Digital Classification of LANDAST TM for Land Cover Mapping of the Pa Wang Phloeng-Khom-Lam Narai National Forest Reserve,
Lop Buri Province, Thailand
Kaew Nualchawee and Lilita Bacareza
Asian Institute of Technology (AIT), Bangkok Thailand
Introduction
The objective of the study was to come up with a land cover map of the Pa Wang Phloeng-Muang Khom-lam Narai National Forest Reserve using various computer-assisted classification of Landsat digital data taken on 18 July 1993 with reference data from 26 January 1994, and to determine practical remote sensing approaches for high classification accuracy result. The demonstration of the stages and final results of the study are presented with focus on the major steps that were employed:
-
Geometric correction of the image
- Pre-classification processing
- Enhancement for reference purpose
- Training sample selection and evaluation
- Digital classification proper
- supervised approach
- Unsupervised approach
- Modified clustering approach
Background
Related Past Studies
In a related study of the area done by kasetsart University in 1984, manual analysis of aerial photographs of scale of 1:15,000 (date 18 November 1983) was performed to identify extent of land cover and land use within the forest reserve. Recently, the Prcticum research Team of the Natural resource Program of AIT conducted another study using Landsat Thematic Mapper (TM) digital data to demonstrate the usefulness of satellite remote sensing in yielding information for land cover changes from 1983 to 1994 in the absence of serial photographs (Practicum Research Study, 1994) Information derived from the two studies served as input to the present study.
The study area
The study area is a part of Khok Charoen District, Lop Buri Province in the upper Central region of Thailand, specifically located in the Yang rak Subdistrict at Latitude 15 degree 15 minutes to 15 degrees 27 minutes North and longituds 100 degrees 52 minutes to 101 degrees East.
The average annual rainfall from 1983 to 1994 was 1,054 mm to 1,324 mm, with the rainy season lasting from May to October leaving November to April a distinctly dry season. Average temperature over the past eleven years was 27.8 degrees Celcius.
The elevation level in the area varies from 80 m to 560 m above mean sea level, with more than 40% of the land area having a slope gradient of 0-2%, while 4.7% has slope gradient of 40%
Vegetation is a mixture of fruit and forest trees. Eucalyptus and Casuarina plantations abound, while the mountains and hills or upland vegetation are dominated by reproduction of salings of Dipterocarpacaea, Myrtaceae and Leguminosae (Practicum research Study, 1994)
Methodology
Selection of the remotely-sensed Data
There was no past experience over the study area as to what season would be best for discrimination of major vegetation cover, etc. The selection of the remotely-sensed data was mainly depended on the availability of the most recent high quality and cloud-free satellite image. In summary, the following images were selected and used for the study:
| Type of image |
Date of acquisition |
Some characteristic |
| Landsat TM |
18 July 1993 |
* 30x30m spatial resolution
*.45-12.5 micron wavelength |
| Landsat TM |
26 January 1993 |
* used only as reference data |
The Image Analysis System Used
The digital analysis was performed at the remote sensing Laboratory (RSL) at AIT, using the ERDAS Image Processing, PC-based System, and CCT was used for data input. The following steps were taken during this study:
Geometric Correction of the Image
A first order polynomial transformation and resampling with nearest neighbor algorithm was used to spatially geo-reference the Landsat image to UTM map projection system Ground control points identifiable at road intersections in reference to topographic maps (1:50,000) and those obtained from field visits through the Global Positioning System (GPS) were used. A root mean square error of pixel (30m) was accepted for the correction process using a total of 8 Ground Control Points (GCPs).
Image Enhancement
The contrast stretch image enhancement was applied to the original image to be used as reference for the interpretation of the raw data. For the study, however, the histogram-equalized stretch was used.
Identification of the different Resource Classes
Based on the field visits to the study area conducted from February to May 1994 and adopting the US Geological Survey classification (Anderson, 1976) land cover classes to be mapped were define, and a classification scheme for the digital analysis was determined. Brief descriptions of the categories are presented as follows.
Table 1. Resource Classes used for digital analysis of the study area.
| |
Level I |
Level II |
| 1. |
Agricultural Land |
Newly Planted cropland
Older cropland
Paddy fields
Tree plantations (orchards) |
| 2. |
Rangeland |
Bushes
Shrubs
Bareland
Grass land/Pasture land |
| 3. |
Forest land |
Forest
Mixed
vegetation |
| 4. |
Water |
Water bodies |
Determination of Training Approaches for Image Classification
In this study, the two basic approaches to training set selection were used, namely uspervised and unsupervised approaches. These two approaches, however, are not always satisfactory for various conditions of environment and variations in cover types, etc. Therefore, additional to the two approaches mentioned, the third approach was also considered, where in both supervised and unsupervised methods were taken into consideration. It involves applying first, a clustering algorithm to the data (the unsupervised way) to define spectral clusters, the results of which can then be manipulated by the analyst for further analysis and training-set determination (the supervised way). Past tests have shown that this "modified (controlled) clustering approach" was judged best because it resulted in savings of man-hours and computer time, as well as having the highest classification accuracy (Fleming, et al., 1975; Rohde, et al., 1977, 1978; Pettinger, 1982) These studies have concluded that the modified clustering approach was best suited when the area are spectrally complex due to variation in vegetation cover and terrain.
