CORRELATION BETWEEN NDVI AND SURFACE

TEMPERATURES USING LANDSAT ETM IMAGERY FOR SAN ANTONIO AREA

CORRELATION BETWEEN CORRELATION BETWEEN NDVI AND SURFACE NDVI AND SURFACE

TEMPERATURES USING TEMPERATURES USING LANDSAT ETM IMAGERY LANDSAT ETM IMAGERY FOR SAN ANTONIO AREAFOR SAN ANTONIO AREA

Remote Sensing ProjectRemote Sensing ProjectByBy

NewfelNewfel MazariMazariFall 2005Fall 2005

ProcedureProcedure

Introduction and ObjectivesIntroduction and ObjectivesSite Date Acquisition and satelliteSite Date Acquisition and satelliteImage Processing and MethodologyImage Processing and MethodologyResults and discussionResults and discussionLimitations and prospects for future workLimitations and prospects for future workConclusionConclusion

Introduction and ObjectivesIntroduction and ObjectivesOur Aim in this study is to correlate between two Our Aim in this study is to correlate between two natural factors:natural factors:

Normalized Difference Vegetation IndexNormalized Difference Vegetation Index NDVINDVILand Surface TemperaturesLand Surface Temperatures LSTLST

Better planning and management of urban areas Better planning and management of urban areas and their surrounding rural lands suggests a use of and their surrounding rural lands suggests a use of a variety of climate factors with their relationship a variety of climate factors with their relationship and implications on the urban climate. Normalized and implications on the urban climate. Normalized Vegetation Index (Vegetation Index (NDVINDVI) is still widely used for ) is still widely used for Urban Heat Islands (Urban Heat Islands (UHIUHI) and urban microclimate ) and urban microclimate studies.studies.

Introduction and ObjectivesIntroduction and Objectives

Considering the variety and complexity of urban Considering the variety and complexity of urban climate studies, our approach is restrictive only to climate studies, our approach is restrictive only to NDVINDVI and and LSTLSTs.s.Factors such as:Factors such as:Topography and elevationTopography and elevationOrientation and shadowOrientation and shadowSoil and moisture content (for both Air and soil)Soil and moisture content (for both Air and soil)Anthropogenic factorsAnthropogenic factorsAre not considered as significant during this studyAre not considered as significant during this study

Site Site -- Date Acquisition Date Acquisition -- satellitesatelliteThe subject of our study is the city of The subject of our study is the city of

San AntonioSan Antonio and itand it’’s surrounding agglomerations s surrounding agglomerations and rural land and rural land

Data Acquisition was provided from Data Acquisition was provided from TexasView TexasView Image is fromImage is from Landsat Enhanced ThematicLandsat Enhanced Thematic

Mapper Plus Mapper Plus (ETM+)(ETM+)Obtained on June 21 2001Obtained on June 21 2001Resolution of 30 X 30mResolution of 30 X 30mCloud cover of 0 %Cloud cover of 0 %June 21 2001 11.00 amJune 21 2001 11.00 am

Image Processing and MethodologyImage Processing and Methodology

IMAGE

ATMOSPHERIC CORRECTIONS

RADIANCE

REFLECTANCE

NDVIBrightness Temperature Tb

LSTLand Use & Land Cover LULC

CORRELATION

Classification of NDVI Classification of LST

RESULTS & CONCLUSION

Image ProcessingImage Processing

ATOMOSPHERIC CORRECTIONSATOMOSPHERIC CORRECTIONSThe image was delivered preprocessedThe image was delivered preprocessed

MethodologyMethodology

NDVI CALCULATIONS RETRIEVALNDVI CALCULATIONS RETRIEVALIs Made by using ETMIs Made by using ETM’’s band 3 and 4s band 3 and 4

NDVI = (NIR – RED) / (NIR + RED)

MethodologyMethodologyBrightness Temperatures CalculationBrightness Temperatures Calculation

TbTb are retrieved by converting spectral radiance are retrieved by converting spectral radiance to to atat--sensorsensor brightnessbrightnesstemperature, the next formula was used:temperature, the next formula was used:

⎟⎟⎠

⎞⎜⎜⎝

⎛+

=1ln 1

2

λLKKT b

TbTb effective ateffective at--Satellite brightness temperature (K)Satellite brightness temperature (K)LLλλ Spectral radiance in W / m2. Spectral radiance in W / m2. sterster..µµmmK1K1 and and K2 K2 are preare pre--launched calibration constantslaunched calibration constants

MethodologyMethodologyLand Surface Temperature CalculationLand Surface Temperature Calculation

The temperature values obtained above are The temperature values obtained above are referenced to as a black body temperature. referenced to as a black body temperature. Therefore corrections for spectral emmisivityTherefore corrections for spectral emmisivityξξ is necessary according to the nature of is necessary according to the nature of LULCLULCIn our case we only use a single In our case we only use a single emissivityemissivity::

ξξ = 0.988= 0.988The corrected The corrected emissivityemissivity LSTLSTs were s were computed using computed using ArthisArthis & Carnahan 1982& Carnahan 1982formulaformula

MethodologyMethodology••Land Surface Temperatures Calculation FormulaLand Surface Temperatures Calculation Formula

( ) ερλ ln/1 b

bt T

TS++

=

Were Were λλ is the wavelength of emitted radianceis the wavelength of emitted radianceρρ = h * c / = h * c / σσσσ Stefan Boltzman constantStefan Boltzman constantc c Light velocityLight velocityh h PlanckPlanck’’s constants constant

MethodologyMethodologyLand Use Land Cover ClassificationLand Use Land Cover Classification LULCLULC

--Initial image classification was made using an Initial image classification was made using an Unsupervised method Unsupervised method ( 10 Classes )( 10 Classes )

--The 10 classes were combined to The 10 classes were combined to 5 Classes5 ClassesBy using Ground Truth VerificationsBy using Ground Truth Verifications

LULCLULC

STATISTICAL RESULTSSTATISTICAL RESULTS

CLASS MINIMUM MAXIMUM MEAN STANDARD DEVIATION

1 -0.915301 -0.858144 -0.563747 0.136549

5 -0.208327 0.225247 0.329211 0.066439

6 0.107639 0.934441 0.757546 0.060163

Statistics for NDVI and LST Classes were computed using ENVI, Only three classes had available statistical parameters due to software memory limitations , the first table for NDVI, the second for LSTs

CLASS MINIMUM MAXIMUM MEAN STANDARD DEVIATION

1 297.516418 322.615387 301.832989 2.084401

5 289.041107 321.200714 308.049680 2.384720

6 298.079437 319.053314 303.268963 1.415670

CORRELATION PLOT FOR CORRELATION PLOT FOR NDVI & LSTsNDVI & LSTs

RESULTS AND DISCUSSIONRESULTS AND DISCUSSION

There is a significant negative correlation There is a significant negative correlation between between NDVINDVI and and LSTsLSTsNDVI NDVI classification method is acceptableclassification method is acceptableLSTs LSTs classification came out surprising, classification came out surprising, due to the limitations of the calculation due to the limitations of the calculation methods, and probably to the complexity methods, and probably to the complexity of the different dependant factors and their of the different dependant factors and their interactions.interactions.

LIMITATIONS AND PROSPECTS LIMITATIONS AND PROSPECTS FOR FUTURE WORKSFOR FUTURE WORKS

LIMITATIONSLIMITATIONSThe methods used to derive The methods used to derive NDVINDVI and and LSTsLSTsneed further refinement and calibrations.need further refinement and calibrations.The use of a single emissive did not meet The use of a single emissive did not meet our expectations.our expectations.The land use and cover The land use and cover LULC LULC needs more needs more accurate ground truth verification.accurate ground truth verification.The image processing software had several The image processing software had several problems of functionality, when considering problems of functionality, when considering a large amount of data. a large amount of data.

PROSPECTS FOR FUTURE PROSPECTS FOR FUTURE WORKSWORKS

FUTURE WORKFUTURE WORKThe subject of study can be a basis for a real The subject of study can be a basis for a real research work in the future, nevertheless research work in the future, nevertheless more data is required.more data is required.Also the combination and correlation of Also the combination and correlation of different sensors and satellites will provide different sensors and satellites will provide better tools to understand the relationship better tools to understand the relationship between between NDVINDVI and and LSTLST

CONCLUSIONCONCLUSION

More advanced methods are required in More advanced methods are required in order to have better results such as:order to have better results such as:Linear Spectral Mixture Analysis Linear Spectral Mixture Analysis LSMALSMAFractal AnalysisFractal AnalysisVegetation Fraction Cover Type Vegetation Fraction Cover Type VFCTVFCTOthersOthers…………………………..