Evaluation of spatial distribution of earth surface temperature in Behbahan during 2000 _ 2014 period using thermal remote sensing

Document Type : Research Paper

Authors

1 Shahid Chamran University of Ahvaz

2 Assistant Professor

3 Associ profe

Abstract

Extended abstract:
Introduction: At present, warmer urban environment is one of the Naagahanh unsustainable urban development is associated with a reduced vegetation. Therefore, knowledge of ground temperature for geosciences, such as changes in the global environment and especially urban weather, is necessary. temperature as one of the most significant climatic parameters can be one of the main factors is tropical in town planning That the leading type of dedicated facility in the city and even determines the structure, shape and texture of urban. Unbridled and unplanned growth of cities, especially big cities of the country are looking to increase their environmental degradation and increasing pollution has been followed. The indiscriminate construction process and reduce the space required for the development of green space as breathing lungs Dramatic changes in the micro-climate in cities, especially in large cities led. Today, because of the importance of thermal remote sensing for environmental studies, many researchers in basic research thermal remote sensing and sensors technology development and application of new thermal data necessary to know.
Methodology: The studied region is between 50 degrees and 19 minutes longitude to 50 degrees and 25 minutes eastern, 30 degrees and 45 minutes latitude to north 30 degrees and 32 minutes in zone 39 located. The highest slope is 69.87 and the lowest slope 1%, the minimum annual temperature 18.1 c° and the maximum annual temperature 32.37 °c. The space of area is 615.6 square kilometers and regional climate is dry based on Domarten method. In this study, spatial distribution and variation of surface temperature were analyzed base on land use maps and vegetation index in the city of BEHBAHAN. For this purpose, ETM+ and OLI images of Landsat satellite were used. After geometric and atmospheric Corrections, images were classified using Maximum Likelihood Algorithm, and temperature changes were evaluated by LCM model.

To extract the surface temperature of three steps:
A: Convert the digital number to radiation
Number of ETM digital conversion of radiation to be used the following formula:
L=LMIN+(LMAX-LMIN)/(QCALMAX-QCALMIN )QCAL-QCALMIN
Where
QCALMIN: The Digital Numeric Value
QCALMAX: Most digital value
QCAL: Digital at the pixel number
LMIN: Spectral Band 6 in the amount of zero emission value DN (W m-2 sr-2 μm-1)
LMAX: radiation value of 6 band in the amount in the DN 255 (W m-2 sr-2 μm-1)
Values LMIN (2.200) and LMAX (10.55) of the extracted image file and Heather in the relationship.
B: Converted spectral radiance temperature blackbody
TB = K2/(Ln(K1/L+1))
ETM using thermal-band data from Planck equation, the temperature blackbody radiation (TB) in which the radiation is assumed to be one, to be converted.
Where
TB: Satellite effective temperature in Kelvins
K1: The first calibration constant equal to 666.09 W m-2 sr-2 μm-1
K2: The first calibration constant equal to 1282.71
L: Spectral radiance sensor (W m-2 sr-2 μm-1)
C; Be corrected radiation
Correction can restore correct radiation surface temperature as well as the quality of information obtained through thermal infrared data is effective. One of the operational and applied to obtain the radiation is (threshold method NDVI) Through the red and near infrared bands were obtained
NDVI = (float(b4)-float(b3))/(float(b4)+float(b3))
Where b4 b3 band 4 and band 3. Finally, after correcting the thermal image and calculate emissivity, land surface temperature is calculated using Equation 6.
LST = TB/([1+[(λ*(TB/p))]*Lnε])
Where
TB: Brightness temperature
λ: Radiance emitted wavelength (μm 5/11)
p= k / hc
Where
h: Planck's constant
c; speed of light
k: Stefan-Boltzmann constant
ε: Emissivity
Results and discussion: The results showed that class 1 of temperature (Temperatures lower than 13°c), which indicates the coolest areas, has the most expansion in northern and southern mountains. In the urban core, this class of temperature was expanded as scattered spots according to urban parks in 2000, but on the contrary in 2014, urban areas were located in the first class of temperature because of significant temperature reduction compared to previous years. Overlaying the map of each Temperature class with land use maps showed that downtown and some parts of bare and agricultural lands in the west areas were located in the first temperature class, most bare and agricultural lands and some parts of pastures were located in second class, most of pasture lands in third class, and a little part of them were located in the fourth class. After the preparation of the temperature map, it attempted to detect the changes and review the changes during the studied period with the model LCM, which is extensively used in IDRISI Tiga software. These changes include reductions, increments and net changes for each class, and transition from one floor to another. The highest temperature drop was observed in the third floor with an average temperature of 16 ° C and the highest increase in the second floor with an average temperature of 14 ° C. This means that in 2013, in the western regions of the area (third floor), the air temperature was reduced and, naturally, the area of this class was lower than in 2000, and reduced area as shown in Fig. 7 and the results are added to the second floor temperature. In fact, the displacement of the area between the second and third temperature classes has occurred, and in general, the temperature has cooled down to 2000.
Conclusion: The severity of the impact of human activity on the environment of cities, as concentrated areas of human use of the environment, depends heavily on the distance from urban centers. The farther away from urban centers and rural areas closer to the effects of human activities on the environment and reduced for certain changes. NDVI increase is the increase in the prevalence of vegetation and land cover is more homogeneous and homogeneous. but NDVI decrease introduce more varied (water, outdoor, bare soil, construction, etc.) and more heterogeneous mosaic of land.

Keywords

Main Subjects

References

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Human Geography Research
Volume 52, Issue 3
October 2020
Pages 817-832

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History

  • Receive Date: 26 June 2017
  • Revise Date: 16 February 2019
  • Accept Date: 16 February 2019

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