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Analiza korelacji pomiędzy wskaźnikami stanu roślinności a wskaźnikami intensywności ognia na przykładzie pożarów leśnych w Grecji w sierpniu 2007 roku

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EN
Analysis of correlation between vegetation and fire intensity indexes: the case of forest fires occurred in Greece in 2007
Języki publikacji
PL
Abstrakty
EN
Forest fires influence significantly on the natural environment, forest management and economy. Therefore, it is very important to predict the susceptibility of the object to damage caused by fire. There are a lot of vegetation indexes obtained from the satellite images which are used to forecast the fire hazard. The aim of the study is to verify which vegetation index is the most appropriate to foresee the fire intensity. The study was carried out for the fi res which occurred in Greece in August 2007. The analyzed area had 190 000 ha. MODIS data were used in the study. The paper presents the results of the correlation analysis for some of vegetation indexes: NDVI – Normalized Difference Vegetation Index, NDII - Normalized Difference Infrared Index, NDWI – Normalized Difference Water Index, GVMI – Global Vegetation Moisture Index, SRWI - Simple Ratio Water Index, SIWSI(6,2) – Shortwave Infrared Water Stress Index and indexes of fi re intensity: BAI – Burned Area Index and NBR – Normalized Burn Ratio. The analysis shows that the vegetation condition is only one of the many factors which control the forest fire intensity. The correlation between vegetation and fire intensity indexes varies depending mainly on weather condition; especially maximum wind speed and the content of water vapour and secondarily on the type of vegetation cover. The most suitable vegetation indexes to predict the fi re intensity resulted to be the NDII and SIWSI(6,2), which present Pearson correlation of 0,66. The correlation is usually better for the BAI fi re intensity index then for the NBR. The only exception is the case of forest.
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Tom
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17--22
Opis fizyczny
Bibliogr. 20 poz., rys., tab., wykr.
Twórcy
autor
  • Wydział Geografii i Studiów Regionalnych Uniwersytetu Warszawskiego
Bibliografia
  • 1. Altobelli, A., Sgambati, A., Bader, F., Fior, G., Magajna. B., Ferrazzo, L., Braut, R., Urrutia, P., Ganis, P., Orlando, S., 2010, Using gvGIS’s Remote Sensing Extention forest fire monitoring. GEOInformatics, 13 (3), 44–47
  • 2. Chuvieco, E., Cocero, D., Riano, D., Mrtin, P., Martinez-Veg, J., de la Riva, J., Perez, F., 2004, Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment, 92, 322–331
  • 3. Ceccato, P., Gobron, N., Flasse, S., Pinty, B., Tarantola, S., 2002, Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1. Theoretical approach. Remote Sensing of Environment, 82, 198–207.
  • 4. Fensholt, R., Sandholt, I., 2003, Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment. Remote Sensing of Environment, 87, 111–121.
  • 5. Fox, D.M., Maselli, F., Carrega, P., 2008, Using SPOT images and field data sampling to map burn severity and vegetation factors affecting post forest fi re erosion risk. Catena 75, 326–335.
  • 6. Gao, B.C., 1996, NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266.
  • 7. Hardyski, M.A., Lemas, V., Smart, R.M., 1983, The influence of soil salinity, growth form, and leaf moisture on the spectral reflectance of spartina alternifolia canopies. Photogrammetric Engineering & Remote Sensing, 49, 77–83.
  • 8. Huete, A.R., 1988, A soil-adjusted vegetation index (SAVI), Remote Sensing of Environment, 25, 295–309.
  • 9. Hunt, E.R., Rock, B.N., Nobel, P.S., 1987, Measurement of leaf relative water content by infrared reflectance. Remote Sensing of Environment, 22, 429–435.
  • 10. Jackson, R.D., Idso, S. B., Reginato, R. J., & Pinter, P. J. ,1981, Canopy temperature as a crop water stress indicator. Water Resources Research, 17, 1133−1138.
  • 11. Jackson, R.D., 1986, Remote sensing of biotic and abiotic plant stress. Annual Review of Phytopathology, 24, 265−286.
  • 12. Karlikowski, T., 1997, Wykorzystanie zdjęć satelitarnych NOAA-AUHRR do wspomagania oceny zagrożenia pożarowego lasu. Prace IBL, 829, 3–72.
  • 13. Key, C., Bensos, N., 2002, Landscape assessment, in fire effects monitoring (FireMon) and inventory protocol: Integration of standardized fi eld data collection techniques and sampling desing with remote sensing to assess fire effects. NPS-USGS National Burn Severity Mapping Project.
  • 14. Martin, M.P., Gomez, I., Chuvieco, E. 2005, Performance of a burned-area index (BAIm) for mapping Mediterranean burned scars from MODIS data. Proceedings of the 5th International Workshop on Remote Sensing and GIS applications to Forest Fire Management: Fire Effects Assessment. (J. Riva, Pérez-Cabello, F. y Chuvieco, E., Eds.). Paris, Universidad de Zaragoza, GOFC GOLD, EARSeL: 193–198.
  • 15. Mycke-Dominko, M., 2003, Teledetekcyjna metoda kategoryzacji zagrożenia pożarowego lasu. Archiwum Fotogrametrii, Kartografii i Teledetekcji, 13
  • 16. Panuelas, J., Filella, I., Biel, C., Serrano, L., 1993, The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing, 14, 1887–1905.
  • 17. Ponomarev, E., 2003, Technologia sporządzania codziennych ocen zagrożenia pożarowego z wykorzystaniem satelitarnej teledetekcji w Regionie Krasnojarskim (Syberia Wschodnia, Rosja). Prace IBL 4, 5–18.
  • 18. Rouse, J.W., Haas, R.W., Schell, J.A., Deering, D.H., Harlan, J.C., 1974, Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation. NASA/GSFC, Greenbelt, MD, USA.
  • 19. Vidal, A., Pinglo, F., Durand, H., Devaux-Ros, C., & Maillet, A. ,1994, Evaluation of a temporal fire risk index in Mediterranean forests from NOAA thermal IR. Remote Sensing of Environment, 49, 296−303.
  • 20. Zarco-Tejada, P.L.J., Rueda, C.A., Ustin, S.L., 2003, Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85, 109–124.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-961b6496-3926-4fe8-a7fd-495919afe516
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