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in the development of methods that integrate
instantaneous value of estimated evapotranspiration over
the whole day.
Recently. the land use/land cover change is very
intense in around urban due to concentration of
population to urban. This land cover change gives large
impact to existing process of water or heat in natural. In
short. land cover change changes the amount of
incoming energy on surfaces, the thermal environment
and it affects energy balances of surfaces.
Because surface energy balances is related to the
evapotranspiration through latent heat flux, the changed
energy balances will effect the local water balances. The
change of water balances over area means the ratio
change of direct runoff, infiltration, evapotranspiration
from precipitation. Decreasing evapotranspiration
means decrease of escaping energy flux from surface as
latent heat, and it induces the increase of local
temperature like heat island(Kondoh, 1991).
The main objective of this research is to calculate the
latent heat flux commonly called evapotranspiration and
expressed in mm of water, over an area. In this paper,
an attempt is made to calculate instantaneous and daily
values of the latent heat flux by solving for LE as a
residual in the surface energy balance equation 1) using
GIS techniques, and simulating of the change of energy
balance according to land use change such as land
reclamation
2. Description of energy balance model
and data collection
The surface energy balance equation is usually
composed of four terms assuming that advection and
storage of heat in the vegetated surface is
negligible(Brutsaert, 1982):
Rn=lE+HA+G ==" 1)
where Rn is net radiation, H is the sensible heat flux, G
is the soil heat flux and LE is the latent heat flux. The
unit of all is W/m”.
e UA smart
land surface Vc
Figure 1. The concept of surface energy balance
model.
Estimates of Rn and G either come from
micrometeorological measurements or come from a
combination of commonly available meteorological data
and remote sensing data. Recent developments in the
latter approach may provide estimates of Rn and G with
primarily remotely sensed information(Jackson, 1985).
From the equation 1). three component of the four
surface energy balance components(ie. net radiation,
soil heat flux and sensible heat flux) were estimated with
remote sensing data(LANDSAT TM) and ground-based
meteorological data. As a result, this enables the
estimation of the remaining term, latent heat flux. Table
1 summarized the used meteorological data and
extracted data from remote sensing and topological data.
Figure 2 depicts the general flow of data processing
employed in this study.
Table 1. Used Data in this study
Remote Sensing data | albedo, a
surface temperature, 7; [ ?C]
vegetation index. NDVI
Meteorological data | air temperature, 7; [^C]
water vapor pressure, e, [mb]
wind speed, 4 [m / s]
cloudiness. n/N
elevation, E{m]
slope, g [degree]
azimuth. ^' [degree]
Topological data
Envionmental effect of
Scenario changed energy balance
Land use/Land cover
Figure 2. Data processing flow employed in this study.
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B4. Vienna 1996
