Abstract
This study aimed to analyze the spectral trend of vegetation with rainfall in El Niño-Southern Oscillation events (ENSO) in the Atlantic Forest, Brazil. Monthly rainfall data were collected from 85 conventional meteorological stations (EMC), data from the Enhanced Vegetation Index 2 (EVI2) and ENSO events (El Niño, La Niña, and Neutral) in the period from 2001 to 2013. Afterwards, state cluster analysis was performed using the results of non-parametric tests. The Mann-Kendall (MK) non-parametric test did not identify a trend pattern in rainfall distribution in the Atlantic Forest. The results for EVI2 by state and region showed that the trend is decreasing in the Northeast Region, except for the states of Alagoas and Pernambuco. Southeast region showed an increasing trend of EVI2 (except for Rio de Janeiro and São Paulo), while the South region showed a decreasing trend. In the Midwest, the trend was significantly decreasing. In the prognosis elaborated for the future, the regions with significant declines of the vegetation were the Northeast and Midwest. This study shows that the Atlantic Forest in some regions of Brazil has been suffering from the growing urbanization process and there is a trend of soil degradation.
References
Alvares, C. A., Stape, J. L., Sentelhas, P. C., Moraes, G., Leonardo, J., & Sparovek, G. (2013). Köppen's climate classification map for Brazil. Meteorologische Zeitschrift, 22(6), 711–728.
Anjos, V. S., Sano, E. E., da Silva Bezerra, H., & Rosa, R. (2013). Caracterização espectro-temporal de pastagens do Triângulo Mineiro utilizando dados modis EVI2 (2000–2010)/Spectral and Temporal Characterization of Pastures from Triangulo Mineiro, State of Minas Gerais, using data MODIS EVI2 (2000–2010). Revista Sociedade & Natureza, 25, 205–215.
BDMEP - Banco de dados Meteorológicos para Ensino e Pesquisa. Disponível em:< http://www.inmet.gov.br/projetos/rede/pesquisa/inicio.php>. Acesso em 13/07/2017.
Cai, W., Wang, G., Santos, A., McPhaden, M. J., Wu, L., Jin, F. F., Timmermann, A., Collins, M., Vecchi, G., Lengaigne, M., England, M. H., Dommenget, D., Takahashi, K., & Guilyardi, E. (2015). Increased frequency of extreme La Niña events under greenhouse warming. Nature Climate Change, 5, 132–137. https://doi.org/10.1038/nclimate2492.
Cañón, J., González, J., & Valdés, J. (2007). Precipitation in the Colorado River basin and its low frequency associations with PDO and ENSO signals. Journal of Hydrology, 333, 252–264. https://doi.org/10.1016/j.jhydrol.2006.08.015.
Capozzoli, C. R., Cardoso, A. D. O., & Ferraz, S. E. T. (2017). River flow variability patterns in Main Brazilian basins and association with climate indices. Revista Brasileira de Meteorologia, 32(2), 243–254. https://doi.org/10.1590/0102-77863220006.
Caúla, R. H., Oliveira-Júnior, J. F., Lyra, G. B., Delgado, R. C., & Heilbron Filho, P. F. L. (2015). Overview of fire foci causes and locations in Brazil based on meteorological satellite data from 1998 to 2011. Environmental Earth Sciences, 74, 1497–1508. https://doi.org/10.1007/s12665-015-4142-z.
Caúla, R. H., Oliveira-Júnior, J. F., Gois, G., Delgado, R. C., Pimentel, L. C. G., & Teodoro, P. E. (2016). Nonparametric statistics applied to fire foci obtained by meteorological satellites and their relationship to the MCD12Q1 product in the state of Rio de Janeiro, Southeast Brazil. Land Degradation & Development, 28(3), 1056–1067. https://doi.org/10.1002/ldr.2574.
Colombo, A. F., & Joly, C. A. (2010). Brazilian Atlantic Forest lato sensu: The most ancient Brazilian forest, and a biodiversity hotspot, is highly threatened by climate change. Brazilian Journal of Biology, 70, 697–708. https://doi.org/10.1590/S1519-69842010000400002.
Da Silva, S. C. P., & Baptista, G. M. D. M. (2015). Análises espectrais da vegetação com dados hyperion e sua relação com a concentração e o fluxo de CO2 em diferentes ambientes na amazônia brasileira. Boletim de Ciêncas Geodésicas, 21, 354–370. https://doi.org/10.1590/S1982-21702015000200020.
Debortoli, N. S., Dubreuil, V., Funatsu, B., Delahaye, F., Oliveira, C. H., Rodrigues-Filho, S., Saito, C. H., & Fetter, R. (2015). Rainfall patterns in the southern Amazon: A chronological perspective (1971–2010). Climatic Change, 132, 251–264. https://doi.org/10.1007/s10584-015-1415-1.
Delgado, R. C., Sediyama, G. C., Costa, M. H., Soares, V. P., & Andrade, R. G. (2012). Classificação espectral de área plantada com a cultura da cana-de-açúcar por meio da árvore de decisão. Revista Engenharia Agrícola, 32, 369–380. https://doi.org/10.1590/S0100-69162012000200017.
Duhan, D., & Pandey, A. (2013). Statistical analysis of long term spatial and temporal trends of precipitation during 1901–2002 at Madhya Pradesh, India. Atmospheric Research, 122, 136–149. https://doi.org/10.1016/j.atmosres.2012.10.010.
Ferreira LKR. 2016. Análise comparativa do desempenho de índices de seca aplicados à região do Alto Jaguaribe - Ceará. Dissertação (Mestrado em Engenharia Civil: Recursos Hídricos) – Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza. 84 f.
Fialho, R. C., & Zinn, Y. L. (2014). Changes in soil organic carbon under Eucalyptus plantations in Brazil: A comparative analysis. Land Degradation & Development, 25, 428–437. https://doi.org/10.1002/ldr.2158.
Freitas, R. D., Arai, E., Adami, M., Ferreira, A. S., Sato, F. Y., Shimabukuro, Y. E., Rosa, R. R., Anderson, L. O., & Rudorff, B. F. T. (2011). Virtual laboratory of remote sensing time series: Visualization of MODIS EVI2 data set over South America. Journal of Computational Interdisciplinary Sciences, 2, 57–68.
Gao, X., Huete, A. R., Ni, W., & Miura, T. (2000). Optical–biophysical relationships of vegetation spectra without background contamination. Remote Sensing of Environment, 74(3), 609–620. https://doi.org/10.1016/S0034-4257(00)00150-4.
Goulart, A. C., Delgado, R. C., Oliveira-Júnior, J. F., Gois, G., & Santos, E. O. (2015). Relação espectro-temporal entre índices de vegetação e a chuva na cidade do Rio de Janeiro. Revista de Ciências Agrárias/Amazonian Journal of Agricultural and Environmental Sciences, 58, 277–283. https://doi.org/10.4322/rca.1990.
Gouveia, S. F., Souza-Alves, J. P., Rattis, L., Dobrovolski, R., Jerusalinsky, L., Beltrão-Mendes, R., & Ferrari, S. F. (2016). Climate and land use changes will degrade the configuration of the landscape for titi monkeys in eastern Brazil. Global Change Biology, 22(6), 2003–2012. https://doi.org/10.1111/gcb.13162.
Goyal, M. K. (2014). Statistical analysis of long term trends of rainfall during 1901–2002 at Assam, India. Water Resources Management, 28, 1501–1515. https://doi.org/10.1007/s11269-014-0529-y.
Grifoni, R. C., Ottone, M. F., & Prenna, E. (2017). Tomographic environmental sections for environmental mitigation devices in historical centers. Energies, 10(3), 351. https://doi.org/10.3390/en10030351.
Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. https://doi.org/10.1016/0034-4257(88)90106-X.
Huete, A. R., Liu, H. Q., Batchily, K., & Van Leeuwen, W. J. D. A. A. (1997). Comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment, 59, 440–451. https://doi.org/10.1016/S0034-4257(96)00112-5.
IBGE – Instituto Brasileiro de Geografia e Estatística. 2010. Atlas nacional do Brasil Milton Santos / IBGE, Diretoria de Geociências. In: Território e meio ambiente Cap. 4: 69–97. Disponível em: https://biblioteca.ibge.gov.br/visualizacao/livros/liv47603_cap4_pt8.pdf. Acesso em: 30/06/2017.
IPCC - Intergovernmental Panel on Climate Change. Disponível em: <http://www.ipcc.ch/>. Acesso em: 30/06/2017.
Jacob, M., Frankl, A., Beeckman, H., Mesfin, G., Hendrickx, M., Guyassa, E., & Nyssen, J. (2015). North Ethiopian afro-alpine tree line dynamics and Forest-cover change since the early 20th century. Land Degradation & Development, 26(7), 654–664. https://doi.org/10.1002/ldr.2320.
Jiang, Z., Huete, A. R., Didan, K., & Miura, T. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112, 3833–3845. https://doi.org/10.1016/j.rse.2008.06.006.
João Hipólito Paiva de Britto Salgueiro, Suzana Maria Gico Lima Montenegro, Eber José de Andrade Pinto, Bernardo Barbosa da Silva, Werônica Meira de Souza, Leidjane Maria Maciel de Oliveira, (2016) Influence of oceanic-atmospheric interactions on extreme events of daily rainfall in the Sub-basin 39 located in Northeastern Brazil. RBRH 21 (4):685–693
Jong, B. T., Ting, M., & Seager, R. (2016). El Niño's impact on California precipitation: Seasonality, regionality, and El Niño intensity. Environmental Research Letters, 11(5), 054021. https://doi.org/10.1088/1748-9326/11/5/054021.
Kaufman, Y. J., & Tanre, D. (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, 30(2), 261–270. https://doi.org/10.1109/36.134076.
Kendall, M. G. 1975. Rank Correlation Methods. London: Charles Griffin, p. 199.
Lima LM. 2013. Aves da Mata Atlântica: riqueza, composição, status, endemismos e conservação. Instituto de Biociências, University of São Paulo, São Paulo, Brazil, Available in: http://www.teses.usp.br/teses/disponiveis/41/41133/tde-17042014-091547. Accessed: 14/07/2017.
Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13, 245–259. https://doi.org/10.2307/1907187.
Marengo, J. A., & Espinoza, J. C. (2016). Extreme seasonal droughts and floods in Amazonia: Causes, trends and impacts. International Journal of Climatology, 36(3), 1033–1050. https://doi.org/10.1002/joc.4420.
Martín, A., Díaz-Raviña, M., & Carballas, T. (2012). Short-and medium-term evolution of soil properties in Atlantic forest ecosystems affected by wildfires. Land Degradation & Development, 23(5), 427–439. https://doi.org/10.1002/ldr.1078.
MMA- Ministério do Meio Ambiente. Disponível em: < http://www.mma.gov.br/port/conama/legiabre.cfm?codlegi=526>. Acesso em 13/07/2017a.
MMA- Ministério do Meio Ambiente. Disponível em: <http://www.mma.gov.br/biomas/mata-atlantica>. Acesso em 13/07/2017b.
Mondal, P. (2011). Quantifying surface gradients with a 2-band enhanced vegetation index (EVI2). Ecological Indicators, 11, 918–924. https://doi.org/10.1016/j.ecolind.2010.10.006.
NOAA/CPC - National Oceanic and Atmospheric Administration/Climate Prediction Center. Disponível em:< http://www.inmet.gov.br/projetos/rede/pesquisa/inicio.php>. Acesso em 13/07/2017.
Nunes, M. T. O., Sousa, G. M., Tomzhinski, G. W., Oliveira-Júnior, J. F., & Fernandes, M. C. (2015). Variáveis Condicionantes na Susceptibilidade de Queimadas e Incêndios no Parque Nacional do Itatiaia. Anuário do Instituto de Geociências (UFRJ. Impresso), 38, 54–62. https://doi.org/10.11137/2015_1_54_62.
Oliveira-Filho, A. T., & Fontes, M. A. L. (2000). Patterns of floristic differentiation among Atlantic forests in southeastern Brazil, and the influence of climate. Biotropica, 32(4b), 793–810. https://doi.org/10.1646/0006-3606(2000)032[0793:POFDAA]2.0.CO;2.
Park, J.-Y., Kug, J.-S., & Park, Y.-G. (2014). An exploratory modeling study on bio-physical processes associated with ENSO. Progress in Oceanography, 124, 28–41. https://doi.org/10.1016/j.pocean.2014.03.013.
Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007). Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences Discussions, 4(2), 439–473.
Pettitt, A. N. (1979). A non-parametric approach to the change-point problem. Applied Statistics, 28, 126–135. https://doi.org/10.2307/2346729.
Poveda, G., Álvarez, D. M., & Rueda, O. A. (2011). Hydro-climatic variability over the Andes of Colombia associated with ENSO: A review of climatic processes and their impact on one of the Earth’s most important biodiversity hotspots. Climate Dynamic, 36, 2233–2249. https://doi.org/10.1007/s00382-010-0931-y.
Rasmusson, E. M., & Carpenter, T. H. (1982). Variations in tropical sea surface temperature and surface wind fields associated with the southern oscillation/El Niño. Monthly Weather Review, 110, 354–384. https://doi.org/10.1175/1520-0493(1982)110%3C0354:VITSST%3E2.0.CO;2.
R Development Core Team (2011). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Accessed 10 Dec 2007. http://www.R-project.org/.
Rougé, C., Ge, Y., & Cai, X. (2013). Detecting gradual and abrupt changes in hydrological records. Advances in Water Resources, 53, 33–44. https://doi.org/10.1016/j.advwatres.2012.09.008.
Salata, F., Golasi, I., Petitti, D., de Lieto Vollaro, E., Coppi, M., & de Lieto Vollaro, A. (2017). Relating microclimate, human thermal comfort and health during heat waves: An analysis of heat island mitigation strategies through a case study in an urban outdoor environment. Sustainable Cities and Society, 30, 79–96. https://doi.org/10.1016/j.scs.2017.01.006.
Santana, M. F., Delgado, R. C., Júnior, J. F. O., de Gois, G., & Teodoro, P. E. (2016). Variabilidade da Mata Atlântica baseado no índice EVI e variáveis climáticas em Cunha-SP, Brasil. Revista de Ciências Agroambientais, 14(1), 37–44.
Santos, G. L., Pereira, M. G., Delgado, R. C., & Torres, J. L. R. (2017a). Natural regeneration in anthropogenic environments due to agricultural use in the cerrado, Uberaba, MG, Brazil. Bioscience Journal, 33(1), 169–176. https://doi.org/10.14393/BJ-v33n1a2017-35036.
Santos, Y. L. F. D., Souza, R. A. F. D., Souza, J. M. D., Andreoli, R. V., Kayano, M. T., Ribeiro, I. O., & Guimarães, P. C. (2017b). Spatio-temporal variability of carbon monoxide over South America using satellite-sensed data from 2003 to 2012. Revista Brasileira de Meteorologia, 32(1), 89–98. https://doi.org/10.1590/0102-778632120150163.
Silva Junior, C. A., Frank, T., & Rodrigues, T. (2014). Discriminação de áreas de soja por meio de imagens EVI/MODIS e análise baseada em geo-objeto. Revista Brasileira de Engenharia Agricola e Ambiental, 18(1), 44–53. https://doi.org/10.1590/S1415-43662014000100007.
Silva, R. F. B., Batistella, M., & Moran, E. F. (2017). Socioeconomic changes and environmental policies as dimensions of regional land transitions in the Atlantic Forest, Brazil. Environmental Science & Policy, 74, 14–22. https://doi.org/10.1016/j.envsci.2017.04.019.
SOS MATA ATLÂNTICA, Disponível em: <https://www.sosma.org.br/nossa-causa/a-mata-atlantica/>. Acesso em 13/07/2017.
Tekleab, S., Mohamed, Y., & Uhlenbrook, S. (2013). Hydro-climatic trends in the Abay/upper Blue Nile basin, Ethiopia. Physics and Chemistry of the Earth, 61-62, 32–42. https://doi.org/10.1016/j.pce.2013.04.017.
Tomasella, J., Pinho, P. F., Borma, L. S., Marengo, J. A., Nobre, C. A., Bittencourt, O. R., & Cuartas, L. A. (2013). The droughts of 1997 and 2005 in Amazonia: Floodplain hydrology and its potential ecological and human impacts. Climatic Change, 116(3–4), 723–746. https://doi.org/10.1007/s10584-012-0508-3.
Varjabedian, R. (2010). Lei da Mata Atlântica: retrocesso ambiental. Estudos Avançados, 24, 147–160. https://doi.org/10.1590/S0103-40142010000100013.
Wanderley, H. S., Sediyama, G. C., Justino, F. B., Alencar, L. P., & Delgado, R. C. (2013). Variabilidade da precipitação no Sertão do São Francisco, estado de Alagoas. Revista Brasileira de Engenharia Agrícola e Ambiental, 17(7), 790–795. https://doi.org/10.1590/S1415-43662013000700014.
Wolter, K. (1987). The southern oscillation in surface circulation and climate over the tropical Atlantic, eastern Pacific, and Indian oceans as captured by cluster analysis. Journal of Climate and Applied Meteorology, 26, 540–558. https://doi.org/10.1175/1520-0450(1987)026%3C0540:TSOISC%3E2.0.CO;2.
Wolter, K., & Timlin, M. S. (2011). El Niño/southern oscillation behaviour since 1871 as diagnosed in an extended multivariate ENSO index (MEI.Ext). International Journal of Climatology, 31, 1074–1087. https://doi.org/10.1002/joc.2336.
Yocom Kent, L. L., Fulé, P. Z., Brown, P. M., Cerano-Paredes, J., Cornejo-Oviedo, E., Cortés Montaño, C., & Skinner, C. N. (2017). Climate drives fire synchrony but local factors control fire regime change in northern Mexico. Ecosphere, 8(3), e01709. https://doi.org/10.1002/ecs2.1709.
Zhang, X. (2015). Reconstruction of a complete global time series of daily vegetation index trajectory from long-term AVHRR data. Remote Sensing of Environment, 156, 457–472. https://doi.org/10.1016/j.rse.2014.10.012.
Zucca, C., Weicheng, W., Leonarda, D., & Maurizio, M. (2015). Assessing the effectiveness of land restoration interventions in dry lands by multitemporal remote sensing – A case study in ouled dlim (Marrakech, Morocco). Land Degradation & Development, 26, 80–91. https://doi.org/10.1002/ldr.2307.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
de Oliveira Souza, T.C., Delgado, R.C., Magistrali, I.C. et al. Spectral trend of vegetation with rainfall in events of El Niño-Southern Oscillation for Atlantic Forest biome, Brazil. Environ Monit Assess 190, 688 (2018). https://doi.org/10.1007/s10661-018-7060-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-018-7060-1