Multi Criteria Decision Making (MCDM) Approach for Mangrove Health Assessment using Geo-informatics Technology

Year 2018, Volume: 5 Issue: 2, 114 - 131, 01.08.2018

Bhumika N. Vaghela Mona G Parmar Hitesh A. Solanki Bhagirath B. Kansara Sumit K. Prajapati Manik H. Kalubarme

https://doi.org/10.30897/ijegeo.412511

Cited By: 7

Abstract

Mangroves are coastal wetland forests established in the intertidal zones of estuaries, backwaters, deltas, creeks, lagoons, marshes and mudflats of tropical and subtropical latitudes. World-wide mangroves are disappearing at an alarming rate. Mangroves form one of the most important ecosystems of coastal areas. In real sense, mangrove is the Kalpvriksh (divine tree which fulfills all the desires) for the coastal communities. It nurtures and safeguards the local ecology of the coastal areas and provides livelihood options to the fishermen and pastoral families. Amongst the maritime States of India, Gujarat has the second highest mangrove cover after West Bengal. Additionally, during last three decades Gujarat has more than doubled its mangrove cover. In Gujarat State, mangroves are well developed in Lakhpat taluka (block) situated in Kachchh district. In recent past, Gulf of Kachchh experienced both natural and anthropogenic changes which made it a distinctive site to analyze how natural processes and anthropogenic activities determine the changes in mangrove vegetation density and health of mangroves in coastal areas.  

Multi-temporal Landsat TM data covering Lakhpat taluka (block) of February-1995, February-2017and Sentinel-2 multi-spectral data (spatial resolution 10 m) of April-2017 was analysed. The mangrove vegetation around the coastal areas was identified and classified into dense and sparse density classes based on Normalized Difference Vegetation Index (NDVI) thresholding approach. The health assessment of mangroves in Lakhpat taluka was attempted using Multi Criteria Decision Making (MCDM) approach including various parameters like mangrove density based on NDVI, Distance of mangroves from human settlement, Distance of mangrove from Industries and Ports which have direct impact of growth and health of mangroves, Erosion/Accretion over the period of last 22 years and availability of Saline water flow during the high tide for good mangrove growth. The buffers layers of various distances for example, 0 to 10 km, 10 to 20 km and 20 to 35 km were generated from the existing mangroves using Sentinel-2 multi-spectral image in GIS environment.  

The results indicate that the NDVI which is single parameter indicating the mangrove stand / vigour, growth condition and resulting health of mangroves in the area. This factor has been given highest weightage as compared to other parameters. The major anthropogenic factors like human Pressure and presence of Industries and Ports have negative impact on the mangrove health. Therefore, it was observed that presence of human settlements and Industries and Ports with the buffer region of 0 to 10 km distances from mangroves are unhealthy or prone to degradation in this region. The results of health assessment are very useful for sustainable planning and management of mangroves in the coastal areas of Lakhpat Taluka. The mangrove restoration and regeneration activity needs to be carried out as suggested by Upadhyay et al., 2015 with active participation of Community Based Organizations (CBOs) to increase the mangrove density as well as mangrove health in this region. 

Keywords

Mangroves, Multi-temporal Landsat TM data, Vegetation Indices (VIs), Multi Criteria Decision Making (MCDM) approach, Mangrove health assessment

References

• Ajai, and Chauhan, H. B., 2017. Mangrove Inventory, Monitoring, and Health Assessment. Coastal Wetlands: Alteration and Remediation pp 573-630, Part of the Coastal Research Library book series (COASTALRL, volume 21).
• Ajai, Bahuguna A, Chauhan, H.B., Sarma, K.S., Bhattacharya S, Ashutosh S, Pandey, C.N., Thangaradjou T, Gnanppazham L, Selvam, V, Nayak, S.R., 2013. Mangrove inventory of India at community level, National Academy Science Letters, 36(1):67-77.doi: 10.1007/S 40009-012=0087-x
• Balmford A, Bruner A, Cooper P, Costanza R , Farber S, Green RE et al (2002). Economic reasons for conserving nature. Science 297: 950- 953.
• Baret, F., & Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35, 161–173.
• Britta S, Jane M, and Duke NC (2005). Water quality in the Great Barrier Reef region: responses of mangrove, sea grass and macro algal communities. Marine Pollution Bulletin 51: 279-296.
• Canham, H.O., 1990. Decision matrices and weighting summation valuation in forest land planning. Northern Journal of Applied Forestry 7, 77–79.
• Chakhar, S. and Martel, J-M., 2003. Enhancing geographical information systems capabilities with multi-criteria evaluation functions. Journal of Geographic Information and Decision Analysis, 7, pp. 47–71.
• Chander, Gyanesh, Markham, Brian L., Helder, Dennis L., 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment 113 (2009) 893–903.
• Chellamani, Prabakaran, Singh, Chandra Prakash & Panigrahy, Sushma, 2014. Assessment of the health status of Indian mangrove ecosystems using multi temporal remote sensing data. Tropical Ecology 55(2): 245-253, 2014 ISSN 0564-3295.
• DasGupta, Rajarshi and Shaw, Rajib, 2013. Cumulative Impacts of Human Interventions and Climate Change on Mangrove Ecosystems of South and Southeast Asia: An Overview. Journal of Ecosystems, Volume 2013 (2013), Article ID 379429, 15 pages.
• De Lange, W.P. and De Lange, P.J. (1994). An appraisal of factors controlling the latitudinal distribution of mangrove (Avicennia marina var. resinifera) in New Zealand. Journal of Coastal Research 10 (3), 539-548.
• DoriRachmawania, Fredinan Yuliandab, Cecep Kusmanac, Mennofatria Boerd, Ety Parwatie, 2016. Study of Mangroves Ecosystem Management at Binalatung in Tarakan City of North Kalimantan, International Journal of Sciences: Basic and Applied Research (IJSBAR) (2016) Volume 26, No. 3, pp 221-234.
• Duke, N.C. (1992). Mangrove floristics and biogeography. In “Tropical Mangrove Ecosystems” (A.I. Robertson and D.M. Alongi, eds), pp.63-100.
• Fensholt, R., & Sandholt, I. (2003). Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semi-arid environment. Remote Sensing of Environment, 87, 1,111–1,121.
• Gao, B. C., 1996. NDWI. A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266.
• Ghulam, A., Li, Z. -L., Qin , Q., Yimit , H., & Wang, J. (2008). Estimating crop water stress with ETM+ NIR and SWIR data. Agricultural and Forest Meteorology, 148, 11,167 9–11.
• Hajkowicz, S., Wheeler, S., Young, D., 2002. An evaluation of options for the Lower Murray reclaimed irrigation areas using multiple criteria analysis. A Paper Presented at the Australian Agricultural and Resource Economics Society, 12–15 February 2002, Canberra.
• Kannan, T., 2014. Change Detection and Health Assessent Modelling of Pichavaram Mangroves by the Application of Remote Sensing and GIS. International Journal of Emerging Technology and Advanced Engineering, Volume 4, Special Issue 4, June 2014, pp. 60 – 68.
• Laaribi, A., chevallier, J.J. and martel, J.M., 1996, A spatial decision aid: a multi-criterion evaluation approach. Computers, Environment and Urban Systems, 20, pp. 351–366.
• Mahapatra, M., Ratheesh, R. and Rajawat, A.S. (2013) Potential Site Selection for Mangrove Plantation along the Kachchh District, Gujarat, India Using Remote Sensing and GIS Techniques. International Journal of Geology, Earth & Environmental Sciences, 3, 18-23.
• Malczewski, J., 1999, GIS and Multi-criteria Decision Analysis (New York: Wiley).
• Muhammad Kamal and Stuart Phinn, 2011. Hyperspectral Data for Mangrove Species Mapping: A Comparison of Pixel-Based and Object-Based Approach, Remote Sensing/ 2011, 3, 2222-2242.
• Nayak, Shailesh & Bahuguna, Anjali, 2001. Application of remote sensing data to monitor mangroves and other coastal vegetation of India. Indian Journal of Marine Sciences Vol. 30(4), December 2001, pp. 195-213.
• Omo O. Omo-Irabor, Samuel B. Olobaniyi, Joe, Akunna, Valentijn Venus, Joseph, M. Maina, Charles Paradzayi, 2011. Mangrove vulnerability modelling in parts of Western Niger Delta, Nigeria using satellite images, GIS techniques and Spatial Multi-Criteria Analysis (SMCA). Environmental Monitoring and Assessment. July 2011, Volume 178, Issue 1–4, pp 39–51.
• Patel Ajay, Singh Vijay, Khalid Mehmood, Kathota Jaydipsinh, Kalubarme, M.H., Pandya C. H., Joshi Nischal and Brahmabhatt Lomesh, 2014. Mapping and Monitoring of Mangroves in the Coastal Districts of Gujarat State using Remote Sensing and Geoinformatics. Asian Journal of Geoinformatics, Vol.14, No.1 (2014), pp. 15 – 26.
• Qureshi, M.E., Harrison, S.R., 2001. A decision support process to compare riparian revegetation options in Scheu Creek catchment in North Queensland. Journal of Environmental Management 62, 101–112.
• Quoc Tuan Vo, NataschaOppelt, Patrick Leinenkugel and Claudia Kuenzer, 2013. Remote Sensing in Mapping Mangrove Ecosystems- An Object-Based Approach, Remote Sensing, Remote Sens. 2013, 5, 183-201.
• Saenger, P. and Snedaker, S.C. (1993). Pantropical trends in mangrove above-ground biomass and annual litter fall. Oecologia 96, 293-299
• Sheppard, S.R.J., 2005. Participatory decision support for sustainable forest management: a framework for planning with local communities at the landscape level in Canada. Canadian Journal of Forest Research 35, 1515–1526.
• Tiezhu Shi, Jue Liu, Zhongwen Hu, Huizeng Liu, Junjie Wang &Guofeng Wu, 2016. New spectral metrics for mangrove forest Identification, REMOTE SENSING LETTERS, 2016, VOL. 7, NO. 9, 885–894.
• Thill, J-C., 1999, Multicriteria Decision-making and Analysis: A Geographic Information Sciences Approach (New York: Ashgate).
• Umroha, Wahyu Adi and Suci Puspita Sari, 2016. Detection of mangrove distribution in Pongok Island. Procedia Environmental Sciences, 33 ( 2016 ) 253 – 257.
• Upadhyay R., Joshi N., Sampat A.C., Verma A.K., Patel A., Singh V., Kathota J. and Kalubarme M.H., 2015. Mangrove Restoration and Regeration Monitoring in Gulf of Kachchh, Gujarat State, India using Remote Sensing and Geo-informatics. International Journal of Geoscience, 6, 299-310.
• Vo, Q. T., Oppelt N, Leinenkugel P, Kuenzer C., 2013. Remote sensing in mapping mangrove ecosystems –an object-based approach. Remote Sens. 2013, 5:183-201.
• Yakowitz, D.S., Weltz, M., 1998. An algorithm for computing multiple attribute additive value measurement ranges under a hierarchy of the criteria: application to farm or rangeland management decisions. In: Beinat, E., Nijkamp, P. (Eds.), Multi-Criteria Analysis for Land-Use Management. Kluwer Academic Publishers, Dordrecht, pp. 163–177.
• Yuvaraj E., Dharanirajan K., Saravanan and Karpoorasundarapandian N, 2014. Evaluation of Vegetation density of the Mangrove forest in South Andaman Island using Remote Sensing and GIS techniques. Int. Res. J. Environment Sci., 2014; 3(8):19–25.