INFLUENCE OF THE RAINFALL SEASONAL VARIABILITY IN THE CAATINGA VEGETATION OF NE BRAZIL BY THE USE OF TIME-SERIES
DOI:
https://doi.org/10.29150/jhrs.v4.1.p31-44Keywords:
Plant phenology of the Caatinga, climatic seasonality, annual cycle of vegetation, time series.Abstract
The climate in the Caatinga, especially precipitation, influences the pattern of spatial and temporal distribution of the vegetation that in turn influences the regional climate from the feedback mechanism of energy flows, water and momentum. This climate-vegetation interaction in the Caatinga is different depending upon the specific climatic pattern considered. The objective of this work is to identify the main phenological and seasonal features of the annual vegetation growth cycles and of the annual rainy season, respectively, in five climatic regions of the Caatinga biome. We use time series (2001-2008) of vegetation indices such as NDVI and LSWI, and precipitation that was derived of TRMM satellite data and surface station data. The results indicate that precipitation variability in the rainy season influences directly the variability of vegetation growth cycles. This influence is not linear but adjusted to a logarithmic function being better fitted with LSWI (r2 = 0.67) than with NDVI (r2 = 0.54). The influence of precipitation on vegetation, using phenological metrics such as start, end, peak and amplitude of the vegetation growing cycles, showed greater lag in climatic regions with higher precipitation in the Caatinga regionReferences
Andrade-Lima, D. 1981. The caatingas dominium. Revista Brasileira de Botânica, v. 4, p. 149-153.
Bustamante Becerra, J.A.; Alvalá, R.C.S.; Von Randow. 2012. C. Seasonal Variability of Vegetation and Its Relationship to Rainfall and Fire in the Brazilian Tropical Savanna. In: Boris Escalante-Ramirez. (Org.). Remote Sensing - Applications. Intech, v. 1, p. 77-98.
Eklundh, L.; Jönsson, P. 2011. Timesat 3.1 Software Manual, Lund University, Sweden.
Ferraz, E.M.N; Rodal, M.J.N; Sampaio, E.V.S.B. 2003. Physiognomy and structure of vegetation along an altitudinal gradient in the semi-arid region of northeastern Brazil. Phytocoenologia, v. 33, p. 71-92.
GAO, B. 1996. NDWI - a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257−266.
INFOCLIMA. 2004. A previsão para o Nordeste do Brasil é de chuvas em torno da normal climatológica no início da estação, com possibilidade de variar de normal a abaixo da média histórica no fim da estação, com alta variabilidade espacial. Boletim de informações climáticas, CPTEC, INPE. Ano 11, 1.
Jönsson, P.; Eklundh, L. 2002. Seasonality extraction and noise removal by function fitting to time-series of satellite sensor data, IEEE Transactions of Geoscience and RemoteSensing, 40, No 8, 1824 – 1832.
Jönsson, P.; Eklundh, L. 2004. Timesat - a program for analyzing time-series of satellite sensor data, Computers and Geosciences, 30, 833 – 845.
Nimer, E. 1989. Climatologia do Brasil. 2a. ed. Rio de Janeiro: IBGE- SUPREN, (Fundação IBGE- SUPREN). Recursos Naturais e Meio Ambiente.
Prentice, K. C. 1990. Bioclimatic distribution of vegetation for general circulation model Joural of. Geophysical Research, vol. 95, n. 11, p. 811 – 830.
Press, W.H.; Teukolsky, S.A.; Vetterling, W.T.; Flannery, B.P. 1994. Numerical Recipes in Fortran. Cambridge University Press.
Silva, E.A.D; Carvalho,S.M.I; Bustamante-Becerra, J.A. 2011. Variabilidade sazonal do clima e da vegetação no Bioma Caatinga. I climatologia da precipitação. In: VI Geonordeste 2011, Feira de Santana- BA.
Tavares, M.C.; Rodal, M.J.N.; Melo, A.L.; Araújo, M.F.L. 2000. Fitossociologia do componente arbóreo de um trecho de Floresta Ombrófi la Montana do Parque Ecológico João Vasconcelos-Sobrinho, Caruaru, Pernambuco. Naturalia, v.26, p. 243-270.
Uvo, C.R.B. 1989. A Zona de Convergência Intertropical (ZCIT) e sua relação com a precipitação da Região Norte do Nordeste Brasileiro. 1989. 82 f. Dissertação de Mestrado em Meteorologia - INPE, São Paulo.
Veloso, H.P.; Rangel-Filho, A.L.R.; Lima, J.C.A. 1991. Classificação da vegetação brasileira, adaptada a um sistema universal. IBGE, Rio de Janeiro.
Wilson, E. H.; Sader, S. A. 2002. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment, v. 80, p. 385-396.
Bustamante Becerra, J.A.; Alvalá, R.C.S.; Von Randow. 2012. C. Seasonal Variability of Vegetation and Its Relationship to Rainfall and Fire in the Brazilian Tropical Savanna. In: Boris Escalante-Ramirez. (Org.). Remote Sensing - Applications. Intech, v. 1, p. 77-98.
Eklundh, L.; Jönsson, P. 2011. Timesat 3.1 Software Manual, Lund University, Sweden.
Ferraz, E.M.N; Rodal, M.J.N; Sampaio, E.V.S.B. 2003. Physiognomy and structure of vegetation along an altitudinal gradient in the semi-arid region of northeastern Brazil. Phytocoenologia, v. 33, p. 71-92.
GAO, B. 1996. NDWI - a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257−266.
INFOCLIMA. 2004. A previsão para o Nordeste do Brasil é de chuvas em torno da normal climatológica no início da estação, com possibilidade de variar de normal a abaixo da média histórica no fim da estação, com alta variabilidade espacial. Boletim de informações climáticas, CPTEC, INPE. Ano 11, 1.
Jönsson, P.; Eklundh, L. 2002. Seasonality extraction and noise removal by function fitting to time-series of satellite sensor data, IEEE Transactions of Geoscience and RemoteSensing, 40, No 8, 1824 – 1832.
Jönsson, P.; Eklundh, L. 2004. Timesat - a program for analyzing time-series of satellite sensor data, Computers and Geosciences, 30, 833 – 845.
Nimer, E. 1989. Climatologia do Brasil. 2a. ed. Rio de Janeiro: IBGE- SUPREN, (Fundação IBGE- SUPREN). Recursos Naturais e Meio Ambiente.
Prentice, K. C. 1990. Bioclimatic distribution of vegetation for general circulation model Joural of. Geophysical Research, vol. 95, n. 11, p. 811 – 830.
Press, W.H.; Teukolsky, S.A.; Vetterling, W.T.; Flannery, B.P. 1994. Numerical Recipes in Fortran. Cambridge University Press.
Silva, E.A.D; Carvalho,S.M.I; Bustamante-Becerra, J.A. 2011. Variabilidade sazonal do clima e da vegetação no Bioma Caatinga. I climatologia da precipitação. In: VI Geonordeste 2011, Feira de Santana- BA.
Tavares, M.C.; Rodal, M.J.N.; Melo, A.L.; Araújo, M.F.L. 2000. Fitossociologia do componente arbóreo de um trecho de Floresta Ombrófi la Montana do Parque Ecológico João Vasconcelos-Sobrinho, Caruaru, Pernambuco. Naturalia, v.26, p. 243-270.
Uvo, C.R.B. 1989. A Zona de Convergência Intertropical (ZCIT) e sua relação com a precipitação da Região Norte do Nordeste Brasileiro. 1989. 82 f. Dissertação de Mestrado em Meteorologia - INPE, São Paulo.
Veloso, H.P.; Rangel-Filho, A.L.R.; Lima, J.C.A. 1991. Classificação da vegetação brasileira, adaptada a um sistema universal. IBGE, Rio de Janeiro.
Wilson, E. H.; Sader, S. A. 2002. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment, v. 80, p. 385-396.
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Published
2014-04-24
How to Cite
Becerra, J. A. B. (2014). INFLUENCE OF THE RAINFALL SEASONAL VARIABILITY IN THE CAATINGA VEGETATION OF NE BRAZIL BY THE USE OF TIME-SERIES. Journal of Hyperspectral Remote Sensing, 4(1), 31–44. https://doi.org/10.29150/jhrs.v4.1.p31-44
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Hyperspectral remote sensing and Atmosphere
