Publication: Spatial Dynamics Evolution of Land use for the Study of the Local Traditional Living Changes
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Issued Date
2023
Resource Type
File Type
application/pdf
Rights Holder(s)
Scopus
Bibliographic Citation
International Journal of Geoinformatics. Vol 19, No.4 (2023), p.37-49
Suggested Citation
Arunpiod C., Phonphan W., Wongsongja N., Utarasakui T., Niemmanee T., Daraneesrisuk J., Thongdara R. Spatial Dynamics Evolution of Land use for the Study of the Local Traditional Living Changes. International Journal of Geoinformatics. Vol 19, No.4 (2023), p.37-49. doi:10.52939/ijg.v19i4.2635 Retrieved from: https://hdl.handle.net/20.500.14740/12595
Other Contributor(s)
Abstract
Land use data can be used to understand patterns of economic behavior, such as the relationship between land use and property values or the impact of land use on environmental factors like air and water quality. The combination of land use data with other data sources and analysis methods can yield significant insights into economic growth and behavior. In this study, the land use and land cover (LULC) were classified using multi-temporal Sentinel-2 imagery (2019 and 2021) and random forest through the Google Earth Engine platform (GGE) with an overall accuracy of more than 89.79%. According to the results of the change detection analysis, there was a 16.96% increase in miscellaneous surface areas and a 15.50% increase in artificial surface areas. These disclose confirm that the sea salt farm, which are the traditional economic function, are losing 37.40%. Furthermore, the CA-Markov model was utilized to predict alterations in land use patterns in the year 2023 through the extrapolation of existing trends. The predicted LULC map of 2023 publicizes the trend of the sea salt farm decreasing, contrasty the artificial surface areas are increasing. In summary, this research reveals the evidence that LULC is strongly related to traditional living changes, and spatial analysis techniques are reasonable and committing tools for study. © 2023, Association for Geoinformation Technology. All rights reserved.