About GAIA GAIA Case Studies Global GIS Agenda 21 Country Data Model Database

"Hot Spot"Areas Investigation of Northern Thailand


UNEP Environment Assessment Programme for Asia and the Pacific (UNEP/EAP-AP) has been involving in the macro scale land cover assessment and monitoring of selected countries in the region. The prime goal of the ongoing land cover assessment and monitoring project is to prepare a wall-to-wall map of the major land cover types of these countries and to monitor the changes across time. The succeeding step is to identify areas undergoing major land cover transformations (called "hot spots") and investigate the "hot spot" areas in detail. The detailed investigation is necessary to identify the nature and extent of land cover transformations, which are occurring at an unprecedented rate and scale in many parts of Asia. Time series analysis of this exercise utilizes NOAA AVHRR 1 km resolution data for the assessment and monitoring and high resolution satellite data such as SPOT, Landsat and IRS-1 for "hot spot" areas investigation. The study aims to produce reliable and harmonized land cover data, utilizing same classification system, same methodology and same data sources for all the countries in the region, needed for regional aggregation and manipulation.

The project now covers ten countries in South and South East Asia including Bangladesh, Cambodia, Lao P.D.R., Myanmar, Nepal, Vietnam, Malaysia, Pakistan, Singapore and Thailand. The analysis of first six countries was completed in 1995 and the analysis of the rest is in progress, targeted to finish in 1996. After the interpretation of 1985/86 and 1992/93 AVHRR data of Vietnam, one "hot spot" area was identified in Mekong River Delta of Southern Vietnam, for further investigation.

The output of the project contributes and fits in the overall framework of environmental assessment of UNEP/EAP-AP (Fig. 1), necessary for informed decision making for preparing legislation and action plans.

Fig. 1.0 Overall framework of environmental assessment of UNEP/EAP-AP
(Source: Surendra Shrestha, UNEP/EAP-AP)


With the rapid economic growth, rapid land use/land cover transformations are going on in many parts of Asia and the Pacific region. The nature and extent of land cover transformation varies from place to place, however. UNEP/EAP-AP's effort in carrying out macro-scale land cover interpretation of multi-temporal and multiseasonal NOAA AVHRR 1 km resolution satellite data of selected countries in South and South East Asia helps to understand the nature and dynamics of ecologically important land cover types. The outcome of six countries namely Bangladesh, Cambodia. Lao P.D.R., Nepal and Vietnam is promising. The analysis of 1985/86 and 1992/93 AVHRR satellite data indicated that rapid land cover change has occurred in the Mekong River Delta during the span of seven years. Available secondary information also provided similar indications. Based on the above information, it was decided to investigate a portion of the Mekong River Delta covering a full scene of SPOT XS data, in detail.

While the analysis of NOAA data helped us to identify "hot spot" area, it is the high-resolution satellite data such as SPOT, IRS and/or Landsat, which provides an opportunity for deeper investigation. With the help of high-resolution satellite data, it is possible to identify nature and extent of land cover transformations in greater detail. The driving forces responsible for the changes can also be identified. The use of remote sensing technology for providing such an information, in a regular basis helps policy makers and planners to formulate effective land use planning for sustainable development based on informed decision making.

Being one of the major economic development region and a region with highest population density of Vietnam, Mekong Delta possesses both opportunities and threats for ecologically sustainable development, now and in the future. Uncertainties that arise from flooding condition and tidal inundation hinder the effective planning and management. Remote Sensing and GIS technology can be an effective tool for regular monitoring, forecasting and dealing with such uncertainties.

The principle objective of this study is to investigate in detail, how agricultural practices such as cropping patterns, cropping intensity, crop types etc. are changing in the Mekong River Delta of Vietnam during recent pasts. The output of the project is expected to be useful for the Vietnamese authorities for sound and effective land use planning and regular monitoring.


Loei is one of the 17 provinces (Changwat) of the Northeastern (Isan) Region of Thailand, which is about 520 kms from Bangkok. The province is boardered with Lao P.D.R. in the north, Udonthani and Nongkhai province in the east, Khon Kaen and Phetchabun province in the south and Phitsanulok province in the west (Fig. 1).

The climate of Loei province is relatively warm in the summer and cool in the winter. This is the only place in Thailand where temperature drops down to 00 C. The general climate of the province has been classified as "Tropical" with the seasonal variation of temperature and rainfall. From November to February, cold and dry air flows into the region from China and from May to October, the southwest monsoon bring a stream of warm moist are from the ocean. The southwest monsoon causes considerable rainfall over much part of the province.

Administratively, Loei has been divided into 12 districts (Amphoe), 89 communes (Tambon), 784 villages and 1 municipal area (Statistical Report of Cgangwat, 1994). The total population of the province is 574,956 persons with a population density of 503.30 person per square kilometers (Statistical Report of Changwat, 1994).

The province is characterized by numerous hill and mountains that resembles with the Northern Region of Thailand. It also consists of valleys running along the rivers. Soils of Loei province are composed of the following types.

  • Soils of recent alluvial plains of variable width along the rivers. They occur only in the valleys of the northern mountains.
  • Soils of nearly level low terraces of semi-recent alluvium. They are widespread in the basins and valleys of the northern mountains.
  • Soils of undulating to rolling old alluvial terraces and fans. They are also widespread in the basins and valleys occurring in higher positions than the low terraces of semi-recent alluvium.
  • Soils of undulating to hilly areas derived from erosion of surface and lava flows, occurring mostly in from of hills and mountains, bordering the alluvial plains and
  • Soils on the hills and mountains that make up the greater part of the region. The soil condition in this area is relatively poor.
  • According to the Depart of Land Development of Thailand, the land capability classification of Loei province consists of five broad groups numbered P-I to P-V for paddy rice and eight groups numbered U-I to U-VIII for upland crops. It is further sub-divided into to sub-groups.

    The total forest area of the province is 7,062.89 square kilometers. There are five national parks, 1 wildlife sanctuary and 20 reserve areas. In 1995, from the management point of view, forest area has been classified into three broad categories as follows.

  • Reserve Area - Resource use is prohibited;
  • Economic Area - Allowed to harvest the selected economic products and;
  • Agricultural Area - Allowed cultivating agricultural crops.
  • The forest vegetation of Loei province is composed of mixed deciduous and dry evergreen forests at lower elevations and of hill evergreen forest and some pine forests above 1000 m elevations.

    Transplanted rice is the predominant crop in the river basin and in valleys. Some part of the paddy cultivation areas composed of recent or semi-recent alluvium with abundant moisture are being used for the cultivation of soybean, sugarcane, tobacco, maize, peanuts, vegetables and fruit trees including longan and mandarin oranges. Shifting cultivation is common in the areas which is being practised on the old alluvium terraces and fans for cultivation of upland rice, maize, groundnut, beans, and cassava.


    4.1 Data Used

    Remote sensing and GIS technology was used for the data analysis. SPOT multispectral satellite data acquired on March 04, 1995 was used as a principal source of information to derive present status of land use/land cover in the study area. A False Color Composite of SPOT XS data is presented in Fig. 2.0. Other secondary information such as topographic maps, soil map, district and provincial boundary map, geological map, land use map, main transportation network and main river & canal network map, etc.were collected from various sources and used in the study.

    4.2 Methods

    An intensive field trip of six days was conducted on November 1995 approximately three months prior to the date of acquisition of the satellite data used. Representative samples of the study area consisting of a number of sample plots were visited. In each sample plot, information such as agricultural crops, cropping patterns, management regimes, dominant vegetation types, topography, soil type, slope, accessibility etc. were collected. Global Positioning Systems (GPS) was used to position ourselves in the satellite imagery. Unstructured interviews with the villagers were also conducted to gain understanding on the various aspects of agricultural practices.

    The SPOT XS image was spatially georeferenced to a Universal Transverse Mercator (UTM) map projection using a non-linear polynomial and resampled with nearest neighbor algorithm. Ground Control Points (GCPs) were selected from the 1:50,000 scale topographic maps. A RMS (Root Mean Square) error of 1 pixel (20 m) was accepted during the non-linear rectification.

    A hybrid approach of supervised and unsupervised classification was deployed in the study. Isodata clustering and iterative labeling was used during the unsupervised classification. Training areas based on the field observation were utilized for the supervised classification. ERDAS Imagine, an image processing software, were used for the analysis. The information form the unsupervised clustering was incorporated in the supervised classified map. The classified map was converted to vector format for further GIS analysis using ARC/INFO 7.0.3 software.

    For the change analysis, Landsat MSS data acquired on 19th. December, 1985 was acquired. The image was georeferenced to UTM projection with a RMS error of +- 1 pixel. Later it was resampled to 20 meters resolution to make it consistent with SPOT XS resolution. Classification was performed in both the images followed by comparison of classified image.

    GIS database was created for different biophysical parameters. The database is in vector format. Spatial analysis was performed using ARC/INFO GIS interface.

    5.0 RESULTS
    5.1 Land Use/Land Cover Types

    The following outlines the major land use/land cover types prevalent in the study area and that can be discerned by the satellite data used. Representative samples of selected land use/land cover classes are presented in Appendix-2.

    Paddy Field: Two types of paddy cultivation are found: rain-fed and irrigated. Rain-fed rice is found in the uplands and in the shifting cultivated areas whereas irrigated rice are found along the river and in valleys.
    Cassava: Cassava is a upland crop that can be found at different stage of growth at any time of the year. Fields ready for plantation, newly planted cassava fields, young cassava plantation, and recently harvested cassava fields are some of the stages observed during the filed visit.
    Soyabean: Soyabean, a cereal crop, can be found in moist areas mostly along the river corridor.
    Eucalyptus Plantation: A large tract of eucalyptus plantation probably affected by forest fire at the time of acquisition of the satellite data was detected in the north-eastern side of the study area.
    Rubber Plantation: Rubber plantation is scattered throughout the study area which is very distinct with its regular shape.
    Other Crops: Other crops are agricultural crops that are not included in the above definition. This includes the mix of banana and/or sugarcane, and/or coconut plantation etc.
    Barren Lands: An area devoid of any vegetation and cover types. This might include abandoned agricultural lands, forest fallow and so on.
    Forest: This includes dense forests mostly inside the national parks, forest reserves, and botanical gardens.
    Hill Forest: Forests poorly stocked and are occurring in the degraded condition along the ridges of mountains.
    Degraded Forest: Forests occurring in a very degraded condition dominated by bamboo. This is one of the major cover types affected by shifting cultivation.
    Waterbodies: This includes rivers, lakes and dams.

    Other Classes: The class represents a mixture of two or more classes that are inseparable in the satellite imagery.

    5.2 Land Use/Land Cover Assessment

    The land use/land cover map prepared with the interpretation of satellite data has been presented in Map 1. The map presents the spatial distribution of land use/land cover types described earlier. The following table provides the areal distribution of these classes.

    Table 1.0 Land Use/land Cover Types of the Study Area
    Land Use/Land Cover Classes Area (in ha.)
    Other Classes 47595.72
    Soybean 10802.72
    Barren Land 57398.76
    E. Plantation 460.84
    Rubber Plantation 3881.84
    Degraded Forest 77385.48
    Scrubland 24005.04
    Paddy Field 21752.28
    Waterbodies 1625.44
    Sand 1257.88
    Hill Forest 118658.64
    Cassava 1997.4
    Other Crops 5873.2
    Forest 7592.72

    The dominant land use/land cover type in the area is forest that includes forest, degraded forest, hill forest, and plantations. Other major land use/land cover types include agricultural lands, other classes, and waterbodies.

    Harvested cassava fields and cassava fields ready for plantation provides similar signature to the barren lands. In all of these cases the reflection is coming from the exposed soil thus making it difficult to classify correctly. Field information collected during the field visit was used to rectify misclassification errors. Rubber plantation and soybean appears, as reds in the false color composite of SPOT XS data. However, rubber plantation are occurring in rectangular shape and soyabean follows the linear shape which helps in discriminating these two classes.

    5.3 Change Assessment

    Visual comparison of Landsat MSS and SPOT XS data as presented below clearly shows the change of forest cover within the span of 11 years (Fig. 13 ).

    Change of forest land to other land use is the main type of land use/land cover change observed in the area. The underlying reasons for the forest cover change are multi-farious. Shifting cultivation, illegal logging, forest fire, and expansion of agricultural land are principal causes among others. Change in the type and intensity of agricultural crops was also noticed. Farmers are moving more towards cash crops cultivating soyabean, sugarcane, rubber and cassava etc.

    Fig. 14 Change Pattern of Forest Cover in the Study Area

    Copyright 1995-2002 by:   ESS   Environmental Software and Services GmbH AUSTRIA