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Accessing the southeastern Brazil 2014 drought severity on the vegetation health by satellite image

Abstract

Droughts are natural events that can cause water scarcity and can consequently have undesired environmental, social and political effects. Because droughts are related to land use and land cover modifications, satellite images are used to monitor and identify drought episodes through indices as Standardized Precipitation Index based on rainfall data and vegetation-based indices as Normalized Difference Vegetation Index (NDVI). Changes in vegetation cover have as impact the increasing of the land surface temperature (LST) that is a significant indicative of drought occurrence. This work explored the NDVI–LST relation through the Vegetation Health Index (VHI) in a tropical environment in Tietê River, State of São Paulo, Brazil, in order to assess changes in vegetation condition in two periods (2000 and 2014). Results showed that stressed areas are coincident with areas presenting high rate of modification in land cover; this areas presented low values of VHI and high values of LST. The worst conditions are verified in 2014, the same period of the most severe drought occurrence that reduced storage capacity in reservoirs in Tietê River.

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References

  1. Adler-Golden SM, Matthew MW, Bernstein LS, Levine RY, Berk A, Richtsmeier SC, Acharya PK, Anderson GP, Felde G, Gardner J, Hoke M, Jeong LS, Pukall B, Ratkowski A, Burke HH (1999) Atmospheric correction for shortwave spectral imagery based on MODTRAN4. SPIE Proc Imaging Spectrom 3753:61–69

    Google Scholar 

  2. Anderson MC, Zolin CA, Sentelhas PC, Hain CR, Semmens K, Yilmaz MT, Gao F, Otkin JA, Tetrault R (2016) The Evaporative stress index as an indicator of agricultural drought in Brazil: an assessment based on crop yield impacts. Remote Sens Environ 174:82–99

    Article  Google Scholar 

  3. Bajgain R, Xiao X, Wagle P, Basara J, Zhou Y (2015) Sensitivity analysis of vegetation indices to drought over two tallgrass prairie sites. ISPRS J Photogramm Remote Sens 108:151–160

    Article  Google Scholar 

  4. Barsi JA, Barker JL, Schott JR (2003) An atmospheric correction parameter calculator for a single thermal band earth-sensing instrument. IEEE Int Geosci Remote Sens Symp 5:3014–3016

    Google Scholar 

  5. Brooks ML, D’antonio CM, Richardson DM, Grace JB, Keeley JE, Ditomaso JM, Hobbs RJ, Pellant M, Pyke D (2004) Effects of invasive alien plants on fire regimes. Bioscience 54:677–688

    Article  Google Scholar 

  6. Canty MJ, Nielsen AA, Schmidt M (2004) Automatic radiometric normalization of multitemporal satellite imagery. Remote Sens Environ 91:441–451

    Article  Google Scholar 

  7. Cao G, Tang Y, Mo W, Wang Y, Li Y, Zhao X (2004) Grazing intensity alters soil respiration in an alpine meadow on the Tibetan plateau. Soil Biol Biochem 36:237–243

    Article  Google Scholar 

  8. Chander G, Markham B (2003) Revised landsat-5 TM radiometric calibration procedures and postcalibration dynamic range. IEEE Trans Geosci Remote Sens 41(11):2674–2677

    Article  Google Scholar 

  9. Coe MT, Latrubesse EM, Ferreira ME, Amsler ML (2011) The effects of deforestation and climate variability on the streamflow of the Araguaia River, Brazil. Biogeochemistry 105:119–131

    Article  Google Scholar 

  10. Coelho CAS, Cardoso DHF, Firpo MAF (2015a) Precipitation diagnostics of an exceptionally dry eventin São Paulo, Brazil. Springer, Wien

    Google Scholar 

  11. Coelho CAS, Oliveira CP, Ambrizzi T, Reboita MS, Carpenedo CB, Campos JLPS, Tomaziello ACN, Pampuch LA, Custódio MS, Dutra LMM, Da Rocha RP, Rehbein A (2015b) The 2014 southeast Brazil austral summer drought: regional scale mechanisms and teleconnections. Clim Dyn 46:3737–3752

    Article  Google Scholar 

  12. Cunha APM, Alvalá RC, Nobre CA, Carvalho MA (2015) Monitoring vegetative drought dynamics in the Brazilian semiarid region. Agric Meteorol 214–215:494–505

    Article  Google Scholar 

  13. Dellamano-Oliveira MJ, Vieira AAH, Rocha O, Colombo V, Sant’Anna CL (2008) Phytoplankton taxonomic composition and temporal changes in a tropical reservoir. Fundam Appl Limnol 171:27–38

    Article  Google Scholar 

  14. Dourado-Neto D, Timm LC, Oliveira JCM, Reichardt K, Bacchi OOS, Tominaga TT, Cássaro FAM (1999) State-space approach for the analysis of soil water content and temperature in a sugarcane crop. Sci Agric 56:1215–1221

    Article  Google Scholar 

  15. Du J, Fang J, Xu W, Shi P (2013) Analysis of dry/wet conditions using the standardized precipitation index and its potential usefulness for drought/flood monitoring in Hunan Province, China. Stoch Environ Res Risk Assess 27:377–387

    Article  Google Scholar 

  16. Flores RJL, Pereira Filho AJ, Karam HA (2016) Estimation of long term low resolution surface urban heat island intensities for tropical cities using MODIS remote sensing data. Urban Clim 17:32–66

    Article  Google Scholar 

  17. Hayes MJ, Svoboda MD, Wilhite DA, Vanyarkho OV (1999) Monitoring the 1996 drought using the standardized precipitation index. Bull Am Meteorol Soc 80:429–438

    Article  Google Scholar 

  18. Henderson-Sellers A, Dickinson RE, Durbidge TB, Kennedy PJ, McGuffie K, Pitman AJ (1993) Tropical deforestation: modeling local to regional scale climate change. J Geophys Res 98:7289–7315

    Article  Google Scholar 

  19. Joly CA, Metzger JP, Tabarelli M (2014) Experiences from the Brazilian Atlantic forest: ecological findings and conservation initiatives. New Phytol 204:459–473

    Article  Google Scholar 

  20. Karnieli A, Agam N, Pinker RT, Anderson M, Imhoff ML, Gutman GG, Panov N, Goldberg A (2010) Use of NDVI and land surface temperature for drought assessment: merits and limitations. J Clim Am Meteorol Soc 24:618–633

    Google Scholar 

  21. Kogan FN (1995) Application of vegetation index and brightness temperature for drought detection. Adv Space Res 15:91–100

    Article  Google Scholar 

  22. Kogan FN (1997) Global drought watch from space. Bull Am Meteorol Soc 78:621–636

    Article  Google Scholar 

  23. Kogan FN (2002) World droughts in the new millennium from AVHRR-based vegetation health indices. Eos Trans Am Geophys Union 83:557–564

    Article  Google Scholar 

  24. Kogan F, Stark R, Gitelson A, Jargalsaikhan L, Dugrajav C, Tsooj S (2004) Derivation of pasture biomass in Mongolia from AVHRR-based vegetation health indices. Int J Remote Sens 14:2889–2896

    Article  Google Scholar 

  25. Kogan F, Guo W, Strashnaia A, Kleshenko A, Chub O, Virchenko O (2015) Modelling and prediction of crop losses from NOAA polar-orbiting operational satellites. Geomat Nat Hazards Risk 7:886–900

    Article  Google Scholar 

  26. Lara LL, Artaxo P, Martinelli LA, Camargo PB, Victoria RL, Ferraz ESB (2005) Properties of aerosols from sugar-cane burning emissions in Southeastern Brazil. Atmos Environ 39:4627–4637

    Article  Google Scholar 

  27. Li Z, Tang B-H, Wu H, Ren H, Yan G, Wan Z, Trigo IF, Sobrino JA (2013) Satellite-derived land surface temperature: current status and perspectives. Remote Sens Environ 131:14–37

    Article  Google Scholar 

  28. Liu WT, Kogan F (2002) Monitoring Brazilian soybean production using NOAA/AVHRR based vegetation condition indices. Int J Remote Sens 23:1161–1179

    Article  Google Scholar 

  29. Magalhães AR (2017) Life and drought in Brazil. In: De Nys E, Engle NL, Magalhaes AR (eds) Drought in Brazil. Proactive management and policy. CRC Press, Boca Raton, FL, p 230

    Google Scholar 

  30. Maia JL, Barbosa AA, Mauad FF, Albertin LL (2008) Uso de Geotecnologias para Análise Espacial da Qualidade da Água no Reservatório de Barra Bonita—SP. Rev Bras de Recur Hídr 2:141–149

    Google Scholar 

  31. Marengo JA, Alves LM (2016) Crise hídrica em São Paulo em 2014: seca e desmatamento. Geousp—Espaço e Tempo (Online). 19(3): 485–494. ISSN: 2179-0892

  32. Martins S, Bernardo N, Ogashawara I, Alcantara E (2016) Support vector machine algorithm optimal parameterization for change detection mapping in Funil Hydroelectric Reservoir (Rio de Janeiro State, Brazil). Model Earth Syst Environ 2:138

    Article  Google Scholar 

  33. McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: Eight Conference on Applied Climatology, pp 179–184

  34. Melo DCD, Scanlon BR, Zhang Z, Wendland E, Yin L (2016) Reservoir storage and hydrological responses to droughts Paraná River basin, south-eastern Brazil. Hydrol Earth Syst Sci 20:4673–4688

    Article  Google Scholar 

  35. Mishra AK, Singh VP (2010) A review of drought concepts. J Hidrol 391:202–216

    Article  Google Scholar 

  36. Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens 66:247–259

    Article  Google Scholar 

  37. Nagendra H, Munroe DK, Southworth J (2004) From pattern to process: landscape fragmentation and the analysis of land use/land cover change. Agric Ecosyst Environ 101:111–115

    Article  Google Scholar 

  38. NASA (2015). Drought shrinking São Paulo Reservoirs. http://earthobservatory.nasa.gov/NaturalHazards/view.php?id=84564&eocn=image&eoci=morenh. Accessed Feb 2017

  39. Nemani R, Pierce L, Running S, Goward S (1993) Developing satellite-derived estimates of surface moisture status. J Appl Meteorol 32:548–557

    Article  Google Scholar 

  40. Nobre CA, Marengo JA, Seluchi ME, Cuartas LA, Alves LM (2016) Some characteristics and impacts of the drougth and water crisis in Southeaster Brazil during 2014 and 2015. J Water Resour Prot 8:252–262

    Article  Google Scholar 

  41. Panday PK, Coe MT, Macedo MN, Lefebvre P, Castanho ADA (2015) Deforestation offsets water balance changes due to climate variability in the Xingu River in eastern Amazonia. J Hydrol 523:822–829

    Article  Google Scholar 

  42. Prado RB, Novo EMLM (2007) Assessment of the space-time relationships between the UHE Barra Bonita trophic state and its drainage basin pollution potential. Soc Nat 19:5–18

    Article  Google Scholar 

  43. Silvério DV, Brando PM, Macedo MN, Beck PSA, Bustamante M, Coe MT (2015) Agricultural expansion dominates climate changes in southeastern Amazonia: the overlooked non-GHG forcing. Environ Res Lett 10:104015

    Article  Google Scholar 

  44. Sobrino JA, Raissouni N (2000) Toward remote sensing methods for land cover dynamic monitoring: application to Morocco. Int J Remote Sens 21:353–366

    Article  Google Scholar 

  45. Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004) Land surface temperature retrieval from Landsat TM5. Remote Sens Environ 90:434–440

    Article  Google Scholar 

  46. Sobrino JA, Jiménez-Muñoz JC, Sòria G, Romaguera M, Guanter L, Moreno J, Plaza A, Martínez P (2008) Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Trans Geosci Remote Sens 46(2):316–327

    Article  Google Scholar 

  47. Teixeira CFA, Damé RCF, Bacelar LCS, Da Silva GM, Do Couto RS (2013) Severity of drought using rainfall indices. Rev Ambiente Água 8:203–213

    Article  Google Scholar 

  48. Tundisi JG, Matsumura-Tundisi T, Abe DS (2008) The ecological dynamics of Barra Bonita (Tietê river, SP, Brazil) reservoir: implications for its biodiversity. Braz J Biol 68:1079–1098

    Article  Google Scholar 

  49. Uriarte M, Yackulic CB, Cooper T, Flynn D, Cortes M, Crk T, Cullman G, McGinty M, Sircely J (2009) Expansion of sugarcane production in São Paulo, Brazil: implications for fire occurrence and respiratory health. Agric Ecosyst Environ 132:48–56

    Article  Google Scholar 

  50. Valor E, Caselles V (1996) Mapping land surface emissivity from NDVI: application to European, African, and South America areas. Remote Sens Environ 57:167–184

    Article  Google Scholar 

  51. Vapnik V (1995) The nature of statistical learning theory. Springer, New York

    Book  Google Scholar 

  52. Yu X, Guo X, Wu Z (2014) Land surface temperature retrieval from Landsat 8 TIRS—comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sens 6:9829–9852

    Article  Google Scholar 

  53. Zedler JB (2003) Wetlands at your service: reducing impacts of agriculture at the watershed scale. The ecological society of America. Front Ecol Environ 1:65–72

    Article  Google Scholar 

Download references

Acknowledgments

Funding was provided by Fundação de Amparo à Pesquisa do Estado de São Paulo (Grant Nos. 2012/19821-1, 2015/21586-9).

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Correspondence to Enner Alcântara.

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Gomes, A.C.C., Bernardo, N. & Alcântara, E. Accessing the southeastern Brazil 2014 drought severity on the vegetation health by satellite image. Nat Hazards 89, 1401–1420 (2017). https://doi.org/10.1007/s11069-017-3029-6

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Keywords

  • Drought monitoring
  • Vegetation health index
  • Standardized precipitation index
  • Land surface temperature