Development of A Simple Method for Detecting Mangrove Using Free Open Source Software

Anang Dwi Purwanto, Erwin Riyanto Ardli


Mangrove forests are becoming attractive natural charms and make everyone to take advantage of the existence of these coastal ecosystems both directly and indirectly. However, the condition of mangrove forests is threatened by their presence due to environmental factors around them. Sustainable mangrove monitoring efforts must always be increased to support the preservation of the mangrove ecosystem. The purpose of this study is to develop a fast and easy mangrove forest identification method based on remote sensing satellite imagery data. The research location chosen was the mangrove area in Segara Anakan, Cilacap. The data image used is Landsat 8 image acquisition on December 3, 2017 with path/row 121/065 obtained from the LAPAN Pustekdata Landsat catalog. The methods used include the Optimum Index Factor (OIF) method for selecting the best channels and the supervised classification method using the Semi-Automatic Classification Plugin (SCP) contained in open source software and provides three algorithm choices for the classification process including Minimum Distance, Maximum Likelihood and Spectral Angle Mapping. The results show the combination of RGB 564 (NIR+SWIR+RED) was the best in the identification of mangrove forests and the Maximum Likelihood classification algorithm was the most optimal in distinguishing mangrove and mangrove classes from both Macro Class and Class levels. The results of the calculation of the area show the mangrove area of 7,037.16 ha. The developed method can produce information on the distribution of mangroves at research sites more quickly, easily, effectively, and efficiently.


Mangrove; OIF; Semi-Automatic Classification Plugin (SCP); Landsat 8

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Alatorre, L.C., Andrés, R.S, Cirujano, S., Beguería, S., & Carrillo, S.S. (2011). Identification of Mangrove Areas by Remote Sensing: The ROC Curve Technique Applied to the Northwestern Mexico Coastal Zone Using Landsat Imagery. Remote Sensing, 3(8), 1568-1583. doi:10.3390/rs3081568.

Bunting, P., Rosenqvist, A., Lucas, R., Rebelo, L.M., Hilarides, L., Thomas, N., Hardy, A., Itoh, T., Shimada, M., & Finlayson, C.M. (2018). The Global Mangrove Watch – a New 2010 Global Baseline of Mangrove Extent. Remote Sensing. 10(1669), 1-19.

Chavez, P.S,, Berlin, G.L., & Sowers, L.B. (1982). Statistical method for selecting Landsat MSS ratios. Journal of Applied Photographic Engineering, 8(1), 23-30.

Congedo, L. (2014). Semi-Automatic Classification Plugin Documentation. Technical Report. DOI: 10.13140/RG.2.1.1219.3524.

Danoedoro, P. (2009, in Indonesian). Remote Sensing for Mangrove Inventory: Potential, Limitations and Data Requirements. Prosiding Sinergi Survei dan Pemetaan Nasional dalam Mendukung Pengelolaan Mangrove Berkelanjutan 2009, 98-113.

Fibriawati, L. (2016, in Indonesian). Atmospheric Correction of SPOT-6 Image With MODTRAN4 Method. Prosiding Seminar Nasional Penginderaan Jauh 2016.

Jaya, I.N.S. (2010, in Indonesian). Digital Image Analysis: Remote Sensing Perspective for Natural Resource Management. Faculty of Forestry. IPB University.

Jhonnerie, R. (2015, in Indonesian). Object and Pixel-based Mangrove Classification Using Multispectral Satellite Imageries at Kembung River, Bengkalis, Riau Province. Dissertation. IPB University.

Kementerian Kehutanan. (2006,in Indonesian). Indonesian Mangrove Forest Data in 2006. Jakarta.

Kuenzer, C., Bluemel, A., Gebhardt, S., Quoc, T.V., Dech, S. (2011). Remote sensing of mangrove ecosystems: A review. Remote Sensing. 3(5), 878-928.

Manoppo, A., Anggraini, N., & Marini, Y. (2015, in Indonesian). Identification of Mangroves with the Optimum Index Factor (OIF) Method on SPOT 6 and Landsat 8 Data in Lingayan Island.. Proceedings MAPIN XX 2015.

Marini, Y., Emiyati., Hawariyah, S., & Hartuti, M. (2014, in Indonesian). Comparison of Supervised Maximum Likelihood Classification Methods with Object-Based Classification for Ponds Inventory in Maros Regency. Prosiding Seminar Nasional Penginderaan Jauh 2014.

Marini, Y., Manoppo, A., & Anggraini, N. (2015, in Indonesian). Determination of RGB Color Composites for Mangrove Identification in Subi Kecil Island using Landsat 8 Data. Buku BungaRampai Mangrove, Jakarta.

Mukhtar, RA., Ramdani, F., & Wicaksono, S.A. (2018, in Indonesian). Mobile GIS Development to Analysis of Land Cover in Trenggalek Regency. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(9), 3416-3424.

Patel, N., & Kaushal, B. (2011). Classification of features selected through Optimum Index Factor (OIF0 for improving classification accuracy. Journal of Forestry Research, 22(1), 99-105. DOI 10.1007/s11676-011-0133-4.

Purwanto, A.D. (2017, in Indonesian). The Spatial Dynamics Model of Mangrove Forest Changes In Segara Anakan, Cilacap. Thesis. Indonesia University.

Purwanto, A.D., Winarso, G., & Julzarika, A. (2018, in Indonesian). Identification of True Mangrove Using OBIA Method Based on Landsat 8 OLI And Landsat 7 ETM+ Imagery. Prosiding Seminar Nasional Geomatika 2018.

Sisodia, P.S., Tiwari, V., & Kumar, A. (2014). Analysis of Supervised Maximum Likelihood Classification for remote sensing image. International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2014 9-11 Mei 2014. Jaipur(IN): p 1-4.

Subardjo, P., & Pribadi, R. (2012, in Indonesian). Study of Changes in the Area of Mangrove Vegetation Using Landsat TM and Landsat 7 ETM + Images from 1998 - 2010 in the Coast of Mimika Regency, Papua. Journal Of Marine Research. (1)1, 146-145.

Susanto., & Asriningrum, W. (2011, in Indonesian). Remote Sensing with Factor Index Value for Mangrove Identification in Batam (Case Study Jandaberhias island). J. Berita Dirgantara, 12(3), 104-109.

Sutanto, A., & Tjahjaningsih, A. (2016, in Indonesian). Radiometric Corrections of Landsat Image Using Semi Automatic Classification Plugin on QGIS Software. Prosiding Seminar Nasional Penginderaan Jauh 2016.

Suwargana, N. (2008, in Indonesian). Analysis of Mangrove Forest Changes using Remote Sensing Data in Bahagia Beach, Muara Gembong, Bekasi. Journal of Remote Sensing and Digital Image Processing, 5, 64-74. diakses tanggal 27 Oktober 2019.