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Mapping of mosquito (Culicidae) breeding sites using predictive geographic information methods

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Application of remote sensing and other GIS methods in the subject of mapping mosquito breeding sites has not been premised accordingly. Despite the facts that the duration of field surveys would be shortened and the effectiveness of action plans would be higher with the supportance of computer modelling techniques. This work aimed to evaluate the applicability of digital elevation models and aerial photographs in the prediction of mosquito breeding sites. Relief analysis was carried out seeking depressions based on elevation models created with TIN and kriging interpolation methods. Spectral analysis was performed on aerial photographs to identify the patches of higher water content or moisture. The results show that terrain analysis can not give acceptable results due to its resolution and accuracy. Spectral analysis of normal (visible range) photographs can be a useful tool in predicting of breeding sites. On grasslands the verificitaion revealed 91% correct results.
Słowa kluczowe
Rocznik
Tom
Strony
255--270
Opis fizyczny
Bibliogr. 23 poz., rys.
Twórcy
autor
  • University of Debrecen, Department of Landscape Protection and Environmental Geography, Debrecen
autor
  • Acrida Conservational Research L.P., Tapolca, Hungary
autor
  • Department of Botany, Hungarian Natural History Museum, Budapest, Hungary
autor
  • University of Debrecen, Department of Landscape Protection and Environmental Geography, Debrecen
  • St-Art Chem L.P., Keszthely, Hungary
Bibliografia
  • [1] Bakos K., 2008: Hyperspectral data processing chain development perspectives for vegetation studies. Tájökológiai Lapok 6 (1-2): 119-125.
  • [2] Bourgeois M.A., Caissie R.M., 1997: Mosquito (Culicidae) breeding habitat distribution above the Petitcodiac River causeway. Greater Moncton Pest Control Commission.
  • [3] Burai P., Lénárt Cs., Bíró T., 2008: Spectral characterization and mapping of sugar beet disease. Cereal Research Communications 36: 811–814.
  • [4] Camara G., Souza R.C.M., Freitas U.M., Garrido J., 1996 .SPRING: Integrating remote sensing and GIS by object-oriented data modelling. Computers & Graphics, 20 (3): 395-403.
  • [5] Hargitai H., Kardeván P., Horváth F., 2006: Analysis of the first Hungarian imaging spectrometric flight and its data for the separation of forest types. Geodézia és Kartográfia 58 (9): 21–34.
  • [6] Hausbeck J., 2004: Mosquito Larvae Monitoring Project 2003 – Madison Metropolitan Area. Madison Department of Public Health, Madison.
  • [7] Hay S.I., Snow R.W., Rogers D.J., 1998: From predicting mosquito habitat to malaria seasons using remotely sensed data: practice, problems and perspectives. Parasitology Today 14 (8): 306–313.
  • [8] Iván Gy., 2007: High resolution digital relief model of Hungary. In: Tóth, T., Tóth, G., Németh, T. & Gaál Z. (eds.): Földminőség, földértékelés és földhasználati információ, Keszthely, MTA TAKI, 193-200.
  • [9] Jordán Gy., 2007a: Digital terrain modelling in a GIS environment. In: Peckham R.J. & Jordan G. (eds): Digital Terrain Modelling. Development and Applications in a Policy Support Environment, Series: Lecture Notes in Geoinformation and Cartography, pp. 1-44.
  • [10] Jordán Gy., 2007b: Adaptive smoothing of valleys in DEMs using TIN interpolation from ridgeline elevations: an application to morphotectonic aspect analysis. Computers & Geosciences 33: 573–585.
  • [11] Jung A., 2005: Use of spectral informations in the research of urban climate. Corvinus Egyetem, Budapest, PhD Thesis 131 p.
  • [12] Lacaux J.P., Tourre Y.M., Vignolles C., Ndione J.A., Lafaye M., 2007: Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal. Remote Sensing of Environment 106: 66-74.
  • [13] Milics G., Burai P., Lénárt Cs., 2008: Pre-harvest prediction of spring barley nitrogen content using hyperspectral imaging. Cereal Research Communications 36: 1863– 1866.
  • [14] Nagy A., Tamás J., Burai P., 2007: Application of advanced technologies for the detection of pollution migration. Cereal Research Communications 35: 805–808.
  • [15] Richards A.J., Jia X., 1999: Remote Sensing Digital Image Analysis. Springer, London.
  • [16] Schäfer M., 2008: Mapping potential mosquito breeding sites using satellite images. Report on methodology tests. Uppsala University
  • [17] Smith M.J., Goodchild M.F., Longley P.A. 2008: Geospatial Analysis – a comprehensive guide to principles, techniques and software tools. 2nd edition, online version: http://www.spatialanalysisonline.com/output/
  • [18] Srivastava A., Nagpal B.N., Saxena R., Subbarao S.K. 2001: Predicted habitat modelling for forest malaria vector species An. dirus in India - A GIS based approach. Curr. Sci. 80: 1129–1134.
  • [19] Srivastava A., Nagpal, B.N., Saxena, R., Eapen, A., Ravindran, K.J., Subbarao, S.K., Rajamanikam, C., Palanisamy, M., Kalra, N.L., Appavoo, N.C. (2003). GIS based malaria information management system for urban malaria scheme in India. Comput Methods Programs Biomed. 71: 63–75.
  • [20] Szabó G., 2006: Analysis of cartographical and geographic information methods’ accuracy in terms of geography. Debreceni Egyetem, Debrecen PhD Thesis, p. 144.
  • [21] Thomas C.J. Lindsay S.W., 2000: Local-scale variation in malaria infection amongst rural Gambian children estimated by satellite remote sensing. Transactions of the Royal Society of Tropical Medicine and Hygiene 94: 159-163.
  • [22] Zou L., Miller S.N., Schmidtmann E.T., 2006: Mosquito larval habitat mapping using remote sensing and GIS: Implications of coalbed methane development and the West Nile Virus. – J. of Medical Entomology 43(5): 1034-1041.
  • [23] Winkler P., 2007: Digital orthophoto programs and 1:10000 national vectoral data of Hungary. In: Tóth, T., Tóth, G., Németh, T. & Gaál, Z. (eds.): Földminőség, földértékelés és földhasználati információ, Keszthely, MTA TAKI, pp. 161-168.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-da6f9cbd-c313-403b-b027-50303c4a4d09
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