Relating Vegetation Cover with Land Surface Temperature and Surface Albedo in Warm Period of Year Using MODIS Imagery in North of Iran

Document Type : Full length article


1 Faculty of Geography and Environmental Science, Department of Geography, Hakim Sabzevari University, Sabzevar, Khorasan Razavi, Iran

2 2Faculty of Marine Science, Department of Marin Biology, Khorramshahr University of Marine Science and Technology


Extended Abstract
The most important indicator of climate in a region is vegetation community. Climate can be different over large areas, because of changes in vegetation community. Vegetation influences weather and climate in its surrounding areas mostly by way of evapotranspiration and albedo so they play a role in the earth’s energy balance. These effects on the earth’s energy balance are taken through air temperature, relative humidity, rainfall, solar radiation and cloud cover on their own micro-weather (Neilson, 1986, Small and Kurc, 2003 and Weiss, et al 2004). Disasters such as drought, flood, forest fire and among them can occur whenever the global energy balance become outside of normal range.
Remote sensing data provide valuable information for vegetation studies because it presents a quick look evaluation of the vegetation conditions. Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and land surface albedo are key biophysical variables for studying land surface processes and surface-atmosphere interactions. These variabels can be calculated by transforming raw satellite data. The aim of this paper was to study relationship between NDVI, LST and land surface albedo due to the impact of vegetation on surface temperature and albedo and identify dryness status in the northern Iran.
Study area is located in the northern part of Iran which covers with dense vegetation such as Hyrcanian forests in the north and sparse vegetation in the southern part of the Alborz Mountain range. This area is an alteration bio-climate region lying between the humid climate in the north and arid and semi-arid climate in the south.
The methodology used in this study consisted of remotely sensed data processes. The remotely sensed data processes involved data acquisition of Moderate Resolution Imaging Spectroradiometer (MODIS), data pre-processing (i.e. atmospheric correction, geometric correction, and data masking), and data processing (i.e. derivation of vegetation, biophysical indices such as NDVI, LST and land surface albedo variables). In this study, MODIS data was selected because the MODIS sensor has several benefits compared to other instruments for example MODIS has a wide swath of 2330 km that covered the entire study area. The MODIS dataset had a variety of products that each contained different levels of data processing. In this study, Level 1B dated July, 2010 (MOD021KM and MYD021KM calibrated and geolocated) was used to derive NDVI and surface albedo, and retrieving LST. This level 1B collection contains calibrated and geolocated radiances at-aperture for all 36 MODIS spectral bands at 1km resolution. In this study, Simplified Model for Atmospheric Correction (SMAC) was used for atmospheric correction of MODIS data.
Absorbtion in visible light (solar radiation) occurs in live green vegetation because of photosynthesis. Scattering (reflectance) of solar energy in the near infrared occurs at the same time. This difference in absorption and reflectance lead us to NDVI. NDVI is an vegitation index which measures this difference to show vegetation density and condition. NDVI value ranges between -1 and +1. The values close to zero means no green vegetation and close to +1 (0.8 - 0.9) represents the highest density of green vegetation. LST is an important parameter in determining the earth radiation budget and heat and moisture flow between the surface and the atmosphere and temperature strongly influence vegetation processes. Thermal bands of MODIS data (band 31 and 32) converted to radiance then converted to brightness temperature using plank law for calculating surface temperature. Albedo is also an important bio-physical indicator of reflecting land surface energy distribution and balance. In the process of broadband albedo retrieval, an empirical regression was used for MODIS data (Liang et al., 2002). Finally, regression and geostatistical approach (e.g. CoKriging) was used in this study to estimate LST and surface albedo using NDVI of MODIS data.
Results and discussion
In this study three criteria such as mean absolute error, root mean square error and mean absolute percentage error have been used to measure the differences between the values estimated for LST and Albedo using regression and CoKriging geostatistical method. The results obtained in this research indicate that the geostatistical method of cokriging has good potential to estimate LST and surface albedo using normalized difference vegetation index. The results of the study show that changes in vegetation cover alter the LST and surface albedo, leading to a local temperature change. Plants and forest have a very low albedo and absorb a large amount of energy. The relationship between normalized difference vegetation index and LST and surface albedo equations were then used to found the surface dryness condition in the study area. The result of 3D feature space of Albedo-NDVI-LST spectral shows that it has a suitable index for extracting drought information. The study revealed that coastal and forested northern slopes of the Alborz Mountain are identified with high normalized difference vegetation value (0/85), minimum surface temperature (23° C) and albedo (7%). The southern part of Alborz Mountain and the central Iran experiences low normalized difference vegetation value (-0/09), high surface temperature of 45 ° C and high surface albedo (38%).
In this study, the relationship between NDVI and LST and surface albedo was analyzed to estimate LST and surface albedo derived by regression and CoKriging methods. It has been recognized that there is a strong correlation between albedo, LST and NDVI. By comparison of the regression relationship among LST, Albedo and NDVI, results exhibit that LST and Albedo are negative correlations with NDVI. In this paper, a 3D feature space of Albedo-NDVI-LST spectral is analyzed for monitoring surface dryness condition. The result represents that surface albedo nad LST is affected by the change of vegetation. This 3D feature space is reliable index to show surface dryness status for soil and plant cover. it is recommended that 3D feature space of Albedo-NDVI-LST spectral can monitor to the surface dryness condition and also it is easy to operate for quick surface dryness assessment. Studies are underway to incorporate other variables in surface dryness condition.


Main Subjects

دشتکیان، ت. و دهقرانی، م. ع. ) 1386 (. بررسی دمای سطح زمین در ارتباط با پورش گیاهی و توسعة رقری با اساتفاده از سانجش از
دور و سامانه های اطلایات جغرافیایی در مناطق بیابانی. مجلة پژوه و سازندگی در منابع طبیعی، شمارة 77 ، 169 - 179 .
علیجانی، ب. و کاویانی، م. ر. ) 1371 (. مبانی آب و هوارناسی، انتشارات سمت. .88
Adab, H. (2014). Remote Sensing based Thermophysical Approach for Detecting Forest Pre-Ignition in Iran. Ph.D. Dissertation, Universiti Teknologi Malaysia (UTM), Faculty of Geoinformation and Real Estate, pp 49-50.
Betts, R.A., Cox, P.M., Lee, S.E. and Woodward, F.I. (1997). Contrasting Physiological and Structural Vegetation Feedbacks in Climate Change Simulations. Nature, Vol. 387, pp 796-799.
Boegh, E., Soegaard, H., Hanan, H., Kabat, P. and Lesch, L. (1998). A Remote Sensing Study of the NDVI-TS Relationship and the Transpiration from Sparse Vegetation in the Sahel based on High Resolution Data. Remote Sensing of Environment, Vol. 69, pp 224–240.
Bounoua, L., Collatz, G.J., Sellers, P.J., Randall, D.A., Dazlich, D.A., Los, S.O., Berry, J.A., Fung, I., Tucker, C.J., Field, C.B. and Jensen, T.G., (1999). Interaction between Vegetation and Climate: Radiative and Physiological Effects of Doubled Atmospheric CO. Journal of Climate, Vol. 12, pp 309-324.
Colwell, J. E. (1974). Vegetation Canopy Reflectance. Remote Sensing of Environment, Vol. 3, pp 175-183.
Hummel, John R., and Ruth A. Reck. (1979). A Global Surface Albedo Model. Journal of Applied Meteorology, Vol. 18, No. 3, pp 239-53.
Karnieli, A., Bayasgalan, M., Bayasgalan, Y., Agam, N., Khudulmur, S. and Tucker, C.J. (2006). Comments on the Use of the Vegetation Health Index over Mongolia, International Journal of Remote Sensing, Vol. 27, pp 2017-2024.
Liang, S., Shuey ,C. J. L., Russ, A. Fang, H., Chen, M., Walthall, C.L., Daughtry, C.S.T., Hunt Jr, R. (2002). Narrow Band to Broad Band Conversions of Land Surface Albedo: II.Validation. Remote Sensing of Environment, Vol. 84, pp 25-41.
Loranty, Michael M., Goetz, Scott, J. and Beck. Pieter S.A. (2011). Tundra Vegetation Effects on Pan-Arctic Albedo. Environmental Research Letters, Vol. 6, No. 2, pp 1-7,024014.
Mu, Qiaozhen, Zhao, Maosheng, Kimball, John S., McDowell, Nathan G. and Running, Steven W. (2013). A Remotely Sensed Global Terrestrial Drought Severity Index. Bulletin of the American Meteorological Society, Vol. 94, No. 1, pp 83-98.
Neilson, R.P. (1986). High-Resolution Climatic Analysis and Southwest Biogeography. Science, Vol. 232, pp 27-34.
Pitman, A.J. (1991). A Simple Parameterization of Sub-Grid Scale Open Water for Climate Models. Climate Dynamics, Vol. 6, pp 99-112.
Prihodko, L. and Goward, S.N. (1997). Estimation of Air Temperature from Remotely Sensed Observations, Remote Sensing of Environment, Vol. 60, pp 335–346.
Rahman, H. and Dedieu, G. (1994). SMAC: A Simplified Method for the Atmospheric Correction of Satellite Measurements in the Solar Spectrum. International Journal of Remote Sensing, Vol. 15, pp 123-143.
Robinove, Charles J., Chavez Jr, Pat S., Gehring, Dale, and Holmgren, Ralph (1981). Arid Land Monitoring using Landsat Albedo Difference Images. Remote Sensing of Environment, Vol. 11, pp 133-56.
Rouse, J.W., Haas, R.H., Schell, J.A. and Deering, D. W. (1973). Monitoring Vegetation Systems in the Great Plains with ERTS. Third ERTS Symposium, NASA SP - 351, Vol. 1, pp 309-311.
Small, E.E. and Kurc, S. (2003). Tight Coupling Between Soil Moisture and the Surface Radiation Budget in Semiarid Environments: Implications for Land-Atmosphere Interactions. Water Resources Research, Vol. 39, No.10, pp 1278.
Sun, D. and Kafatos, M. (2007). Note on the NDVI-LST Relationship and the Use of Temperature-Related Drought Indices over North America. Geophysical Research Letters, Vol.34, L24406, pp 1-4.
Wan, Z., Wang, P. and Li., X. (2004). Using MODIS Land Surface Temperature and Normalized DifferenceVegetation Index Products for Monitoring Drought in the Southern Great Plains, USA. International Journal of Remote Sensing, Vol. 25, No. 1, pp 61-72.
Wang, J., Rich, P.M. and Price, K.P. (2003). Temporal responses of NDVI to precipitation and temperature in the central great plains, USA. International Journal of Remote Sensing, Vol. 24, No. 11, pp 2345-2364.
Weiss, J.L., Gutzler, D.S., Coonrod, J.E.A. and Dahm, C.N. (2004). Seasonal and Inter-Annual Relationships between Vegetation and Climate in Central New Mexico, USA. Journal of Arid Environments, Vol. 57, pp 507-534.
Xue, Y., Sellers, P.J., Kinter, J.L. and Shukla, J. (1991). A Simplified Biosphere Model for Global Climate Studies. Journal of Climate, Vol. 4, pp 345-364.
Yao, Y., Liang, S., Cheng, J., Liu, S., Fisher, J.B., Zhang, X., Jia, K., Zhao, X., Qin, Q., Zhao, B., Han, S., Zhou, G., Zhou, G., Li, Y. and Zhao, S. (2013). MODIS-Driven Estimation of Terrestrial Latent Heat Flux in China Based on a Modified Priestley-Taylor Algorithm. Agricultural and Forest Meteorology, Vol. 171-172, pp 187-202.
Zhou, Chuncheng, Lingling, Ma, Xinhong, Wang and Shi, Qiu (2010). A Thin Cloud Removal Method for Optical Remote Sensing Imagery Based on Spatial Variogram. 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), Canada, 23-25 Sept.