E3S Web Conf.
Volume 202, 2020The 5th International Conference on Energy, Environmental and Information System (ICENIS 2020)
|Number of page(s)||9|
|Section||Green Infrastructure and Resilience|
|Published online||10 November 2020|
The Mapping of Land Use Using Object-Based Image Analysis (OBIA) in Klaten Regency
1 Geodetic Engineering Department, Diponegoro University, Indonesia
2 Planologi Engineering Department, Diponegoro University, Indonesia
3 Computer Engineering Department, Diponegoro University, Indonesia
4 Civil Engineering Department, Diponegoro University, Indonesia
* Corresponding author: firstname.lastname@example.org
Klaten Regency is one of the regencies in Central Java Province that has an increasing population every year. This can cause an increase in built-up land for human activities. The built-up land needs to be monitored so that the construction is in accordance with the regional development plan so that it does not cause problems such as the occurrence of critical land. Therefore, it is necessary to monitor land use regularly. One method for monitoring land use is the remote sensing method. The remote sensing method is much more efficient in mapping land use because without having to survey the field. The remote sensing method utilizes satellite imagery data that can be processed for land use classification. This study uses the sentinel 2 satellite image data with the Object-Based Image Analysis (OBIA) algorithm to obtain land use classification. Sentinel 2 satellite imagery is a medium resolution image category with a spatial resolution of 10 meters. The land use classification can be used to see the distribution of built-up land in Klaten Regency without having to conduct a field survey. The results of the study obtained a segmentation scale parameter value of 60 and a merge scale parameter value of 85. The classification results obtained by 5 types of land use with OBIA. Agricultural land use dominates with an area of 50% of the total area.
Key words: Land Use / Object-Based Image Analysis / Remote Sensing
© The Authors, published by EDP Sciences, 2020
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