Soil Resource Mapping and its Nutrient Status in Two ...

International Journal of Engineering Research & Technology (IJERT) RACEE-2015 Conference Proceedings

Soil Resource Mapping and its Nutrient Status in Two Blocks of Koraput District, Odisha using GIS Technology Kakoli Banerjee1#, Rakesh Paul1 & B. B. Dash2 1

Department of Biodiversity and Conservation of Natural Resources Central University of Orissa, Landiguda, Koraput Regional Research and Technology Transfer Station (Coastal Zone) Orissa University of Agriculture and Technology, Bhubaneshwar, Odisha 751003

2

Abstract- − In India about 25 Mha and in Odisha 0.75 Mha of land covering 11 districts are affected with iron toxicity and iron toxic plant contains deficient level of N, P, K, Ca & Mg. Black soils in Odisha occur in parts of Kalahandi, Bolangir, Koraput (Kalimela), Ganjam (Aska), Puri (Balugaon), Angul and Padmapur area covering 0.96 Mha, usually contain more than 30 per cent clay with cracks in summer and swells on wetting. The sandy and coarse loamy coastal soils have electrical conductivity 15-25 dS m-1 and are characterized by chlorides of Na, Mg, and Ca. The red, laterite and lateritic group soils in the state of Odisha constitute more than 75 per cent of the total land area. Various soil productivity constraints affecting the agricultural production in the state of Odisha includes low pH and low cation exchange capacity, low organic matter content and low available N & K status, deficiency of Ca, Mg & S as well as micro nutrients like Zn, B & Mo, inherent deficiencies and fixation of soluble P, toxicity of Fe, Al & Mn. With this background, a study was undertaken in two blocks of Koraput district for mapping the secondary and micro-nutrients in agriculture soils during 2012-2014. Surface soils (0 to 30 cm) was investigated for Ca, Mg along with diethylene-triamine pentaacetic acid (DTPA)-extractable Zn, Cu, Mn, Fe and hot water extractable S and B in relation to some chemical properties in approximately 280 representative soils. Secondary data of soil nutrients from 2012-2014 and the digital boundary map of Koraput district were collected from State Soil Testing Laboratory and National Information Center respectively. Then the soil nutrient data were linked with the sampling spots’ Geocoordinates in the GIS software. The analyzed soil nutrient data (mean variation in available Ca and Mg were 2.68- 3.14 and 1.14 - 1.38 ppm, respectively and that of DTPA-extractable Zn, Cu, Mn, Fe and hot water extractable S and B were 0.48 - 1.26, 1.143.25, 18.17- 20.36, 37.77 - 48.77 and 16.21 - 25.55, 0.44 - 0.57 ppm, respectively. The GIS maps were generated in the CSV (Comma Separated Values) format and were incorporated into the Arc-GIS software to prepare the Interpolated maps by giving low to high range values to expresses the soil nutrient status in different colour exhibits. The soil fertility maps show that most of these nutrient elements are either in low or very low concentrations and at some places it is data deficient. This study thus ascertains the level of nutrient depletion and can find ways for management of such nutrients in this area. Keywords: Agricultural soil; secondary nutrient; micro-nutrient; soil fertility maps

I.

INTRODUCTION

The total land area of the world exceeds 13 billion-hectare but less than half of it can be used for agricultural activity including grazing. Potentially arable land constitutes 3031

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million ha of which 2154 million ha are potentially cultivable in developing countries and 877 million ha are in developed countries. About 1461 million ha is cultivated of which 784 million ha & 677 million ha are in developing and developed countries respectively [1]. A new study has revealed that a majority of the soils in India are deficient in secondary nutrients and micronutrients vital for both plant and human health. With the growing realization that agro-forestry is a practical, low cost alternatives for food production as well as environmental protection, forest departments of many countries are integrating agro-forestry programmes with conventional silvicultural practices [2]. Most agro-forestry systems constitute sustainable land use and help to improve soil in a number of ways. Some of these beneficial effects are evidenced in a number of experiments carried out in different parts of the world [3,4]. Through agro-forestry, many countries could not only minimize the land degradation but also increased the production [5,6,7]. It is a new area of study with a potential for assuring land sustainability. But we are lagging behind in this context. The study – conducted by the Indian Institute of Soil Science (IISS), a key research body of the Indian Council of Agricultural Research – found that the soils of as many as 174 districts across 13 states were deficient in secondary nutrients like sulphur and micronutrients like zinc, boron, iron, manganese and copper. The chemical analysis of 70,759 soil samples collected from Andhra Pradesh, Assam, Bihar, Gujarat, Haryana, Madhya Pradesh, Maharashtra, Odisha, Punjab, Tamil Nadu, Uttar Pradesh, Uttarakhand and West Bengal found zinc to be deficient by 39.9 per cent and sulphur by 27.8 per cent. The boron deficit was found to be 20 percent while iron, manganese and copper were deficient by 12.9, 6 and 4.3 percent, respectively [8]. The consistently low yield of almost all crops in states like Odisha, West Bengal, Gujarat and Bihar despite application of nitrogen, phosphorus, potassium and zinc has been attributed to the deficiency of boron. The removal of secondary nutrients and micronutrients was found to be the highest in the transGangetic part of the Indo Gangetic plains, the food bowl of the country. Tamil Nadu recorded the maximum zinc deficit of 62.2 per cent while West Bengal was the lowest at 8.5 per cent (http://www.dailymail.co.uk/indiahome/indianews/article-

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2738639/Most-Indian-soils-deficient-crucialnutrients.html#ixzz3CpWAIR65). The conservative estimates showed that the demand for food grains would increase from 192 million tonnes in 2000 to 355 million tonnes in 2030. Contrary to increasing food demands, the factor productivity and rate of response of crops to applied fertilizers under intensive cropping systems are declining year after year. The current status of nutrient use efficiency is quite low in case of P (15-20%), N (30-50%), S (8-12%), Zn (2-5%), Fe (1-2%) and Cu (1-2%) due to deterioration in chemical, physical and biological health of the soils [9]. Continuous cropping leads to decline in organic C levels by 50-70% to equilibrium levels dictated by climate and precipitation. The major reasons identified for soil health deterioration are: wide nutrient gap between nutrient demand and supply, high nutrient turn over in soil-plant system coupled with low and imbalanced fertilizer use, emerging deficiencies of secondary and micronutrients in soils, soil acidity, nutrient leaching in sandy soils, nutrient fixation in red, laterite and clayey soils, impeded drainage in swellshrink soils, soil salinization and sodification etc. With the advent of Remote Sensing technology satellite data are being used for mapping, monitoring and assessment of various soil resources in given areas. Space- borne remotely sensed data being repetitive and multispectral in nature is an ideal choice for use in monitoring database management tool offering solutions for planning and problem analysis related to biodiversity. Remote Sensing and GIS techniques are becoming fast and effective tools for extracting information of complex and dynamic natures. Keeping all these things, on the basis of reviews, an effort has been made to highlight the nutrient status of Koraput district so that it may serve as a tool for further research, planning or development works. II.

MATERIALS AND METHODS

The present study is aimed at use of Remote Sensing and Geographical Information System (GIS) techniques for representation of nutrient status in soils of Koraput district of Odisha. A. Site Selection Two blocks namely Koraput and Semiliguda in Koraput district (Fig.1) have been selected for the study. Soil samples were collected from different locations in these blocks during post harvest season. The co-ordinates of the sampling points were collected through hand held Global Positioning System (GPS). In Koraput block 14 major panchayats comprising of 42 villages and in Semiliguda block 17 panchayats comprising of 84 villages were selected for the purpose. At the initial stage soil samples from the year marked villages were collected from points whose Geo Coordinates were noted by a GPS. These collected samples were processed, labelled and analysed in the Soil Testing Laboratory at Semiliguda during the year 2012-2014. The analysis was carried out following standard methods of analysis as prescribed and accepted by IARI (Indian Agricultural Research Institute). The various parameters such as available nutrients i.e.Calcium (Ca), Magnesium (Mg), Manganese (Mn), Sulphur (S), Copper (Cu), Zinc (Zn), Boron (B), Iron (Fe) have been carried out as per guidelines of IARI.

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B. Analysis of soil samples The DTPA (Diethylene triamine pentaacetic acid) extraction of the soil samples were carried out for extracting the available nutrient element estimation which are under reference. The standard graph was prepared by using standard solution of different nutrient elements and the observed data for the particular nutrient was found out from the standard graph. The soil samples (5gm) were first prepared with DTPA (100ml) which was then filtered and the filtrate was collected. The extractant (DTPA) can extract all available nutrient elements bound to the soil particles. The estimation in the extractant was done using Atomic Absorption Spectrophotometer based on the principle of emission spectroscopy. C. Preparation of digital soil fertility map of Koraput and Semiliguda The map showing the boundary of Semiliguda and Koraput blocks of Koraput district was obtained from National Information Centre (NIC). The map was scanned and taken into the Arc-GIS software. This map was then Geo referenced using Arc- GIS tools by taking reference points using a toposheet of 1:50,000 scale. The block boundary of the two blocks was digitized using on screen digitization method in GIS platform. The sampling locations collected through GPS were put in the map and a base map was prepared sharing the boundary of the blocks and sampling locations. The soil data for each locations were gathered in Excel format. Then the soil data were linked with the sampling points in GIS software. The analysed soil data in the excel format was converted to CSV format for incorporation in the Arc-GIS software. By right clicking over the Geo-referenced map a set of tables appear where these CSV data were inserted. The soil sampling points (from where samples were taken) then appear in small denotations inside the map. By clicking the individual points inside the map the characteristic properties in the CSV format were displayed. A base map displaying the location of soil samples were prepared first. Soil characteristics like available Calcium (Ca), available Magnesium (Mg), available Manganese (Mn), available Sulphur (S), available Copper (Cu), available Zinc (Zn), available Boron (B),available Iron (Fe) were expressed by giving low to high range values in different colour exhibits. III.

RESULTS AND DISCUSSION

Agriculture is the backbone of Indian economy contributing about 40% towards gross national production and providing livelihood to about 70% of the population. Therefore for a primary agrarian country like India accurate and timely information on biodiversity status can only help sustainable crop production to meet the demand for food. This can be achieved if the natural resources like soil and water are well managed. For the best management practices to be followed one have to visualize the existing condition as well as feasibility of improved management which can support to achieve the target. Soil nutrient is the most important factor which sustains the growth of all plant life on earth starting from the agricultural cops to forests (Table 1).

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The present world population of 6 billion is expected to reach 8 billion by the year 2030. It is expected that most of the increase in population would occur in developing countries here. Nearly 1 billion people suffer from chronic malnutrition. Many developing countries face major challenges to achieve food, fibre, fodder, fuel, income, equity and social justice in a sustainable manner, considering available per capita land area, severe scarcity of fresh water resources and particular socio-economic conditions. Higher crop productivity, income, employment and environmental services will have to be achieved from the land that is already being farmed. The Indian population, which increased from 683 million in 1981 to 1210 million in 2010, is estimated to reach 1412 million in 2025 and to 1475 million in 2030. To feed the projected population of 1.48 billion by 2030 India need to produce 350 million tonnes of food grains [8]. The expanded food needs of future must be met through intensive agriculture without much expansion in the arable land. The per capita arable land decreased from 0.34 ha in 1950-51 to 0.15 ha in 2000-01 and is expected to shrink to 0.08 ha in 2025 and to 0.07 ha in 2030. So the current food-grains production of 218 mt (2009-10) is produced from the net arable land of 141 m ha. Soil and water management form the basis for sustainable system of productive agriculture. This research work highlights the preparation of digital soil nutrient status map in two major blocks of Koraput district (namely Koraput and Semiliguda). The soils of these areas are mainly Red and Laterite having a high composition of Iron and Aluminium. The basic characters of these soils are well drained, low water holding capacity, highly acidic, very good fixation of phosphate nutrients. These soils were considered rich in plant nutrients during last two decades, but with advancement of technology and improved economic conditions of the local population, the heavy exploitation of natural resources of these soils has taken place within a very short period. This has reflected on sudden changes in the biodiversity. The major problem of low yield was expected due to depletion of micronutrients and trace elements such as Ca, Mg, Mn, S, Cu, Fe, Zn, B. 2+ The Map for Calcium (Ca ) indicates the Calcium micronutrient status in the Koraput and Semiliguda blocks of Koraput district (Fig.2). Each point in the map shows the Calcium status of individual sampling sites under study representing the value in ppm (parts per million). The 2+ Interpolated map for Calcium (Ca ) expresses the soil characteristic which is prepared by interpolating the CSV data format to the map using spatial analysing tool. Five continuous intervals are made using different pseudo-colours 2+ to show the variation in the status of Calcium (Ca ). The 2+ variation in the average amount of Calcium (Ca ) ranged from 2.676260 ppm to 3.137460 ppm which is shown in the legend of the map with different pseudo colour ranges. 2+ The Map for Magnesium (Mg ) indicates the 2+ Magnesium (Mg ) micronutrient status in the Koraput and Semiliguda blocks of Koraput district (Fig. 2). Each point in 2+ the map shows the Magnesium (Mg ) status of individual

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sampling sites under study representing the value in ppm (parts per million). The Interpolated map for Magnesium 2+ (Mg ) expresses the soil characteristic which is prepared by interpolating the CSV data format to the map using spatial analysing tool. Five continuous intervals are made using different pseudo colours to show the variation in the 2+ status of Magnesium (Mg ). The variation in the average 2+ amount of Magnesium (Mg ) ranged from 37.767300 ppm to 48.773200 ppm which is shown in the legend of the map with different pseudo colour ranges. The Map for Copper (Cu) indicates the Copper micronutrient status in the Koraput and Semiliguda blocks of Koraput district (Fig.3). Each point in the map shows the Copper (Cu) status of individual sampling sites under study representing the value in ppm (parts per million). The Interpolated map for Copper (Cu) expresses the soil characteristic which is prepared by interpolating the CSV data format to the map using spatial analysing tool. Five continuous intervals are made using different pseudo colours to show the variation in the status of Copper (Cu). The variation in the average amount of Copper (Cu) ranged from 1.143280 ppm to 3.248240 ppm which is shown in the legend of the map with different pseudo colour ranges. 2+ The Map for Iron indicates the Iron (Fe ) micronutrient status in the Koraput and Semiliguda blocks of Koraput 2+ district (Fig.3). Each point in the map shows the Iron (Fe ) status of individual sampling sites under study representing the value in ppm (parts per million). The Interpolated 2+ map for Iron (Fe ) expresses the soil characteristic which is prepared by interpolating the CSV data format to the map using spatial analysing tool. Five continuous intervals are made with different pseudo colours to show the 2+ variation in the Iron (Fe ) status. The variation in the 2+ average amount of Iron (Fe ) ranged from 37.767300 ppm to 48.773200 ppm which is shown in the legend of the map with different pseudo colour ranges. 2+ The Map for Manganese (Mn ) indicates the Manganese 2+ (Mn ) micronutrient status in the Koraput and Semiliguda blocks of Koraput district (Fig.3). Each point in the map 2+ shows the Manganese (Mn ) status of individual sampling sites under study representing the value in ppm (parts per 2+ million). The Interpolated map for Manganese (Mn ) expresses the soil characteristic which is prepared by interpolating the CSV data format to the map using spatial analysing tool. Five continuous intervals are made using different pseudo colours to show the variation in the 2+ status of Manganese (Mn ). The variation in the average 2+ amount of Manganese (Mn ) ranged from 18.165000 ppm to 20.362800 ppm which is shown in the legend of the map with different pseudo colour ranges. The Map for Sulphur (S) indicates the Sulphur (S) micronutrient status in the Koraput and Semiliguda blocks of Koraput district (Fig.3). Each point in the map shows the

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Sulphur (S) status of individual sampling sites under study representing the value in ppm (parts per million). The Interpolated map for Sulphur (S) expresses the soil characteristic which is prepared by interpolating the CSV data format to the map using spatial analysing tool. Five continuous intervals are made using different pseudo-colours to show the variation in the status of Sulphur (S). The variation in the average amount of Sulphur (S) ranged from 16.213900 ppm to 25.553600 ppm which is shown in the legend of the map with different pseudo colour ranges. The Map for Zinc (Zn) indicates the Zinc (Zn) micronutrient status in the Koraput and Semiliguda blocks of Koraput district (Fig. 3). Each point in the map shows the Zinc (Zn) status of individual sampling sites under study representing the value in ppm (parts per million). The Interpolated map for Zinc (Zn) expresses the soil characteristic which is prepared by interpolating the CSV data format to the map using spatial analysing tool. Five continuous intervals are made using different pseudo colours to show the variation in the status of Zinc (Zn). The variation in the average amount of Zinc (Zn) ranged from 0.483167 ppm to 1.256870 ppm which is shown in the legend of the map with different pseudo colour ranges. The Map for Boron (B) indicates the Boron (B) micronutrient status in the Koraput and Semiliguda blocks of Koraput district (Fig. 3). Each point in the map shows the Boron(B) status of individual sampling sites under study representing the value in ppm (parts per million). The Interpolated map for Boron (B) expresses the soil characteristic which is prepared by interpolating the CSV data format to the map using spatial analysing tool. Five continuous intervals are made using different pseudo colours to show the variation in the status of Boron (B). The variation in the average amount of Boron (B) ranged from 0.438796 ppm to 0.571277 ppm which is shown in the legend of the map with different pseudo colour ranges. Soil resource inventory gives an idea of its potentialities and limitations for effective exploitations. The soil maps are required on different scales to meet the requirement of planning at various scales. Soil degradation (loss of biodiversity) refers to a decline in the soil’s natural ability for sustenance of vegetation through adverse changes in its physicochemical attributes [ 1 0 ] . Several soil characters have been mapped and monitored using IRS satellite data following both visual and digital techniques [ 1 1 ] . Precision agriculture also known as prescription farming which is a need of the hour, Variable Rate Technology (VRT) and Site Specific Agriculture ( S S A ) is considered as the st agricultural system of the 21 century. Agro-biodiversity studies involve the integrated technologies such as GPS, GIS, VRT crop models and yield monitors. Accurately prepared digital soil maps depicting the biodiversity within the soil can translate results of soil properties i.e. secondary and micronutrient status to a spatial coverage for whole field.

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From the data it is very clear that most of these nutrient elements are either in low or very low status and at some places it is beyond available state. Individual nutrient element analysis for each sample has been placed in the ArcGIS software to plot a Interpolated map indicating it’s available status in a low to high colour format. These maps can be well studied to ascertain the level of nutrient depletion and management of such nutrients in that area. This type of expression of natural resource characters definitely helps to study the effects of the changing soil characteristics on the biodiversity of the area. IV. REFERENCES R. Dudal, “Land degradation in a world perspective”. Jr. Soil and Water Conserv., vol. 37(5), 1982, pp. 245-249. [2] M. S. Swaminathan, “The promise of agroforestry for ecological and nutritional security”, in Agroforestry and decade of development, H. W. Steppler and P. K. R. Nair, Eds., ICRAF, Nairobi, Kenya, 1987, p. 30. [3] P.K.R. Nair, “Soil productivity under agroforestry”, in Agroforestry: realities, possibilities and potentials, H.K. Gholz, Ed.,. Dordrecht, Netherlands: Nijhoff and ICRAF, 1987, pp. 21-30. [4] A. Young, “The environmental basis of agroforestry”, in Meteorology and agroforestry: proceedings of an international workshop on the application of meteorology to agroforestry systems planning and management, T. Darnhofer and W.E. Rcifsnyder, Eds., Nairobi: ICRAF, 1989b, pp. 29-48. [5] GTI, “Environmental training modules in Agriculture”, Graduate Training Institute (GTI), Bangladesh Agricultural University, Mymensingh, 1995, p. 85. [6] R. P. Mishra and M. Sarim, “Social security through social fencing”, in A conference on sustainable development, International Institute for Environment and Development London, England, 28-30 April, 1987. [7] M. S. Swaminathan, “Preface in desertification and its control”, ICAR, New Delhi, India, 1977. [8] M.K. Hasan, and K.M. Alam, “Land degradation situation in Bangladesh and role of agroforestry on farm research division”, Bangladesh Agricultural Research Institute, Gazipur, Bangladesh School of Agriculture and Rural Development, Bangladesh Open University, Gazipur, Bangladesh, 2006. [9] K.S. Reddy, “Vision 2030”, Indian Institute of Soil Sciences, Bhopal, 2011. [10] R. Lal, and B.A. Stewart, “Advances in soil science, soil degradation”, New York: Springer Verlag, 1990, p. 349. [11] R.S. Dwivedi, “Spatio-temporal characterization of soil degradation”, Jr. Trop. Ecol., vol. 43(1), 2002, pp. 75-90. [1]

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TABLE 1. IMPORTANCE OF SECONDARY AND MICRONUTRIENTS FOR PLANTS Nutrients Importance · Utilized for Continuous cell division and formation · Involved in nitrogen metabolism Calcium(Ca2+) ·Reduces plant respiration ·Aids translocation of photosynthesis from leaves to fruiting organs Secondary ·Increases fruit set Nutrients ·Essential for nut development in peanuts ·Stimulates microbial activity in the soil ·Key element of chlorophyll production ·Improves utilization and mobility of 2+ Magnesium(Mg ) phosphorus ·Activator and component of many plant enzymes ·Directly related to grass tetany ·Increases iron utilization in plants ·Influences earliness and uniformity of maturity ·Catalyzes several plant processes ·Major function in photosynthesis Copper(Cu2+) ·Major function in reproductive stages ·Indirect role in chlorophyll production ·Increases sugar content ·Intensifies colour Micronutrients ·Improves flavour of fruits and vegetables ·Promotes formation of chlorophyll 2+ Iron(Fe ) ·Acts as an oxygen carrier ·Reactions involving cell division and growth ·Functions as a part of certain enzyme Manganese(Mn2+) systems ·Aids in chlorophyll synthesis ·Increases the availability of P and Ca ·Integral part of amino acids Sulphur(S) ·Helps develop enzymes and vitamins ·Promotes nodule formation on legumes ·Aids in seed production ·Necessary in chlorophyll formation (though it isn’t one of the constituents) ·Aids plant growth hormones and enzyme system Zinc(Zn2+) ·Necessary for chlorophyll production ·Necessary for carbohydrate formation ·Necessary for starch formation ·Aids in seed formation ·Essential of germination of pollen Boron(B) grains and growth of pollen tubes ·Essential for seed and cell wall formation ·Promotes maturity ·Necessary for sugar translocation ·Affects nitrogen and carbohydrate

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Fig 1. Map showing the sampling sites

Fig. 2: Map showing secondary nutrients status in the sampling sites

Fig. 3. Map showing micronutrient status in the sampling sites

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International Journal of Engineering Research & Technology (IJERT) RACEE-2015 Conference Proceedings

Monitoring the Mangrove Forest Cover Change of Bhitarkanika National Park using GIS and Remote Sensing Technique Kakoli Banerjee1#, Gobinda Bal1 and Khitish Chandra Moharana1 1

Department of Biodiversity and Conservation of Natural Resources Central University of Orissa, Landiguda, Koraput

Abstract- − Increasing anthropogenic activities and intense biotic pressure poses a serious impact on coastal environment, especially mangrove ecosystem and Bhitarkanika mangrove ecosystem is no exception to it. Remote Sensing and GIS serves as important tool to provide up-to date information which is the primary requirement for conservation, planning and restoration of mangroves. The present study deals with periodic assessment and monitoring of the mangroves of Bhitarkanika National Park, Orissa, India. Satellite data of Landsat ETM + for year 2000, IRS R2 LISS III for both the years 2006 and 2015 respectively were used along with other spatial and non-spatial data to find out the changes that occurred in mangrove forest and other land cover categories. It was found that the National Park occupied 73.29 % of mangrove forest, 14.80% of agricultural land, 1.02% of aquaculture ponds, 0.76% of settlement, 0.34% of roadways, 5.83% of drainage, 3.38% of scrub forest and 0.58% of grassland. The data were verified through ground truth data collected through random field sampling. It was inferred from the study that there is a loss of 64.40 ha (1.52%) of mangrove forest and 112.28 ha of mudflats (30.6%) during the study period. An increase in aquaculture pond area of 34.84 ha (56.47%), settlements of 18.86 ha (40.83 %) and scrub forest of 80.29 ha (64. 39%) is observed during the study over the last 15 years. This clearly depicts that increase in anthropogenic activities by local villagers and other anthropogenic causes have led to the LULC changes over the study period. An immediate step should be taken by the State Forest Department, NGO’S and policy makers, to monitor the status of mangrove vegetation and LULC pattern so as to

conserve the floral and faunal biodiversity of Bhitarkanika mangrove ecosystem. Keywords: Mangrove; LULC; Bhitarkanika National Park

I.

Floristic

Composition;

INTRODUCTION

Mangrove forests are among the most productive and biologically important ecosystems of the world because they provide important and unique ecosystem goods and services to human society. The forests help stabilizing shorelines and reduce the devastating impact of natural disasters such as tsunamis and hurricanes. They also provide breeding and nursing grounds for marine and pelagic species, and food, medicine, fuel and building materials for local communities. Mangroves, including associated soils, could sequester approximately 22.8 million metric tons of carbon each year. Covering only 0.1% of the earth’s continental surface, the forests account for 11% of the total input of terrestrial carbon into the ocean [1] and 10% of the terrestrial dissolved organic carbon (DOC) exported to the ocean [2]. The total mangrove area of the world has been assessed to be approximately 18.15 million hectares. India's mangrove wetlands range from 6,81,000 ha [3] to 5,00,000 ha [4]. The distribution of mangrove forest in India and Orissa is given in Table1.

TABLE 1: MANGROVES COVER DATA OF INDIA AND ORISSA (Km2) Places

Years 1997

1999

2001

2003

2005

2009

2011

2013

India

4737

4871

4482

4448

4581

4639

4663

4628

Orissa

211

213

215

207

203

221

222

213

Balasore

3

3

3

4

4

3

4

2

Bhadrak

17

18

19

21

20

23

23

21

Jagatsinghpur

10

10

5

8

4

7

7

7

Kendrapara

181

184

192

180

175

187

187

183

Source-state Forest Report, FSI :(1997-2013)

The mangrove forests of Bhitarkanika mangrove ecosystem, located in the state of Orissa are the second largest mangrove forest of mainland India [5]. Developmental activities such as construction of jetties, roads, and defence structures, missile testing site, inshore fisheries by mechanized vessels and the proposal of a

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major port threaten the existence of this unique ecosystem [6,7]. Spatial characteristics and extent of anthropogenic disturbances (Table 2) affecting the mangrove forests of Bhitarkanika situated along the east coast of Odisha, India was examined by using remotely sensed data and GIS, supplemented with socio-economic surveys which shows

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that 30% of the major forest classes to be under high to very high levels of disturbance especially at easy access points [8]. TABLE 2: RESOURCE USE AND DEPENDENCY OF LOCAL PEOPLE IN VILLAGES LOCATED IN BHITARKANIKA CONSERVATION AREA, INDIA (N = 324 HOUSEHOLDS) Mean Quantity (kg/hhld/annum)

Resource Use Total consumption of fuel

2205.0 +104.2 312.0 +32.2

Fuel wood

Fuel wood from Park Fuel wood from homesteads Cow dung, farm refuse, others

Fish

Fish caught from the Park

98.0 +28.3

Used as rafters

343.0 +36.9

As roof supports

27.0 +4.3

Honey Thatching materials (Phoenix paludosa)

525.0 +239.7

Timber Non Wood Forest product

21.0 +2.35 1949.0 +375.0

49.0 +8.7

Source: Ambastha et al., (2010) Fig. 1. Location map

The satellite remote sensing applications in forestry offer great advantages and can lead to better monitoring, planning and management of forest resources in the country. Synoptic coverage, imaging access to inaccessible areas, concurrently temporal viewings enables to holistically understand and monitor forest environment. It also assists, in determining forested area, forest health, afforestation or deforestation activities and overall impacts. The mangrove ecosystem is both dynamic and complex in nature. The conventional field surveys are extremely difficult to carry out in swampy areas. Hence, cost effective and accurate mapping techniques are required for timely monitoring of mangroves [9]. Remote sensing technology serves as a valuable tool for gathering accurate information with time and cost effectiveness [10]. Land use/land cover (LULC) changes are key elements of the global environmental change processes [11,12]. Remote sensing technique is emerging as increasingly important tool for mapping and timely monitoring the mangroves [9]. Application of remote sensing has now made it possible to study the changes in land cover in less time, at low cost and with better accuracy [13]. In this background, the present paper aims to monitor the changes in the LULC pattern of Bhitarkanika National Park over time in order to pinpoint the causes of such change. II.

MATERIALS AND METHODS

A. Description of study site The mangrove forests of Bhitarkanika National Park are found largely within Bhitarkanika Wildlife Sanctuary (BWS) between 860 45′ E to 870 50′ E longitudes and 200 40′ N to 200 48′ N latitudes in the lower reaches of the Dhamra-Pathsala-Maipura Rivers (Fig. 1).

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The climate in this 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 300 C in summer to 150C in winter [14]. In 1988, an area of 672 km2 of these forests was declared as a Wildlife Sanctuary with a core area of 145 km2 that was designated as National Park. Floral and faunal diversity of the area includes more than 300 plant species [15] of mangroves and non-mangroves, 31 species of mammals representing 25 genera and 14 families [5], 29 species of reptiles with four species of turtles and 174 species of birds [16]. It is a critical habitat of the endangered Saltwater Crocodile (Crocodylus porosus) and the nesting ground of the Olive Ridley Sea Turtles (Lepidochelys olivacea) [17]. Considering its ecological and social value the area has been identified as a Ramsar Site [18]. The tree flora is primarily confined to a few families such as Apocynaceae, Avicenniaceae, Sonneratiaceae, Tamaricaceae, Bignoniaceae, Euphorbiaceae, Leguminosae, Meliaceae, Plumbaginaceae, Rhizophoraceae etc. Air breathing roots or pneumatophores are seen dominantly only in Avicennia, Lumnitzera and Sonneratia apetala where it is sharply pointed, vertical and woody. Vertically flattened roots are seen in Heritiera and Xylocarpus where the surface roots are vertically flattened and look finger like projections. In most of the mangroves, leaves are thick and fleshy which possess aqueous tissues, mucilage and stone cells and have shrunken stomata. B. Analysis of data Landsat L7 ETM+ satellite image having resolution 30 m of December 2000 and Indian Remote Sensing Satellite (IRS-Resourcesat-2, LISS-III) satellite images having

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International Journal of Engineering Research & Technology (IJERT) RACEE-2015 Conference Proceedings

spatial resolution of 23.5 m of February 2006 and April 2015 were used respectively for image interpretation in the entire study. These data were procured from NRSC (National Remote Sensing Centre), Hyderabad (IRS-R2) and Earth Explorer image harnessing site (Landsat L7 ETM+). Survey of India toposheet (SOI, 1979) was also used for georectification of data. Reference map of ORSAC (Orissa Space Application Centre) was used for image interpretation. Digital image processing technique has been adopted for classification of LULC classes occurring in Bhitarkanika National Park. Procured raw images of three different years were taken for band composition and generation of FCC (False Colour Composites) images and then georegistered to UTM (Universal Transverse Mercator) projection of WGS (World Geodetic System) 84 datum. Georeferencing of the images have been done by using the existing georeferenced satellite imagery of ORSAC. All the satellite images were interpreted in visual interpretation method. In this method, field truth collected from mangrove area was taken into consideration and the tones, textures of the images were also taken into consideration for interpretation. River, island and other land parts of estuarine area were also interpreted visually. The interpreted layers were stored in GIS platform and all these items were also separated through non-spatial value like

land use category. Through this process land use and land cover maps of three different years have been created using ArcGIS 10.1 software (Fig.2). The georectified images were classified into 50 classes. The Unsupervised Classification Maps and False Colour Composite maps were verified by ground truth verification of land use and vegetation types. III.

RESULTS AND DISCUSSION

Bhitarkanika mangrove ecosystem flourishes in the deltaic region, formed by the rich alluvial deposits of Brahmani and Baitarani River. Brahmani has a drainage basin of 8570 km2, length of 365 km and peak discharge of 14,150 m3/s, while Baitarni River has drainage basin of 8570 km2 lengths of 365 km and peak discharge of 14,150 m3/s [19]. It receives inputs of untreated domestic and industrial wastes (including organic matter, oil and heavy metal). These mangroves received attention for it floristic and faunistic diversity. The present study was undertaken to understand the prevailing situation of the inputs due to anthropogenic pressure on these mangrove dominated estuarine ecosystem. The area statistics of forest and land cover types as interpreted from satellite imagery is given in (Table 3).

TABLE 3: CHANGE PATTERN OF AREA (HA) OF LULC CLASSES OF BHITARKANIKA NATIONAL PARK FROM 2000 TO 2015

893.127 61.691 351.761 35.129 4421.685 20.525 203.823

Change in Area 2000-2006 -30.76 -1.85 +5.25 +2.11 +56.83 +1.85 -44.12

Change in Area 2006-2015 -46.26 +36.7 +1.37 -0.16 -124.22 0 +124.41

Change in Area 2000-2015 -77.03 +34.84 +6.62 +1.94 -64.40 +1.85 +80.29

46.191

+10.68

+8.17

+18.86

SL. no.

LULC classes

2000

2006

2015

1 2 3 4 5 6 7

Agricultural Land Aquaculture Pond Drainage Grass Land Mangrove Forest Road Scrub Forest

970.157 26.85 345.135 33.18 4489.076 18.673 123.533

939.392 24.991 350.391 35.298 4545.914 20.525 79.404

8

Settlement

27.328

38.017

The landscape comprises of 73.29% of forest land (mangrove) and 20.29% of matrix comprising private agricultural land, sand-barren land, mud-flat, aquaculture pond and trees. Interspersed grasslands and water bodies occupied 0.58 % and 5.83 % area of the landscape respectively. Thus, the landscape represents a complex of forest- grassland and wetland interspersed with agriculture. Two forest classes were identified namely mangrove forest (Heritiera fomes, Excoecaria

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% of change from 20002015 -7.94 % + 56.47% +1 .882% 5.52% - 1.52% +9.9% + 64. 39 % + 40.83 %

agallocha, Avicennia sp. Rhizophora sp. etc) occurring in the fringe area of the National Park and scrub forest are dominated by (Thespesia populneoides, Salicornia brachiata, Lumnitzera racemosa. Cynometra ramiflora, Acanthus ilicifolius, Porteresia coarctata, Suaeda nudiflora, Suaeda maritima). The different land use patterns and the change in land use from the year 2000 to 2015 is represented in Fig. 2.

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International Journal of Engineering Research & Technology (IJERT) RACEE-2015 Conference Proceedings

Fig. 2. Map showing changes in LULC of Bhitarkanika National Park

Despite the protected status and a ban on resource extraction, local extractions from the Park are considerable. In average 14% of the total fuel wood consumed in each of the household i.e. around 312 kg come from the forests in the form of dead or felled wood collected during the dry season which are used for house construction, non wood forest product (NWFP), fisheries, fodder and on much smaller scales for construction of jetties, forest pathways, small gap bridges, boats, fish traps, and mooring poles (Table 2). From Table 3, it is clear that most of the landscapes are occupied by mangrove forests whose area has been increased from 2000 to 2006 but gradually decreased from 2006 to 2015. The most important activity carried out in Bhitarkanika National Park is agriculture where as aquaculture, roadways construction and settlement is negligible. However, there is an increasing trend of settlements, roadways and development of aquaculture ponds over the years from 2000 to 2015 which is an alarming signal for sustainability of mangrove forests. To understand the trend of change in the Land Use and Land Cover (LULC) pattern in different categories, maps have been prepared taking the data of fifteen years (2000-2015) as represented in Fig.2, which is also supported by the ANOVA analysis at 1% level of significance (Table 4). Agriculture is the most important source of income for Orissa; two thirds of the state budget is produced by agriculture which employs 80% of the population. The agricultural land has gradually decreased from 970.16 ha to 939.39 ha (3.17%) and 939.93 ha to 893.13 ha (4.92 %) during 2000-2006 and 2006-2015 respectively showing a total decrease of 7.94% during the period of fifteen years which may be due to the conversion of the agricultural land to the aquacultural pond for the purpose of shrimp culture. There has been a great hike in aquaculture pond area from 26.85 ha to 61.69 ha (129.76 %) as shown in Table 3. The decrease in the agricultural land and increase in the aquacultural pond area are also confirmed through ANOVA (p