Share This Article:

Inexpensive Method to Assess Mangroves Forest through the Use of Open Source Software and Data Available Freely in Public Domain

Abstract Full-Text HTML XML Download Download as PDF (Size:1443KB) PP. 43-57
DOI: 10.4236/jgis.2015.71004    3,126 Downloads   4,379 Views   Citations


Mapping and assessment of mangrove environment are crucial since the mangrove has an important role in the process of human-environment interaction. In Indonesia alone, 25% of South East Asia's mangroves available are under threat. Recognizing the availability and the ability of new sensor of Landsat data, this study investigates the use of Landsat ETM + 7 and Landsat 8, acquired in 2002 and 2013 respectively, for assessing the extent of mangroves along the South Sulawesi’s coastline. For each year, a supervised classification of the mangrove was performed using open source GRASS GIS software. The resulting maps were then compared to quantify the change. Field work activities were conducted and confirmed with the changes that occurred in the study area.  Considering the accuracy assessment, our study shows that the RGB composite color-supervised classification is better than band ratio-supervised classification methods. By linking the open source software with the Landsat data and Google Earth satellite imagery that is available in public domain, mangroves forest conversion and changes can be observed remotely. Ground truth surveys confirmed that, decreases of mangroves forest is due to the expansion of fishpond activity. This technique could potentially allow rapid, inexpensive remote monitoring of cascading, indirect effects of human activities to mangroves forest.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Ramdani, F. , Rahman, S. and Setiani, P. (2015) Inexpensive Method to Assess Mangroves Forest through the Use of Open Source Software and Data Available Freely in Public Domain. Journal of Geographic Information System, 7, 43-57. doi: 10.4236/jgis.2015.71004.


[1] Yanagisawa, H., et al. (2009) The Reduction Effects of Mangrove Forest on a Tsunami Based on Field Surveys at Pakarang Cape, Thailand and Numerical Analysis. Estuarine, Coastal and Shelf Science, 81, 27-37.
[2] Das, S. and Crépin, A.-S. (2013) Mangroves Can Provide Protection against Wind Damage during Storms. Estuarine, Coastal and Shelf Science, 134, 98-107.
[3] Saenger, P., Hegerl, E.J. and Davie, J.D.S. (1983) Global Status of Mangrove Ecosystems by the Working Group on Mangrove Ecosystems of the IUCN Commission on Ecology in Cooperation with the United Nations Environment Programme and the World Wild Life Fund. The Environment, 3, 1-88.
[4] Brander, L.M., Wagtendonk, A.J., Hussain, S.S., McVittie, A., Verburg, P.H., deGroot, R.S. and van der Ploeg, S. (2013) Ecosystem Service Values for Mangroves in Southeast Asia: A Meta-Analysis and Value Transfer Application. Ecosystem Service, 1, 62-69.
[5] Forestry Paper FAO 153 (2007) The World’s Mangroves 1980-2005. Food and Agriculture Organization of the United Nations, Rome.
[6] Burbridge, P.R. (1982) Management of Mangrove Exploitation in Indonesia. Applied Geography, 2, 39-54.
[7] Choong, E.T., Wirakusumah, R.S. and Achmadi, S.S. (1990) Mangrove Forest Resources in Indonesia. Forest Ecology and Management, 33/34, 45-57.
[8] Dutrieux, E. (1991) Study of the Ecological Functioning of the Mahakam Delta (East Kalimantan, Indonesia). Estuary, Coastal and Shelf Science, 32, 415-420.
[9] Verheugt, W.J.M., Purwoko, A., Danielsen, F., Skov, H. and Kadarisman, R. (1991) Integrating Mangrove and Swamp Forests Conservation with Coastal Lowland Development; the BanyuasinSembilang Swamps Case Study, South Sumatra Province, Indonesia. Landscape and Urban Planning, 20, 85-94.
[10] Ruitenbeek, H.J. (1994) Modelling Economy-Ecology Linkages in Mangroves: Economic Evidence for Promoting Conservation in Bintuni Bay, Indonesia. Ecological Economics, 10, 233-247.
[11] Babo, N.R. and Froehlich, J.W. (1998) Community-Based Mangrove Rehabilitation: A Lesson Learned from East Sinjai, South Sulawesi, Indonesia. The World Bank, Washington DC.
[12] Armitage, D. (2004) Socio-Institutional Dynamics and the Political Ecology of Mangrove Forest Conservation in Central Sulawesi, Indonesia. Continental Shelf Research, 24, 2535-2551.
[13] Sidik, A.S. (2008) The Changes of Mangrove Ecosystem in Mahakam Delta, Indonesia: A Complex Social Environmental Pattern of Linkages in Resources Utilization. Proceedings of the South China Sea Conference 2008 on the South China Sea: Sustaining Ocean Productivities, Maritime Communities and the Climate, Kuantan, 25-29 November 2008, 27-46.
[14] Duke, N.C., Meynecke, J.O., Dittmann, S., Ellison, A.M., Anger, K., Berger, U., Cannicci, S., Diele, K., Ewel, K.C., Field, C.D., Koedam, N., Lee, S.Y., Marchand, C., Nordhaus, I. and Dahdouh-Guebas, F. (2007) A World without Mangroves? Science, 317, 41b-42b.
[15] Alongi, D.M. (2002) Present State and Future of the World’s Mangrove Forests. Environmental Conservation, 29, 331-349.
[16] Giri, C., Zhu, Z., Tieszen, L.L., Singh, A., Gillette, S. and Kelmelis, J.A. (2008) Mangrove Forest Distributions and Dynamics (1975-2005) of the Tsunami-Affected Region of Asia. Journal of Biogeography, 35, 519-528.
[17] Yuwono, E., Jennerjahn, T.C., Nordhaus, I., Ardli, E.R., Sastranegara, M.H. and Pribadi, R. (2007) Ecological Status of Segara Anakan, Indonesia: A Mangrove-Fringed Lagoon Affected by Human Activities. Asian Journal of Water, Environment and Pollution, 4, 61-70.
[18] Dahuri, R., et al. (2001) Pengelolaan Sumber Daya Wilayah Pesisirdan Lautan Secara Terpadu. Pradnya Paramita, Bogor.
[19] Ardli, E.R. (2007) Spatial and Temporal Dynamics of Mangrove Conversion at the Segara Anakan Cilacap, Java, Indonesia. In: Yuwono, E., Jennerjahn, T., Sastanegara, M.H. and Sukardi, P., Eds., Synopsis of Ecological and Socio-Economic Aspects of Tropicalcoastal Ecosystem with Special Reference to Segara Anakan, Research Institute, University of Jenderal Soedirman, Purwokerto, 11-20.
[20] Gilman, E., Ellison, J., Duke, N.C. and Field, C. (2008) Threats to Mangroves from Climate Change and Adaptation Options: A Review. Aquatic Botany, 89, 237-250.
[21] Soegiarto, A. (1984) The Mangrove Ecosystem in Indonesia, Its Problems and Management. In: Teas, H.J., Ed., Physiology and Management of Mangroves, Dr W. Junk Publishers, The Hague, 69-78.
[22] Souza-Filho, P.W.M., Martins, E.S.F. and Costa, F.R. (2006) Using Mangroves as a Geological Indicator of Coastal Changes in the Bragan?a Macrotidal Flat, Brazilian Amazon: A Remote Sensing Data Approach. Ocean & Coastal Management, 49, 462-475.
[23] Li, M.S., Mao, L.J., Shen, W.J., Liu, S.Q. and Wei, A.S. (2013) Change and Fragmentation Trends of Zhanjiang Mangrove Forests in Southern China Using Multi-Temporal Landsat Imagery (1977-2010). Estuarine, Coastal and Shelf Science, 130, 111-120.
[24] Nascimento, W.R., Souza-Filho, P.W.M., Proisy, C., Lucas, R.M. and Rosenqvist, A. (2013) Mapping Changes in the Largest Continuous Amazonian Mangrove Belt Using Object-Based Classification of Multisensor Satellite Imagery. Estuarine, Coastal and Shelf Science, 117, 83-93.
[25] NASA (2013) Landsat 8 Instruments.
[26] NASA (1998) Landsat 7 Science Data Users Handbook.
[27] USGS (2013) Using the USGS Landsat 8 Product.
[28] Shapiro, M. and Westervelt, J. (1992) R.MAPCALC: An Algebra for GIS and Image Processing. Technical Report, US-Army CERL, Champaign.
[29] Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. and Harlan, J.C. (1974) Monitoring the Venal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. NASA/GSCF Final Report, Greenbelt.
[30] Gao, B.C. (1996) NDWI—A Normalized Difference Vegetation Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sensing of Environment, 58, 257-266.
[31] Pohl, C. and van Genderen, J.L. (1998) Multisensor Image Fusion in Remote Sensing: Concepts, Methods and Application. International Journal of Remote Sensing, 19, 823-854.
[32] Rahman, M., Ullah, R., Lan, M., Sri Sumantyo, J.T., Kuze, H. and Tateishi, R. (2013) Comparison of Landsat Image Classification Methods for Detecting Mangrove Forests in Sundarbans. International Journal of Remote Sensing, 34, 1041-1056.
[33] Singh, A. and Harrison, A. (1985) Standardized Principal Components. International Journal of Remote Sensing, 6, 883-896.
[34] Lillesand, T.M. and Kiefer, R.W. (2000) Remote Sensing and Image Interpretation. John Wiley and Sons, New York.
[35] Rogerson, P.A. (2002) Change Detection Thresholds for Remotely Sensed Images. Journal of Geographical Systems, 4, 85-97.
[36] Davis, J.C. (2002) Statistics and Data Analysis in Geology. Wiley, New York, 638.
[37] Giri, C., Ochieng, E., Tieszen, L.L., Zhu, Z., Singh, A., Loveland, T., Masek, J. and Duke, N. (2011) Global Distribution of Mangroves Forests of the World Using Earth Observation Satellite Data. In Supplement to: Giri, et al. 2011, UNEP World Conservation Monitoring Centre, Cambridge.
[38] Campbell, J.B. and Wynne, R.H. (2011) Introduction to Remote Sensing. 5th Edition Guilford, New York, 667 p.
[39] Amri, A. (2005) Community Participation in Rehabilitation, Conservation and Management of Mangroves: Lessons from Coastal Areas of South Sulawesi, Indonesia. African Study Monographs, 29, 19-30.
[40] Kirui, K.B., Kairo, J.G., Bosire, J., Viergever, K.M., Rudra, S., Huxham, M. and Briers, R.A. (2013) Mapping of Mangrove Forest Land Cover Change along the Kenya Coastline Using Landsat Imagery. Ocean & Coastal Management, 83, 19-24.
[41] Santos, L.C.M., Matos, H.R., Schaeffer-Novelli, Y., Cunha-Lignon, M., Bitencourt, M.D., Koedam, N. and Dahdouh-Guebas, F. (2014) Anthropogenic Activities on Mangrove Areas (S?o Francisco River Estuary, Brazil Northeast): A GIS-Based Analysis of CBERS and SPOT Images to Aid in Local Management. Ocean & Coastal Management, 89, 39-50.

comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.