Assessing the Potential of LAPAN-A3 Data for Landuse/landcover Mapping

Zylshal Zylshal(1*), Rachmad Wirawan(2), Dony Kushardono(3)

(1) Indonesian National Institute of Aeronautics and Space (LAPAN)
(2) Universitas Negeri Malang
(3) Indonesian National Institute of Aeronautics and Space (LAPAN)
(*) Corresponding Author


LAPAN-A3 / LAPAN-IPB is the third generation of micro-satellite developed by Indonesian National Institute of Aeronautics and Space (LAPAN). The satellite carries a multispectral push-broom sensor that can record the earth's surface at the visible and near-infrared spectrum. Being launched in June 2016, there has no been many publications related to the use of LAPAN-A3 multispectral data for landuse/landcover (LULC) mapping. This paper aims to provide information regarding the use of LAPAN-A3 data for the LULC extraction maximum likelihood algorithm as well as neural network and then evaluate the results. The LAPAN-A3 image was geometrically corrected by using Landsat-8 OLI image as reference data. Three test areas with a size of 1200x945 pixels are then selected for pixel-based classification with the two aforementioned algorithms. For comparison, both LAPAN-A3 and Landsat-8 data were classified for 3 test areas. Accuracy assessment was performed on both datasets using manually interpreted SPOT-6 Pansharpened image as reference data. Preliminary results showed that LAPAN-A3 were able to extract  10 different LULC classes, comprises of built-up area, forest, rivers, fishponds, shrubs, wetland forests, rice fields, sea, agricultural land, and bare soil. The overall accuracy of LAPAN-A3 data is generally lower than Landsat-8, which ranges from 49.76% to 71.74%. These results illustrate the potential of LAPAN-A3 data to derive LULC information. The lack of necessary parameters to perform radiometric correction and blurring effect are several issues that need to be solved to improve the accuracy LULC. 


LAPAN-A3;Landsat-8;LULC;Maximum Likelihood;Neural Network

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Bailly, J. S., Arnaud, M., & Puech, C. (2007). Boosting: a classification method for remote sensing. International Journal of Remote Sensing, 28(7), 1687–1710. Bensebaa, K., Banon, G. J. F., & Fonseca, L. M. G. (2012). Spatial resolution estimation of CBERS-1 and CBERS-2 CCD cameras. International Journal of Remote Sensing, 33(2), 604–629. Bensebaa, K., Banon, G. J. F., Fonseca, L. M. G., & Erthal, G. J. (2008). On-orbit Spatial Resolution Estimation of CBERS-2 Imaging System Using Ideal Edge Target. In E. Damiani, K. Yétongnon, P. Schelkens, A. Dipanda, L. Legrand, & R. Chbeir (Eds.), Signal Processing for Image Enhancement and Multimedia Processing (pp. 37–48). Boston, MA: Springer US. Bischof, H., Pinz, A. J., & Schneiden, W. (1992). Multispectral Classification of Landsat-Images Using Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 30(3), 482–490. BSN. (2014). Landcover classification - Part 1: Coarse and Medium scale. Badan Standardisasi Nasional. (in Indonesian) Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measrurement, XX(1), 37–46. Congalton, R. G., & Green, K. (2009). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. CRC Press/Taylor & Francis. Cracknell, a P. (1998). Synergy in remote sensing - what’ s in a pixel ? International Journal of Remote Sensing, 19(11), 2025–2047. Dai, X. (1998). The effects of image misregistration on the accuracy of remotely sensed change detection. IEEE Transactions on Geoscience and Remote Sensing, 36(5 PART 1), 1566–1577. Danoedoro, P. (2012). Introduction to Digital Remote Sensing. Yogyakarta: Penerbit ANDI. (In Indonesian) Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., & Li, X. (2016). Water bodies’ mapping from Sentinel-2 imagery with Modified Normalized Difference Water Index at 10-m spatial resolution produced by sharpening the swir band. Remote Sensing, 8(4). Foody, G., McCulloch, M., & Yates, W. (1995). Classification of remotely sensed data by an artificial neural network: issues related to training data characteristics. Photogrammetric Engineering & Remote Sensing, 61(4), 391–401. Retrieved from Gandhi, V. (2004). Image Classification Based on Textural Features using Artificial Neural Network ( ANN ). Journal of The Institution of Engineers (India), 84, 72–77. Hakim, P. R., Hasbi, W., & Syafrudin, A. H. (2014). ADCS requirements of Lapan-A3 satellite based on image geometry analysis. Proceeding - ICARES 2014: 2014 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, 142–146. Hakim, P. R., & Permala, R. (2017). Analysis of LAPAN-IPB image lossless compression using differential pulse code modulation and huffman coding. In IOP Conference Series: Earth and Environmental Science (Vol. 54, p. 12096). IOP Publishing. Hakim, P. R., Syafrudin, A. H., & Utama, S. (2017). Band Co-registration Distortion Modeling of LAPAN-A3 Multispectral Imager Based on Satellite Attitude. The 4th International Symposium on LISAT 2017. Bogor, Indonesia. Han, N., Wu, J., Tahmassebi, A. R. S., Xu, H. W., & Wang, K. (2011). NDVI-based lacunarity texture for improving identification of torreya using object-oriented method. Agricultural Sciences in China, 10(9), 1431–1444. Hasbi, W., & Suhermanto. (2013). Development of LAPAN-A3 / IPB Satellite an Experimental Remote Sensing Microsatellite. In 34th Asian Conference on Remote Sensing 2013, ACRS 2013 (p. 8). Bali. Retrieved from Hepner, G. F., Logan, T., Pitter, N., & Bryant, N. (1990). Artificial Neural Network Classification Using a Minimal Training Set : Comparison to Conventional Supervised Classification. Photogrammetric Engineering & Remote Sensing, 56(4), 469–473. Jensen, J. R. (2005). Introductory Digital Image Processing: A Remote Sensing Perspective (3rd Ed.). Pearson Education. Junior, J. M., & Tommaselli, A. M. G. (2013). Exterior orientation of CBERS-2B imagery using multi-feature control and orbital data. ISPRS Journal of Photogrammetry and Remote Sensing, 79, 219–225. Kanellopoulos, I., Wilkinson, G. G., & Megier, J. (1993). Integration of neural network and statistical image classification for land cover mapping. In Proceedings of IGARSS ’93 - IEEE International Geoscience and Remote Sensing Symposium (pp. 511–513). Tokyo, Japan: IEEE. Kavzoglu, T., & Mather, P. M. (2003). The use of backpropagating artificial neural networks in land cover classification. International Journal of Remote Sensing, 24(23), 4907–4938. Knipling, E. B. (1970). Physical and Physiological Basis for the Reflectance of Visible and Near Infrared Radiation from Vegetation. Remote Sensing of Environment, 1, 155–159. Lee, D. H., Yang, J. Y., Seo, D. C., Song, J. H., Chung, J. H., & Lim, H. S. (2011). Image restoration of the asymmetric point spread function of a high-resolution remote sensing satellite with time-delayed integration. Advances in Space Research, 47(4), 690–701. 10.1016/j.asr.2010.10.006 Li, G., Li, X., Li, G., Wen, W., Wang, H., Chen, L., … Deng, F. (2013). Comparison of spectral characteristics between China HJ1-CCD and landsat 5 TM imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(1), 139–148. Liu, X. H., Skidmore, A. K., & Van Oosten, H. (2002). Integration of classification methods for improvement of land-cover map accuracy. ISPRS Journal of Photogrammetry and Remote Sensing, 56(4), 257–268. McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. Nugroho, J. T., Zylshal, Z., & Kushardono, D. (2017). LAPAN-A3 Satellite Data Analysis for Land Cover Classification (Case Study: Toba Lake Area, North Sumatra). Paper presented at Seminar Nasional Penginderaan Jauh. Depok, Jawa Barat. Parker, J. a, Kenyon, R. V, & Troxel, D. E. (1983). Comparison of interpolation methods for image resampling. IEEE Transactions on Medical Imaging, 2(1), 31–39. PUSTEKSAT. (2016). LAPAN-A3 Satellite Technical Specification. Retrieved January 16, 2017, from Richards, J. A. (1993). Remote Sensing Digital Image Analysis. Berlin/Heidelberg: Springer-Verlag. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learining Internal Representations by Error Propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Vol. 1, pp. 318–362). MIT Press. Schowengerdt, R. A. (1983). Techniques for image processing and classification in remote sensing. Academic Press. Retrieved from Setiawan, Y., Prasetyo, L. B., Pawitan, H., Wijayanto, A. K., Permatasari, P. A., Syartinilia, & Liyantono. (2017). An Evaluation of the Use of Lapan-A3/IPB Spectral Features to Identify Agricultural Land Use Types in Java, Indonesia. In The 4th International Symposium on LISAT 2017. Bogor, Indonesia. Tahir, A. M., Hakim, P. R., Syafruddin, A. H., Cagak, J., Km, S., & Indonesia, B. (2016). Image-Focusing Quality Improvement on Lapan-A3 Satellite Multispectral Imager. Jurnal Teknologi Dirgantara, 14(1), 37–50. Retrieved from Tucker, C. J., & Sellers, P. J. (1986). Satellite remote sensing of primary production. International Journal of Remote Sensing, 7(11), 1395–1416. USGS. (2012). Landsat Data Continuity Mission ( Ldcm ) Mission Data Data Format Control Book ( Dfcb ), (October). Veraverbeke, S., Gitas, I., Katagis, T., Polychronaki, A., Somers, B., & Goossens, R. (2012). Assessing post-fire vegetation recovery using red-near infrared vegetation indices: Accounting for background and vegetation variability. ISPRS Journal of Photogrammetry and Remote Sensing, 68(1), 28–39. Wahid, D. A., & Akiyama, T. (2007). Possibilities of landuse / landcover classification using ALOS AVNIR-2 in Takayama. Journal of the Japan Society of Photogrammetry and Remote Sensing, 46(5), 56–67. Weng, Q. (2012). Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sensing of Environment, 117, 34–49. Yoshioka, H., Huete, A. R., & Miura, T. (2000). Derivation of vegetation isoline equations in red-NIR reflectance space. IEEE Transactions on Geoscience and Remote Sensing, 38(2 I), 838–848. Zylshal, Z., Sari, N. M., Nugroho, J. T., & Kushardono, D. (2017). Comparison of Spectral Characteristic between LAPAN-A3 and Sentinel-2A. Paper presented at The 5th Geoinformation Science Symposium. Yogyakarta, Indonesia.


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