APPLICATION OF LAPAN A3 SATELLITE DATA FOR THE IDENTIFICATION OF PADDY FIELDS USING OBJECT BASED IMAGE ANALYSIS (OBIA)

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.