THE EFFECT OF MINIMUM NOISE FRACTION ON MULTISPECTRAL IMAGERY DATA FOR VEGETATION CANOPY DENSITY MODELLING

*Akbar Muammar Syarif  -  Department of Geography Information Science, Universitas Gadjah Mada, Indonesia
Ignatius Salivian Wisnu Kumara  -  Universitas Gadjah Mada, Indonesia
Received: 21 Dec 2017; Published: 25 Oct 2018.
Open Access License URL: http://creativecommons.org/licenses/by-nc-sa/4.0

Citation Format:
Article Info
Section: Articles
Language: EN
Statistics: 993 112
Abstract

Minimum Noise Fraction (MNF) is known as one of the methods to minimize noise on hyper spectral imagery. In addition, there are not many studies have tried to show the effect of MNF transform on multispectral data. This study purposes to determine the effect of MNF transform on the accuracy level of vegetation density modeling using 10 meters Sentinel-2A spatial resolution (multispectral data) and to know the cause. The study area is located in parts of Sapporo City, Hokkaido, Japan. Vegetation density is modeled through vegetation index approach, Normalized Difference Vegetation Index (NDVI). The results show that the coefficient correlation of vegetation density data and vegetation index regression after MNF transformation (0.801623) has higher value than the same regression without the MNF (0.794481). However, better correlation does not represent the better accuracy on vegetation density modeling. Accuracy calculation through standard error of estimate shows the use of MNF in multispectral data for vegetation density modeling causes the decrease of model accuracy value. The accuracy of vegetation density model without involving MNF transformation reached 91.4%, while the model accuracy through MNF transformation before vegetation density modeling reached 90.89%. The insignificant increased accuracy is occurred due to the limited number of multispectral image information compared to hyper spectral image data.     

    


 

Keywords: Minimum Noise Fraction (MNF), multispectral, Sentinel 2A, vegetation canopy density

Article Metrics:

  1. Bhandari, A., Kumar, A., & Singh, G. (2012). Feature Extraction Using Normalized Difference Vegetation Index (NDVI): A Case Study of Jabalpur City, Procedia Technology, 6(2012) pp 612-621 doi: Crossref]

    " target="_blank">[Crossref]
  2. Boardman, J. W., & Kruse, F. A., 1994, Automated Spectral Analysis: A Geological Example Using AVIRIS Data, North Grapevine Mountains, Nevada: in Proceedings, ERIM Tenth Thematic Conference on Geologic Remote Sensing, Environmental Research Institute of Michigan, Ann Arbor, MI, pp. I-407 - I-418.

  3. Chen, L., Yang, X., & Zhen, G. (2017). Potential of Sentinel-2 Data for Alteration Extraction in Coal-bed Methane Reservoirs. Ore Geology Reviews, (2017) [http://dx.doi.org/10.1016/j.oregeorev.2017.10.009">Crossref]

  4. Danoedoro, P. (2012). Pengantar Penginderaan Jauh Digital. Yogyakarta: Andi Offset.

  5. Ebadi, L., Shafri, H.M., Mansor, S., & Ashurov, R. (2013). A Review of Applying Second-generation Wavelets for Noise Removal from Remote Sensing Data. Environmental Earth Sciences, 70(6), pp 2679–2690 [http://dx.doi.org/10.1007/s12665-013-2325-z">Crossref]

  6. Exelis. (2015). Background: MNF Transform. In part of ENVI Help by Exelis Visual Information Solutions, Inc. a subsidiary of Harris Corporation. USA.

  7. Fauzan, M.A., Kumara, I.S.W., Yogyantoro, R.N., …., & Wicaksono, P. (2017). Assessing the Capability of Sentinel-2 Data for Mapping Seagrass Percent Cover in Jerowaru, East Lombok. Indonesian Journal of Geography, 49(1) pp. 121 – 134. [https://doi.org/10.22146/ijg.28407">Crossref]

  8. Frassy, F., Via, G., Maianti, P., & Gianinetto, M. (2013). Minimum Noise Fraction Transform for Improving the Classification of Airborne Hyperspectral Data: Two Case Studies. In 5th Workshop on Hyperspectral Image and Signal Proceeding: Evolution in Remote Sensing. June 2013. [https://doi.org/10.1109/whispers.2013.8080626">Crossref]

  9. Green, A. A., Berman, M., Switzer, P., & Craig, M.D. (1988). A Transformation for Ordering Multispectral Data in Terms of Image Quality with Implications for Noise Removal. IEEE Transactions on Geoscience and Remote Sensing, 26(1), pp 65–74 [http://dx.doi.org/10.1109/36.3001">Crossref]

  10. Jia, X.P., Kuo, B., & Crawford, M.M. (2013). Feature Mining for Hyperspectral Image Classification. Proc. IEEE Transactions on Geoscience and Remote Sensing, 101(3), pp 676–697 [http://dx.doi.org/10.1109/JPROC.2012.2229082">Crossref]

  11. Lantzanakis, G., Mitraka, Z., & Chrysoulakis, N. (2016). Comparison of Physically & Image Based Atmospheric Correction Methods for Sentinel-2 Satellite Imagery. In Proc. of SPIE Vol. 9688, Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016)  [http://dx.doi.org/10.1117/12.2242889">Crossref]

  12. Letexier, D., & Bourennane, S. (2008). Noise Removal from Hyperspectral Images by Multidimensional Filtering. IEEE Transactions on Geoscience and Remote Sensing, 46(7), pp 2061–2069. [https://doi.org/10.1109/tgrs.2008.916641">Crossref]

  13. Luo, G., Chen, G., Tian, L., Qin, K. & Qian, S. (2016). Minimum Noise Fraction versus Principal Component Analysis as a Preprocessing Step for Hyperspectral Imagery Denoising, Canadian Journal of Remote Sensing, 42(2), pp 106-116. [http://dx.doi.org/10.1080/07038992.2016.1160772">Crossref]

  14. Navarro, G., Caballero, I., Silva, G., Parra, P., Vazquez, A., & Caldeira, R. (2017). Evaluation of Forest Fire on Madeira Island Using Sentinel-2A MSI Imagery. Int. J. Appl. Earth Obs. Geoinformation, 58(2017) pp 97-106 [http://dx.doi.org/10.1016/j.jag.2017.02.003">Crossref]

  15. Rouse, J.W., Hass, R.H., Schell, J.A., & Deering, D.W. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS. In Proceedings of Third Earth Resources Technology Satellite-1 Symposium SP-351, pp 3010-3017

  16. Traganos, D. & Reinartz, P. (2017). Mapping Mediterranean Seagrasses with Sentinel-2 Imagery, Marine Pollution Bulletin, (2017) [http://dx.doi.org/10.1016/j.marpolbul.2017.06.075">Crossref]


No citation recorded.