CORRELATION BETWEEN NDVI AND SURFACE TEMPERATURES USING LANDSAT ETM IMAGERY FOR SAN ANTONIO AREA. Remote Sensing Project By Newfel Mazari Fall 2005

Transcription

1 CORRELATION BETWEEN NDVI AND SURFACE TEMPERATURES USING LANDSAT ETM IMAGERY FOR SAN ANTONIO AREA Remote Sensing Project By Newfel Mazari Fall 2005

2 Procedure Introduction and Objectives Site Date Acquisition and satellite Image Processing and Methodology Results and discussion Limitations and prospects for future work Conclusion

3 Introduction and Objectives Our Aim in this study is to correlate between two natural factors: Normalized Difference Vegetation Index NDVI Land Surface Temperatures LST Better planning and management of urban areas and their surrounding rural lands suggests a use of a variety of climate factors with their relationship and implications on the urban climate. Normalized Vegetation Index (NDVI( NDVI) ) is still widely used for Urban Heat Islands (UHI( UHI) ) and urban microclimate studies.

4 Introduction and Objectives Considering the variety and complexity of urban climate studies, our approach is restrictive only to NDVI and LSTs. Factors such as: Topography and elevation Orientation and shadow Soil and moisture content (for both Air and soil) Anthropogenic factors Are not considered as significant during this study

5 Site - Date Acquisition - satellite The subject of our study is the city of San Antonio and it s s surrounding agglomerations and rural land Data Acquisition was provided from TexasView Image is from Landsat Enhanced Thematic Mapper Plus (ETM+) Obtained on June Resolution of 30 X 30m Cloud cover of 0 % June am

6 Image Processing and Methodology IMAGE ATMOSPHERIC CORRECTIONS RADIANCE Land Use & Land Cover LULC Classification of NDVI REFLECTANCE NDVI LST CORRELATION Brightness Temperature Tb Classification of LST RESULTS & CONCLUSION

7 Image Processing ATOMOSPHERIC CORRECTIONS The image was delivered preprocessed Methodology NDVI CALCULATIONS RETRIEVAL Is Made by using ETM s s band 3 and 4 NDVI = (NIR RED) / (NIR + RED)

8 Methodology Brightness Temperatures Calculation Tb are retrieved by converting spectral radiance to at-sensor brightness temperature, the next formula was used: T b = K K ln 1 L λ Tb effective at-satellite brightness temperature (K) Lλ Spectral radiance in W / m2. ster.µm K1 and K2 are pre-launched calibration constants 2 + 1

9 Methodology Land Land Surface Temperature Calculation The temperature values obtained above are referenced to as a black body temperature. Therefore corrections for spectral emmisivity ξ is necessary according to the nature of LULC In our case we only use a single emissivity: ξ = The corrected emissivity LSTs s were computed using Arthis & Carnahan 1982 formula

10 Methodology Land Surface Temperatures Calculation Formula S t = T b 1+ ( λ+ Tb/ ρ) lnε Were λ is the wavelength of emitted radiance ρ = h * c / σ σ Stefan Boltzman constant c Light velocity h Planck s s constant

11 Methodology Land Land Use Land Cover Classification LULC -Initial image classification was made using an Unsupervised method ( 10 Classes ) -The The 10 classes were combined to 5 Classes By using Ground Truth Verifications

12 LULC

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15 STATISTICAL RESULTS 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 CLASS MINIMUM MAXIMUM MEAN STANDARD DEVIATION

16 CORRELATION PLOT FOR NDVI & LSTs

17 RESULTS AND DISCUSSION There is a significant negative correlation between NDVI and LSTs NDVI classification method is acceptable LSTs classification came out surprising, due to the limitations of the calculation methods, and probably to the complexity of the different dependant factors and their interactions.

18 LIMITATIONS AND PROSPECTS FOR FUTURE WORKS LIMITATIONS The methods used to derive NDVI and LSTs need further refinement and calibrations. The use of a single emissive did not meet our expectations. The land use and cover LULC needs more accurate ground truth verification. The image processing software had several problems of functionality, when considering a large amount of data.

19 PROSPECTS FOR FUTURE WORKS FUTURE WORK The subject of study can be a basis for a real research work in the future, nevertheless more data is required. Also the combination and correlation of different sensors and satellites will provide better tools to understand the relationship between NDVI and LST

20 CONCLUSION More advanced methods are required in order to have better results such as: Linear Spectral Mixture Analysis LSMA Fractal Analysis Vegetation Fraction Cover Type VFCT Others.