Mukhoriyah Mukhoriyah, Dony Kushardono


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.


paddy field, LAPAN A3, Landsat 8, object based image analysis (OBIA), supervised classification

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Affan M., Faizah, & Dahlan. (2010). Land Cover Change Analysis Using Satellite Images. Jurnal Natural. 10(1): 50-55.

Ardiansyah, & Sawitri Subyanto, A. S. (2015). Identification of Paddy FIelds Using NDVI and PCA in Landsat 8 Image (Case Study: Demak Regency, Central Java). UNDIP Geodetic Journal. 4 (4), 316–324.

Jaya I.N.S. (2010). Digital Image Analysis Remote Sensing Perspective for Natural Resource Management. Faculty of Forestry, Bogor Agricultural University.

Judianto, C.T., & Nasser, E.N. (2015). The Analysis of LAPAN-A3/IPB Satellite Image Data Simulation Using High Data Rate Modem. ScienceDirect. Procedia Environmental Sciences, 24, 285-296.

Nugroho, J. T., Zylshal, Z., & Kushardono, D. (2018). LAPAN-A3 Satellite Data Analysis for Land Cover Classification (Case Study: Toba Lake Area, North Sumatra). International Journal of Remote Sensing and Earth Sciences (IJReSES), 15 (1), 71-80. j.ijreses.2018.v15.a2782

Nugroho, U. C., Susanto, Yudhatama, D., & Mukhoriyah. (2015). Identification of Tin Mining Land Using Maximum Likelihood Guided Method in Landsat. Globe Magazine, 17 (1), 9-15.

Setiawan, Y., Prasetyo, L. B., Pawitan, H., Liyantono, L., Syartinilia, S., Wijayanto, A. K., & Hakim, P. R. (2018). Utilization of Satellite Data Fusion LAPAN-A3 / Ipb and Landsat 8 for Monitoring Rice Fields. Journal of Natural Resources and Environmental Management, 8 (1), 67–76.

Setiawan, Y., & Yoshino, K. (2014). Detecting land-use change from seasonal vegetation dynamics on a regional scale with MODIS EVI 250-m time-series imagery. Journal of Land Use Science, 9 (3), 304-330. 1747423X.2013.786151

Singawilastra, D. H., Wikantika., K., & Harto, A. B. (2016). Analysis and Validation of the 2010 Ministry of Agriculture's 2010 Paddy Field Survey Results Based on the OBIA Classification Approach (Study Cases, Juntiyuat District, Indramayu District). ResearchGate. DOI: 10.13140/RG.2.2.34258.81609

Tahir. A.M., Hakim. P.R., & Syafruddin, A.H. (2016). Image-Focusing Quality Improvement on LAPAN-A3 Satellite Multispectral Imager. Aerospace Technology Journal, 14 (1 ), 37-50.

Wibowo, T. S. (2012). Application of object-based image analysis (OBIA) for land-use change detection using ALOS-2 AVNIR. Indonesian Earth Journal, I (3), 130-138.

Wibowo, T. W., & Danoedoro, P. (2010). Comparison of Multispectral Classification with Object Oriented Classification for Alos Avnir-2 Image-Based Land Cover Extraction. Thesis, Gajah Madja University.

Yaisa, S.N., Yanuarsyah, I., & Hudjimartsu, S. A. (2019). Identification of Paddy Field with Object-Base Image Analysis (OBIA) Combination on Aerial Image Images. . Semnati, 2, 227-231. index.php/semnati/article/view/295.

Zylshal, Z., Sari, N. M., Nugroho, J. T., & Kushardono, D. (2017). Comparison of

Spectral Characteristics between LAPAN-A3 and Sentinel-2A. The 5th Geoinformation Science Symposium, IOP Conference Series: Earth and Environmental Science, 98, 012051. DOI: 10.1088/1755-1315/98/1/01205


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