1.2 The Time Series Analyses: Significance and System
Availability
Time-series analysis has been widely used in the area of global
climate change. This type of analysis is categorized as
environmental statistics (Smith, 1999). Dealing with time series
data, there are several typical questions one might want to
know such as identifying the time focus dependence of certain
climate condition, recognizing pattern, forecasting phenomena,
search or establish mitigation strategy based on identified
parameters or variables and finally time series analysis is also
the type of analysis that lead to other significance analysis
based on the acclaimed correlations. As for Malaysia, our
typical Monsoons (2 inter-monsoons and 2 main monsoons)
can be the time focus dependence.
Climate change studies that involves time series analyses
revolves in the issues of precipitation, and urban heat island
(Cicek and Turkoglu 2005; Dixon and Mote 2010; Ghazanfari
et al. 2009; Kishtawal et al. 2010). Recognition of pattern will
enable hierarchical classification scheme over a large set of
data. This recognition allowed researcher to perform predictive
analyses. The bases of predictive results are usually based on
spatial pattern, changes pattern, movement pattern etc.
The change in urban albedo has significant impact on the
evolution of UHI, thus yielding a more reasonable intensity of
UHI relative to the actually observed value. Ducham and
Hamm (2006) plotted daily albedo with respect to the daily
precipitation and NDVI and their time series analyses indicate
that the dependency of albedo on precipitation is relatively
high.
In due respect to the above information, being such an
important element in our climate condition and realizing the
importance of knowing its pattern in monitoring and mitigating
strategies for adapting or securing our environment as it is
mentioned in (Bala et al. 2007; Lawrence and Chase 2010) it is
an urge to identify the albedo pattern changes of Malaysia. This
finding will become handy to the local climate researcher in
order to improvise or maybe calibrate any of the climate
models available to suits Malaysia climate conditions.
2. METHODS FOR DATA PROCESSING AND
SPATIAL DATA REPRODUCTION
2.1 Study Area
MODIS land surface product MCD43A3 at horizontal tile 38
and vertical tile 08 is downloaded via Land Process Distributed
Active Archived Center (LPDAAC) website. These images
were been processed every 8 daily sinusoidal at 500m spatial
resolution. The grid covers west part of Malaysia which also
known as Peninsular Malaysia. Its area is 131,598 square
kilometers (50,810 square miles). It shares a land border with
Thailand in the north (Figure 1).
Malaysia is one of the countries classified as tropical climate
where hot and humid are the common characters. The average
air temperature ranges from 20°C — 35?C and occasionally
exceeded 35?C depending on the variation of Monsoons. The
Peninsular Malaysia experiencing quite a remarkable monsoon
variation. Notably along the coastline area where and extreme
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
events of rainfall and flood can be expected during specific
monsoon.
«
AP.
Y Legend
ERE Wang Valey
EZ] istrict Boundary LT hen
0. 39500 75.000 153.003
Figure 1. The Peninsular Malaysia
2.2 Data Retrieval and Acquisition
The familiarization of MODIS data can simply be done through
visiting the http://modis.gsfc.nasa.gov/index.php website. One
has to be familiar with its spectral, spatial and temporal
resolution. As each of its products have their very own
specification. MODIS temporal resolution varies in term of what
kind of information one wants to retrieve. For example, the land
surface albedo product (MCD43A43) is available at sixteen
daily coverages while the land cover data (MCD12Q1) is
available yearly. Therefore, the specification recognition and
familiarization is importance at the beginning in order to avoid
mistakes in data acquisition process.
There are four experts software are used in this study. Two of
them are commercially off the shelf scientific software known as
ArcGIS 9.3 and ERDAS Imagine 9.1. ArcGIS 9.3 is used to
process the spatial data and for mapping purposes. The spatial
interpolation technique is conducted using this software. Erdas
Imagine 9.1 mainly used for satellite image processing such as
image density slicing, classification and conversion.
The other two tools are the 6S code and the MRT Tools. The 6S
code is an application develops to simulate the solar radiation
on the ground and in the atmosphere (Kotchenova and Vermote
2007; Kotchenova et al. 2006). The 6S stand for Second
Simulation of a Satellite Signal in the Solar Spectrum is a basic
code Radiative Transfer (RT) code. This code used for
calculation of lookup tables in MODIS atmospheric correction
algorithm (Justice et al. 2002; Vermote et al. 2002). This code is
chose as it is not restricted to specific sensor, test site and object
class which is very important when some parameters are
impossible or difficult to obtain (Zhao et al. 2001).
The MRT Tools 4.0 was released in February 2008. It is a
product in collaboration of Land Processes DAAC USGS Earth
Resources Observation and Science (EROS) Center with the
Department of Mathematics and Computer Science South
Dakota School of Mines and Technology. This software is used
