Macro-scale, Multi-temporal Land Cover Assessment and Monitoring of Thailand




2.0 Thailand : Study Area

2.1 Location and Physical Characteristics

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.

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 of Thailand

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.

2.2 Present Land Cover

Thailand is well endowed with cultivable land which represents a significant portion of the country's total area. Agricultural land supports rice paddy, upland crops, para rubber, oil palm and perennial crops etc. Both evergreen and deciduous forests including mangrove forests can be found in Thailand. Other land cover types found in Thailand are urban areas, waterbodies others that includes abandoned land, marsh land, swamp, rock-outcrop, beach and pasture land.

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.

Table 2.0 Forest types, forest distribution, elevation range and dominant species found in Thailand

Forest enchrochment and commercial logging are two leading factors responsible for forest destruction . Converstion to shrimp ponds, salt pand and paddy field is typical problem for mangrove forest destruction. The Status of forest areas in Thailand form 1961 to 1993 has been presented below.

3.0 Methodology

3.1 Data Acquisition

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.

Fig. 2 : FCC of 27 December 1985	Fig. 3 : FCC of 31 January 1993

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.

3.2 Pre-processing

AVHRR data pre-processing mainly consisted of: data extraction and noise removal, radiometric calibration, geometric correction, and cloud masking procedures.

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 points.

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.

3.3 Classification

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.