Natural Hazards

, Volume 89, Issue 3, pp 1401–1420 | Cite as

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

  • Ana Carolina Campos Gomes
  • Nariane Bernardo
  • Enner Alcântara
Original Paper

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.

Keywords

Drought monitoring Vegetation health index Standardized precipitation index Land surface temperature 

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 3753:61–69Google 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 174:82–99CrossRefGoogle 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 108:151–160CrossRefGoogle 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 5:3014–3016Google 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 54:677–688CrossRefGoogle Scholar
  6. Canty MJ, Nielsen AA, Schmidt M (2004) Automatic radiometric normalization of multitemporal satellite imagery. Remote Sens Environ 91:441–451CrossRefGoogle 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 36:237–243CrossRefGoogle 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):2674–2677CrossRefGoogle 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 105:119–131CrossRefGoogle Scholar
  10. Coelho CAS, Cardoso DHF, Firpo MAF (2015a) Precipitation diagnostics of an exceptionally dry eventin São Paulo, Brazil. Springer, WienGoogle 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 46:3737–3752CrossRefGoogle Scholar
  12. Cunha APM, Alvalá RC, Nobre CA, Carvalho MA (2015) Monitoring vegetative drought dynamics in the Brazilian semiarid region. Agric Meteorol 214–215:494–505CrossRefGoogle 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 171:27–38CrossRefGoogle 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 56:1215–1221CrossRefGoogle 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 27:377–387CrossRefGoogle 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 17:32–66CrossRefGoogle Scholar
  17. Hayes MJ, Svoboda MD, Wilhite DA, Vanyarkho OV (1999) Monitoring the 1996 drought using the standardized precipitation index. Bull Am Meteorol Soc 80:429–438CrossRefGoogle 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 98:7289–7315CrossRefGoogle Scholar
  19. Joly CA, Metzger JP, Tabarelli M (2014) Experiences from the Brazilian Atlantic forest: ecological findings and conservation initiatives. New Phytol 204:459–473CrossRefGoogle 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 24:618–633Google Scholar
  21. Kogan FN (1995) Application of vegetation index and brightness temperature for drought detection. Adv Space Res 15:91–100CrossRefGoogle Scholar
  22. Kogan FN (1997) Global drought watch from space. Bull Am Meteorol Soc 78:621–636CrossRefGoogle Scholar
  23. Kogan FN (2002) World droughts in the new millennium from AVHRR-based vegetation health indices. Eos Trans Am Geophys Union 83:557–564CrossRefGoogle 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 14:2889–2896CrossRefGoogle 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 7:886–900CrossRefGoogle 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 39:4627–4637CrossRefGoogle 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 131:14–37CrossRefGoogle Scholar
  28. Liu WT, Kogan F (2002) Monitoring Brazilian soybean production using NOAA/AVHRR based vegetation condition indices. Int J Remote Sens 23:1161–1179CrossRefGoogle 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 230Google 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 2:141–149Google 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: 2179-0892Google Scholar
  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 2:138CrossRefGoogle 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 179–184Google Scholar
  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 20:4673–4688CrossRefGoogle Scholar
  35. Mishra AK, Singh VP (2010) A review of drought concepts. J Hidrol 391:202–216CrossRefGoogle Scholar
  36. Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens 66:247–259CrossRefGoogle 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 101:111–115CrossRefGoogle 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 32:548–557CrossRefGoogle 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 8:252–262CrossRefGoogle 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 523:822–829CrossRefGoogle 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 19:5–18CrossRefGoogle 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 10:104015CrossRefGoogle Scholar
  44. Sobrino JA, Raissouni N (2000) Toward remote sensing methods for land cover dynamic monitoring: application to Morocco. Int J Remote Sens 21:353–366CrossRefGoogle Scholar
  45. Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004) Land surface temperature retrieval from Landsat TM5. Remote Sens Environ 90:434–440CrossRefGoogle 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):316–327CrossRefGoogle Scholar
  47. Teixeira CFA, Damé RCF, Bacelar LCS, Da Silva GM, Do Couto RS (2013) Severity of drought using rainfall indices. Rev Ambiente Água 8:203–213CrossRefGoogle 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 68:1079–1098CrossRefGoogle 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 132:48–56CrossRefGoogle Scholar
  50. Valor E, Caselles V (1996) Mapping land surface emissivity from NDVI: application to European, African, and South America areas. Remote Sens Environ 57:167–184CrossRefGoogle Scholar
  51. Vapnik V (1995) The nature of statistical learning theory. Springer, New YorkCrossRefGoogle 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 6:9829–9852CrossRefGoogle 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 1:65–72CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Department of CartographySão Paulo State UniversityPresidente PrudenteBrazil
  2. 2.Department of Environmental EngineeringSão Paulo State UniversitySão PauloBrazil

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