Application of LAPAN A3 satellite data for the identification of paddy fields using Object Based Image Analysis (OBIA)

Mukhoriyah and Dony Kushardono (2021) Application of LAPAN A3 satellite data for the identification of paddy fields using Object Based Image Analysis (OBIA). International Journal of Remote Sensing and Earth Sciences, 18 (1). pp. 33-42. ISSN 0216-6739

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The role of agriculture is directly related to SDG No.2, which is running a programme until 2030 to reduce national poverty, eradicate hunger by increasing food security and improving nutrition and support sustainable agriculture. Problems faced include the reduction in agricultural land, which results in lower rice production, and the limited information on the monitoring of paddy fields using spatial data. The purpose of this study is to identify paddy fields using LAPAN A3 satellite imagery based on OBIA classification. The data used were from LAPAN A3 multispectral imagery dated 19 June 2017, Landsat 8 imagery dated 17 June 2017, DEM SRTM (BIG), and the Administrative Boundary Map (BIG). The analysis method was segmentation by grouping image pixels, and supervised classification by taking several sample areas based on Random Stratified Sampling. The results will be carried using a confusion matrix. The classification results produced four classes; watery paddy fields, vegetation paddy fields, fallow paddy fields, and non-paddy fields, using of the green, red, and NIR bands for the LAPAN A3 data. From the results of the segmentation process, there remain some oversegmented features in the appearance of the same object. Oversegmentation is due to an inaccurate value assignment to each algorithm parameter when the segmentation process is performed. For example, watery paddy fields appear almost the same as open land (fallow paddy fields), the water object is darker purple. The visual classification results (Landsat 8 data) are considered as the reference for the digital classification results (LAPAN A3). Forty-eight samples were taken and divided into four classes, with each class consisting of 12 samples. The results of the accuracy test show that the total accuracy of the object-based digital classification for visual classification is 62.5% with a Kappa accuracy value of 0.5. The conclusion is that LAPAN A3 data can be used to identify paddy fields based on spectral resolution and to complement Landsat 8 data. To improve the accuracy of the classification results, more samples and the correct RGB composition are needed.

Item Type: Article
Uncontrolled Keywords: paddy field, LAPAN A3, Landsat 8, object based image analysis (OBIA), supervised classification
Subjects: Taksonomi LAPAN > Teknologi Penginderaan Jauh > Penelitian, Pengkajian, dan Pengembangan > Pemanfaatan Penginderaan Jauh
Divisions: LAPAN > Deputi Penginderaan Jauh > Pusat Pemanfaatan Penginderaan jauh
Depositing User: Administrator Repository
Date Deposited: 19 Nov 2021 10:41
Last Modified: 31 Oct 2022 01:05

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