Volume 1, Issue 4, November 2017, Page: 103-109
Geostatistical Modeling of Air Temperature Using Thermal Remote Sensing
Masoud Minaei, Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran
Foad Minaei, Faculty of Geography, University of Tehran, Tehran, Iran
Received: Mar. 15, 2017;       Accepted: May 15, 2017;       Published: Jul. 12, 2017
DOI: 10.11648/j.ajese.20170104.11      View  1800      Downloads  117
Geographic Information Systems and spatial interpolation are the most often used geographic sciences for spatial analysis and visualization of temperature to use in hydrological studies. According to dependency of nature of thermal bands data to temperature, using thermal remote sensing images as auxiliary data can be useful in air temperature spatial interpolation. In light of these considerations, we used Landsat thermal bands together with Kriging and Co-kriging geostatistical methods for four seasons to interpolate mean temperature in Northeast of Iran as a region with low density of gauge distribution. Using Landsat (instead of for instance MODIS) is firstly to provide requirement of mentioned science. Secondly, help to provide deeper understand in case of “climatic neighborhood” concept. To assess the efficiency of the method cross validation indicators were used. Thermal images used in this study increase the accuracy for the winter and autumn in comparison to unused outputs. The provided results for spring and summer were good too. Also, the spatial impacts of thermal images on the results of autumn and spring are significant. This research indicated that using thermal images as auxiliary data have potential to improve spatial prediction accuracy and quality. At the end, we know that number of our observation stations are too low and considering the Kriging requirements like normal distribution and stationarity is toilsome but we should consider that this problem exist in the regions with low density of gauges and should find a way to enhance the air temperature interpolation in these cases.
Interpolation, Kriging, Thermal Co-Kriging, Golestan, Environmental Studies
To cite this article
Masoud Minaei, Foad Minaei, Geostatistical Modeling of Air Temperature Using Thermal Remote Sensing, American Journal of Environmental Science and Engineering. Vol. 1, No. 4, 2017, pp. 103-109. doi: 10.11648/j.ajese.20170104.11
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This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ArcGIS 10 help., 2013. Cross Validation (Geostatisical Analyst) [Online]. ESRI, http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#//00300000000z000000. [Accessed 16 August 2013].
Benavides, R. Montes, F. Rubio, A. Osoro K., 2007. Geostatistical modelling of air temperature in a mountainous region of Northern Spain. Agricultural and Forest Meteorology, 146( 3–4), 173-188.
Boi, P. Fiori, M. Canu S., 2011. High spatial resolution interpolation of monthly temperatures of Sardinia. Meteorol. Appl, 18, 475–482. DOI: 10.1002/met.243.
Chen, C. Yue, T. Dai, H. Tian M., 2013. The smoothness of HASM. International Journal of Geographical Information Science, DOI: 10.1080/13658816.2013.787146.
Delbari, M. Afrasiab, P. Jahani, S., 2013. Spatial interpolation of monthly and annual rainfall in northeast of Iran. Meteorol Atmos Phys. DOI: 1007/s00703-013-0273-5.
Jabot, E. Zin, I. Lebel, T. Gautheron, A. Obled, C., 2012. Spatial interpolation of sub-daily air temperatures for snow and hydrologic applications in mesoscale Alpine catchments. Hydrol. Process, 26, 2618–2630.
Joyce, K. E. Wright, K. C, Samsonov, S. V. Ambrosia, V. G., 2009. Remote sensing and the disaster management cycle, Advances in Geoscience and Remote Sensing, Gary Jedlovec (Ed.), ISBN: 978-953-307-005-6, InTech, Available from: http://www.intechopen.com/books/advances-in-geoscience-andremote-sensing/remote-sensing-and-the-disaster-management-cycle.
Kalivas, D. P. Kollias, V. J. Apostolidis, E. H., 2013. Evaluation of three spatial interpolation methods to estimate forest volume in the municipal forest of the Greek island Skyros. Geo-spatial Information Science, 16 (2), 100-112. DOI: 10.1080/10095020.2013.766398.
Kyriakidis, P. C. Goodchild, M. F., 2006. On the prediction error variance of three common spatial interpolation schemes. International Journal of Geographical Information Science, 20(8), 823-855. DOI: 10.1080/13658810600711279
Li, X. Cheng, G. Lu, L., 2005. Spatial analysis of air temperature in Qinghai-Tibet Plateau. Arctic Antarct. Alpine Res, 37 (2), 246–252.
Meng, Q. 2006. Geostatistical prediction and mapping for large area forest inventory using remote sensing data. UCGIS Summer Symposium. www.ucgis.org/summer2006/studentpapers/Mengqm_July03_2006.pdf. Accessed on August 20th 2013.
Meng, Q. Liu, Z. Borders B.E., 2013. Assessment of regression kriging for spatial interpolation – comparisons of seven GIS interpolation methods. Cartography and Geographic Information Science, 40 (1), 28-39. DOI: 10.1080/15230406.2013.762138.
Minaei, M. Irannezhad, M., 2016. Spatio-temporal trend analysis of precipitation, temperature, and river discharge in the northeast of Iran in recent decades. Theor Appl Climatol (2016). doi:10.1007/s00704-016-1963-y.
Minaei, M.; Kainz, W., 2016. Watershed Land Cover/Land Use Mapping Using Remote Sensing and Data Mining in Gorganrood, Iran. ISPRS Int. J. Geo-Inf., 5, 57.
Oliver, M. A. Webster, R., 1990. Kriging: a method of interpolation for geographical information systems. International Journal of Geographical Information Systems, 4(3), 313-332. DOI: 10.1080/02693799008941549.
Prakash, A., 2000. Thermal remote sensing: concepts, issues and applications. International Archives of Photogrammetry and Remote Sensing, XXXIII (Part B1), 239-243.
Ren-Ping, Z. Jing, G. Tian-Gang, L. Qi-Sheng, F. Aimaiti, Y., 2016. Comparing interpolation techniques for annual temperature mapping across Xinjiang region. 6th Digital Earth Summit; Beijing; China; 7 July 2016 through 8 July 2016; Code 124956. DOI: 10.1088/1755-1315/46/1/012028.
USGS website., 2013. What are the band designations for the Landsat satellites? http://landsat.usgs.gov/band_designations_landsat_satellites.php. Accessed on September 2013.
Wang, S. Q. Liu, E. P. Zhang, H. J. Wu, W., 2011. Comparison of spatial interpolation methods for soil available P in a hilly area. International Conference on Computer Distributed Control and Intelligent Environmental Monitoring, 2011-2014. DOI 10.1109/CDCIEM.2011.367.
Wentz, E. A. Peuquet, D. J. Anderson, S., 2010. An ensemble approach to space–time interpolation. International Journal of Geographical Information Science, 24(9), 1309-1325, DOI: 10.1080/13658816.2010.488238.
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