42
Figure 4.5 Time series of monthly average of precipitation
The monthly average temperature in Sumatra ranges between 26°C to 28°C. The pattern of monthly average temperature showed that monthly
temperature in Sumatra reach highest temperature value during May and June.
Figure 4.6 Time series of monthly average temperature
Monthly temperature and monthly water deficit used to provide information of T
scalar
and W
scalar
. T
scalar
is computed with reference to derivation of optimal temperatures T
opt
for plant production. W
scalar
is the effect of water deficit on plant photosynthesis. The result of estimation T
scalar
and W
scalar
as shown in mean T
scalar
and W
scalar
in Figure 4.7.
50 150
250 350
450 550
P re
ci pi
tat ion
m m
m on
th
Monthly Period
Monthly Average Precipitation Over Sumatra, Year 2001 - 2009
Mean Rainfall
26 26.5
27 27.5
28
T em
pe rat
ur e
o C el
ci us
Monthly Period
Monthly Average Temperature Over Sumatra, Year 2001 - 2009
Mean Temp
43
Figure 4.7 Time series of annual distribution T
scalar
and W
scalar
in Sumatra
Optimum temperature for plant productivity based to estimation monthly average of optimal temperature and effect of water deficit as show in Figure 4.7.
Plants or vegetation have a better growth rate response if the conditions change during different development stages. Humidity should be kept high. Temperature
should not be too hot. When in the flowering stage, a cooler temperature is desired with humidity not more than 50.
This study has used an example of estimation optimal temperature for plant and water deficit during normal climate condition in year 2005 and during
abnormal climate condition El Nino and La Nina in year 2006 and 2007. During normal condition mean average of optimal temperature reached value 0.8° and in
abnormal condition the values increased to reached value 0.9° El Nino event and decreased to 0.8° La Nina event. Mean average water deficit during normal and
abnormal climate condition only have small changes or stable. Refer to estimation result of optimal temperature and water deficit, this
condition is affected to EVI and FPAR distribution. During normal or abnormal climate condition, the effect of optimal temperature and water deficit is not clearly
seen to EVI distribution in Sumatra. EVI distribution in climate variability relatively constant with average mean value reached to 0.58. That is why, EVI as
indication of global spatially greenness vegetation condition still occurred during normal or abnormal climate condition.
0.70 0.80
0.90 1.00
T sc
al ar an
d W
sc al
ar V al
ue
Monthly Period
Monthly Average Tscalar - Wscalar Over Sumatra, Year 2001 - 2009
Mean Tscalar Mean Wstress
44 Respectively, the effect of optimal temperature and water deficit is clearly
seen in FPAR distribution. During normal climate condition, average of FPAR value reached to 0.4 meanwhile in abnormal climate condition, average of FPAR
value reached to 0.2 and 0.3 El Nino and La Nina event. As stated in previous sub-chapter, FPAR measures the proportion of available radiation in the
photosynthetically active wavelengths that are absorbed by a canopy. Photosynthetic temperature curves of plant adapted to various light growth
conditions differed in the position of optimum temperature and in the value of the photosynthetic rate. During normal climate condition, FPAR value reached to
high value compare to abnormal climate condition. This condition explained that temperature and water has optimal value for plant growth and photosynthesis.
Meanwhile during abnormal climate condition, temperature and water not in optimal condition for plant growth. Limited of waters condition because of high
temperature regulates the evapotranspiration and thus influences the variation of soil moisture and this condition affected to limited photosynthesis activity. As the
result the FPAR value decreased during abnormal climate condition El Nino and La Nina event.
Temperature has a direct impact on vegetation photosynthesis and soil decomposition. On
the other hand, the temperature regulates the evapotranspiration and thus influences the variation of soil moisture, which is an
important factor for vegetation growth and soil decomposition. Through this, the temperature thus has an indirect impact on the vegetation growth and soil
decomposition. This indirect influence has not yet been studied Zeng et al., 2005.
4.4 Net Primary Production NPP
Net primary productivity NPP is the fundamental process in biosphere functioning and is needed for assessing the carbon balance at regional and global
scales. Changes in NPP could arise due to anthropogenic effects and climate change, and directly affect human and animal food supplies. Net primary
productivity NPP is an important component of the carbon cycle and a key
45 indicator of ecosystem performance. Vegetation also acts as a source, or sink, for
the greenhouse gas CO
2
. Gross primary productivity GPP is the total amount of atmospheric carbon CO
2
assimilated by vegetation. Several methods of estimating NPP over large areas have been established.
NPP in this research was estimated based on the utilization of remotely sensed to provide information of the monthly NPP flux, defined as net fixation of CO
2
by vegetation, is computed in NASA-CASA Carnegie Ames Stanford Approach
model on the basis of light-use efficiency Monteith, 1972. Monthly production of plant biomass is estimated as a product of time-varying surface solar irradiance,
Sr, and EVI from the MODIS satellite, times a constant light utilization efficiency term e
max
that is modified by time-varying stress scalar terms for temperature T and moisture W effects Eq. 1.
The e
max
term is set uniformly at 0.39 gCMJ−1 PAR, a value that derives from calibration of predicted annual NPP to previous field estimates Potter et al.,
1993. This 950 model calibration has been validated globally by comparing predicted annual NPP to more than 1900 field measurements of NPP Zheng et
al., 2003; Potter et al., 2003. The model uses the same e
max
value for all vegetation types in the Amazon, but allows predicted light use efficiency to be
regulated by monthly climate variations that vary across the region.
4.4.1. Estimation of NPP for Sumatra
The data sets used to derive NPP are gridded between longitude 94.5 W to 108 E and latitude -6.5 S to 6.0 N. Estimation of NPP value obtained from
MODIS EVI and MODIS FPAR data with gridded type for each cell by 1 km x 1 km. The size of grid cell is 1621 for longitude size and 1501 for latitude size.
With a long-term MODIS EVI and MODIS FPAR combined with climatic dataset, it is possible to detect the trends and inter-annual variability in global
NPP over Sumatra terrestrial. Sumatra terrestrial has area around 47.7 million hectare. Result of estimation of NPP distribution in Sumatra has reached the
maximum value of NPP occurred in year 2007 and minimum value of NPP occurred in year 2006.
