Comparison of Land Use Classification Methods Using Landsat 9 Data in Eastern Economic Corridor, Thailand

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Weeraphart Khunrattanasiri
Jutamad Srikongruk
Santi Suksard
Vissanu Domrongsutsiri

Abstract

Visual interpretation of satellite data, supervised classification (maximum likelihood) and a random forest (RF) algorithm were used in the Eastern Economic Corridor (EEC), Thailand to classify land use into 5 categories: forest; water body; agriculture; urban and built-up land; and miscellaneous land. The information was interpreted from Landsat 9 OLI-2 data at a spatial resolution of 30 m. In addition, the GIS overlay technique was used to compare the results of land use classification between visual interpretation and the automated analysis techniques.


Based on the results, 13,343.03 km2 of the Landsat 9 OLI-2 data within the EEC could be classified, with visual interpretation producing the highest overall accuracy (97.30%), followed by the RF algorithm (67.57%) and then the maximum likelihood method (64.86%). The comparison revealed that 8,441.25 km2 (63.26% of the EEC), encompassing all 5 land use categories, matched with the visual interpretation results when applying the RF algorithm and supervised classification methods via the GIS overlay technique. Thus, the RF algorithm outperformed supervised classification in accurately classifying land use within the EEC.

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Khunrattanasiri, W., Srikongruk, J., Suksard, S., & Domrongsutsiri, V. (2024). Comparison of Land Use Classification Methods Using Landsat 9 Data in Eastern Economic Corridor, Thailand . Thai Journal of Forestry, 43(2), 51–61. retrieved from https://li01.tci-thaijo.org/index.php/tjf/article/view/262376
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Original Articles

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