INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 5, No 4, 2015

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1 INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 5, No 4, 2015 Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0 Research article ISSN Remote sensing and GIS based sea level rise inundation assessment of Bhitarkanika forest and adjacent eco-fragile Scientist, Environment and Sustainability dept., CSIR-IMMT, Bhubaneswar manish@immt.res.in ABSTRACT Remote sensing and GIS approach were utilized for inundation assessment mainly based on accurate digital elevation map (DEM) and land cover (Level II classification) prepared using high spatial resolution (2.5 m) stereo pair CARTOSAT-1 image and LISS III image of respectively. ERDAS-LPS 9.3 software along with Arc Map 10.0 was utilized for generating DEM which was overlaid on land cover for inundation assessment. In terms of area, the total forest land cover ( hectare) was classified into four major forest classes i.e. littoral mangrove forest (16234 hectare, 96%) > scrub forest (442.3 hectare, 2.6 %) > evergreen nonmangrove forest (215.6 hectare, 1.3 %) > deciduous open forest (17.2 hectare, 0.1 %). The other ecologically fragile land covers like water bodies (unlined canals/drains, perennial lakes/ponds, dry river streams, perennial river streams, wetlands (inland natural and coastal wetlands) and wastelands (open scrubland, dense scrubland and sandy coastal area) occupied 6480 hectare, hectare and hectare respectively. Based on elevation, land cover area were classified into five inundation sensitive zones viz. very high (up to 0.5 m elevation), high ( m), medium ( m), low ( m) and very low (> 3.5 m) sensitive zone. For littoral mangrove forest, scrub forest and evergreen non-mangrove forest 10 % ( hectare), 7 % (36.6 hectare) and 12 % (25.1 hectare) of their total area were in very high sensitive zone. In both the inland and coastal wetland, the maximum area was under high sensitive zone ( m elevation) i.e hectare and hectares respectively. The present work has delineated vulnerable land cover and its findings will help in planning adaptation measures to minimize the risk due to SLR. Keywords: Sea level rise (SLR), Land cover, Digital elevation model (DEM), Mangrove forest 1. Introduction India having about 7500 km long coastline has been identified to be vulnerable due to accelerated sea level rise (UNEP, 1989). SLR is causing flood, salinisation, erosion, siltation and degradation of ecosystem. The inundation of coastal area due to sea level rise (SLR) and climate change possesses a great threat to natural resources and biodiversity of coastal ecosystem (Nicholls and Cazenave, 2010; Marfai and King 2007). Natural ecosystem along the coast and estuary like mangrove forest, salt marshes, wetlands, coastal sand dunes and beaches etc. are sensitive to changes in sea level as they are dependent on tidal rhythm of sea for their sustenance. McLeod and Salm, 2006 and IUCN, 1989 highlighted that sea-level rise is the greatest climate change challenge that mangrove ecosystems will face. Ellison, (1993) and Field, (1995) reflected about SLR influence on mangroves and revealed that accretion rates in mangrove forests may be large enough to compensate for the presentday rise in sea level. In contrast with faster rate of SLR beyond capacity of mangroves to adapt, mangroves forest will be lost (Woodroffe and Grindrod, 1991; Hashimoto et al., 2006). Submitted on April 2015 published on May

2 Also, many mangrove forests (and other coastal wetlands) may experience local soil surface movements, usually subsidence, which adds to the rate of inundation (Syvitski et al., 2009; Webb et al., 2013). It reflects the complexity of vulnerability assessment of mangrove ecosystem due to SLR. SLR future projections varied depending on consideration of different factors like green house gas emission scenario lead global warming, sea water expansion, glacier and polar ice melt contribution etc. IPCC (2007) projection of 0.18 to 0.59 m SLR by the end of this century is re-projected to 0.26 to 0.98 m during as reported by Church et.al Considering influence of melting of ice sheet towards SLR, Rahmstorf 2007; Pfeer et al. 2008; Traill et al SLR projections was 0.5 to 2.3 m. Greenland and Antarctic ice sheets melting can even increase the SLR by 70 m as reported by Hansen et al It reflects about uncertainties and complexities associated with accurate SLR prediction for coastal inundation assessment. Therefore, the present inundation assessment of Bhitarkanika mangrove forest due to SLR is based on remote sensing and GIS derived land elevation information. Several studies were undertaken worldwide and in India for assessing coastal inundation using topography map and SRTM image (Demirkesen et.al and 2008; Kumar et al., 2010; Rao et al., 2008 and 2011; Natesan and Parthasarathy, 2010). Remote sensing and GIS derived digital elevation map (DEM) were utilised as an important tool for inundation risk assessment (Yang and Rystedt, 2002; Bates et al., 2004; Demirkesen et al., 2007). Ksiksi et al. (2012) estimated about 1.67 Km 2, 14.0 Km 2 and Km 2 mangrove forest along Abu Dhabi will be severely affected due to 0.5 m, 1.5 m and 3.0 m SLR respectively. Very few site specific studies were undertaken regarding inundation assessment due to SLR in ecologically sensitive mangrove forest region, especially in India, which are mainly located in low lying and gently sloping estuarine and coastal zone. Mangroves in India account for about 3 % of the global mangroves and 8 % of Asian mangroves (SFR, 2009; FAO, 2007) and are very important for ecology, environment and society. The present study targets the SLR inundation assessment of Bhitarkanika mangrove forest and surrounding ecofragile area, the second largest patch of mangrove forest in India using CARTOSAT-1 derived DEM. The inundation assessment will help in forecasting the most vulnerable forest land cover area due to SLR and thereby contribute for better management and adaptation plan for ecologically sensitive mangrove forest and surrounding ecological area. 2. Study area The deltaic mangrove swamps of Bhitarkanika Wildlife Sanctuary are extremely low lying and subjected to regular tidal inundation. The general elevation above mean tide level is between 1.5 and 2 meters (Dani and Kar, 1999). It lies within to N and to E as integral part of Odisha coastal plain fringing Bay of Bengal, India (Figure 1). The area has great ecological and economic importance due to presence of India s second largest mainland mangrove patch of Bhitarkanika forest (Patnaik et al., 1995) and Dhamra Port. Considering its ecological and social values the Bhitarkanika area has been identified as a Ramsar site (Hussain and Badola, 2008). The mangrove forest area is interspersed with numerous small rivulets, creeks, and channels which are under continuous tidal influence. The climate in the area is tropical and annual rainfall averages 1670 mm with the main rainfall occurring during the months of August and September. The temperature ranges from 30 0 C in summer to 15 0 C in winter (Kar and Bustard, 1986). It is badly affected by tropical cyclone originating in Bay of Bengal, Indian Ocean. 675

3 3. Methodology Figure 1: Location of the Bhitarkanika forest The present study utilized remote sensing and Geographical Information System (GIS) tools towards land cover inundation assessment due to sea level rise along with primary survey. Land cover map of study area was prepared using IRS P-6 LISS-III resourcesat (2009) satellite image. LISS-III satellite data having spatial resolution 23.5 m were imported into ERDAS IMAGINE 9.3 image processing software, geo-corrected using reference image and projected UTM/WGS 84 projection. Land cover map was extracted through visual interpretation technique following unsupervised classification method. Besides satellite data, Survey of India (SOI) topo-sheets number 73 L/13, L/14, P/1 and P/2 was also referred along with reconnaissance survey ground truthing to increase the accuracy of land cover classification. CARTOSAT-1 stereo pair PAN image with spatial resolution of 2.5 m was used for digital elevation map (DEM) generation with the help of ERDAS IMAGINE with Leica Photogrammetric Suite (LPS) v 9.3. DEM has been generated with Rational Polynomial Co-efficient (RPC) and using RPC along with Ground Control Points (GCP S). DEM validation has been carried out with the DGPS spread over distributed ground control points. The root mean square (RMS) error (in elevation) of the DEM was equal to 0.14 m. Forest tree height data were also utilized in DEM preparation. The basic information regarding satellite image and overall methodology for land cover map along with DEM is given in Table 1 and Figure 2 respectively. Land cover map was overlayed on the DEM to assess the impact of SLR due to inundation. Table 1: Satellite image utilized for DEM and land cover analysis Data product Date and year Path/row Source Resourcesat (LISS-III) 3 rd February, /58 NRSC, India Cartosat-1 (PAN) 17 th January, /301 NRSC, India Cartosat-1 (PAN) 28 th January, /301 NRSC, India 676

4 Figure 2: Flow-diagram for Land Cover map and Digital Elevation Map (DEM) generation 4. Results 4.1 Land Cover classification of Bhitarkanika mangrove forest and nearby eco-fragile area Based on detailed interpretation of LISS-III resourcesat image (2009), toposheets ( 73 L/13, 73 L/14, 73 P/1 and 73 P/2 ) and reconnaissance survey, Bhitarkanika forest area and surrounding area was classified into 20 level II land cover classes, falling under 6 level I land cover classes viz. agriculture, forest, built-up area, water bodies, wetland and wasteland (Fig. 3). The Bhitarkanika forest area and surrounding was predominantly covered with rainfed kharif agriculture land ( hectare, %) followed by littoral swamp mangrove forest (16234 hectare, %), perennial river and stream water body ( hectare, 9.08 %) and rural built-up (2934 hectare, 4.23 %) accounting for about 87 % of total study area. The forest land cover were mainly classified as littoral mangrove swamp forest > scrub forest (442.3 hectare) > semi-evergreen forest (215.6 hectare) > deciduous open forest (17.2 hectare). Besides forest, the surrounding eco-fragile land covers mainly consists of water bodies (6480 hectare) > wetland ( hectare) > wasteland ( hectare). The major water bodies with decreasing area were classified as perennial river and stream > unlined canal and drains > perennial ponds and lake > dry river and streams. Wetlands of the study area are classified as coastal and inland wetland occupying hectare and hectare respectively. For the wastelands, three major land use classes delineated were open scrub land, dense scrubland and coastal sandy area. Among wasteland, coastal sandy occupied maximum area of hectare followed by open scrub land (270.5 hectare) and dense scrub land (217.5 hectare) (Figure 3 and Table 2). 677

5 Figure 3: LISS-III derived land cover map (level II) of Bhitarkanika mangrove forest and surrounding Table 2: Level II Land cover classes of Bhitarkanika forest and surrounding ecologically fragile areas Land covers classes Area Percentage Level -I Level-II (hectare) (%) Agriculture Aquaculture/ Pisciculture Crop Land -Rabi Crop Crop Land Kharif Crop Crop Land Two crop area Plantation-Agricultural Plantation Built-up Built Up (Rural) Built up (Urban)-residential Forest Evergreen/ Semi Evergreen Deciduous (Dry/Moist)- Open Littoral/ Swamp Forest (Mangrove/Forest ) Scrub Forest Water-bodies Canal/Drain-Unlined Lakes/ Ponds-Perennial River/Stream-Dry River/Stream-Perennial Wetlands Coastal Natural Inland Natural Wasteland Sandy area-coastal Scrub land-dense scrub Scrub land - Open scrub Total Area (Hectare)

6 4.2 Land cover and sensitivity classes High spatial resolution (2.5 m) CARTOSAT-1 image was utilized for digital elevation map (DEM) generation with the help of ERDAS LPS 9.3 digital image processing software. The area was delineated with increase in 0.5 m elevation at regular interval along with below mean sea level (MSL) Figure 4. For inundation assessment due to potential sea level rise (SLR), study area was classified into five sensitive classes. Lowest elevation area up-to 0.5 m was classified as very high sensitive class. The area with m, m, m and > 3.5 m elevation were classified as high, medium, low and very low sensitive zone. Figure 4: Digital elevation map (DEM) of Bhitarkanika forest and surroundings Figure 5: Stack column diagram of land cover and land elevation sensitive class 679

7 4.3 Forest land cover The forest cover was sub-classified into four level II land covers i.e. evergreen non-mangrove forest, deciduous open forest, littoral mangrove forest and scrub forest. In terms of area, the maximum forest land cover was by littoral mangrove forest (16234 hectare) followed by scrub forest (442.3 hectare), evergreen non-mangrove forest (215.5 hectare) and deciduous open forest (17.2 hectare). The proportion of total forest land cover (16909 hectare) under very high, high, medium, low and very low sensitive zone were (9.5 %), (19.1%), (42.1%), (19.5%) and hectare (9.8%) respectively. In all the five sensitive zones, highest area were occupied by littoral mangrove forest ( hectare, hectare, hectare, and hectare) followed scrub forest (33.5 hectare, hectare, hectare, 65.7 hectare and 3.5 hectare). All the deciduous forest area was delineated under low sensitive zone i.e m elevation (Table 3 and Figure 6). Table 3: Forest land covers and elevation-sensitive zone Elevation (m) and sensitivity zone Below 0.5 m (Very High) m (High) m (Medium) m (Low) Above 3.5 m (Very Low) Total area (ha) Forest Land cover area (in hectare) Littoral Evergreen Non- Deciduous Mangrove Mangrove Forest Open Forest Scrub Forest Total Area (ha) (9.5) (19.1) (42.1) (19.5) (9.8) (100) Figure 6: Pie diagram of forest covers (%) under different elevation-sensitivity zone 680

8 For littoral mangrove forest, scrub forest and evergreen non-mangrove forest 10 %, 7 % and 12 % of their total area were most sensitive towards SLR i.e. distributed below 0.5 m elevation. In case of littoral mangrove forest (42 %) and scrub forest (44 %) most of their area was covered under medium sensitive zone (Figure 6). 4.4 Water-bodies The water bodies of the study area were mainly classified into 4 level II land cover classes as canal/drain, perennial lakes/ponds, river stream (dry) and perennial river stream. In terms of area, the maximum water-bodies land cover was by perennial river streams ( hectare) > canal/drain (78.0 hectare) > perennial lakes/ponds (56.8 hectare) > dry river/stream (45.6 hectare). The proportion of total water-bodies (6480 hectare) under very high, high, medium, low and very low sensitive zone were (16.9 %), (16.7 %), (25.2 %), (36.7 %) and hectare (4.5 %) respectively. In very high sensitive zone, only perennial river stream occupied the water-bodies land cover. Majority of the canal/drain land cover was under low (41.6 hectare) and very low sensitive zone (36.3 hectare). About 1.8 and 14.3 hectare of perennial lake and pond were delineated under high and medium sensitive zone (Figure 7). 4.5 Wetland Figure 7: Water-bodies land covers vis-à-vis elevation/sensitive zone The wet-land of the study area were classified into 2 level II land cover classes as coastal natural wetland and inland natural wetland land cover. In terms of area, the inland natural wetland ( hectare, %) occupied larger area than the coastal natural wetland (808.7 hectare, 39.1 %). The proportion of total wetland ( hectare) area under very high, high, medium, low and very low sensitive zone were 277 (13.4 %), (48.7 %), (15.9 %), (13.7 %) and hectare (8.3 %) respectively. In very high sensitive zone, relatively higher area and proportion of coastal natural wetland (146.5 hectare, 18.1 %) was estimated than inland natural wetland (130.3 hectare, 10.3 %). In both the inland and coastal wetland, the maximum area was under high sensitive zone ( m elevation) i.e hectare and hectares respectively. Very less coastal natural wetland (2.4 hectare, 0.2 %) was in very low sensitive zone (Figure 8). 681

9 4.6 Wasteland Figure 8: wetland land cover vis-à-vis elevation/sensitive zone The wastelands of the study area were classified into 3 level II land cover classes as open scrub wasteland, dense scrub wasteland and coastal sandy area. In terms of area, the coastal sandy area (630.8 hectare, 56.3 %) occupied larger area than the open scrub wasteland (270.5 hectare, 24.1 %) and dense scrubland wasteland (217.5 hectare, 19.4 %). The proportion of total wasteland ( hectare) area under very high, high, medium, low and very low sensitive zone were (41.4 %), (19.1 %), 34.5 (3.1 %), 376 (33.6 %) and 30.9 hectare (2.8 %) respectively. Larger area and proportion of coastal sandy wasteland (436.3 hectare, 69.1 %) was recorded in very high sensitive zone, whereas only 27.1 hectare (10.0 %) open scrub wasteland was found in very high sensitive zone. Almost all the dense scrub wasteland was recorded under low sensitive zone (Figure 9). Figure 9: Wasteland cover vis-à-vis elevation/sensitive zone 682

10 5. Discussion and conclusions Worldwide Inundation risk of low lying coastal areas due to SLR is important global warming induced challenge and its consequences will vary from region to region. Based on complexities of coastal systems and their regional variations, Nicholls and Mimura (1998) and Rahmstorf (2012) suggested for site specific study for realistic understanding of influence and responses of coastal area towards SLR. In this regard, extremely low lying delataic slopes Bhitarkanika forest subjected to regular tide inundation (Dani and Kar 1999) and a Ramsar wetland site since 2002 was selected for inundation assessment. Remote sensing and GIS approach were utilized for inundation assessment and land cover (up to level II class) were classified into five sensitive zone very high to very low based on more accurate CARTOSAT-1 derived land elevation dataset of Bhitarkanika forest along with surrounding ecologically fragile area (like water bodies, wetlands and wasteland. For forest, water bodies, wetlands and wastelands land cover 9.5 %, 16.9 %, 13.4 % and 41.4 % of their respective area was below 0.5 m elevation i.e. under very high sensitive zone (IPCC, 2007 projection of SLR by about 0.59 m by 2100 year). The finding also highlights that among different forest classes in very high sensitive area, littoral mangrove dense forest occupied the maximum area and therefore will be mostly affected due to SLR. As canals/drains and perennial ponds/lakes were located in higher elevation and so they are least vulnerable towards inundation. Under wasteland land cover, most of the sandy coastal areas are under very high sensitive area. Usha Natesan and Anitha Parthasarathy (2010) reported that with 1 m SLR along the coastal zone of Kanyakumari District, Tamilnadu will affect 7 % of the total land area as vulnerable. Murli and Kumar (2015) reported about Km2 coastal land of Cochin will be inundated due to 1 m rise in sea level. Depending on site-specific factors, sea-level rise is likely to show negative impacts on mangrove and other eco-fragile areas due to inundation, increased saline intrusion and erosion (Saenger, 2002). Although the present study solely targeted towards inundation assessment based on elevation, but it has focussed on site specific not yet assessed ecologically important area due to SLR. There is an urgent need for site specific detailed study regarding potential SLR inundation and vulnerability assessment in low lying coastal area for better response and site specific adaptation environmental management planning. Also, comprehensive approach should be adopted to assess about saline water intrusion, erosion, subsidence and ecosystem response towards SLR. Acknowledgements The study was part of project work enabling activities for preparation of India s second national communication to the UNFCCC (contract no.ind / /wii-688) funded by UNDP-GEF-MoEF. Author acknowledges ORSAC, Bhubaneswar for their GIS work and Forest and Environment department, Odisha for necessary permission and support. Sincere thanks to Prof. B. K. Mishra, director CSIR-IMMT, Bhubaneswar and project team for sharing their experience. 6. References 1. Bates, P. D, (2004), Remote sensing and flood inundation modeling, Hydrological Processes, 18(13), pp Church, J.A. et al., (2013), Sea Level Change, In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 683

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