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.
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ASPRS 2005 Annual Conference
Baltimore, Maryland • March 7-11, 2005
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ASPRS 2005 Annual Conference
Baltimore, Maryland • March 7-11, 2005