An Analysis of Frequency Distribution of LST in Iran

Document Type : Research Paper

Author

Abstract

Any land feature (lake, sand dune, playa, forest, …) has its own thermal properties (specific heat and thermal conductivity). So, different land features show different frequency distribution of LST. On the other hand, environmental changes affect the LST. As a result, the frequency distribution of LST over a given area may change by deforestation, desertification, drying of lakes, and disappearing of snowcaps. The high spatial and temporal resolution of MODIS LST data makes it possible to investigate the changes in the frequency distribution of LST. Using 15 years of LST data shows that the frequency distribution of LST in Iran depend on altitude, atmospheric moisture, snowcaps, surface and subsurface water respectively. Different land features such as lakes, river systems, deserts, playas, sand dunes, forest, and urban areas possess their LST frequency signature. Consequently, LST frequency distribution may be used as a powerful tool to delineate such land features. At the same time, some environmental changes like drying of lakes, deforestation and land use changes may be detected and tracked using frequency analysis of LST.

Keywords

References

-   Benali, A., Carvalho, A. C., Nunes, J. P., Carvalhais, N., & Santos, A. (2012). Estimating air surface temperature in Portugal using MODIS LST data. Remote Sensing of Environment, 124, 108-121.
https://www.researchgate.net/publication/230660797_Estimating_air_surface_temperature_in_Portugal_using_MODIS_LST_data
-   Colombi, A., De Michele, C., Pepe, M., Rampini, A., & Michele, C. D. (2007). Estimation of daily mean air temperature from MODIS LST in Alpine areas. EARSeL eProceedings, 6(1), 38-46.
https://pdfs.semanticscholar.org/813f/55d5e136c702a03f9108414a701b79cc18dd.pdf
-   Guangmeng, G., & Mei, Z. (2004). Using MODIS land surface temperature to evaluate forest fire risk of northeast China. IEEE Geoscience and Remote sensing letters, 1(2), 98-100.
https://www.researchgate.net/publication/3449515_Using_MODIS_Land_Surface_Temperature_to_Evaluate_Forest_Fire_Risk_of_Northeast_China
-   Hengl, T., Heuvelink, G. B., Tadić, M. P., & Pebesma, E. J(2012). Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images. Theoretical and applied climatology, 107(1-2), 265-277.
-   Imhoff, M. L., Zhang, P., Wolfe, R. E., & Bounoua, L (2010). Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sensing of Environment, 114(3), 504-513.
-   Keikhosravi Kiany, M.S., & Masoodian, S. A. (2017). Identification of Seasonal Snow-covered Seasons of Iran based on MODIS Data. Geography and Environmental Planning, 63(3), 33-48.
https://www.sid.ir/en/Journal/ViewPaper.aspx?ID=567945
 
-   Langer, M., Westermann, S., & Boike, J. (2010). Spatial and temporal variations of summer surface temperatures of wet polygonal tundra in Siberia-implications for MODIS LST based permafrost monitoring. Remote Sensing of Environment, 114(9), 2059-2069.
https://www.researchgate.net/publication/222113865_Spatial_and_temporal_variations_of_summer_surface_temperatures_of_wet_polygonal_tundra_in_Siberia_-_implications_for_MODIS_LST_based_permafrost_monitoring
-   Liu, Y., Hiyama, T., & Yamaguchi, Y. (2006). Scaling of land surface temperature using satellite data: A case examination on ASTER and MODIS products over a heterogeneous terrain area. Remote Sensing of Environment, 105 (2), 115-128.
https://www.researchgate.net/publication/222946781_Scaling_of_land_surface_temperature_using_satellite_data_A_case_examination_on_ASTER_and_MODIS_products_over_a_heterogeneous_terrain_are
-   Masoodian, S. A. (2011). The climate of Iran. Iran: Sharia Toos Publication
https://scholar.google.com/citations?user=-_N2LhoAAAAJ&hl=en
-   Mildrexler, D. J., Zhao, M., & Running, S. W. (2011). Satellite finds highest land skin temperatures on earth. Bulletin of the American Meteorological Society, 92(7), 855-860.
https://watermark.silverchair.com/2011bams3067_1.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysg
-   Mostovoy, G. V., King, R., Reddy, K. R., & Kakani, V. G. (2005, May). Using MODIS LST data for high-resolution estimates of daily air temperature over Mississippi. In Analysis of Multi-Temporal Remote Sensing Images, 2005 International Workshop on the (PP.76-80).IEEE.
https://ieeexplore.ieee.org/abstract/document/1469844
 
-   Neteler, M. (2010). Estimating daily land surface temperatures in mountainous environments by reconstructed MODIS LST data. Remote sensing, 2(1), 333-351.
file:///C:/Documents%20and%20Settings/Kh.Mir%20Teymuri/My%20Documents/Downloads/remotesensing-02-00333.pdf
-   Son, N. T., Chen, C. F., Chen, C. R., Chang, L. Y., & Minh, V. Q (2012). Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data. International Journal of Applied Earth Observation & Geoinformation, 18, 417-427.
https://www.researchgate.net/publication/329642889_Monitoring_drought_via_TVDI_derived_from_2000_-_2015_MODIS_data_a_case_ study_of_Vietnamese_Mekong_River_Delta
-   Wang, X., & Li, Y (2016). Predicting urban heat island circulation using CFD. Building and Environment, 99, 82-97.
-   Westermann, S., Langer, M., & Boike, J. (2011). Spatial and temporal variations of summer surface temperatures of high-arctic tundra on Svalbard-implications for MODIS LST based permafrost monitoring. Remote Sensing of Environment, 115(3), 908-922.
-   Zhou, B., Lauwaet, D., Hooyberghs, H., De Ridder, K., Kropp, J. P., & Rybski, D (2016). Assessing seasonality in the surface urban heat island of London. Journal of Applied Meteorology and Climatology, 55(3), 493-505.
https://www.researchgate.net/publication/283697230_Assessing_Seasonality_in_the_Surface_Urban_Heat_Island_of_London
Geography and Development
Volume 18, Issue 60 - Serial Number 60
September 2020
Pages 21-30

Files

History

  • Receive Date: 14 September 2020
  • Accept Date: 14 September 2020

Share

How to cite

Statistics