Abstract
In Brazil and other countries, most electric-sector enterprises do not present a systematic space–time evaluation of their structures to identify environmental vulnerabilities. Thus, this study aims to analyze the susceptibility of mass movements in transmission lines, in the Serra da Mantiqueira region (Brazil), subject to the effects of tropical rains to operationalize a dynamic platform of analysis and alertness in the most critical areas. For this study, static and dynamic data were collected in the region from public and private sources. Next, multiple criteria were defined through the analytic hierarchy process (AHP). Using geographic information systems (GIS) and map algebra, it was possible to determine a susceptibility map in five classes. Subsequently, based on the identified areas and dynamic meteorological and hydrological data, a real-time platform was operationalized for monitoring, analysis, and alerts to environmental risks. Consequently, a geographic database with a regional coverage (14,000 km2) was generated, involving seven criteria: slope, distance of transmission lines, drainage density, soil use, soil type, fracture and failure density, and precipitation. The susceptibility classes found in the study region were very low (1.5%), low (12%), average (34.9%), high (45.3%), and very high (6.2%). It was also possible to identify different mass movements in areas close to the transmission lines, as well as other risk elements such as dwellings, roads, reservoir borders, and telecommunications towers. The operationalized monitoring platform allowed the establishment of dynamic analyses on the occurrence of extreme natural events, by sending notifications and an online map of the affected areas. Thus, this platform developed in this study can become an instrument of evaluation, monitoring, and management for the public management and regulatory agencies of the electric sector.
This is a preview of subscription content, log in via an institution to check access.
Adapted by the author for use in the monitoring of power transmission lines from INPE
Similar content being viewed by others
References
ANA (2013) Hydrographic network data. https://metadados.ana.gov.br/geonetwork/srv/pt/main.home. Accessed 25 Jun 2015
ANEEL (2004) Manual de fiscalização da transmissão 2004. In: Man. Fisc. da Transm. 2004. http://www.aneel.gov.br/arquivos/PDF/manual.de.transmissão.pdf. Accessed 25 Jun 2015
ANEEL (2015) SIGEL. In: Sist. Informações Geográficas do Set. Elétrico. https://sigel.aneel.gov.br/portal/home/. Accessed 28 Apr 2016
Blackbridge (2013) RapidEye images. http://geocatalogo.mma.gov.br/. Accessed 12 Dec 2015
Corominas J, van Westen C, Frattini P et al (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73:209–263. https://doi.org/10.1007/s10064-013-0538-8
CPRM (2011a) Seleção de municípios críticos a deslizamentos. Rio de Janeiro/RJ. https://www.cprm.gov.br/publique/media/gestao_territorial/riscos_geologicos/apresentacao_susc.pdf. Accessed 11 Nov 2016
CPRM (2011b) Levantamento da geodiversidade. Brasília/DF
CPRM (2017) GeoSGB. http://geosgb.cprm.gov.br/geosgb/downloads.html. Accessed 21 Feb 2018
CPTEC (2016) Climatological conditions and rainfall data. http://previsaonumerica.cptec.inpe.br/bam. Accessed 11 Aug 2016
Egas HM, Graminha CA, De Andrade MRM (2011) Análise preliminar da ocorrência de avalanche de rocha na bacia do rio do entupido. Município de Queluz, SP, pp 1–10
EMBRAPA (2011) Mapa de Solos do Brasil. Brasília/DF. https://www.embrapa.br/image/journal/article?img_id=14124385&t=1467725744269. Accessed 7 July 2016
EPE (2014) Balanço Energético Nacional 2014: Ano base 2013. Rio de Janeiro/RJ. http://epe.gov.br/pt/publicacoes-dados-abertos/publicacoes/Balanco-Energetico-Nacional-2014. Accessed 25 June 2015
Faria DGM, Augusto Filho O (2013) Aplicação do Processo de Análise Hierárquica (AHP) no mapeamento de perigo de escorregamentos em áreas urbanas. Rev Inst Geol 34:23–44. https://doi.org/10.5935/0100-929X.20130002
Gimenes FBQ, Filho OA (2013) Mapas de fragilidade ambiental utilizando o processo de análise hierárquica (AHP) e sistema de informação geográfica (SIG). In: An XVI Simpósio Bras Sensoriamento Remoto—SBSR, pp 6564–6571
Gorsevski PV, Donevska KR, Mitrovski CD, Frizado JP (2012) Integrating multi-criteria evaluation techniques with geographic information systems for landfill site selection: a case study using ordered weighted average. Waste Manag 32:287–296. https://doi.org/10.1016/j.wasman.2011.09.023
IBGE (2005) Nota Técnica SIRGAS 2000. Rio de Janeiro/RJ. geoftp.ibge.gov.br/nota_tecnica_termino_periodo_transicao_sirgas2000. Accessed 7 July 2016
IBGE (2009) Manual Técnico de Geomorfologia. Rio de Janeiro/RJ. https://biblioteca.ibge.gov.br/visualizacao/livros/liv66620.pdf. Accessed 7 July 2016
IBGE (2010) Brazilian Census Data 2010. In: Informações Socio-Demográficas. www.ibge.gov.br. Accessed 20 Mar 2015
INPE (2013) Manual Do Usuário. São José dos Campos. http://www.terrama2.dpi.inpe.br/manuais. Accessed 25 Aug 2016
Kumar S, Vaidya OS (2006) Analytic hierarchy process: an overview of applications. Eur J Oper Res 169:1–29. https://doi.org/10.1016/j.ejor.2004.04.028
Liao T, Hu D, Li X, Chen Y (2011) Landslide vulnerability evaluation: A case study from Sichuan, China. In: Proceedings of the 2011 19th International conference geoinformatics, geoinformatics 2008–2011. doi: 10.1109/GeoInformatics.2011.5981061
Meinhardt M, Fink M, Tünschel H (2015) Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology 234:80–97. https://doi.org/10.1016/j.geomorph.2014.12.042
Mossri P (1998) Garganta do Embaú. https://pt.wikipedia.org/wiki/Garganta_do_Embaú. Accessed 15 Nov 2015
NASA/METI (2015) ASTER GDEM 2. https://lpdaac.usgs.gov/data_access/usgs_earthexplorer. Accessed 22 Aug 2015
Quan H-C, Lee B-G (2012) GIS-based landslide susceptibility mapping using analytic hierarchy process and artificial neural network in Jeju (Korea). KSCE J Civ Eng 16:1258–1266. https://doi.org/10.1007/s12205-012-1242-0
Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15:234–281. https://doi.org/10.1016/0022-2496(77)90033-5
Soares P (2008) Mapa de infiltração do alto e médio Vale do Paraíba do Sul com base em elementos do meio físico e na precipitação. Ambient Agua 3:26–42. https://doi.org/10.4136/ambi-agua.40
Taheri K, Gutiérrez F, Mohseni H et al (2015) Sinkhole susceptibility mapping using the analytical hierarchy process (AHP) and magnitude–frequency relationships: a case study in Hamadan province, Iran. Geomorphology 234:64–79. https://doi.org/10.1016/j.geomorph.2015.01.005
USGS (2013) Landsat-8 images. https://landsat.gsfc.nasa.gov/. Accessed 23 Feb 2016
van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102:112–131. https://doi.org/10.1016/j.enggeo.2008.03.010
Acknowledgements
We are grateful to the INPE for providing the rainfall data in Brazil (forecast and observation data), to Prof. Dr. Eymar S. S. Lopes by the technical support to the platform for monitoring, analysis and alert to environmental risks (TerraMA2). The ASTER Global Digital Elevation Model V002 data product was retrieved from the online Data Pool, courtesy of the NASA Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, https://lpdaac.usgs.gov/data_access/data_pool. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Junqueira, A.M., Andrade, M.R.M., Mendes, T.S.G. et al. Landslide susceptibility mapping for transmission lines: dynamic monitoring, analysis and alerts for extreme natural events. Environ Earth Sci 79, 46 (2020). https://doi.org/10.1007/s12665-019-8750-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-019-8750-x