SPATIAL AND TEMPORAL VEGETATION VARIABILITY IN AFRICA: AN APPLICATION OF TEMPORAL MAP ALGEBRA Jeremy Mennis Department of Geography and Urban Studies Temple University 1115 W. Berks St., 309 Gladfelter Hall Philadelphia, PA 19122 jmennis@temple.edu ABSTRACT This research investigates the impact of ENSO on African vegetation intensity during 1982-1999 over different vegetation types in three regions: the western Sahel, eastern Africa, and southern Africa. Data are derived primarily from the AVHRR sensor. Mean NDVI anomaly and spatial and temporal anomaly variance were tabulated using temporal map algebra, an extension of conventional map algebra for spatio-temporal data handling. In eastern Africa, ENSO cold phase is associated with enhanced vegetation intensity, particularly for woodland and open shrubland, and ENSO warm phase is associated with enhanced vegetation intensity in shrubland. This pattern continues through the growing season. In the western Sahel, ENSO cold phase is associated with enhanced vegetation intensity in shrubland and grassland, and ENSO warm phase is associated with suppressed vegetation intensity for all vegetation types. This pattern continues, though muted, through the growing season. In southern Africa, ENSO cold (warm) phase is associated with enhanced (suppressed) vegetation intensity. For shrubland, this pattern continues through the growing season, while for woodland and grassland it is reversed – ENSO cold phase is associated with suppressed vegetation intensity. For nearly all vegetation types in all study regions, ENSO cold phase is associated with higher spatial variance in NDVI anomaly, particularly for croplands. These differences in spatial variance are generally minimized during the following growing season. In eastern Africa, ENSO warm phase is associated with higher temporal variance over wooded grassland and closed shrubland. In the western Sahel and southern Africa ENSO cold phase is associated with higher temporal variance. INTRODUCTION Previous research has shown that El Niño/Southern Oscillation (ENSO), a cyclical pattern in the coupled oceanatmosphere system associated with changes in the equatorial Walker cell circulation (Carleton, 1998), impacts precipitation and temperature, and consequently vegetation, in various regions across the globe (Ropelewski and Halpert, 1987). The issue of vegetation variation in Africa, and its relationship to ENSO, is of particularly interest because of crop failure and consequent food scarcity in times of environmental stress. It has been shown that some regions of Africa tend to undergo drought during an ENSO warm phase, weakening vegetation intensity, while other regions tend to undergo anomalously rainy conditions, enhancing vegetation intensity (Anyamba and Eastman, 1996). These vegetation responses may occur simultaneously with sea surface temperature changes, or may occur months later due to the time it takes for vegetation to respond to changes in precipitation and temperature, as well because of normal seasonal vegetation fluctuations. The present research investigates the impact of ENSO on the variation in vegetation intensity over different land covers in three regions of Africa: the western Sahel, eastern Africa, and southern Africa (Figure 1). Data describing African ENSO phase, vegetation intensity, and land cover are derived from satellite imagery. The analysis is performed using an analytical approach called temporal map algebra, which was developed by the author as a means to extend conventional map algebra to the analysis of time series of imagery. ASPRS 2005 Annual Conference Baltimore, Maryland • March 7-11, 2005 Figure 1. The three study regions: eastern Africa (East), western Sahel (Sahel), and southern Africa (South). DATA Vegetation intensity over the years 1982 through 1999 is observed using Normalized Difference Vegetation Index (NDVI) data derived from the Advanced Very High Resolution Radiometer (AVHRR), a sensor on board the National Oceanic and Atmospheric Administration (NOAA) series of polar-orbiting satellites. NDVI is calculated as (Channel 2 – Channel 1) / (Channel 2 + Channel 1) where Channel 1 and Channel 2 are reflectance values captured in the red and near-infrared wavelengths, respectively. Monthly NDVI data were acquired from NASA Goddard Space Flight Center’s Distributed Active Archive Center (DAAC). These 8 km resolution data, part of the Pathfinder AVHRR Land (PAL) program, have undergone preprocessing to account for sensor degradation; compositing was used to reduce cloud contamination (Holben, 1986). Land cover data were acquired from the University of Maryland Global Land Cover Facility (GLCF). These 8 km resolution data were derived from AVHRR data with the aid of higher resolution imagery used for classification purposes (Hansen et al,. 2000). Table 1 reports the land covers for each of the three study regions. All study regions are dominated by woodland, wooded grassland, and shrubland (Figure 2). We focus on the following vegetated land covers in this analysis: woodland, wooded grassland, closed shrubland, open shrubland, grassland, and cropland. ENSO phase data, indicating whether each month is associated with an ENSO warm, cold, or neutral phase, were acquired from the NOAA-Cooperative Institute for Research in Environmental Sciences’ (CIRES) Climate Diagnostics Center (Smith and Sardeshmukh, 2000). These data were generated by calculating the five month running mean of the Southern Oscillation Index (SOI) and sea surface temperature for Niño 3.4, a region of the equatorial Pacific often used to track ENSO. Months with anomalies beyond the twentieth percentile in both SOI and sea surface temperature running means were identified as ENSO warm and cold phase months. The remaining months were classified as neutral phase. ENSO warm phases are encoded for certain months of 1982-3, 1987, 1991, 1993, 1994-5, and 1997-8. An ENSO cold phase is encoded for September through November 1988. Because of the potential lag in vegetation response to ENSO forcing, another ENSO time series was created that focused only on the growing season for each study region. These data encoded for each growing season month, whether that month was preceeded by an ENSO warm, cold, or neutral phase since the past summer. Growing season for the ASPRS 2005 Annual Conference Baltimore, Maryland • March 7-11, 2005 Table 1. Land Cover as a Percent of Total Area Land Cover Water Evergreen Broadleaf Forest Deciduous Broadleaf Forest Woodland Wooded Grassland Closed Shrubland Open Shrubland Grassland Cropland Bare Ground E. Africa 0 2 1 15 20 16 15 27 3 0 W. Sahel 0 2 1 18 39 7 4 4 8 16 S. Africa 1 0 14 44 25 6 7 2 1 western Sahel and southern Africa study regions were identified as August through September and January through March, respectively (Kogan, 1998; Anyamba et al., 2002). Because eastern Africa has two distinct growing seasons due to a bimodal distribution of precipitation, the growing season was identified as both March through April and September through November (Anyamaba et al., 2002). For purpose of discussion, the two ENSO time series data sets are referred to as the ‘simultaneous’ and ‘ growing season’ ENSO phase data, respectively. Visual inspection of the NDVI data revealed two data quality issues. First, for a handful of months, entire sections of Africa were simply missing from the time series of imagery. Second, in many of the monthly images which provided complete coverage over Africa, the imagery appeared ‘spotted’ with unusually high pixel values. These high pixel A B C Figure 2. Land cover for eastern Africa (A), western Sahel (B), and southern Africa. (C). ASPRS 2005 Annual Conference Baltimore, Maryland • March 7-11, 2005 values did not continue from one month to another in the same location, suggesting that they are errors and do not reflect any actual characteristic of the earth surface. To address the first issue, entire monthly images that were missing portions of Africa were regarded as ‘no data’ in the time series. A multi-step procedure was used to address the second issue. First, we visually identified those images that contained the unusually high pixel values. For each of these images, a 5x5 cell window mean filter was passed over the image, producing a new image in which each cell contained the mean of its neighbors (and not including the focal cell in the calculation). The original and filtered images were then compared; if an original NDVI value was greater two times, or less than half, the filter value, it was assumed to be an error and was replaced with the filter value. As an additional preprocessing step, the departure from the monthly mean for each pixel was calculated for each NDVI image in order to separate the seasonal variation from other influences on NDVI value. This was done by calculating a single image for each month which represented the monthly mean value for each pixel. The appropriate monthly mean image was then subtracted from each of the original NDVI images to yield a time series of 216 monthly NDVI anomaly images extending from January 1982 – December 1999. METHODS The analysis was performed using temporal map algebra, a data processing language for spatio-temporal raster data (Mennis et al., in press), such as time series of remotely sensed imagery (Mennis and Viger, 2004). Temporal map algebra is an extension of the conventional map algebra (Tomlin, 1990) that forms the basis for raster data handling in many geographic information system (GIS) and remote sensing image processing software packages. In temporal map algebra, the conventional local, focal, and zonal functions have been extended to operate on threedimensional ‘space-time data cubes’ in which two dimensions encode planimetric position and the third dimension encodes time. Whereas a conventional raster grid is composed of a tessellation of square grid cells, a space-time data cube is composed of a tessellation of cubic space-time ‘elements’. A comparison of conventional map algebra functions with their temporal map algebra counterparts helps to illuminate how temporal map algebra works. Conventional local functions take as input two grids and output a grid in which the value of each output grid cell is derived from the values of the analogous cell position in the input grids, as in a raster overlay. The temporal map algebra analog can be envisioned as the superposition of two threedimensional space-time data cubes, instead of the overlay of two grids. Conventional focal functions are similar to filter operations in image processing; they take as input a single grid and generate an output grid in which the value of each cell is derived from the values of the cells within a predefined neighborhood around that cell position. In temporal map algebra, the focal window is applied to the space-time cube, so that neighborhoods may extend in space, in time, or in space and time simultaneously. Conventional zonal functions take as input a value grid and a zone grid and generate a table summarizing the values in the value grid according to the zones in the zone grid. Because temporal map algebra operates on space-time data cubes, the value data and zone data may be spatial, temporal, or spatio-temporal. Thus, the value data may summarized according to zones that extend across space, through time, or over space and time simultaneously. As a prototype implementation, temporal map algebra was implemented in the scripting language IDL (Research Systems, Inc.). The space-time data cube was implemented as a simple three-dimensional array of the form [row, column, timestep] and a select set of local, focal, and zonal temporal map algebra functions were developed. For more information on temporal (and multidimensional) map algebra, including current development efforts to create open source and interoperable, spatio-temporal raster data handling software in JAVA, visit the project web site at http://astro.temple.edu/~jmennis/research/mma. Temporal map algebra is used here to summarize NDVI anomaly values during different ENSO phases and over different land covers for each study region. First, mean NDVI anomaly is calculated for each simultaneous and growing season ENSO phase. Second, mean NDVI anomaly is calculated for each land cover and simultaneous and growing season ENSO phase. In the third step, temporal map algebra focal functions are used to generate space-time data cubes of the spatial and temporal variance of NDVI anomaly. A series of spatial variance cubes are calculated using focal functions that employ a series of spatial-only neighborhoods, including rectangular focal windows with radii of 1x1, 3x3, 5x5, 7x7, 9x9, 11x11, and 13x13. A series of temporal variance cubes are calculated using focal ASPRS 2005 Annual Conference Baltimore, Maryland • March 7-11, 2005 functions that employ a series of temporal-only neighborhoods. Here, the variance of a space-time cube element is calculated based on the values of the elements that precede and follow it in time, and not the values of its spatial neighbors. The temporal neighborhoods include neighborhood ‘radii’ of 1, 2, 4, 5, and 6 months. Note that a six month radii indicates that the variance of an element is computed based on the values of all the elements contained within a year-long window centered on the focal element. RESULTS Table 2 reports the mean NDVI anomaly for each ENSO phase using both the simultaneous and growing season ENSO phase data. In eastern Africa, NDVI tends to be higher during warm and cold ENSO phases. In the western Sahel and southern Africa, ENSO cold phases are associated with higher vegetation intensity and warm phases with lower vegetation intensity. In the growing season, these differences among ENSO phases remain consistent in eastern Africa. In southern Africa, an ENSO cold phase tends to be followed by enhanced vegetation intensity during the following growing season. Table 2. Mean NDVI Anomaly by Simultaneous and Growing Season ENSO Phase ENSO Phase Cold Neutral Warm Simultaneous ENSO Phase E. Africa W. Sahel S. Africa 0.0059 0.0148 0.0364 -0.0022 0.0037 0.0041 0.0067 -0.0128 -0.0154 Growing Season ENSO Phase E. Africa W. Sahel S. Africa 0.0031 -0.0041 0.0228 -0.0027 0.0081 -0.0023 0.0069 -0.0098 0.0002 Figure 3 shows the mean NDVI anomaly for each ENSO phase combination over different land covers using both the simultaneous and growing season ENSO phase data. For each study region, each vegetation type tends to respond differently to cold and warm ENSO phases. In eastern Africa, ENSO cold phase is generally associated with enhanced vegetation intensity, particularly for woodland and open shrubland. The exception is for wooded grassland, which exhibits suppressed vegetation intensity for an ENSO cold phase. ENSO warm phase is associated with enhanced vegetation intensity in shrubland. This pattern continues through the growing season, though notably cropland also emerges as having a positive vegetation response.to the ENSO cold phase. In the western Sahel, ENSO cold phase is associated with enhanced vegetation intensity in shrubland and grassland. ENSO warm phase is associated with suppressed vegetation intensity for all vegetated covers, particularly for woodland and cropland. This warm phase pattern continues, though muted, through the growing season. Also during the growing season, ENSO cold phase is associated with suppressed vegetation intensity in grassland and enhanced vegetation intensity in cropland. In southern Africa, ENSO cold (warm) phase is associated with enhanced (suppressed) vegetation intensity. For shrubland, this pattern continues through the growing season, while for woodland and grassland it is reversed – ENSO cold phase is associated with suppressed vegetation intensity. Figures 4 and 5 show the mean spatial variance of the NDVI anomaly for each land cover for each ENSO phase, using the simultaneous and growing season ENSO data, respectively. For brevity the figures show only the wooded grassland, open shrubland, and grassland land covers. The variance was calculated using a series of seven temporal map algebra focal functions, each employing a differently sized spatial-only focal window. The radius of the focal window was increased over the seven functions from a radius of one to three to five cells, and so on up to a radius of 13 cells. The application of these functions results in seven mean NDVI anomaly variance values for each land cover/ENSO phase/study region combination, which are then plotted in the Figure 4 and 5 graphs. It is worth noting that it is potentially misleading to compare the variance among different land covers as they differ in their spatial extent; smaller land covers will likely be subject to greater spatial variance because the focal window will cross into a variety of land cover types. Rather, the graphs should be used to compare the variance signature of the different ENSO phases for a single land cover. ASPRS 2005 Annual Conference Baltimore, Maryland • March 7-11, 2005 0.07 0.05 0.07 A 0.05 0.03 0.03 0.01 0.01 -0.01 -0.01 -0.03 -0.03 Woodlnd W. Grass. C. Shrub. O. Shrub. Grassland Cropland 0.07 0.05 Woodlnd W. Grass. C. Shrub. O. Shrub. Grassland Cropland W. Grass. C. Shrub. O. Shrub. Grassland Cropland W. Grass. C. Shrub. O. Shrub. Grassland Cropland 0.07 C 0.05 0.03 0.03 0.01 0.01 -0.01 -0.01 -0.03 D -0.03 Woodlnd W. Grass. C. Shrub. O. Shrub. Grassland Cropland 0.07 0.05 B Woodlnd 0.07 E 0.05 0.03 0.03 0.01 0.01 -0.01 -0.01 -0.03 F -0.03 Woodlnd W. Grass. C. Shrub. O. Shrub. Grassland Cropland Woodlnd Figure 3. Mean NDVI anomaly (shown on the Y axis) over different land covers during different ENSO phases: eastern Africa (A and B), western Sahel (C and D), and southern Africa (E and F). Purple bar is cold phase; red bar is neutral phase; and yellow bar is warm phase. Note that graph sets A, C, E and B, D, F are generated using the simultaneous and growing season ENSO phase data, respectively. The increase of the spatial variance with increasing focal window radius, as indicated in all Figure 4 and 5 graphs, is an expression of positive spatial autocorrelation – similar NDVI anomaly values tend to occur near one another. For nearly all land covers, ENSO cold phase is associated with higher spatial variance, particularly for croplands. Exceptions occur in the southern African study region where there is a relatively weaker elevated ENSO cold phase spatial variance for croplands and no difference in spatial variance among ENSO phases for closed shrubland. These differences in spatial variance are generally minimized during the following growing season (Figure 5). Figure 6 and 7 present results similar to those of Figures 4 and 5 but report the mean temporal, instead of spatial, variance of the NDVI anomaly. The temporal variance is computed by generating neighborhoods that extend both before and after the element upon which the focal function is centered, but do not extend to any of that element’s spatial neighbors. Analogous to the method for generating the spatial variance graphs in Figures 4 and 5, the focal ASPRS 2005 Annual Conference Baltimore, Maryland • March 7-11, 2005 East Africa: Wooded Grassland Sahel: Wooded Grassland 0.006 0.005 0.004 0.003 0.002 0.001 0.008 Mn NDVI Anomaly Variance 0.007 0.000 0.007 0.006 0.005 0.004 0.003 0.002 0.001 0.000 1 3 5 7 9 11 13 East Africa: Closed Shrubland 5 7 9 11 0.006 0.005 0.004 0.003 0.002 0.001 0.000 9 11 0.005 0.004 0.003 0.002 0.001 3 5 7 9 11 0.003 0.002 0.001 0.003 0.002 0.001 3 9 11 13 5 7 9 11 13 Spatial Neighborhood (X,Y cell radius) Southern Africa: Cropland 0.008 0.007 0.006 0.005 0.004 0.003 0.002 0.007 0.006 0.005 0.004 0.003 0.002 0.001 0.000 Spatial Neighborhood (X,Y cell radius) 13 0.004 1 0.001 0.000 11 0.005 13 Mn NDVI Anomaly Variance Mn NDVI Anomaly Variance 0.004 9 0.000 1 0.008 0.005 7 0.006 Sahel: Cropland 0.006 5 0.007 Spatial Neighborhood (X,Y cell radius) 0.007 7 3 Southern Africa: Closed Shrubland 0.006 13 0.008 5 0.001 Spatial Neighborhood (X,Y cell radius) 0.007 East Africa: Cropland 3 0.002 0.008 Spatial Neighborhood (X,Y cell radius) 1 0.003 1 0.000 7 0.004 13 Mn NDVI Anomaly Variance 0.007 5 0.005 Sahel: Closed Shrubland Mn NDVI Anomaly Variance Mn NDVI Anomaly Variance 3 0.008 3 0.006 Spatial Neighborhood (X,Y cell radius) 0.008 1 0.007 0.000 1 Spatial Neighborhood (X,Y cell radius) Mn NDVI Anomaly Variance Southern Africa: Wooded Grassland 0.008 Mn NDVI Anomaly Variance Mn NDVI Anomaly Variance 0.008 0.000 1 3 5 7 9 11 13 Spatial Neighborhood (X,Y cell radius) 1 3 5 7 9 11 13 Spatial Neighborhood (X,Y cell radius) Figure 4. Trend in the mean NDVI anomaly spatial variance for different ENSO phases (simultaneous ENSO phase data) at increasing spatial-only neighborhoods. Green, blue, and red lines indicate cold, neutral, and warm ENSO phase, respectively. neighborhood temporal ‘radius’ was incrementally increased from one month to two to three, and so on up to a six month radius. Some interesting patterns can be identified in Figure 6. First, eastern Africa and southern Africa both show increasing temporal variance with an increase in temporal radius, indicating temporal autocorrelation. The results for the western Sahel, however, indicate that in this region similar NDVI anomaly values cluster very weakly in time, if at all. Additionally, in eastern Africa ENSO warm phase is associated with higher temporal variance over wooded grassland and closed shrubland. In the western Sahel and southern Africa, however, ENSO cold phase is associated with higher temporal variance in NDVI anomaly. When the growing season ENSO phase data are used, the ENSO cold phase is associated with the lowest temporal NDVI anomaly variance in the western Sahel. Also of note is the arc shape of the temporal variance signature for land covers in southern Africa. For these land covers, for all ENSO phases, temporal variance increases with an increasing focal neighborhood, peaks at a focal neighborhood radius of approximately four months, then declines as the focal neighborhood expands further. This pattern suggests an NDVI anomaly at a given location may remain on the order of eight months around the growing season before shifting to a new anomaly pattern. ASPRS 2005 Annual Conference Baltimore, Maryland • March 7-11, 2005 East Africa: Wooded Grassland Sahel: Wooded Grassland 0.006 0.005 0.004 0.003 0.002 0.001 0.008 Mn NDVI Anomaly Variance 0.007 0.000 0.007 0.006 0.005 0.004 0.003 0.002 0.001 0.000 1 3 5 7 9 11 13 East Africa: Closed Shrubland 5 7 9 11 0.006 0.005 0.004 0.003 0.002 0.001 0.000 9 0.003 0.002 0.001 1 11 0.006 0.005 0.004 0.003 0.002 0.001 0.005 0.004 0.003 0.002 0.001 3 5 7 9 11 13 1 Sahel: Cropland 0.007 Mn NDVI Anomaly Variance 0.007 Mn NDVI Anomaly Variance 0.007 0.006 0.005 0.004 0.003 0.002 0.001 0.000 3 5 7 9 11 Spatial Neighborhood (X,Y cell radius) 13 7 9 11 13 0.006 0.005 0.004 0.003 0.002 0.001 0.000 1 5 Southern Africa: Cropland 0.008 0.001 3 Spatial Neighborhood (X,Y cell radius) 0.008 0.002 13 0.000 1 East Africa: Cropland 0.003 11 0.006 0.008 0.004 9 0.007 Spatial Neighborhood (X,Y cell radius) 0.005 7 0.008 Spatial Neighborhood (X,Y cell radius) 0.006 5 Southern Africa: Closed Shrubland 0.007 13 3 Spatial Neighborhood (X,Y cell radius) 0.000 7 0.004 13 Mn NDVI Anomaly Variance Mn NDVI Anomaly Variance Mn NDVI Anomaly Variance 3 0.008 5 0.005 Sahel: Closed Shrubland 0.007 3 0.006 Spatial Neighborhood (X,Y cell radius) 0.008 1 0.007 0.000 1 Spatial Neighborhood (X,Y cell radius) Mn NDVI Anomaly Variance Southern Africa: Wooded Grassland 0.008 Mn NDVI Anomaly Variance Mn NDVI Anomaly Variance 0.008 0.000 1 3 5 7 9 11 13 Spatial Neighborhood (X,Y cell radius) 1 3 5 7 9 11 13 Spatial Neighborhood (X,Y cell radius) Figure 5. Trend in the mean NDVI anomaly spatial variance for different ENSO phases (growing season ENSO phase data) at increasing spatial-only neighborhoods. Green, blue, and red lines indicate cold, neutral, and warm ENSO phase, respectively. CONCLUSION The results presented here are generally consistent with previous research that has shown that ENSO cold (warm) phases are associated with wetter (drier) than normal conditions in southern Africa, producing enhanced vegetation intensity (Kogan, 1998). For eastern Africa, the opposite has been found – ENSO warm phase is associated with an increase in precipitation and hence enhanced vegetation intensity (Anyamba et al., 2001). Unlike previous research, however, this study shows that vegetation response to ENSO phase varies markedly among different vegetation types within particular regions of Africa. In addition, this study also shows that vegetation types vary in the timing of their response to ENSO phase. Of the three study regions, southern Africa has the most consistent response to ENSO phase across vegetation types, with the cold phase associated with enhanced vegetation intensity and the warm phase associated with weakened vegetation intensity. Shrubland appears to be the vegetation type most directly affected by ENSO phase, particularly via an increase in vegetation intensity during ENSO cold phase in the western Sahel and in the following growing season in southern Africa. Results from the study of spatial and temporal variance are less conclusive. They do suggest, however, that in general ENSO cold and warm phases ASPRS 2005 Annual Conference Baltimore, Maryland • March 7-11, 2005 East Africa: Wooded Grassland Sahel: Wooded Grassland 0.007 0.006 0.005 0.004 0.003 0.002 0.009 Mn NDVI Anomaly Variance 0.008 0.001 0.008 0.007 0.006 0.005 0.004 0.003 0.002 0.001 1 2 3 4 5 6 East Africa: Closed Shrubland 3 4 5 0.007 0.006 0.005 0.004 0.003 0.002 0.001 5 0.006 0.005 0.004 0.003 0.002 2 3 4 5 0.004 0.003 0.002 0.001 0.005 0.004 0.003 0.002 1 5 Temporal Neighborhood (Months) 6 2 3 4 5 6 Temporal Neighborhood (Months) Southern Africa: Cropland 0.009 0.008 0.007 0.006 0.005 0.004 0.003 0.002 0.001 4 6 0.006 6 Mn NDVI Anomaly Variance Mn NDVI Anomaly Variance 0.005 5 0.007 Sahel: Cropland 0.006 4 0.001 1 0.009 0.007 3 0.008 Temporal Neighborhood (Months) 0.008 2 Southern Africa: Closed Shrubland 0.007 East Africa: Cropland 3 0.002 Temporal Neighborhood (Months) 0.008 6 0.009 2 0.003 0.009 Temporal Neighborhood (Months) 1 0.004 1 0.001 4 0.005 6 Mn NDVI Anomaly Variance 0.008 3 0.006 Sahel: Closed Shrubland Mn NDVI Anomaly Variance Mn NDVI Anomaly Variance 2 0.009 2 0.007 Temporal Neighborhood (Months) 0.009 1 0.008 0.001 1 Temporal Neighborhood (Months) Mn NDVI Anomaly Variance Southern Africa: Wooded Grassland 0.009 Mn NDVI Anomaly Variance Mn NDVI Anomaly Variance 0.009 0.008 0.007 0.006 0.005 0.004 0.003 0.002 0.001 1 2 3 4 5 6 Temporal Neighborhood (Months) 1 2 3 4 5 6 Temporal Neighborhood (Months) Figure 6. Trend in the mean NDVI anomaly temporal variance for different ENSO phases (simultaneous ENSO phase data) at increasing temporal-only neighborhoods. Green, blue, and red lines indicate cold, neutral, and warm ENSO phase, respectively. tend to increase variation in vegetation intensity over space and time, though these patterns tend to fade by the following growing season. It is important to note some uncertainties in the analysis presented here. First, only one ENSO cold phase, in late 1988, is represented in the study. This event may not be representative of a typical ENSO cold phase, and its duration and time of year necessarily influences calculations of NDVI anomaly and variance when compared to other longer-lasting ENSO warm phase events. Second, while some attempt was made to address the issue of temporal lag in vegetation response to ENSO phase forcing, the use of the growing season ENSO phase data may not capture lagged vegetation responses that do not occur within the confines of the conventional growing season. In fact, ENSO cold and warm phases may disrupt normal vegetation cycles. In future research, these issues will be addressed by tracking individual ENSO events, as opposed to summarizing data for all ENSO events of a particular phase type, and finer granularity temporal lags will be applied to capture a wider range of temporal vegetation response to ENSO forcing. ASPRS 2005 Annual Conference Baltimore, Maryland • March 7-11, 2005 East Africa: Wooded Grassland Sahel: Wooded Grassland 0.007 0.006 0.005 0.004 0.003 0.002 0.009 Mn NDVI Anomaly Variance 0.008 0.001 0.008 0.007 0.006 0.005 0.004 0.003 0.002 0.001 1 2 3 4 5 6 East Africa: Closed Shrubland 3 4 5 0.007 0.006 0.005 0.004 0.003 0.002 0.001 5 0.006 0.005 0.004 0.003 0.002 2 3 4 5 0.004 0.003 0.002 0.001 0.005 0.004 0.003 0.002 1 5 Temporal Neighborhood (Months) 6 2 3 4 5 6 Temporal Neighborhood (Months) Southern Africa: Cropland 0.009 0.008 0.007 0.006 0.005 0.004 0.003 0.002 0.001 4 6 0.006 6 Mn NDVI Anomaly Variance Mn NDVI Anomaly Variance 0.005 5 0.001 1 0.009 0.006 4 0.007 Sahel: Cropland 0.007 3 0.008 Temporal Neighborhood (Months) 0.008 2 Southern Africa: Closed Shrubland 0.007 East Africa: Cropland 3 0.002 Temporal Neighborhood (Months) 0.008 6 0.009 2 0.003 0.009 Temporal Neighborhood (Months) 1 0.004 1 0.001 4 0.005 6 Mn NDVI Anomaly Variance 0.008 3 0.006 Sahel: Closed Shrubland Mn NDVI Anomaly Variance Mn NDVI Anomaly Variance 2 0.009 2 0.007 Temporal Neighborhood (Months) 0.009 1 0.008 0.001 1 Temporal Neighborhood (Months) Mn NDVI Anomaly Variance Southern Africa: Wooded Grassland 0.009 Mn NDVI Anomaly Variance Mn NDVI Anomaly Variance 0.009 0.008 0.007 0.006 0.005 0.004 0.003 0.002 0.001 1 2 3 4 5 6 Temporal Neighborhood (Months) 1 2 3 4 5 6 Temporal Neighborhood (Months) Figure 7. Trend in the mean NDVI anomaly temporal variance for different ENSO phases (growing season ENSO phase data) at increasing temporal-only neighborhoods. Green, blue, and red lines indicate cold, neutral, and warm ENSO phase, respectively. REFERENCES Anyamba, A., and J. R. Eastman (1996). Interannual variability of NDVI over Africa and its relation to El Niño / Southern Oscillation. International Journal of Remote Sensing, 17(13):2533-2548. Anyamba, A., C.J. Tucker, and J.R. Eastman (2001). NDVI anomaly patterns over Africa during the 1997/98 ENSO warm event. International Journal of Remote Sensing, 22:1847-1859. Anyamba, A., C.J. Tucker, and R. Mahoney (2002). From El Niño to La Niña: vegetation response patterns over east and southern Africa during the 1997-1998 period. Journal of Climate, 15:3096-3103. Carleton, A.M. (1998). Ocean-atmosphere interactions. In Encyclopedia of Environmental Analysis and Remediation (eds. R.A. Meyers), John Wiley and Sons, New York, pp. 3151-3188. Hansen, M., R. DeFries, J. R. G. Townshend, and R. Sohlberg (2000). Global land cover classification at 1km resolution using a decision tree classifier. International Journal of Remote Sensing, 21:1331-1365. Holben, B.N. (1986). Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing, 7:1417-1434. Kogan, F.N. (1998). A typical pattern of vegetation conditions in sourthern Africa during El Niño years detected from AVHRR data using three-channel numerical index. International Journal of Remote Sensing, 19:3689-3695. ASPRS 2005 Annual Conference Baltimore, Maryland • March 7-11, 2005 Mennis, J., R. Viger, and C.D. Tomlin (in press). Cubic map algebra functions for spatio-temporal analysis. Cartography and Geographic Information Science. Mennis, J. and R. Viger (2004). Analyzing time series of satellite imagery using temporal map algebra. In Proceedings of the ASPRS Annual Conference, May 23-27, Denver, CO. Ropelewski, C.F. and M.S. Halpert (1987). Global and regional scale precipitation patterns associated with El Niño/Southern Oscillation. Monthly Weather Review, 115:1606-1626. Smith, C.A. and P. Sardeshmukh (2000). The effect of ENSO on the intraseasonal variance of surface temperature in winter. International Journal of Climatology, 20:1543-1557. Tomlin, C. D. (1990). Geographic Information Systems and Cartographic Modeling. Prentice Hall, Englewood Cliffs, New Jersey. ASPRS 2005 Annual Conference Baltimore, Maryland • March 7-11, 2005