Expert classification for Land Cover Mapping of Bang Pakong Watershed, Thailand
Siam Lawawirojwong
Geo-Informatics and Space Technology Development Agency, Bangkok, Thailand
Tel: +66-2-5790116 Fax: +66-2-5790116
E-mail: siam@gistda.or.th
Sura Pattanakiat (Ph.D), Charlie Navanugraha (Ph.D)
Associate Professor, Faculty of Environment and Resource Studies
Mahidol University, Salaya, Nakhonpathom, Thailand
Tel: +66-2- 4415000 Fax: +66-2- 4419510
E-mail: enspt@mahidol.ac.th,
encnv@mahidol.ac.th
Prasong Sanguantham
Assistant Professor, Faculty of Forestry (M.S.)
Kasetsart University, Chatuchak, Bangkok, Thailand
Tel: +66-2- 9428108 Fax: +66-2- 9428108
E-mail: fformgt@ku.ac.th
ABSTRACT
The main objective of this research is to generate a knowledge-based and to develop the expert
classification using Landsat-7 (ETM+) imagery for land cover classification of Bang Pakong
watershed. Image preparations included radiometric and atmospheric corrections, geometric
correction, image enhancement, image mosaic, and image subset.
The expert classification applied the unsupervised classification (ISODATA clustering method)
and knowledge-based operation which including spectral characters, GIS data (DEM and soil
moisture regime) and spatial models (clump model, NDVI model, mean NDVI per zone model,
WI model, mean WI per zone model, slope model, and aspect model) to classify the conditions
for land cover category identification. The land cover categories are identified as residential and
open space area, abandoned land, mixed deciduous forest, mangrove forest and wetland, paddy
field, other vegetation, and water bodies. The percentages of accuracy for each land cover
categories using maximum likelihood classification are 62.5, 64.29, 81.82, 75, 64.71, 66.67, and
54.55, respectively. Meanwhile, the percentages of accuracy for the expert classification are 75,
78.57, 90.91, 87.5, 70.59, 76.19, and 72.73, respectively. Therefore, the accuracy for each land
cover category from the expert classification is higher than the maximum likelihood
classification. Furthermore, the overall accuracy of the expert classification is about 78%, and
the maximum likelihood classification is only about 67%. Thus the accuracy of the expert
classification is about 11% higher than the maximum likelihood classification.
INTRODUCTION
In the past, while dealing with image classification for land cover mapping, quite often only
gray level, texture, geometric characteristics, and the knowledge from optical spectrum bands
are used to classify land cover from satellite image. The benefit from such basis was limited in
ability of land cover classification, which could not identify precisely on land cover.
The classification of land cover requires sophisticated skill and techniques. Traditionally, there
were inefficiencies for not only upgrading the classification accuracy by simple spectral
classification technology but also increasing its categories. For this reason, this study provides
the land cover classification knowledge-based system by system language model which is done
through AI (Artificial Intelligence) combined with the information from RS (Remote Sensing)
and GIS (Geographic Information System). It is called expert classification.
With the assistance of the expert classification, it is feasible to integrate spectral information
from remote sensing and transformed data from GIS to set as the reference material for land
cover classification. Through integrated database and parameters, the related uncertainty levels
data can be classified. The classification procedure must relatively fit in with true land cover
condition, and also have a great effect on land cover classification. Furthermore, it also avoids
the inconvenience of classification procedures towards further classification.
In addition, to apply and develop the knowledge-based into a better condition of land cover
classification, the focus is aimed at building up the knowledge-based system with spectral
information in remote sensing and the indirect data in GIS, and inference from knowledge in
special domain range to make classification decision.
On the other hand, it is possible to hope to promote the accuracy of image classification which
can serve as a powerful tool to apply on environmental monitoring or changing detection in the
future.
