Metodología para evaluar la vulnerabilidad ambiental en los ecosistemas de páramo asociada a los usos del suelo: Caso de estudio complejo de páramos Pisba (Boyacá, Colombia)
ilustraciones, graficas, mapas
- Autores:
-
Ramirez Gomez, Juan Camilo
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/81889
- Palabra clave:
- 350 - Administración pública y ciencia militar::354 - Administración pública de la economía y el medio ambiente
AREAS PROTEGIDAS
PROTECCION DEL MEDIO AMBIENTE
Protected areas
Environmental protection-8a. ed.
Vulnerabilidad ambiental
Análisis espacial
Análisis multicriterio
Páramos
Páramo de Pisba
Spatial analysis
Environmental vulnerability
Spatial analysis
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
id |
UNACIONAL2_14387053244a9c5cf184b675b82ae380 |
---|---|
oai_identifier_str |
oai:repositorio.unal.edu.co:unal/81889 |
network_acronym_str |
UNACIONAL2 |
network_name_str |
Universidad Nacional de Colombia |
repository_id_str |
|
dc.title.spa.fl_str_mv |
Metodología para evaluar la vulnerabilidad ambiental en los ecosistemas de páramo asociada a los usos del suelo: Caso de estudio complejo de páramos Pisba (Boyacá, Colombia) |
dc.title.translated.eng.fl_str_mv |
Methodology to assess environmental vulnerability in moorland ecosystems associated with land use: A case study of the Pisba moorland complex (Boyacá, Colombia) |
title |
Metodología para evaluar la vulnerabilidad ambiental en los ecosistemas de páramo asociada a los usos del suelo: Caso de estudio complejo de páramos Pisba (Boyacá, Colombia) |
spellingShingle |
Metodología para evaluar la vulnerabilidad ambiental en los ecosistemas de páramo asociada a los usos del suelo: Caso de estudio complejo de páramos Pisba (Boyacá, Colombia) 350 - Administración pública y ciencia militar::354 - Administración pública de la economía y el medio ambiente AREAS PROTEGIDAS PROTECCION DEL MEDIO AMBIENTE Protected areas Environmental protection-8a. ed. Vulnerabilidad ambiental Análisis espacial Análisis multicriterio Páramos Páramo de Pisba Spatial analysis Environmental vulnerability Spatial analysis |
title_short |
Metodología para evaluar la vulnerabilidad ambiental en los ecosistemas de páramo asociada a los usos del suelo: Caso de estudio complejo de páramos Pisba (Boyacá, Colombia) |
title_full |
Metodología para evaluar la vulnerabilidad ambiental en los ecosistemas de páramo asociada a los usos del suelo: Caso de estudio complejo de páramos Pisba (Boyacá, Colombia) |
title_fullStr |
Metodología para evaluar la vulnerabilidad ambiental en los ecosistemas de páramo asociada a los usos del suelo: Caso de estudio complejo de páramos Pisba (Boyacá, Colombia) |
title_full_unstemmed |
Metodología para evaluar la vulnerabilidad ambiental en los ecosistemas de páramo asociada a los usos del suelo: Caso de estudio complejo de páramos Pisba (Boyacá, Colombia) |
title_sort |
Metodología para evaluar la vulnerabilidad ambiental en los ecosistemas de páramo asociada a los usos del suelo: Caso de estudio complejo de páramos Pisba (Boyacá, Colombia) |
dc.creator.fl_str_mv |
Ramirez Gomez, Juan Camilo |
dc.contributor.advisor.none.fl_str_mv |
Rodríguez Eraso, Nelly |
dc.contributor.author.none.fl_str_mv |
Ramirez Gomez, Juan Camilo |
dc.subject.ddc.spa.fl_str_mv |
350 - Administración pública y ciencia militar::354 - Administración pública de la economía y el medio ambiente |
topic |
350 - Administración pública y ciencia militar::354 - Administración pública de la economía y el medio ambiente AREAS PROTEGIDAS PROTECCION DEL MEDIO AMBIENTE Protected areas Environmental protection-8a. ed. Vulnerabilidad ambiental Análisis espacial Análisis multicriterio Páramos Páramo de Pisba Spatial analysis Environmental vulnerability Spatial analysis |
dc.subject.lemb.spa.fl_str_mv |
AREAS PROTEGIDAS PROTECCION DEL MEDIO AMBIENTE |
dc.subject.lemb.eng.fl_str_mv |
Protected areas Environmental protection-8a. ed. |
dc.subject.proposal.spa.fl_str_mv |
Vulnerabilidad ambiental Análisis espacial Análisis multicriterio Páramos Páramo de Pisba |
dc.subject.proposal.eng.fl_str_mv |
Spatial analysis Environmental vulnerability Spatial analysis |
description |
ilustraciones, graficas, mapas |
publishDate |
2022 |
dc.date.accessioned.none.fl_str_mv |
2022-08-12T18:22:36Z |
dc.date.available.none.fl_str_mv |
2022-08-12T18:22:36Z |
dc.date.issued.none.fl_str_mv |
2022-08-12 |
dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
status_str |
acceptedVersion |
dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/81889 |
dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
url |
https://repositorio.unal.edu.co/handle/unal/81889 https://repositorio.unal.edu.co/ |
identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
dc.language.iso.spa.fl_str_mv |
spa |
language |
spa |
dc.relation.indexed.spa.fl_str_mv |
RedCol LaReferencia |
dc.relation.references.spa.fl_str_mv |
Adger, W.N., 2006. Vulnerability. Global Environmental Change, 16(3), pp.268–281. Available at: https://linkinghub.elsevier.com/retrieve/pii/S0959378006000422. Adriano Coronel, 2012. GESTIÓN DE RIESGOS en Gestión de proyectos. Wiki-EOI. Aryafar, A., Yousefi, S. & Doulati Ardejani, F., 2013. The weight of interaction of mining activities: Groundwater in environmental impact assessment using fuzzy analytical hierarchy process (FAHP). Environmental Earth Sciences, 68(8), pp.2313–2324. Ascough, J.C. et al., 2008. Future research challenges for incorporation of uncertainty in environmental and ecological decision-making. Ecological Modelling, 219(3–4), pp.383–399. Awange, J.L. & Kyalo Kiema, J.B., 2013. Environmental Geoinformatics, Available at: http://www.springer.com/series/3234%0Ahttp://link.springer.com/10.1007/978-3-642-34085-7. Bagdanavičiute, I. & Valiunas, J., 2013. GIS-based land suitability analysis integrating multi-criteria evaluation for the allocation of potential pollution sources. Environmental Earth Sciences, 68(6), pp.1797–1812. Bar, S., Parida, B.R. & Pandey, A.C., 2020. Landsat-8 and Sentinel-2 based Forest fire burn area mapping using machine learning algorithms on GEE cloud platform over Uttarakhand, Western Himalaya. Remote Sensing Applications: Society and Environment, 18(January), p.100324. Available at: https://doi.org/10.1016/j.rsase.2020.100324. Barredo, J.I. & Bosque-Sendra, J., 1998. Multi-criteria evaluation methods for ordinal data in a GIS environment. Geographical Systems, 5(4), pp.313–327. Baxter, M.J., 1979. THE APPLICATION OF LOGIT REGRESSION ANALYSIS TO PRODUCTION-CONSTRAINED GRAVITY MODELS*. Journal of Regional Science, 19(2), pp.171–177. Available at: http://doi.wiley.com/10.1111/j.1467-9787.1979.tb00583.x [Accessed September 26, 2020]. occo, G., Mendoza, M. & Masera, O.R., 2001. La dinámica del cambio del uso del suelo en Michoacán.Una propuesta metodológica para el estudio de los procesos de deforestación. Investigaciones Geograficas, 44, pp.18–38. Bourgoin, C. et al., 2020. Assessing the ecological vulnerability of forest landscape to agricultural frontier expansion in the Central Highlands of Vietnam. International Journal of Applied Earth Observation and Geoinformation, 84(July 2019), p.101958. Available at: https://doi.org/10.1016/j.jag.2019.101958. Breiman, L., 2001. Random forests. Machine Learning, pp.1–122. Cabrera, M. & Ramírez, W., 2014. Restauración ecológica de los páramos en colombia: transformación y herramientas para su conservación. Calvo González, J., 2006. La Fragilidad de Páramos. , (June), pp.123–144. CAR, C., 2018. Guía ilustrativa sobre Analisis de la vulnerabilidad territorial ante el cambio climatico, Available at: https://www.car.gov.co/uploads/files/5cc8af9bc943b.pdf. Cárdenas, M.F., 2016. Ecohydrology of paramos in Colombia: vulnerability to climate change and land use. , p.139. Available at: http://www.bdigital.unal.edu.co/56394/. Cavazzana, G.H. et al., 2016. Natural and environmental vulnerability along the touristic “Estradas parque pantanal” by GIS algebraic mapping. In Handbook of Environmental Chemistry. Springer Verlag, pp. 209–226. Available at: https://link-springer-com.ezproxy.unal.edu.co/chapter/10.1007/698_2014_328 [Accessed September 27, 2020]. Choudhary, K., Boori, M.S. & Kupriyanov, A., 2018. Spatial modelling for natural and environmental vulnerability through remote sensing and GIS in Astrakhan, Russia. Egyptian Journal of Remote Sensing and Space Science, 21(2), pp.139–147. Available at: https://doi.org/10.1016/j.ejrs.2017.05.003. Cinner, J.E. et al., 2013. Evaluating social and ecological vulnerability of coral reef fisheries to climate change. PloS one, 8(9). Correa Ayram, C.A. et al., 2020. Spatiotemporal evaluation of the human footprint in Colombia: Four decades of anthropic impact in highly biodiverse ecosystems. Ecological Indicators, 117(June), p.106630. Available at: https://doi.org/10.1016/j.ecolind.2020.106630. Cresso, M. et al., 2020. Future Climate Change Renders Unsuitable Conditions for Paramo Ecosystems in Colombia. Sustainability, 12(20), p.8373. Available at: https://www.mdpi.com/2071-1050/12/20/8373 [Accessed November 15, 2020]. Duguy, B. et al., 2012. Modelling the ecological vulnerability to forest fires in mediterranean ecosystems using geographic information technologies. Environmental Management, 50(6), pp.1012–1026. Durán Gil, C.A., 2017. Análisis espacial de las condiciones de vulnerabilidad social, económica, física y ambiental en el territorio colombiano. Perspectiva Geográfica, 22(1), pp.11–32. Eastman, R., 2012. IDRISI Selva. Guía para SIG y procesamiento de imágenes. Clark University, 53(9), p.321. Echeverría, M., Rosero, C. & Bravo, L., 2018. Vulnerabilidad a nivel de ecosistema de Páramo frente al Cambio Climático en la zona de Igualata Parroquia San Isidro, Cantón Guano Provincia de Chimborazo. Revista del Instituto de Investigación de la Facultad de Ingeniería Geológica, Minera, Metalurgica y Geográfica, 20(39), pp.137–148. Eguiguren-Velepucha, P.A. et al., 2016. Tropical ecosystems vulnerability to climate change in southern Ecuador. Tropical Conservation Science, 9(4), p.194008291666800. Available at: http://journals.sagepub.com/doi/10.1177/1940082916668007 [Accessed November 15, 2020]. Elisa Zanella, M., Wanderley Correia Dantas, E. & Luís Sampaio Olímpio, J., 2012. a Vulnerabilidade Natural E Ambiental Do Município De Fortaleza/Ce. Boletim Goiano de Geografia, 31(2), pp.13–27. ESRI, 2016. Comprender el análisis de distancia euclidiana—Ayuda. ArcGIS for Desktop. Available at: https://pro.arcgis.com/es/pro-app/2.8/tool-reference/spatial-analyst/understanding-euclidean-distance-analysis.htm [Accessed July 14, 2022]. Etter, A. et al., 2020. Assessing restoration priorities for high-risk ecosystems: An application of the IUCN red list of ecosystems. Land Use Policy, 99(42), p.104874. Available at: https://doi.org/10.1016/j.landusepol.2020.104874. Fischer, M. & Getis, A., 1997. Recent Developments in Spatial Analysis – Spatial Statistics, Behavioural Modelling and Computational Intelligence. , (February 2016), p.433pp. Galik, E., 2013. Tecnología del Sistema de Información Geográfica (SIG). Garavito, G., Gómez Zárate, D.P. & Palacio Tamayo, D., 2018. Gobernanza territorial en los páramos Chingaza y Sumapaz-Cruz Verde. Una comparación de sus principales actores y problemáticas. Perspectiva Geográfica, 23(1), pp.11–30. Gerardo, López, E. & Mendoza, Manuel, B., 2001. Predicción del cambio de cobertura y uso de suelo. Estudio de caso de la Ciudad de Morelia. Investigaciones geográficas, 28(45), p.134. Available at: http://pbidi.unam.mx:8080/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=cat02029a&AN=tes.TES01000666406&lang=es&site=eds-live. Getis, A., 1995. Cliff, A.D. and Ord, J.K. 1973: Spatial autocorrelation. London: Pion. Progress in Human Geography, 19(2), pp.245–249. Available at: http://journals.sagepub.com/doi/10.1177/030913259501900205 [Accessed September 26, 2020]. Gomarasca, M.A., 2010. Basics of geomatics. Applied Geomatics, 2(3), pp.137–146. Guarderas, P., Smith, F. & Dufrene, M., 2021. Land use land cover dynamics through time and their proximate drivers of change in a tropical mountain system : a case study in a highland landscape of northern Ecuador Land use transitions and drivers of change in a highland landscape of northern Ecuador. He, H.S., DeZonia, B.E. & Mladenoff, D.J., 2001. Erratum: An aggregation index (AI) to quantify spatial patterns of landscapes (Landscape Ecology (200) 15 (591-601)). Landscape Ecology, 16(1), p.87. Hong, W. et al., 2016. Establishing an ecological vulnerability assessment indicator system for spatial recognition and management of ecologically vulnerable areas in highly urbanized regions: A case study of Shenzhen, China. Ecological Indicators, 69, pp.540–547. Available at: https://doi.org/10.1016/j.landusepol.2017.01.010. [Accessed November 15, 2020]. Huang, P.H., Tsai, J.S. & Lin, W.T., 2010. Using multiple-criteria decision-making techniques for eco-environmental vulnerability assessment: A case study on the Chi-Jia-Wan Stream watershed, Taiwan. Environmental Monitoring and Assessment, 168(1–4), pp.141–158. DEAM, 2012. Fuertes impactos del cambio climático en los páramos de Colombia, Bogotá. Ippolito, A. et al., 2010. Ecological vulnerability analysis: A river basin case study. Science of the Total Environment, 408(18), pp.3880–3890. Available at: http://dx.doi.org/10.1016/j.scitotenv.2009.10.002. ang, W.S., Engel, B. & Yeum, C.M., 2020. Integrated environmental modeling for efficient aquifer vulnerability assessment using machine learning. Environmental Modelling & Software, 124, p.104602. Jeanneth, H., 2002. Aproximación a un modelo para la evaluación de la vulnerabilidad de las coberturas vegetales de Colombia ante un posible cambio climático utilizando SIG. Meteorología Colombiana, 6, pp.55–63. Jones, D. & Tamiz, M., 2016. Multiple criteria Decision Analysis, Jonse, a P.C. & Coutinho, L.C., 2016. Remote Sensing and Spatial Decision Support System. Comparative and General Pharmacology, 00(1), pp.17–19. Juzga, M.A., 2016. COMPARACIÓN DE ÍNDICES DE VEGETACIÓN EN EL CERRO DE LA CONEJERA DE LA CIUDAD DE BOGOTÁ. , p.2016. Kangas, J. et al., 2000. Improving the quality of landscape ecological forest planning by utilising advanced decision-support tools. Forest Ecology and Management, 132(2–3), pp.157–171. Khan, S., 2012. Vulnerability assessments and their planning implications: A case study of the Hutt Valley, New Zealand. Natural Hazards, 64(2), pp.1587–1607. Kia, M.B. et al., 2012. An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental Earth Sciences, 67(1), pp.251–264. De Lange, H.J. et al., 2010. Ecological vulnerability in risk assessment - A review and perspectives. Science of the Total Environment, 408(18), pp.3871–3879. Available at: http://dx.doi.org/10.1016/j.scitotenv.2009.11.009. Li, A. et al., 2006. Eco-environmental vulnerability evaluation in mountainous region using remote sensing and GIS - A case study in the upper reaches of Minjiang River, China. Ecological Modelling, 192(1–2), pp.175–187. Li, J. & Heap, A.D., 2008. A Review of Spatial Interpolation Methods for Environmental Scientists. Australian Geological Survey Organisation, GeoCat# 68(2008/23), p.154. Li, Y. et al., 2017. Mapping the hotspots and coldspots of ecosystem services in conservation priority setting. Journal of Geographical Sciences, 27(6), pp.681–696. Liao, X., Li, W. & Hou, J., 2013. Application of GIS Based Ecological Vulnerability Evaluation in Environmental Impact Assessment of Master Plan of Coal Mining Area. Procedia Environmental Sciences, 18, pp.271–276. Available at: http://dx.doi.org/10.1016/j.proenv.2013.04.035. Liu, Y. et al., 2020. Variability in Regional Ecological Vulnerability: A Case Study of Sichuan Province, China. International Journal of Disaster Risk Science, pp.1–13. Available at: https://doi.org/10.1007/s13753-020-00295-6 [Accessed November 15, 2020]. Lollino, G. et al., 2015. Slope Stability Analyses of the Proposed Reconstituted Slope of the Quarry Heights Drive Landslide, Durban, South Africa. Engineering Geology for Society and Territory - Volume 2: Landslide Processes, 2, pp.1–2177. Longley, P.A. et al., 2005. Geographical Information Systems and Science, Available at: http://www.amazon.com/Geographic-Information-Systems-Science-Longley/dp/0470870001/ref=sr_1_2?ie=UTF8&qid=1430849641&sr=8-2&keywords=Geographical+Information+Systems+and+Science+longley+2005. Luo, D., Caldas, M.M. & Goodin, D.G., 2021. Estimating environmental vulnerability in the Cerrado with machine learning and Twitter data. Journal of Environmental Management, 289, p.112502. Malczewski, J., 1999. GIS and Multicriteria Decision Analysis, Malekmohammadi, B. & Jahanishakib, F., 2017. Vulnerability assessment of wetland landscape ecosystem services using driver-pressure-state-impact-response (DPSIR) model. Ecological Indicators, 82(July), pp.293–303. Available at: http://dx.doi.org/10.1016/j.ecolind.2017.06.060. Mattivi, P. et al., 2019. TWI computation: a comparison of different open source GISs. Open Geospatial Data, Software and Standards 2019 4:1, 4(1), pp.1–12. Available at: https://opengeospatialdata.springeropen.com/articles/10.1186/s40965-019-0066-y [Accessed July 14, 2022]. Meneses Moreno, L.H. et al., 2006. Plan De Manejo Parque Nacional Natural. , p.313. Mercier, A. et al., 2019. Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest–Agriculture Mosaics in Temperate and Tropical Landscapes. Remote Sensing 2019, Vol. 11, Page 979, 11(8), p.979. Available at: https://www.mdpi.com/2072-4292/11/8/979/htm [Accessed March 30, 2022]. Ministerio Medio del Medio Ambiente, 2002. Programa para el manejjo sostenible y restauración de ecosistemas de alta montaña. The Journal of Rural Health, 11(3), pp.177–184. Mohamed, S.A., 2020. Coastal vulnerability assessment using GIS-Based multicriteria analysis of Alexandria-northwestern Nile Delta, Egypt. Journal of African Earth Sciences, 163, p.103751. Available at: https://www-sciencedirect-com.ezproxy.unal.edu.co/science/article/pii/S1464343X20300029 [Accessed September 27, 2020]. Moizo Marrubio, P., 2004. La percepción remota y la tecnología SIG: una aplicación en Ecología de Paisaje. Geofocus: Revista Internacional de Ciencia y Tecnología de la Información Geográfica, (4), p.10. Morales, M. et al., 2007. Atlas de páramos de Colombia, Instituto de Investigación de Recursos Biológicos Alexander von Humboldt. Available at: http://repository.humboldt.org.co/handle/20.500.11761/35044#.YoOwPRqwbuE.mendeley. Moreno Ortegón, C.D. & Palma Barragan, J.D., 2016. Vulnerabilidad Ecológica Del Complejo De Páramos Chilí-Barragán a Los Incrementos De Temperatura En Un Escenario De Cambio Climático. , p.307. Naciones Unidas, 2009. 2009 UNISDR Terminología sobre Reducción del Riesgo de Desastres. Estrategia Internacional para la Reducción de Desastres de las Naciones Unidas (UNISDR), p.43. Nandy, S. et al., 2015. Environmental vulnerability assessment of eco-development zone of Great Himalayan National Park, Himachal Pradesh, India. Ecological Indicators, 57, pp.182–195. Available at: http://dx.doi.org/10.1016/j.ecolind.2015.04.024. Nguyen, K.A. & Liou, Y.A., 2019. Global mapping of eco-environmental vulnerability from human and nature disturbances. Science of the Total Environment, 664, pp.995–1004. Available at: https://doi.org/10.1016/j.scitotenv.2019.01.407. Niu, L. et al., 2021. Degradation of river ecological quality in Tibet plateau with overgrazing: A quantitative assessment using biotic integrity index improved by random forest. Ecological Indicators, 120, p.106948. Omann, I., Stocker, A. & Jäger, J., 2009. Climate change as a threat to biodiversity: An application of the DPSIR approach. Ecological Economics, 69(1), pp.24–31. Available at: http://dx.doi.org/10.1016/j.ecolecon.2009.01.003. Osorio Fernández, A.Y., 2015. Explotación minera en el páramo de Pisba-Boyacá. Abadín J (2007).Agricultura sostenible en los Andes Tropicales. Importancia de la Materia Orgánica en la conservación de la fertilidad del suelo, pp.1–21. Available at: https://repository.unimilitar.edu.co/handle/10654/7798#.XoJMWjUEz2I.mendeley. Palacios Saldaña, R. & Pacheco Bonrostro, J., 2016. Los métodos de decisión multicriterio discretos. Un punto de vista racional aplicado a la toma de decisiones. Anáhuac Journal, 16(1), pp.47–78. Available at: http://search.ebscohost.com/login.aspx?direct=true&db=fap&AN=126288689&lang=es&site=eds-live. Pei, H. et al., 2015. Methods and applications for ecological vulnerability evaluation in a hyper-arid oasis: a case study of the Turpan Oasis, China. Environmental Earth Sciences, 74(2), pp.1449–1461. Available at: http://dx.doi.org/10.1007/s12665-015-4134-z. Perring, M.P. et al., 2016. Global environmental change effects on ecosystems: The importance of land-use legacies. Global Change Biology, 22(4), pp.1361–1371. Peyre, G. et al., 2021. Mapping the páramo land-cover in the Northern Andes. International Journal of Remote Sensing, 42(20), pp.7777–7797. Available at: https://www.tandfonline.com/doi/abs/10.1080/01431161.2021.1964709 [Accessed March 30, 2022]. Rezaei, F., Safavi, H.R. & Ahmadi, A., 2013. Groundwater vulnerability assessment using fuzzy logic: A case study in the zayandehrood aquifers, Iran. Environmental Management, 51(1), pp.267–277. Riitters, K. et al., 2000. Global-scale patterns of forest fragmentation. Ecology and Society, 4(2). Rivera, D. & Rodríguez, C., 2011. Guía divulgativa de criterios para la delimitación de páramos de Colombia, Rivera Ospina, D. & Rodríguez, C., 2011. Guía divulgativa de criterios para la delimitación de páramos de Colombia. Salvati, L. et al., 2013. Landscape changes and environmental quality: The evolution of land vulnerability and potential resilience to degradation in Italy. Regional Environmental Change, 13(6), pp.1223–1233. Sarmiento Pinzón, C.E. et al., 2013. Aportes a la conservación estratégica de los páramos de Colombia : actualización de la cartografía de los complejos de páramo a escala 1:100.000, Available at: http://repository.humboldt.org.co/handle/20.500.11761/31406#.XR0GZNq7t58.mendeley. SGC, 2017. Guía Metodológica para la Zonificación de Amenaza por Movimientos en Masa Escala 1:25000, Song, G. et al., 2010. The ecological vulnerability evaluation in southwestern mountain region of China based on GIS and AHP method. Procedia Environmental Sciences, 2(5), pp.465–475. Available at: http://dx.doi.org/10.1016/j.proenv.2010.10.051. Thiemann, F. & Sester, M., 2018. Trends in Spatial Analysis and Modelling. Turner, B.L. et al., 2003. A framework for vulnerability analysis in sustainability science. Proceedings of the National Academy of Sciences, 100(14), pp.8074–8079. Available at: http://www.pnas.org/lookup/doi/10.1073/pnas.1231335100. Senisterra, G.E., Gaspari, F.J. & Delgado, M.I., 2015. Zonificación de la vulnerabilidad ambiental en una cuenca serrana rural, Argentina. , pp.38–58. Valencia, J.B. et al., 2020. Climate Vulnerability Assessment of the Espeletia Complex on Páramo Sky Islands in the Northern Andes. Frontiers in Ecology and Evolution, 8, p.565708. Available at: https://www.igac.gov.co [Accessed November 15, 2020]. Vargas, O., 2013. Disturbios en los páramos andinos. , (March), pp.39–57. Available at: https://www.researchgate.net/publication/260438569_Disturbios_en_los_paramos_andinos. Xiaolei, Z. et al., 2011. Assessment of eco-environment vulnerability in the northeastern margin of the Qinghai-Tibetan Plateau, China. Environmental Earth Sciences, 63(4), pp.667–674. Available at: https://link-springer-com.ezproxy.unal.edu.co/article/10.1007/s12665-010-0731-z [Accessed September 27, 2020]. Yang, F., Ma, C. & Fang, H., 2022. Simulation of critical transitions and vulnerability assessment of Tibetan Plateau key ecosystems. Journal of Mountain Science 2022 19:3, 19(3), pp.673–688. Available at: https://link-springer-com.ezproxy.unal.edu.co/article/10.1007/s11629-021-6960-7 [Accessed March 30, 2022]. Yang, S. et al., 2015. Screening of social vulnerability to natural hazards in China. Natural Hazards, 76(1), pp.1–18. Available at: https://link-springer-com.ezproxy.unal.edu.co/article/10.1007%2Fs11069-014-1225-1 [Accessed November 15, 2020]. Yi, C. & Jackson, N., 2021. A review of measuring ecosystem resilience to disturbance. Environmental Research Letters, 16(5). Zadeh, L. a., 1965. Fuzzy sets. Information and Control, 8(3), pp.338–353. Zadeh, L.A., 1971. Fuzzy Semantics. Information Sciences, 3, pp.159–176. Zhang, X. et al., 2017. Ecological vulnerability assessment based on PSSR in Yellow River Delta. Journal of Cleaner Production, 167, pp.1106–1111. Available at: http://dx.doi.org/10.1016/j.jclepro.2017.04.106. |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.license.spa.fl_str_mv |
Atribución-NoComercial-SinDerivadas 4.0 Internacional |
dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Atribución-NoComercial-SinDerivadas 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.format.extent.spa.fl_str_mv |
117 páginas |
dc.format.mimetype.spa.fl_str_mv |
application/pdf |
dc.coverage.region.none.fl_str_mv |
Pisba, Boyacá |
dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
dc.publisher.program.spa.fl_str_mv |
Bogotá - Ciencias Agrarias - Maestría en Geomática |
dc.publisher.department.spa.fl_str_mv |
Escuela de posgrados |
dc.publisher.faculty.spa.fl_str_mv |
Facultad de Ciencias Agrarias |
dc.publisher.place.spa.fl_str_mv |
Bogotá, Colombia |
dc.publisher.branch.spa.fl_str_mv |
Universidad Nacional de Colombia - Sede Bogotá |
institution |
Universidad Nacional de Colombia |
bitstream.url.fl_str_mv |
https://repositorio.unal.edu.co/bitstream/unal/81889/1/1018462280.2022.pdf https://repositorio.unal.edu.co/bitstream/unal/81889/2/license.txt https://repositorio.unal.edu.co/bitstream/unal/81889/3/1018462280.2022.pdf.jpg |
bitstream.checksum.fl_str_mv |
4238acea222f2d0f2108ead144f0069e 8153f7789df02f0a4c9e079953658ab2 65dd071be57dcba05c3ff238d1c36bd5 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
repository.mail.fl_str_mv |
repositorio_nal@unal.edu.co |
_version_ |
1814089299116687360 |
spelling |
Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Rodríguez Eraso, Nellyff7f44184949401b01f35fcd5a2a1082600Ramirez Gomez, Juan Camilo0e3f904bd8385e979a7d30fdcda3e4342022-08-12T18:22:36Z2022-08-12T18:22:36Z2022-08-12https://repositorio.unal.edu.co/handle/unal/81889Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, graficas, mapasLa evaluación de la vulnerabilidad es esencial para la toma de decisiones en el marco de la gestión ambiental y la conservación de los ecosistemas. Igualmente, es un proceso complejo que combinar factores biofísicos, sociales y económicos, donde el uso de la geomática, brinda un soporte conceptual y técnico para su integración y análisis. El presente trabajo se enfocó en proponer y aplicar una metodología para evaluar la vulnerabilidad del Complejo de Páramos del Pisba (Departamento de Boyacá) asociada a la cobertura del suelo, aplicando análisis espacial y un enfoque de jerarquía analítica (AHP). Se identificaron 25 variables agrupadas en 11 factores que reflejan la sensibilidad, exposición y resiliencia del ecosistema trabajadas a una escala de 1:25.000. Mediante un análisis de percepción remota, geoestadística, distancias euclidianas y análisis del paisaje, los factores con mayor incidencia fueron: estado de la vegetación, condiciones abióticas, actividades agropecuarias e incendios. Los bordes occidentales del páramo tienden a ser más vulnerables, asociados a los cambios en coberturas por actividades antrópicas. Se encontró que el 18,06% del área de estudio presenta una vulnerabilidad alta, 21,96% una vulnerabilidad media y 59,98% vulnerabilidad baja, donde los hotspots están ubicados en zonas de borde. En general el Complejo de Pisba tienen cerca de 46461 ha en categorías alta y media de vulnerabilidad, siendo las coberturas naturales más afectadas los bosques (10,6% y 32,82% en vulnerabilidad alta y media respectivamente), seguido por la vegetación de paramo (10,18% y 18,18% en vulnerabilidad alta y media respectivamente). Se espera que la presente investigación, se pueda replicar en los diferentes páramos de del país, previendo diferencias intra-regionales, que ayuden a generar acciones urgentes de manejo en estos ecosistemas estratégico, a partir de información espacial disponible, usando elementos que desde la Geomática como ciencia, soportan la identificación y análisis de la vulnerabilidad ambiental para la planificación ambiental del territorio. (Texto tomado de la fuente)Vulnerability assessment is essential for decision-making in the framework of environmental management and ecosystem conservation. Likewise, it is a complex process that combines biophysical, social, and economic factors, where the use of geomatics provides conceptual and technical support for its integration and analysis. The present work focused on proposing and applying a methodology to assess the vulnerability of the Pisba moorland complex (Department of Boyacá) associated with land cover, applying spatial analysis and an analytical hierarchy approach (AHP). 25 variables grouped into 11 factors that reflect the sensitivity, exposure, and resilience of the ecosystem were identified. Through an analysis of remote sensing, geostatistics, Euclidean distances, and landscape analysis, the factors with the highest incidence were: the state of the vegetation, abiotic conditions, agricultural activities, and fires. The western edges of the páramo tend to be more vulnerable, associated with changes in coverage due to anthropic activities. It was found that 18.06% of the study area has high vulnerability, 21.96% medium vulnerability, and 59.98% low vulnerability, where the hotspots are located in border areas. In general, the Pisba Complex has about 46,461 ha in high and medium vulnerability categories, with the most affected natural cover being forests (10.6% and 32.82% in high and medium vulnerability, respectively), followed by the vegetation of moorland (10.18% and 18.18% in high and medium vulnerability, respectively). It is expected that this research can be replicated in the different moors of the country, anticipating intra-regional differences that help generate urgent management actions in these strategic ecosystems, based on available spatial information, use of spatial methodologies that support the identification, and environmental vulnerability analysis.MaestríaMagíster en GeomáticaGeoinformación para el uso sostenible de los recursos naturales117 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias Agrarias - Maestría en GeomáticaEscuela de posgradosFacultad de Ciencias AgrariasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá350 - Administración pública y ciencia militar::354 - Administración pública de la economía y el medio ambienteAREAS PROTEGIDASPROTECCION DEL MEDIO AMBIENTEProtected areasEnvironmental protection-8a. ed.Vulnerabilidad ambientalAnálisis espacialAnálisis multicriterioPáramosPáramo de PisbaSpatial analysisEnvironmental vulnerabilitySpatial analysisMetodología para evaluar la vulnerabilidad ambiental en los ecosistemas de páramo asociada a los usos del suelo: Caso de estudio complejo de páramos Pisba (Boyacá, Colombia)Methodology to assess environmental vulnerability in moorland ecosystems associated with land use: A case study of the Pisba moorland complex (Boyacá, Colombia)Trabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMPisba, BoyacáRedColLaReferenciaAdger, W.N., 2006. Vulnerability. Global Environmental Change, 16(3), pp.268–281. Available at: https://linkinghub.elsevier.com/retrieve/pii/S0959378006000422.Adriano Coronel, 2012. GESTIÓN DE RIESGOS en Gestión de proyectos. Wiki-EOI.Aryafar, A., Yousefi, S. & Doulati Ardejani, F., 2013. The weight of interaction of mining activities: Groundwater in environmental impact assessment using fuzzy analytical hierarchy process (FAHP). Environmental Earth Sciences, 68(8), pp.2313–2324.Ascough, J.C. et al., 2008. Future research challenges for incorporation of uncertainty in environmental and ecological decision-making. Ecological Modelling, 219(3–4), pp.383–399.Awange, J.L. & Kyalo Kiema, J.B., 2013. Environmental Geoinformatics, Available at: http://www.springer.com/series/3234%0Ahttp://link.springer.com/10.1007/978-3-642-34085-7.Bagdanavičiute, I. & Valiunas, J., 2013. GIS-based land suitability analysis integrating multi-criteria evaluation for the allocation of potential pollution sources. Environmental Earth Sciences, 68(6), pp.1797–1812.Bar, S., Parida, B.R. & Pandey, A.C., 2020. Landsat-8 and Sentinel-2 based Forest fire burn area mapping using machine learning algorithms on GEE cloud platform over Uttarakhand, Western Himalaya. Remote Sensing Applications: Society and Environment, 18(January), p.100324. Available at: https://doi.org/10.1016/j.rsase.2020.100324.Barredo, J.I. & Bosque-Sendra, J., 1998. Multi-criteria evaluation methods for ordinal data in a GIS environment. Geographical Systems, 5(4), pp.313–327.Baxter, M.J., 1979. THE APPLICATION OF LOGIT REGRESSION ANALYSIS TO PRODUCTION-CONSTRAINED GRAVITY MODELS*. Journal of Regional Science, 19(2), pp.171–177. Available at: http://doi.wiley.com/10.1111/j.1467-9787.1979.tb00583.x [Accessed September 26, 2020].occo, G., Mendoza, M. & Masera, O.R., 2001. La dinámica del cambio del uso del suelo en Michoacán.Una propuesta metodológica para el estudio de los procesos de deforestación. Investigaciones Geograficas, 44, pp.18–38.Bourgoin, C. et al., 2020. Assessing the ecological vulnerability of forest landscape to agricultural frontier expansion in the Central Highlands of Vietnam. International Journal of Applied Earth Observation and Geoinformation, 84(July 2019), p.101958. Available at: https://doi.org/10.1016/j.jag.2019.101958.Breiman, L., 2001. Random forests. Machine Learning, pp.1–122.Cabrera, M. & Ramírez, W., 2014. Restauración ecológica de los páramos en colombia: transformación y herramientas para su conservación.Calvo González, J., 2006. La Fragilidad de Páramos. , (June), pp.123–144.CAR, C., 2018. Guía ilustrativa sobre Analisis de la vulnerabilidad territorial ante el cambio climatico, Available at: https://www.car.gov.co/uploads/files/5cc8af9bc943b.pdf.Cárdenas, M.F., 2016. Ecohydrology of paramos in Colombia: vulnerability to climate change and land use. , p.139. Available at: http://www.bdigital.unal.edu.co/56394/.Cavazzana, G.H. et al., 2016. Natural and environmental vulnerability along the touristic “Estradas parque pantanal” by GIS algebraic mapping. In Handbook of Environmental Chemistry. Springer Verlag, pp. 209–226. Available at: https://link-springer-com.ezproxy.unal.edu.co/chapter/10.1007/698_2014_328 [Accessed September 27, 2020].Choudhary, K., Boori, M.S. & Kupriyanov, A., 2018. Spatial modelling for natural and environmental vulnerability through remote sensing and GIS in Astrakhan, Russia. Egyptian Journal of Remote Sensing and Space Science, 21(2), pp.139–147. Available at: https://doi.org/10.1016/j.ejrs.2017.05.003.Cinner, J.E. et al., 2013. Evaluating social and ecological vulnerability of coral reef fisheries to climate change. PloS one, 8(9).Correa Ayram, C.A. et al., 2020. Spatiotemporal evaluation of the human footprint in Colombia: Four decades of anthropic impact in highly biodiverse ecosystems. Ecological Indicators, 117(June), p.106630. Available at: https://doi.org/10.1016/j.ecolind.2020.106630.Cresso, M. et al., 2020. Future Climate Change Renders Unsuitable Conditions for Paramo Ecosystems in Colombia. Sustainability, 12(20), p.8373. Available at: https://www.mdpi.com/2071-1050/12/20/8373 [Accessed November 15, 2020].Duguy, B. et al., 2012. Modelling the ecological vulnerability to forest fires in mediterranean ecosystems using geographic information technologies. Environmental Management, 50(6), pp.1012–1026.Durán Gil, C.A., 2017. Análisis espacial de las condiciones de vulnerabilidad social, económica, física y ambiental en el territorio colombiano. Perspectiva Geográfica, 22(1), pp.11–32.Eastman, R., 2012. IDRISI Selva. Guía para SIG y procesamiento de imágenes. Clark University, 53(9), p.321.Echeverría, M., Rosero, C. & Bravo, L., 2018. Vulnerabilidad a nivel de ecosistema de Páramo frente al Cambio Climático en la zona de Igualata Parroquia San Isidro, Cantón Guano Provincia de Chimborazo. Revista del Instituto de Investigación de la Facultad de Ingeniería Geológica, Minera, Metalurgica y Geográfica, 20(39), pp.137–148.Eguiguren-Velepucha, P.A. et al., 2016. Tropical ecosystems vulnerability to climate change in southern Ecuador. Tropical Conservation Science, 9(4), p.194008291666800. Available at: http://journals.sagepub.com/doi/10.1177/1940082916668007 [Accessed November 15, 2020].Elisa Zanella, M., Wanderley Correia Dantas, E. & Luís Sampaio Olímpio, J., 2012. a Vulnerabilidade Natural E Ambiental Do Município De Fortaleza/Ce. Boletim Goiano de Geografia, 31(2), pp.13–27.ESRI, 2016. Comprender el análisis de distancia euclidiana—Ayuda. ArcGIS for Desktop. Available at: https://pro.arcgis.com/es/pro-app/2.8/tool-reference/spatial-analyst/understanding-euclidean-distance-analysis.htm [Accessed July 14, 2022].Etter, A. et al., 2020. Assessing restoration priorities for high-risk ecosystems: An application of the IUCN red list of ecosystems. Land Use Policy, 99(42), p.104874. Available at: https://doi.org/10.1016/j.landusepol.2020.104874.Fischer, M. & Getis, A., 1997. Recent Developments in Spatial Analysis – Spatial Statistics, Behavioural Modelling and Computational Intelligence. , (February 2016), p.433pp.Galik, E., 2013. Tecnología del Sistema de Información Geográfica (SIG).Garavito, G., Gómez Zárate, D.P. & Palacio Tamayo, D., 2018. Gobernanza territorial en los páramos Chingaza y Sumapaz-Cruz Verde. Una comparación de sus principales actores y problemáticas. Perspectiva Geográfica, 23(1), pp.11–30.Gerardo, López, E. & Mendoza, Manuel, B., 2001. Predicción del cambio de cobertura y uso de suelo. Estudio de caso de la Ciudad de Morelia. Investigaciones geográficas, 28(45), p.134. Available at: http://pbidi.unam.mx:8080/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=cat02029a&AN=tes.TES01000666406&lang=es&site=eds-live.Getis, A., 1995. Cliff, A.D. and Ord, J.K. 1973: Spatial autocorrelation. London: Pion. Progress in Human Geography, 19(2), pp.245–249. Available at: http://journals.sagepub.com/doi/10.1177/030913259501900205 [Accessed September 26, 2020].Gomarasca, M.A., 2010. Basics of geomatics. Applied Geomatics, 2(3), pp.137–146.Guarderas, P., Smith, F. & Dufrene, M., 2021. Land use land cover dynamics through time and their proximate drivers of change in a tropical mountain system : a case study in a highland landscape of northern Ecuador Land use transitions and drivers of change in a highland landscape of northern Ecuador.He, H.S., DeZonia, B.E. & Mladenoff, D.J., 2001. Erratum: An aggregation index (AI) to quantify spatial patterns of landscapes (Landscape Ecology (200) 15 (591-601)). Landscape Ecology, 16(1), p.87.Hong, W. et al., 2016. Establishing an ecological vulnerability assessment indicator system for spatial recognition and management of ecologically vulnerable areas in highly urbanized regions: A case study of Shenzhen, China. Ecological Indicators, 69, pp.540–547. Available at: https://doi.org/10.1016/j.landusepol.2017.01.010. [Accessed November 15, 2020].Huang, P.H., Tsai, J.S. & Lin, W.T., 2010. Using multiple-criteria decision-making techniques for eco-environmental vulnerability assessment: A case study on the Chi-Jia-Wan Stream watershed, Taiwan. Environmental Monitoring and Assessment, 168(1–4), pp.141–158.DEAM, 2012. Fuertes impactos del cambio climático en los páramos de Colombia, Bogotá.Ippolito, A. et al., 2010. Ecological vulnerability analysis: A river basin case study. Science of the Total Environment, 408(18), pp.3880–3890. Available at: http://dx.doi.org/10.1016/j.scitotenv.2009.10.002.ang, W.S., Engel, B. & Yeum, C.M., 2020. Integrated environmental modeling for efficient aquifer vulnerability assessment using machine learning. Environmental Modelling & Software, 124, p.104602.Jeanneth, H., 2002. Aproximación a un modelo para la evaluación de la vulnerabilidad de las coberturas vegetales de Colombia ante un posible cambio climático utilizando SIG. Meteorología Colombiana, 6, pp.55–63.Jones, D. & Tamiz, M., 2016. Multiple criteria Decision Analysis,Jonse, a P.C. & Coutinho, L.C., 2016. Remote Sensing and Spatial Decision Support System. Comparative and General Pharmacology, 00(1), pp.17–19.Juzga, M.A., 2016. COMPARACIÓN DE ÍNDICES DE VEGETACIÓN EN EL CERRO DE LA CONEJERA DE LA CIUDAD DE BOGOTÁ. , p.2016.Kangas, J. et al., 2000. Improving the quality of landscape ecological forest planning by utilising advanced decision-support tools. Forest Ecology and Management, 132(2–3), pp.157–171.Khan, S., 2012. Vulnerability assessments and their planning implications: A case study of the Hutt Valley, New Zealand. Natural Hazards, 64(2), pp.1587–1607.Kia, M.B. et al., 2012. An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental Earth Sciences, 67(1), pp.251–264.De Lange, H.J. et al., 2010. Ecological vulnerability in risk assessment - A review and perspectives. Science of the Total Environment, 408(18), pp.3871–3879. Available at: http://dx.doi.org/10.1016/j.scitotenv.2009.11.009.Li, A. et al., 2006. Eco-environmental vulnerability evaluation in mountainous region using remote sensing and GIS - A case study in the upper reaches of Minjiang River, China. Ecological Modelling, 192(1–2), pp.175–187.Li, J. & Heap, A.D., 2008. A Review of Spatial Interpolation Methods for Environmental Scientists. Australian Geological Survey Organisation, GeoCat# 68(2008/23), p.154.Li, Y. et al., 2017. Mapping the hotspots and coldspots of ecosystem services in conservation priority setting. Journal of Geographical Sciences, 27(6), pp.681–696.Liao, X., Li, W. & Hou, J., 2013. Application of GIS Based Ecological Vulnerability Evaluation in Environmental Impact Assessment of Master Plan of Coal Mining Area. Procedia Environmental Sciences, 18, pp.271–276. Available at: http://dx.doi.org/10.1016/j.proenv.2013.04.035.Liu, Y. et al., 2020. Variability in Regional Ecological Vulnerability: A Case Study of Sichuan Province, China. International Journal of Disaster Risk Science, pp.1–13. Available at: https://doi.org/10.1007/s13753-020-00295-6 [Accessed November 15, 2020].Lollino, G. et al., 2015. Slope Stability Analyses of the Proposed Reconstituted Slope of the Quarry Heights Drive Landslide, Durban, South Africa. Engineering Geology for Society and Territory - Volume 2: Landslide Processes, 2, pp.1–2177.Longley, P.A. et al., 2005. Geographical Information Systems and Science, Available at: http://www.amazon.com/Geographic-Information-Systems-Science-Longley/dp/0470870001/ref=sr_1_2?ie=UTF8&qid=1430849641&sr=8-2&keywords=Geographical+Information+Systems+and+Science+longley+2005.Luo, D., Caldas, M.M. & Goodin, D.G., 2021. Estimating environmental vulnerability in the Cerrado with machine learning and Twitter data. Journal of Environmental Management, 289, p.112502.Malczewski, J., 1999. GIS and Multicriteria Decision Analysis,Malekmohammadi, B. & Jahanishakib, F., 2017. Vulnerability assessment of wetland landscape ecosystem services using driver-pressure-state-impact-response (DPSIR) model. Ecological Indicators, 82(July), pp.293–303. Available at: http://dx.doi.org/10.1016/j.ecolind.2017.06.060.Mattivi, P. et al., 2019. TWI computation: a comparison of different open source GISs. Open Geospatial Data, Software and Standards 2019 4:1, 4(1), pp.1–12. Available at: https://opengeospatialdata.springeropen.com/articles/10.1186/s40965-019-0066-y [Accessed July 14, 2022].Meneses Moreno, L.H. et al., 2006. Plan De Manejo Parque Nacional Natural. , p.313.Mercier, A. et al., 2019. Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest–Agriculture Mosaics in Temperate and Tropical Landscapes. Remote Sensing 2019, Vol. 11, Page 979, 11(8), p.979. Available at: https://www.mdpi.com/2072-4292/11/8/979/htm [Accessed March 30, 2022].Ministerio Medio del Medio Ambiente, 2002. Programa para el manejjo sostenible y restauración de ecosistemas de alta montaña. The Journal of Rural Health, 11(3), pp.177–184.Mohamed, S.A., 2020. Coastal vulnerability assessment using GIS-Based multicriteria analysis of Alexandria-northwestern Nile Delta, Egypt. Journal of African Earth Sciences, 163, p.103751. Available at: https://www-sciencedirect-com.ezproxy.unal.edu.co/science/article/pii/S1464343X20300029 [Accessed September 27, 2020].Moizo Marrubio, P., 2004. La percepción remota y la tecnología SIG: una aplicación en Ecología de Paisaje. Geofocus: Revista Internacional de Ciencia y Tecnología de la Información Geográfica, (4), p.10.Morales, M. et al., 2007. Atlas de páramos de Colombia, Instituto de Investigación de Recursos Biológicos Alexander von Humboldt. Available at: http://repository.humboldt.org.co/handle/20.500.11761/35044#.YoOwPRqwbuE.mendeley.Moreno Ortegón, C.D. & Palma Barragan, J.D., 2016. Vulnerabilidad Ecológica Del Complejo De Páramos Chilí-Barragán a Los Incrementos De Temperatura En Un Escenario De Cambio Climático. , p.307.Naciones Unidas, 2009. 2009 UNISDR Terminología sobre Reducción del Riesgo de Desastres. Estrategia Internacional para la Reducción de Desastres de las Naciones Unidas (UNISDR), p.43.Nandy, S. et al., 2015. Environmental vulnerability assessment of eco-development zone of Great Himalayan National Park, Himachal Pradesh, India. Ecological Indicators, 57, pp.182–195. Available at: http://dx.doi.org/10.1016/j.ecolind.2015.04.024.Nguyen, K.A. & Liou, Y.A., 2019. Global mapping of eco-environmental vulnerability from human and nature disturbances. Science of the Total Environment, 664, pp.995–1004. Available at: https://doi.org/10.1016/j.scitotenv.2019.01.407.Niu, L. et al., 2021. Degradation of river ecological quality in Tibet plateau with overgrazing: A quantitative assessment using biotic integrity index improved by random forest. Ecological Indicators, 120, p.106948.Omann, I., Stocker, A. & Jäger, J., 2009. Climate change as a threat to biodiversity: An application of the DPSIR approach. Ecological Economics, 69(1), pp.24–31. Available at: http://dx.doi.org/10.1016/j.ecolecon.2009.01.003.Osorio Fernández, A.Y., 2015. Explotación minera en el páramo de Pisba-Boyacá. Abadín J (2007).Agricultura sostenible en los Andes Tropicales. Importancia de la Materia Orgánica en la conservación de la fertilidad del suelo, pp.1–21. Available at: https://repository.unimilitar.edu.co/handle/10654/7798#.XoJMWjUEz2I.mendeley.Palacios Saldaña, R. & Pacheco Bonrostro, J., 2016. Los métodos de decisión multicriterio discretos. Un punto de vista racional aplicado a la toma de decisiones. Anáhuac Journal, 16(1), pp.47–78. Available at: http://search.ebscohost.com/login.aspx?direct=true&db=fap&AN=126288689&lang=es&site=eds-live.Pei, H. et al., 2015. Methods and applications for ecological vulnerability evaluation in a hyper-arid oasis: a case study of the Turpan Oasis, China. Environmental Earth Sciences, 74(2), pp.1449–1461. Available at: http://dx.doi.org/10.1007/s12665-015-4134-z.Perring, M.P. et al., 2016. Global environmental change effects on ecosystems: The importance of land-use legacies. Global Change Biology, 22(4), pp.1361–1371.Peyre, G. et al., 2021. Mapping the páramo land-cover in the Northern Andes. International Journal of Remote Sensing, 42(20), pp.7777–7797. Available at: https://www.tandfonline.com/doi/abs/10.1080/01431161.2021.1964709 [Accessed March 30, 2022].Rezaei, F., Safavi, H.R. & Ahmadi, A., 2013. Groundwater vulnerability assessment using fuzzy logic: A case study in the zayandehrood aquifers, Iran. Environmental Management, 51(1), pp.267–277.Riitters, K. et al., 2000. Global-scale patterns of forest fragmentation. Ecology and Society, 4(2).Rivera, D. & Rodríguez, C., 2011. Guía divulgativa de criterios para la delimitación de páramos de Colombia,Rivera Ospina, D. & Rodríguez, C., 2011. Guía divulgativa de criterios para la delimitación de páramos de Colombia.Salvati, L. et al., 2013. Landscape changes and environmental quality: The evolution of land vulnerability and potential resilience to degradation in Italy. Regional Environmental Change, 13(6), pp.1223–1233.Sarmiento Pinzón, C.E. et al., 2013. Aportes a la conservación estratégica de los páramos de Colombia : actualización de la cartografía de los complejos de páramo a escala 1:100.000, Available at: http://repository.humboldt.org.co/handle/20.500.11761/31406#.XR0GZNq7t58.mendeley.SGC, 2017. Guía Metodológica para la Zonificación de Amenaza por Movimientos en Masa Escala 1:25000,Song, G. et al., 2010. The ecological vulnerability evaluation in southwestern mountain region of China based on GIS and AHP method. Procedia Environmental Sciences, 2(5), pp.465–475. Available at: http://dx.doi.org/10.1016/j.proenv.2010.10.051.Thiemann, F. & Sester, M., 2018. Trends in Spatial Analysis and Modelling.Turner, B.L. et al., 2003. A framework for vulnerability analysis in sustainability science. Proceedings of the National Academy of Sciences, 100(14), pp.8074–8079. Available at: http://www.pnas.org/lookup/doi/10.1073/pnas.1231335100.Senisterra, G.E., Gaspari, F.J. & Delgado, M.I., 2015. Zonificación de la vulnerabilidad ambiental en una cuenca serrana rural, Argentina. , pp.38–58.Valencia, J.B. et al., 2020. Climate Vulnerability Assessment of the Espeletia Complex on Páramo Sky Islands in the Northern Andes. Frontiers in Ecology and Evolution, 8, p.565708. Available at: https://www.igac.gov.co [Accessed November 15, 2020].Vargas, O., 2013. Disturbios en los páramos andinos. , (March), pp.39–57. Available at: https://www.researchgate.net/publication/260438569_Disturbios_en_los_paramos_andinos.Xiaolei, Z. et al., 2011. Assessment of eco-environment vulnerability in the northeastern margin of the Qinghai-Tibetan Plateau, China. Environmental Earth Sciences, 63(4), pp.667–674. Available at: https://link-springer-com.ezproxy.unal.edu.co/article/10.1007/s12665-010-0731-z [Accessed September 27, 2020].Yang, F., Ma, C. & Fang, H., 2022. Simulation of critical transitions and vulnerability assessment of Tibetan Plateau key ecosystems. Journal of Mountain Science 2022 19:3, 19(3), pp.673–688. Available at: https://link-springer-com.ezproxy.unal.edu.co/article/10.1007/s11629-021-6960-7 [Accessed March 30, 2022].Yang, S. et al., 2015. Screening of social vulnerability to natural hazards in China. Natural Hazards, 76(1), pp.1–18. Available at: https://link-springer-com.ezproxy.unal.edu.co/article/10.1007%2Fs11069-014-1225-1 [Accessed November 15, 2020].Yi, C. & Jackson, N., 2021. A review of measuring ecosystem resilience to disturbance. Environmental Research Letters, 16(5).Zadeh, L. a., 1965. Fuzzy sets. Information and Control, 8(3), pp.338–353.Zadeh, L.A., 1971. Fuzzy Semantics. Information Sciences, 3, pp.159–176.Zhang, X. et al., 2017. Ecological vulnerability assessment based on PSSR in Yellow River Delta. Journal of Cleaner Production, 167, pp.1106–1111. Available at: http://dx.doi.org/10.1016/j.jclepro.2017.04.106.AdministradoresBibliotecariosConsejerosEstudiantesGrupos comunitariosInvestigadoresMaestrosMedios de comunicaciónProveedores de ayuda financiera para estudiantesPúblico generalORIGINAL1018462280.2022.pdf1018462280.2022.pdfTesis de Maestría en Geomáticaapplication/pdf6583644https://repositorio.unal.edu.co/bitstream/unal/81889/1/1018462280.2022.pdf4238acea222f2d0f2108ead144f0069eMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81889/2/license.txt8153f7789df02f0a4c9e079953658ab2MD52THUMBNAIL1018462280.2022.pdf.jpg1018462280.2022.pdf.jpgGenerated Thumbnailimage/jpeg5713https://repositorio.unal.edu.co/bitstream/unal/81889/3/1018462280.2022.pdf.jpg65dd071be57dcba05c3ff238d1c36bd5MD53unal/81889oai:repositorio.unal.edu.co:unal/818892024-08-08 23:11:42.05Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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 |