Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Article
  • Published:
Modeling Earth Systems and Environment Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • 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

    Article  Google Scholar 

  • 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, 97

  • 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

    Article  Google Scholar 

  • Alves JJA, Araújo MA, Nascimento SS (2009) Degradação da Caatinga: uma investigação ecogeográfica. Revista Caatinga 22(3):126–135

    Google Scholar 

  • 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 2:1–10

    Google Scholar 

  • 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)

  • 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):420–429

    Article  Google Scholar 

  • 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):90–112

    Google Scholar 

  • 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):754–762

    Article  Google Scholar 

  • 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):549–565

    Article  Google Scholar 

  • 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):579–589

    Article  Google Scholar 

  • 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):614–622

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Beltrame AV (1994) Diagnóstico do meio físico de bacias hidrográficas: modelo e aplicação. Florianópolis: Ed. da UFSC

  • Bezerra LMA (2015) Análise dos impactos socioambientais decorrentes da mineração na chapada do Araripe, Nova Olinda, Ceará. Geosaberes Revista de Estudos Geoeducacionais 6(2):79–89

    Google Scholar 

  • 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

  • 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

  • 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

  • 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

    Article  Google Scholar 

  • Campos SAC, Ferreira MDP, Coelho AB, De Lima JE (2015) Degradação ambiental agropecuária no bioma Caatinga. Revista Econômica do Nordeste 46(3):155–170

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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):539–548

    Article  Google Scholar 

  • 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 10:240–260

    Google Scholar 

  • 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):97–107

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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):1476–1498

    Article  Google Scholar 

  • Gao BC (1996) NDWI—a Normalized Difference Water Index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58(3):257–266

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 

  • 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):301–314

    Google Scholar 

  • Grömping U (2006) Relative importance for linear regression in R: the package relaimpo. J Stat Softw 17(1):1–27

    Article  Google Scholar 

  • 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 9–20

  • 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

    Article  Google Scholar 

  • 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

  • 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

    Article  Google Scholar 

  • Hyndman RJ, Khandakar Y (2008) Automatic time series forecasting: the forecast package for R. J Stat Softw 26(3):1–22

    Google Scholar 

  • 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

  • INMET-Instituto Nacional de Meteorologia (1993) Normais Climatológicas 1961–1990. INMET, Brasília

    Google Scholar 

  • Iqbal M (1983) An introduction to solar radiation. Academic Press Canadian, New York, p 390

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Kosmas C, Kairis O (2017) Environmental hazards methodologies for risk assessment and management. Chapter 6 - Land desertification. IWA Publishing, pp 211–246

  • 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

    Article  Google Scholar 

  • 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):841–846

    Google Scholar 

  • 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 1:14–22

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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):1425–1432

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

  • 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

    Article  Google Scholar 

  • 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):28–38

    Google Scholar 

  • 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):143–163

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 4:1–17

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004) Land surface temperature retrieval from LANDSAT TM 5. Remote Sens Environ 90(4):434–440

    Article  Google Scholar 

  • 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):22–30

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Teixeira DB, Teixeira LM, Costa CA (2016) Correlation between precipitation and vegetation indexes under preserved Caatinga condition. J Hyperspectral Remote Sens 6(1):21–30

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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):79–82

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Postgraduate Program in Meteorology, Unidade Acadêmica de Ciências (UACA), Federal University of Campina Grande (UFCG), Campina Grande, Paraíba, 58428-830, Brazil

    Dimas de Barros Santiago

  2. Institute of Atmospheric Sciences (ICAT), Federal University of Alagoas (UFAL), Maceió, Alagoas, 57072-260, Brazil

    Washington Luiz Félix Correia Filho & José Francisco de Oliveira-Júnior

  3. Postgraduate Program in Biosystems Engineering (PGEB), Federal Fluminense University (UFF), Niterói, Rio de Janeiro, 24220-900, Brazil

    José Francisco de Oliveira-Júnior

  4. Department of Geography, State University of Mato Grosso (UNEMAT), Sinop, Mato Grosso, 78555-000, Brazil

    Carlos Antonio da Silva Junior

Authors
  1. Dimas de Barros Santiago
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Washington Luiz Félix Correia Filho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José Francisco de Oliveira-Júnior
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carlos Antonio da Silva Junior
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dimas de Barros Santiago.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Barros Santiago, D.d., Correia Filho, W.L.F., de Oliveira-Júnior, J.F. et al. Mathematical modeling and use of orbital products in the environmental degradation of the Araripe Forest in the Brazilian Northeast. Model. Earth Syst. Environ. 5, 1429–1441 (2019). https://doi.org/10.1007/s40808-019-00614-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40808-019-00614-x

Keywords

Search

Navigation