Abstract
This study investigated the
long-term trends in vegetation and rainfall and the extent and rate of
vegetation change over the Bani river Basin at multiple spatial and
temporal scales in relation to local and regional drivers. Monthly 8-km
Normalized Difference Vegetation Index (NDVI) timeseries data from 1982
to 2011 was derived from 10-day Satellite Pour l’Observation de la Terre
vegetation product (SPOT-VGT) at 1-km (1998-2011) and 15-day GIMMS
(Global Inventories Monitoring and Modelling Systems) at 8-km satellite
data (1982-2006). Gridded rainfall data at 8-km grid resolution was
created from 40 meteorological stations and complemented with Tropical
Rainfall Measurement Mission (TRMM) data. A Mann Kendall (MK) trend
analysis was used to determine the trend for each dataset using monthly
and annual time-series. This analysis produced some indicators like
Kendall’s tau, p-value and Theil-Sen. The p-value estimator (p-value
less than 0.07) was used in this study to show the significance of the
trend.
Trend analysis revealed that within the study area
vegetation greening trends are mostly associated with areas where
natural vegetation is still well represented. From the results 934
pixels (49% of the study area) showed a positive trend while 155 pixels
(8% of the study area) showed a negative trend significant at p-value
less than 0.07. During the same period rainfall had increased by about
17 mm, translating into a positive trend for almost the entire study
area. Vegetation productivity in the study area is dependent on rainfall
which varies greatly temporally and spatially. The linear Pearson
correlation was used to estimate the relationship between NDVI and
rainfall for every pixel at monthly interval for the growing season
data. Comparing their long-term mean the result showed a good
correlation between the two datasets with an R value of 0.98. Four (4)
reference areas were used to explain and cross verify representative
areas that exhibit either entirely negative MK-trends or entirely
positive MK-trends over the monitoring period.
These reference
areas were selected based on their trend in rainfall and NDVI and their
NDVI long-term departure. Free 30-meter Landsat images were acquired for
the four reference areas for the following three intervals: 1984 and
1986, 1999 and 2000 and 2009 and 2010. Land Use/Land Cover (LULC) change
was then quantified and the rate of land conversion was determined.
LULC variables included urban, Cropland and natural vegetation
(Shrublands, Steppe, Open Trees and Closed Trees). For the entire
period, the class ‘Natural Vegetation’ decreased between 22.83% and
63.47% from its initial area for areas (1) and (2), while the iv
decrease was 8.35% for area (3) and 13.39% for area (4). The class
‘Cropland’ increased for 564.86% in area (3); 62.17% in area (4); 35.79%
in area (2) and 16.22% in area (1). To investigate whether there is a
relationship between NDVI, rainfall and LULC change, LULC variables were
correlated with long-term trend in rainfall and NDVI. The results
showed there is a positive correlation between increases in rainfall and
some land cover classes, while some classes such as settlements were
negatively correlated with vegetation productivity trends. Croplands and
Natural Vegetation were positively correlated (r=0.89) with rainfall
while settlements have a negative correlation with NDVI time series
trends (r=-0.57). Despite the fact that rainfall is the major
determinant of vegetation cover dynamics in the study area, it appears
that other human-induced factors such as urbanisation have negatively
influenced the change in vegetation cover. The results provide spatially
explicit and temporally good and rich information of vegetation
productivity dynamics and its drivers at landscape scale. This is an
important input for assessing the impact of climate change on vegetation
for biophysical modelling. It also improves our knowledge of the
drivers of vegetation productivity changes.
The study suggests that NDVI can be useful for general vegetation cover monitoring and planning. Future studies
need to also look at the effect of vegetation cover change in regard to other landscape
components such as specifically population density and soil degradation.
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