Thailand, centrally located in the Indochina Peninsula, is one of the most developed and wealthiest countries in Southeast Asia. It is bordered by Cambodia and Laos on the east, Laos and Myanmar on the north, Myanmar on the west and Malaysia and Gulf of Thailand on the south. It is bounded between 50 40' and 200 30' North latitude and 900 70' and 1050 45' East latitude (Fig. 1). The total of area of Thailand is 513,115 sq. kms.
Fig. 1 Location Map of Thailand
Physiographically, the country has been divided into six regions viz.
Central plain, Southeast coast, Northeast plateau, Central highlands, North
and West continental highlands and Peninsular Thailand. Due to these variations,
Thailand possesses tremendous natural and cultural diversity. The forest
vegetation for example, ranges from pine forests on the north to lowland
rain forests or tropical mangrove forests on the south.
The climate of Thailand is defined as "humid tropical" which
is influenced by the seasonal monsoon and the local topography. Two distinct
types of climate are recognised : tropical rain forest climate and tropical
savannah climate. The tropical rain forest climate is characterised by
uniformly high temperature and heavy rainfall without possessing any distinct
dry season. The tropical savannah climate on the other hand is characterised
by less precipitation with three distinct seasons. The rainy season extends
from May to October, hot dry season from March to April and cold dry season
from November to February. The average annual precipitation and temperature
varies from region to region. The following table provides information
on the generalised climatic data for the six physiographic regions of Thailand.
Table 1.0 Generalised Climatic Data for the six physiographic regions
Source: Meteorological Department, Thailand
The total population of the country is 58.34 Million (Ongsomwang, 1995)
with a population density of 113.7 persons/sq. km. The following figure
provides information on the growth of the population and population density
during last 15 years.
Evergreen and deciduous forests are the two principle forest types, abundant in various parts of the country. The distribution of these forests depends on climatic, edaphic, topographic and biological factors. Evergreen forests can further be sub-divided into tropical evergreen forests, tropical rain forests, dry evergreen forests, hill evergreen forests, coniferous forests and swamp forest. Both freshwater and mangrove swamp forests can be found. Deciduous forests are sub-divided into mixed deciduous , dry deciduous and savannah forests.
The following table provides information on the distribution, elevation
range and dominant species of various forest types found in Thailand.
A number of NOAA AVHRR HRPT and LAC data were acquired from different sources including NOAA NESDIS (USA), EROS Data Center (USA), NRCT (Thailand) and SMA/SMC (China) (Table 3). In general at least four scenes for the harvest season and four for the summer season were acquired for each country covering two time frames 1985-1986 and 1992-1993. Afternoon pass of NOAA-9 for 1985-1986 and NOAA-11 for 1992-1993 were selected for the study. Sample images of pre-processed 1985/86 and 1992/93 NOAA AVHRR data has been presented in the Figure 2 and 3 respectively.
Click on either image for the full size file
Table 3. Acquired NOAA AVHRR Data for Thailand
Phenological characteristics of the vegetation and hence the seasonality
were given due consideration in procuring the AVHRR data. Basically data
representing two seasons viz. harvest season and summer season were selected
for each country. Acquiring summer season data has its clear advantage
that it facilitates discriminating deciduous and evergreen forests. The
selection of these data sets that exhibit complementary information was
found to be informative in distinguishing different forest types and also
in distinguishing forest from agricultural lands.
NOAA AVHRR HRPT data were analysed using PC ERDAS and IDRISI image processing
softwares. Further analysis were performed in GIS (ARC/INFO software) environs.
In-house software has been written for the down loading, band selection,
calibration, geometric correction and cloud masking of AVHRR data as pre-processing
steps. Fig. 2 shows the flowchart of the methodology adopted.
Fig. 4 An Overview of Methodology Used
AVHRR data pre-processing mainly consisted of: data extraction and noise
removal, radiometric calibration, geometric correction, and cloud masking
The HRPT Level-1B received in packed format were converted from BIL
to BSQ format using appropriate programs. The original radiometric resolution
of 10 bit pixel values was maintained by using two bytes for each pixel
for all 5 channels. The bad/noisy lines were than identified by visual
inspection of each channel of an image. All such bad/noisy lines were marked
as being areas of "no data" by assigning zero values.
Radiometric calibration were performed based on the procedures outlined
by European Space Agency (ESA) Handbook on "SHARP LEVEL-2 : Development
Procedures and Format Specifications" and by NOAA Technical Memorandum
NESS 107 on "Data Extraction and Calibration of TIROS-N/NOAA Radiometers".
Due to the lack of readily available atmospheric data in South and South
East Asia atmospheric correction was not performed. Besides, although several
possible approaches for the correction of water vapour absorption and aerosol
scattering exist, there is presently no agreement on an acceptable method
for atmospheric corrections. Some methods need further validation and are
far from straight forward (IGBP, 1992).
The bi-directional reflectance effect caused by the viewing geometry
and surface angular anisotropy also affects the AVHRR channels 1 and 2
(Gutman 1990). The bi-directional effect depends upon the vegetation type
and could differ from one type to another. In order to correct the effect
of viewing direction, angular corrections should be developed for different
vegetation types and different seasons. However, images taken at large
view angles (off-nadir views) which fall at the extreme of the scan line
was excluded and thus, such effects, due to atmospheric scattering and
absorption, and viewing geometry, are partly reduced.
A two step procedure has been used for the geometric correction of AVHRR
images. The images were first resampled to a reference map projection based
on location data generated by orbital model navigation and then further
corrected by a linear first order rectification based on ground control
Interactive visual cloud masking procedure was used to identify the
threshold value for clouds. Use of such an interactive cloud screening
procedure proved to be highly effective in removing the clouds without
losing useful data.
Finally, country masks was generated by rasterizing the vector boundaries
of the study area obtained form the World Data Bank II.
Unsupervised classification was performed followed by interacting labelling.
Secondary information were fully utilised during the analysis. Field trips
were organised to collect secondary data and for results validation.