Skip to main content
SpringerLink
Log in

Anthropic Exposure Indicator for River Basins Based on Landscape Characterization and Fuzzy Inference

  • WATER RESOURCES AND THE REGIME OF WATER BODIES
  • Published:
Water Resources Aims and scope Submit manuscript

Abstract

The objective of this study is to develop an anthropic exposure indicator for river basins using quantitative and qualitative aspects of the landscape and morphometric analysis based on fuzzy logic and geoprocessing. The indicator was developed from a Mamdani type fuzzy inference system by integrating information regarding the calculation of the anthropic transformation index and the circularity index of the river basin and its watersheds. The anthropic transformation was obtained from the mapping of land and forest use plotted by visual interpretation of the orthorectified multispectral satellite image of RapidEye. The circularity index was calculated using the area of the territorial limits of the study area. The basin presented thirteen classes of uses, with a greater predominance of the anthropic agricultural area, occupied by temporary crops in approximately 3472 ha (36.33%). The vegetation cover has a greater predominance of forest fragments of dense Ombrophylous forest that measure approximately 3589 ha (37.05%). The indicator showed a medium to high anthropogenic exposure for the basin. Watershed 8 showed a high to very high exposure. The exposure indicator is a tool that details the anthropic exposure of watersheds based on the reality of the activities that occur within it and the morphometric capacity. It can be used for similar areas.

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.

Similar content being viewed by others

REFERENCES

  1. Anomaa, G.M.M.M., Ranjith, P.U., Stephen, H.A., Claire, B., and Allen, T., Use of fuzzy rainfall–runoff predictions for claypan watersheds with conservation buffers in Northeast Missouri, J. Hydrol. (Amsterdam, Neth.), 2014, vol. 517, pp. 1008–1018.

    Google Scholar 

  2. Badar, B., Romshoo, S.A., and Khan, M.A., Integrating biophysical and socioeconomic information for prioritizing watersheds in a Kashmir Himalayan lake: a remote sensing and GIS approach, Environ. Monit. Assess., 2013, vol. 185, pp. 6419–6445.

    Article  Google Scholar 

  3. Blum, C.T., Roderjan, C.V., and Galvão, F., Composição florística e distribuição altitudinal de epífitas vasculares da Floresta Ombrófila Densa na Serra da Prata, Morretes, Paraná, Brasil, Biota Neotr., 2012, vol. 11, pp. 141–159.

    Article  Google Scholar 

  4. Bosa, D.M., Pacheco, D., Pasetto, M.R., and Santos, R., Florística e estrutura do componente arbóreo de uma floresta ombrófila densa montana em Santa Catarina, Brasil, Rev. Árvore, 2015, vol. 39, pp. 49–58.

    Article  Google Scholar 

  5. Brazilian Institute of Geography and Statistics— IBGE, Manual técnico da vegetação brasileira, Rio de Janeiro: IBGE, 2012.

    Google Scholar 

  6. Brazilian Institute of Geography and Statistics— IBGE, Manual técnico de uso da terra, Rio de Janeiro: IBGE, 2013, 3 Ed.

    Google Scholar 

  7. Brazilian Institute of Geography and Statistics—IBGE, Produção Agrícola Municipal. Culturas Temporárias e permanentes, Rio de Janeiro: IBGE, 2013, vol. 40.

    Google Scholar 

  8. Cardoso, C.A., Dias, H.C.T., Soares, C.P.B., and Martins, S.V., Caracterização morfométrica da bacia hidrográfica do rio Debossan, Nova Friburgo, RJ, Rev. Árvore, 2006, vol. 30, pp. 241–248.

    Article  Google Scholar 

  9. Chidthong, Y., Tanaka, H., and Supharatid, S., Developing ahybrid multi-model for peak floodforecasting, Hydrol. Process, 2009, vol. 23, pp. 1725–1738.

    Article  Google Scholar 

  10. Dubreuil, V., Fante, K.P., Planchon, O., and Santanna Neto, J.L., Les types de climats annuels au Brésil: une application de la classification de Köppen de 1961 à 2015. EchoGéo, 2017, vol. 41, pp. 1–28.

    Google Scholar 

  11. Environmental Systems Research Institute—ESRI, ArcGis 10.5, 2016.

  12. Faustino, A.B., Ramos, F.F., and Silva, S.M.P., Dinâmica temporal do uso e cobertura do solo na Bacia hidrográfica do Rio Doce (RN) com base em sensoriamento remoto e SIG: uma contribuição aos estudos ambientais, Sociedade e Território, 2014, vol. 26, pp. 18–30.

    Google Scholar 

  13. Giordano, R. and Liersch, S.L., A fuzzy GIS-based system to integrate local and technical knowledge in soil salinity monitoring, Environ. Model. Soft., vol. 36, 2012, pp. 49–63.

    Article  Google Scholar 

  14. Gomes, H.M., Fuzzy logic for structural system control, Latin Am. J. Solids Struct., vol. 9, 2012, pp. 111–129.

    Article  Google Scholar 

  15. Gouveia, R.G.L., Aplicação do índice de transformação antrópica na análise multitemporal da bacia do Córrego do Bezerro Vermelho em Tangará da Serra-MT, Rev. Árvore, 2013, vol. 37, pp. 1045–1054.

    Article  Google Scholar 

  16. Güçlü, Y.S. and Şen, Z., Hydrograph estimation with fuzzy chain model, J. Hydrol. (Amsterdam, Neth.), 2016, vol. 538, pp. 587–597.

    Google Scholar 

  17. Ilaloo, M., A comparative study of fuzzy logic approach for landslide susceptibility mapping using GIS: An experience of Karaj dam basin in Iran, Procedia Social and Behavioral Sci., 2011, vol. 19, pp. 668–676.

    Article  Google Scholar 

  18. Jacquin, A.P. and Shamseldin, A.Y., Review of the application of fuzzy inference systems in river flow forecasting, J. Hydroinf., 2009, vol. 11, pp. 202–210.

    Article  Google Scholar 

  19. Klir, G.J., Foundations of fuzzy set theory and fuzzy logic: a historical overview, Int. J. General Syst., 2001, vol. 30, pp. 91–132.

    Article  Google Scholar 

  20. Krueger, T., Page, T., Hubacek, K., Smith L., and Hi-scock, K., The role of expert opinion in environmental modelling, Environ. Model. Soft., 2012, vol. 36, pp. 4–18.

    Article  Google Scholar 

  21. Lémechev, T., On hydrological heterogeneity catchment morphology and catchment response, J. Hydrol. (Amsterdam, Neth.), 1982, vol. 100, pp. 357–375.

    Google Scholar 

  22. Lohani, A.K., Kumar, R., and Sigh, R.D., Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques, J. Hydrol. (Amsterdam, Neth.), 2012, vol. 442–443, pp. 23–35.

    Google Scholar 

  23. Lopes, E.R.N., Souza, J.C., Sousa, J.P.S., Albuquerque Filho, J.L., and Lourenço, R.W., Modelagem ambiental de bacias hidrográficas: caracterização morfométrica e pedológica da bacia do rio Una—Ibiúna, Brasil, Geosul, 2018, vol. 33, n. 66, pp. 105–127.

    Article  Google Scholar 

  24. Lopes, E.R.N., Sales, J.C.A., Sousa, J.A.P., Amorim, A.T., Albuquerque Filho, J.L., and Lourenço, R.W., Losses on the Atlantic Mata Vegetation Induced by Land Use Changes, CERNE, 2018, vol. 24, no. 2, pp. 121–132.

    Article  Google Scholar 

  25. Lourenço, R.W., Landim, P.M.B., Rosa, A.H., Roveda, J.A.F., Martins, A.C.G., and Fraceto, L.F.F., Mapping soil pollution by spatial analysis and fuzzy classification, Environ. Earth Sci., 2010, vol. 60, pp. 495–504.

    Article  Google Scholar 

  26. Lourenço, R.W., Silva, D.C.C., Martins, A.C.G., Sales, J.C.A., Roveda, S.R.M.M., and Roveda, J.A.F., Use of fuzzy systems in the elaboration of an anthropic pressure indicator to evaluate the remaining forest fragments, Environ. Earth Sci., 2015, vol. 74, pp. 2481–2488.

    Article  Google Scholar 

  27. Malik, M.I. and Bhat, M.S., Integrated approach for prioritizing watersheds for management: a study of lidder catchment of Kashmir Himalayas, Environ. Manag., 2014, vol. 54, pp. 1267–1287.

    Article  Google Scholar 

  28. Moura, M.C.F. and Oliveira, L.C.S., Atividade agrícola: produção, impacto e sustentabilidade, Revista Iber-o-Americana de Ciências Ambientais, 2013, vol. 4, pp. 6–14.

    Article  Google Scholar 

  29. Narumalani, S., Hlady, J.T., and Jensen, J.R., Information extraction from remotely sensed data, in Manual of Geospatial Sci. Technol., Bossler, J.D., Londres: Taylor & Francis, 2002, pp. 298–324.

  30. Pereira, P., Brevik, E.C., Muñoz Rojas, M., Miller, B.A., Smetanova, A., Depellegrin, D., Misiune, L., Novara, A., and Cerda, A., Soil Mapping and Processes Modeling for Sustainable Land Management, 2017.

  31. Pradhan, B., Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia, Environ. Earth Sci., 2011, vol. 63, pp. 329–349.

    Article  Google Scholar 

  32. Reid, L.M., Understanding and evaluating cumulative watershed impacts, in Cumulative Watershed Effects of Fuel Management in the Western United States, Elliot, W.J., Miller, I.S., and Audin, L., United States Department of Agriculture Forest Service, Rocky Mountain Research Station, Fort Collins, CO, General Tech. Rep. RMRS-GTR-231, 2010, pp. 277–298.

  33. Rodrigues, C.A.G. and Hott, M.C., Dinâmica da vegetação natural no nordeste do estado de São Paulo, entre 1988 e 2003, Rev. Árvore, 2010, vol. 34, pp. 881–887.

    Article  Google Scholar 

  34. Rodrigues, L.C., Neves, S.M.A.S., Neves, R.J., Galvanin, E.A.S., and Silva, J.S.V., Avaliação do grau de transformação antrópica da paisagem da bacia do rio Queima-Pé, Mato Grosso, Brasil, Revista Brasileira de Ciências Ambientais, 2014, no. 32, pp. 52–64.

  35. Roy, J. and Saha, S., GIS-based gully erosion susceptibility evaluation using frequency ratio, cosine amplitude and logistic regression ensembled with fuzzy logic in Hinglo River Basin, India, Remote Sens. Applic.: Soc. Environ., 2019, vol. 15, pp. 1–15.

    Google Scholar 

  36. Seabra, V.S., Xavier, R.A., Damasceno, J., and Dornellas, P.C., Mapeamento do uso e cobertura do solo da bacia do rio Taperoá: região semiárida do estado da Paraíba, Caminhos de Geografia, 2014, vol. 15, no. 50, pp. 127–137.

    Google Scholar 

  37. Silvert, W., Índices fuzzy environment conditions, Ecol. Model., 2000, vol. 130, pp. 111–119.

    Article  Google Scholar 

  38. Souza, J.C., Sales, J.C.A., Lopes, E.R.N., Rove-da, J.A.F., Roveda, S.R.M.M., and Lourenço, R.W., Valuation methodology of laminar erosion potential using fuzzy inference systems in a Brazilian savanna, Environ. Monit. Assess., 2019, pp. 191–624.

  39. The Mathworks. Fuzzy Logic Toolbox™ User’s Guide, Copyright 1995–2014 by The MathWorks Inc., 2014.

  40. Tonello, K.C., Dias, H.C.T., Souza, A.L., Ribeiro, C.A.A.S., and Leite, F.P., Morfometria da bacia hidrográfica da Cachoeira das Pombas, Guanhães – MG, Rev. Árvore, 2006, vol. 30, pp. 849–857.

    Article  Google Scholar 

  41. Vaeza, R.F., Filho, Oliveira Filho, P.C., Maia, A.G., and Disperati, A.A., Uso e ocupação do solo em bacia hidrográfica urbana a partir de imagens orbitais de alta resolução, Floresta e Ambiente, 2010, vol. 17, pp. 23–29.

    Article  Google Scholar 

  42. Zadeh, L.A., Fuzzy sets. Information and control, 1965, vol. 8, pp. 338–353.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Federal University of the South of Bahia, Universitary Campus Sosígenes Costa, 45810-000, Porto Seguro, Bahia, Brazil

    Elfany Reis do Nascimento Lopes

  2. State Goiás University, Doctor Deusdete Ferreira de Moura Avenue, 76600-000, Goiás City, Goiás, Brazil

    José Carlos de Souza

  3. São Paulo State University (UNESP), Science and Technology Institute, Sorocaba, Geoprocessing and Environmental Mathematical Modeling Laboratory, 18087-180, São Paulo, Brazil

    Jocy Ana Paixão de Sousa & Roberto Wagner Lourenço

  4. Technological Research Institute of São Paulo (IPT), University City, 05508-901, São Paulo, Brazil

    José Luiz Albuquerque Filho

Authors
  1. Elfany Reis do Nascimento Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José Carlos de Souza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jocy Ana Paixão de Sousa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José Luiz Albuquerque Filho
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Roberto Wagner Lourenço
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Elfany Reis do Nascimento Lopes, José Carlos de Souza, Jocy Ana Paixão de Sousa, José Luiz Albuquerque Filho or Roberto Wagner Lourenço.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Elfany Reis do Nascimento Lopes, Carlos de Souza, J., Paixão de Sousa, J.A. et al. Anthropic Exposure Indicator for River Basins Based on Landscape Characterization and Fuzzy Inference. Water Resour 48, 29–40 (2021). https://doi.org/10.1134/S0097807821010140

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0097807821010140

Keywords:

Search

Navigation