Cold Season’s Air Temperature Geostatistical Modeling: Considering the Landsat Thermal Band and Snow Cover Area

جغرافیا و مخاطرات محیطی

Article 5, Volume 6, Issue 4 - Serial Number 24, January 2018, Pages 159-172   

Document Type: Research Article

DOI: 10.22067/geo.v6i4.62549

Author

Masoud Minaei^*

Ferdowsi University of Mashhad

Abstract

Providing climatic data like temperature in good spatial resolution is a key requirement for many geographical, ecological and bioclimatic research. With this in mind, various related studies use thermal remote sensing images as auxiliary data to enhance the air temperature interpolation outcomes. That’s while normally summer season images are used as auxiliary data and less attention has been paid to winter season acquired images which are often covered by snowy areas. With this in mind, the Snow Covered Area (SCA) extent impacts on air temperature interpolation were investigated. The data used were temperature data and four Landsat thermal images of December 1986 and 1999. To calculate the area of snow cover, band combination and NDSI index were used. Results show that Thermal Co-Kriging (TCK) of December 1986 provide better results with more snow affected thermal image. While in 1999 although different results were obtained but the best selected output did not show impacts of different snow cover area. These results revealed that probably the SCA extent threshold could be different and could be found with more research. Finally, we know that number of our observation stations are too low and considering the Kriging requirements like normal distribution and stationarity are toilsome but we should consider that this problem exists in the regions with low density of gauges and should find a way to enhance the air temperature interpolation in these cases. At the end, using high resolution, Landsat thermal bands improve our ability to explain and visualize local temperature variability into a variety of applications such as deriving temperature dependent climatic variables, species distribution modelling and assessments of fire risk.

Keywords

Interpolation; Thermal co-kriging; Kriging; Golestan

References

Alsamamra, H., Ruiz-Arias, J. A., Pozo-Vazquez, D., & Tovar-Pescador, J. (2009). A comparative study of ordinary and residual kriging techniques for mapping global solar radiation over southern Spain. Agricultural and Forest Meteorology, 149(8), 1343-1357.

ArcGIS Help. (2014). Fundamentals of panchromatic sharpening. Retrieved from http://resources.arcgis.com/en/help/main/10.1/index.html#//009t000000mw000000

Arundel, S. T. (2005). Using spatial models to establish climatic limiters of plant species' distributions. Ecological Modelling, 182(2), 159-181.

Attorre, F., Alfo, M., De Sanctis, M., Francesconi, F., & Bruno, F. (2007). Comparison of interpolation methods for mapping climatic and bioclimatic variables at regional scale. International Journal of Climatology, 27(13), 1825-1843.

Bakr, N., Weindorf, D. C., Bahnassy, M. H., Marei, S. M., & El-Badawi, M. M. (2010). Monitoring land cover changes in a newly reclaimed area of Egypt using multi-temporal Landsat data. Applied Geography, 30(4), 592-605.

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. Meteorological Applications, 18(4), 475-482.

Chen, C. F., Yue, T. X., Dai, H. L., & Tian, M. Y. (2013). The smoothness of HASM. International Journal of Geographical Information Science, 27(8), 1651-1667.

Crawford, C. J., Manson, S. M., Bauer, M. E., & Hall, D. K. (2013). Multitemporal snow cover mapping in mountainous terrain for Landsat climate data record development. Remote Sensing of Environment, 135, 224-233.

Cristobal, J., Ninyerola, M., & Pons, X. (2008). Modeling air temperature through a combination of remote sensing and GIS data. Journal of Geophysical Research-Atmospheres, 113(D13), 1-13.

Delbari, M., Afrasiab, P., & Jahani, S. (2013). Spatial interpolation of monthly and annual rainfall in northeast of Iran. Meteorology and Atmospheric Physics, 122(1-2), 103-113.

Dozier, J. (1989). Spectral signature of Alpine snow cover from the Landsat thematic mapper. Remote Sensing of Environment, 28, 9-22.

Erdenetuya, M., Khishigsuren, P., Davaa, G., & Otgontugs, M. (2006, June). Glacier change estimation using Landsat TM data. Paper presented at the ISPRS Tokyo 2006 Symposium Technical Commission VI. Tokyo.

Farooq, A. (2015). Spectral reflectance of land covers. Retrieved from http://www.geol-amu.org/notes/m1r-1-8.htm.

Hancock, S., Baxter, R., Evans, J., & Huntley, B. (2013). Evaluating global snow water equivalent products for testing land surface models. Remote Sensing of Environment, 128, 107-117.

Hengl, T., Heuvelink, G. B. M., Tadic, 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.

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. Hydrological Processes, 26(17), 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.). InTech.

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.

Klein, A. G., & Isacks, B. L. (1999). Spectral mixture analysis of Landsat thematic mapper images applied to the detection of the transient snowline on tropical Andean glaciers. Global and Planetary Change, 22(1-4), 139-154.

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.

Li, X., Cheng, G. D., & Lu, L. (2005). Spatial analysis of air temperature in the Qinghai-Tibet Plateau. Arctic Antarctic and Alpine Research, 37(2), 246-252.

Meng, Q. (2006). Geostatistical prediction and mapping for large area forest inventory using remote sensing data. 2006 UCGIS Summer Symposium. www.ucgis.org/summer2006/ studentpapers/Mengqm_July03_2006.pdf

Meng, Q. (2014). Regression kriging versus geographically weighted regression for spatial interpolation. International Journal of Advanced Remote Sensing and GIS, 3(1), 606-615.

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.

Minaei, M., & Irannezhad, M. (2016). Spatio-temporal trend analysis of precipitation, temperature, and river discharge in the northeast of Iran in recent decades. Theoretical and Applied Climatology, 131(1-2), 167-179.

Minaei, M., & Kainz, W. (2016). Watershed land cover/land use mapping using remote sensing and data mining in Gorganrood, Iran. ISPRS International Journal of Geo-Information, 5(5), 1-16.

Minaei, M., & Minaei, F. (2017). Geostatistical modeling of air temperature using thermal remote sensing. Environment and Sustainability, 1(4), 103-109.

Moteallemi, A., Bina, B., Minaei, M., & Mortezaie, S. (2017). The evaluation of noise pollution at Samen district in Mashhad by means of Geographic Information System (GIS)". International Journal of Occupational Hygiene, 9(4), 31-46.

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.

Prakash, A. (2000, July). Thermal remote sensing: Concepts, issues and applications. Paper presented at the ISPRS, Amsterdam. ISPRS Archives – 33 (B1).

Riggs, G. A., Hall, D. K., & Salomonson, V. V. (1994, August). A snow index for the landsat thematic mapper and moderate resolution imaging spectroradiometer. Paper presented at the Geoscience and Remote Sensing Symposium, IGARSS 94. Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation. International, Pasadena, CA, 1994, pp. 1942-1944 vol.4.

Statistical-Center-of-Iran. (2012). Iranian population and housing census 2011 - Golestan Province General Results. Tehran: Statistical Center of Iran.

Stewart, S. B., & Nitschke, C. R. (2017). Improving temperature interpolation using MODIS LST and local topography: A comparison of methods in south east Australia. International Journal of Climatology, 37(7), 3098-3110.

USGS. (2013). What are the band designations for the Landsat satellites? Retrieved from http://landsat.usgs.gov/band_designations_landsat_satellites.php.

Wang, S., Liu, E., Zhang, H., & Wu, W. (2011, February). Comparison of spatial interpolation methods for soil available P in a hilly area. Paper presented at 2011 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring, Changsha.

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.

Wolter, P. T., Berkley, E. A., Peckham, S. D., Singh, A., & Townsend, P. A. (2012). Exploiting tree shadows on snow for estimating forest basal area using Landsat data. Remote Sensing of Environment, 121, 69-79.

Yin, D. M., Cao, X., Chen, X. H., Shao, Y. J., & Chen, J. (2013). Comparison of automatic thresholding methods for snow-cover mapping using Landsat TM imagery. International Journal of Remote Sensing, 34(19), 6529-6538.

Zaksek, K., & Schroedter-Homscheidt, M. (2009). Parameterization of air temperature in high temporal and spatial resolution from a combination of the SEVIRI and MODIS instruments. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4), 414-421.

Zheng, X., Zhu, J. J., & Yan, Q. L. (2013). Monthly Air Temperatures over Northern China Estimated by Integrating MODIS Data with GIS Techniques. Journal of Applied Meteorology and Climatology, 52(9), 1987-2000.