Analytical procedure for physicochemical parameters of NDWC during 2015.
\r\n\t
",isbn:"978-1-83968-930-7",printIsbn:"978-1-83968-929-1",pdfIsbn:"978-1-83968-931-4",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"f159c09dab49a9bc6239b42660d8e8ec",bookSignature:"Dr. Yongxia Zhou",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10310.jpg",keywords:"Brain Science, Brain-Computer Interface, Imaging of Neural Networks, Brain Networks, Brain Function, Molecular Imaging, Brain and Mind, Functional Imaging, Multimodal Imaging, Neuroplasticity Enhancement, Learning, Memory",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 28th 2020",dateEndSecondStepPublish:"October 26th 2020",dateEndThirdStepPublish:"December 25th 2020",dateEndFourthStepPublish:"March 15th 2021",dateEndFifthStepPublish:"May 14th 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Yongxia Zhou had completed her Ph.D. from the University of Southern California in Biomedical imaging (2004) and had been trained and worked as a neuroimaging scientist in several prestigious institutes including Columbia University, New York University, University of Pennsylvania. 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The desert area is approximately 23,000 km2 semiarid, receiving most of its water 88–135 mm of annual rainfall sporadically during the season of monsoon. These wetlands have rich variety of floral and faunal life such, as various aquatic plant species and different animal species of fishes, amphibians, reptiles, birds and mammals. However, the region is of diverse ecological value for the biodiversity of plant and animal species. These wetlands are distributed in different districts which usually start from Ghotki, Sukkur, Khairpur and ends in Sanghar District [1, 2, 3, 4]. In the Nara Canal region, the ground water level usually varies around 76 mm. The capacity of recharging these wetlands in the region is very low due to low rainfall. The level of water table varies between 2.5 and 5 m. In the nearest lands to the Nara Canal, the water level increases up to 10–18 m. Most of the wetlands were developed from sand dunes while a few were developed by deposition of silt [5]. Irrigation system on Nara Canal is contained from upper Nara between Sukkur Barrage to the south Jamrao Headwaters and includes major canals such as Jamarao, Mithrao, Khipro and Thar [1, 2, 3, 4, 5, 6]. The area is geographically part of the Indus Basin and is composed of alluvial sediments which are deposited by previous and current different branches of the rivers. The sediments of the area are carried by Indus River which have tertiary shale and limestone basement. The sediments are composed of acolin sands that have previously been deposited during the Pleistocene Epoch. The composition of soil is from sandy to loamy and some part of soil is scarcely made up with the loamy sands. The color of soils is generally from brown to gray brown with the 5–15% mixture of CaCO3. The soil is usually composed of nan-saline, non-sodic mixed with poor organic materials having range of pH from 7.8 to 8.4. The developing hypersaline wetlands are common due to the overflow from Nara Canal [5]. The region is distributed with the sandy and steep hills which are locally famous as “patt”, “Tars” or “Tals”. The main source of water for the agriculture and other activities is Nara Canal which extends up to 4–5 km from both sides of canal [5] (Figure 1).
\n\n
\n
Map of study area of Nara Desert wild life sanctuary.
The area has high wind velocity with the huge amounts of shifting of sand hills and have high temperature with higher soil radiation in the summer and observed very short rainfall and high evapo-transpiration. In the Nara Desert the average minimum temperature is 20°C and the maximum temperature is 45°C. In the summer, the hottest months are from May to July, when the temperature increases from 45 to 51°C. In the winter season, the lower temperature ranges from 20 to 28°C for the months of December to January. In the region, the annual rainfall from 88 to 135 mm occurs during the months from July to September [1, 2, 3, 4, 6]. The region of NDWC includes about more than 225 small to medium and some large sized-wetlands; some of these are seasonal and most are permanent. The Nara Canal is the largest canal of Sindh Province and covers more than 108 million hectares. On the both sides of Nara Canal the area is covered with woodland, riverine forest, scrub and desert scrubs. In the region, the source of water for wetlands is the seepage from the Nara Canal [1, 2, 3, 4, 7].
\nThe climate of the area is mainly arid having high temperatures and late summer rains observed. The seasonal rainfall is varied and is less than 250–300 mm and rainy season usually starts from June to September. Before the monsoon season, the average temperature is exceeding 45°C in the desert region and in the plains of NDWS the average temperature between 30 and 40°C. The wetland complex is recognized of great hydrological values as 98% Nara Canal water is used for agriculture and only 2% water is used for domestic and drinking purposes. The wetland complex of Nara Canal is 361.6 km long and 90–135 m wide. The maximum water depth of wetland complex is 7.5 m. The highest water discharges of Nara Canal are reported in the months of May–July and the minimum water flow in the August [1, 2, 5].
\nAccording to the census survey report of 2016, the total population of Nara Taluka is counted 160,985. The target area of Nara Taluka the total population can be estimated to be more than 60,000. The major communities are living in the area study are Baradin, Baloch, Chang, Shard, Syed, Rapper, Dashi, Maleah, Khaskheli, Channa, Sahta, Gopang, Bhurgari, Rind, Nizamani, Mirbahar, Khoso, Wassan, Deewan, Ibupoto, Kakepoto, Rajar, Mirani, and Macchi. From these communities, a few numbers of people are engaged in artisan work, trade, business and job in government departments etc. The Livestock and agriculture are the major source of income of local communities. Cotton and wheat are the main crops cultivated in the Nara while sugarcane, barely, oil seed, pulses, vegetable and fodder are also cultivated. The peoples of the area are living in the worst condition and they have least health facilities, drinking water, education and they do not have available basic life facilities. In spite of low productivity of area, the desert area sustains relatively higher human (1.05 m) and livestock (1.25 m) populations was reported, respectively. The livestock is the major source of income, meat and wool in the area. The overexploitation of vegetation by the grazing animals and the cutting of trees and shrubs for fuel purposes have resulted in environmental degradation that threatens the natural resources in this area. There is no developed transportation system in the Nara Desert. Due to low literacy rate in the area, there is a lack of employment opportunities [5].
\nIn the region of NDWC the richest biodiversity comprises a mosaic habitat of sandy hills, canals, forests, agriculture fields, freshwater and hypersaline wetlands. The area has the richest plant biodiversity consisting of 160 plant species belonging to 118 genera and 45 families were recorded [8, 9, 10]. The seasonal crops include sugarcane, cotton, wheat, barley and sunflower. The major vegetation in the sandy habitats are Prosopis cineraria, Acacia niloticus, Salvadora oleoides, Dalbergia sissoo, Tamarix aphylla, Melia azedarach, Populus spp. and Calotropis procera [5, 6, 11, 12, 13]. In the Nara Desert, the various drought-tolerant plant species, such as cactuses and succulents (Agaves spp.), Aerva javanica, Calligonum polygonoides, Crotalaria burhia, Capparis decidua, Dipterygium glaucum, Tephrosia villosa, Aristida adscensionis, Cassia, Tephrosia uniflora and Cassia italic were recorded [5]. In the zone of Nara Desert, a mixed vegetation of shrubs and plants, such as Typha spp., Hydrilla verticillata, Paspalum distichum, Polygonum hyaropier, Urticularia lotus, Nelumbium nuciferum, Desmostachya bininata, Phragmites karka, Saccharum bengalensis and Tamarix indica were also reported [5, 8, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23].
\nThe region has been received the high socio-economic values for the local community which is dependent on the agriculture, livestock, fish farms and freshwater wetlands. In this area, the small to medium villages are scattered and their major economies are agriculture and livestock [1, 2, 5, 17, 24]. In this zone, the variety of different wild mammalian species includes Hemiechnus auritus, Caracal caracal, Felis chaus, Felis margarita, Herpestes edwardsi, Herpestes javanicus, Canis lupus pallipes, Vulpes zerda, Hyaena hyaena, Mellivora capensis, Manis crassicaudata, Gazella bennettii, Hyelaphus porcinus, Sus scrofa cristatus, Lepus tibetanus, Funambulus pennantii, Hystrix indica, Lutrogale perspicilletta, Lutra lutra and Prionailurus viverrinus [5, 25, 26, 27]. The NDWC has the richest biodiversity of native and migratory avian fauna of which more than 78 avian species were recorded [5, 7, 24, 25, 28, 29, 30]. From this region, two threatened species of Indian-backed vulture and houbara bustard were recorded. The indigenous bird species of myna, crow, sparrow, red-wattled lapwing, white-tailed plover, and stilt were commonly observed. Two bird species of large-pied wagtail (Motacilla maderaspatensis) and rock bunting (Emberiza cia) were the first time reported in this region [5]. Some important bird species categorized by International Union for Conservation of Nature (IUNCN) Red List as Least Concern species include the Indian darter, black or red-naped ibis, ruddy Shel duck; as Vulnerable, the marbled teal, and as Near Threatened, the ferruginous duck [5, 23, 24, 28, 29, 30, 31, 32].
\nFor the collection of data the study was carried out in Nara Desert Wildlife Sanctuary which is located between 26°28° N and 68°70° N (Elevation 50–115 m) in the province of Sindh, Pakistan For the collection of flora species, the direct method/observation was applied during the diurnal period. To collect the faunal species, the direct and indirect methods were applied. For the collection of important ecological data of floristical and faunistical species, the field work for diurnal and nocturnal surveys were conducted randomly.
\nFor the various physicochemical parameters, water from 10 randomly selected stations from a few selected lakes of NDWC were sampled monthly from January to December 2015. The samples were collected from two different sampling sites of upper surface and lower bottom layers and were kept in (Van Dorn Plastic Bottles 1.5 liter) during the collection period. After the samples were kept in 10% nitric acid for 24 hours and rinsed with the distilled water. Water samples were mixed in acid-washed container, rinsed with distilled water, and then stored at 4°C for further analysis. For quality assurance, the samples were analyzed in duplicate through careful standardization and samples examined. Physicochemical analysis was performed by the standardized methods [33]. Chemical properties of water samples including water temperature, depth, and transparency were measured with the Secchi Disk. The temperature was measured by a mercury thermometer immersed into a water depth of 15 cm for 2–5 mins. The pH was measured by of an Orion Model 420 pH meter. The EC, TDS, and Na parameters were measured by a WTW 320 conductivity meter. Alkalinity, hardness, Cl and phosphate were analyses conducted by the standardized methods as recognized by WHO [34]. Titration method (2310) was used to measure Ca, HCO3 and HCO. Mg and K were analyzed by spectrometry. For the analysis of BOD and DO, the Winkler method and a Jenway Model 9071 Oxygen Meter were used, respectively (Table 1).
\nParameters | \nAbbreviation | \nUnits | \nProcedure | \n
---|---|---|---|
Temperature | \nTemp | \n°C | \nMercury thermometer | \n
pH | \npH | \npH Unit | \npH meter | \n
Electrical conductivity | \nEC | \nMu/Scm | \nConductivity meter | \n
Total dissolved solids | \nTDS | \nmg L−1 | \nWTW 320 | \n
Turbidity | \nTurb | \nNTU | \nNephlometric turbidity meter | \n
Calcium | \nCa | \nmg L−1 | \nTitration method (EDTA) | \n
Magnesium | \nMg | \nmg L−1 | \nTitration method (EDTA) | \n
Hardness | \nHard | \nmg L−1 | \nTitration (silver nitrate) | \n
Carbonate | \nHCO | \nppm | \nTitration (2310) | \n
Bi-carbonate | \nHCO3 | \nppm | \nTitration (2310) | \n
Alkalinity | \nAlkal | \nmg L−1 | \nTitration (silver nitrate) | \n
Chlorides | \nCl | \nmg L−1 | \nTitration (silver nitrate) | \n
Sodium | \nNa | \nmg L−1 | \nWTW (320) | \n
Potassium | \nK | \nmg L−1 | \nTitration method (EDTA) | \n
Sulphate | \nSO4 | \nmg L−1 | \nTitration (2310) | \n
Biological oxygen Demand | \nBOD | \nmg L−1 | \nWinkler method | \n
Dissolved oxygen | \nDO | \nmg L−1 | \nWinkler method | \n
Analytical procedure for physicochemical parameters of NDWC during 2015.
Flora: In the region of Nara Desert a variety of different floral species of aquatic as well as terrestrial plants, herbs, shrubs and drought resistant plant species were recorded (Table 2).
\nS. No# | \nScientific Name | \nCommon Name | \nLocal Name | \n
---|---|---|---|
1. | \nAcacia nilotica | \nThorn mimosa, Bubul | \nBubar | \n
2. | \nDalbergia sissoo roxb | \nSisu, Tali | \nTalehi | \n
3. | \nAlhagi maurorum | \nCamel-thorn bush | \nKandero | \n
4. | \nAlbizia durraz | \nSiris | \nSareenhn | \n
5. | \nMimosa pudica L. | \nChui-mui, Lajwanti | \nSharam Booti | \n
6. | \nProsopis juliflora | \nMosquite | \nDeevi | \n
7. | \nProsopis cinreria | \nJamal gotha | \nKandi | \n
8. | \nTamarind indica linn | \nImli | \nGidaamri | \n
9. | \nTrigodela L. | \nProshan, kakpie | \nHurbo | \n
10. | \nOcimum L. | \nBasil | \nNazbu | \n
11. | \nGrevia L. | \nPhalsa | \nPharva | \n
12. | \nAzadirachia indica | \nNeem | \nNim | \n
13. | \nFicus benghalensis L. | \nBanyan tree | \nBarr | \n
14. | \nFicus religiosa L. | \nSacred Fig | \nPipal | \n
15. | \nMorus alba L. | \nWhite Mulberry | \nToot | \n
16. | \nEucalyptus camaldulensis | \nRed Gum, Eucalyptus | \nSafedo | \n
17. | \nZizephus mauritiana Lam | \nBerry | \nBaer | \n
18. | \nSalvadora persica L. | \nPeelu | \nKhabbar | \n
19. | \nCordia gharaf | \nGondni | \nGaiduri | \n
20. | \nTamarix passerinoides | \nTamarisk | \nLayee | \n
21. | \nPennisatum glaicum L. | \nBajra | \nBajhari | \n
22. | \nZea mays L. | \nCorn | \nMakai | \n
23. | \nDesmostachya bipinnata | \nDub, Halfa grass | \nDrubh | \n
24. | \nCalotropis procera | \nMilk Weed | \nAkk | \n
25. | \nEruka sativa | \nSalad Rocket | \nJanmbho | \n
26. | \nOpentia ficus indica L. | \nCactus | \nThohar | \n
27. | \nCapparix decidus | \nKapparis | \nKirar | \n
28. | \nSuaeda fruiticosa | \nShrubby Seablight | \nLaani | \n
29. | \nCitrullus colocynchis L. | \nBitter Apple | \nTooh | \n
30. | \nCalligonum polygonoides | \nPhog | \nPhog | \n
31. | \nAerva javanica | \nKopak Bush | \nBooh | \n
31. | \nTamarix aphylla | \nTamarisk | \nLao | \n
32. | \nSalvadora oleoides | \nJaal | \nJaar | \n
33. | \nCrotolaria burhia | \nBurhia Rattlepod | \nSoma | \n
34. | \nDipterygium glaucum | \nSafrawi | \nPhair | \n
35. | \nAristida adscensionis | \nSixweeks Threeawn | \nLumb Gaah | \n
36. | \nCassia italic | \nCassia, Golden tree | \nGhora wal | \n
37. | \nTephrotia uniflora | \nSenegal | \nSiringh/Andhari | \n
38. | \nTeophrotia villosa | \nCreeping Thistle | \nPhoodno | \n
39. | \nTypha latifolia | \nCattail | \nKanahn | \n
40. | \nTypha angusta | \nCattail | \nKaani | \n
41. | \nPaspalum distichum | \nKnotgrass | \nNaru Gaah | \n
42. | \nHydrilla verticillata | \nHydrilla | \nHydrilla | \n
43. | \nNyphaea lotus | \nWhite Lotus | \nKanwal | \n
44. | \nPolygonum hyaropier | \nBlake’s Knotweed | \nAnjbar | \n
45. | \nUrticulara lotus | \nWater Lotus | \nKanwal | \n
46. | \nNelumbium nuciferum | \nNelumbium | \nKanwal | \n
Flora of NDWS.
Fauna: Amphibians: In the Nara Desert three amphibian species belonging from two families of Ranidae and Bufonidae were reported (Table 3).
\nS. No# | \nScientific Name | \nCommon Name | \n
---|---|---|
1. | \nCrocodylus palustris | \nMugger crocodile | \n
2. | \nKuchuga tecta | \nSaw-back turtle | \n
3. | \nKuchuga smithi | \nBrown turtle | \n
4. | \nGeoclemys hemiltonii | \nSpotted-pond turtle | \n
5. | \nCanis aureus | \nAsiatic jackal | \n
6. | \nFellis chaus | \nJungle cat | \n
7. | \nPrionailurus viverrinus | \nFishing cat | \n
8. | \nFelis silvestris | \nDesert cat | \n
9. | \nVulpes vulpes | \nRed fox | \n
10. | \nLutrogale perspicillata | \nSmooth-coated otter | \n
11. | \nHerpestes javanicus | \nSmall Indian mongoose | \n
12. | \nHerpestes edwardsi | \nGray mongoose | \n
13. | \nAxis porcinus | \nHog deer | \n
14. | \nSus scrofa | \nIndian wild boar | \n
15. | \nFunambulus pennanti | \nPalm squirrel | \n
16. | \nGerbilus nanus | \nBalochistan gerbill | \n
17. | \nHemiechinus collaris | \nLong-eared hedgehog | \n
18. | \nHystrix indica | \nIndian crested porcupine | \n
19. | \nLepus nigricollis | \nDesert hare | \n
20. | \nMeriones hurrianae | \nIndian desert jird | \n
21. | \nMus musculus | \nHouse mouse | \n
22. | \nTatera indica | \nIndian gerbil | \n
23. | \nAspiderestes gangeticus | \nIndian soft shell turtle | \n
24. | \nLissemys punctate punctata | \nIndian flapshell turtle | \n
25. | \nNaja naja naja | \nIndian cobra | \n
26. | \nEchis carinatus | \nSaw-scaled viper | \n
27. | \nEryx johni | \nIndian sand boa | \n
28. | \nLytorhynchus paradoxus | \nSindh awlheaded sand snake | \n
29. | \nPlatyceps rhodorchis | \nCliff racer platyceps | \n
30. | \nPlatyceps ventromaculatus | \nGlossy-bellied racer | \n
31. | \nXenochrophid piscator | \nCheckered keelback | \n
31. | \nCalotes versicolor | \nTree lizard | \n
32. | \nTrapelus megalonyx | \nAfghan ground agama | \n
33. | \nHemidactylus brookii | \nYellow-bellied house gecko | \n
34. | \nHemidactylus brooki | \nSpotted Indian house gecko | \n
35. | \nCyrtopodion scaber | \nKeeled rock gecko | \n
36. | \nOphoimorus raithmai | \nThree-fingered sand-fish | \n
37. | \nOphoimorus tridactylus | \nIndian sand swimmer | \n
38. | \nEutrophis macularia | \nBronze grass skink | \n
39. | \nVaranus bengalensis | \nBengal monitor | \n
40. | \nVaranus griseus | \nDesert monitor | \n
41. | \nAcanthodactylus cantoris | \nIndian fringetoed sandy lizard | \n
42. | \nBufo stomaticus | \nMarbled toad | \n
43. | \nHoplobatrachus tigerinus | \nBull frog | \n
44. | \nRana cyanophlyctis | \nSkittering frog | \n
45. | \nTachybaptus ruficollis | \nLittle grebe | \n
46. | \nPodiceps nigricollis | \nBlack-necked grebe | \n
47. | \nPhalacrocorax niger | \nLittle cormorant | \n
48. | \nPhalacrocorax carbo | \nLarge cormorant | \n
49. | \nPhalacrocorax fuscicollis | \nIndian darter | \n
50. | \nArdea cinerea | \nGray heron | \n
51. | \nArdea purpurea | \nPurple heron | \n
52. | \nArdeola grayii | \nIndian pond heron | \n
53. | \nBubulcus ibis | \nCattle egret | \n
54. | \nEgretta alba | \nLarge egret | \n
55. | \nEgretta intermedia | \nIntermediate egret | \n
56. | \nEgretta garzetta | \nLittle egret | \n
57. | \nEgretta gularis | \nReef heron | \n
58. | \nIxobrychus minutus | \nLittle bittern | \n
59. | \nIxobrychus sinensis | \nYellow bittern | \n
60. | \nTadorna ferruginea | \nRuddy shelduck | \n
61. | \nMarmaronetta angustirostris | \nMarbled teal | \n
62. | \nAnas crecca | \nCommon teal | \n
63. | \nAnas platyrhynchos | \nMallard | \n
64. | \nAnas strepera | \nGadwall | \n
65. | \nAnas clypeata | \nShoveller | \n
66. | \nAythya ferina | \nCommon pochard | \n
67. | \nAythya nyroca | \nFerruginous duck | \n
68. | \nAythya fuligula | \nTufted duck | \n
69. | \nAythya collaris | \nRing-necked duck | \n
70. | \nElanus caeruleus | \nBlackwinged kite | \n
71. | \nMilvus migrans | \nCommon kite | \n
72. | \nHaliastur indus | \nBrahminy kite | \n
73. | \nAccipiter badius | \nCentral Asian shikra | \n
74. | \nButastur teesa | \nWhite-eyed buzzard | \n
75. | \nCircus aeruginosus | \nMarsh harrier | \n
76. | \nFalco tinnunculus | \nCommon kestrel | \n
77. | \nPandion haliaetus | \nOsprey | \n
78. | \nFrancolinus pondicerianus | \nGray partridge | \n
79. | \nFrancolinus francolinus | \nBlack partridge | \n
80. | \nAmaurornis phoenicurus | \nWhite-breasted waterhen | \n
81. | \nGallinula chloropus | \nIndian moorhen | \n
82. | \nPorphyrio porphyrio | \nPurple moorhen | \n
83. | \nFulica atra | \nCommon coot | \n
84. | \nCharadrius leschenaultia | \nGreater sand plover | \n
85. | \nVanellus indicus | \nRedwattled lapwing | \n
86. | \nVanellus leucurus | \nWhite tailed plover | \n
87. | \nCharadrius dubius | \nLittle ringed plover | \n
88. | \nCharadrius alexadrinus | \nKentish plover | \n
89. | \nCharadrius mongolus | \nLesser sand plover | \n
90. | \nNumenius arquata | \nCurlew | \n
91. | \nNumenius phaeopus | \nWhimbler | \n
92. | \nLimosa lapponica | \nBartailed godwit | \n
93. | \nTringa totanus | \nCommon redshank | \n
94. | \nTringa stagnatilis | \nMarsh sandpiper | \n
95. | \nTringa nebularia | \nGreen shank | \n
96. | \nTringa glareola | \nWood sandpiper | \n
97. | \nTringa hypoleucos | \nCommon sandpiper | \n
98. | \nGallinnago gallinago | \nCommon snipe | \n
99. | \nCalidris minutus | \nLittle stint | \n
100. | \nCalidris alpine | \nDunlin | \n
101. | \nPhilomachus pugnax | \nRuff | \n
102. | \nHimantopus himantopus | \nBlackwinged stilt | \n
103. | \nLarus heuglini | \nHeuglin’s gull | \n
104. | \nLarus brunnicephalus | \nBrown headed gull | \n
105. | \nLarus ridibundus | \nBlack Headed gull | \n
106. | \nLarus genei | \nSlenderbilled gull | \n
107. | \nGelochelidon nlitica | \nGull-billed tern | \n
108. | \nHydroprogne caspia | \nCaspian tern | \n
109. | \nSterna aurantia | \nIndian River tern | \n
110. | \nSterna acuticauda | \nBlackbellied tern | \n
111. | \nSterna albifrons | \nLittle tern | \n
112. | \nSterna sendvicensis | \nSandwitch tern | \n
113. | \nColumba livia | \nBlue rock pigeon | \n
114. | \nStreptopelia decaocto | \nRing dove | \n
115. | \nSterptopelia senegalensis | \nLittle brown dove | \n
116. | \nCentropus sinensis | \nCrown pheasant | \n
117. | \nKetupa zeylonensis | \nBrown fish owl | \n
118. | \nAthene brama | \nSpotted owlet | \n
119. | \nCeryle rudis | \nPied kingfisher | \n
120. | \nAlcedo athis | \nCommon kingfisher | \n
121. | \nHalcyon smyrnensis | \nWhitebreasted kingfisher | \n
122. | \nMerops orientalis | \nGreen-bee eater | \n
123. | \nMerops persicus | \nBlue-cheeked bee eater | \n
124. | \nCorcias benghalensis | \nIndian roller | \n
125. | \nUpupa epops | \nCommon hoopoe | \n
126. | \nAmomanes deserti | \nDesert lark | \n
127. | \nCalendrella brachydactyla | \nGreat short-toed lark | \n
128. | \nGalerida cristata | \nCrested lark | \n
129. | \nRiparia diluta | \nPale sand martin | \n
130. | \nHirundo fuligula | \nCrag/rock martin | \n
131. | \nHirundo rustica | \nBarn or common swallow | \n
132. | \nLanius isabellinus | \nRufous tailed or Isabelline shrike | \n
133. | \nLanus meridionalis | \nSouthern gray shrike | \n
134. | \nLanius vittatus | \nBay backed shrike | \n
135. | \nDicrurus adsimilis | \nBlack drongo | \n
136. | \nAcridotheres adsimilis | \nIndian myna | \n
137. | \nSturnus vulgaris | \nCommon | \n
138. | \nPhoenicurus ochruros | \nStarling | \n
139. | \nOenanthe albonigra | \nHume’s wheatear | \n
140. | \nSaxicoloides fulicata | \nIndian robin | \n
141. | \nSaxicola caprata | \nPied robin chat | \n
142. | \nOenanthe isabellina | \nIsabelline wheatear | \n
143. | \nOenanthe sdeserti | \nDesert wheatear | \n
144. | \nCorvus splendens | \nHouse crow | \n
145. | \nDendrocitta vagabunda | \nTree pie | \n
146. | \nPrinia flaviventris | \nYellow bellied prinia | \n
147. | \nPrinia burnesii | \nRufous vented prinia | \n
148. | \nPycnonotus leucogenys | \nWhite-cheeked bulbul | \n
149. | \nPycnonotus cafer | \nRed-vented bulbul | \n
150. | \nTurdoides caudatus | \nCommon babbler | \n
151. | \nTurdoides earlie | \nStriated babbler | \n
152. | \nTurdoides striata | \nJungle babbler | \n
153. | \nRhipidura aureola | \nWhite browed fantail | \n
154. | \nPhylloscopus collybita | \nCommon chiffchaff | \n
155. | \nAcrocephalous stentoreus | \nClamorous reed warbler | \n
156. | \nSylvia curruca | \nLesser whitethroat sylvia | \n
157. | \nPhylloscopus trochiloides | \nGreenish warbler | \n
158. | \nMotacilla alba | \nWhite wagtail | \n
159. | \nMotacilla flava | \nYellow wagtail | \n
160. | \nNectarinia asiatica | \nPurple sunbird | \n
161. | \nPasser domesticus | \nHouse sparrow | \n
162. | \nPasser pyrrhonotus | \nSindh jungle sparrow | \n
Fauna of NDWS.
Reptiles: Region of Nara Desert is considered rich in herpeto-fauna with 24 reptilian species belonging to three orders and 12 families. Out of the 24-reptilian species, 2 were herbivores, 13 were carnivores and 9 were insectivores. A famous indigenous Vulnerable reptilian species of marsh crocodile (Crocodylus palustris) was also reported from the Nara Canal and its adjacent territories (Figure 2) (Table 3).
\nA view of marsh crocodile.
Birds: In the region of NDWC, these wetlands are recognized as the major habitats for the variety of rare and endangered migratory birds. In NDWC, 118 bird species belonging to 13 orders and 35 families belonging were reported. Fifty-nine birds were native species while 53 birds were migratory species. The important migratory birds were the marbled teal, Anan angustirostris (Vulnerable-threatened) and the ferruginous duck, Aythya nyroca and the Indian darter, Anhyinga rufa (Near-threatened) (Table 3).
\nSmall mammals: From the habitat of NDWC the small mammal populations belonging to 3 orders and 5 families were also reported. The small mammals included 5 granivores, 2 herbivores and 1 omnivore (Table 3).
\nLarge Mammals: Large Mammals: Twenty-five species of large mammals were reported. From order Carnivora, 10 species included the jungle cat, jackal, small Indian mongoose, gray mongoose, wolf and red fox and from the Order Artiodactyla, the wild boar (Table 3) (Figure 3–6).
\nA view of Lake in NDWC.
A view of plantation in NDWC.
A view of Desert dune in NDWC.
A view of Typha in NDWC.
The highest air temperature of 45°C was measured in July and the lowest of 20°C was measured in January (Tables 4, 11). The highest and lowest water temperatures were 42 and 17°C, respectively (Table 4). The highest and lowest values (9.3 and 6.9) for pH were measured in November and December, respectively (Tables 11, 12). The highest and lowest values (9120 and 364 mu/Scm) for EC were measured in November and May, respectively (Tables 11, 13). The highest and lowest values (1042 and 214 mg/L) for TDS were measured in March and May, respectively (Tables 7, 13). The highest and lowest values (186 and 0.20 NTU) for turbidity were measured in the months of January – September, respectively (Tables 7, 9). The highest and lowest values (1214 and 6 mg/L) for Ca were measured in January and November, respectively (Tables 8, 10). The highest and lowest values (876 and 12 mg/L) for Mg were measured in March and May, respectively (Tables 8, 13). The highest and lowest values (5536 and 140 mg/L) for hardness were measured in March and May, respectively (Tables 7, 13). The highest and lowest values (71.6 and 2.0 mg/L) for alkalinity were measured in November and September, respectively (Tables 8, 11). The highest and lowest values (3418 and 22 mg/L) for Cl were measured in March and May, respectively (Tables 7, 13). The highest and lowest values (1231 and 21 mg/L) for Na were measured in November and May, respectively (Tables 10, 13). The highest and lowest values (182 and 3 m/L) for K were measured in January and July, respectively (Tables 7, 13). The highest and lowest values (2980 and 38 mg/L) for SO4 were measured in November and May, respectively (Tables 7, 13). The highest and lowest values (3580 and 0.5 mg/L) for HCO3 were measured in November and July (Tables 8, 11) while the value of 0 for HCO was measured in all the months of the study period (Tables 4–13). The highest and lowest values (4.6 and 2.7 mg/L) for BOD were measured in January and July, respectively (Tables 9, 10). The highest and lowest values (6.1 and 3.7 mg/L) for DO were measured in November and July, respectively (Tables 4, 10).
\nParameters | \nMonths | \n|||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | \nFeb | \nMar | \nApr | \nMay | \nJun | \nJul | \nAug | \nSep | \nOct | \nNov | \nDec | \n|
Temp: Air | \n20 | \n25 | \n28 | \n32 | \n38 | \n43 | \n45 | \n42 | \n32 | \n28 | \n23 | \n20 | \n
Tem: Water | \n17 | \n21 | \n23 | \n28 | \n29 | \n38 | \n33 | \n37 | \n29 | \n24 | \n20 | \n17 | \n
pH | \n8.2 | \n8.1 | \n8.3 | \n8.2 | \n8.0 | \n7.9 | \n7.9 | \n8.0 | \n7.8 | \n7.9 | \n8.0 | \n8.1 | \n
EC | \n985 | \n915 | \n954 | \n885 | \n875 | \n870 | \n850 | \n860 | \n820 | \n890 | \n970 | \n930 | \n
TDS | \n630 | \n618 | \n613 | \n605 | \n590 | \n598 | \n600 | \n620 | \n580 | \n612 | \n640 | \n624 | \n
Turb | \n30 | \n35 | \n32 | \n38 | \n40 | \n42 | \n45 | \n37 | \n28 | \n33 | \n35 | \n41 | \n
Ca | \n40 | \n38 | \n52 | \n50 | \n58 | \n52 | \n65 | \n54 | \n35 | \n34 | \n45 | \n42 | \n
Mg | \n27 | \n30 | \n37 | \n32 | \n35 | \n33 | \n40 | \n25 | \n23 | \n29 | \n24 | \n38 | \n
Hard | \n210 | \n234 | \n270 | \n218 | \n245 | \n240 | \n256 | \n228 | \n205 | \n215 | \n225 | \n220 | \n
Alkal | \n3.5 | \n3.7 | \n3.8 | \n3.9 | \n3.4 | \n3.7 | \n4.0 | \n3.8 | \n3.2 | \n3.5 | \n3.6 | \n3.7 | \n
Cl | \n131 | \n127 | \n122 | \n132 | \n115 | \n142 | \n145 | \n130 | \n120 | \n124 | \n140 | \n135 | \n
Na | \n128 | \n143 | \n145 | \n135 | \n120 | \n128 | \n150 | \n132 | \n110 | \n122 | \n130 | \n137 | \n
K | \n18 | \n20 | \n21 | \n23 | \n19 | \n20 | \n25 | \n21 | \n16 | \n17 | \n20 | \n18 | \n
SO4 | \n128 | \n138 | \n140 | \n98 | \n32 | \n110 | \n116 | \n114 | \n105 | \n118 | \n120 | \n132 | \n
HCO | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n
HCO3 | \n175 | \n152 | \n162 | \n147 | \n150 | \n134 | \n130 | \n170 | \n140 | \n153 | \n165 | \n160 | \n
BOD | \n3.0 | \n3.5 | \n3.2 | \n3.0 | \n2.9 | \n3.3 | \n2.8 | \n3.1 | \n3.5 | \n3.4 | \n3.8 | \n3.7 | \n
DO | \n4.8 | \n4.5 | \n4.9 | \n4.2 | \n3.7 | \n4.0 | \n3.9 | \n4.3 | \n4.2 | \n4.6 | \n4.1 | \n4.7 | \n
Physicochemical analysis of water sample of station 1. Gunjo Bhanbharo Lake.
Parameters | \nMonths | \n|||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | \nFeb | \nMar | \nApr | \nMay | \nJun | \nJul | \nAug | \nSep | \nOct | \nNov | \nDec | \n|
Temp: Air | \n20 | \n25 | \n26 | \n30 | \n32 | \n37 | \n42 | \n38 | \n32 | \n27 | \n23 | \n20 | \n
Tem: Water | \n16 | \n21 | \n23 | \n26 | \n29 | \n33 | \n38 | \n35 | \n29 | \n24 | \n20 | \n17 | \n
pH | \n8.2 | \n8.0 | \n8.1 | \n7.9 | \n8.0 | \n7.7 | \n7.8 | \n8.1 | \n7.6 | \n8.0 | \n7.9 | \n8.2 | \n
EC | \n2860 | \n2478 | \n2730 | \n2380 | \n2595 | \n2517 | \n2370 | \n2678 | \n2173 | \n2594 | \n2436 | \n2247 | \n
TDS | \n1830 | \n1800 | \n16,500 | \n1700 | \n1540 | \n1460 | \n1285 | \n1678 | \n1240 | \n1315 | \n1464 | \n1780 | \n
Turb | \n33 | \n24 | \n30 | \n22 | \n25 | \n19 | \n21 | \n20 | \n18 | \n26 | \n28 | \n29 | \n
Ca | \n140 | \n137 | \n150 | \n145 | \n130 | \n142 | \n120 | \n126 | \n114 | \n118 | \n132 | \n122 | \n
Mg | \n60 | \n48 | \n75 | \n70 | \n52 | \n65 | \n45 | \n44 | \n42 | \n58 | \n53 | \n66 | \n
Hard | \n600 | \n652 | \n680 | \n575 | \n580 | \n638 | \n590 | \n618 | \n563 | \n598 | \n620 | \n640 | \n
Alkal | \n7.0 | \n8.2 | \n12.0 | \n9.1 | \n8.0 | \n8.5 | \n10.0 | \n7.8 | \n6.0 | \n11.0 | \n9.0 | \n8.4 | \n
Cl | \n192 | \n185 | \n210 | \n190 | \n170 | \n192 | \n165 | \n204 | \n154 | \n167 | \n184 | \n180 | \n
Na | \n382 | \n370 | \n350 | \n308 | \n315 | \n338 | \n286 | \n268 | \n250 | \n317 | \n375 | \n347 | \n
K | \n32 | \n36 | \n38 | \n32 | \n28 | \n25 | \n24 | \n33 | \n20 | \n24 | \n27 | \n23 | \n
SO4 | \n778 | \n754 | \n725 | \n674 | \n635 | \n653 | \n605 | \n725 | \n582 | \n762 | \n683 | \n697 | \n
HCO | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n
HCO3 | \n350 | \n270 | \n285 | \n315 | \n260 | \n290 | \n250 | \n305 | \n250 | \n360 | \n376 | \n326 | \n
BOD | \n3.5 | \n3.4 | \n3.2 | \n3.6 | \n3.6 | \n3.7 | \n3.8 | \n3.5 | \n3.7 | \n3.3 | \n3.8 | \n3.4 | \n
DO | \n5.0 | \n4.9 | \n4.8 | \n4..7 | \n4.9 | \n4.5 | \n4.8 | \n4.8 | \n4.6 | \n4.3 | \n4.2 | \n4.7 | \n
Physicochemical analysis of water sample of station 2. Bachal Bhanbharo Lake.
Parameters | \nMonths | \n|||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | \nFeb | \nMar | \nApr | \nMay | \nJun | \nJul | \nAug | \nSep | \nOct | \nNov | \nDec | \n|
Temp: Air | \n21 | \n24 | \n26 | \n30 | \n32 | \n37 | \n43 | \n36 | \n32 | \n27 | \n23 | \n20 | \n
Tem: Water | \n18 | \n20 | \n23 | \n28 | \n29 | \n34 | \n39 | \n32 | \n29 | \n24 | \n20 | \n17 | \n
pH | \n8.0 | \n7.9 | \n7.6 | \n7.7 | \n7.8 | \n7.6 | \n7.5 | \n7.8 | \n7.3 | \n7.5 | \n7.9 | \n7.7 | \n
EC | \n925 | \n876 | \n856 | \n815 | \n814 | \n764 | \n725 | \n780 | \n705 | \n802 | \n883 | \n865 | \n
TDS | \n586 | \n562 | \n547 | \n540 | \n510 | \n487 | \n462 | \n516 | \n443 | \n550 | \n532 | \n580 | \n
Turb | \n9.2 | \n11.4 | \n11.9 | \n10.7 | \n10.4 | \n11.9 | \n12.3 | \n10.9 | \n11.2 | \n11.0 | \n11.6 | \n10.8 | \n
Ca | \n82 | \n74 | \n68 | \n80 | \n54 | \n67 | \n45 | \n56 | \n40 | \n65 | \n75 | \n70 | \n
Mg | \n74 | \n70 | \n62 | \n51 | \n52 | \n58 | \n48 | \n66 | \n42 | \n54 | \n68 | \n62 | \n
Hard | \n538 | \n518 | \n426 | \n487 | \n410 | \n468 | \n430 | \n500 | \n410 | \n485 | \n520 | \n508 | \n
Alkal | \n3.5 | \n3.2 | \n2.6 | \n2.8 | \n2.5 | \n2.9 | \n2.2 | \n2.8 | \n2.1 | \n3.1 | \n3.2 | \n3.4 | \n
Cl | \n168 | \n156 | \n139 | \n141 | \n130 | \n127 | \n118 | \n1162 | \n106 | \n149 | \n156 | \n150 | \n
Na | \n42 | \n33 | \n38 | \n41 | \n34 | \n35 | \n32 | \n36 | \n27 | \n40 | \n38 | \n39 | \n
K | \n40 | \n34 | \n32 | \n29 | \n30 | \n35 | \n34 | \n47 | \n28 | \n37 | \n36 | \n33 | \n
SO4 | \n208 | \n193 | \n195 | \n165 | \n173 | \n182 | \n164 | \n158 | \n153 | \n168 | \n192 | \n187 | \n
HCO | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n
HCO3 | \n142 | \n137 | \n130 | \n129 | \n123 | \n132 | \n120 | \n125 | \n105 | \n139 | \n134 | \n138 | \n
BOD | \n3.9 | \n3.6 | \n3.8 | \n3.8 | \n3.6 | \n3.8 | \n3.7 | \n3.6 | \n3.5 | \n3.7 | \n3.9 | \n3.8 | \n
DO | \n5.2 | \n4.9 | \n5.1 | \n5.0 | \n4.8 | \n4.7 | \n4.8 | \n4.9 | \n4.6 | \n5.1 | \n5.1 | \n5.0 | \n
Physicochemical analysis of water sample of station 3. Skebi Lake.
Parameters | \nMonths | \n|||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | \nFeb | \nMar | \nApr | \nMay | \nJun | \nJul | \nAug | \nSep | \nOct | \nNov | \nDec | \n|
Temp: Air | \n21 | \n23 | \n26 | \n31 | \n32 | \n39 | \n44 | \n40 | \n32 | \n26 | \n23 | \n20 | \n
Tem: Water | \n19 | \n20 | \n23 | \n27 | \n29 | \n36 | \n40 | \n36 | \n29 | \n23 | \n20 | \n17 | \n
pH | \n8.5 | \n8.6 | \n9.0 | \n8.5 | \n8.7 | \n8.7 | \n8.5 | \n8.8 | \n8.2 | \n8.9 | \n8.4 | \n8.6 | \n
EC | \n1327 | \n2190 | \n1270 | \n1250 | \n1194 | \n1230 | \n1054 | \n1180 | \n983 | \n1246 | \n1127 | \n1110 | \n
TDS | \n9827 | \n8965 | \n10,420 | \n9547 | \n8974 | \n9657 | \n7590 | \n8540 | \n6580 | \n7890 | \n8936 | \n9570 | \n
Turb | \n0.27 | \n0.23 | \n0.29 | \n0.24 | \n0.25 | \n0.27 | \n0.23 | \n0.25 | \n0.20 | \n0.26 | \n0.29 | \n0.28 | \n
Ca | \n728 | \n745 | \n772 | \n710 | \n685 | \n715 | \n680 | \n672 | \n582 | \n720 | \n782 | \n730 | \n
Mg | \n838 | \n782 | \n876 | \n816 | \n782 | \n725 | \n678 | \n698 | \n645 | \n763 | \n812 | \n804 | \n
Hard | \n5428 | \n5160 | \n5536 | \n5096 | \n4528 | \n4826 | \n3974 | \n4976 | \n3792 | \n5120 | \n5265 | \n5380 | \n
Alkal | \n3.8 | \n3.5 | \n3.4 | \n3.3 | \n3.2 | \n3.7 | \n3.1 | \n3.2 | \n3.0 | \n3.7 | \n3.6 | \n3.5 | \n
Cl | \n3329 | \n3289 | \n3418 | \n3370 | \n3236 | \n3190 | \n3075 | \n2987 | \n2868 | \n3145 | \n3185 | \n3276 | \n
Na | \n52 | \n50 | \n46 | \n54 | \n58 | \n45 | \n48 | \n47 | \n42 | \n48 | \n49 | \n50 | \n
K | \n182 | \n159 | \n170 | \n180 | \n162 | \n150 | \n156 | \n169 | \n140 | \n160 | \n172 | \n157 | \n
SO4 | \n2980 | \n2896 | \n2937 | \n2765 | \n2830 | \n2696 | \n2589 | \n2752 | \n2438 | \n2686 | \n2845 | \n2810 | \n
HCO | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n
HCO3 | \n182 | \n153 | \n170 | \n167 | \n162 | \n146 | \n156 | \n149 | \n140 | \n148 | \n172 | \n160 | \n
BOD | \n4.2 | \n4.1 | \n4.0 | \n3.9 | \n4.0 | \n4.0 | \n3.9 | \n3.9 | \n3.8 | \n4.0 | \n4.1 | \n4.0 | \n
DO | \n5.5 | \n5.3 | \n5.4 | \n5.2 | \n5.3 | \n5.4 | \n5.1 | \n5.2 | \n5.0 | \n5.3 | \n5.2 | \n5.1 | \n
Physicochemical analysis of water sample of station 4. Tooti Lake.
Parameters | \nMonths | \n|||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | \nFeb | \nMar | \nApr | \nMay | \nJun | \nJul | \nAug | \nSep | \nOct | \nNov | \nDec | \n|
Temp: Air | \n21 | \n25 | \n26 | \n33 | \n37 | \n41 | \n42 | \n39 | \n32 | \n26 | \n23 | \n20 | \n
Tem: Water | \n19 | \n22 | \n23 | \n30 | \n33 | \n38 | \n39 | \n36 | \n29 | \n23 | \n20 | \n17 | \n
pH | \n9.2 | \n9.0 | \n9.1 | \n8.7 | \n8.8 | \n9.0 | \n8.5 | \n8.6 | \n8.3 | \n8.4 | \n8.9 | \n8.8 | \n
EC | \n7984 | \n7878 | \n7920 | \n7636 | \n7180 | \n7280 | \n6568 | \n6892 | \n5872 | \n6972 | \n6217 | \n7684 | \n
TDS | \n5123 | \n4976 | \n5068 | \n4783 | \n4837 | \n4690 | \n4287 | \n4528 | \n4027 | \n4686 | \n4839 | \n5074 | \n
Turb | \n38 | \n32 | \n33 | \n37 | \n30 | \n36 | \n28 | \n29 | \n24 | \n31 | \n35 | \n34 | \n
Ca | \n1214 | \n1180 | \n1174 | \n1168 | \n1034 | \n1149 | \n983 | \n1084 | \n845 | \n987 | \n1128 | \n1068 | \n
Mg | \n394 | \n376 | \n374 | \n310 | \n345 | \n355 | \n285 | \n367 | \n262 | \n296 | \n312 | \n342 | \n
Hard | \n3985 | \n3884 | \n3923 | \n3810 | \n3812 | \n3792 | \n3529 | \n3626 | \n3428 | \n3590 | \n3782 | \n3888 | \n
Alkal | \n2.9 | \n2.3 | \n2.64 | \n2.4 | \n2.5 | \n2.2 | \n2.1 | \n2.5 | \n2.0 | \n2.6 | \n2.8 | \n2.7 | \n
Cl | \n147 | \n140 | \n132 | \n130 | \n127 | \n129 | \n120 | \n125 | \n108 | \n134 | \n142 | \n128 | \n
Na | \n75 | \n72 | \n69 | \n71 | \n62 | \n73 | \n56 | \n54 | \n48 | \n66 | \n72 | \n70 | \n
K | \n23 | \n21 | \n18 | \n19 | \n16 | \n20 | \n14 | \n22 | \n15 | \n17 | \n21 | \n22 | \n
SO4 | \n1915 | \n1880 | \n1819 | \n1792 | \n1725 | \n1682 | \n1528 | \n1632 | \n1372 | \n1575 | \n1882 | \n1794 | \n
HCO | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n
HCO3 | \n0.9 | \n0.6 | \n0.8 | \n0.6 | \n0.7 | \n0.8 | \n0.5 | \n0.7 | \n0.6 | \n0.7 | \n0.8 | \n0.6 | \n
BOD | \n4.5 | \n4.4 | \n4.2 | \n4.3 | \n4.1 | \n4.2 | \n4.0 | \n4.1 | \n3.9 | \n4.3 | \n4.2 | \n4.0 | \n
DO | \n5.4 | \n5.1 | \n5.3 | \n5.1 | \n5.2 | \n5.3 | \n5.1 | \n5.2 | \n5.0 | \n5.3 | \n5.2 | \n5.1 | \n
Physicochemical analysis of water sample of station 5. Dangewari Lake.
Parameters | \nMonths | \n|||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | \nFeb | \nMar | \nApr | \nMay | \nJun | \nJul | \nAug | \nSep | \nOct | \nNov | \nDec | \n|
Temp: Air | \n21 | \n24 | \n26 | \n34 | \n38 | \n43 | \n44 | \n38 | \n32 | \n28 | \n23 | \n20 | \n
Tem: Water | \n19 | \n21 | \n23 | \n30 | \n35 | \n40 | \n41 | \n34 | \n29 | \n24 | \n20 | \n17 | \n
pH | \n9.0 | \n8.8 | \n8.9 | \n8.6 | \n8.5 | \n8.7 | \n8.3 | \n8.4 | \n8.1 | \n8.3 | \n8.6 | \n8.8 | \n
EC | \n8912 | \n8842 | \n8720 | \n8254 | \n7837 | \n7632 | \n6865 | \n6540 | \n5392 | \n6934 | \n6836 | \n7894 | \n
TDS | \n5610 | \n5495 | \n5580 | \n5324 | \n4872 | \n4670 | \n4583 | \n4264 | \n3983 | \n4685 | \n4892 | \n4975 | \n
Turb | \n186 | \n183 | \n180 | \n172 | \n162 | \n148 | \n152 | \n138 | \n106 | \n162 | \n170 | \n180 | \n
Ca | \n492 | \n471 | \n482 | \n465 | \n385 | \n390 | \n328 | \n387 | \n295 | \n348 | \n412 | \n426 | \n
Mg | \n782 | \n735 | \n757 | \n708 | \n628 | \n615 | \n573 | \n628 | \n412 | \n593 | \n684 | \n710 | \n
Hard | \n4427 | \n4350 | \n4321 | \n4230 | \n4082 | \n4150 | \n3862 | \n3764 | \n3429 | \n3927 | \n4128 | \n4250 | \n
Alkal | \n2.8 | \n2.4 | \n2.6 | \n2.3 | \n2.4 | \n2.5 | \n2.2 | \n2.6 | \n2.1 | \n2.4 | \n2.5 | \n2.7 | \n
Cl | \n142 | \n137 | \n130 | \n120 | \n126 | \n119 | \n114 | \n121 | \n108 | \n133 | \n125 | \n138 | \n
Na | \n54 | \n49 | \n46 | \n44 | \n42 | \n43 | \n37 | \n39 | \n32 | \n46 | \n41 | \n40 | \n
K | \n8.0 | \n8.4 | \n4.0 | \n7.5 | \n5.0 | \n6.8 | \n6.0 | \n8.7 | \n7.0 | \n6.5 | \n9.0 | \n8.9 | \n
SO4 | \n2286 | \n2175 | \n2213 | \n2098 | \n2145 | \n1945 | \n2096 | \n2190 | \n1827 | \n1670 | \n1273 | \n1450 | \n
HCO | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n
HCO3 | \n1.2 | \n1.1 | \n1.06 | \n1.3 | \n1.03 | \n1.2 | \n1.1 | \n1.3 | \n1.0 | \n1.1 | \n1.4 | \n1.2 | \n
BOD | \n4.6 | \n4.2 | \n4.3 | \n4.4 | \n4.2 | \n4.5 | \n4.1 | \n4.2 | \n3.9 | \n4.0 | \n4.3 | \n4.2 | \n
DO | \n5.1 | \n4.8 | \n5.0 | \n4.9 | \n5.0 | \n5.0 | \n4.9 | \n4.8 | \n4.7 | \n4.8 | \n5.0 | \n4.9 | \n
Physicochemical analysis of water sample of station 6. Kharari Lake.
Parameters | \nMonths | \n|||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | \nFeb | \nMar | \nApr | \nMay | \nJun | \nJul | \nAug | \nSep | \nOct | \nNov | \nDec | \n|
Temp: Air | \n20 | \n24 | \n26 | \n30 | \n37 | \n41 | \n44 | \n38 | \n32 | \n27 | \n23 | \n20 | \n
Tem: Water | \n18 | \n21 | \n23 | \n27 | \n34 | \n37 | \n40 | \n34 | \n29 | \n24 | \n20 | \n17 | \n
pH | \n8.0 | \n7.8 | \n7.9 | \n7.6 | \n7.7 | \n7.9 | \n7.5 | \n7.8 | \n7.6 | \n7.8 | \n7.7 | \n7.9 | \n
EC | \n5489 | \n5370 | \n5282 | \n5685 | \n4827 | \n5120 | \n4628 | \n4952 | \n5273 | \n5734 | \n6190 | \n5856 | \n
TDS | \n3186 | \n3256 | \n2973 | \n3080 | \n2863 | \n2965 | \n2682 | \n2792 | \n2854 | \n3124 | \n3340 | \n3260 | \n
Turb | \n12.6 | \n11.8 | \n12.0 | \n11.3 | \n10.0 | \n9.2 | \n8.0 | \n10.4 | \n9.0 | \n9.8 | \n11.9 | \n10.5 | \n
Ca | \n22 | \n18 | \n20 | \n19 | \n18 | \n17 | \n15 | \n21 | \n13 | \n11 | \n6 | \n10 | \n
Mg | \n294 | \n265 | \n240 | \n217 | \n235 | \n247 | \n192 | \n230 | \n205 | \n208 | \n273 | \n253 | \n
Hard | \n1082 | \n978 | \n926 | \n912 | \n884 | \n890 | \n836 | \n992 | \n928 | \n985 | \n1180 | \n1005 | \n
Alkal | \n20.5 | \n19.6 | \n`19.8 | \n18.7 | \n19.2 | \n20.2 | \n16.2 | \n17.6 | \n18.5 | \n19.9 | \n21.4 | \n20.7 | \n
Cl | \n603 | \n568 | \n583 | \n590 | \n535 | \n528 | \n483 | \n610 | \n528 | \n630 | \n666 | \n642 | \n
Na | \n787 | \n740 | \n782 | \n714 | \n739 | \n728 | \n626 | \n775 | \n712 | \n805 | \n821 | \n794 | \n
K | \n38 | \n35 | \n31 | \n34 | \n29 | \n32 | \n26 | \n36 | \n28 | \n30 | \n32 | \n33 | \n
SO4 | \n937 | \n883 | \n826 | \n694 | \n793 | \n782 | \n638 | \n728 | \n875 | \n635 | \n1040 | \n868 | \n
HCO | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n
HCO3 | \n980 | \n940 | \n826 | \n860 | \n793 | \n836 | \n782 | \n872 | \n863 | \n945 | \n1070 | \n984 | \n
BOD | \n3.7 | \n3.6 | \n3.1 | \n3.5 | \n2.9 | \n3.4 | \n2.7 | \n3.3 | \n2.9 | \n3.2 | \n3.0 | \n4.4 | \n
DO | \n5.6 | \n5.8 | \n5.4 | \n5.7 | \n5.3 | \n5.5 | \n5.2 | \n5.8 | \n5.7 | \n5.9 | \n6.1 | \n6.0 | \n
Physicochemical analysis of water sample of station 7. Raja Pathan Lake.
Parameters | \nMonths | \n|||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | \nFeb | \nMar | \nApr | \nMay | \nJun | \nJul | \nAug | \nSep | \nOct | \nNov | \nDec | \n|
Temp: Air | \n21 | \n25 | \n26 | \n34 | \n39 | \n42 | \n45 | \n40 | \n32 | \n26 | \n23 | \n20 | \n
Tem: Water | \n19 | \n22 | \n23 | \n30 | \n36 | \n39 | \n41 | \n36 | \n29 | \n23 | \n20 | \n17 | \n
pH | \n8.8 | \n8.7 | \n8.4 | \n8.6 | \n8.2 | \n8.4 | \n8.5 | \n8.3 | \n9.0 | \n9.1 | \n9.3 | \n8.9 | \n
EC | \n8379 | \n8239 | \n7838 | \n5673 | \n4570 | \n6890 | \n5820 | \n7346 | \n7739 | \n8215 | \n9120 | \n8764 | \n
TDS | \n1529 | \n1432 | \n1482 | \n1380 | \n1273 | \n1446 | \n1382 | \n1457 | \n1528 | \n1590 | \n1690 | \n1568 | \n
Turb | \n0.27 | \n0.25 | \n0.26 | \n0.28 | \n0.22 | \n0.23 | \n0.21 | \n0.24 | \n0.25 | \n0.27 | \n0.29 | \n0.22 | \n
Ca | \n49 | \n45 | \n38 | \n46 | \n30 | \n42 | \n36 | \n47 | \n40 | \n51 | \n54 | \n50 | \n
Mg | \n329 | \n348 | \n230 | \n297 | \n186 | \n245 | \n210 | \n305 | \n236 | \n264 | \n397 | \n322 | \n
Hard | \n1626 | \n1486 | \n1327 | \n1336 | \n1182 | \n1479 | \n1268 | \n1504 | \n1529 | \n1545 | \n1770 | \n1654 | \n
Alkal | \n67.4 | \n59 | \n48 | \n51 | \n43 | \n49 | \n50 | \n62 | \n60.2 | \n57 | \n71.6 | \n68 | \n
Cl | \n320 | \n316 | \n257 | \n286 | \n216 | \n265 | \n237 | \n248 | \n285 | \n317 | \n397 | \n304 | \n
Na | \n1125 | \n1030 | \n937 | \n879 | \n792 | \n915 | \n845 | \n1056 | \n1026 | \n1187 | \n1231 | \n1115 | \n
K | \n67 | \n70 | \n53 | \n56 | \n47 | \n58 | \n52 | \n60 | \n62 | \n57 | \n72 | \n55 | \n
SO4 | \n386 | \n357 | \n274 | \n290 | \n218 | \n287 | \n274 | \n370 | \n326 | \n365 | \n403 | \n378 | \n
HCO | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n
HCO3 | \n3428 | \n3294 | \n3028 | \n3125 | \n2793 | \n2880 | \n2983 | \n3145 | \n3218 | \n3472 | \n3580 | \n3356 | \n
BOD | \n3.9 | \n3.6 | \n3.2 | \n3.3 | \n3.4 | \n3.7 | \n3.5 | \n3.8 | \n3.6 | \n3.5 | \n3.8 | \n3.7 | \n
DO | \n5.6 | \n5.5 | \n4.8 | \n5.3 | \n5.0 | \n5.1 | \n5.2 | \n5.0 | \n5.4 | \n5.7 | \n5.8 | \n5.2 | \n
Physicochemical analysis of water sample of station 8. Old Nara Lake.
Parameters | \nMonths | \n|||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | \nFeb | \nMar | \nApr | \nMay | \nJun | \nJul | \nAug | \nSep | \nOct | \nNov | \nDec | \n|
Temp: Air | \n21 | \n23 | \n26 | \n34 | \n38 | \n41 | \n42 | \n37 | \n32 | \n28 | \n23 | \n20 | \n
Tem: Water | \n19 | \n20 | \n23 | \n30 | \n35 | \n38 | \n39 | \n34 | \n29 | \n25 | \n20 | \n17 | \n
pH | \n7.9 | \n7.7 | \n7.8 | \n7.6 | \n7.3 | \n7.5 | \n7.1 | \n7.4 | \n7.0 | \n7.3 | \n6.9 | \n7.2 | \n
EC | \n1263 | \n1143 | \n1172 | \n1092 | \n938 | \n1286 | \n1027 | \n1167 | \n1263 | \n1342 | \n1495 | \n1275 | \n
TDS | \n729 | \n720 | \n628 | \n612 | \n510 | \n664 | \n543 | \n675 | \n692 | \n754 | \n808 | \n785 | \n
Turb | \n31 | \n32 | \n28 | \n29 | \n25 | \n26 | \n27 | \n28 | \n30 | \n27 | \n33 | \n30 | \n
Ca | \n58 | \n55 | \n50 | \n52 | \n40 | \n48 | \n46 | \n50 | \n53 | \n57 | \n64 | \n60 | \n
Mg | \n49 | \n47 | \n46 | \n43 | \n36 | \n44 | \n42 | \n45 | \n47 | \n53 | \n56 | \n51 | \n
Hard | \n328 | \n345 | \n275 | \n263 | \n204 | \n289 | \n232 | \n254 | \n245 | \n316 | \n390 | \n327 | \n
Alkal | \n4.3 | \n4.4 | \n4.0 | \n4.1 | \n3.9 | \n4.7 | \n4.1 | \n4.4 | \n4.2 | \n4.5 | \n4.8 | \n4.6 | \n
Cl | \n152 | \n148 | \n138 | \n119 | \n110 | \n134 | \n126 | \n160 | \n135 | \n145 | \n166 | \n141 | \n
Na | \n141 | \n128 | \n136 | \n130 | \n106 | \n128 | \n124 | \n133 | \n130 | \n140 | \n150 | \n137 | \n
K | \n17 | \n14 | \n16 | \n15 | \n12 | \n13 | \n4 | \n11 | \n5 | \n9 | \n18 | \n17 | \n
SO4 | \n239 | \n215 | \n143 | \n178 | \n137 | \n198 | \n153 | \n232 | \n172 | \n214 | \n260 | \n243 | \n
HCO | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n
HCO3 | \n210 | \n225 | \n189 | \n194 | \n145 | \n178 | \n162 | \n196 | \n186 | \n224 | \n240 | \n210 | \n
BOD | \n3.8 | \n3.7 | \n3.5 | \n3.6 | \n3.3 | \n3.5 | \n3.0 | \n3.4 | \n3.1 | \n3.2 | \n3.6 | \n3.3 | \n
DO | \n5.0 | \n4.8 | \n4.9 | \n4.9 | \n4.8 | \n4.7 | \n4.8 | \n4.8 | \n4.9 | \n4.8 | \n5.0 | \n4.8 | \n
Physicochemical analysis of water sample of station 9. Saedo Pattan Lake.
Parameters | \nMonths | \n|||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | \nFeb | \nMar | \nApr | \nMay | \nJun | \nJul | \nAug | \nSep | \nOct | \nNov | \nDec | \n|
Temp: Air | \n21 | \n24 | \n26 | \n33 | \n35 | \n40 | \n43 | \n38 | \n32 | \n27 | \n23 | \n20 | \n
Tem: Water | \n19 | \n21 | \n3 | \n30 | \n31 | \n36 | \n40 | \n34 | \n29 | \n24 | \n20 | \n17 | \n
pH | \n7.6 | \n7.4 | \n7.2 | \n7.3 | \n7.0 | \n7.2 | \n6.9 | \n7.1 | \n7.0 | \n7.5 | \n7.3 | \n7.1 | \n
EC | \n528 | \n512 | \n489 | \n537 | \n364 | \n464 | \n387 | \n486 | \n472 | \n515 | \n596 | \n564 | \n
TDS | \n312 | \n302 | \n307 | \n289 | \n214 | \n289 | \n230 | \n267 | \n278 | \n307 | \n321 | \n298 | \n
Turb | \n162 | \n157 | \n147 | \n137 | \n117 | \n118 | \n26 | \n127 | \n145 | \n167 | \n180 | \n172 | \n
Ca | \n47 | \n49 | \n53 | \n51 | \n38 | \n45 | \n42 | \n51 | \n48 | \n50 | \n60 | \n54 | \n
Mg | \n18 | \n15 | \n17 | \n13 | \n4 | \n12 | \n15 | \n14 | \n16 | \n15 | \n19 | \n17 | \n
Hard | \n210 | \n208 | \n203 | \n198 | \n140 | \n212 | \n154 | \n168 | \n182 | \n185 | \n230 | \n217 | \n
Alkal | \n3.5 | \n3.3 | \n3.4 | \n3.2 | \n3.1 | \n3.3 | \n3.0 | \n3.5 | \n3.1 | \n3.2 | \n3.6 | \n3.4 | \n
Cl | \n30 | \n31 | \n29 | \n27 | \n22 | \n28 | \n25 | \n26 | \n28 | \n30 | \n32 | \n29 | \n
Na | \n31 | \n28 | \n30 | \n25 | \n21 | \n24 | \n27 | \n26 | \n29 | \n28 | \n33 | \n30 | \n
K | \n7.0 | \n6.8 | \n6.0 | \n6.2 | \n5.0 | \n5.4 | \n3.0 | \n4.8 | \n5.0 | \n5.9 | \n4.0 | \n4.7 | \n
SO4 | \n61 | \n55 | \n47 | \n44 | \n38 | \n43 | \n40 | \n49 | \n46 | \n56 | \n69 | \n62 | \n
HCO | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n
HCO3 | \n171 | \n156 | \n154 | \n128 | \n104 | \n145 | \n120 | \n138 | \n125 | \n167 | \n180 | \n171 | \n
BOD | \n3.9 | \n3.7 | \n3.5 | \n3.8 | \n3.3 | \n3.4 | \n3.1 | \n3.6 | \n3.2 | \n3.5 | \n3.8 | \n3.4 | \n
DO | \n5.0 | \n4.8 | \n4.9 | \n4.8 | \n4.7 | \n4.8 | \n4.6 | \n4.9 | \n4.8 | \n4.7 | \n5.0 | \n4.9 | \n
Analysis of physicochemical parameters of water sample for station 10. Nara Canal Chundiko.
The Nara Desert Wetland Complex (NDWC) encompasses sandy dunes, steep hills and includes low lying flat zones associated with different natural wetlands formed from the seepage of Nara Canal. These different wetlands are the major perennial source of water for the agricultural lands, local communities, wildlife and grazing livestock. NDWC comprises more than 225 seasonal and permanent, small, medium and large-sized lakes/wetlands. The total area of Nara Canal is distributed from Sorah (Sukkur) to Head Jamrao about 108,960 hectares and Nara Canal was declared in 1972 as a Game Reserve area for the protection of wild animals. The NDWC is also recognized as an essential potential Ramsar Site [1, 2, 3, 4, 5]. The different floral habitation distributed in the Nara Desert includes phytoplankton, reed vegetation, herbs, shrubs and trees. The area is ecologically-rich with the faunal biodiversity which includes zooplankton, invertebrates, fishes, amphibians, reptiles, birds, small and large mammals. The NDWC has received high economic, social, floral and faunal habitats, and aquatic biodiversity values because the local communities are directly or indirectly dependent on these natural sustainable resources [1, 2, 3, 4, 7, 29, 35] (Figure 7).
\nA view of Nara Canal Khairpur.
In the area the water quality and recharged by the Nara Canal is mainly sweet and acceptable ranges of TDS between 500 and 800 ppm except hypersaline lakes. The hypersaline water of desert area is mainly observed brackish which have TDS between 10,000 and 28,000 pp. [5, 36].
\nThe climate of the area is mainly arid having high temperatures and late summer rains observed. The seasonal rainfall is varied and is less than 250–300 mm and rainy season usually starts from June to September. Before the monsoon season, the average temperature is exceeding 45°C in the desert region and in the plains of NDWS the average temperature between 30 and 40°C. The wetland complex is recognized of great hydrological values as 98% Nara Canal water is used for agriculture and only 2% water is used for domestic and drinking purposes. The wetland complex of Nara Canal is 361.6 km long and 90–135 m wide. The maximum water depth of wetland complex is 7.5 m. The highest water discharges of Nara Canal are reported in the months of May–July and the minimum water flow in the August [1, 2, 3, 4, 7].
\nThe assessment of physicochemical parameters such as pH and alkalinity revealed that the lake water has mostly remained alkaline during the whole study period except the Nara Canal station due to its input of rain water. The range of hardness was higher in most of the selected lakes of NDWC. The acceptable level of hardness in lake waters is recognized as 200 mg/L by the World Health Organization [34]. The physicochemical parameters of Na and Mg, as well as the EC, TDS concentrations were found to be higher than the WHO standard. The Na is the major solute that can also affect aquatic biodiversity [37]; Na concentrations during the study period were higher than WHO accepted standard in most of the wetlands in the NDWC. The WHO recommended that the tolerable level of Cl is 250 mg/L [34]. In this study, the various wetlands had higher Cl concentration than the acceptable WHO standard. However, the concentrations of Ca, K, SO4, HCO3, BOD and DO were higher than the acceptable WHO standard in the most of selected wetlands during the study period.
\nIn the study area, quality of water is mainly sweet and acceptable for drinking purposes. The range of Total Dissolved Solids (TDS) reported between 500 and 800 ppm. In the area, there are also few brackish wetlands reported and TDS varies between 10,000 and 28,000 due to recharge of insufficient amount of water. The quality of ground water is mainly dominated by sulfate, chloride, calcium and magnesium ions [16, 38, 39]. The conductivity (or TDS) is major parameter along with pH in recognizing the water quality. The values of both parameters is considered acceptable in freshwater lakes while it is otherwise considered the saline lakes. If the value of turbidity is higher than considering alkaline water while above the WHO standard level of 5 NTU. The value of higher turbidity may be due to discharge of waste materials and agriculture run off. The Nara Canal is originates from the Indus River. The water in the Indus River is generally contaminated carrying organic and inorganic polluted particles load from the upstream due to anthropogenic activities. The Sindh Environmental Protection Agency (SEPA 2002) recorded that the value of BOD in Indus River is exceeds more than 6.5 mg/L, which is also recognized by Global Environmental Monitoring System (GEMS) the water of Indus River is highly polluted. According to microbiological analysis of water by WWF-Pakistan (2007) confirmed that in the two sites the presence of fecal coliform. The availability of Fecal coliform in the water system is considering harmful for the human population consumption which may cause water borne disease. In freshwater bodies the availability of Fecal coliform is an indicator of contamination with the human and animal excreta [5, 7, 36, 38, 40, 41].
\nThe parameters of water were only collected to examine the quality of water for the purpose of drinking. Although, it has also been reported that the more than 100,000 fisherman population who were directly associated with the fishery occupation have suffered a lot in the recent decades. In the wetland complexes the higher amount of inflow of saline effluent has causing in the devastation of the lake [7, 40].
\nHunting: In the study area, the hunting for recreation is observed common and uncontrolled. However, the region is protected but there is no effective implementation of the wildlife laws for the wild animals. Due to hunting pressure, this is also leading to imbalance between the predator and prey species.
\nForaging of Livestock: The large amount of grazing livestock in the area together with the recent climatic changes is degrading the food chain in the ecosystem dynamics.
\nCutting trees: In the study area, the cutting of trees in the adjoining desert region for continuous practice of conversion of lands into agricultural fields which is affecting the wild population.
\nDevelopmental activities: In this modern era the human population is increasing in higher rate and habitation, the developmental activities in the region and conversion of land for the purpose of agriculture has been damaging the wild habitat and ultimately increasing stress on the existing wildlife.
\nRecommendation:
\nControlled hunting: To control the hunting the check posts should be established for keeping vigilance at important points on uncontrolled hunting. Due to shortage of infrastructure in Sindh Wildlife Department like as transport system and staff failed to stop hunting so that the officials must enhance staff and transport. The Wildlife authorities must consider strengthening of Sindh Wildlife Department in the region.
\nEcotourism: The area of Nara Wetland Complex is a best site for promoting ecotourism. For sighting of wildlife and bird watching the watch towers at potential points and other facilities should be developed for promoting community based conservation tourism. The people of local community should be participated and benefited from all this tourism activates. From local community the youth should be trained as a volunteers and co-guides. These health activities will provide the incentives to the local community as a source of income generating activity and an alternative livelihood source.
\nPromote participatory wildlife management and conservation: For promoting participation in wildlife management and conservation the institutional capacity of community based organizations in the region should be developed.
\nThis research study on “Ecology and Environmental Assessment of Nara Desert Wetland Complex, (NDWC) Khairpur, Sindh-Pakistan” was financially supported by University of Sindh Jamshoro, Sindh- Pakistan.
\nThe research study of “Ecology and Environmental Assessment of Nara Desert Wetland Complex, (NDWC) Khairpur, Sindh-Pakistan”; has there is no conflict of interest.
I am very thankful to WWF-Pakistan and Sindh Wildlife Department for their support during the collection of data in the studied areas of NDWC.
\nTem | temperature |
pH | pH |
EC | electrical conductivity |
TDS | total dissolved solids |
Tur | turbidity |
Ca | calcium |
Mg | magnesium |
Hard | hardness |
HCO | carbonate |
HCO3 | bi-carbonate |
Alkal | alkalinity |
Cl | chlorides |
Na | sodium |
K | potassium |
SO4 | sulfate |
BOD | biological oxygen demand |
DO | dissolved oxygen |
NDWC | Nara Desert wetland complex |
WHO | World Health Organization |
WWF | Worldwide Fund for Nature |
Nowadays, a significant size of information is managed and transmitted, and mainly, images have involved prodigious status, particularly in recognition field. Thus, it is important to decrease the size of the data via compression algorithms which can allow their storage and their transmission while using limited resources. Compression is employed to overcome this problem and keep more files. Mainly multimedia files need more storage space than other types of files. Images represent the largest part of the most used multimedia files in almost all fields. Unlike other types of files, a huge amount of image data requires more resources for storage and transmission on computer networks, and compression is therefore presented as an inevitable tool with the aim of more maneuverability of this data. Today, several compression formats exist while presenting their limits (degradations, size, duration, etc.) on somewhat particular images (text images and background areas).
To overcome this difficulty, scientists are constantly developing new techniques to compress images in order to find a perfectionist compression method that can largely conserve storage space and preserve the quality of the source file.
The compression methods that exist tend to introduce the theory of fractals, which appears to be a strong instrument for boosting image quality and reducing the resources required. Nowadays, the image files intricated in several specific programs are characterized by massive data, pixel correlation, and similarity. Under such conditions, the old compression methods appear to be unsuitable for this mission because of their need for significant encoding time. On the other hand, the recently developed fractal image compression techniques offer better compression qualities [1, 2, 3]. These methods are built on the principle that shapes (fractals) can better represent usual scenes than traditional geometric forms. A number of fractal image compression techniques are presented in a lot of works; they include an image coding agreeing to more security and less degradation (Jeng et al. [4], Li [5], and Han [6]). These methods have revealed an important in information, reduced statistical characteristics of chaotic patterns, and weakness in statistical cryptanalysis. In addition, metaheuristics are likewise employed to optimize compression, like genetic algorithms [7], ant colonies [8], and optimization by particle swarms [9]. These methods have ability to create a partition constructed on a region which improves the compression ratio and maintains better the decompressed image quality.
In this chapter, we try for the first time to apply natural metaheuristics on fractal compression, by suggesting new methods which associate, both the bat-inspired algorithm (BIA) and the wolf pack algorithm (WPA) with fractal image compression (FIC) to speed up encoding and optimize both file size and image quality. The main objective of using these algorithms is its property of search for global solution and its capacity to generate very satisfactory results quickly and for less means compared to similar techniques. The algorithms also use fewer parameters and without initial approximation of the unidentified parameters. This document is organized as follows; Section 1 presents an introduction in the study context. Next, a summary of the fractal image compression is detailed in Section 2. Section 3 summarizes the natural metaheuristics and presents those used in this work, namely, the WPA and the BIA. In Section 4, we give a review of some related works. After that, we present our proposed methods in Sections 5 and 6. Experiments are explained in Section 7. Finally, the conclusion appears in Section 8.
Fractal compression is a new method of irreversible image compression [10]. It was adapted by Hutchinson [11] and Barnsley and Demko [12]. It searches for self-similarities among the diverse image blocks [13] and only keeps the parameters of the contractual transformation in place of the pixels of the image. Like this, we can build an estimate as close as possible to the source image by detecting the redundancy of forms at several scales and try to eliminate these redundancies in the original image so that the result is precise enough to be accepted.
The FIC is founded on an iterated function system f, a limited group of contractions defined on a metrical space Rn by the relation:
This contraction can be in several shapes depending on technical constraints. It can be done several points of the original image and carry them nearer to the compressed one. This reflection is named “affine transformation”; then, each sub-block of the original image will be submitted either a rotation at an angle, a scale, or a translation (transformed using eight isometries) according to Equation 2:
where T is a linear transformation, Rn → Rn is a vector, and b ∈ Rn is a vector. Practically, the general principle of fractal compression is to try to find the finest matching domain blocks for each range block in order to minimize the distance metric. The code that follows illustrates this idea well:
Enter the original image
Create a partitioning of Range Blocks R
Create a partitioning of Domain figures
For all figures Destination Make
For all figures Sources Make
For all the defined transformations Make
Apply the transformation to the Range Blocks
Adjust the average of the pixel colors
Apply the reduction from the Range Blocks to the Domain Blocks
Calculate the error between the result and the Domain Blocks
If the error is minimal for the destination figure Then
Save the modifications made
End if
End For
Write the saved values in the output file
End For
End For
This process is realized following the relation:
where v () is the function of the contraction which aims to modify Di. Thereafter, the nearest block Bi is sought for all Ri blocks by calculating the error between Bi and Ri, and we can use, for example, the Hausdorff distance defined by Equation 4:
where d(B, R) = max b ϵ A min r ϵ B ||b-r||.
Or using the Euclidean distance described by the equation:
where n represents the pixel’s number in Ri and Bi blocks. d should be as minimum as possible for blocks that look alike.
Fractal decompression consists of the reconstruction of Ri from the blocks Bi which appear identical the most by practicing the contraction used in compression.
In recent years, several studies have concentrated on the development of accelerated decryption procedures [14, 15, 16] with the aim of preserving image quality. Their principle is to choose a random image as the original one and execute an affine transformation like defined in Equations (6) and (7), founded on the fractal ciphers obtained by itself. This act is repeated recursively till the reconstructed image were satisfactory:
where I is a contractive or isometric spatial transformation, D is a domain block, R is a range block, and S is the reconstructed image.
In fact, FIC is becoming among the most promising methods for image compression for its significant compression ratio (CR) and preservation of quality. Its beginnings date from the 1990s when Jacquin [17, 18] introduced the first method of image compression; its principle is partitioning the image into two tiling blocks: the range and domain blocks.
The domain blocks are double the size of the range blocks and overlap such that a new domain block starts at each pixel. The main idea of this compression is to find the nearest domain block in concordance with each range block, to determine the right contractual transformation, and to store all these parameters. This principle was exciting; however it remained limited to local applications because it consumes a lot of CPU time. Since then, researchers have constantly presented new techniques to reduce the compression time; Thomas and Deravi [19] link range blocks and brand them more adaptive with image content by using the region-growing method. Cardinal [20] presented an alike principle; it is founded on a geometrical partition of the grayscale image block feature space. The experimental evaluations with earlier published methods illustrate an important enhancement in encoding time with practically better quality. He et al. [21] have used the normalized block with the aim to evade the extreme search in corresponding block. Chong and Pi [22] proposed a new adaptive search method to decrease the calculation complexity of fractal encoding to discard a big number of unqualified domain blocks so as to speed up FIC.
Other studies have been presented on new aspects to improve the way of research like the encoding via the Fourier transform [23], special image features [24], and discrete cosine transform inner product [25]. The majority of existing methods rely on a corresponding error threshold to limit the search. Lately, Lin and Wu [26] have defined another way of search built on image block edge property, which proves suitable results. Furthermore, many research articles have been published over the past decade; they increased the quality of the compressed image through the use of metaheuristics without resorting to more resources in the coding process.
Heuristics refer to the set of techniques that can solve several problems by maximizing gains and decreasing the resource’s consumption; however, the optimal solution cannot be certain if, in the investigation space, there is an intersection between the local and global solution [27]. Natural heuristics represent a large family of heuristics which are inspired from communal conduct of animals existing in societies like assemblages of birds, ant colonies, or grouping of fish. They are founded on the principle of populations of entities who cooperate and develop rendering to reciprocal precepts. These techniques allow the invention of procedures which can resolve hard problems by dividing control. These approaches form a famous prototype which is effectively employed as a prodigious tool for resolving difficult problems [28] with less resource consumption. Several researches [29, 30] illustrate that these systems have an effective potential to manage various situations and could be adapted to bring solutions to diversified optimization problems.
The wolf pack algorithm (WPA) [31] belongs to the family of bioinspired techniques which can be used to estimate resolutions for numerous optimization problems. WPA is a metaheuristic built on the population invoked by the social hunting behavior of wolves. It basically involves hunting wolves, tracking down prey, and capturing it under the orders of a leader wolf. The wolf pack includes the strongest and most intelligent wolf chef. He is responsible for controlling the pack. Its decisions are always based on the surrounding environment: prey, pack wolves, and other hunters. The pack is divided into two families of wolves: scoot and ferocious.
The scoot wolves move autonomously in the milieu and adjust its way according to the concentration of the odor of the prey. When a prey is found, scoot wolves cry and transmit info by sound to the leading wolf who guesses the distance to reach this prey; it calls the furious wolves and quickly displaces towards the cry. The prey is then caught and is shared conferring to the nature of each wolf: from the sturdiest to the feeblest. Subsequently, feeble wolves could die from absence of nutrition. In this manner the pack ensures a certain dynamic and robustness at all times.
WPA is performed as follows:
In a search environment ℝn, any wolf i denotes an elementary solution to the problem, at a location xi.
Initially, wolves are distributed chaotically in the environment.
At any instant t, the wolf i passes from the location xti to the location xt + 1i. The choice of the following location is updated by rendering the following equation:
where λ represents a vector randomly distributed in the interval [−1,1] and xg designates the location of the chief wolf. After a static sum of repetitions, which corresponds to a research stage, the wolf of the finest result gets converted to a leader one; feeble wolves (bad solutions) will be wiped out and substituted with a novel group of wolves in an arbitrary manner.
The bat-inspired algorithm (BIA) [32] belongs to the family of metaheuristics inspired by nature, introduced by Yang and founded on the echolocation comportment of bats. Bats have a system identical to radar except that radars use electromagnetic waves while bats use ultrasonic waves (of frequency inaudible to humans). Bats move and hunt with high-performance sonar. By another way, bats are distinguished by an extraordinary steering mechanism allowing them to differentiate between an obstacle and a prey, which allows them to hunt even all. The impulses produced by bats can be linked almost to the hunting plans of bats. Often, the pulses are between 25 and 150 kHz at a static frequency; they only persist for 8–10 ms. Bats generate between 10 and 20 ultrasonic sound eruptions every second, each of them stays between 5 and 20 ms. However, when bats seek their prey and feel so close, they can increase the rate of emission of sound eruptions up to 200/s. This proves the extraordinary ability of bats to process signals. Assuming that the speed of sound in air is v = 340 m/s, the wavelength λ of the ultrasonic sound therefore manifests with a continuous frequency f:
The wavelengths vary between 2 and 14 mm and are equivalent to the size of the bat’s prey, for a representative frequency between 25 and 150 kHz. The pulses produced by bats can spread an imposing sound intensity of 110 dB, but quite favorably, these pulses remain in the ultrasonic domain. The intensity of the pulse can take different stages, such as very strong when bats are chasing and weak at a quiet sound when they mark their prey. These short pulsations usually have a wandering range of a few meters which depends on the frequency.
In reality, bats combine all of their senses to effectively detect prey and navigate more easily. Here, we are only interested in echolocation and the behaviors that accompany it. To create new optimization techniques, the echolocation of bats can be transformed into an optimized objective function.
In a search space Ri, the bats fly randomly using the speed Vi in location (solution) Xi using velocity Vi. They produce pulses at a static wavelength λ with a variable frequency f and an intensity A (differs from a big positive A0 to a smallest constant value Amin) to hunt for prey. When the bats choice the finest results, they choose a local result from the best selected ones.
In 2005, Dervis Karaboga proposed a new iterative optimization method based on artificial bee colonies (ABC). This technique is based on three different classes of bees, (a) bee used, (b) spectator bee, and (c) scout bee. The spectator bees waiting in the store obtain data concerning the sources of nectar revealed earlier from the employees. Then they choose a usable nutrition source built on the received information. Scout bees arbitrarily search for nutriment in the area for [33].
In 2006, Cristian Martinez presented an enhanced image compression using the ant colony technique. The basic idea is that ants always seek and find the shortest path from nest to food source using the pheromone. For fractal compression, the pheromone is positioned on the range block i and the domain block j. The pheromone matrix is rectangular (not symmetrical) where the lines designate range blocks (image blocks) and the columns indicate domain blocks (blocks to transform). Then, the ants build routes by choosing a block of domain j for each block of range i. the solution will be found on the basis of updating the pheromone and heuristic information [34]. The result proposes similar image quality to that obtained with a deterministic way while minimizing the calculation time by 34%.
In 2009, many of studies were focalized on FIC: Chakrapani and Soundara Rajan [35] have created a new fractal image compression founded on a genetic algorithm in the intention of optimizing the encoding time for an acceptable image quality. The results give improved performance over exhaustive search.
In the work of Xing-yuan, Fan-ping, and Shu-guo [36], a spatial correlation hybrid genetic algorithm that uses the features of the fractal and divided iterative function system is proposed. It consists of two stages. The first uses spatial correlation in the images for the range and the domain blocks in order to exploit the local optima. The second one is based on a genetic-simulated annealing algorithm (SAGA) to find the global optima if the local optima is not satisfied. In order to escape early convergence, the algorithm approves that the dyadic mutation operator takes place in place of the traditional operator.
In 2010, Chakrapani et al. have enhanced the fractal image compression using particle swarm optimization (PSO) technique [37]. PSO is used to speed up the search of the nearest finest match block for a definite block to be encoded. This method illustrates that the recovered image quality can be conserved when in comparison with the full-search FIC.
In 2016, Shaimaa S. Al-Bundi et al. [38] use an upgraded genetic algorithm to enhance the exploration space in the target image by good estimation to the global optimum in an only execution.
In 2017, Al-Saidi N.M.G et al. [39] optimize the fractal image compression by introducing the harmony search algorithm. This strategy searches for the best solution through singing a song; this proposed technique offers splendid performances in terms of image quality, reduced computation time, and storage space when compared to other methods.
In 2018, we [40] used the wolf pack algorithm to improve the FIC; the idea is to take the entire image for the search space where this space is divided into blocks; scooters wolves roam the environment to find other smaller and similar blocks. They examine the entire space and select the blocks with the best physical shape. By this method, the encoding time was considerably reduced, and we also obtained a better compression rate.
In 2019, we have [41] chosen to improve the FIC by using the bat-inspired algorithm. Our tow proposed methods are detailed and well explained in Sections 5 and 6, respectively.
We assume an image of m x n pixels as exploration space, represented by an array P where each pixel is considered as a cell and on a byte (gray pixel). The resulting image C of m/2 × n/2 pixels is reached by following the steps:
Divide the entire image into tiny nonoverlapping ri blocks of size s × s (with s << m). More simply, we will proceed with blocks of square size of b x b; this partition called range block will be represented by RN = {r1, r2,…, rN}.
For all the blocks ri, the scooting wolves roam the space in order to find a di of size 2b × 2b similar with ri while respecting the parameters mentioned above. A fitness value f (di) will be assigned for each block di according to Eq. (6). The block di is taken for prey.
After the hunting wolves have inspected the entire space and for all ri blocks, di blocks with the best physical form are selected. It will be mapped according to Eqs. (4) and (5).
If no improvement is made to the wolf chef solution, the process will be stopped after a fixed number of iterations.
The adapted WPA algorithm for FIC is showed as:
Algorithm 1. FIC-WPA
Initialization:
Generate ri, (i = 1 … N)
For each ri, f(ri) ← 0, (i = 1 … N)
While not (stopping criteria)
it←0
While not (Iter-scoot < It)
Pick random numbers: λ ϵ [−1,1]
For each ri do
xi ← xi + λ|xg − xi|
If (f(bxi) < f(g)) g ← i
Endif
Endfor
ri ← v(di)
Update It
EndWhile
EndWhile
End For.
The similar idea employed in the wolf pack algorithm and the BAT algorithm for FIC is completed through the succeeding phases:
The whole image is scouted randomly by bats with the use of loudness L and frequency F.
Each block is compared to all of its neighboring blocks by bats for its degree of homogeneity as a function of volume and frequency. If they meet a criterion (color_level_block - neighboring color level ≤ frequency), we create a domain block of size L * L whose value is only the mean of the domain block.
When the bats roam the whole picture. The iteration will be stopped.
After decomposing the image into domain blocks, the bats’ position themselves at xi, and the size of the blksz block will be saved in a sparse S.
By eliminating the solution with the smaller block, we try to find the best solution in this step.
In order to calculate the compression ratio, we will use Huffman encoding to store all information (locations, block sizes, and values).
Finally, and to reconstruct the image, we will use Huffman decoding to regenerate the image data of the compressed image.
Algorithm 2. Proposed FIC-BAT
Initialization: Generate bats (Number_bats = 1..N) //
Begin
Loudness L;
Frequency F;
While not (stopping criteria)
For each bat
If similarity (distance) = 1
Create domain block;
Store location in vectors I,J;
Store block sizes in vector blksz;
Else
Store location in vectors I,J;
Store block sizes in vector blksz;
End-if
End For
End-while
Search for best solutions;
Store the locations and block sizes in a sparse S;
End.
The resolution presents an aspect that should not be ignored when trying to test the efficiency of an approach. For our circumstance, we will take into consideration the three test images (Lena, Barbara, and cameraman) with different resolutions (16 * 16, 32 * 32, 64 * 64, 128 * 128, 256 * 256) so that we can see the impact of this factor on the quality quantities (compression ratio, compression time, EQM, PSNR) (Figures 1 and 2).
Tested images (before compression). (a) Cameraman, (b) Lena, and (c) Barbara.
Decompressed tested images (after WPA compression). (a) Cameraman, (b) Lena, and (c) Barbara.
From the test images with a reconstructed resolution of 256 * 256, it can be seen that the quality of the images is acceptable to a very good degree. And to be more exact, we must refer to calculable measures.
The table below presents the results obtained by applying our approach to the above images with different resolutions:
The results obtained (detailed in Table 1) illustrate firstly the agreement between image resolution and encoding time, which demonstrates that our proposed method is significantly responsive to image resolution; it is also clear that the image quality is in contrast linked to it (image quality degrades quickly as resolution increases). This is clearly shown by the amplitudes of the PSNR and the MSE.
Tested image | Resolution | Compression time (s) | Decompression time (s) | Compression ratio | MSE | PSNR (dB) |
---|---|---|---|---|---|---|
Cameraman | 16*16 | 0.184 | 0.818 | 1.098 | 2.617 | 37.999 |
32*32 | 0.446 | 0.802 | 1.174 | 4.079 | 36.173 | |
64*64 | 2.451 | 0.816 | 1.420 | 6.853 | 31.509 | |
128*128 | 41.284 | 0.810 | 1.653 | 8.931 | 30.096 | |
256*256 | 774.438 | 0.897 | 1.741 | 9.152 | 31.605 | |
Lena | 16*16 | 0.185 | 0.832 | 1.095 | 7.730 | 33.616 |
32*32 | 0.507 | 0.801 | 1.125 | 5.861 | 34.451 | |
64*64 | 2.290 | 0.791 | 1.287 | 8.401 | 33.550 | |
128*128 | 34.257 | 0.821 | 1.480 | 9.547 | 32.641 | |
256*256 | 668.810 | 0.850 | 1.596 | 9.282 | 33.305 | |
Barbara | 16*16 | 0.225 | 0.812 | 1.019 | 1.352 | 38.070 |
32*32 | 0.607 | 0.763 | 1.055 | 4.064 | 36.067 | |
64*64 | 1.868 | 0.781 | 1.152 | 6.259 | 35.060 | |
128*128 | 22.713 | 0.793 | 1.310 | 11.093 | 32.348 | |
256*256 | 509.406 | 0.861 | 1.447 | 11.115 | 33.288 |
Variation in image resolution.
The compression ratio remains relative to the resolution where we indicate that our method interferes in this measurement. Wolves appear when the quantity of blocks is very huge (higher resolution) by contributing a higher compression rate than that existing in lesser resolutions.
Decompression time stays optimized and is similar for almost all methods. This is due to the decompression process which is not complex compared to the compression process.
We will now focus on comparing our proposed method with some other techniques. The following table (Table 2) shows the notable alteration between our method and the others:
Images de test | Methods | PSNR (dB) | Time (s) | Ratio (CR) |
---|---|---|---|---|
Lena 128*128 | FIC-WPA | 32.641 | 34.257 | 1.480 |
Suman K. Mitra et al.’s works [7] | 30.22 | / | 1.059 | |
Vishvas V. Kalunge et al.’s works [42] | / | 67 | / | |
Lena 256*256 | FIC-WPA | 33.305 | 668.810 | 1.596 |
Y. Chakrapani et al.’s works [37] | 26.22 | 2370 | 1.3 | |
Exhaustive search | 32.69 | 8400 | 1.3 | |
DWSR [31] | 25.8212 | 56.4247 | 1.56355 | |
PSO-RCQP [43] | 27.089 | 6.453 | 1.6392 | |
Cameraman 256*256 | FIC-WPA | 31.605 | 774.438 | 1.741 |
PSO-RCQP [43] | 26.686 | 268 | 1.8212 | |
Barbara 128*128 | FIC-WPA | 32.348 | 22.713 | 1.310 |
Vishvas V. Kalunge et al.’s works [42] | / | 66 | / | |
Barbara 256*256 | FIC-WPA | 33.288 | 509.406 | 1.447 |
Y. Chakrapani et al works [37] | 28.34 | 2500 | 1.2 | |
Exhaustive search | 32.84 | 8400 | 1.2 |
Comparison of the FIC-WPA with other techniques.
Bat’s number: In Table 3, we will examine the images of the cameraman and Lena in order to extract the adequate number of bats that will be used in our approach.
Images | Number of bats | Compression time | Decompression time | Compression ratio | PSNR | MSE |
---|---|---|---|---|---|---|
Cameraman | 2 | 0.488 | 0.705 | 1.385 | 31.608 | 10.088 |
4 | 0.459 | 0.707 | 1.366 | 30.934 | 8.563 | |
8 | 0.452 | 0.729 | 1.372 | 31.216 | 9.417 | |
16 | 0.451 | 0.749 | 1.349 | 30.934 | 8.879 | |
32 | 0.509 | 0.843 | 1.355 | 29.827 | 10.045 | |
64 | 0.472 | 0.919 | 1.365 | 30.412 | 10.405 | |
128 | 0.478 | 0.749 | 1.348 | 31.895 | 9.663 | |
256 | 0.475 | 0.883 | 1.335 | 30.989 | 8.899 | |
512 | 0.457 | 0.750 | 1.392 | 29.997 | 9.870 | |
Lena | 2 | 0.518 | 0.739 | 1.303 | 30.629 | 14.440 |
4 | 0.548 | 0.727 | 1.315 | 30.083 | 15.612 | |
8 | 0.505 | 0.716 | 1.311 | 30.071 | 14.929 | |
16 | 0.514 | 0.713 | 1.306 | 29.984 | 15.348 | |
32 | 0.563 | 0.784 | 1.299 | 30.452 | 15.037 | |
64 | 0.562 | 0.779 | 1.298 | 31.228 | 13.887 | |
128 | 0.512 | 0.715 | 1.306 | 30.669 | 15.603 | |
256 | 0.518 | 0.709 | 1.320 | 30.389 | 16.118 | |
512 | 0.520 | 0.702 | 1.321 | 29.856 | 14.710 |
Testing the number of bats.
Loudness:Table 4 presents some tests carried out on an additional stress which is loudness, with the objective of taking into consideration the good value for the test.
Image | Loudness | Compression time | Decompression time | Compression ratio | PSNR | MSE |
---|---|---|---|---|---|---|
Cameraman | 2 | 0.453 | 0.720 | 1.267 | 33.788 | 6.531 |
3 | 0.452 | 0.729 | 1.372 | 31.216 | 9.417 | |
4 | 0.458 | 0.721 | 1.376 | 33.022 | 7.807 | |
5 | 0.469 | 0.702 | 1.355 | 30.220 | 9.375 | |
6 | 0.438 | 0.727 | 1.359 | 30.119 | 9.027 | |
7 | 0.460 | 0.723 | 1.348 | 28.971 | 9.130 | |
8 | 0.442 | 0.727 | 1.279 | 34.284 | 5.600 | |
9 | 0.455 | 0.736 | 1.220 | 30.813 | 6.962 | |
10 | 0.468 | 0.757 | 1.205 | 33.859 | 5.012 | |
11 | 0.472 | 0.755 | 1.187 | 33.548 | 4.362 | |
Lena | 2 | 0.530 | 0.735 | 1.248 | 31.781 | 14.848 |
3 | 0.505 | 0.716 | 1.311 | 30.071 | 14.929 | |
4 | 0.537 | 0.726 | 1.324 | 29.230 | 16.853 | |
5 | 0.586 | 0.751 | 1.299 | 30.536 | 14.382 | |
6 | 0.519 | 0.713 | 1.244 | 30.812 | 13.766 | |
7 | 0.516 | 0.715 | 1.223 | 31.290 | 12.190 | |
8 | 0.503 | 0.756 | 1.228 | 31.032 | 11.917 | |
9 | 0.495 | 0.740 | 1.204 | 32.213 | 9.056 | |
10 | 0.492 | 0.761 | 1.194 | 31.873 | 8.372 | |
11 | 0.488 | 0.737 | 1.192 | 32.050 | 9.695 |
Testing the values of loudness.
Frequency: The last test table (Table 5) is made to pick up the best frequencies that will be used in our algorithm.
Image | Frequency | Compression time | Decompression Time | Compression ratio | PSNR | MSE |
---|---|---|---|---|---|---|
Cameraman | 20 | 0.490 | 0.714 | 1.118 | 40.341 | 1.526 |
30 | 0.458 | 0.698 | 1.209 | 33.576 | 4.4806 | |
40 | 0.442 | 0.727 | 1.279 | 34.284 | 5.600 | |
50 | 0.460 | 0.738 | 1.357 | 29.463 | 11.990 | |
60 | 0.435 | 0.706 | 1.422 | 26.887 | 15.954 | |
70 | 0.453 | 0.716 | 1.586 | 23.701 | 22.989 | |
80 | 0.467 | 0.702 | 1.722 | 23.769 | 33.415 | |
Lena | 20 | 0.495 | 0.755 | 1.066 | 42.199 | 1.904 |
30 | 0.512 | 0.735 | 1.099 | 35.522 | 4.947 | |
40 | 0.503 | 0.756 | 1.228 | 31.032 | 11.917 | |
50 | 0.514 | 0.718 | 1.359 | 27.649 | 23.370 | |
60 | 0.549 | 0.745 | 1.590 | 23.577 | 39.059 | |
70 | 0.521 | 0.717 | 1.850 | 22.204 | 50.050 | |
80 | 0.646 | 0.720 | 2.064 | 20.869 | 58.349 |
Testing different values of frequency.
As we can see from the previous tables, the best choices are given by 8 for the number of bats, 8 for the intensity, and 30 for the frequency, so the treatment parameters are as follows:
The number of iterations is in the interval of [10,100].
The number of bats is fixed at 8.
Loudness 8, Frequency 30.
We experienced the proposed approach on five images through diverse resolutions. Table 6 illustrates certain quality measurement liable on the resolutions.
Test image | Resolution | Compression time (s) | Decompression time (s) | Compression ratio | MSE | PSNR (dB) |
---|---|---|---|---|---|---|
Blonde women | 32*32 | 0.455 | 0.692 | 1.223 | 9.643 | 32.645 |
64*64 | 2.581 | 0.698 | 1.430 | 13.100 | 31.678 | |
128*128 | 34.634 | 0.712 | 1.712 | 14.165 | 30.853 | |
256*256 | 811.291 | 0.763 | 1.741 | 13.150 | 32.065 | |
Lena | 32*32 | 0.522 | 0.704 | 1.109 | 5.651 | 34.695 |
64*64 | 2.193 | 0.704 | 1.282 | 8.722 | 33.380 | |
128*128 | 33.376 | 0.720 | 1.486 | 9.799 | 32.909 | |
256*256 | 732.345 | 0.763 | 1.604 | 9.660 | 33.115 | |
Cameraman | 32*32 | 0.467 | 0.832 | 1.211 | 3.628 | 36.328 |
64*64 | 2.608 | 0.719 | 1.440 | 7.743 | 30.258 | |
128*128 | 43.854 | 0.691 | 1.658 | 9.086 | 31.078 | |
256*256 | 732.011 | 0.977 | 1.678 | 9.626 | 31.095 | |
Living room | 32*32 | 0.558 | 0.687 | 1.219 | 13.475 | 31.210 |
64*64 | 2.092 | 0.692 | 1.355 | 14.584 | 31.414 | |
128*128 | 26.347 | 0.710 | 1.487 | 14.584 | 31.322 | |
256*256 | 478.219 | 0.767 | 1.555 | 14.138 | 31.599 | |
Mandrill | 32*32 | 0.598 | 0.554 | 1.211 | 11.936 | 31.674 |
64*64 | 2.376 | 0.889 | 1.422 | 18.377 | 30.555 | |
128*128 | 22.190 | 0.732 | 1.419 | 18.435 | 30.582 | |
256*256 | 334.636 | 0.795 | 1.368 | 16.404 | 30.737 |
Testing the image resolutions.
Figure 3 shows the image of blonde women before and after proposed compression.
An image of Compressed and decompressed blond women.
In Figure 4, we show a cameraman image before and after proposed compression,
An image of compressed and decompressed cameramen.
Figures 5 and 6 describe separately Lena and living room images before and after applying the proposed compression.
An image of compressed and decompressed Lena.
An image of compressed and decompressed living room.
Finally, we conclude our sequence of assessments with mandrill image before and after proposed compression, in Figure 7.
An image of compressed and decompressed Mandrill.
As we can observe, the images’ quality is very suitable.
And to approve this effect, Table 6 explores additional quality measure.
The table shows that our approach is significantly sensitive to changing resolutions. It is also clear that the quality of the images is inversely linked to the resolution (as soon as the resolution increases, the quality of the images degrades) which is well proven by the MSE and PSNR measurements.
We note that our proposed method has a remarkable effect on compression ratio and remains relative to the resolution; the bats then show themselves, when the number of blocks is greater (more resolution) by offering a compression ratio greater than that present in the lower resolutions.
On the other hand, the decompression process does not include any complexity compared to the compression process which gives us a decompression time which remains optimized and similar for almost all resolutions.
Finally, and to be in the set of techniques which try to optimize fractal compression, we will draw up a comparison of our approach with certain existing methods. Table 7 clearly shows the remarkable difference between our method and the others.
Test image | Methods | PSNR (dB) | Compression time (s) | Compression ratio |
---|---|---|---|---|
Lena 128*128 | BIA | 32.909 | 33.376 | 1.486 |
Suman K. Mitra et al works [7] | 30.22 | / | 1.059 | |
Vishvas V. Kalunge et al works [42] | / | 67 | / | |
Lena 256*256 | BIA | 33.115 | 732.345 | 1.604 |
Y. Chakrapani et al.’s works [37] | 26.22 | 2370 | 1.3 | |
Exhaustive search | 32.69 | 8400 | 1.3 | |
DWSR [44] | 25.8212 | 56.4247 | 1.56355 | |
PSO-RCQP [43] | 27.089 | 6.453 | 1.6392 | |
Cameraman 256*256 | BIA | 31.095 | 732.011 | 1.678 |
PSO-RCQP [43] | 26.686 | 268 | 1.8212 | |
Barbara 128*128 | BIA | 32.176 | 21.478 | 1.312 |
Vishvas V. Kalunge et al.’s works [42] | / | 66 | / |
Proposed approach versus other methods.
At first glance, our proposed method represents new work which has led to satisfactory results. It proportionally retains the quality offered after compression. Reduced time remains very satisfactory, and the compression rate is better than that offered by most of the methods below.
In this chapter, we explained the possibility of optimizing fractal image compression using bioinspired metaheuristics. We focused on the application and, for the first time, recent metaheuristics which are, respectively, the wolf pack algorithm and the bat-inspired algorithm in order to improve the performance of fractal image compression. The results demonstrate the efficiency of the algorithms considered.
Compared to other optimization metaheuristics, our approaches offer better results in many aspects, mainly the encoding and decoding time, the size, and the quality.
In addition, proposed approaches demonstrate the ability of these techniques to manage image compression.
No potential conflict of interest was reported by the authors.
I would to thank the persons that have participated in this work.
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\n\n4.1 The Corresponding Author represents and warrants that the Chapter does not and will not breach any applicable law or the rights of any third party and, specifically, that the Chapter contains no matter that is defamatory or that infringes any literary or proprietary rights, intellectual property rights, or any rights of privacy. The Corresponding Author warrants and represents that: (i) the Chapter is the original work of themselves and any Co-Author and is not copied wholly or substantially from any other work or material or any other source; (ii) the Chapter has not been formally published in any other peer-reviewed journal or in a book or edited collection, and is not under consideration for any such publication; (iii) they themselves and any Co-Author are qualifying persons under section 154 of the Copyright, Designs and Patents Act 1988; (iv) they themselves and any Co-Author have not assigned and will not during the term of this Publication Agreement purport to assign any of the rights granted to IntechOpen under this Publication Agreement; and (v) the rights granted by this Publication Agreement are free from any security interest, option, mortgage, charge or lien.
\n\nThe Corresponding Author also warrants and represents that: (i) they have the full power to enter into this Publication Agreement on their own behalf and on behalf of each Co-Author; and (ii) they have the necessary rights and/or title in and to the Chapter to grant IntechOpen, on behalf of themselves and any Co-Author, the rights and licenses expressed to be granted in this Publication Agreement. If the Chapter was prepared jointly by the Corresponding Author and any Co-Author, the Corresponding Author warrants and represents that: (i) each Co-Author agrees to the submission, license and publication of the Chapter on the terms of this Publication Agreement; and (ii) they have the authority to enter into this Publication Agreement on behalf of and bind each Co-Author. The Corresponding Author shall: (i) ensure each Co-Author complies with all relevant provisions of this Publication Agreement, including those relating to confidentiality, performance and standards, as if a party to this Publication Agreement; and (ii) remain primarily liable for all acts and/or omissions of each such Co-Author.
\n\nThe Corresponding Author agrees to indemnify and hold IntechOpen harmless against all liabilities, costs, expenses, damages and losses and all reasonable legal costs and expenses suffered or incurred by IntechOpen arising out of or in connection with any breach of the aforementioned representations and warranties. This indemnity shall not cover IntechOpen to the extent that a claim under it results from IntechOpen's negligence or willful misconduct.
\n\n4.2 Nothing in this Publication Agreement shall have the effect of excluding or limiting any liability for death or personal injury caused by negligence or any other liability that cannot be excluded or limited by applicable law.
\n\n5. TERMINATION
\n\n5.1 IntechOpen has a right to terminate this Publication Agreement for quality, program, technical or other reasons with immediate effect, including without limitation (i) if the Corresponding Author or any Co-Author commits a material breach of this Publication Agreement; (ii) if the Corresponding Author or any Co-Author (being an individual) is the subject of a bankruptcy petition, application or order; or (iii) if the Corresponding Author or any Co-Author (being a company) commences negotiations with all or any class of its creditors with a view to rescheduling any of its debts, or makes a proposal for or enters into any compromise or arrangement with any of its creditors.
\n\nIn case of termination, IntechOpen will notify the Corresponding Author, in writing, of the decision.
\n\n6. INTECHOPEN’S DUTIES AND RIGHTS
\n\n6.1 Unless prevented from doing so by events outside its reasonable control, IntechOpen, in its discretion, agrees to publish the Chapter attributing it to the Corresponding Author and any Co-Author.
\n\n6.2 IntechOpen has the right to use the Corresponding Author’s and any Co-Author’s names and likeness in connection with scientific dissemination, retrieval, archiving, web hosting and promotion and marketing of the Chapter and has the right to contact the Corresponding Author and any Co-Author until the Chapter is publicly available on any platform owned and/or operated by IntechOpen.
\n\n6.3 IntechOpen is granted the authority to enforce the rights from this Publication Agreement, on behalf of the Corresponding Author and any Co-Author, against third parties (for example in cases of plagiarism or copyright infringements). In respect of any such infringement or suspected infringement of the copyright in the Chapter, IntechOpen shall have absolute discretion in addressing any such infringement which is likely to affect IntechOpen's rights under this Publication Agreement, including issuing and conducting proceedings against the suspected infringer.
\n\n7. MISCELLANEOUS
\n\n7.1 Further Assurance: The Corresponding Author shall and will ensure that any relevant third party (including any Co-Author) shall, execute and deliver whatever further documents or deeds and perform such acts as IntechOpen reasonably requires from time to time for the purpose of giving IntechOpen the full benefit of the provisions of this Publication Agreement.
\n\n7.2 Third Party Rights: A person who is not a party to this Publication Agreement may not enforce any of its provisions under the Contracts (Rights of Third Parties) Act 1999.
\n\n7.3 Entire Agreement: This Publication Agreement constitutes the entire agreement between the parties in relation to its subject matter. It replaces and extinguishes all prior agreements, draft agreements, arrangements, collateral warranties, collateral contracts, statements, assurances, representations and undertakings of any nature made by or on behalf of the parties, whether oral or written, in relation to that subject matter. Each party acknowledges that in entering into this Publication Agreement it has not relied upon any oral or written statements, collateral or other warranties, assurances, representations or undertakings which were made by or on behalf of the other party in relation to the subject matter of this Publication Agreement at any time before its signature (together "Pre-Contractual Statements"), other than those which are set out in this Publication Agreement. Each party hereby waives all rights and remedies which might otherwise be available to it in relation to such Pre-Contractual Statements. Nothing in this clause shall exclude or restrict the liability of either party arising out of its pre-contract fraudulent misrepresentation or fraudulent concealment.
\n\n7.4 Waiver: No failure or delay by a party to exercise any right or remedy provided under this Publication Agreement or by law shall constitute a waiver of that or any other right or remedy, nor shall it preclude or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall preclude or restrict the further exercise of that or any other right or remedy.
\n\n7.5 Variation: No variation of this Publication Agreement shall be effective unless it is in writing and signed by the parties (or their duly authorized representatives).
\n\n7.6 Severance: If any provision or part-provision of this Publication Agreement is or becomes invalid, illegal or unenforceable, it shall be deemed modified to the minimum extent necessary to make it valid, legal and enforceable. If such modification is not possible, the relevant provision or part-provision shall be deemed deleted.
\n\nAny modification to or deletion of a provision or part-provision under this clause shall not affect the validity and enforceability of the rest of this Publication Agreement.
\n\n7.7 No partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Corresponding Author or any Co-Author, nor authorize any party to make or enter into any commitments for or on behalf of any other party.
\n\n7.8 Governing law: This Publication Agreement and any dispute or claim (including non-contractual disputes or claims) arising out of or in connection with it or its subject matter or formation shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of or in connection with this Publication Agreement (including any non-contractual disputes or claims).
\n\nLast updated: 2020-11-27
\n\n\n\n
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