6.2 Identifying Time-lag Relationships between Vegetation Condition and Climate to Produce Vegetation Outlook Maps and Monitor Drought

The temporal and spatial vegetation dynamics is highly dependent on many different environmental and biophysical factors. Among these, climate is one of the most important factors that influence the growth and condition of vegetation (Propastin et al., 2006). The complexity of the relationships between vegetation and climate; climate and oceanic dynamics; and the impacts of the combination of ocean-atmosphere interaction on vegetation result in a huge challenge in monitoring drought patterns and its temporal and spatial effects on vegetation. Traditionally, climate-based drought indicators such as the Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI) have been used for drought monitoring (Wilhite, 2000; Hayes et al., 1999; Wells et al., 2004). However, most climate-based drought monitoring approaches have a limited spatial precision at which drought patterns can be mapped because the indices are calculated from point-based, meteorological measurements collected at weather station locations. In addition, weather stations are scarce in remote areas and not uniformly distributed. As a result, most climate-based drought indices maps depict broad-scale point-based data using statisticalbased spatial interpolation techniques and the level of spatial detail in those patterns is highly dependent on the density and distribution of weather stations. Therefore, climate-based drought indices can be enhanced through integration with remote sensing data to be useful for local-scale drought planning and monitoring activities. Remotely sensed data from the Advanced Very High Resolution Radiometer (AVHRR) have been widely used to monitor vegetation over large areas with relatively higher spatial resolution (e.g., 1-km, 4-km, and 16-km) than the climate data sets commonly used for drought monitoring (DeBeurs and Henebry 2004; Townshend et al. 1987; Tucker et al. 1985). Several studies indicated that remote sensing data has become a common process in quantitative description of vegetation cover that can be used to address temporal and spatial relationships between climate and vegetation including the eventual lagged relationships of climate (e.g., precipitation and temperature) to vegetation response (Camberlin et al., 2007; Groeneveld and __________________________ * Corresponding author address: Tsegaye Tadesse, National Drought Mitigation Center, Univ. of Nebraska Lincoln, NE 68583-0988; e-mail: [email protected]. Baugh, 2007; Anyamba and Tucker, 2005; Seaquist et al., 2005; Roerink et al.,2003). This quantitative description of vegetation can be used to identify and predict the vegetation stress during drought. In addition, satellite observations are a valuable source of timely, spatially-continuous information for monitoring vegetation dynamics and conditions. Thus, recent advances in remote sensing observations, improvements in the spatial and temporal coverage of weather stations, and improved computational capabilities and statistical analysis techniques have enhanced our capabilities to monitor drought and project its impact on vegetation conditions over large geographic areas. Studying past and present droughts in relation to climatological, oceanic, and atmospheric parameters could help mitigate future drought impacts on society by improving our understanding of the drought hazard (Tadesse et al, 2005a). Improvements in short-, medium-, and long-range climate predictions enhance our capability to monitor vegetation conditions and develop better drought early warning and knowledgebased decision support systems. However, the complexities of drought characteristics, as well as the highly variable temporal and spatial relationships of climate-vegetation interactions, make the prediction of drought and its impacts on vegetation very challenging. Because of this, for better vegetation monitoring and more accurate assessment of the impacts of drought, a better understanding of how long it takes for a vegetation to respond after a precipitation event occurred is essential (Camberlin et al., 2007; Diodato, 2007; Foody, 2003; Goward and Prince, 1995). In addition, determining how this precipitation-vegetation response relationship varies both in space and time (i.e., geographically and across the growing season) is a fundamental research question to improving drought monitoring and prediction. The overall objective of the study was to assess the nature of temporal and spatial relationships between climate (e.g., precipitation and temperature), oceanic dynamics (e.g., sea surface temperature change in the Pacific and Atlantic ocean), and the vegetation condition, as measured by the satellite-derived standardized seasonal greenness (SSG) in the central United States (Figure 1) using 17 years of data (i.e., 1989 to 2005). Preliminary results of this study are presented in this paper.