Advertisement

Modeling Earth Systems and Environment

, Volume 5, Issue 4, pp 1429–1441 | Cite as

Mathematical modeling and use of orbital products in the environmental degradation of the Araripe Forest in the Brazilian Northeast

  • Dimas de Barros SantiagoEmail author
  • Washington Luiz Félix Correia Filho
  • José Francisco de Oliveira-Júnior
  • Carlos Antonio da Silva Junior
Original Article

Abstract

Vegetation cover is indispensable in the process of inhibiting environmental degradation. In the Northeast of Brazil, especially in the Araripe Nacional Forest (FLONA), this problem is related to the removal of vegetation for industrial and domestic use, in addition to the expansion of livestock. Thus, the objective of this work was to evaluate the environmental degradation in the area of FLONA from orbital products via remote sensing with the aid of mathematical modeling. For this, two orbital images of the orbit 65, point 217 were used for processing and obtaining the variables: (1st) July 7, 2003 from TM/Landsat-5 and (2nd) July 15, 2018 from OLI/Landsat-8. In mathematical modeling, the multiple linear regression (MRL) model was applied to the orbital products: land surface temperature (LST), normalized burn ratio (NBR), Normalized Difference Moisture Index, Normalized Difference Water Index (NDWI) to estimate the Soil Adjusted Vegetation Index (SAVI) and hence to predict the Normalized Difference Vegetation Index (NDVI). All the processing to obtain the results was carried out in the software R version 3.4-1. O NDVI pointed out a significant increase of 72.05% in dense vegetation, from 158.33 to 272.40 km2. However, vegetation is more likely to suffer from stress due to the increase in LST at 5 °C, which increased from 17.5 to 25.0 °C, reaching its highest value of 42 °C in July 2011. The MRL results indicated that the models have an excellent predictive capacity in the estimation of degradation, with R2 value greater than 92% of the explained variance. In addition, the MAE and root mean square error were less than 0.03 for both models. The models pointed out that SAVI, NBR and NDWI are responsible for the variability of NDVI in environmental degradation of FLONA. Highlight for the theoretical-conceptual model that can be applied to any semi-arid and highly-sensitive region to changes in the rainfall pattern.

Keywords

Vegetal cover Orbital products Use and occupation of the soil 

Notes

Acknowledgements

The first author thanks the Coordination of Improvement of Higher Education Personnel (CAPES) by the Postgraduate Scholarship at doctoral level. The third author thanks the Brazilian National Council for Scientific and Technological Development (CNPq) for the Productivity Grant in Research process number 306410/2015-0.

References

  1. Accioly LJO, Pachêco AC, Thomaz CC, Lopes OF, Oliveira MAJ (2002) Relações empíricas entre a estrutura da vegetação e dados do sensor TM/LANDSAT. Revista Brasileira de Engenharia Agrícola e Ambiental 6(3):492–498.  https://doi.org/10.1590/S1415-43662002000300019 CrossRefGoogle Scholar
  2. Allen RG, Tasumi M, Trezza R, Waters R, Bastiaanssen W (2002) SEBAL (surface energy balance algorithms for land). Advance training and users manual—Idaho implementation, version, 1, 97Google Scholar
  3. Althoff TD, Menezes RSC, Carvalho AL, Pinto AS, Santiago GACF, Ometo JPHB, Von Randow C, Sampaio EVSB (2016) Climate change impacts on the sustainability of the firewood harvest and vegetation and soil carbon stocks in a tropical dry forest in Santa Teresinha Municipality, Northeast Brazil. For Ecol Manag 360:367–375.  https://doi.org/10.1016/j.foreco.2015.10.001 CrossRefGoogle Scholar
  4. Alves JJA, Araújo MA, Nascimento SS (2009) Degradação da Caatinga: uma investigação ecogeográfica. Revista Caatinga 22(3):126–135Google Scholar
  5. Alves CCE, Bezerra LMA, Costa Matias AC (2011) A importância da Conservação/Preservação Ambiental da Floresta Nacional do Araripe para a Região do Cariri–Ceará/Brasil. Revista Geográfica de América Central 2:1–10Google Scholar
  6. Alves LER, Correia Filho WLF, Gomes HB, Oliveira-Junior JF, Sanches FO (2019) Space-temporal evaluation of changes in soil use and soil cover and temperature in the metropolitan region of Baixada Santista. Biosci J (online, in press) Google Scholar
  7. Aquino DDN, Rocha Neto OCD, Moreira MA, Teixeira ADS, Andrade EMD (2018) Use of remote sensing to identify areas at risk of degradation in the semi-arid region. Revista Ciência Agronômica 49(3):420–429CrossRefGoogle Scholar
  8. Araújo EVSB, Socorro BAM, Sampaio YSB (2005) Impactos ambientais da agricultura no processo de desertificação no Nordeste do Brasil. Revista de Geografia (Recife) 22(1):90–112Google Scholar
  9. Araújo AO, Mendonça LAR, Lima MGS, Feitosa JV, Silva FJA, Ness RLL, Frischkorn H, Simplício AAF, Kerntopf MR (2013) Modificações nas propriedades dos solos de uma área de manejo florestal na Chapada do Araripe. Revista Brasileira de Ciências do Solo 37(3):754–762CrossRefGoogle Scholar
  10. Ayoubi S, Sahrawat KL (2011) Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in northern Iran. Arch Agron Soil Sci 57(5):549–565CrossRefGoogle Scholar
  11. Ayoubi S, Jabbari M, Khademi H (2018) Multiple linear modeling between soil properties, magnetic susceptibility and heavy metals in various land uses. Model Earth Syst Environ 4(2):579–589CrossRefGoogle Scholar
  12. Barbosa IS, Andrade LA, Almeida JAP (2009) Evolução da cobertura vegetal e uso agrícola do solo no Município de Lagoa Seca, PB. Revista Brasileira de Engenharia Agrícola e Ambiental 13(5):614–622Google Scholar
  13. Bélanger E, Lucotte M, Moingt M, Paquet S, Oestreicher J, Rozon C (2017) Altered nature of terrestrial organic matter transferred to aquatic systems following deforestation in the Amazon. Appl Geochem 87:136–145.  https://doi.org/10.1016/j.apgeochem.2017.10.016 CrossRefGoogle Scholar
  14. Beltrame AV (1994) Diagnóstico do meio físico de bacias hidrográficas: modelo e aplicação. Florianópolis: Ed. da UFSCGoogle Scholar
  15. Bezerra LMA (2015) Análise dos impactos socioambientais decorrentes da mineração na chapada do Araripe, Nova Olinda, Ceará. Geosaberes Revista de Estudos Geoeducacionais 6(2):79–89Google Scholar
  16. Brazil (1946) Ministério da Casa Civil. Lei de Nº. 9.226/1946—Criação da Floresta Nacional do Araripe-Apodi. http://www.planalto.gov.br/ccivil_03/Decreto-Lei/1937-1946/Del9226.htm. Accessed 19 Jan 2019
  17. Brazil (1997) Ministério da Casa Civil. Decreto n°148 de 04 de agosto de 1997—Criação da Área de Proteção Ambiental da Chapada do Araripe. http://www.planalto.gov.br/ccivil_03/DNN/Anterior%20a%202000/1997/Dnn5587.htm. Accessed 19 Jan 2019
  18. Brazil (2012) Ministério da Casa Civil. Decreto de Lei de n°12.651 de 25 de maio de 2012—Novo Código Florestal. http://www.planalto.gov.br/ccivil_03/_Ato2011-2014/2012/Lei/L12651.htm. Accessed 4 Feb 2019
  19. Bullock EL, Woodcock CE, Olofsson P (2018) Monitoring tropical forest degradation using spectral unmixing and landsat time series analysis. Remote Sens Environ.  https://doi.org/10.1016/j.rse.2018.11.011 CrossRefGoogle Scholar
  20. Campos SAC, Ferreira MDP, Coelho AB, De Lima JE (2015) Degradação ambiental agropecuária no bioma Caatinga. Revista Econômica do Nordeste 46(3):155–170Google Scholar
  21. Cattaneo A (2008) Regional comparative advantage, location of agriculture, and deforestation in Brazil. J Sustain For 27(1–2):25–42.  https://doi.org/10.1080/10549810802225200 CrossRefGoogle Scholar
  22. Caúla RH, Oliveira-Júnior JF, Lyra GB, Delgado RC, Heilbron Filho PFL (2015) Overview of fire foci causes and locations in Brazil based on meteorological satellite data from 1998 to 2011. Environ Earth Sci (Print) 74(2):1497–1508.  https://doi.org/10.1007/s12665-015-4142-z CrossRefGoogle Scholar
  23. Cunha JE, Rufino IA, Silva BB, Chaves IB (2012) Dinâmica da cobertura vegetal para a Bacia de São João do Rio do Peixe, PB, utilizando-se sensoriamento remoto. Revista Brasileira de Engenharia Agrícola e Ambiental 16(5):539–548CrossRefGoogle Scholar
  24. Da Silva JAA, da Rocha KD, Ferreira RLC, Tavares JÁ (2015) Produtividade Volumétrica de Clones de Eucalyptus Spp. no Polo Gesseiro do Araripe, Pernambuco. Anais da Academia Pernambucana de Ciência Agronômica 10:240–260Google Scholar
  25. De Andrade CB, de Oliveira LMM, Omena JAM, Villar AC, Gusmão L, Rodrigues DFB (2018) Avaliação de índices de vegetação e características fisiográficas no Sertão Pernambucano. Revista Brasileira de Meio Ambiente 4(1):97–107Google Scholar
  26. Eckert S, Hüsler F, Liniger H, Hodel E (2015) Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia. J Arid Environ 113:16–28.  https://doi.org/10.1016/j.jaridenv.2014.09.001 CrossRefGoogle Scholar
  27. Fernandes F, Barbosa MP, Moraes Neto JM (2013) Caracterização do Uso das Terras e das Áreas em Riscos de Desertificação em Parte da Floresta Nacional do Araripe (FLONA): Municípios de Barbalha e do Crato, Estado do Ceará. Revista Brasileira de Geografia Física 6(5):1476–1498CrossRefGoogle Scholar
  28. Gao BC (1996) NDWI—a Normalized Difference Water Index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58(3):257–266CrossRefGoogle Scholar
  29. Ghazoul J, Burivalova Z, Garcia-Ulloa J, King LA (2015) Conceptualizing forest degradation. Trends Ecol Evol 30(10):622–632.  https://doi.org/10.1016/j.tree.2015.08.001 CrossRefGoogle Scholar
  30. Goers L, Lawson J, Garen E (2012) Economic drivers of tropical deforestation for agriculture. In: Ashton MS, Tyrrel ML, Spalding D, Gentry B (eds) Managing forest carbon in a changing climate. Springer, Dordrecht, pp 305–320.  https://doi.org/10.1007/978-94-007-2232-3_14 Google Scholar
  31. Gois G, Souza JL, Silva PRT, Oliveira Júnior JF (2005) Caracterização da Desertificação no Estado de Alagoas Utilizando Variáveis Climáticas. Revista Brasileira de Meteorologia 20(2):301–314Google Scholar
  32. Grömping U (2006) Relative importance for linear regression in R: the package relaimpo. J Stat Softw 17(1):1–27CrossRefGoogle Scholar
  33. Guerra AJT, Sampaio JJA (1996) Processos erosivos acelerados, movimentos de massa e assoreamento na cidade do Crato-CE. Anuário do Instituto de Geociências—UFRJ, vol 19, pp 9–20Google Scholar
  34. Guha S, Govil H, Dey A, Gill N (2018) Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy. Eur J Remote Sens 51(1):667–678.  https://doi.org/10.1080/22797254.2018.1474494 CrossRefGoogle Scholar
  35. Hijmans RJ (2019) Raster: geographic data analysis and modeling. R package version 2.8-19. https://CRAN.R-project.org/package=raster. Accessed 5 Jan 2019
  36. Huete AR (1988) A soil-adjusted vegetation index (SAVI). Rem Sens Environ 25(3):295–309.  https://doi.org/10.1016/0034-4257(88)90106-X CrossRefGoogle Scholar
  37. Hyndman RJ, Khandakar Y (2008) Automatic time series forecasting: the forecast package for R. J Stat Softw 26(3):1–22Google Scholar
  38. Hyndman R, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O’Hara-Wild M, Petropoulos F, Razbash S, Wang E, Yasmeen F (2019) forecast: forecasting functions for time series and linear models. R package version 8.7. http://pkg.robjhyndman.com/forecast. Accessed 5 Jan 2019
  39. INMET-Instituto Nacional de Meteorologia (1993) Normais Climatológicas 1961–1990. INMET, BrasíliaGoogle Scholar
  40. Iqbal M (1983) An introduction to solar radiation. Academic Press Canadian, New York, p 390Google Scholar
  41. Ji L, Zhang L, Wylie B (2009) Analysis of dynamic thresholds for the Normalized Difference Water Index. Photogramm Eng Remote Sens 75(11):1307–1317.  https://doi.org/10.14358/PERS.75.11.1307 CrossRefGoogle Scholar
  42. Jin S, Sader SA (2005) Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sens Environ 94:364–372.  https://doi.org/10.1016/j.rse.2004.10.012 CrossRefGoogle Scholar
  43. Johnson CN, Balmford A, Brook BW, Buettel JC, Galetti M, Guangchun L, Wilmshurst JM (2017) Biodiversity losses and conservation responses in the anthropocene. Science 356(6335):270–275.  https://doi.org/10.1126/science.aam9317 CrossRefGoogle Scholar
  44. Kosmas C, Kairis O (2017) Environmental hazards methodologies for risk assessment and management. Chapter 6 - Land desertification. IWA Publishing, pp 211–246Google Scholar
  45. Labrière N, Locatelli B, Laumonier Y, Freycon V, Bernoux M (2015) Soil erosion in the humid tropics: a systematic quantitative review. Agric Ecosyst Environ 203:127–139.  https://doi.org/10.1016/j.agee.2015.01.027 CrossRefGoogle Scholar
  46. Lim HS, Jafri M, Abdullah K, Alsultan S (2012) Application of a simple mono window land surface temperature algorithm from Landsat ETM over Al Qassim, Saudi Arabia. Sains Malaysiana 41(7):841–846Google Scholar
  47. Lobato RB, Menezes J, Lima LA, Sapienza JÁ (2010) Índice de Vegetação por Diferença Normalizada para Análise da Redução da Mata Atlântica na Região Costeira do Distrito de Tamoios Cabo Frio/RJ. Caderno de Estudos Geoambientais 1:14–22Google Scholar
  48. Markham BL, Barker LL (1987) Thematic mapper bandpass solar exoatmospherical irradiances. Int J Remote Sens 8(3):517–523.  https://doi.org/10.1080/01431168708948658 CrossRefGoogle Scholar
  49. McFeeters SK (1996) The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int J Remote Sens 17(7):1425–1432CrossRefGoogle Scholar
  50. Mendonça LAR, Vásquez MAN, Feitosa JV, Oliveira JF, Franca RM, Vásquez EMF, Frischkorn H (2009) Avaliação da capacidade de infiltração de solos submetidos a diferentes tipos de manejo. Revista de Engenharia Sanitária e Ambiental 14(1):89–98.  https://doi.org/10.1590/S1413-41522009000100010 CrossRefGoogle Scholar
  51. Miatto RC, Wright IJ, Batalha MA (2016) Relationships between soil nutrient status and nutrient-related leaf traits in Brazilian cerrado and seasonal forest communities. Plant Soil 404(1–2):13.  https://doi.org/10.1007/s11104-016-2796-2 CrossRefGoogle Scholar
  52. Oliveira Souza TC, Delgado RC, Magistrali IC, Dos Santos GL, Carvalho DC, Teodoro PE, Silva Júnior CA, Caúla RH (2018) Spectral trend of vegetation with rainfall in events of El Niño-Southern Oscillation for Atlantic Forest biome, Brazil. Environ Monit Assess 190(11):688.  https://doi.org/10.1007/s10661-018-7060-1 CrossRefGoogle Scholar
  53. Quintano C, Fernández-Manso A, Calvo L, Marcos E, Valbuena L (2015) Land surface temperature as potential indicator of burn severity in forest Mediterranean ecosystems. Int J Appl Earth Obs Geoinf 36:1–12.  https://doi.org/10.1016/j.jag.2014.10.015 CrossRefGoogle Scholar
  54. R Development Core Team (2019) R: a language and environment for statistical computing version 3.4-1. R Foundation for Statistical Computing, Vienna. http://www.r-project.org. ISBN 3-900051-07-0. Accessed 5 Jan 2019
  55. Ribeiro JB, Borgo M, Maranho LT (2013) Áreas protegidas de Curitiba (PR, Brasil) como sumidouros de CO2. Floresta 43(2):181–190.  https://doi.org/10.5380/rf.v43i2.27380 CrossRefGoogle Scholar
  56. Sá IIS, Galvíncio JD, Beserra MS, Sá IB (2008) Uso do índice de vegetação da diferença normalizada (IVDN) para caracterização da cobertura vegetal da região do Araripe pernambucano. Revista Brasileira de Geografia Física 1(1):28–38Google Scholar
  57. Sá IIS, Galvíncio JD, Beserra MS, Sá IB (2010) Cobertura Vegetal e Uso da Terra na Região Araripe Pernambucana. Mercator-Revista de Geografia da UFC 9(19):143–163Google Scholar
  58. Salvati L, Kosmas C, Kairi O, Karavitis C, Acikalin S, Belgacem A, Solé-Benet A, Chaker M, Fassouli V, Gokceoglu C, Gungor H, Hessel R, Khatteli H, Kounalaki A, Laouina A, Ocakoglu F, Ouessar M, Ritsema C, Sghaier M, Sonmez H, Taamallah H, Tezcan L, Vente J (2014) Unveiling soil degradation and desertification risk in the Mediterranean basin: a data mining analysis of the relationships between biophysical and socioeconomic factors in agro-forest landscapes. J Environ Plan Manag.  https://doi.org/10.1080/09640568.2014.958609 CrossRefGoogle Scholar
  59. Santos FA, De Aquino CMS (2015) Análise da dinâmica do Índice de Vegetação por Diferença Normalizada (NDVI), dos aspectos econômicos e suas relações com a desertificação/degradação ambiental em Castelo do Piauí, Piauí, Brasil. Revista Electrónica de Investigação e Desenvolvimento 4:1–17Google Scholar
  60. Santos GL, Pereira MG, Delgado RC, Torres JLR (2017) Natural regeneration in anthropogenic environments due to agricultural use in the cerrado, Uberaba, MG, Brazil. Biosci J (Online) 33(1):169–176.  https://doi.org/10.14393/BJ-v33n1a2017-35036 CrossRefGoogle Scholar
  61. Silva Junior CHL, Aragão LEOC, Fonseca MG, Almeida CT, Vedovato LB, Anderson LO (2018a) Deforestation-induced fragmentation increases forest fire occurrence in central Brazilian Amazonia. Forests 9(6):305.  https://doi.org/10.3390/f9060305 CrossRefGoogle Scholar
  62. Silva Junior CA, Coutinho A, Oliveira-Júnior JF, Lima MG, Shakir M, Gois G, Johann J, Teodoro PE (2018b) Analysis of the impact on vegetation caused by abrupt deforestation via orbital sensor in the environmental disaster of Mariana, Brazil. Land Use Policy 76:10–20.  https://doi.org/10.1016/j.landusepol.2018.04.019 CrossRefGoogle Scholar
  63. Silveira EMDO, Mello JMD, Acerbi Júnior FW, Reis AAD, Withey KD, Ruiz LA (2017) Characterizing landscape spatial heterogeneity using semivariogram parameters derived from NDVI images. Cerne 23(4):413–422.  https://doi.org/10.1590/01047760201723042370 CrossRefGoogle Scholar
  64. Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004) Land surface temperature retrieval from LANDSAT TM 5. Remote Sens Environ 90(4):434–440CrossRefGoogle Scholar
  65. Sousa RF, Barbosa MP, Sousa Junior SP, Nery AR, Lima NA (2008) Estudo da evolução espaço-temporal da cobertura vegetal do município de Boa Vista-PB, utilizando Geoprocessamento. Revista Caatinga 21(3):22–30Google Scholar
  66. Soverel NO, Perrakis DD, Coops NC (2010) Estimating burn severity from Landsat dNBR and RdNBR indices across western Canada. Remote Sens Environ 114(9):1896–1909.  https://doi.org/10.1016/j.rse.2010.03.013 CrossRefGoogle Scholar
  67. Teixeira DB, Teixeira LM, Costa CA (2016) Correlation between precipitation and vegetation indexes under preserved Caatinga condition. J Hyperspectral Remote Sens 6(1):21–30Google Scholar
  68. Vale Júnior JFD, Nicodem S, Melo VF, Uchôa SCP, Cruz DLDS (2016) Characterization of organic matter under different pedoenvironments in the Viruá National Park, in northern Amazon. Revista Brasileira de Ciência do Solo 40:e0140480.  https://doi.org/10.1590/18069657rbcs20140480 CrossRefGoogle Scholar
  69. Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30(1):79–82CrossRefGoogle Scholar
  70. Wilson EH, Sader SA (2002) Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sens Environ 80(3):385–396.  https://doi.org/10.1016/S0034-4257(01)00318-2 CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Postgraduate Program in Meteorology, Unidade Acadêmica de Ciências (UACA)Federal University of Campina Grande (UFCG)Campina GrandeBrazil
  2. 2.Institute of Atmospheric Sciences (ICAT)Federal University of Alagoas (UFAL)MaceióBrazil
  3. 3.Postgraduate Program in Biosystems Engineering (PGEB)Federal Fluminense University (UFF)NiteróiBrazil
  4. 4.Department of GeographyState University of Mato Grosso (UNEMAT)SinopBrazil

Personalised recommendations