Spatial Variation of Vegetation Moisture Mapping Using Advanced Spaceborne Thermal Emission & Reflection Radiometer (ASTER) Data

Abstract

Drought is a recurrent phenomenon in Jharkhand. It affects the livelihoods of the majority of its people, particularly tribals and dalits living in rural areas. Twelve of the 24 districts of the state, covering 43% of the total land area, are covered under the Drought Prone Areas Programme (DPAP). Hunger and starvation deaths are reported almost every year. Vegetation moisture content is one of the key parameters in drought monitoring, agricultural modelling and forest health mapping. In this paper the three different approaches is described using Advanced Spaceborne Thermal Emission & Reflection Radiometer (ASTER) data for measuring the vegetation moisture content in a part of Palamu Commissionaire of Jharkhand state, which is prone to severe drought. ASTER thermal data was used to calculate land surface temperature using Normalized Differential Vegetation Index (NDVI) emissivity correction method. Reflective bands are used to determine NDVI, Modified Soil Adjustment Vegetation Index (MSAVI) & Normalised Differential Water Index (NDWI). The three different vegetation moisture estimation methods namely MSAVI – LST (land surface temperature) feature space identification, NDWI & Vegetation Dryness Index (VDI) is applied to determine the vegetation moisture level. The results of three methods were classified and final moisture content map was produced. The result was validated using rainfall data of study area. This study indicates that by proper pre-processing of ASTER data, it can be used to estimate the land surface temperature and vegetation moisture content and can be used for drought prediction.

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V. Singh, R. Satpathy, R. Parveen and A. Jeyaseelan, "Spatial Variation of Vegetation Moisture Mapping Using Advanced Spaceborne Thermal Emission & Reflection Radiometer (ASTER) Data," Journal of Environmental Protection, Vol. 1 No. 4, 2010, pp. 448-455. doi: 10.4236/jep.2010.14052.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] D. A. Wilhite and M. H. Glantz, “Understanding the Dr- ought Phenomenon: The Role of Definitions,” Water International, Vol. 10, No. 3, 1985, pp. 111-120.
[2] A. F. H. Goetz, “Imaging Spectrometry for Remote Sensing Vision to Reality in 15 Years,” SPIE International Society for Optical Engineers, Vol. 12, 1995, p. 2480.
[3] J. W. Rouse, R. H. Hass, J. A. Schell and D. W. Deering, “Monitoring Vegetation Systems in the Great Plains with ERTS,” Proceedings of 3rd Earth Resource Satellite-1 Symposium, NASA SP-351, Greenbelt, 1974, pp. 310- -317.
[4] E. F. Lambin and D. Ehrlich, “The Surface Temperature-Vegetation Index Space for Land Cover and Land- Cover Change Analysis,” International Journal of Remote Sensing, Vol. 17, No. 17, 1996, pp. 1087-1105.
[5] ASTER Reference Guide Version, “Earth Remote Sensing Data Analysis Centre 1.0,” March 2003.
[6] ITT Visual Information Solutions, “ENVI User’s Guide,” Version 4.5, 2008.
[7] B. L. Markham and J. L. Barkewr, “Landsat MSS and TM Post Calibration Dynamic Ranges, Exoatmospheric Reflectance and At-Satellite Temperatures,” EOSAT Landsat Technical Notes 1, 1986, pp. 3-8.
[8] R. P. Gupta, “Remote Sensing Geology,” Springer-Verlag Berlin Heidelberg, Germany, 1991.
[9] V. Caselles, C. Coll, E. Valor and E. Rubio, “Mapping Land Surface Emissivity Using AVHRR Data: Application to La Mancha, Spain,” Remote Sensing Review, Vol. 12, No. 3-4, 1995, pp. 311-333.
[10] W. C. Snyder, Z. Wan, Y. Zhang and Y. Z. Feng, “ClasSification-Based Emissivity for Land Surface Temperature Measurement from Space,” International Journal of Remote Sensing, Vol. 19, No. 14, 1998, pp. 2753-2774.
[11] P. Dash, F. M. Gottsche, F. S. Olesen and H. Fischer, “Land Surface Temperature and Emissivity Estimation from Passive Sensor Data: Theory and Practice Current Trends,” International Journal of Remote Sensing, Vol. 23, No. 13, 2002, pp. 2563-2594.
[12] van de Griend, A. A. and M. Owe, “On the Relationship Between Thermal Emissivity and the Normalized Difference Vegetation Index for Natural Surfaces,” International Journal of Remote Sensing, Vol. 14, No. 6, 1993, pp. 1119-1131.
[13] E. Valor and V. Caselles, “Mapping Land Surface Emissivity from NDVI: Application to European, African, and South American Areas,” Remote Sensing of Environment, Vol. 57, No. 3, 1996, pp. 167-184.
[14] T. N. Carlson and D. A. Ripley, “On the Relation Between NDVI, Fractional Vegetation Cover and Leaf Area Index,” Remote Sensing of Environment, Vol. 62, No. 3, 1997, pp. 241-252.
[15] J. Qi, A. Chehbouni, A. R. Huete, Y. Kerr and S. Sorooshian, “A Modified Soil Adjusted Vegetation Index (MSAVI),” Remote Sensing of Environment, Vol. 48, 1994, pp. 119-126.
[16] A. K. M. Hossain, G. Easson and V. K. Boken, “Mapping Spatial Variation in Surface Moisture Using Reflective and Thermal ASTER Imagery for Southern Africa,” ASPRS 2006 Annual Conference, Reno, 1-5 May 2006.
[17] G. Tian, L. B. Jupp, F. Qiang, W. Bingfang and H. Qin, “Drought Monitoring of Crop Using Remote Sensing and GIS,” Journal of Remote Sensing, Vol. 6, No. (Suppl.), 2002, pp. 145-152.
[18] B. C. Gao, “NDWI – A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space,” Remote Sensing of Environment, Vol. 58, No. 3, 1996, pp. 257-266.
[19] M. Maki, M. Ishiahra and M. Tamura, “Estimation of Leaf Water Status to Monitor the Risk of Forest Fires by Using Remotely Sensed Data,” Remote Sensing of Environment, Vol. 90, No. 3, 2004, pp. 441-450.

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