Generación de un índice espectro-temporal para la identificación de zonas afectadas por deforestación usando imágenes Landsat
dc.contributor.advisor | Lizarazo Salcedo, Ivan Alberto | spa |
dc.contributor.author | Hurtado Abril, Jose Leonardo | spa |
dc.date.accessioned | 2020-03-18T20:16:19Z | spa |
dc.date.available | 2020-03-18T20:16:19Z | spa |
dc.date.issued | 2019 | spa |
dc.description | ilustraciones, gráficas | spa |
dc.description.abstract | La presente investigación tiene como propósito la creación de un índice espectro-temporal enfocado en la detección de áreas afectadas por deforestación en cualquier lugar y en cualquier momento del tiempo al igual que la formulación de un criterio para evaluar severidad, el cual es validado en la selva amazónica colombiana. Para ello se propusó un minucioso estudio de series de tiempo de datos satelitales Landsat con énfasis en el manejo y descarga de resultados usando el algoritmo Landtrendr mediante el análisis de variabilidad espectral de las bandas del espectro infrarrojo y los índices temáticos de vegetación normalizada, de suelo ajustado y de área quemada. Una vez generado el índice espectro-temporal basado en el análisis de series de tiempo se realizaron pruebas y validaciones en diversas zonas de la Amazonia colombiana y en diferentes intervalos temporales para evaluar la calidad de los resultados obtenidos. Se generaron mapas derivados del índice temático de deforestación usando tablas de clasificación de su grado de severidad. Finalmente, se utilizó el concepto del objeto de deforestación en un entorno de objetos geográficos donde la totalidad de la metodología y flujo de procesos propuestos se basó en los resultados derivados del índice espectro-temporal y se evaluaron usando una matriz de exactitud basada en objetos tomando como referencia los polígonos oficiales del Sistema de Monitoreo de Bosque y Carbono. (Texto tomado de la fuente). | spa |
dc.description.abstract | The purpose of this research is to create a spectrum-time index focused on the detection of areas analyzed by deforestation anywhere in the world and at any time in time, as well as the formulation of a criterion for assessing severity. For this, a thorough study of Landsat satellite data time series was carried out with emphasis on the management and downloading of results using the Landtrendr algorithm through the analysis of spectral variability of the infrared spectrum bands and the thematic indices of normalized soil vegetation. adjusted and burned area. Once the spectrum-time index was generated based on the analysis of time series, tests and validations were analyzed in different areas of the Colombian Amazon and at different time intervals to assess the quality of the results obtained. Maps derived from the thematic deforestation index were generated using classification tables of their degree of severity. Finally, the concept of the object of deforestation in an environment of geographical objects where the entire methodology and the flow of proposed processes was based on the results derived from the spectrum-time index and were evaluated using an accuracy matrix based on objects taking as reference the official polygons of the Forest and Carbon Monitoring System. | eng |
dc.description.additional | Magíster en Geomatica. Línea de Investigación: Geoinformación para el uso sostenible de los recursos naturales | spa |
dc.description.curriculararea | Ciencias Agronómicas | spa |
dc.description.degreelevel | Maestría | spa |
dc.description.degreename | Magíster en Geomática | spa |
dc.description.notes | Incluye anexos | spa |
dc.description.researcharea | Geoinformación para el uso sostenible de los recursos naturales | spa |
dc.format.extent | xx, 152 páginas | spa |
dc.format.mimetype | application/pdf | spa |
dc.identifier.instname | Universidad Nacional de Colombia | spa |
dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia | spa |
dc.identifier.repourl | https://repositorio.unal.edu.co/ | |
dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/76106 | |
dc.language.iso | spa | spa |
dc.publisher | Universidad Nacional de Colombia | spa |
dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá | spa |
dc.publisher.department | Departamento de Agronomía | spa |
dc.publisher.faculty | Facultad de Ciencias Agrarias | spa |
dc.publisher.place | Bogotá, Colombia | spa |
dc.publisher.program | Bogotá - Ciencias Agrarias - Maestría en Geomática | spa |
dc.relation.references | Arango, M., Aramburo, M. A., & Olaya, Y. (2012). Gestión y ambiente. Problemática de Los Pasivos Ambientales Mineros En Colombia, 15(3), 125–133. Retrieved from https://revistas.unal.edu.co/index.php/gestion/article/view/36286 | spa |
dc.relation.references | Araque, L., & Jiménez, A. (2009). Caracterización de firma espectral a partir de sensores remotos para el manejo de sanidad vegetal en el cultivo de palma de aceite. Revista Palmas, 30(3), 63–79. | spa |
dc.relation.references | Armenteras, D., Gibbes, C., Anaya, J. A., & Dávalos, L. M. (2017). Integrating remotely sensed fires for predicting deforestation for REDD+. Ecological Applications, 27(4), 1294–1304. https://doi.org/10.1002/eap.1522 | spa |
dc.relation.references | Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., & Heynen, M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3–4), 239–258. https://doi.org/10.1016/j.isprsjprs.2003.10.002 | spa |
dc.relation.references | Ben Abbes, A., Bounouh, O., Farah, I. R., de Jong, R., & Martínez, B. (2018). Comparative study of three satellite image time-series decomposition methods for vegetation change detection. European Journal of Remote Sensing, 51(1), 607–615. https://doi.org/10.1080/22797254.2018.1465360 | spa |
dc.relation.references | Blaschke, T. (2010b). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16. https://doi.org/10.1016/J.ISPRSJPRS.2009.06.004 | spa |
dc.relation.references | Blaschke, Thomas, & Strobl, J. (2015). What’ s wrong with pixels ? Some recent developments interfacing remote sensing and GIS. Interfacing Remote Sensing and GIS, (October), 1–7. | spa |
dc.relation.references | Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., & Gorelick, N. (2018). A LandTrendr multispectral ensemble for forest disturbance detection A R T I C L E I N F O. Remote Sensing of Environment, 205, 131–140. https://doi.org/10.1016/j.rse.2017.11.015 | spa |
dc.relation.references | Dávalos, L. M., Bejarano, A. C., Hall, M. A., Correa, H. L., Corthals, A., & Espejo, O. J. (2011). Forests and drugs: Coca-driven deforestation in tropical biodiversity hotspots. Environmental Science and Technology, 45(4), 1219–1277. https://doi.org/10.1021/es102373d | spa |
dc.relation.references | Defries, R., Achard, F., Brown, S., Herold, M., Murdiyarso, D., Schlamadinger, B., & De Souza, C. (2007). Earth observations for estimating greenhouse gas emissions from deforestation in developing countries. 10(4), 385–394. https://doi.org/10.1016/j.envsci.2007.01.010 | spa |
dc.relation.references | Dutrieux, L. P., Verbesselt, J., Kooistra, L., & Herold, M. (2015). Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia. ISPRS Journal of Photogrammetry and Remote Sensing, 107, 112–125. https://doi.org/10.1016/J.ISPRSJPRS.2015.03.015 | spa |
dc.relation.references | Espejo, J. (2016). Desarrollo de una metodología para estimación de la deforestación mediante el análisis multitemporal de imágenes multiespectrales en un entorno de análisis basado en objetos geográficos ( GEOBIA ). Desarrollo de una metodología para estimación de la defor. Universidad Distrital Francisco Jose de Caldas. | spa |
dc.relation.references | Fraser, R. (2000). Hotspot and NDVI Differencing Synergy (HANDS) A New Technique for Burned Area Mapping over Boreal Forest. Remote Sensing of Environment, 74(3), 362–376. https://doi.org/10.1016/S0034-4257(00)00078-X | spa |
dc.relation.references | Garay Salamanca, L. J., Cabrera Leal, M., Espitia Zamora, J. E., Fierro Morales, J., Negrete Montes, R., Pardo Becerra, L. A., … Vargas Valencia, F. (2013). Minería en Colombia Fundamentos para superar el modelo extractivista. Retrieved from http://cdn.ipsnoticias.net/documentos/Mineria-en-Colombia-2013.pdf | spa |
dc.relation.references | Gobron, N., Pinty, B., Verstraete, M. M., & Widlowski, J.-L. (2000). Advanced Vegetation Indices Optimized for Up-Coming Sensors: Design, Performance, and Applications. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 38. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=885197 | spa |
dc.relation.references | Hansen, M. C., Potapov, P. V, Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., … Townshend, J. R. G. (2013). High-resolution global maps of 21st-century forest cover change. Science (New York, N.Y.), 342(6160), 850–853. https://doi.org/10.1126/science.1244693 | spa |
dc.relation.references | Huete, A. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment Fingerprinting Australian ecosystem threats from climate change and biodiversity loss View project Impacts of extreme hydro-meteorological conditions on ecosystem functioning and produ. REMOTE SENSING OF ENVIRONMEN, 25, 295–309. | spa |
dc.relation.references | Hussain, M., Wolfgang, L., Dyk, A., Ortlepp, S., Schmullius, C., & Tinis, S. (2018). Integrating Geographic Object Based Image Analysis with the Open Data Cube for multi-resolution, multi-sensor hyper-temporal image analysis using CCDC for deforestation monitoring in Canada. | spa |
dc.relation.references | Kennedy, R. E., Yang, Z., & Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. Remote Sensing of Environment, 114(12), 2897–2910. https://doi.org/10.1016/j.rse.2010.07.008 | spa |
dc.relation.references | Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., & Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. Remote Sensing, 10(5), 691. https://doi.org/10.3390/rs10050691 | spa |
dc.relation.references | Marti, L., Sanchez-Pi, N., Molina, J. M., & Bicharra Garcia, A. C. (2014). (PDF) YASA: Yet Another Time Series Segmentation Algorithm for Anomaly Detection in Big Data Problems. Springer International Publishing, (0302–9743), 697–708. https://doi.org/10.1007/978-3-319-07617-1_61 | spa |
dc.relation.references | Molinier, M., Astola, H., Räty, T., & Woodcock, C. (2018). Timely and semi-automatic detection of forest logging events in boreal forest using all available landsat data. International Geoscience and Remote Sensing Symposium (IGARSS), 2018-July, 1730–1733. https://doi.org/10.1109/IGARSS.2018.8518112 | spa |
dc.relation.references | Negret, P. J., Watson, J. E. M., Possingham, H. P., Sonter, L., Jones, K. R., Suarez, C., … Maron, M. (2019). Emerging evidence that armed conflict and coca cultivation influence deforestation patterns Optimal monitoring of Australian Vertebrate Fauna View project Cost-effective Conservation Planning View project Emerging evidence that armed conflict and coca cul. https://doi.org/10.1016/j.biocon.2019.07.021 | spa |
dc.relation.references | Ramirez, S., & Lizarazo, I. (2014). classification of cloud masses from weather imagery using machine learning algorithms. https://doi.org/0120-6230 | spa |
dc.relation.references | Ruiz Posse, E. J., Bocco, M., & Bisonard, E. M. (2012). Enfermedades del maíz producidas por virus y mollicutes en Argentina | Instituto Nacional de Tecnología Agropecuaria. INTA, 179–190. Retrieved from https://inta.gob.ar/noticias/enfermedades-del-maiz-producidas-por-virus-y-mollicutes-en-argentina | spa |
dc.relation.references | Schultz, M., Clevers, J. G. P. W., Clevers, J. G. P. W., Carter, S., Verbesselt, J., Avitabile, V., … Herold, M. (2016). Performance of vegetation indices from Landsat time series in deforestation monitoring Copernicus Global Land Service: Dynamic Land Cover View project Finite element modelling of trees based on terrestrial LiDAR scanning data View project Performance of v. International Journal of Applied Earth Observations and Geoinformation, 52, 318–327. https://doi.org/10.1016/j.jag.2016.06.020 | spa |
dc.relation.references | Strachan, I. B., Pattey, E., & Boisvert, J. B. (2002). Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance. Remote Sensing of Environment, 80(2), 213–224. https://doi.org/10.1016/S0034-4257(01)00299-1 | spa |
dc.relation.references | T.Ahamed, L.Tian, Y.Zhang, K. C. T. (2011). A review of remote sensing methods for biomass feedstock production. Biomass and Bioenergy, 35(7), 2455–2469. https://doi.org/10.1016/J.BIOMBIOE.2011.02.028 | spa |
dc.relation.references | Verbesselt, J. (2017). Change detection and monitoring - WUR. Retrieved from https://www.wur.nl/en/Research-Results/Chair-groups/Environmental-Sciences/Laboratory-of-Geo-information-Science-and-Remote-Sensing/Research/Integrated-land- | spa |
dc.relation.references | Verified Carbon Standard. (2018). Methodology Assessment Report: Baseline and Monitoring Methodology for Avoiding Planned Deforestation of Undrained Peat Swamp Forests Methodology Element Title Baseline and Monitoring Methodology for Avoiding Planned Deforestation of Undrained Peat Swamp . Retrieved from www.scscertified.com | spa |
dc.relation.references | Willington, E., Nolasco, M., & Bocco, M. (2013). Clasificación supervisada de suelos de uso agrícola en la zona central de Córdoba (Argentina): comparación de distintos algoritmos sobre imágenes Landsat. Congreso Argentino de AgroInformatica. Retrieved from http://42jaiio.sadio.org.ar/proceedings/simposios/Trabajos/CAI/17.pdf | spa |
dc.relation.references | Zhang, Y., Liu, Q. Y., Luan, R. S., Liu, X. B., Zhou, G. C., Jiang, J. Y., … Li, Z. F. (2012). Spatial-temporal analysis of malaria and the effect of environmental factors on its incidence in Yongcheng, China, 2006-2010. BMC Public Health, 12(1), 1. https://doi.org/10.1186/1471-2458-12-544 | spa |
dc.relation.references | Zhu, Z., Woodcock, C. E., Holden, C., & Yang, Z. (2015). Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time. Remote Sensing of Environment, 162, 67–83. https://doi.org/10.1016/j.rse.2015.02.009 | spa |
dc.relation.references | Aguayo, P. M. (2013). Apuntes de Teledetección: Índices de vegetación. | spa |
dc.relation.references | Al-Khaier, F. (2003). Soil Salinity Detection Using Satellite Remote Sensing. | spa |
dc.relation.references | BBC. (2019). Incendios en el Amazonas: el número récord de fuegos que afectan a Brasil y también arrasan otros países de Sudamérica - BBC News Mundo. Retrieved September 18, 2019, from https://www.bbc.com/mundo/noticias-america-latina49426794 | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.rights.license | Atribución-NoComercial-SinDerivadas 4.0 Internacional | spa |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | spa |
dc.subject.agrovoc | Imágenes por satélites | spa |
dc.subject.agrovoc | satellite imagery | eng |
dc.subject.agrovoc | Deforestación | spa |
dc.subject.agrovoc | deforestation | eng |
dc.subject.agrovoc | Análisis espectral | spa |
dc.subject.agrovoc | spectral analysis | eng |
dc.subject.ddc | 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería | spa |
dc.subject.proposal | Deforestación | spa |
dc.subject.proposal | Deforestation | eng |
dc.subject.proposal | Series de tiempo | spa |
dc.subject.proposal | Time series | eng |
dc.subject.proposal | Landtrendr | spa |
dc.subject.proposal | Landtrendr | eng |
dc.subject.proposal | Spectrum-time index | eng |
dc.subject.proposal | Índice espectro-temporal | spa |
dc.subject.proposal | GEOBIA | eng |
dc.subject.proposal | GEOBIA | spa |
dc.title | Generación de un índice espectro-temporal para la identificación de zonas afectadas por deforestación usando imágenes Landsat | spa |
dc.title.alternative | Generation of a spectro-temporal index for the identification of areas identified by deforestation using Landsat images | eng |
dc.type | Trabajo de grado - Maestría | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_bdcc | spa |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/masterThesis | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/TM | spa |
dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
dcterms.audience.professionaldevelopment | Estudiantes | spa |
dcterms.audience.professionaldevelopment | Investigadores | spa |
dcterms.audience.professionaldevelopment | Público general | spa |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |
Archivos
Bloque original
1 - 1 de 1
Cargando...
- Nombre:
- tesis 090320.pdf
- Tamaño:
- 7.75 MB
- Formato:
- Adobe Portable Document Format
- Descripción:
- Tesis de Maestría en Geomática
Bloque de licencias
1 - 1 de 1
No hay miniatura disponible
- Nombre:
- license.txt
- Tamaño:
- 3.9 KB
- Formato:
- Item-specific license agreed upon to submission
- Descripción: