Appraisal of land use/land cover of mangrove forest ecosystem using support vector machine

Environmental Earth Sciences

Volumn 71, Issue 5, 2014, Pages 2245-2255

  Singh, Sudhir Kumar ^a   Srivastava, Prashant K ^b   Gupta, Manika ^c   Thakur, Jay Krishna ^d   Mukherjee, Saumitra ^e  

^a UNIVERSITY OF ALLAHABAD   (India)

^b UNIVERSITY OF BRISTOL   (United Kingdom)

^c INDIAN INSTITUTE OF TECHNOLOGY DELHI   (India)

^d MARTIN LUTHER UNIVERSITY HALLE WITTENBERG   (Germany)

^e JAWAHARLAL NEHRU UNIVERSITY   (India)

Author keywords

Land use land cover; Mangrove forest; Pichavaram forest; Support vector machine; Urbanization indices

Indexed keywords

EID: 84893850117     PISSN: 18666280     EISSN: 18666299     Source Type: Journal    
DOI: 10.1007/s12665-013-2628-0     Document Type: Article

References (48)

1. Bala Krishna Prasad M, Ramanathan A, Alongi D, Kannan L (2006) Seasonal variations and decadal trends in concentrations of dissolved inorganic nutrients in Pichavaram mangrove waters, southeast India. Bull Mar Sci 79(2): 287-300.
2. Bazi Y, Melgani F (2006) Toward an optimal SVM classification system for hyperspectral remote sensing images. IEEE Trans Geosci Remote Sens 44(11): 3374-3385.
3. Bishop YM, Fienberg SE et al. (2007) Discrete multivariate analysis theory and practice. MIT Press, Cambridge.
4. 2 flux from mangrove surrounding waters. Geophys Res Lett 30(11): 1558.
5. Census Data (1991) Directorate of Census. Government of India, Tamil Nadu.
6. Census Data (2001) Directorate of Census. Government of India, Tamil Nadu.
7. Dixon B, Candade N (2008) Multispectral landuse classification using neural networks and support vector machines: one or the other, or both? Int J Remote Sens 29(4): 1185-1206.
8. Donato D, Kauffman J, Mackenzie R, Ainsworth A, Pfleeger A (2012) Whole-island carbon stocks in the tropical Pacific: implications for mangrove conservation and upland restoration. J Environ Manage 97: 89-96.
9. Foody GM, Mathur A (2004) A relative evaluation of multiclass image classification by support vector machines. IEEE Trans Geosci Remote Sens 42(6): 1335-1343.
10. Green K, Kempka D, Lackey L (1994) Using remote sensing to detect and monitor land-cover and land-use change. Photogramm Eng Remote Sens 60(3): 331-337.
11. Gupta M, Srivastava PK (2010) Integrating GIS and remote sensing for identification of groundwater potential zones in the hilly terrain of Pavagarh, Gujarat, India. Water Int 35(2): 233-245. doi: 10. 1080/02508061003664419.
12. Hua S, Sun Z (2001) Support vector machine approach for protein subcellular localization prediction. Bioinformatics 17(8): 721-728.
13. Huang C, Davis L, Townshend J (2002) An assessment of support vector machines for land cover classification. Int J Remote Sens 23(4): 725-749.
14. Islam T, Rico-Ramirez MA, Han DW, Srivastava PK (2012) Artificial intelligence techniques for clutter identification with polarimetric radar signatures. Atmos Res 109: 95-113. doi: 10. 1016/j. atmosres. 2012. 02. 007.
15. Jensen JR (1996) Introductory digital image processing: a remote sensing perspective, 2nd edn. Prentice-Hall Inc, New Jersey.
16. Kavzoglu T, Colkesen I (2009) A kernel functions analysis for support vector machines for land cover classification. Int J Appl Earth Obs Geoinf 11(5): 352-359.
17. Keuchel J, Naumann S, Heiler M, Siegmund A (2003) Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data. Remote Sens Environ 86(4): 530-541.
18. Lau K, Wu Q (2008) Local prediction of non-linear time series using support vector regression. Pattern Recogn 41(5): 1539-1547.
19. Li D-C, Liu C-W (2010) A class possibility based kernel to increase classification accuracy for small data sets using support vector machines. Expert Syst Appl 37(4): 3104-3110.
20. Lillesand TM, Kiefer RW, Chipman JW (2004) Remote sensing and image interpretation, 5th edn. John Wiley & Sons Ltd, New York.
21. Mouat DA, Mahin GG, Lancaster J (1993) Remote sensing techniques in the analysis of change detection. Geocarto Int 8(2): 39-50.
22. Mukherjee S (2004) Textbook of environmental remote sensing. Macmillan India Ltd, New Delhi.
23. Mukherjee S, Shashtri S, Singh C, Srivastava PK, Gupta M (2009) Effect of canal on land use/land cover using remote sensing and GIS. J Indian Soc Remote Sens 37(3): 527-537.
24. Otukei J, Blaschke T (2010) Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. Int J Appl Earth Obs Geoinf 12: S27-S31.
25. Pal M, Foody GM (2010) Feature selection for classification of hyperspectral data by SVM. IEEE Trans Geosci Remote Sens 48(5): 2297-2307.
26. Pal M, Mather P (2004) Assessment of the effectiveness of support vector machines for hyperspectral data. Future Gener Comput Syst 20(7): 1215-1225.
27. Patel D, Dholakia M, Naresh N, Srivastava PK (2012) Water harvesting structure positioning by using geo-visualization concept and prioritization of mini-watersheds through morphometric analysis in the lower Tapi basin. J Indian Soc Remote Sens 40(2): 299-312. doi: 10. 1007/s12524-011-0147-6.
28. Patel DP, Gajjar CA, Srivastava PK (2013) Prioritization of Malesari mini-watersheds through morphometric analysis: a remote sensing and GIS perspective. Environ Earth Sci 69: 2643-2656.
29. Petropoulos GP, Kontoes C, Keramitsoglou I (2011) Burnt area delineation from a uni-temporal perspective based on Landsat TM imagery classification using Support Vector Machines. Int J Appl Earth Obs Geoinf 13(1): 70-80.
30. Prasad MBK, Ramanathan A (2008) Distribution of rare earth elements in the Pichavaram mangrove sediments of the southeast coast of India. J Coastal Res 24(sp1): 126-134.
31. Ranjan RK, Ramanathan A, Singh G (2008) Evaluation of geochemical impact of tsunami on Pichavaram mangrove ecosystem, southeast coast of India. Environ Geol 55(3): 687-697.
32. Ranjan RK, Routh J, Ramanathan A (2010) Bulk organic matter characteristics in the Pichavaram mangrove-estuarine complex, south-eastern India. Appl Geochem 25(8): 1176-1186.
33. Sanchez-Hernandez C, Boyd DS, Foody GM (2007) Mapping specific habitats from remotely sensed imagery: support vector machine and support vector data description based classification of coastal saltmarsh habitats. Ecol Inform 2(2): 83-88.
34. Song C, Woodcock CE, Seto KC, Lenney MP, Macomber SA (2001) Classification and change detection using Landsat TM data: when and how to correct atmospheric effects? Remote Sens Environ 75(2): 230-244.
35. Srivastava PK, Mukherjee S, Gupta M (2008) Groundwater quality assessment and its relation to land use/land cover using remote sensing and GIS. In: Proceedings of international groundwater conference on groundwater use-efficiency and sustainability: groundwater and drinking water issues, Jaipur, India, pp 19-22.
36. Srivastava PK, Mukherjee S, Gupta M (2010) Impact of urbanization on land use/land cover change using remote sensing and GIS: a case study. Int J Ecol Econ Stat 18(S10): 106-117.
37. Srivastava PK, Gupta M, Mukherjee S (2012a) Mapping spatial distribution of pollutants in groundwater of a tropical area of India using remote sensing and GIS. Appl Geomat 4(1): 21-32. doi: 10. 1007/s12518-011-0072-y.
38. Srivastava PK, Kiran G, Gupta M, Sharma N, Prasad K (2012b) A study on distribution of heavy metal contamination in the vegetables using GIS and analytical technique. Int J Ecol Dev 21(1): 89-99.
39. Srivastava PK, Han D, Gupta M, Mukherjee S (2012c) Integrated framework for monitoring groundwater pollution using a geographical information system and multivariate analysis. Hydrol Sci J 57(7): 1453-1472. doi: 10. 1080/02626667. 2012. 716156.
40. Srivastava PK, Han D, Rico-Ramirez MA, Bray M, Islam T (2012d) Selection of classification techniques for land use/land cover change investigation. Adv Space Res 50(9): 1250-1265. doi: 10. 1016/j. asr. 2012. 06. 032.
41. Srivastava PK, Singh S, Gupta M, Thakur JK, Mukherjee S (2012e) Modeling impact of land use change trajectories on groundwater quality using remote sensing and GIS. Environ Eng Manage J (in press).
42. Szuster BW, Chen Q, Borger M (2011) A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones. Appl Geogr 31(2): 525-532.
43. Thakur J, Srivastava PK, Singh S, Vekerdy Z (2012) Ecological monitoring of wetlands in semi-arid region of Konya closed Basin, Turkey. Reg Environ Change 12(1): 133-144. doi: 10. 1007/s10113-011-0241-x.
44. Tong S, Chang E (2001) Support vector machine active learning for image retrieval. In: Proceedings of the ninth ACM international conference on multimedia. ACM, New York, pp 107-118.
45. Vapnik VN, Chervonenkis AY (1971) On the uniform convergence of relative frequencies of events to their probabilities. Theory Probab Appl 16(2): 264-280.
46. Waske B, Benediktsson JA (2007) Fusion of support vector machines for classification of multisensor data. IEEE Trans Geosci Remote Sens 45(12): 3858-3866.
47. Yao X, Tham L, Dai F (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101(4): 572-582.
48. Zhu G, Blumberg DG (2002) Classification using ASTER data and SVM algorithms: the case study of Beer Sheva, Israel. Remote Sens Environ 80(2): 233-240.