Drought is a multi-causal and complex environmental problem and can have serious socioeconomic consequences. Recently, the IPCC (the Intergovernmental Panel on Climate Change) in the Fourth Assessment Report (AR4) concluded that South Asia and the Middle East would be subject to severe and prolonged droughts due to global climate change, particularly increase in greenhouse gases in the atmosphere (IPCC, 2007). Drought is a climate-related natural disaster, the effects of which are exacerbated by human activities. Sometimes drought affects large regions and even several countries for a long period of time. Drought has a serious impact on the food productivity of an area and also on the life expectancy of the inhabitants. The consequences of drought involve socioeconomic and ecological issues (WGA, 1996) (Jeyaseelan, 2005; Pongracza et al., 1996). Iran, which includes arid zones, has been periodically endangered by drought events, which have devastatingly affected society and the environment (Shamsipour et al., 2008). Therefore, studying drought requires different sources of datasets. In other words, when developing a regional planning project for sustainable development, the acquisition of updated data is essential, especially for countries with arid and semi-arid climates. Recent innovations in remote sensing methods have brought new solutions for studying environmental problems in geosciences. In assessing natural risks such as drought, remote sensing provides rapid and instantaneous spatial data on natural phenomena; they are useful in decision making and weather forecasting (Sunyurp et al., 2004). Remote sensing drought monitoring depends on the factors causing drought (Jeyaseelan, 2005). Drought indicators and variables, obtained through remote sensing data, may involve some uncertainties, induced by the sensitivity of the factors or their dependence on meteorological and environmental conditions. Furthermore, some non-standard algorithms may lead to incorrect estimation of drought intensity. More effective methods to increase the accuracy of evaluation and analysis of remotely sensed data are applying models that can combine into data layers. Geographic information systems (GIS) are used to combine layers of data in drought modeling. Recently, spatial technologies, such as RS and GIS, and numerical modeling techniques have been developed as powerful tools for ecological assessment of the environment (Krivtsov, 2004; MacMillan et al., 2004; Store and Jokimäki, 2003). The use of these technologies not only provides a platform to support multilevel and hierarchically integrated analyzes of resources and the environment, but also integrates the information obtained into a comparative theoretical analysis of the ecosystem. Meanwhile, Plummer (2000) argued that prospects for combining ecological models and remote sensing data would focus on accuracy estimation, issues of spatial and temporal scale, and long-term comprehensive data sets..