temperature and bands 4 and 5 were usedfor calculating theNDVIThemetadata of the satellite imagesused in the algorithm is presented in Table 1

21 Top of Atmospheric Spectral Radiance Thefirst step of thealgorithm is the input of Band 10 After inputting band 10 inthe background the tool uses formulas taken from the USGSwebpage for retrieving the top of atmospheric (TOA) spectralradiance (119871120582)

119871120582 = 119872119871lowast 119876cal + 119860119871 minus 119874119894 (1)

where119872119871represents the band-specific multiplicative rescal-

ing factor 119876cal is the Band 10 image 119860119871is the band-specific

additive rescaling factor and 119874119894is the correction for Band 10

[6]

22 Conversion of Radiance to At-Sensor Temperature Afterthe digital numbers (DNs) are converted to reflection theTIRS band data should be converted from spectral radianceto brightness temperature (BT) using the thermal constantsprovided in the metadata file The following equation is

Table 1 Metadata of the satellite images

Thermal constant Band 101198701

1321081198702

77789Rescaling factor Band 10

119872119871

0000342119860119871

01Correction Band 10

119874119894

029

used in the toolrsquos algorithm to convert reflectance to BT[7]

BT = 1198702

ln [(1198701119871120582) + 1]

minus 27315 (2)

where1198701and119870

2stand for the band-specific thermal conver-

sion constants from the metadataFor obtaining the results in Celsius the radiant tem-

perature is revised by adding the absolute zero (approxminus27315∘C) [8]

Journal of Sensors 3

23 NDVI Method for Emissivity Correction

231 Calculating NDVI Landsat visible and near-infraredbands were used for calculating the Normal DifferenceVegetation Index (NDVI) The importance of estimating theNDVI is essential since the amount of vegetation present isan important factor and NDVI can be used to infer generalvegetation condition [9] The calculation of the NDVI isimportant because afterward the proportion of the vegeta-tion (119875V) should be calculated and they are highly relatedwiththe NDVI and emissivity (120576) should be calculated which isrelated to the 119875V

NDVI = NIR (band 5) minus 119877 (band 4)NIR (band 5) + 119877 (band 4)

(3)

where NIR represents the near-infrared band (Band 5) and 119877represents the red band (Band 4)

232 Calculating the Proportion of Vegetation 119875V is cal-culated according to (4) A method for calculating 119875V [4]suggests using the NDVI values for vegetation and soil(NDVIV = 05 andNDVI119904 = 02) to apply in global conditions[10]

119875V = (NDVI minusNDVI

119904

NDVIV minus NDVI119904)

2

(4)

However since the NDVI values differ for every area thevalue for vegetated surfaces 05may be too lowGlobal valuesfrom NDVI can be calculated from at-surface reflectivitiesbut it would not be possible to establish global values inthe case of an NDVI computed from TOA reflectivitiessince NDVIV and NDVI

119904will depend on the atmospheric

conditions [11]

233 Calculating Land Surface Emissivity The land surfaceemissivity (LSE (120576)) must be known in order to estimate LSTsince the LSE is a proportionality factor that scales blackbodyradiance (Planckrsquos law) to predict emitted radiance and itis the efficiency of transmitting thermal energy across thesurface into the atmosphere [12] The determination of theground emissivity is calculated conditionally as suggested in[10]

120576120582= 120576V120582119875V + 120576119904120582 (1 minus 119875V) + 119862120582 (5)

where 120576V and 120576119904 are the vegetation and soil emissivitiesrespectively and 119862 represents the surface roughness (119862 = 0for homogenous and flat surfaces) taken as a constant valueof 0005 [13] The condition can be represented with thefollowing formula and the emissivity constant values shownin Table 1 [4]

120576120582

=

120576119904120582 NDVI lt NDVI

119904

120576V120582119875V + 120576119904120582 (1 minus 119875V) + 119862 NDVI119904le NDVI le NDVIV

120576119904120582+ 119862 NDVI gt NDVIV

(6)

When the NDVI is less than 0 it is classified as water andthe emissivity value of 0991 is assigned For NDVI valuesbetween 0 and 02 it is considered that the land is coveredwith soil and the emissivity value of 0996 is assigned Valuesbetween 02 and 05 are considered mixtures of soil andvegetation cover and (6) is applied to retrieve the emissivityIn the last case when the NDVI value is greater than 05 itis considered to be covered with vegetation and the value of0973 is assigned

The last step of retrieving the LST or the emissivity-corrected land surface temperature 119879

119904is computed as follows

[14]

119879119904=

BT1 + [(120582BT120588) ln 120576

120582] (7)

where 119879119904is the LST in Celsius (∘C (2)) BT is at-sensor BT

(∘C) 120582 is the wavelength of emitted radiance (for which thepeak response and the average of the limitingwavelength (120582 =10895) [15] will be used) 120576

120582is the emissivity calculated in (6)

and

120588 = ℎ119888

120590= 1438 times 10

minus2mK (8)

where 120590 is the Boltzmann constant (138 times 10minus23 JK) ℎ isPlanckrsquos constant (6626 times 10minus34 J s) and 119888 is the velocity oflight (2998 times 108ms) [9]

3 LST Validation

The two major LST validation models are through groundmeasurements or near-surface air temperature [16 17] TheLST results comparingwith the groundmeasurements resultsmay have an error up to 5∘C in the case of Srivastava et al theaccuracy of the results in some area showeddifference ofplusmn2∘Cwith actual ground temperaturemeasurements According toLiu and Zhang another method using the mean near-surfaceair temperature to verify the retrieved LST results showed thatthe LST retrieving error is about 07∘C For the validation sixrepresentative points have been used

For the validation of the final retrieved LST results in thepresented tool the mean near-surface air temperature wasused [18] but with bigger amount of data and taking not onlythe mean temperature but also the actual temperature in thegiven pixel at themoment of the satellite passing over the areafor 27 representative points

The comparison was made with air temperature whichis different and can sometimes result in big differencessince the resolution of LANDSAT 8 for the used bandsis 100m for the thermal band and 30m for the red andNIR bands The LST was calculated and taken for thepixel in which the meteorological station fell Sometimesthe differences can be very big depending on the weathercondition and other factors [19] It should also be takeninto consideration that there is 11 to 2 metersrsquo differ-ence between the LST and the air temperature whichmeans that differences in the temperatures are normal andexpected


N

EW

S

(a)

(b)

(c)

(d)

Figure 2 Application of algorithm in Ontario and Quebec Canada (a) Geographic location of Ontario in Canada (b) frames of satelliteimages of study areas (c) first case located betweenToronto andHuntsville (d) second case located in surrounding area of the city ofMoncton

31 Application of the Algorithm to Ontario and QuebecCanada Hourly data were collected from the CanadianWeather and Meteorology website (httpclimateweathergcca) and used for comparison with the retrieved LST forwhich according to the available data satellite images weredownloaded for 02052015 (Toronto area) and 04062015(Moncton area) for the areas shown in Figure 2

The study area was the Canadian provinces of Ontarioand Quebec (Figure 2) One satellite image was downloadedfrom each of the two provinces These areas were chosenbecause of their specifications That is both study areasincluded water urban areas and green areas

32 Comparison of LST Validation Results To compare theresults two different satellite images from two differentdates in two different areas were chosen according to theavailable data After downloading the satellite images fromhttpearthexplorerusgsgov LSTs were retrieved in ERDASusing the algorithm presented in this paper In the first casethe satellite image was located between Toronto and the cityof Huntsville near Lake Simcoe in Ontario Canada For thisarea 20 meteorological stations were found but only 16 ofthem were used for the accuracy assessment because of thepresence of clouds or other unwanted events The differencesbetween the retrieved LSTs and the air temperatures anddetails on the stations are presented in Table 2 and Figure 3

In the second case the study area was located in thearea surrounding the city of Moncton and included part ofNew Brunswick Prince Edward Island and Nova Scotia inCanada For this area we found 11 meteorological stationsand all of them were used for the accuracy assessment Thedetails are presented in Table 3 and Figure 4

Table 2 Emissivity of representative terrestrialmaterials for LAND-SAT 8 TIRS Band 10

Terrestrial material Water Building Soil VegetationEmissivity 0991 0962 0966 0973

4 Conclusion

This paper presented a new LST software tool and itsalgorithm created in ERDAS for calculating the LST fromLANDSAT 8 TIRS The algorithm was derived using theobserved thermal radiance of the TIRS Band 10 of LANDSAT8 TIRS To verify the final retrieved LST results the near-surface air temperature method was used From the anal-ysis of the two areas in Canada from two different datesthe standard deviation calculated for the first case basedon 16 meteorological stations was 24∘C and that for thesecond case based on 11 stations was 27∘C It should bementioned that sometimes the difference between the near-surface temperature and the LST can be drastic since weare comparing two different temperatures in different places(ground temperature and 11 to 20m off the ground) Itshould also be taken into consideration that the resolutionof the LANDSAT 8 TIRS data is 100m for the thermal bandand 30m for the red and NIR bands Values smaller thanminus5∘C in the two cases were considered to be clouds or otherunwanted events on the satellite images since the data werefrom springtime it was not expected From Tables 3 and 4and Figures 3 and 4 it can be concluded that for the firstcase the smallest difference between the LST retrieved fromthe presented tool and the near-air temperaturewas 07∘Cand


Collingwood

Lagoon City

Pa Udora Strong

Barrie Oro

Mono Centre

Pa Atmos Erin

Borden Awos

Uxbridge WestPa Uxbridge TarisPa Claremont Silo FarmPa Atmos Claremont

Toronto Button Ville A

Egbert CSPa Tornto

Inter

Pa Atmos Vaughan

Pa Hardwood Mountain Bike Park

Pa Caledon E Park

Pa Atmos Bran

Pa Atmos Vaughan

Pa Markham North

N

E

S

W S

Meteorological stations

ltminus5∘C

minus5ndash3∘C3ndash6∘C6ndash9∘C9-10∘C

24-25∘C20ndash24∘C17ndash20∘C10ndash17∘C

gtminus25∘C

Toyota

0 5 10 20(km)

LST (∘C)

Figure 3 Retrieved LST image and meteorological stations from first study area used in accuracy assessment


N

EW

S

0 5 10 20(km)

Doaktown Auto RCS

Buctouche CDA Cs

Parsboro

Mechanic Settlement

Gagetown Awos

Pa Atmos Erin

Gagetown A

Napan Auto

Fundy Park (Alma) Cs

Saint Jhon A

Moncton Intl A


ltminus5∘C


10-11∘C

13ndash17∘C11ndash13∘C

17ndash20∘Cgtminus20

∘C

LST (∘C)

Figure 4 Retrieved LST image and meteorological stations from second study area used in accuracy assessment


Table 3 Details and differences of station from first study case

ST name Data 12pm LST Difference Latitude Longitude 119867

Barrie-Oro 199 209 minus10 44∘2910158400000010158401015840 79∘3310158400000010158401015840 28900mPa Hardwood Mountain Bike Park 192 249 minus57 44∘3110158400890010158401015840 79∘3510158402420010158401015840 33450mBorden Awos 200 207 minus07 44∘1610158402000010158401015840 79∘5410158404200010158401015840 22250mPa Udora Strong 198 223 minus25 44∘1510158400360010158401015840 79∘1210158401830010158401015840 26650mLagoon City 107 92 15 44∘3210158405000010158401015840 79∘1310158400000010158401015840 22070mCollingwood 138 187 minus49 44∘3010158400000010158401015840 80∘1310158400000010158401015840 17980mMono Centre 191 184 07 44∘0110158405610010158401015840 80∘0110158402801010158401015840 43600mUxbridge West 194 223 minus29 44∘0510158405400010158401015840 79∘0910158404902010158401015840 32500mPa Uxbridge Taris 197 232 minus35 44∘0310158401600010158401015840 79∘0610158405500010158401015840 35950mPa Atmos Vaughan 209 179 31 43∘5110158404770010158401015840 79∘3210158402890010158401015840 25400mPa Angus Glen Golf Club 204 233 minus29 43∘5410158402980010158401015840 79∘1910158402340010158401015840 23050mToronto Buttonville A 212 233 minus21 43∘5110158404400010158401015840 79∘2210158401200010158401015840 19810mPa Claremont Silo Farm 198 218 minus20 43∘5910158403790010158401015840 79∘0510158404390010158401015840 26350mPa Markham North Toyota 216 274 minus58 43∘4910158400100010158401015840 79∘2010158403381010158401015840 18750mPa Atmos Claremont 215 235 minus20 43∘5610158400980010158401015840 79∘0510158400540010158401015840 16700mPa Atmos Erin 198 216 minus18 43∘4910158403362010158401015840 80∘0710158401284010158401015840 47000m

Table 4 Details and differences of station from second study case


Mechanic Settlement 84 86 minus02 45∘4110158403704010158401015840 65∘0910158405404010158401015840 40300mMoncton Intl A 153 143 10 46∘0610158404400010158401015840 64∘4010158404300010158401015840 7070mFundy Park (Alma) Cs 124 102 22 45∘3610158400000010158401015840 64∘5710158400000010158401015840 4270mBuctouche Cda Cs 151 149 02 46∘2510158404900610158401015840 64∘4610158400500910158401015840 3590mNappan Auto 154 133 21 45∘4510158403440010158401015840 64∘1410158402920010158401015840 1980mDoaktown Auto Rcs 159 115 44 46∘3510158400609010158401015840 66∘0010158403507110158401015840 4300mSaint John A 151 125 26 45∘1810158405800010158401015840 65∘5310158402400010158401015840 10880mParrsboro 159 81 78 45∘2410158404800010158401015840 64∘2010158404900010158401015840 3090mGagetown A 150 102 48 45∘5010158400000010158401015840 66∘2610158400000010158401015840 5060mGagetown Awos A 163 186 minus23 45∘5010158402000010158401015840 66∘2610158405900010158401015840 Summerside 139 117 22 46∘2610158402800010158401015840 63∘5010158401700010158401015840 1220m

the biggest was 58∘C In the second study case the smallestdifference was 02∘C and the biggest was 78∘C

The tool can be significant since it is opening an opportu-nity tomany researchers to get to the LST values easy and theycan apply them in a number of researchs The tool presentedin this paper produced quite good results considering that theaccuracy assessment was made with near-air temperaturesand it can be used for different types of research For futurestudies the tool should be refined with in situmeasurementsof LST

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] A Rajeshwari and N Mani ldquoEstimation of land surface tem-perature of dindigul district using landsat 8 datardquo InternationalJournal of Research in Engineering and Technology vol 3 no 5pp 122ndash126 2014

[2] F Becker and Z-L Li ldquoTowards a local split window methodover land surfacesrdquo International Journal of Remote Sensing vol11 no 3 pp 369ndash393 1990

[3] S UstinManual of Remote Sensing Remote Sensing for NaturalResource Management and Environmental Monitoring JohnWiley amp Sons Hoboken NJ USA 2004

[4] F Wang Z Qin C Song L Tu A Karnieli and S Zhao ldquoAnimproved mono-window algorithm for land surface temper-ature retrieval from landsat 8 thermal infrared sensor datardquoRemote Sensing vol 7 no 4 pp 4268ndash4289 2015

[5] Q Q Sun J J Tan and Y H Xu ldquoAn ERDAS image processingmethod for retrieving LST and describing urban heat evolutiona case study in the Pearl River Delta Region in South ChinardquoEnvironmental Earth Sciences vol 59 no 5 pp 1047ndash1055 2009

[6] J A Barsi J R Schott S J Hook N G Raqueno B LMarkham and R G Radocinski ldquoLandsat-8 thermal infraredsensor (TIRS) vicarious radiometric calibrationrdquo Remote Sens-ing vol 6 no 11 pp 11607ndash11626 2014

[7] USGS 2013 httplandsatusgsgovLandsat8 Using Productphp


[8] H-Q Xu and B-Q Chen ldquoRemote sensing of the urban heatisland and its changes in Xiamen City of SE Chinardquo Journal ofEnvironmental Sciences vol 16 no 2 pp 276ndash281 2004

[9] Q H Weng D S Lu and J Schubring ldquoEstimation ofland surface temperature-vegetation abundance relationship forurban heat island studiesrdquo Remote Sensing of Environment vol89 no 4 pp 467ndash483 2004

[10] J A Sobrino J C Jimenez-Munoz andL Paolini ldquoLand surfacetemperature retrieval fromLANDSATTM5rdquoRemote Sensing ofEnvironment vol 90 no 4 pp 434ndash440 2004

[11] J C Jimenez-Munoz J A Sobrino A Plaza L Guanter JMoreno and P Martınez ldquoComparison between fractionalvegetation cover retrievals from vegetation indices and spectralmixture analysis case study of PROBACHRIS data over anagricultural areardquo Sensors vol 9 no 2 pp 768ndash793 2009

[12] J C Jimenez-Munoz J A Sobrino A Gillespie D Saboland W T Gustafson ldquoImproved land surface emissivities overagricultural areas using ASTER NDVIrdquo Remote Sensing ofEnvironment vol 103 no 4 pp 474ndash487 2006

[13] J A Sobrino and N Raissouni ldquoToward remote sensingmethods for land cover dynamic monitoring application toMoroccordquo International Journal of Remote Sensing vol 21 no2 pp 353ndash366 2000

[14] M Stathopoulou and C Cartalis ldquoDaytime urban heat islandsfrom Landsat ETM+ and Corine land cover data an applicationto major cities in Greecerdquo Solar Energy vol 81 no 3 pp 358ndash368 2007

[15] B L Markham and J L Barker ldquoSpectral characterization ofthe LandsatThematic Mapper sensorsrdquo International Journal ofRemote Sensing vol 6 no 5 pp 697ndash716 1985

[16] Z-L Li B-H Tang HWu et al ldquoSatellite-derived land surfacetemperature current status and perspectivesrdquo Remote Sensingof Environment vol 131 pp 14ndash37 2013

[17] P K Srivastava T J Majumdar and A K BhattacharyaldquoSurface temperature estimation in Singhbhum Shear Zone ofIndia using Landsat-7 ETM+ thermal infrared datardquo Advancesin Space Research vol 43 no 10 pp 1563ndash1574 2009

[18] L Liu and Y Z Zhang ldquoUrban heat island analysis using thelandsat TM data and ASTER data a case study in Hong KongrdquoRemote Sensing vol 3 no 7 pp 1535ndash1552 2011

[19] K Gallo R Hale D Tarpley and Y Yu ldquoEvaluation of the rela-tionship between air and land surface temperature under clear-and cloudy-sky conditionsrdquo Journal of Applied Meteorology andClimatology vol 50 no 3 pp 767ndash775 2011

International Journal of

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RoboticsJournal of

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Active and Passive Electronic Components

Control Scienceand Engineering

Journal of



RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



Input Band 10 Input Band 4 Input Band 5

Top of atmosphericspectral radiance(see Section 21)

Calculating NDVI(see Section 231)

Determination ofground emissivity(see Section 233)

Calculating LST(see (7))

LST result

Conversions ofradians to at-sensor

temperature(see Section 22)

Calculatingproportion ofvegetation P

(see Section 232)

Figure 1 Flowchart for LST retrieval

image can be retrieved following the steps of Figure 1 Thedata of Landsat 8 is available at the Earth Explorer websitefree of charge In this study the TIR band 10 was used toestimate brightness temperature and bands 4 and 5 were usedfor calculating theNDVIThemetadata of the satellite imagesused in the algorithm is presented in Table 1

21 Top of Atmospheric Spectral Radiance Thefirst step of thealgorithm is the input of Band 10 After inputting band 10 inthe background the tool uses formulas taken from the USGSwebpage for retrieving the top of atmospheric (TOA) spectralradiance (119871120582)

119871120582 = 119872119871lowast 119876cal + 119860119871 minus 119874119894 (1)

where119872119871represents the band-specific multiplicative rescal-

ing factor 119876cal is the Band 10 image 119860119871is the band-specific

additive rescaling factor and 119874119894is the correction for Band 10

[6]

22 Conversion of Radiance to At-Sensor Temperature Afterthe digital numbers (DNs) are converted to reflection theTIRS band data should be converted from spectral radianceto brightness temperature (BT) using the thermal constantsprovided in the metadata file The following equation is

Table 1 Metadata of the satellite images

Thermal constant Band 101198701

1321081198702

77789Rescaling factor Band 10

119872119871

0000342119860119871

01Correction Band 10

119874119894

029

used in the toolrsquos algorithm to convert reflectance to BT[7]

BT = 1198702

ln [(1198701119871120582) + 1]

minus 27315 (2)

where1198701and119870

2stand for the band-specific thermal conver-

sion constants from the metadataFor obtaining the results in Celsius the radiant tem-

perature is revised by adding the absolute zero (approxminus27315∘C) [8]





(3)




119904


2

(4)



conditions [11]


120576120582= 120576V120582119875V + 120576119904120582 (1 minus 119875V) + 119862120582 (5)


120576120582

=

120576119904120582 NDVI lt NDVI

119904


120576119904120582+ 119862 NDVI gt NDVIV

(6)




[14]

119879119904=

BT1 + [(120582BT120588) ln 120576

120582] (7)




and

120588 = ℎ119888

120590= 1438 times 10

minus2mK (8)


3 LST Validation





N

EW

S

(a)

(b)

(c)

(d)








4 Conclusion



Collingwood

Lagoon City

Pa Udora Strong

Barrie Oro

Mono Centre

Pa Atmos Erin

Borden Awos



Egbert CSPa Tornto

Inter

Pa Atmos Vaughan


Pa Caledon E Park

Pa Atmos Bran

Pa Atmos Vaughan

Pa Markham North

N

E

S

W S


ltminus5∘C



gtminus25∘C

Toyota

0 5 10 20(km)

LST (∘C)



N

EW

S

0 5 10 20(km)

Doaktown Auto RCS

Buctouche CDA Cs

Parsboro

Mechanic Settlement

Gagetown Awos

Pa Atmos Erin

Gagetown A

Napan Auto


Saint Jhon A

Moncton Intl A


ltminus5∘C


10-11∘C



∘C

LST (∘C)













References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation













(3)




119904


2

(4)



conditions [11]


120576120582= 120576V120582119875V + 120576119904120582 (1 minus 119875V) + 119862120582 (5)


120576120582

=

120576119904120582 NDVI lt NDVI

119904


120576119904120582+ 119862 NDVI gt NDVIV

(6)




[14]

119879119904=

BT1 + [(120582BT120588) ln 120576

120582] (7)




and

120588 = ℎ119888

120590= 1438 times 10

minus2mK (8)


3 LST Validation





N

EW

S

(a)

(b)

(c)

(d)








4 Conclusion



Collingwood

Lagoon City

Pa Udora Strong

Barrie Oro

Mono Centre

Pa Atmos Erin

Borden Awos



Egbert CSPa Tornto

Inter

Pa Atmos Vaughan


Pa Caledon E Park

Pa Atmos Bran

Pa Atmos Vaughan

Pa Markham North

N

E

S

W S


ltminus5∘C



gtminus25∘C

Toyota

0 5 10 20(km)

LST (∘C)



N

EW

S

0 5 10 20(km)

Doaktown Auto RCS

Buctouche CDA Cs

Parsboro

Mechanic Settlement

Gagetown Awos

Pa Atmos Erin

Gagetown A

Napan Auto


Saint Jhon A

Moncton Intl A


ltminus5∘C


10-11∘C



∘C

LST (∘C)













References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










N

EW

S

(a)

(b)

(c)

(d)








4 Conclusion



Collingwood

Lagoon City

Pa Udora Strong

Barrie Oro

Mono Centre

Pa Atmos Erin

Borden Awos



Egbert CSPa Tornto

Inter

Pa Atmos Vaughan


Pa Caledon E Park

Pa Atmos Bran

Pa Atmos Vaughan

Pa Markham North

N

E

S

W S


ltminus5∘C



gtminus25∘C

Toyota

0 5 10 20(km)

LST (∘C)



N

EW

S

0 5 10 20(km)

Doaktown Auto RCS

Buctouche CDA Cs

Parsboro

Mechanic Settlement

Gagetown Awos

Pa Atmos Erin

Gagetown A

Napan Auto


Saint Jhon A

Moncton Intl A


ltminus5∘C


10-11∘C



∘C

LST (∘C)













References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Collingwood

Lagoon City

Pa Udora Strong

Barrie Oro

Mono Centre

Pa Atmos Erin

Borden Awos



Egbert CSPa Tornto

Inter

Pa Atmos Vaughan


Pa Caledon E Park

Pa Atmos Bran

Pa Atmos Vaughan

Pa Markham North

N

E

S

W S


ltminus5∘C



gtminus25∘C

Toyota

0 5 10 20(km)

LST (∘C)



N

EW

S

0 5 10 20(km)

Doaktown Auto RCS

Buctouche CDA Cs

Parsboro

Mechanic Settlement

Gagetown Awos

Pa Atmos Erin

Gagetown A

Napan Auto


Saint Jhon A

Moncton Intl A


ltminus5∘C


10-11∘C



∘C

LST (∘C)













References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










N

EW

S

0 5 10 20(km)

Doaktown Auto RCS

Buctouche CDA Cs

Parsboro

Mechanic Settlement

Gagetown Awos

Pa Atmos Erin

Gagetown A

Napan Auto


Saint Jhon A

Moncton Intl A


ltminus5∘C


10-11∘C



∘C

LST (∘C)













References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation




















References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation
























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









Research Article Algorithm for Automated Mapping of Land ...(Moncton area) for the areas shown in...

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Transcript of Research Article Algorithm for Automated Mapping of Land ...(Moncton area) for the areas shown in...