Spatial Analysis of Environmental Hazards

Spatial Analysis of Environmental Hazards

Spatio-temporal monitoring of agricultural drought using remotely sensed data (Case study of Markazi province of Iran)

Authors
Abstract
As a result of climate change and reduction in rainfall during the last decade, drought has become big problem in the world, especially in arid and semi-arid areas such as Iran. Therefore drought monitoring and management is great of important. In contrast with the traditional methods which are based on the ground stations measurements and meteorological drought monitoring, using the remote sensing techniques and satellite imagery have become a useful tool for spatio-temporal monitoring of agricultural drought. But using of this technique and its results still need to be evaluated and calibrated for different areas.

The aim of this survey is to study the spatial and temporal patterns of drought using remote sensing and the regional meteorological data in the Markazi province. For this purpose, the MODIS satellite data between the years of 2000-2013 have been used to monitor and derived vegetation indices. Drought indices based on satellite data including the Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Temperature Vegetation Dryness Index (TVDI), and Soil Water Index (SWI) were obtained from the MODIS satellite data for the period of study for different temporal scales (seasonal, biannual and annul).Then, correlation between obtained results from satellite data and standardized precipitation index (SPI) have been analyzed in all time periods.

Results show that study area has a low to medium vegetation cover. According to the results, the climate situation of the study area is more compatible with the seasonal results of the VCI, and VCI was selected as the best indicator for agricultural drought monitoring in the study are. The obtained results from the applying of VCI over the area show the drought condition in 2000 and 2008 and the wetness in 2009 and 2010 during the study period.
Keywords

Bhuiyan, C. 2008. Desert Vegetation during Droughts: Response and Sensitivity. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII: 907-912.
Bonaccorso, B.; Bordi, I.; Cancelliere, A.; Rossi, G.; and Sutera, A. 2003. Spatial variability of drought: An analysis of the SPI in Sicily. Water Resources Management, 17: 273–296.
Caccamo, G.; Chisholm, L. A.; Bradstock, R. A.; and Puotinen, M. L. 2011. Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems. Remote Sensing of Environment,115: 2626-2639.
Jang, J. D. 2004. Evaluation of thermal-water stress of forest in southern Qubec from satellite image. University of Laval, Faculty of forest and Geomantic, Quebec.
Kanada. Jupp, D. L. B.; Tian, G.; Mcvicar, T. R.; Qin, Y.; and Fuqin, L. 1998. Soil moisture and drought monitoring using remote sensing, Theoretical background and methods. CSIRO: Astrulia.
Kogan, F. N. 2001. Contribution of remote sensing to drought early warning. National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite Data and Information Services (NESDIS), Washington: DC. U.S.A.
Kogan, F.N. 1995. Application of vegetation index and brightness temperature for drought detection. Adv, Space Res, 15(11): 91– 100.
Kogan, F.N. 1997. Global drought watch from space. Bulletin of the American Meteorological Society, 78: 621-636.
Krzanowski, W.J. 1987. Selection of variables to preserve multivariate data structure, using principal components. Journal of the Royal Statistical Society, 36(1): 22–33.
McKee, T. B.; Doesken, N. J.; and Kleist, J. 1993. The relationship of drought frequency and duration11 to time scales. Preprints, 8th Conference on Applied Climatology, Anaheim.
Owrangi, M. A.; Adamowski, J.; Rahnemaei, N.; Mohammadzadeh, A.; and Afshin Sharifan, R. 2011. Drought monitoring methodology based on AVHRR Image and SPOT vegetation maps. Journal of Water Resource and Protection, 3: 325-334.
Price, J.C. 1990. Using spatial context in satellite data to infer regional scale evapotranspiration. IEEE Trans, Geoscience and Remote Sensing, 28: 940–948.
Rhee, J. 2010. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sensing of Environment, 114: 2875–2887.
Roswintiarti, O.; Oarwati, S.; and Angraini, N. 2010. Potential drought monitoring over agriculture area in Java Island, Indonesia. Indonesian National Institute of Aeronautics and Space (LAPAN), Progress Report of SAFE Prototype Year.
Rouse, J. W.; Haas, Jr. R.; Schell, H. J. A.; and Deering, D.W. 1973. Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium, NASA: 309-317.
Sandholt, I.; Rasmussen, K.; and Andersen, J. 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of soil moisture status. Remote Sensing of Environment, 79 (2): 213-224.
Shahabfar, A.; Ghulam, A.; and Eitzinger, J. 2012. Drought monitoring in Iran using the perpendicular drought indices. International Journal of Applied Earth Observation and Geoinformation, 18: 119-127.
Thenkabail, P.S.; Enclona, E. A.; Ashton, M. S.; Legg, C.; and Jean De Dieu, M. 2004. The use of remote sensing data for drought assessment and monitoring in Southwest Asia. Sri Lanka: International Water Management Institute.
Tsakiris, G.; and Vangelis, H. 2004. Towards a drought watch system based on spatial SPI. Water Resources Management, 18: 1-12.
Tucker, C. J. 1979. Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing of the Environment, 8: 127-150.
Zhang, A.; & and Jia, G. 2013. Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sensing of Environment, 134: 1223.