Sengupta, M. and Dalwani, R. (Editors). 2008 Proceedings of Taal2007: The 12th World Lake Conference: 617-638

Monitoring and Modelling of Chilika Environment Using Remote Sensing Data U. S. Panda and P. K. Mohanty Department of Marine Sciences, Berhampur University, Berhampur- 760 007, Orissa, India Email: mail2usp@gmail.com

ABSTRACT The Chilika Lagoon (N 190 28’-190 54’; E 850 06’-850 35’) on the east coast of India in the Orissa state is the largest lagoonal system in the subcontinent and is one of the largest tropical lagoons of the world. Sedimentation, both from riverine discharge and disintegration of macrophytes, choking of the outer channel, shifting of the inlet mouth, decline in water area and increase in vegetated area, and the opening of the new inlet mouth are the dominant processes influencing the cotemporary phase of lagoon transformation in the Chilika. Remote sensing offers promise in the detection and delineation of the functional elements of lagoon transformation. In order to check the deteriorating condition of the lagoon a new inlet mouth was opened on 23 September 2000. Therefore time series Indian Remote Sensing Satellite Data (IRS 1D and P6 LISS-III) along with field observations in the post mouth opening period have been used in the present study for a qualitative and quantitative evaluation of the components involved in the transformation and to obtain estimates of their distribution, abundance and physical state. Long term monitoring of important water quality parameters such as salinity, temperature, depth, dissolved oxygen, turbidity, secchi depth, water nutrients, pH, total suspended solid, chlorophyll concentration etc in the time-space continuum were carried out during the period 2001 to 2006. Based on the circulation / hydrodynamics of the lagoon, the lagoon is divided into four sectors. The northern sector receives discharges of the floodwaters from the tributaries of the river Mahanadi. The southern sector is relatively smaller and does not show much seasonal variation in salinity. The central sector has features intermediate of the other sectors. The eastern sector, which is a narrow and constricted outer channel, connects the lagoon with the Bay of Bengal and the tidal effects are important in this area. Thus, due to its complicated geomorphology, the circulation in the lagoon corresponding to the different sectors is very complex. Interest in detailed analysis of the circulation, biotic and abiotic factors affecting the lake and its limnology is due to the threat to the lagoon from various factors – Eutrophication, weed proliferation, siltation, industrial pollution and depletion of bioresources. The present paper deals with the numerical simulation of seasonal circulation in the lagoon using Mike 21 hydrodynamic flexible mesh model. The model is capable of accurate simulation of tidal elevations and salinity structure throughout the lagoon and is presented in this paper.

INTRODUCTION: Located on the shores, lagoons are the coastal areas of extraordinary importance in terms of natural surroundings. Due to their general morphological features, they have a very sensitive naturally dynamic balance in all aspects. Although the lagoons have direct connection to the sea, they still display fairly different characteristics than those of the sea as far as the hydrodynamic structure; ecological features and water quality are concerned. Therefore, they have to be protected to maintain the native wild life. On the other hand, because of their sensitive natural balance, they are affected, to a great extent, by the changes in the hydrodynamic and mor-phological conditions arising out of the artificial influences. It is fairly frequent that especially the lagoons near the estuaries are affected by the problems carried to the sea by the rivers that start from the sources outside coastal areas. The physical processes in large lakes are complicated due to the flow regime, effluent discharge from urban runoffs

and treated/untreated pollution sources. Therefore, it is important to understand and analyze the individual contributory factors – changes in the landscape, climate variability, eutrophication, effects of species recruitment events etc – since the physical, biological and chemical variability results from a combination of these effects and their interaction. A long-term multi – disciplinary investigation of the lakes should include the physico – chemical, hydrological and biological parameters.

Application of the coastal zones management plan covering the studies aimed at protecting the lagoons is of vital importance in maintaining the natural balance of the areas in question. However, establishing such a plan and applying it can only be possible by means of healthy data. Difficulties at this point emanate from the shortcomings in eliciting data by means of classical methods. In many of the lagoons, it may even prove impossible to identify the past conditions. In other words, protection of the natural balance of the lagoons can be possible by using a monitoring programme to be set in

connection with a healthy, systematic and manageable data system. Continuation of ground studies is essential but not sufficient in establishing the monitoring programmes on a sound basis. A reliable monitoring programme can be designed only by using relatively new technologies such as Remote Sensing and formation of GIS.

Remote sensing data can be used to obtain almost any information that is typically obtained from maps. In many regions of the world, remotely sensed data, and particularly Landsat or Spot data, may be the only source of good cartographic information. Drainage basin area and the drainage network are easily obtained from good imagery, even in remote regions. There have also been a number of studies to extract quantitative geomorphic information from Landsat imagery (Haralick, 1985). Once basic measurements have been taken from the imagery, the usual physiographic descriptors can be calculated, such as basin shape, circularity, and stream orders.

The use of remotely sensed data for water resources monitoring and management is basically for mapping. The problem is usually one of identifying a land-water boundary or delineating geological and geomorphic characteristics of an area or determining land use with respect to consumptive use of water. Both analysis of imagery and digital classification techniques have been used successfully (Campbell, 1996 and Jensen, 1996).

Information about the coastal lines is very important for decision-making in coastal management. For the coastal planning, coastal zone management, monitoring of river, and river mouth monitoring and management, GIS is one of the important tools which provides scientific data, integrates coastal spatial and non-spatial information, and operations and supports decision making processes. In this study measuring depth in shallow areas by using IRS-1D/P6 data were investigated with the observed bathymetric data.

Delineation of land-water boundaries depends on the relative spectral characteristics of soil, vegetation, and water. The very low reflectance of water in the near-infrared region of the spectrum makes this waveband the obvious choice for identifying and measuring surface water.

Spectral qualities of water bodies are determined by the interaction of several factors, including the radiation incident to the water surface, optical properties of the water due to turbidity, roughness of the surface, angles of observation, and illumination, and, in some instances, reflection of light from the bottom. As incident light strikes the water surface, some is reflected back to the atmosphere; this reflected radiation carries little information about the water itself, although it may convey information about roughness of the surface and, and therefore, about wind and waves. Instead, the spectral properties (i.e., ‘‘Color’’) of a water body

are determined largely by energy that is scattered and reflected within the water body itself, known as volume reflection because it occurs over a range of depths rather than at the surface. Some of this energy is directed back towards the surface, where it again passes into the atmosphere, and then to the sensor. This light, sometimes known as under light is the primary source of the color of a water body (Engman, 1991).

In environmental monitoring application of remote sensing, the influence of the atmosphere can be especially important. The atmosphere, of course, alters the spectral properties of incident radiation and also influences the characteristics of the reflected signal. Although these influences are also present in remote sensing of land surfaces, they assume special significance in hydrologic studies, in part because such studies often depend on subtle spectral differences (easily lost in atmospheric haze) and also perhaps because much of the hydrologic information is carried by the short wavelengths that are most easily scattered by the atmosphere. Information concerning water depth and configuration of the ocean floor is one of the most basic forms of hydrographic data. Bathymetry is especially important near coastlines, in harbors, and near shoals and banks, where shallow water can present hazards to navigation and where changes can occur rapidly as sedimentation, erosion, and scouring of channels alters underwater topography.

In addition to the observational and remote sensing aspects, a comprehensive multidisciplinary approach based on mathematical modelling of the lagoon environment is formulated using Mike 21 Hydrodynamic Flexible Mesh (HD FM) model of DHI water & Environment. DESCRIPTION OF STUDY AREA The Chilika lagoon (N 190 28’-190 54’; E 850 06’-850

35’) on the east coast of India in the Orissa State (Figure 1) is the largest lagoonal system in the subcontinent and is one of the largest tropical lagoons of the world. The average length and breadth of the lagoon are about 65 km and 16 km respectively. The water area of the lagoon is variable from a maximum of 992 km2 during rainy season to 815 km2 during summer (Mohanty et.al, 2001). At the northern end, tributaries of the Mahanadi River, such as Daya, Nuna and Bhargavi join the lagoon (Figure 1) and are responsible for the large fresh water and sediment influx to the lagoon. The lagoon is separated from the Bay of Bengal by sand bar of 60 km in length. The lagoon has two mouths, a 24 km long narrow and curved channel which runs parallel to the coast to join the Bay of Bengal near Arakhakuda, and the new mouth at Sipakuda at a distance of 8 km from the main body of the lagoon. Table 1 gives the recent physiography and physico-chemical characteristics of Chilika lagoon. Chilika

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Lagoon is a very dynamic ecosystem and is a unique assemblage of freshwater, brackish and saline water ecosystems. The water quality of the lagoon changes widely with onset of different seasons and is exhibiting different ecological characteristics in localized pockets. The lagoon has been classified into four different sectors such as northern sector, central sector, southern sector and outer channel, according to its physico-chemical and biological characteristics (Pal and Mohanty, 2002).

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MONITORING OF CHILIKA LAGOON THOUGH FIELD OBSERVATIONS AND ANALYSES Observations on water quality parameters such as depth, secchi depth, water temperature, salinity, pH, dissolved oxygen (DO), biological oxygen demand (BOD), water nutrients (ammonia, nitrate, nitrite and phosphate) and chlorophyll were carried out during Summer and Winter seasons covering about 30 stations in the body of the lagoon from 2001 to 2006. There seven stations in the southern sector, ten in the central and northern sectors and three in the outer channel region. Stations in the respective sector are represented as SS, CS, NS and OC (Figure 2). The stations have been fixed at uniform distance through Global Positioning System (GPS) with an error

margin of ± 6 to 8 meter. Samples were collected to observe the spatial and temporal variations of different environmental parameters of the lagoon during May and December. Most of the water quality parameters were analysed following Parsons et al. (1984).

The summary of hydrographic and water quality parameters observed in Chilika lagoon during summer and winter seasons from 2001 to 2006 are presented in Table 2. Hydrographic and water quality parameters in the lagoon show significant spatial and temporal variability. After opening of the new mouth outer channel records highest depth followed by southern, central and northern sectors. Similarly, other parameters show distinct spatial distributional trend. However, the seasonal variability is remarkable and dominates over interannual variability. In all the four sectors of the lagoon, pH, salinity, BOD, Chlorophyll-a, Phaeopigment and TSS are more during summer than those during winter while depth, dissolved oxygen and water nutrients show a opposite trend.

Summary of the hydrological and biological features of the lagoon, known till now are given in Table 1a. and 1b. respectively (after Panigrahy, 2005).

North ern

Sec tor

Central

Secto r

Southe

rn

Sector

Gajapati NagarGouranga Patna

Ram bhaSabulia

Keshapur

Pa thara

ChandraputBa lugaon

Nair iBaulabandha

Ba radihi

So rana

Ka lupa ragha t Mangalajodi

Bhusandpur

Garh Rorang

Jadupur

Dah ikhiaGangadharpur

Sa tap ada

Arkhakuda

Ma luda

Old Mouth

New Mouth

NalabanaIsland

10 0 10 20 Kilometers

N

EW

S

C H I L I K A L A G O O N

Bay of Bengal

Central Sector

NalabanaNorthern Sector

Pa lur Canal

Railway track

Sourthern Sector

Vil lagesð

Rivers and rivulates

Road (NH)

I N D I A

O R I S S A

Figure 1. Index map of Chilika Lagoon showing different sectors, rivers and inlet mouth to Bay of Bengal.

Table 1a. Physiography and physico-chemical characteristics of Chilika lagoon. Physiography Location Latitude 19º 28’-19º 54’

Longitude 85º 06’ – 85º 35’ Boundaries East- Bay of Bengal

West- Rocky Hills of Eastern Ghats North- Alluvial plain of Mahanadi delta South- Rocky Hills of Eastern Ghats

Water spread area (sq. km) Summer months: 906 Rainy months: 1165

Shape Pear shaped Length and Breadth (km) Length: 65

Breadth; 2 (minimum), 16 (maximum) No. of rivers and rivulets draining into the lake

10

No. of islands inside the lake 106 Age ~ 5000 years Lake mouth One (near Arakhakuda) Major ecological divisions Four - Northern sector - Central sector - Southern sector - Outer channel area Depth (m) 0.38-4.2 Average: 1.35 Physicochemical properties Temperature (°C) Surface water 17.5-32.0, Vertical gradient: ~1 Salinity (ppt) Traces-36.0, vertical gradient: ~2 pH 7.6-10.0, vertical gradient: <1 Dissolved Oxygen (mg/l) 1.3-13.4 Nutrients (mg/l) Nitrate Traces-0.19 Phosphate Traces-0.18 Silicate 0.10-0.60 Major Elements (ppm) Calcium 24-330 Magnesium 87-1380 Sodium 295-13500 Potassium 17-428 Chloride 604-19650 Sulphate 104-3000 Bicarbonate 90-159 Trace Elements (ppm) Copper 0.02-0.04 Zinc 0.025-0.19 Iron (%) 0.12-0.32 Table 1b. Biological characteristics of Chilika lagoon. Phytoplankton and Primary Productivity Species composition Mishra et al. (1988) 62

• Diatoms - 43 • Dinoflagellates - 8 • Blue-green algae -7 • Green algae - 4

Raman et al. (1990) 96 • Diatoms - 60 • Dinoflagellates - 8 • Blue-green algae - 13 • Green algae – 16 • Euglenoids - 4

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Phytoplankton Density Mishra et al. (1988) 1.62 – 2.95 x 10 6

Panigrahy (1985) 1.78 x 10 3 - 1.49 x 10 5 Relative abundance (%) • Diatoms – 67.3

• Dinoflagellates – 25.9 • Blue-green algae –3.5 • Green algae – 2.3

Phytoplankton pigment (mg m-3) • Total chlorophyll - (ND – 67.05) • Chlorophyll-a - (ND – 13.38) • Chlorophyll-b - (ND – 18.60) • Chlorophyll-c - (ND – 59.03)

Primary productivity ( mg C m-3 hr-1) 6.80 to 68.82 Zooplankton and secondary productivity Plankton volume (ml l –1) 0.03-0.27 Faunistic composition 170 species (26 groups) Relative abundance of dominant groups (%) • Copepoda – (43.0-70.0)

• Veligers – (5.0-30.0) • Naupli – (16.0-18.00) • Protozoans – 7.0 • Rotifers – 4.0 • Polychaetes – 2.0 • Mysids - 1.5

Biomass (gm m-3) 0.3213 - 3.3048 Macrophytes Dominant species • Potamogeton pectinatus

• Halophila ovata • Najas gramineae • N. falcioulata • Rupia maritima • Eichornia crassipes • Sciripus articulatus • Gracillaria verucosa

Spread area (km 2) • 1973 – 20 • 1977 – 60 • 1982 – 100 • 1985 – 200 • 1991 - 440

Zoobenthos Total no. of species 62 Dominant groups Foraminifera, Nematoda, Polychaeta, Copoepoda,

Ostracoda, Isopoda, Amphipoda, Gastropoda and Bivalvia

Biomass (gm m-2) Northern Sector –11.1 Central sector – 18.3 Outer channel area – 13.8 Southern sector – 11.1

Average annual production (gm m-2) 13.54 – 16.5 Fisheries Fish catch (tonnes) 1986-87 to 1991-92 Minimum – 4185.0

Maximum- 8815.7 Average – 6034.2

Rate of fish production (kg ha –1) 65 - 122 Faunistic composition Icthyofauna – 166 species

Prawn – 21 species Crabs – 5 species

Important fisheries • Mullets (Mugil cephalus, M. macrolepis) • Sciaenids (Pseudosciaena coiber) • Threadfins (Eleutheronema tetradactylum) • Perches (Lates calcarifer) • Prawns (Panaeus indicus, P.monodon, Metapeneaus dobsonii, M. monoceros) • Crabs (Scylla serrata, Neptunus pealagicus)

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Fishery economy Fisherfolk population – 1,00,000 (Approx.) Fisherman villages – 122 Boats in operation – 2365 Nets in operation – 21000 Traps in operation – 1,51,000 Per capita income – Rs. 1500 Govt. revenue- Rs. 8-10 crores

Types of fisheries in practice as on 1991 Jano (109), Prawn (71), Bahan 933), Dian (88), Uthapani (9)

Birds and Mammals No. of species of birds 151 (26 families)

Migratory (96), resident (55) Mammals Dolphin, Sea cow ND: Not Detected

85.1 85.15 85.2 85.25 85.3 85.35 85.4 85.45 85.5 85.55 85.6 85.65

85.1 85.15 85.2 85.25 85.3 85.35 85.4 85.45 85.5 85.55 85.6 85.65

19.5

19.55

19.6

19.65

19.7

19.75

19.8

19.85

19.5

19.55

19.6

19.65

19.7

19.75

19.8

19.85

SS1

SS2

SS3

SS4

SS5

SS6SS7

CS1

CS2

CS3

CS4CS5

CS6

CS7CS8

CS9

CS10

NS1NS2

NS3

NS4

NS5

NS6

NS7

NS8NS9

NS10

OC1

OC2 OC3

Figure 2. Map showing the station locations of Chilika Lagoon.

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Table 2: The summery (Maximum, minimum, average and standard deviation for whole season, southern sector, central sector, northern sector and outer channel) of hydrographic and water quality parameters observed in Chilika lagoon during Summer and Winter seasons from 2001 to 2006.

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REMOTE SENSING OF CHILIKA LAGOON It is well known that remotely sensed data acquired from the Indian Remote Sensing satellites have the potential to provide meaningful information on the water quality, silt load, productivity and vegetation coverage of lagoons/lake, estuaries and other coastal water bodies with a cost effective procedure and better synopticity unobtainable using conventional methods. Witzig and Whitehurst (1981) provided an excellent review of the literature describing the application of remote sensing to surface water quality studies. Therefore, in the present study attempts have been made to monitor the water quality and hydrographic features, vegetation coverage during summer and winter. Two Indian Remote Sensing satellite (IRS) Linear Imaging Self Scan sensor (LISS-III) data (Path/Row-20/54) on 6 May, 2003 and IRS P6 data on 19 February 2005 were procured from the National Remote Sensing Agency (NRSA), Hyderabad covering the Chilika region. The geometric registration and analysis of digital satellite data was accomplished through Environmental Research Design Analysis Software (ERDAS) Imagine Software. False Colour Composite (FCC) The display colour assignment for any band of a multispectral image can be done in an entirely arbitrary manner. In this case, the colour of a target in the displayed image does not have any resemblance to its actual colour. The resulting product is known as a false colour composite image. A very common false colour composite scheme for displaying a IRS multispectral image is shown below: R = Band 3 (NIR band) G = Band 2 (red band) B = Band 1 (green band)

This false colour composite scheme allows vegetation to be detected readily in the image. In this type of false colour composite images, vegetation appears in different shades of red depending on the

types and conditions of the vegetation, since it has a high reflectance in the NIR band. It is a method of displaying multi-band (multi-channel) imagery. By assigning three of the image bands to the fundamental colours red, blue and green, one can produce a colour image. The blue band in the original image is often affected by atmospheric effects such as haze, and is therefore usually left out. When the assigned image bands do not correspond to the frequencies of red, blue and green the output image will appear in colours that are not intuitive or natural. For instance, different types of vegetation might appear as blue, red, green or yellow. Intuitively, vegetation would appear green. Such an image is known as a false colour composite. It is useful for extraction of information which is difficult to discern in the original imageries.

In the FCC images of Chilika lagoon the emergent vegetation are shown in red, water in deep blue to light blue and the black colour depicts under water/submerged vegetation inside the lagoon. False colour composite for summer and winter are discussed with representative data sets.

The FCC during summer (Figure 3a) depicts the areas of emergent vegetation (red), submerged vegetation (black), deep water (deep blue) and shallow water (light blue) regions and fairly agree with field observations. Nalabana Island during summer is very distinct with reddish tinge and suggests that the Island is dry during summer and covered with sparse emergent vegetation. Nalabana Island is completely submerged and the submerged vegetation (dark colour) is distinctly shown. During monsoon and post monsoon seasons large influx of fresh water to the lagoon occurs through the rivers (Figure 1). Therefore, the water quality and vegetation cover changes as compared to summer. Figure 3b depicts the features of Chilika lagoon during winter. The areas of emergent and submerged vegetation, deep and shallow water regions are very well delineated in the FCC. It is very much distinct that the vegetated area (emergent and floating) during winter is less as compared to summer. However, submerged vegetation during winter is more than that during summer.

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Figure 3. False Colour Composite Images of Chilika Lagoon (a) Summer- May 2003 and (b) Winter- February, 2005 Image Classification and Analysis A human analyst attempting to classify features in an image uses the elements of visual interpretation (tone, shape, size, pattern, texture, shadow, and association) to identify homogeneous groups of pixels which represent various features or land cover classes of interest. Digital image classification uses the spectral information represented by the digital numbers in one or more spectral bands, and attempts to classify each individual pixel based on this spectral information. This type of classification is termed spectral pattern recognition. In either case, the objective is to assign all pixels in the image to particular classes or themes (e.g. water, coniferous forest, deciduous forest, corn, wheat, etc.). The resulting classified image is comprised of a mosaic of pixels, each of which belongs to a particular theme, and is essentially a thematic "map" of the original image.

When talking about classes, we need to distinguish between information classes and spectral classes. Information classes are those categories of interest that the analyst is actually trying to identify in the imagery. Spectral classes are groups of pixels that are uniform (or near-similar)

with respect to their brightness values in the different spectral channels of the data.

Satellite data sets representing summer and winter were digitally analyzed and the maximum likelihood classification was used to obtain spectral signature of different classes during summer (May 2003) and winter (February 2005) seasons.

Six different classes during summer (Figure 4a) and winter (Figure 4b) were obtained and the areas covered under different classes were estimated. The six classes are deep water, shallow water, emergent vegetation, free floating vegetation, submerged vegetation and inland vegetation. Excluding the inland vegetation, the changes those are apparent from summer (May, 2003) to winter (February 2005) are increased in the area of deep water, shallow water and submerged vegetation while there is significant increase in the emergent and free floating vegetation. Thus, the remarkable changes from summer to winter are reduction in the vegetation free water area and enhancement in the vegetated area. The results agree with the observation of Pal and Mohanty (2002) and Panda et.al. (2007) for the pre-mouth opening period which showed minimum vegetation free water area and maximum vegetated area during summer.

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Figure 4: Classified Images of Chilika Lagoon (a) Summer- May 2003 and (b) Winter- February, 2005 Normalized difference Vegetation Index (NDVI) Since early instruments of Earth Observation, such as NASA's ERTS and NOAA’s AVHRR, acquired data in the red and near-infrared, it was natural to exploit the strong differences in plant reflectance to determine their spatial distribution in these satellite images. The NDVI is calculated from these individual measurements as follows:

NIR REDNDVINIR RED

−=

+ where RED and NIR stand for the spectral reflectance measurements acquired in the red and near-infrared regions, respectively. These spectral reflectances are themselves ratios of the reflected over the incoming radiation in each spectral band individually. Hence, they take on values between 0.0 and 1.0. By design, the NDVI itself varies between -1.0 and +1.0.

Subsequent work has shown that the NDVI is directly related to the photosynthetic capacity and hence energy absorption of plant canopies (Sellers 1985 and Myneni et al. 1995).

In addition to the simplicity of the algorithm and its capacity to broadly distinguish vegetated

areas from other surface types, the NDVI also has the advantage of compressing the size of the data to be manipulated by a factor 2 (or more), since it replaces the two spectral bands by a single new field (eventually coded on 8 bits instead of the 10 or more bits of the original data).

The users of NDVI have tended to estimate a large number of vegetation properties from the value of this index. Typical examples include the Leaf Area Index, Biomass, chlorophyll concentration in leaves, plant productivity, fractional vegetation cover, accumulated rainfall, etc. Such relations are often derived by correlating space-derived NDVI values with ground-measured values of these variables. This approach raises further issues related to the spatial scale associated with the measurements, as satellite sensors always measure radiation quantities for areas substantially larger than those sampled by field instruments.

In spite of many possible perturbing factors upon the NDVI, it remains a valuable quantitative vegetation monitoring tool when the photosynthetic capacity of the land surface needs to be studied at the appropriate spatial scale for various phenomena.

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NDVI Image IRS 1D LISS-III

DOP: 23 May 2003

NDVI Image IRS 1D LISS-III

DOP: 05 Feb 2005

Figure 5: Normalized Difference Vegetation Index images of Chilika Lagoon (a) Summer- May 2003 and (b) Winter- February, 2005

Satellite data sets representing summer and

winter were digitally analyzed normalized difference vegetation index maps for May 2003 and February 2005. This Vegetation index (NDVI= [Infrared (IR)-Red (R)] / [Infrared (IR) + Red (R)]) is a convenient parameter for expressing the multi-spectral response of vegetation vigour in Chilika lagoon .

The NDVI images for summer (Figure 5a) and winter (Figure 5b) were obtained and the areas covered under different classes are apparent. The deep water, shallow water, emergent vegetation, free floating vegetation, submerged vegetation and inland vegetation are clearly apparent and shows the difference during two different seasons. MODELLING OF CHILIKA LAGOON USING MIKE 21 MODEL MIKE 21 is a two-dimensional or area model based on Flexible Mesh (FM) approach (21 refers to two dimensions horizontally and one dimension vertically), developed and maintained by Danish Hydraulic Institute- Water, Environment and Health, Denmark. It is one of the most sophisticated models available worldwide to simulate water environment such as oceans, coasts and estuaries. It is very much suited for the environments where stratification (i.e., vertical) of the system can be neglected. Its usefulness is specially been observed where the usual fixed grid systems are unable to adjust to the domains with arbitrary geometries. It provides a flexibility to fit into such domains. It has inherited advantages to represent islands and complex

bathymetry more precisely, resolution can be placed on the boundary layers in an optimal manner, the problem of stair casing near land boundary can also be avoided and grids with dynamically changing resolution are possible.

The MIKE 21 FM has five modules to aid in modelling studies for the wide range of objectives. The hydrodynamic (HD) module is the basic computational component of the entire MIKE 21 FM system. Rest all follow the HD module and are used for the various purposes as their name suggest, i.e., Transport (TR) for advection and dispersion studies, ECO Lab (EL) for ecological parameters, Mud Transport (MT) and Sand transport (ST) for sedimentation studies. In order to achieve the objectives stated above, in the present study used HD module of Mike 21 Modelling system and the results are discussed below. Hydrodynamic Module The HD module simulates water level variations and flows in response to a variety of forcing functions in lagoons, estuaries and coastal regions. The HD module calculates the resulting flow and distribution of salt and temperature subjected to a variety of forcing, sources and boundary conditions.

The HD module is based on numerical solution of two-dimensional shallow water equations – the depth integrated incompressible Reynolds averaged Navier-Stokes equations (DHI, 2007). Thus, model is based on the continuity and momentum equations,

627

following principle of conservation of mass and momentum in both x and y directions. Model Equations Mass equations: Continuity

estd

yq

xp

t−=

∂∂

=∂∂

+∂∂

+∂∂ ξ

The momentum equation has nine components

representing effects of time deviation, convective momentum, cross momentum, gravity, bed resistance, eddy viscosity, coriolis, wind resistance/ fiction effect, density / barometric pressure gradient as given below.

Momentum equation: X direction

0)(

2

2

2

2

22

222

=∂

∂+−Ω−⎟⎟

⎠

⎞⎜⎜⎝

⎛∂∂

+∂∂

−+

+⎟⎠⎞

⎜⎝⎛∂∂

+⎟⎠⎞

⎜⎝⎛

∂∂

+⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

+∂∂

xphfVV

yq

xpE

hCqpgp

xgh

hpq

yhp

xtp a

wxq ρ

ξ

Momentum equation: Y direction

0)(

2

2

2

2

22

222

=∂

∂+−Ω−⎟⎟

⎠

⎞⎜⎜⎝

⎛∂∂

+∂∂

−+

+⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

+⎟⎠⎞

⎜⎝⎛

∂∂

+⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

+∂∂

yphfVV

yq

xpE

hCqpgq

ygh

hpq

xhq

ytq a

wyp ρ

ξ

(1) (2) (3) (4) (5) (6) (7) (8) (9) Where, x , = Spatial coordinates (m) yξ = Surface elevation or water levels above datum (m) t = Time (s) p , = Momentum or flux densities in x and y

direction respectively (m3/s/m)

q

d = Time varying depth of water (m) s = Source magnitude; (m3/s) e = Evaporation rate; (mm/day) h = Water depth (m) g = Acceleration due to gravity (m/s2) C = Chezy resistance (m1/2/s) E = Eddy viscosity coefficient (m2/s) Ω = Coriolis parameter, latitude dependent (s-

1) f = Wind friction factor (dimensionless)

V = Wind speed (m/s)

xV , = Wind speed component in x and y direction (m/s)

yV

wρ = Density of water (kg/m2)

aρ = Atmospheric pressure (kg/m/s2) The spatial discretisation of equations is performed using a cell-centered finite volume method. The

spatial domain is discretised by the subdivision of the continuum into non-overlapping elements/cells. In the horizontal plane an unstructured grid is used comprising of triangle elements. An approximate Riemann solver is used for convective fluxes, which makes it possible to handle the discontinuous solutions. For the time integration an explicit Euler method is used. Due to the stability restriction using an explicit scheme the time step interval must be selected so that the Courant-Friendrich-Levy (CFL) number is less than 1. A variable time step interval is used in the calculation and it is determined so that the CFL number is less than a critical CFL number in all computational nodes. To control the time step it is also possible to specify a minimum time step and a maximum time step. Looking to the requirement of this study with 89100 time step, the time step interval is restricted to 30 seconds which produces the CFL number less than the critical CFL number (0.8). Model Set-up and Calibration Calibrating a numerical model is an essential step in any modelling study. A well-calibrated model allows the testing and prediction of impacts from certain events that cannot be derived through measurements or may be too complex to predict or difficult to quantify without the help of an advanced numerical tool.

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Specifications for Model Setup Parameter Value Setup file Mesh and Bathymetry Chilika.mesh (4905 nodes) Simulation Period 2006-05-01 00:00 – 2006-05-31 00:00 (31 days) Time step interval 30 sec No. of Time Steps 89100 Solution Technique Low order, fast algorithm

Minimum time step: 0.01 s Maximum time step: 120 s Critical CFL number: 0.8

Density Function of temperature and salinity (Reference temperature 180C and Salinity 32 psu)

Eddy viscosity Type: Smagorinsky formulation (constant ~0.5) Enable Flood and Dry Drying depth 0.01m

Flooding depth 0.05 m Wetting depth 0.1 m

Initial Surface Level -0.98 m Wind Varying in time, constant in domain:

*.dfs0 (different files for May and December) Wind Friction Varying with wind speed

0.001255 at 7 m/s 0.002425 at 25 m/s

Source River input at north =constant value May 9.76 m3/s and December 11.72 m3/s

North Boundary Water_level_sipakuda.dfs1 extracted from Mike C-MAP Resistance Manning number: Constant Value 32 m1/3/s Result files Type: 2D Horizontal; Format: Area Series. CPU Simulation Time About 15 hours 29 minutes with a 2.0 GHz PC, 512 MB DDR2 RAM

The model results are dependent upon a sound model set-up and reliable input information; thus, it is necessary to document the set-up and its performance. The numerical modelling carried out in the present study was aimed at circulation and water quality analysis through HD module. The module was mainly driven by boundary conditions extracted from databases containing tidal constituents, temperature, salinity and wind.

For the present study, the model was set up for two seasons (one month each) i.e. during summer (May, 2005) and for winter (December, 2005). The following section briefly describes the user setup and configuration of model applied to simulate the hydrodynamic modelling in Chilika lagoon. Model domain and bathymetry

The model domain was selected from 850 04’ to 850 43’ East and 190 27’ to 190 55’ North. Bathymetry reflects the geometry of the region. The model bathymetries were prepared, based on the information from British Admiralty Sea Maps

(extracted from a software product named C-MAP in digital form) and toposheets of Chilika region prepared by the Survey of India. The bathymetry map has been validated with the GPS observations collected during field survey in Chilika lagoon. All bathymetries used Chart Datum (CD), i.e. a datum set approximately equal to Lowest Astronomical Tide (LAT), and the projection used was lat/long. The mesh file for the Chilika lagoon (Figure 6) has been created considering the computational grid, water depths and boundary information. The mesh has been obtained with triangles without small angles (the perfect mesh has equilateral triangles), with smooth boundaries, with high resolutions in areas of special interest and based on valid xyz data obtained from field observations and Mike C-MAP.

Large angles and high resolutions in a mesh are contradicting with the need for short simulation times. The resolution of the mesh, combined with the water depths and chosen time-step governs the Courant numbers in a model set-up. The maximum Courant number shall be less than 0.5. So the simulation time dependency on the triangulation of

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the mesh, relates not only to the number of nodes in the mesh, but also the resulting Courant numbers. As a result of this, the effect on simulation time of a fine resolution at deep water can be relatively high compared to a high resolution at shallow water. Figure 7 represents the bathymetry of the region obtained from the mesh file and depth profile.

Simulated water currents in Chilika lagoon Water currents in Chilika lagoon were simulated for flood and ebb conditions during May, 2006 and December, 2006. Figure 8 and 9 depicts the simulated water currents in Chilika lagoon for flood and ebb conditions during summer (May 2006). There are two figures in each figure 8 to 11, the first one is the full view of the lagoon and the second one presents a clear and expanded view of the water

current in the outer channel region. It is observed that flood currents near the channel region are of higher order which is connected with the influence of tidal intrusion in to the lagoon. The lower order water current in the main body of the lagoon implies that the lagoon’s main land is not much influenced by the tidal current and moreover a still water.

Similarly, Figure 9 and 10 represents the simulated water current for flood and ebb conditions during winter (December, 2006) in Chilika lagoon. During winter also the current is higher order of 0.4-0.5 m/s in the channel region and suddenly decreased to the order 0.2 m/s in the main body of the lagoon. From the field knowledge it can be delineated that due to less river inputs during these two months the water current in the main body of the lagoon is of very lower order.

Figure 6: The flexible mesh structure of Chilika lagoon which is the basic input to the model.

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Bathymetry [m]Above 0-0.25 - 0-0.5 - -0.25

-0.75 - -0.5-1 - -0.75

-1.25 - -1-1.5 - -1.25

-1.75 - -1.5-2 - -1.75

-2.25 - -2-2.5 - -2.25

-2.75 - -2.5-3 - -2.75

-3.25 - -3-3.5 - -3.25

Below -3.5Undefined Value

0:00:00 12/30/1899 85.10 85.15 85.20 85.25 85.30 85.35 85.40 85.45 85.50 85.55 85.60 85.65 85.70

19.46

19.48

19.50

19.52

19.54

19.56

19.58

19.60

19.62

19.64

19.66

19.68

19.70

19.72

19.74

19.76

19.78

19.80

19.82

19.84

19.86

19.88

19.90

Figure 7: The bathymetry of Chilika lagoon used in the Mike model.

Current speed [m/s]Above 0.350.325 - 0.35

0.3 - 0.3250.275 - 0.30.25 - 0.275

0.225 - 0.250.2 - 0.225

0.175 - 0.20.15 - 0.175

0.125 - 0.150.1 - 0.125

0.075 - 0.10.05 - 0.075

0.025 - 0.050 - 0.025

Below 0Undefined Value

3:30:00 5/1/2006 Time Step 6 of 1416. 85.10 85.15 85.20 85.25 85.30 85.35 85.40 85.45 85.50 85.55 85.60 85.65 85.70

19.46

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19.62

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19.74

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19.80

19.82

19.84

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19.88

19.90

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Current speed [m/s]Above 0.350.325 - 0.35

0.3 - 0.3250.275 - 0.3

0.25 - 0.2750.225 - 0.25

0.2 - 0.2250.175 - 0.2

0.15 - 0.1750.125 - 0.15

0.1 - 0.1250.075 - 0.1

0.05 - 0.0750.025 - 0.05

0 - 0.025Below 0Undefined Value

3:30:00 5/1/2006 Time Step 6 of 1416. 85.38 85.39 85.40 85.41 85.42 85.43 85.44 85.45 85.46 85.47 85.48 85.49 85.50 85.51 85.52 85.53 85.54

19.60

19.61

19.62

19.63

19.64

19.65

19.66

19.67

19.68

19.69

19.70

19.71

19.72

19.73

19.74

Figure 8: Simulation of flood current in during May 2006 a) full view, b) expanded view of the channel.

Current speed [m/s]Above 0.560.52 - 0.560.48 - 0.520.44 - 0.480.4 - 0.44

0.36 - 0.40.32 - 0.360.28 - 0.320.24 - 0.280.2 - 0.24

0.16 - 0.20.12 - 0.160.08 - 0.120.04 - 0.08

0 - 0.04Below 0Undefined Value

20:00:00 5/1/2006 Time Step 39 of 1416. 85.10 85.15 85.20 85.25 85.30 85.35 85.40 85.45 85.50 85.55 85.60 85.65 85.70

19.46

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Current speed [m/s]Above 0.560.52 - 0.560.48 - 0.520.44 - 0.480.4 - 0.44

0.36 - 0.40.32 - 0.360.28 - 0.320.24 - 0.280.2 - 0.24

0.16 - 0.20.12 - 0.160.08 - 0.120.04 - 0.08

0 - 0.04Below 0Undefined Value

20:00:00 5/1/2006 Time Step 39 of 1416. 85.38 85.39 85.40 85.41 85.42 85.43 85.44 85.45 85.46 85.47 85.48 85.49 85.50 85.51 85.52 85.53 85.54

19.60

19.61

19.62

19.63

19.64

19.65

19.66

19.67

19.68

19.69

19.70

19.71

19.72

19.73

19.74

Figure 9: Simulation of ebb current in during May 2006 a) full view, b) expanded view of the channel.

Surface elevation [m]Above 1.71.65 - 1.71.6 - 1.65

1.55 - 1.61.5 - 1.55

1.45 - 1.51.4 - 1.45

1.35 - 1.41.3 - 1.35

1.25 - 1.31.2 - 1.25

1.15 - 1.21.1 - 1.15

1.05 - 1.11 - 1.05

Below 1Undefined Value

21:30:00 12/1/2006 Time Step 42 of 1175. 85.10 85.15 85.20 85.25 85.30 85.35 85.40 85.45 85.50 85.55 85.60 85.65 85.70

19.46

19.48

19.50

19.52

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Surface elevation [m]Above 1.75

1.7 - 1.751.65 - 1.71.6 - 1.65

1.55 - 1.61.5 - 1.55

1.45 - 1.51.4 - 1.45

1.35 - 1.41.3 - 1.35

1.25 - 1.31.2 - 1.25

1.15 - 1.21.1 - 1.15

1.05 - 1.1Below 1.05Undefined Value

20:30:00 12/1/2006 Time Step 40 of 1175. 85.44 85.45 85.46 85.47 85.48 85.49 85.50 85.51 85.52 85.53 85.54 85.55 85.56 85.57

19.61

19.62

19.63

19.64

19.65

19.66

19.67

19.68

19.69

19.70

19.71

19.72

Figure 10: Simulation of flood current in during December, 2006 a) full view, b) expanded view of the channel.

Surface elevation [m]Above 1.061.04 - 1.061.02 - 1.04

1 - 1.020.98 - 10.96 - 0.980.94 - 0.960.92 - 0.940.9 - 0.92

0.88 - 0.90.86 - 0.880.84 - 0.860.82 - 0.840.8 - 0.82

0.78 - 0.8Below 0.78Undefined Value

13:30:00 12/1/2006 Time Step 26 of 1175. 85.10 85.15 85.20 85.25 85.30 85.35 85.40 85.45 85.50 85.55 85.60 85.65 85.70

19.46

19.48

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Surface elevation [m]Above 1.025

1 - 1.0250.975 - 1

0.95 - 0.9750.925 - 0.95

0.9 - 0.9250.875 - 0.9

0.85 - 0.8750.825 - 0.85

0.8 - 0.8250.775 - 0.8

0.75 - 0.7750.725 - 0.75

0.7 - 0.7250.675 - 0.7Below 0.675Undefined Value

12:30:00 12/1/2006 Time Step 24 of 1175. 85.40 85.41 85.42 85.43 85.44 85.45 85.46 85.47 85.48 85.49 85.50 85.51 85.52 85.53 85.54 85.55 85.56 85.57

19.59

19.60

19.61

19.62

19.63

19.64

19.65

19.66

19.67

19.68

19.69

19.70

19.71

19.72

19.73

Figure 11: Simulation of ebb current in during December 2006 a) full view, b) expanded view of the channel.

Inlet Mouth (85.510565, 19.665059) [m]Magarmukha (85.438641, 19.679867) [m]Northern Secotr (85.453449, 19.803619) [m]Central Sector (85.341332, 19.736983) [m]Southern Sector (85.178444, 19.603711) [m]

00:002006-05-03

00:0005-08

00:0005-13

00:0005-18

00:0005-23

00:0005-28

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

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00:002006-12-04

00:0012-09

00:0012-14

00:0012-19

00:0012-24

00:0012-29

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

Figure 12: Simulation Water levels at five stations (1. Inlet mouth, 2. Muggarmukha, 3. Northern Sector, 4. Central Sector and 5. Southern Sector) during a) May 2006 and b)December, 2006 Table 4: Simulated and observed salinity ranges during summer and winter in different sectors of the Chilika lagoon. Summer (May) Winter (December) Sector Simulated

Salinity in PSU Observed Salinity in PSU

Simulated Salinity in PSU

Observed Salinity in PSU

Northern Sector 7.61-29.62 04.00-30.00 0.48-4.80 0- 6.00 central sector 9.49-27.06 14.00-30.00 2.24-8.13 2-20 (major portion

2-4) southern sector 13.94-18.13 14.00-20.00 7.22-10.71 4.00-10.00 Outer Channel 30.2- 33.8 30.00-33.00 2.19-15.20 20-24

Simulated Salinity in Chilika lagoon Simulated Surface Water Level in Chilika lagoon Figure 13 depicts salinity during May and December, 2006. Table 4 presents the simulated and observed salinity ranges during May and December, 2006 for outer channel, northern sector, central sector and southern sector. During summer (May 2006), the salinity ranges closely agree with the observed salinity ranges (Table 4). Similarly, for December, 2006 except for the outer channel there exists close agreement between the simulated and the observed salinity ranges.

Figure 12 depicts the simulated surface water levels at five stations during summer and winter, 2006 in the lagoon. The five stations in each region Inlet mouth, Muggarmukha, Northern Sector, Central Sector and Southern Sector (co-ordinates of stations are given in the figure) shows that the surface water level is decreases from outer channels to the main body of the lagoon. The outer channel is absolutely fluctuate with the tidal heights and subsequently decreases into the lagoon. During summer the water level gets as high as 2.4 m, whereas during winter it is above 2.5m.

636

Salinity [PSU]Above 32

30 - 3228 - 3026 - 2824 - 2622 - 2420 - 2218 - 2016 - 1814 - 1612 - 1410 - 128 - 106 - 84 - 6

Below 4Undefined Value

0:30:00 5/16/2006 Time Step 720 of 1416. 85.10 85.15 85.20 85.25 85.30 85.35 85.40 85.45 85.50 85.55 85.60 85.65 85.70

19.46

19.48

19.50

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19.54

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19.62

19.64

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19.72

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19.80

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19.86

19.88

19.90

Salinity [PSU]Above 24

22 - 2420 - 2218 - 2016 - 1814 - 1612 - 1410 - 128 - 106 - 84 - 62 - 40 - 2

-2 - 0-4 - -2

Below -4Undefined Value

12:30:00 12/3/2006 Time Step 120 of 1175. 85.10 85.15 85.20 85.25 85.30 85.35 85.40 85.45 85.50 85.55 85.60 85.65 85.70

19.46

19.48

19.50

19.52

19.54

19.56

19.58

19.60

19.62

19.64

19.66

19.68

19.70

19.72

19.74

19.76

19.78

19.80

19.82

19.84

19.86

19.88

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t1

Figure 13: Salinity during December, 2006

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ACKNOWLEDGEMENTS One of the authors (USP) acknowledges the financial assistance offered by the Council of Scientific and Industrial Research (CSIR), Government of India in terms of Senior Research Fellowship. Thanks are also due to DHI Water & Environment, New Delhi for providing facilities to carry out the modelling work at their office. REFERENCES Campbell, James B. 1996. Introduction to Remote

Sensing, 2nd Ed.; Virginia Polytechnic Institute and State University, The Guilford Press

DHI (Danish Hydraulic Institute – Water, Environment and Health). 2007 MIKE 21 flow model FM: hydrodynamic module – user guide. Hørsholm, Denmark:

Engman, E.T.; Gurney, R.J. Remote Sensing in Hydrology; Chapman and Hall: UK, 1991.

Haralick, R.M.; Wang, S.; Shapiro, L.G.; Campbell, J.B. Extraction of drainage networks by using a consistent labeling technique. Remote Sensing Environ. 1985, 18, 163-175.

Jensen, J.R. A remote sensing perspective. Introductory Digital Image Processing; Prentice Hall, 1996.

Mohanty, P. K., Pal, S. R., and Mishra, P. K., 2001. Monitoring ecological conditions of a coastal lagoon using IRS Data: A case study in Chilka, East Coast

of India. Journal of coastal Research Special Issue 34, (ICS 2000 Proceedings) pp459-469.

Myneni, R. B., F. G. Hall, P.J. Sellers, and A.L. Marshak (1995) 'The interpretation of spectral vegetation indexes', IEEE Transactions on Geoscience and Remote Sensing, 33, 481-486.

Pal, S. R., and Mohanty, P. K., 2002. Use of IRS-1B data for change detection in Water quality and vegetation of Chilka lagoon, east coast of India. International Journal. Remote Sensing, 23(6): 1027-1042.

Panda., U. S, Mohanty., P.K., Pal., S.R., Mishra., P. and Jayaraman., G. 2007. Spatial and interannual variability in Chilika lagoon: a study based on field observations and remote sensing data In the book Conservation, Restoration and management of lakes and coastal wetlands,Eds. P. K. Mohanty, Capital Publishing Company, New Delhi. pp 240-267

Panigrahi, S.H., 2005. Seasonal variability of phytoplankton productivity and related physico – chemical parameters in the Chilika lake and its adjoining sea, Berhampur University, Doctor of Philosophy’s thesis, 258p.

Parsons, T. R.; MAITA, Y., and LALLI, C. M., 1984. A manual of Chemical and Biological Methods for Seawater Analysis, Pergamon Press, New York, 173p.

Sellers, P. J. (1985) 'Canopy reflectance, photosynthesis, and transpiration', International Journal of Remote Sensing, 6, 1335-1372.

Witzig, S.,and Whitehurst,C., 1981. Literature review of the current use and technology of MSS digital data for lake tropic classification, Proceedings of the 1981 Fall Meeting of the American Society of Phtotogrammetry, San Francisco, pp.1-20.

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