Dinâmica da floresta do Parque Estadual do Turvo com índices de vegetação

Rosa, Paulo Afonso; Breunig, Fábio Marcelo; Balbinot, Rafaelo; Galvão, Lênio Soares

doi:10.4322/floram.2013.034

Resumos

Este trabalho analisou a relação entre índices de vegetação e precipitação, no Parque Estadual do Turvo (PET), localizado no noroeste do Estado do Rio Grande do Sul (RS). O Normalized Difference Vegetation Index (NDVI) e o Enhanced Vegetation Index (EVI) foram determinados a partir de imagens do Moderate Resolution Imaging Spectroradiometer (MODIS/Terra). Dados de precipitação do Tropical Rainfall Measuring Mission (TRMM) permitiram o cálculo do índice de estiagem. Foram identificados nove meses com precipitações abaixo da média, 92 meses com precipitações normais e 50 meses com valores acima da média. Os ciclos anuais de NDVI e EVI não apresentaram relação conclusiva com a precipitação anormal, mostrando que a floresta apresenta resiliência para suportar pequenos períodos de seca. O NDVI apresentou maior estabilidade no verão e no inverno.

sensoriamento remoto; séries temporais; chuva; resiliência

In this study, we evaluated the relation between vegetation indices and rainfall in the 'Parque Estadual do Turvo' (PET), located in the northwest of Rio Grande do Sul (RS) state. The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were determined from images of the Moderate Resolution Imaging Spectroradiometer (MODIS/Terra). Rainfall data from the Tropical Rainfall Measuring Mission (TRMM) allowed the calculation of the dry spell index. Data analysis showed that nine months presented rainfall below the average; 92 months had rainfall close to the average; and 50 months presented rainfall above the average. The annual cycles of NDVI and EVI did not present conclusive correlation with the abnormal rainfall data, showing that the forest is resilient to short dry periods. The NDVI showed more stability in the summer and winter.

remote sensing; time series; rain; resilience

Atkinson PM, Dash J, Jeganathan C. Amazon vegetation greenness as measured by satellite sensors over the last decade. Geophysical Research Letters 2011;38(19). http://dx.doi.org/10.1029/2011GL049118
Baret F, Guyot G. Potential and Limits of Vegetation Índices for LAI e APAR Assessment. Remote Sensing of Environment 1991;3:161-173. http://dx.doi.org/10.1016/0034-4257(91)90009-U
Baret F, Guyot G, Major D. TSAVI: a vegetation index which minimizes soil brightness effects on LAI or APAR estimation. In: Proceedings of the Canadian symposium on remote sensing; 1989; Vancouver. Ottawa: Canadian Remote Sensing Society; 1989.
Booth BBB, Jones CD, Collins M, Totterdell IJ, Cox PM, Sitch S, et al. High sensitivity of future global warming to land carbon cycle processes. Environmental Research Letters 2012;7(2):024002. http://dx.doi.org/10.1088/1748-9326/7/2/024002
Brack P, Bueno RM, Falkemberg DB, Paiva MRC, Sobral M, Stehmann JR. Levantamento florístico do Parque Estadual do Turvo, Tenente Portela, Rio Grande do Sul, Brasil. Roessléria 1985;7:69-94.
Castro ALC. Desastres naturais Brasília: Ministério do Planejamento e Orçamento; 1996.
Center for Ocean-Land-Atmosphere Studies - COLA. Grid Analysis and Display System (GrADS). IGES / COLA 4041 Powder Mill Road, Suite 30 Calverton, MD 20705-3106 USA, 2010 [Internet]. [cited 2010 Dec 20]. Available from: http://www.iges.org/grads/
Centro de Previsão de Tempo e Estudos Climáticos - CPTEC. El Niño e La Niña [cited 2013 July 21]. Available from: http://enos.cptec.inpe.br/
Derrubadas. Prefeitura Municipal. Dados gerais [Internet]. [cited 2012 Dec 11]. Available from: http://www.derrubadas-rs.com.br/siteantigo/index_arquivos/page0001.htm
Dwyer J, Schmidt G. The MODIS reprojection tool. In: Qu JJ, Gao W, Kafatos M, Murphy RE, Salomonson VV. Earth Science Satellite Remote Sensing Berlin Heidelberg: Springer Berlin Heidelberg; 2006. p. 162-177. http://dx.doi.org/10.1007/978-3-540-37294-3_9
Franchito SH, Rao VB, Vasques AC, Santo CME, Conforte JC. Validation of TRMM precipitation radar monthly rainfall estimates over Brazil. Journal of Geophysical Research 2009; 114: D02105 1-9. http://dx.doi.org/10.1029/2007JD009580
Galvão LS, Breunig FM, Santos JR , Moura YM. View-illumination effects on hyperspectral vegetation indices in the Amazonian tropical forest. International Journal of Applied Earth Observation and Geoinformation 2013;21:291-300. http://dx.doi.org/10.1016/j.jag.2012.07.005
Gunderson LH. Ecological resilience - in theory and application. Annual Review of Ecology and Systematics 2000;31:425-439. http://dx.doi.org/10.1016/j.jag.2012.07.005
Huete AR, Hua H, Qi J, Chehbouni A, Van Leeuwen WJD. Normalization of multidirectional red and NIR reflectances with the SAVI. Remote Sensing of Environment 1992;41:143-154. http://dx.doi.org/10.1016/0034-4257(92)90074-T
Huete A, Didan k , Miura T, Rodriguez EP, Gao X, Ferreira LG. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 2002;83(1-2):195-213. http://dx.doi.org/10.1016/S0034-4257(02)00096-2
Huntingford C, Zelazowski P, Galbraith D, Mercado LM, Sitch S, Fisher R, et al. Simulated resilience of tropical rainforests to CO₂-induced climate change. Nature Geoscience 2013;6:268-273. http://dx.doi.org/10.1038/ngeo1741
Instituto Brasileiro de Geografia e Estatística - IBGE. Folhas SH 22 Porto Alegre e parte das folhas SH 21 e SI 22 Lagoa Mirim: Levantamento de Recursos Naturais. Geologia, geomorfologia, pedologia, vegetação, uso potencial da terra. Rio de Janeiro; 1986. v. 33.
Instituto Brasileiro de Geografia e Estatística - IBGE. Manual técnico da vegetação brasileira 2. ed. Rio de Janeiro; 2012.
Irgang BE. A mata do alto Uruguai no RS. Ciência e Cultura 1980;(32):323-324.
ITT Visual Information Solutions - ITT VIS. ENVI TM- Environment for Visualizing for Images [Internet]. 4.5 ed. ^®ITT Visual Information Solutions; 2008. Available from: www.ittvis.com
Justice CO, Vermote E, Townshend JRG, DeFries R, Roy DP, Hall DK, et al. The Moderate Resolution Imaging Spectroradiometer (MODIS): Land Remote Sensing for Global Change Research. IEEE Transactions on Geoscience and Remote Sensing 1998;36(4):1228-1249. http://dx.doi.org/10.1109/36.701075
Justice CO, Townshend JRG, Vermote EF, Masuoka E, Wolfe RE, Saleous N, et al. An overview of MODIS Land data processing and product status. Remote Sensing of Environment 2002;83(1-2):3-15. http://dx.doi.org/10.1016/S0034-4257(02)00084-6
Lee R, Yu F, Price KP. Evaluating vegetation phonological patterns in Inner Mongolia using NDVI timeseries analysis. International Journal of Remote Sensing 2002;23(12):2505-2512. http://dx.doi.org/10.1080/01431160110106087
Leite PF. Contribuição ao conhecimento fitoecológico do sul do Brasil. Ciência e Ambiente 2002;24:51-73.
Miura T, Huete AR, Yoshioka H, Holben BN. An error and sensitivity analysis of atmospheric resistant vegetation indices derived from dark target-based atmospheric correction. Remote Sensing of Environment 2001;78(3):284-298. http://dx.doi.org/10.1016/S0034-4257(01)00223-1
Parque Florestal Estadual do Turvo. Aspectos legais [Internet]. [cited 2012 Dec 13]. Available from: http://www.derrubadas-rs.com.br/turvo.htm
Passow MJ. TRMM - Tropical Rainfall Measuring Mission: Bringing remote sensing of precipitation into your classroom. Terræ Didatica 2010;6(1):03-08. [cited 2013 Jan 31]. Available from: http://ppegeo.igc.usp.br/pdf/ted/v6n1/v6n1a02.pdf
Phillips OL, Aragão LEOC, Lewis SL, Fisher JB, Lloyd J, López-González G, et al. Drought sensitivity of the Amazon rainforest. Science 2009;323(5919):1344-7. http://dx.doi.org/10.1126/science.1164033
Ponzoni FJ. Comportamento espectral da vegetação. In: Menezes PR, Madeira JS Nº. Sensoriamento Remoto: reflectância de alvos naturais. Brasília: UnB/Embrapa; 2001.157-199.
Rio Grande do Sul. Secretaria Estadual do Meio Ambiente do Estado do Rio Grande do Sul (SEMA - RS). Plano de manejo do Parque Estadual do Turvo Porto Alegre: Divisão de Unidades de Conservação do Estado do Rio Grande do Sul; 2005.
Rouse JW, Haas RH, Schell JA, Deering DW. Monitoring vegetation systems in the Great Plains with ERTS. In: Proceedings of the Earth Resources Technology Satellite-1 Symposium; 1973; Washington. Washington: NASA; 1973.
Santos AR, Peluzio TMO, Saito NS. Spring 5.1.2: passo a passo: Aplicações Práticas. Alegre: CAUFES; 2010. [cited 2012 Dec 10]. Available from: http://www.mundogeomatica.com.br/Livros/Livro_Spring_5.1.2_Aplicacoes_Praticas/Livro_SPRING_512_PassoaPasso_Aplica%C3%A7%C3%A3o_Pr%C3%A1tica.pdf
Silva L, Costa JF Fº, Lima VER, Almeida HA. Análise do albedo e do índice de vegetação da diferença normalizada (NDVI) em uma bacia hidrográfica, usando o SEBAL - Surface Energy Balance Algorithm for Land. In: Anais do XIII Simpósio Brasileiro de Geografia Física Aplicada; 2009; Viçosa. Viçosa: UFV; 2009. [cited 2012 Dec 15]. Available from: http://www.geo.ufv.br/simposio/simposio/trabalhos/trabalhos_completos/eixo1/054.pdf
Vibrans AC, Schramm VF. Perfil temporal de EVI/NDVI na bacia hidrográfica do rio Itajaí em Santa Catarina. In: Anais do Simpósio Brasileiro de Sensoriamento Remoto (SBSR); 1999; Natal. Natal: INPE; 2009. PMid:19943939 PMCid:PMC2789042.
Zhang X, Friedl MA, Schaaf CB, Strahler AH, Hodges JCF, Gao F, et al. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 2003;84:471-475. http://dx.doi.org/10.1016/S0034-4257(02)00135-9

Datas de Publicação

Publicação nesta coleção
01 Out 2013
Data do Fascículo
Dez 2013

Histórico

Recebido
01 Abr 2013
Aceito
17 Ago 2013

This work is licensed under a Creative Commons Attribution 4.0 International License.

[1] Atkinson PM, Dash J, Jeganathan C. Amazon vegetation greenness as measured by satellite sensors over the last decade. Geophysical Research Letters 2011;38(19). http://dx.doi.org/10.1029/2011GL049118

[2] Baret F, Guyot G. Potential and Limits of Vegetation Índices for LAI e APAR Assessment. Remote Sensing of Environment 1991;3:161-173. http://dx.doi.org/10.1016/0034-4257(91)90009-U

[3] Baret F, Guyot G, Major D. TSAVI: a vegetation index which minimizes soil brightness effects on LAI or APAR estimation. In: Proceedings of the Canadian symposium on remote sensing; 1989; Vancouver. Ottawa: Canadian Remote Sensing Society; 1989.

[4] Booth BBB, Jones CD, Collins M, Totterdell IJ, Cox PM, Sitch S, et al. High sensitivity of future global warming to land carbon cycle processes. Environmental Research Letters 2012;7(2):024002. http://dx.doi.org/10.1088/1748-9326/7/2/024002

[5] Brack P, Bueno RM, Falkemberg DB, Paiva MRC, Sobral M, Stehmann JR. Levantamento florístico do Parque Estadual do Turvo, Tenente Portela, Rio Grande do Sul, Brasil. Roessléria 1985;7:69-94.

[6] Castro ALC. Desastres naturais Brasília: Ministério do Planejamento e Orçamento; 1996.

[7] Center for Ocean-Land-Atmosphere Studies - COLA. Grid Analysis and Display System (GrADS). IGES / COLA 4041 Powder Mill Road, Suite 30 Calverton, MD 20705-3106 USA, 2010 [Internet]. [cited 2010 Dec 20]. Available from: http://www.iges.org/grads/

[8] Centro de Previsão de Tempo e Estudos Climáticos - CPTEC. El Niño e La Niña [cited 2013 July 21]. Available from: http://enos.cptec.inpe.br/

[9] Derrubadas. Prefeitura Municipal. Dados gerais [Internet]. [cited 2012 Dec 11]. Available from: http://www.derrubadas-rs.com.br/siteantigo/index_arquivos/page0001.htm

[10] Dwyer J, Schmidt G. The MODIS reprojection tool. In: Qu JJ, Gao W, Kafatos M, Murphy RE, Salomonson VV. Earth Science Satellite Remote Sensing Berlin Heidelberg: Springer Berlin Heidelberg; 2006. p. 162-177. http://dx.doi.org/10.1007/978-3-540-37294-3_9

[11] Franchito SH, Rao VB, Vasques AC, Santo CME, Conforte JC. Validation of TRMM precipitation radar monthly rainfall estimates over Brazil. Journal of Geophysical Research 2009; 114: D02105 1-9. http://dx.doi.org/10.1029/2007JD009580

[12] Galvão LS, Breunig FM, Santos JR , Moura YM. View-illumination effects on hyperspectral vegetation indices in the Amazonian tropical forest. International Journal of Applied Earth Observation and Geoinformation 2013;21:291-300. http://dx.doi.org/10.1016/j.jag.2012.07.005

[13] Gunderson LH. Ecological resilience - in theory and application. Annual Review of Ecology and Systematics 2000;31:425-439. http://dx.doi.org/10.1016/j.jag.2012.07.005

[14] Huete AR, Hua H, Qi J, Chehbouni A, Van Leeuwen WJD. Normalization of multidirectional red and NIR reflectances with the SAVI. Remote Sensing of Environment 1992;41:143-154. http://dx.doi.org/10.1016/0034-4257(92)90074-T

[15] Huete A, Didan k , Miura T, Rodriguez EP, Gao X, Ferreira LG. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 2002;83(1-2):195-213. http://dx.doi.org/10.1016/S0034-4257(02)00096-2

[16] Huntingford C, Zelazowski P, Galbraith D, Mercado LM, Sitch S, Fisher R, et al. Simulated resilience of tropical rainforests to CO₂-induced climate change. Nature Geoscience 2013;6:268-273. http://dx.doi.org/10.1038/ngeo1741

[17] Instituto Brasileiro de Geografia e Estatística - IBGE. Folhas SH 22 Porto Alegre e parte das folhas SH 21 e SI 22 Lagoa Mirim: Levantamento de Recursos Naturais. Geologia, geomorfologia, pedologia, vegetação, uso potencial da terra. Rio de Janeiro; 1986. v. 33.

[18] Instituto Brasileiro de Geografia e Estatística - IBGE. Manual técnico da vegetação brasileira 2. ed. Rio de Janeiro; 2012.

[19] Irgang BE. A mata do alto Uruguai no RS. Ciência e Cultura 1980;(32):323-324.

[20] ITT Visual Information Solutions - ITT VIS. ENVI TM- Environment for Visualizing for Images [Internet]. 4.5 ed. ^®ITT Visual Information Solutions; 2008. Available from: www.ittvis.com

[21] Justice CO, Vermote E, Townshend JRG, DeFries R, Roy DP, Hall DK, et al. The Moderate Resolution Imaging Spectroradiometer (MODIS): Land Remote Sensing for Global Change Research. IEEE Transactions on Geoscience and Remote Sensing 1998;36(4):1228-1249. http://dx.doi.org/10.1109/36.701075

[22] Justice CO, Townshend JRG, Vermote EF, Masuoka E, Wolfe RE, Saleous N, et al. An overview of MODIS Land data processing and product status. Remote Sensing of Environment 2002;83(1-2):3-15. http://dx.doi.org/10.1016/S0034-4257(02)00084-6

[23] Lee R, Yu F, Price KP. Evaluating vegetation phonological patterns in Inner Mongolia using NDVI timeseries analysis. International Journal of Remote Sensing 2002;23(12):2505-2512. http://dx.doi.org/10.1080/01431160110106087

[24] Leite PF. Contribuição ao conhecimento fitoecológico do sul do Brasil. Ciência e Ambiente 2002;24:51-73.

[25] Miura T, Huete AR, Yoshioka H, Holben BN. An error and sensitivity analysis of atmospheric resistant vegetation indices derived from dark target-based atmospheric correction. Remote Sensing of Environment 2001;78(3):284-298. http://dx.doi.org/10.1016/S0034-4257(01)00223-1

[26] Parque Florestal Estadual do Turvo. Aspectos legais [Internet]. [cited 2012 Dec 13]. Available from: http://www.derrubadas-rs.com.br/turvo.htm

[27] Passow MJ. TRMM - Tropical Rainfall Measuring Mission: Bringing remote sensing of precipitation into your classroom. Terræ Didatica 2010;6(1):03-08. [cited 2013 Jan 31]. Available from: http://ppegeo.igc.usp.br/pdf/ted/v6n1/v6n1a02.pdf

[28] Phillips OL, Aragão LEOC, Lewis SL, Fisher JB, Lloyd J, López-González G, et al. Drought sensitivity of the Amazon rainforest. Science 2009;323(5919):1344-7. http://dx.doi.org/10.1126/science.1164033

[29] Ponzoni FJ. Comportamento espectral da vegetação. In: Menezes PR, Madeira JS Nº. Sensoriamento Remoto: reflectância de alvos naturais. Brasília: UnB/Embrapa; 2001.157-199.

[30] Rio Grande do Sul. Secretaria Estadual do Meio Ambiente do Estado do Rio Grande do Sul (SEMA - RS). Plano de manejo do Parque Estadual do Turvo Porto Alegre: Divisão de Unidades de Conservação do Estado do Rio Grande do Sul; 2005.

[31] Rouse JW, Haas RH, Schell JA, Deering DW. Monitoring vegetation systems in the Great Plains with ERTS. In: Proceedings of the Earth Resources Technology Satellite-1 Symposium; 1973; Washington. Washington: NASA; 1973.

[32] Santos AR, Peluzio TMO, Saito NS. Spring 5.1.2: passo a passo: Aplicações Práticas. Alegre: CAUFES; 2010. [cited 2012 Dec 10]. Available from: http://www.mundogeomatica.com.br/Livros/Livro_Spring_5.1.2_Aplicacoes_Praticas/Livro_SPRING_512_PassoaPasso_Aplica%C3%A7%C3%A3o_Pr%C3%A1tica.pdf

[33] Silva L, Costa JF Fº, Lima VER, Almeida HA. Análise do albedo e do índice de vegetação da diferença normalizada (NDVI) em uma bacia hidrográfica, usando o SEBAL - Surface Energy Balance Algorithm for Land. In: Anais do XIII Simpósio Brasileiro de Geografia Física Aplicada; 2009; Viçosa. Viçosa: UFV; 2009. [cited 2012 Dec 15]. Available from: http://www.geo.ufv.br/simposio/simposio/trabalhos/trabalhos_completos/eixo1/054.pdf

[34] Vibrans AC, Schramm VF. Perfil temporal de EVI/NDVI na bacia hidrográfica do rio Itajaí em Santa Catarina. In: Anais do Simpósio Brasileiro de Sensoriamento Remoto (SBSR); 1999; Natal. Natal: INPE; 2009. PMid:19943939 PMCid:PMC2789042.

[35] Zhang X, Friedl MA, Schaaf CB, Strahler AH, Hodges JCF, Gao F, et al. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 2003;84:471-475. http://dx.doi.org/10.1016/S0034-4257(02)00135-9

Brasil

Brasil

Dinâmica da floresta do Parque Estadual do Turvo com índices de vegetação

Dynamics of the forest from the 'Parque Estadual do Turvo' with vegetation indices

Resumos

Datas de Publicação

Histórico