plant diversity assessment at landscape level in jamnagar district ...

ISSN 2321 – 9149 Research Article

IJAEES (2013) Vol.1, No.1, 23-35 International Journal of Advancement in Earth and Environmental Sciences

PLANT DIVERSITY ASSESSMENT AT LANDSCAPE LEVEL IN JAMNAGAR DISTRICT, GUJARAT USING SATELLITE REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM Ganesh Datt BhattI* I*

The Global Open University, Dimapur Nagaland- 797109, India(*[email protected])

---------------------------------------------------------------------------------------------------------------------------------------------------------------ABSTRACT: The research article attempts to assess the plant diversity assessment at landscape level in Jamnagar district, Gujarat using satellite remote sensing and GIS using Landsat-MSS data with 60 m spatial resolution. The vegetation types/land use map was prepared using on-screen visual interpretation techniques for mapping and stratifications. Six vegetation classes viz; Thorn forest, Mangrove forest, Mangrove scrub, Scrub, Prosopis juliflora scrub and Grassland and ten land use classes viz; Orchard, Agriculture, Barren land, Mine, Mud flat, Salt-affected land, Salt pan, Waterbody, Wetland and Settlement were prepared and categorized. The vegetation classes together account 4.61 % of total geographical area. The largest forest area is occupied by Thorn forest (1.37 %). The overall classification accuracy of vegetation types/land use map was found to be 83.73 % with Khat value 0.79. The highest Shannon-Weaver diversity of index was observed in Mangrove forest (2.38) followed, Thorn forest (2.01), Mangrove scrub (1.41) and Scrub (1.21) respectively. The study area needs proper conservation planning and management strategies for natural resources, especially the mangrove species in the Marine National Park, Jamnagar district, Gujarat. The satellite data and its output products will be useful for preparation of working and management plans.

Keywords: Satellite imagery, Mapping, On-screen visual interpretation, Vegetation types/land use, Diversity assessment. ---------------------------------------------------------------------------------------------------------------------------------------------------------------1. INTRODUCTION: Increasing and maintaining biodiversity on this planet earth is a very important objective of conservation, planning and management (1). Biodiversity indicators are helpful to establish and monitor the levels of biodiversity in terrestrial and aquatic ecosystems. The number of biodiversity indicators is vast and range from gene to landscape patterns depending on spatial scales. The monitoring of biodiversity surrogate measures have been proposed, which are closely correlated with direct measures of biodiversity, but are easier to measure. These surrogate measures indices account for three basic floral diversity aspects viz; diversity of tree locations, species diversity and the diversity of tree dimensions, in terms of girth and height [(2), (3)]. Field observations of a large variety of forest structures and tree species provide a range of habitats for different species [(4), (5), (6)]. Landscape parameter is emphasized by patch size, forest type, number, shape, heterogeneity and edge features indicate spatial organization of vegetation types, biotic disturbance and habitat stability [(7), (8), (9)]. Floral diversity indices also gives good qualitative and quantitative assessment of forest structures which is a key pre-requisite for understanding the interactions between patterns and processes in forest ecosystems [(10), (11)]. The floral diversity indices are also important input variables for the reconstruction of forest structures used in spatially explicit growth models and computer visualizations [(12), (13)]. There are two different techniques of data collection, mapping and sampling. Mapping involves the full spatial enumeration of all forest floras within a large observation window. Mapping is very common in ecological studies and the corresponding data allows the application of powerful statistics and detailed analyses of plant interactions [(14), (15)]. However, often summary characteristics for larger geographic entities, such as forest boundary, forest enterprises, political regions and whole countries, are required for management, conservation planning and policy decision making. In this way sample design methods are the only feasible option. Since the observation windows are the sample plots, which are used for this purpose and are comparatively small, floral diversity and structural indices are naturally more suitable other than

G.D. Bhatt / International Journal of Advancement in Earth and Environmental Sciences, Vol.1, No. 1

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sophisticated characteristics from spatial statistics. This also offers the opportunity of combining the sample method of diversity measures with existing forest resource inventories (16) and adds significant value to traditional forest inventory with a comparatively low additional effort. Such a combination of inventory objectives clearly facilitates the concepts of multipurpose forestry-based techniques for sustainable management (17). The humid tropical forests in the eastern Himalaya and North-East India, which harbor about 5,000 endemic species, are very rich in plant wealth (18). Species richness of these forests has been recognized by [(19), (20), (21), (22-24), (25)]. Various studies have been carried out to quantify floral diversity assessment and to understand the ecology of forest communities of this region [(26), (27), (28), (29)]. Several researchers have studied floristic diversity in the humid tropical forests of the Western Ghats of India and other biodiversity-rich areas in the Indian subcontinent [(30), (31), (32), (33)]. Different natural vegetation inventories and vegetation types are essential for landscape level biodiversity analysis. The tropical forests are rich in floral diversity (34). The importance of spatial heterogeneity to floral diversity has been well documented (35). In this context a research study was conducted in the Gujarat region with special context to Jamnagar. We have used remotely sensed data and GIS to create maps that would aid in stratifying the habitats to guide biodiversity sampling in and around the Marine National Park, Jamnagar district, Gujarat. The objectives of the research were: (1) to prepare vegetation type/land use map using two season satellite data (dry and wet) on 1:50,000 scale (2) based on the area of vegetation type/land use map 0.0001 % area was sampled. In the present study an attempt was made to emphasis the study of spatial patterns of plant diversity assessment at landscape level using satellite remote sensing and GIS. 2. STUDY AREA: Gujarat can be divided into eight agro-climatic zones. These are: southern hills, southern Gujarat, middle Gujarat, north Gujarat, northwest arid, north Saurashtra and south Saurashtra. These diverse agro-climatic zones, defined on the basis of the prevailing climatic conditions, are also responsible for the great diversity of flora and fauna in the State. With four major hill ranges terminating in the State, it has very interesting topographical features. These hill ranges are Sahyadri and Satpura ranges in the south, Vindhyan range in the centre and the Aravalli range in the north. These hill ranges, except the Sahyadri, have their western most limits in the State. Sahyadri has its northern most limits in Gujarat. Most of the forests in Gujarat are now restricted to its hilly tracts. Four major rivers viz., Narmada, Tapi, Mahi and Sabarmati pass through the state. Gujarat is located on the Tropic of Cancer and is, therefore, characterized by its subtropical climate (36). The presence of four hill ranges with geological, topographical and seraphic features, has largely contributed to the diverse physical conditions in the State. Historically, geographically, economically and ecologically, Gujarat is a unique (longest coastline area, variation in rainfall, temperature, as well as floral and faunal wealth.) state of India. It has immense biological wealth. However, being the second most industrialized State of India, it has witnessed a heavy pressure on this wealth. But, at the same time, one finds the initiatives and success stories in the field of biodiversity conservation (37). Gujarat has the longest coastline area covered by 1650 km2 and also contributes the high mangrove diversity after the Sundarbans delta of West Bengal. Biogeographically, Gujarat can be classified into four major zones viz., the Indian desert, the semi-arid, the Western Ghats and the coastal areas. Each zone differs from each other in terms of the floral and faunal assemblages (38). The study area is bounded in the north by the Gulf of Kutch, in the east by Rajkot district, the south by Porbandar district, and in the west lies the Arabian Sea. The study area lies between 21° 42' and 23° 14' N latitude and 69° 00' and 70° 40' E longitude, covering an area of 14,125 km2 (Fig. I). Marine National Park (MNP) is situated on the southern shore of the Gulf of Kutch in the Jamnagar district. The average temperature is about 26°C and the summer and winter temperatures are 41°C and 22°C respectively. The relative humidity is 70-75 %. The annual rainfall varies from 100-600 mm (39). As per Champion and Seth classification scheme (1968), the main forest types in the study area are: Mangrove forest (4B/TS1), Mangrove scrub (4B/TS2), Desert thorn forest (6B/CI) and Dry grassland (5/DS4) (40). The main plant species in this forest are: Avicennia marina (Forssk.) Vierh., Avicennia officinalis L., Acanthus ilicifolius L., Avicennia marina subsp. marina, Ceriops tagal (Perr.) C.B.Rob., Bruguiera cylindrica (L.) Blume, Bruguiera gymnorhiza (L.) Lam., Rhizophora mucronata Lam., Sonneratia apetala Buch.-Ham., Acacia nilotica (L.) Delile, Acacia senegal (L.) Willd., Salvadora oleoides Decne., Salvadora persica L., Balanites aegyptiaca (L.) Delile, Flacourtia indica (Burm.f.) Merr. and Prosopis juliflora (Sw.) DC.

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Fig. I: Location of the study area.

3. MATERIAL AND METHODS: The Landsat Multispectral Scanner (MSS), launched on-board Landsat 1st July 1972, was the world's first earth observation satellite. The MSS provided four spectral bands of earth surface reflectance in the visible and near-infrared regions of the electromagnetic spectrum at a nominal 60 m spatial resolution. The Landsat MSS data were used for the present study during the period of 1976 to 1979 (Table I). The Landsat MSS satellite data was procured from Global Land cover Facility (GLCF) website www.glcf.umd.edu. The dark pixel subtraction technique was used to remove the haze from the image (41). The mosaic satellite imagery of November, 1976, 1978, January, 1979 and September, 1977 were used with Lambert Conic Conformal (LCC) projection and WGS 84 datum (Fig. II), (Fig. III). The nearest neighbor resampling method was used for mosaic of all the scenes. The mosaic of two scenes was made using feather overlap function and the study area was extracted using the districts boundary of Survey of India toposheet (41L, 41K, 41J, 41G and 41F on 1: 2,50, scale was used. S. No. 1. 2. 3. 4.

Satellite Landsat-III -do-do-do-

Sensor MSS -do-do-do-

Path-Row 161-45 161-44 162-45 162-44

Date of pass 06 Nov. 1976 26 Sept. 1977 24 Nov. 1978 17 Jan. 1979

Table I: Satellite imagery used in the present study.

The complete methodology is shown in Fig. IV. The ground truthing was done from September 2008 to November 2011. The on-screen visual interpretation technique was used to delineate the vegetation types/land uses in the study area based on the interpretation key (Fig. V). Seven vegetation types and ten land uses were mapped (Plate - I). The overall vegetation types/land use classification accuracy was aimed more than 83.76 %. Table II show various vegetations types and land use categories in study area. The accuracy was assessed using stratified random sampling design. A total of 25 sample points were decided based on the size of the stratum. A minimum of 3 sample points were laid for the smallest stratum. The sample points was randomly checked on field and a confusion matrix was generated (Table III). The


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percentage fraction of the correctly classified points out of total points for each class was taken as measure of classification accuracy (42). The stratified random sampling with 0.002 % intensity was carried out for analyzing all the vegetation types. A 31.62 m × 31.62 m plot size was worked out to be optimal by species-area-curve method for sampling (Fig. VI). In this connection the plot sizes of 0.1 ha (31.62 m x 31.62 m) and 1 ha square plot was considered. The sample plot is reached on ground based GPS locations. For sampling of shrub species one plot of 5 m x 5 m in center was laid. For herbaceous plants, four plots of 1 m x 1m in opposite corners were laid (Fig. VII). Nested sample plots were followed within each sample plot to record shrubs, herbs, climbers, epiphytes and lianas information. A total of 25 tree plots, 25 shrub plots of 5×5m size and 100 herb plots of 1×1m size were laid in different vegetation types. The shrub and herb plots were laid inside the tree plots. At each sample plot the circumference at breast height (cbh) of all tree species was recorded. The individuals with cbh ≥30 cm were considered as trees and the saplings were considered as shrubs and seedlings as herbs. For shrubs, the total numbers of tillers of each species were counted and the circumference at ground level was recorded. For herbs, only numbers of individuals were recorded.

Fig. II: False Color Composite (wet season).


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Fig. III: False Color Composite (dry season).

Fig. IV: Methodology.

During the fieldwork, various study area characteristics viz; (i) vegetation types, (ii) slope, (iii) aspect, (iv) topography, (v) latitude, longitude and the altitude, (vi) tonal characteristics on FCC, (vii) evidence of disturbance viz., lopping,


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grazing, fire, etc., were recorded. Floral diversity assessment at landscape level in vegetation type-wise was calculated using Shannon-Weaver diversity index (43). The formula is used for floral diversity assessment is:

ln = natural log = the index value and ni the IVI or number of species and N is total IVI or total number of species in that habitat type is also called as information index and can also be used to describe the landscape diversity index.

Fig. V: Vegetation types/land use map of Jamnagar district, Gujarat.

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Fig. VI: Species area curve.

S. No.

Vegetation types/land use

Area (km2)

Area (%)

1. Thorn forest 193.86 1.37 2. Mangrove forest 190.05 1.35 3. Mangrove scrub 38.12 0.27 5. Scrub 111.88 0.79 6. Prosopis juliflora scrub 78.88 0.56 7. Grassland 38.01 0.27 8. Orchard 10.03 0.07 9. Agriculture 7948.11 56.27 10. Barren land 963.21 6.82 11. Mine 3.62 0.03 12. Mud flat 567.80 4.02 13. Salt-affected land 301.91 2.14 14. Salt pan 144.92 1.03 15. Waterbody 3355.12 23.75 16. Wetland 43.17 0.31 17. Settlement 136.41 0.97 Total 14125.10 100.00 Table II: Area under different vegetation types/land use categories.

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Vegetation types/land use M MS TH SC P GR OR AG BL M MF SL SP WB WL S Total

M 6 1 7

MS 1 5 6

TH 6 6

SC 10 1 11

P 8 1 9

GR 1 4 1 1 7

OR 5 5

AG 1 1 3 5

BL 4 1 5

M 2 2

MF 1 4 5

SL 4 1 5

SP 1 3 4

WB 3 1 4

WL 1 4 5

S 2 2

Total 7 6 6 11 10 4 7 4 5 3 4 5 4 4 5 3 88

Table III: Accuracy assessment of vegetation types/land use map.

Overall accuracy = 83.73 %

Khat value = 0.79

M- Mangrove forest, MS- Mangrove scrub, TH- Thorn forest, SC- Scrub, P- Prosopis juliflora scrub, GR- Grassland, OR- Orchard, AG- Agriculture, BL- Barren land, M- Mine, MF- Mud flat, SL- Salt-affected land, SP- Salt pan, WB- Waterbody, WL- Wetland, S- Settlement.


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Fig. VII: Sample plot design.

S.No.

Variable

1. 2. 3.

M

Tree Shrub Herb

Shannon-Weaver Diversity Index 2.38 1.04 2.72

4. 5. 6.

MS

Tree Shrub Herb

1.41 0.04 0.78

7. 8. 9.

TH

Tree Shrub Herb

2.01 1.38 1.55

10. 11. 12.

P

Tree Shrub Herb

0.32 1.08 2.81

13. 14.

SC

Tree Shrub

1.21 1.16

Herb

0.43

15.

Table IV: Shannon-Weaver diversity index in different vegetation types.

M- Mangrove forest, MS- Mangrove scrub, TH- Thorn forest, P- Prosopis juliflora scrub, SC- Scrub. 4. RESULT AND DISCUSSIONS: The on-screen visual interpretation techniques of Landsat MSS satellite imagery facilitated identification and mapping of Thorn forest, Mangrove forest, Mangrove scrub, Forest plantation, Scrub, Prosopis juliflora scrub, Grassland (vegetation classes) and Orchard, Agriculture, Barren land, Mine, Mud flat, Saltaffected land, Salt pan, Waterbody, Wetland, Settlement (land use classes) were prepared (Fig. IV). The six vegetation classes and ten land uses classes were prepared. Table II shows the area covered by different vegetation types/land use classes in the study area. The vegetation classes’ together account for 4.61 % of the geographical area, of which is maximum forest area is covered by Thorn forest 1.37 %. Grassland occupies a very small area (0.027 %). Among land use categories the Agriculture (56.27 %) occupies maximum area followed by Waterbody (23.75 %) and Mine (0.03 %).


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Table III shows the overall classification accuracy was found to be (83.76 %) with kappa statistic 0.79. The ShannonWeaver diversity of index was calculated using vegetation/phytosociological data. The vegetation data was collected in different vegetation types in and around the Marine National Park, Jamnagar, Gujarat. The highest tree Shannon-Weaver diversity index was observed in Mangrove forest (2.38) followed by, Thorn forest (2.01), Mangrove scrub (1.41) and Scrub (1.21). Similarly the diversity index was also calculated in shrubs and herbs. The highest shrub Shannon-Weaver diversity index was observed in Thorn forest (1.38) followed by Scrub (1.55), Mangrove forest (1.41) and Mangrove scrub (0.04) respectively. The herb diversity was also observed in these vegetation types. The highest Shannon-Weaver diversity index was also observed in Prosopis juliflora scrub (2.81) followed by Mangrove forest (2.72), Thorn forest (1.55), Mangrove scrub (0.78) and Scrub (0.43) respectively. Table IV gives the overall status of Shannon-Weaver diversity index in different vegetation types. The related studies on coastal region of Gujarat have mainly focused on the Gulf of Kachchh. 11 different species of mangroves have been reported from the state of Gujarat [(44-55)]. The available information includes the presence of Sonneratia apetala at river Tapi [(54), (55)] Bruguiera cylindrica in the Umargaon creek [(48),(49),(50)], and Rhizophora mucronata at sea coasts near Bulsar (now Valsad district) (52). In this addition, the general occurrence of Acanthus ilicifolius in the estuarine area of South Gujarat of Navsari district has also been reported [(54), (55), (36)]. The Survey of India topographical maps from 1965, showed no mangroves in our survey area. The Forest Survey of India 2011, reports shows a mangrove area of 1058 km2 for the entire Gujarat. 5. CONCLUSIONS: Plant diversity assessment is tried at local and regional levels to understand the present status and to make effective management strategies for conservation planning. In this connection, sampling techniques and measurements methods are followed based on objectives of the studies and in majority of the studies are based on the availability of time, money and manpower is the major constrain. The relation to sampling techniques, measurements, sample size and possibility of incorporating other techniques in plant diversity studies such as remote sensing and GIS tool is very useful. The measurement of stem size in the field is the major issue in diversity studies where, unanimous decision should be achieved among the studies in relation to the threshold of girth of stem considered to be a tree and the height at which it is to be taken from the ground. Sample size in plant diversity study is an important issue, which determines the success and failure of a diversity study to bring out the true diversity status. Much attention should be paid in this issue in determination of the sample size, and distribution of the samples. Although remote sensing is a handy tool to study the vegetation at landscape level, applicability of the same at species level is impractical at the present context with available advancements. Much advancements and researches should be made in this line to apply this technology at species level characterization at landscape level using satellite remote sensing and GIS. ACKNOWLEDGEMENT: Author gratefully acknowledges the support and encouragement received from Chancellor, Dr. P.R. Trivedi, The Global Open University, Nagaland, during the course of this study. The author is thankful to Director, Marine National Park, Jamnagar, Gujarat for the study. Author gratefully acknowledges Maj. R.M. Thakur, Center Inchage Dehradun circle for necessary guidance and suggestions. The author gratefully acknowledges two anonymous reviewers for his valuables suggestions for the improvement of this research article. REFERENCES: 1. 2. 3. 4.

Hunter Jr., L.M., 1999, Maintaining biodiversity in forest ecosystems, Cambridge University Press, Cambridge. Gadow, K.V., 1999, Waldstruktur and diversitat, forest structure and diversity, Allgemeine Forst-Jagdztg, 170: 117-122. Pommerening, A., 2002, Approaches to quantifying forest structures, Forest, 75: 305-324. Fuller, A.K., Harrison, D.J. and Lachowski, H.J., 2004, Stand scale effects of partial harvesting and clear cutting on small mammals and forest structure, Journal of Forest Ecology and Management, 191: 373-386. 5. Lexerod, N. and Eid, T., 2006, An evaluation of different diameter diversity indices based on criteria related to forest management planning, Journal of Forest Ecology and Management, 222: 17-28. 6. Shirley, S., 2004, The influence of habitat diversity and structure on bird use of riparian buffer strips in coastal forests of British Columbia, Canada, Canadian Journal Forest Research, 34: 1499-1510. 7. Turner, M.G., 1989, Landscape ecology: The effect of pattern and process, Annual Review Ecology Systematic, 20: 171-197. 8. Li, H. and Reynolds, J.F., 1993, A new contagion index to quantify special pattern of landscape, Landscape Ecology, 8: 155162. 9. Ritters, K.H., O’Neill, R.V, Hunsaker C. T., Wickham, J.D., Yankee, D.H., Timmins, S., P., Jones, K.B. and Jackson, B.L., 1995, A factor analysis of landscape pattern and structure merits, Landscape Ecology, 10: 23-40. 10. Aguirre, O., Hui, G., Gadow, K.V. and Jimenez, J., 2003, An analysis of spatial forest structure using neighborhood-based variables, Journal of Forest Ecology and Management, 183: 137-145.


33

11. Pommerening, A., 2006, Evaluating structural indices by reversing forest structural analysis, Journal of Forest Ecology and Management, 224: 266-277. 12. Hasenauer, H, 2006, Sustainable forest management, growth models for Europe, Springer, Berlin and Heidelberg. 13. Pommerening, A. and Stoyan, D., 2008, Reconstructing spatial tree point patterns from nearest neighbour summary statistics measured in small sub-windows, Canadian Journal of Forest Research, 38: 1110-1122. 14. Dale, M.R.T., 1999, Spatial pattern analysis in plant ecology, Cambridge University Press, Cambridge. 15. Illian, J., Penttinen, A., Stoyan, H. and Stoyan, D., 2008, Statistical analysis and modelling of spatial point patterns, John Wiley and Sons, Chichester. 16. Sterba, H., 2008, Diversity indices based on angle count sampling and their interrelationships when used in forest inventories, Forestry, 81: 587–597. 17. Pommerening, A. and Murphy, S., 2004, A review of the history, definitions and methods of continuous cover forestry with special attention to afforestation and restocking, Forestry, 77: 27-44. 18. Olson M.D., Dinerstein E., Mittermeier R.A., Myers N., Thomsen J.B. and da Fonseca G.A.B., 1998, Biodiversity hotspots and major tropical wilderness areas: approaches to setting conservation priorities, Conservation Biology, 12: 516-520. 19. Hooker, J.D., 1872-1897, Flora of British India, Vols. 1-7., L. Reeve and Co., Ashford, Kent, U.K. 20. Khiewtam, R.S. and Ramakrishnan, P.S., 1993, Litter and fine root dynamics of relic sacred groove forest of Cherrapunjee in northeastern India, Journal of Forest Ecology and Management, 60: 327-344. 21. Rao R.S. and Panigrahi, G., 1961, Distribution of vegetational types and their dominant species in Eastern India, Indian Botanical Society, 40: 274–285. 22. Rao, A.S., 1969, Orchids of Khasi and Jaintia Hills, Bulletin Botanical Survey of India, 11: 115–123. 23. Rao, A.S., 1974, The Vegetation and phytogeography of Assam-Burma, In: Mani M.S. (Eds.), Ecology and biogeography in India, W. Junk, The Hague, The Netherlands, pp. 204-205. 24. Rao, A.S., 1977, Floristic studies in north-eastern India (Old Assam Region), Bulletin Botanical Survey of India, 19: 56–60. 25. Balakrishnan, N.P., 1981-1983, Flora of Jowai and Vicinity, Meghalaya, Vol.-II, Botanical Survey of India, Howrah, India. 26. Khan, M.L., Rai, J.P.N. and Tripathi, R.S., 1987, Population structure of some tree species in disturbed and protected subtropical forests of north-east India, Acta Oecologica, 8: 247-255. 27. Khiewtam, R.S. and Ramakrishnan, P.S., 1993, Litter and fine root dynamics of relic sacred groove forest of Cherrapunjee in northeastern India, Journal of Forest Ecology and Management, 60:327-344. 28. Rao, P., Barik, S.K., Pandey, H.N. and Tripathi, R.S., 1997, Tree seed germination and seedling establishment in tree-fall gaps and understorey in a sub-tropical forest of northeast India, Australian Journal Ecology, 22: 136–145. 29. Jamir, S.A., 2000, Studies on plant biodiversity, community structure and population behavior of dominant tree species of some sacred grooves of Jaintia Hills, Meghalaya, Ph.D. Thesis, North-Eastern Hill University, Shillong, India. 30. Pascal, J.P. and Pelissier, R., 1996, Structure and floristic composition of a tropical rain forest in south-west India, Tropical Ecology, 12: 191–214. 31. Parthasarathy, N. and Karthikeyan, R., 1997, Plant biodiversity inventory and conservation of two tropical dry evergreen forests on the Coromandal Coast, South India, Biodiversity Conservation, 6:1063-1083. 32. Ayyappan, N. and Parthasarathy, N., 1999, Biodiversity inventory of trees in a large-scale permanent plot of tropical evergreen forest at Varagalaiar, Anamalais, Western Ghats, India, Biodiversity Conservation, 8: 1533-1554. 33. Parthasarathy, N., 2001, Changes in forest composition and structure in three sites of tropical evergreen forest around Sengaltheri, Western Ghats, Current Science, 80: 389-393. 34. Padalia, H., Chauhan, N., Porwal, M.C. and Roy, P.S., 2004, Phytosociological observations on tree species diversity of Andaman Islands, India, Current Science, 87(6): 799-806. 35. FWhittakar, R. H. 1972, Evolution of measurement of species diversity, Taxon, 21: 213-251. 36. IIRS, 2011, Biodiversity characterization at landscape level in North-West India and Lakshadweep islands using satellite remote sensing and geographic information system, Bishen Singh Mahendra Pal Singh, Dehradun. 37. Gujarat Ecology Commission, 1996, Status of biological diversity of Gujarat, Vadodara, 38. Rodgers, W.A. and Panwar, S.H., 1988, Biogeographical classification of India, New Forest, Dehradun, India. 39. Patel, P.P., 1997, Ecoregions of Gujarat, Gujarat Ecology Commission, Vadodara, Gujarat, India. 40. Champion, H.G. and Seth, S.K., 1968, A revised survey of forest types of India, Government of India Publications, New Delhi. th 41. Lillesand, T.M. and Kiefer, R.W., 2004, Remote sensing and image interpretation, 5 Edition John Wiley and Sons, New York. 42. Story, M. and Congalton, R., 1986, Accuracy assessment: a user’s perspective, Photogrammetlic Engineering and Remote Sensing, 52(3): 397-399. 43. Shannon, C.E. and Weaver, W., 1949, The mathematical theory of communication, University of Illinois Press, Urbana. 44. Anonymous, 1987, Mangrove in India - Status Report, Ministry of Environment and Forest, New Delhi, India. 45. Anonymous, 1992, Coastal Environment, Space Application Centre, Indian Space Research Organization, Ahmedabad, India. 46. Anonymous, 1998, An Anthology of Indian Mangroves. Environmental Information System, Ministry of Environment and Forest, New Delhi, India. 47. Chavan, S.A, 1985, Status of Mangrove Ecosystem in Gulf Of Kachchh In: Proc. Symposium Endangered Marine Animals and Marine Parks, Marine Biology Association of India, Cochin, India, pp. 475-482. 48. Kothari, M.J. and Rao, K.M. 1991a, Environmental impact on mangroves in Gujarat, Indian Botanic Contactor, 8: 51-57. 49. Kothari, M.J. and Rao, K.M. 1991b, Two new records for Goa and Gujarat, Indian Botanic Contactor, 8: 59-61. 50. Kothari, M.J. & Singh, N.P. 1998. Mangrove diversity along the North-West coast of India. Journal of Eco. and Tax. Bot., 22: 571-585.


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51. Rajendran, N. & Baskara Sanjeevi, S. 2004. Flowering Plants and Ferns in Mangrove Ecosystem of India, An Identification Manual. Environmental Information System (ENVIS), Centre for Advanced study in Marine Biology, Annamalai University, India. 52. Shah, D.G., Bahuguna, A., Deshmukh, B., Nayak, S.R., Singh H.S. & Patel, B.H. 2005. Zoning and monitoring dominant mangrove communities of a part of the Marine National Park, Gulf of Kachchh. Journal of the Ind. Soc. of Rem. Sens., 33: 155-163. 53. Shah, G.L. 1978. Flora of Gujarat State. Sardar Patel University, Vallabh Vidyanagar, Anand, Gujarat, India. 54. Singh, H.S. 2002. Mangroves in Gujarat, Current Status and Strategy for Conservation. Gujarat. Ecological Education and Research Foundation, Gandhinagar, India. 55. Singh, H.S. 2006. Mangroves and Their Environment, With Emphasis on Mangroves in Gujarat. Gujarat Forest Department, Gandhinagar, India.

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(a)

(b)

(c)

(d)

(e)

(f)

Plate - I: Vegetation types in the study area ; (a) Thorn forest, (b) Mangrove forest, (c) Mangrove Scrub, (d) Forest plantation, (e) P. Juliflora Scrub, (f) Grassland.