Un nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-Nariño
ilustraciones, mapas, tablas
- Autores:
-
Lozano Arias, Laura
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/86135
- Palabra clave:
- 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Secuestro de carbono
Carbon sequestration
Estimación de las existencias de carbono
Carbon stock assessments
Mangles
Control remoto
Remote control
Biomasa
Manglar
Teledetección
Reservas de carbono
Aprendizaje automático
Biomass
Carbon stocks
Machine learning
Mangrove
Remote sensing
Worldview-2
Tumaco
Nariño
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
| id |
UNACIONAL2_53ea791b4a7b30940d64502882727e68 |
|---|---|
| oai_identifier_str |
oai:repositorio.unal.edu.co:unal/86135 |
| network_acronym_str |
UNACIONAL2 |
| network_name_str |
Universidad Nacional de Colombia |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Un nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-Nariño |
| dc.title.translated.eng.fl_str_mv |
A new approach for estimating living biomass and stored carbon in mangrove forests using remote sensing and machine learning: Tumaco-Nariño case study. |
| title |
Un nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-Nariño |
| spellingShingle |
Un nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-Nariño 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Secuestro de carbono Carbon sequestration Estimación de las existencias de carbono Carbon stock assessments Mangles Control remoto Remote control Biomasa Manglar Teledetección Reservas de carbono Aprendizaje automático Biomass Carbon stocks Machine learning Mangrove Remote sensing Worldview-2 Tumaco Nariño |
| title_short |
Un nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-Nariño |
| title_full |
Un nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-Nariño |
| title_fullStr |
Un nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-Nariño |
| title_full_unstemmed |
Un nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-Nariño |
| title_sort |
Un nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-Nariño |
| dc.creator.fl_str_mv |
Lozano Arias, Laura |
| dc.contributor.advisor.none.fl_str_mv |
Selvaraj, John Josephraj Gallego Pérez, Bryan Ernesto |
| dc.contributor.author.none.fl_str_mv |
Lozano Arias, Laura |
| dc.contributor.researchgroup.spa.fl_str_mv |
Recursos Hidrobiológicos |
| dc.contributor.orcid.spa.fl_str_mv |
Lozano Arias, Laura [0009-0008-7538-0067] |
| dc.contributor.cvlac.spa.fl_str_mv |
Lozano Arias, Laura [1144200812] |
| dc.contributor.researchgate.spa.fl_str_mv |
Lozano Arias, Laura [https://www.researchgate.net/profile/Laura-Lozano-23] |
| dc.subject.ddc.spa.fl_str_mv |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería |
| topic |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Secuestro de carbono Carbon sequestration Estimación de las existencias de carbono Carbon stock assessments Mangles Control remoto Remote control Biomasa Manglar Teledetección Reservas de carbono Aprendizaje automático Biomass Carbon stocks Machine learning Mangrove Remote sensing Worldview-2 Tumaco Nariño |
| dc.subject.agrovoc.none.fl_str_mv |
Secuestro de carbono Carbon sequestration Estimación de las existencias de carbono Carbon stock assessments Mangles Control remoto Remote control |
| dc.subject.proposal.spa.fl_str_mv |
Biomasa Manglar Teledetección Reservas de carbono Aprendizaje automático |
| dc.subject.proposal.eng.fl_str_mv |
Biomass Carbon stocks Machine learning Mangrove Remote sensing Worldview-2 |
| dc.subject.unesco.none.fl_str_mv |
Tumaco Nariño |
| description |
ilustraciones, mapas, tablas |
| publishDate |
2023 |
| dc.date.issued.none.fl_str_mv |
2023-12-01 |
| dc.date.accessioned.none.fl_str_mv |
2024-05-22T18:29:24Z |
| dc.date.available.none.fl_str_mv |
2024-05-22T18:29:24Z |
| 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/86135 |
| 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/86135 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.references.spa.fl_str_mv |
Ahmad, A., Gilani, H., & Ahmad, S. R. (2021). Forest aboveground biomass estimation and mapping through high-resolution optical satellite imagery—a literature review. Forests, 12(7), 914. https://doi.org/10.3390/F12070914/S1 Alboukadel, K. (2023). RDocumentation. Factoextra. fviz_contrib (1.0.7). https://www.rdocumentation.org/packages/factoextra/versions/1.0.7/topics/fviz_contrib Aldrich, R. C., Bailey, W. F., & Heller, R. C. (1959). Large Scale 70 mm. Color Photography Techniques and Equipment and Their Application to a Forest Sampling Problem. Photogrammetric Engineering, 25, 747–754. FAO. (2007). The world’s mangroves 1980-2005. In FAO Forestry Paper (Vol. 153). FAO, Yigini, Y., Olmedo, G. F., Reiter, S., Baritz, R., Viatkin, K., & Vargas, R. R. (2018). Soil Organic Carbon Mapping Cookbook. 2nd Edition, Rome. https://fao-gsp.github.io/SOC-Mapping-Cookbook/mappingMethods.html#rf Congalton, R. G., & Green, K. (2008). Assessing the accuracy of remotely sensed data: Principles and practices, second edition. In Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition. Anand, A., Pandey, P. C., Petropoulos, G. P., Pavlides, A., Srivastava, P. K., Sharma, J. K., & Malhi, R. K. M. (2020). Use of hyperion for mangrove forest carbon stock assessment in bhitarkanika forest reserve: A contribution towards blue carbon initiative. Remote Sensing, 12(4). https://doi.org/10.3390/rs12040597 Escobar, H. A. T. (2010). Documento síntesis: Caracterización, Diagnóstico y Zonificación de los Manglares en el Departamento de Nariño. Forkuor, G., Benewinde Zoungrana, J. B., Dimobe, K., Ouattara, B., Vadrevu, K. P., & Tondoh, J. E. (2020). Above-ground biomass mapping in West African dryland forest using Sentinel-1 and 2 datasets - A case study. Remote Sensing of Environment, 236(November 2019), 111496. https://doi.org/10.1016/j.rse.2019.111496 Gao, B.-C. (1996). NDWI - A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water From Space. REMOTE SENS. ENVIRON, 7212, 257–266. GAFMEX. (2023). Clinómetro Electrónico Ecii D Haglöf. https://gafmex.com/clinometro-electronico-ecii-d-haglof/ Gao, Y., Yu, G., Yang, T., Jia, Y., He, N., & Zhuang, J. (2016). New insight into global blue carbon estimation under human activity in land-sea interaction area: A case study of China. Earth-Science Reviews, 159, 36–46. https://doi.org/10.1016/J.EARSCIREV.2016.05.003 Donato, D. C., Kauffman, J. B., Murdiyarso, D., Kurnianto, S., Stidham, M., & Kanninen, M. (2011). Mangroves among the most carbon-rich forests in the tropics. Nature Geoscience, 4(5), 293–297. https://doi.org/10.1038/ngeo1123 Dimar. (2020). Compilación Oceanográfica de la Cuenca Pacífica Colombiana II (Vol. 9). Editorial Dimar. Digital Globe. (2010). DigitalGlobe Core Imagery Products Guide. 8–11. https://www.satimagingcorp.com/satellite-sensors/worldview-3 Darmawan, S., Takeuchi, W., Vetrita, Y., Wikantika, K., & Sari, D. K. (2015). Impact of topography and tidal height on ALOS palsar polarimetric measurements to estimate aboveground biomass of mangrove forest in Indonesia. Journal of Sensors, 2015. https://doi.org/10.1155/2015/641798 Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics Surveys, 4, 40–79. https://doi.org/10.1214/09-SS054 Awad, M., & Khanna, R. (2015). Efficient Learning Machines. Theories, Concepts, and Applications for Engineers and System Designers. Apress, Berkeley, CA. Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007). Random Forest for classification in ecology. Ecology, 88(11), 2783–2792. https://doi.org/10.1890/07-0539.1 Basith, A., & Prastyani, R. (2020). Evaluating ACOMP , FLAASH AND QUAC on WORLDVIEW-3 for Satellite Derived Bathymetry (SDB) in shallow water. 46(3), 151–158. Cantera, J. R., Thomassin, B. A., & Arnaud, P. M. (1999). Faunal zonation and assemblages in the Pacific Colombian mangroves. In Hydrobiologia (Vol. 413). Castellanos-Galindo, G. A., Kluger, L. C., Camargo, M. A., Cantera, J., Mancera Pineda, J. E., Blanco-Libreros, J. F., & Wolff, M. (2021). Mangrove research in Colombia: Temporal trends, geographical coverage and research gaps. Estuarine, Coastal and Shelf Science, 248(October 2019). https://doi.org/10.1016/j.ecss.2020.106799 Chave, J., Andalo, C., Brown, S., Cairns, M. A., Chambers, J. Q., Eamus, D., Fölster, H., Fromard, F., Higuchi, N., Kira, T., Lescure, J. P., Nelson, B. W., Ogawa, H., Puig, H., Riéra, B., & Yamakura, T. (2005). Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 145(1), 87–99. https://doi.org/10.1007/S00442-005-0100-X Bunting, P., Rosenqvist, A., Hilarides, L., Lucas, R. M., Thomas, N., Tadono, T., Worthington, T. A., Spalding, M., Murray, N. J., & Rebelo, L. M. (2022). Global Mangrove Extent Change 1996–2020: Global Mangrove Watch Version 3.0. Remote Sensing, 14(15). https://doi.org/10.3390/rs14153657 Bunting, P. (2020). Class Accuracy (V1.0.0). https://github.com/remotesensinginfo/classaccuracy/releases/tag/qgis3_v1.0.0 Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324/METRICS Bolivar, J. M., Gutierrez-Velez, V. H., & Sierra, C. A. (2018). Carbon stocks in aboveground biomass for Colombian mangroves with associated uncertainties. Regional Studies in Marine Science, 18, 145–155. https://doi.org/10.1016/j.rsma.2017.12.011 Bolar, K. (2019). STAT V.0.1.0 (0.1.0). https://cran.r-project.org/web/packages/STAT/index.html Bishop, C. (2008). Pattern Recognition and Machine Learning. Springer Verlag. http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop - Pattern Recognition And Machine Learning - Springer 2006.pdf Bindu, G., Rajan, P., Jishnu, E. S., & Ajith Joseph, K. (2020). Carbon stock assessment of mangroves using remote sensing and geographic information system. Egyptian Journal of Remote Sensing and Space Science, 23(1), 1–9. https://doi.org/10.1016/j.ejrs.2018.04.006 Beeston, M., Cuyvers, L., & Vermilye, J. (2020). Blue Carbon: Mind the Gap. October, 16. https://gallifrey.foundation/wp-content/uploads/2020/10/Blue-Carbon-Mind-the-Gap-V2.2.pdf Zhu, Y., Liu, K., Liu, L., Wang, S., & Liu, H. (2015). Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images. Remote Sensing 2015, Vol. 7, Pages 12192-12214, 7(9), 12192–12214. https://doi.org/10.3390/RS70912192 Zhu, Y., Liu, K., Liu, L., Myint, S. W., Wang, S., Liu, H., & He, Z. (2017). Exploring the potential of world view-2 red-edge band-based vegetation indices for estimation of mangrove leaf area index with machine learning algorithms. Remote Sensing, 9(10). https://doi.org/10.3390/rs9101060 Zhu, Y., Liu, K., Liu, L., Myint, S. W., Wang, S., Cao, J., & Wu, Z. (2020). Estimating and Mapping Mangrove Biomass Dynamic Change Using WorldView-2 Images and Digital Surface Models. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 2123–2134. https://doi.org/10.1109/JSTARS.2020.2989500 Zhang, M., Lin, H., Zeng, S., Li, J., Shi, J., & Wang, G. (2013). Impacts of plot location errors on accuracy of mapping and scaling up aboveground forest carbon using sample plot and landsat tm data. IEEE Geoscience and Remote Sensing Letters, 10(6), 1483–1487. https://doi.org/10.1109/LGRS.2013.2260719 Zhu, J. J., & Yan, B. (2022). Blue carbon sink function and carbon neutrality potential of mangroves. Science of the Total Environment, 822, 153438. https://doi.org/10.1016/j.scitotenv.2022.153438 Wicaksono, P., Danoedoro, P., Hartono, H., Nehren, U., & Ribbe, L. (2011). Preliminary work of mangrove ecosystem carbon stock mapping in small island using remote sensing: above and below ground carbon stock mapping on medium resolution satellite image. Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII, 8174, 81741B. https://doi.org/10.1117/12.897926 Toosi, N. B., Soffianian, A. R., Fakheran, S., Pourmanafi, S., Ginzler, C., & Waser, L. T. (2019). Comparing different classification algorithms for monitoring mangrove cover changes in southern Iran. Global Ecology and Conservation, 19, e00662. https://doi.org/10.1016/J.GECCO.2019.E00662 Wicaksono, P., Danoedoro, P., Hartono, & Nehren, U. (2016). Mangrove biomass carbon stock mapping of the Karimunjawa Islands using multispectral remote sensing. International Journal of Remote Sensing, 37(1), 26–52. https://doi.org/10.1080/01431161.2015.1117679 Yepes, A., Duque, Á., Navarrete, D., & Philips, J. (2011). Protocolo para la estimación nacional y subnacional de biomasa de biomasa-carbono en Colombia. Instituto de Hidrología, Meteorología y Estudios Ambientales-IDEAM. https://www.researchgate.net/publication/269107473_What_is_governance/link/548173090cf22525dcb61443/download%0Ahttp://www.econ.upf.edu/~reynal/Civil wars_12December2010.pdf%0Ahttps://think-asia.org/handle/11540/8282%0Ahttps://www.jstor.org/stable/41857625 Yepes, A., Zapata, M., Bolivar, J., Monsalve, A., Espinosa, S. M., Sierra-Correa, P. C., & Sierra, A. (2016). Ecuaciones alométricas de biomasa aérea para la estimación de los contenidos de carbono en manglares del Caribe Colombiano. In Rev. Biol. Trop. (Int. J. Trop. Biol. ISSN (Vol. 64, Issue 2). Zanne, A. E., Lopez-Gonzalez, G., Coomes, D. A., Ilic, J., Jansen, S., Lewis, S. L., Miller, R. B., Swenson, N. G., Wiemann, M. C., & Chave, J. (2009). Global wood density database. Dryad. http://hdl.handle.net/10255/dryad.235 Wang, L., Jia, M., Yin, D., & Tian, J. (2019). A review of remote sensing for mangrove forests: 1956–2018. Remote Sensing of Environment, 231(May). https://doi.org/10.1016/j.rse.2019.111223 Nellemann, C., Corcoran, E., Duarte, C. M., Valdés, L., De Young, C., Fonseca, L., & Grimsditch, G. (2009). Blue carbon: A Rapid Response Assessment. In Environment. http://www.grida.no/files/publications/blue-carbon/BlueCarbon_screen.pdf Naciones Unidas, & Asamblea General. (2015). Transformar nuestro mundo: la Agenda 2030 para el Desarrollo Sostenible. Nguyen, H. H., Nghia, N. H., Nguyen, H. T. T., Le, A. T., Tran, L. T. N., Duong, L. V. K., Bohm, S., & Furniss, M. J. (2020). Classification Methods for Mapping Mangrove Extents and Drivers of Change in Thanh Hoa Province, Vietnam during 2005-2018. Forest and Society, 4(1), 225–242. https://doi.org/10.24259/FS.V4I1.9295 Pham, T. D., Yokoya, N., Bui, D. T., Yoshino, K., & Friess, D. A. (2019). Remote sensing approaches for monitoring mangrove species, structure, and biomass: Opportunities and challenges. Remote Sensing, 11(3), 1–24. https://doi.org/10.3390/rs11030230 Nesha, M. K., Hussin, Y. A., van Leeuwen, L. M., & Sulistioadi, Y. B. (2020). Modeling and mapping aboveground biomass of the restored mangroves using ALOS-2 PALSAR-2 in East Kalimantan, Indonesia. International Journal of Applied Earth Observation and Geoinformation, 91, 102158. https://doi.org/10.1016/J.JAG.2020.102158 Mutanga, O., Adam, E., & Cho, M. A. (2012). High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. International Journal of Applied Earth Observation and Geoinformation, 18, 399–406. https://doi.org/10.1016/j.jag.2012.03.012 Navarrete-Ramírez, S. M., & Rodríguez-Rincón, A. M. (2014). Protocolo IndicadorCondición Tendencia Bosques de Manglar. Indicadores de monitoreo biológico del Subsistema de Áreas Marinas Protegidas (SAMP): Vol. No. 67 (Invemar, GEF, & PNUD (eds.)). Serie de Publicaciones Generales del Invemar. INVEMAR, UNIVALLE, & INCIVA. (2006). Valoración de la biodiversidad marina y costera de Bahía Málaga (Valle del Cauca), como uno de los instrumentos necesarios para que sea considerada un área protegida. http://www.invemar.org.co/redcostera1/invemar/docs/9860IF_BIOMALAGA2007.pdf Gross, J., Flores, E. E., & Schwendenmann, L. (2014). Stand structure and aboveground biomass of a Pelliciera rhizophorae mangrove forest, gulf of Monitjo Ramsar Site, Pacific Coast, Panama. Wetlands, 34(1), 55–65. https://doi.org/10.1007/s13157-013-0482-1 Gupta, K., Mukhopadhyay, A., Giri, S., Chanda, A., Datta Majumdar, S., Samanta, S., Mitra, D., Samal, R. N., Pattnaik, A. K., & Hazra, S. (2018). An index for discrimination of mangroves from non-mangroves using LANDSAT 8 OLI imagery. MethodsX, 5, 1129–1139. https://doi.org/10.1016/j.mex.2018.09.011 Google, & Maxar Technologies. (2021). Maps Data. https://earth.google.com/web/ Giri, C., Ochieng, E., Tieszen, L. L., Zhu, Z., Singh, A., Loveland, T., Masek, J., & Duke, N. (2011). Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20(1), 154–159. https://doi.org/10.1111/j.1466-8238.2010.00584.x Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. https://doi.org/10.1007/978-0-387-84858-7 Gonzalez, R. C., & Woods, R. E. (2008). Digital Image Processing Third Edition Library of Congress Cataloging-in-Publication Data on File. Hickey, S. M., Callow, N. J., Phinn, S., Lovelock, C. E., & Duarte, C. M. (2018). Spatial complexities in aboveground carbon stocks of a semi-arid mangrove community: A remote sensing height-biomass-carbon approach. Estuarine, Coastal and Shelf Science, 200, 194–201. https://doi.org/10.1016/j.ecss.2017.11.004 Howard, J., Hoyt, S., Isensee, K., Pidgeon, E., & Telszewski, M. (2014). Coastal Blue Carbon: Methods for Assessing Carbon Stocks and Emissions Factors in Mangroves, Tidal Salt Marshes, and Seagrass Meadows. Conservation International, Intergovernmental Oceanographic Commission of UNESCO, International Union for Conservation of Nature. Arlington, Virginia, USA., 1–180. www.ioc.unesco.org Hu, T., Zhang, Y. Y., Su, Y., Zheng, Y., Lin, G., & Guo, Q. (2020). Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data. Remote Sensing 2020, Vol. 12, Page 1690, 12(10), 1690. https://doi.org/10.3390/RS12101690 Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. https://doi.org/10.1016/0034-4257(88)90106-X Hutchison, J., Manica, A., Swetnam, R., Balmford, A., & Spalding, M. (2014). Predicting global patterns in mangrove forest biomass. Conservation Letters, 7(3), 233–240. https://doi.org/10.1111/conl.12060 Ideam. (2010). Leyenda nacional de coberturas de la tierra. Metodología CORINE Land Cover adaptada para Colombia, escala 1:100.000. In Area: Vol. TH-62-04-1 (Issue 257). Thuy, H. L. T., Tan, M. T., Van, T. T. T., Bien, L. B., Ha, N. M., & Nhung, N. T. (2020). Using sentinel image data and plot survey for the assessment of biomass and carbon stock in coastal forests of Thai Binh Province, Vietnam. Applied Ecology and Environmental Research, 18(6), 7499–7514. https://doi.org/10.15666/aeer/1806_74997514 Twilley, R. R., Chen, R. H., & Hargis, T. (1992). Carbon sinks in mangroves and their aplications to carbon budget of tropical coastal ecosystems. Water, Air, and Soil Pollution 64:, 64, 265–288. https://doi.org/10.1021/es983796z Vu, T. D., Takeuchi, W., & Van, N. A. (2014). Carbon Stock Calculating and Forest Change Assessment Toward REDD+ Activities for The Mangrove Forest in Vietnam. Transactions of the Japan Society for Aeronautical and Space Sciences, Aerospace Technology Japan, 12(ists29), Pn_23-Pn_31. https://doi.org/10.2322/tastj.12.pn_23 Wang, G., Zhang, M., Gertner, G. Z., Oyana, T., McRoberts, R. E., & Ge, H. (2011). Uncertainties of mapping aboveground forest carbon due to plot locations using national forest inventory plot and remotely sensed data. Scandinavian Journal of Forest Research, 26(4), 360–373. https://doi.org/10.1080/02827581.2011.564204 Takeuchi, W., Darmawan, S., Nakazono, E., Vetrita, Y., Winarso, G., Dien, V. T., Oo, K. S., Wikantika, K., Sari, D. K., Darmawan, S., Nakazono, E., Vetrita, Y., Winarso, G., Dien, V. T., Oo, K. S., Wikantika, K., & Sari, D. K. (2016). Carbon stock calculation and forest change assessment toward REDD+ activities for the mangrove forests in Southeast Asia. Institue of Industrial Science The University of Tokyo, Japan, 2. http://www.asienreisender.de/mangroveforest.html Kogut, P. (2021). Teledetección: Tipos Y Aplicaciones De Los Sensores Remotos. https://eos.com/es/blog/teledeteccion/ Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C., & Hunt, T. (2023). Caret V.6.0-94 (6.0-94). https://cran.r-project.org/web/packages/caret/index.html Lu, D. (2006). The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing, 27(7), 1297–1328. https://doi.org/10.1080/01431160500486732 Khorram, S., Nelson, S. A. C., Koch, F. H., & van der Wiele, C. F. (2012). Remote Sensing. https://doi.org/10.1007/978-1-4614-3103-9 Kershaw, J. A., Ducey, M. J., Beers, T. W., & Husch, B. (2016). Sampling Designs in Forest Inventories. In Forest Mensuration (pp. 305–360). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118902028.ch10 Kauffman, J. B., & Donato, D. C. (2012). Protocols for the measurement, monitoring and reporting of structure, biomass and carbon stocks in mangrove forests. Working Paper 86. CIFOR, Bogor, Indonesia. Working Paper 86, CIFOR, January, 50. Kauffman, J. B., & Cole, T. G. (2010). Micronesian mangrove forest structure and tree responses to a severe typhoon. Wetlands, 30(6), 1077–1084. https://doi.org/10.1007/S13157-010-0114-Y/METRICS Jiang, Z., Huete, A. R., Didan, K., & Miura, T. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112(10), 3833–3845. https://doi.org/10.1016/J.RSE.2008.06.006 Jiang, Y., Zhang, L., Yan, M., Qi, J., Fu, T., Fan, S., & Chen, B. (2021). High-Resolution Mangrove Forests Classification with Machine Learning Using Worldview and UAV Hyperspectral Data. Remote Sensing 2021, Vol. 13, Page 1529, 13(8), 1529. https://doi.org/10.3390/RS13081529 Jachowski, N. R. A., Quak, M. S. Y., Friess, D. A., Duangnamon, D., Webb, E. L., & Ziegler, A. D. (2013). Mangrove biomass estimation in Southwest Thailand using machine learning. Applied Geography, 45, 311–321. https://doi.org/10.1016/J.APGEOG.2013.09.024 Lu, D., Chen, Q., Wang, G., Liu, L., Li, G., & Moran, E. (2016). A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. In International Journal of Digital Earth (Vol. 9, Issue 1, pp. 63–105). https://doi.org/10.1080/17538947.2014.990526 Luo, Y. M., Huang, D. T., Liu, P. Z., & Feng, H. M. (2016). An novel random forests and its application to the classification of mangroves remote sensing image. Multimedia Tools and Applications, 75(16), 9707–9722. https://doi.org/10.1007/s11042-015-2906-9 MAXAR Technologies. (2020, July). Multispectral reference guide. https://resources.maxar.com/white-papers/multispectral-reference-guide McCarthy, M. J., & Halls, J. N. (2014). Habitat mapping and change assessment of coastal environments: An examination of worldview-2, quickbird, and ikonos satellite imagery and airborne lidar for mapping barrier island habitats. ISPRS International Journal of Geo-Information, 3(1), 297–325. https://doi.org/10.3390/ijgi3010297 McLeod, E., Chmura, G. L., Bouillon, S., Salm, R., Björk, M., Duarte, C. M., Lovelock, C. E., Schlesinger, W. H., & Silliman, B. R. (2011). A blueprint for blue carbon: Toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Frontiers in Ecology and the Environment, 9(10), 552–560. https://doi.org/10.1890/110004 Mejía-Rentería, J. C., Castellanos-Galindo, G. A., Cantera-Kintz, J. R., & Hamilton, S. E. (2018). A comparison of Colombian Pacific mangrove extent estimations: Implications for the conservation of a unique Neotropical tidal forest. Estuarine, Coastal and Shelf Science, 212(July), 233–240. https://doi.org/10.1016/j.ecss.2018.07.020 Microsoft Bing, & Maxar Technologies. (2022). Bing Maps. https://www.bing.com/maps?cc=es Millennium Ecosystem Assessment. (2005). Ecosystems and Human Well-being : Current State and Trends, Volume 1 (R. Hassan, R. Scholes, & N. Ash (eds.)). Island Press. http://www.millenniumassessment.org//en/products.global.condition.aspx Monsalve, A., & Ramírez, G. (2015). Caracterización de la estructura y contenido de carbono de los bosques de manglar en el área de jurisdicción del Consejo comunitario La Plata, Bahía Málaga, Valle del Cauca (Vol. 57, Issue 4). Mukhtar, E., Raynaldo, A., & Novarino, W. (2021). Carbon stock mapping using mangrove discrimination indices in mandeh bay, west sumatra. AACL Bioflux, 14(1), 430–440. Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. In Remote Sensing of Environment (Vol. 148, pp. 42–57). Elsevier Inc. https://doi.org/10.1016/j.rse.2014.02.015 Palacios- penaranda, M. L. (2017). Evaluación del almacenamiento de carbono como servicio ecosistémico en bosques de manglar de la costa pacífica colombiana. [UNIVERSIDAD DEL VALLE]. https://bibliotecadigital.univalle.edu.co/handle/10893/14485 Rahman, M. M., Lagomasino, D., Lee, S. K., Fatoyinbo, T., Ahmed, I., & Kanzaki, M. (2019). Improved assessment of mangrove forests in Sundarbans East Wildlife Sanctuary using WorldView 2 and TanDEM-X high resolution imagery. Remote Sensing in Ecology and Conservation, 5(2), 136–149. https://doi.org/10.1002/rse2.105 Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95–107. https://doi.org/10.1016/0034-4257(95)00186-7 Rovai, A. S., Twilley, R. R., Castañeda-Moya, E., Midway, S. R., Friess, D. A., Trettin, C. C., Bukoski, J. J., Stovall, A. E. L., Pagliosa, P. R., Fonseca, A. L., Mackenzie, R. A., Aslan, A., Sasmito, S. D., Sillanpää, M., Cole, T. G., Purbopuspito, J., Warren, M. W., Murdiyarso, D., Mofu, W., … Riul, P. (2021). Macroecological patterns of forest structure and allometric scaling in mangrove forests. Global Ecology and Biogeography, 30(5), 1000–1013. https://doi.org/10.1111/GEB.13268 Rwanga, S. S., Ndambuki, J. M., Rwanga, S. S., & Ndambuki, J. M. (2017). Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS. International Journal of Geosciences, 8(4), 611–622. https://doi.org/10.4236/IJG.2017.84033 Saeys, Y., Inza, I., & Larrañaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics (Oxford, England), 23(19), 2507–2517. https://doi.org/10.1093/BIOINFORMATICS/BTM344 Quang, N. H., Quinn, C. H., Carrie, R., Stringer, L. C., Hue, L. T. Van, Hackney, C. R., & Tan, D. Van. (2022). Comparisons of regression and machine learning methods for estimating mangrove above-ground biomass using multiple remote sensing data in the red River Estuaries of Vietnam. Remote Sensing Applications: Society and Environment, 26(November 2021), 100725. https://doi.org/10.1016/j.rsase.2022.100725 Qiu, P., Wang, D., Zou, X., Yang, X., Xie, G., Xu, S., & Zhong, Z. (2019). Finer Resolution Estimation and Mapping of Mangrove Biomass Using UAV LiDAR and WorldView-2 Data. Forest, 10(817). https://doi.org/10.3390/f10100871 QGIS. (2023, February 25). QGIS 3.28. https://www.qgis.org/en/site/ Pham, T. D., Xia, J., Thang Ha, N., Tien Bui, D., Nhu Le, N., & Tekeuchi, W. (2019). A review of remote sensing approaches for monitoring blue carbon ecosystems: Mangroves, sea grasses and salt marshes during 2010–2018. Sensors (Switzerland), 19(8). https://doi.org/10.3390/s19081933 Pham, T. D., Le, N. N., Ha, N. T., Nguyen, L. V., Xia, J., Yokoya, N., To, T. T., Trinh, H. X., Kieu, L. Q., & Takeuchi, W. (2020). Estimating mangrove above-ground biomass using extreme gradient boosting decision trees algorithm with fused sentinel-2 and ALOS-2 PALSAR-2 data in can Gio biosphere reserve, Vietnam. In Remote Sensing (Vol. 12, Issue 5). https://doi.org/10.3390/rs12050777 Perumal, K., & Bhaskaran, R. (2010). Supervised Classification Performance of Multispectral Images. Journal of Computing, 2(2), 124–129. http://arxiv.org/abs/1002.4046 Perea Ardila, M. A., & Murillo Sandoval, P. J. (2022). La ganancia de manglar y sus implicaciones en el reservorio de Carbono del Parque Nacional Natural Sanquianga en Colombia. Ecosistemas, 31(3), 2386. https://doi.org/10.7818/ecos.2386 Perea Ardila, M. A., Leal Villamil, J., & Oviedo Barrero, F. (2021). Caracterización espectral y monitoreo de bosque de manglar con teledetección en el litoral Pacífico Colombiano: Bajo Baudó, Chocó. LA GRANJA: REVISTA DE CIENCIAS DE LA VIDA, 34(C), 27–44. https://doi.org/http://doi.org/10.17163/lgr.n34.2021.02 Edición Perea-Ardila, M. A., Oviedo-Barrero, F., & Leal-Villamil, J. (2019). Cartografía de bosques de manglar mediante imágenes de sensores remotos: estudio de caso Buenaventura, Colombia. Revista de Teledetección, 53, 73. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., & Thirion, B. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830. http://scikit-learn.sourceforge.net. Palacios-Peñaranda, M. L., Cantera-Kintz, J. R., & Peña-Salamanca, E. J. (2019). Carbon stocks in mangrove forests of the Colombian Pacific. Estuarine, Coastal and Shelf Science, 227(April 2018), 106299. https://doi.org/10.1016/j.ecss.2019.106299 Saldarriaga, J. G., Duque, A. J., & Álvarez, E. (2011). Modelos para la estimación de la biomasa y el carbono en diferentes tipos de bosque del choco biogeográfico, Colombia: manglar, guandal y bosques de colina localizados en los Consejos Comunitarios de Bajo Mira y Concosta a partir de los datos colectados e. Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM), Ministerio de Medio Ambiente, Vivienda y Desarrollo Territorial (MAVDT), Fundación Natura, Fundación Gordon y Betty Moore – USAID. Santos, T., & Freire, S. (2015). Testing the Contribution of WorldView-2 Improved Spectral Resolution for Extracting Vegetation Cover in Urban Environments. Canadian Journal of Remote Sensing, 41(6), 505–514. https://doi.org/10.1080/07038992.2015.1110011 SciPy Community. (2024). SciPy documentation. SciPy v1.12.0 Manual. https://docs.scipy.org/doc/scipy/index.html Selvaraj, J. J., & Gallego-Pérez, B. E. (2023). Estimating mangrove aboveground biomass in the Colombian Pacific coast: A multisensor and machine learning approach. Heliyon, 9, 2405–8440. https://doi.org/10.1016/j.heliyon.2023.e20745 Simard, M., Fatoyinbo, L., Smetanka, C., Rivera-Monroy, V. H., Castañeda-Moya, E., Thomas, N., & Van der Stocken, T. (2019). Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nature Geoscience, 12(1), 40–45. https://doi.org/10.1038/s41561-018-0279-1 Sinha, S., Jeganathan, C., Sharma, L. K., & Nathawat, M. S. (2015). A review of radar remote sensing for biomass estimation. International Journal of Environmental Science and Technology 2015 12:5, 12(5), 1779–1792. https://doi.org/10.1007/S13762-015-0750-0 Simard, M., Rivera-Monroy, V. H., Mancera-Pineda, J. E., Castañeda-Moya, E., & Twilley, R. R. (2008). A systematic method for 3D mapping of mangrove forests based on Shuttle Radar Topography Mission elevation data, ICEsat/GLAS waveforms and field data: Application to Ciénaga Grande de Santa Marta, Colombia. Remote Sensing of Environment, 112(5), 2131–2144. https://doi.org/10.1016/j.rse.2007.10.012 Spalding, M. D., & Leal, M. (2021). The State of the World’s Mangroves 2021. Global Mangrove Alliance. |
| 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 |
xiv, 82 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.coverage.temporal.none.fl_str_mv |
http://vocab.getty.edu/page/tgn/1024046 |
| dc.coverage.region.none.fl_str_mv |
Tumaco, Nariño, Colombia |
| dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.publisher.program.spa.fl_str_mv |
Palmira - Ingeniería y Administración - Maestría en Ingeniería - Ingeniería Ambiental |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad de Ingeniería y Administración |
| dc.publisher.place.spa.fl_str_mv |
Palmira, Valle del Cauca, Colombia |
| dc.publisher.branch.spa.fl_str_mv |
Universidad Nacional de Colombia - Sede Palmira |
| institution |
Universidad Nacional de Colombia |
| bitstream.url.fl_str_mv |
https://repositorio.unal.edu.co/bitstream/unal/86135/1/license.txt https://repositorio.unal.edu.co/bitstream/unal/86135/2/1144200812.2024.pdf https://repositorio.unal.edu.co/bitstream/unal/86135/3/1144200812.2024.pdf.jpg |
| bitstream.checksum.fl_str_mv |
eb34b1cf90b7e1103fc9dfd26be24b4a 07639ee190f34706854fff07e9a3c39f 354dd9e8f8933982f7330e40600fc4a1 |
| 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_ |
1814089232390553600 |
| 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_abf2Selvaraj, John Josephraj539c2f732e0e2883867d8ca4e8fd6141Gallego Pérez, Bryan Ernesto7ab1c2e2868563c0c1e812576c47092fLozano Arias, Laura545fd70b73e45864c53a3749702309fb600Recursos HidrobiológicosLozano Arias, Laura [0009-0008-7538-0067]Lozano Arias, Laura [1144200812]Lozano Arias, Laura [https://www.researchgate.net/profile/Laura-Lozano-23]http://vocab.getty.edu/page/tgn/1024046Tumaco, Nariño, Colombia2024-05-22T18:29:24Z2024-05-22T18:29:24Z2023-12-01https://repositorio.unal.edu.co/handle/unal/86135Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, mapas, tablasLos manglares desempeñan un papel crucial en la mitigación del cambio climático al absorber y retener hasta cinco veces más carbono que otros bosques. Es importante determinar la biomasa viva y el carbono almacenado en estos ecosistemas para proporcionar una base sólida en la planificación y gestión gubernamental. Este estudio presenta un enfoque innovador ya que utiliza herramientas de teledetección junto con datos recolectados en campo, imágenes Worldview 2 y evalúa dos algoritmos de aprendizaje automático, Random Forest y Support Vector Regression. El caso de estudio en el manglar de Tumaco, Nariño, incluyó el cálculo de la superficie del bosque por medio de una clasificación supervisada, la estimación de la biomasa viva (aboveground y belowground) y el carbono almacenado, y la evaluación de los modelos. Los resultados revelaron una precisión global del 87% en la clasificación de coberturas, con valores promedio de 192.50 ± 102.78 para la biomasa aérea, 79.95 ± 56.85 para la biomasa subterránea y 127.43 ± 73.49 para el carbono almacenado. El modelo basado en Random Forest destacó con un rendimiento sobresaliente, mostrando un RMSE de 140.68 ± 98.76 y un R2 de 0.78 ± 0.28, superando a modelos globales. Adicionalmente, se evidenció que los índices espectrales fortalecen la capacidad del modelo para explicar y predecir la biomasa aérea. Se sugiere explorar el uso de imágenes Lidar y datos SAR para mejorar la precisión en estudios locales con mayor resolución espacial. (Texto tomado de la fuente)Mangroves play a crucial role in climate change mitigation by absorbing and sequestering up to five times more carbon than other forests. It is important to determine the living biomass and carbon stored in these ecosystems to provide a sound basis for government planning and management. This study presents an innovative approach using remote sensing tools together with field collected data, using Worldview 2 imagery and evaluating two machine learning algorithms, Random Forest and Support Vector Regression. The case study in the mangrove forest of Tumaco, Nariño, included the calculation of forest area by supervised classification, estimation of live biomass (aboveground and belowground) and carbon stock, and evaluation of the models. The results revealed an overall accuracy of 87% in cover classification, with average values of 192.50 ± 102.78 for aboveground biomass, 79.95 ± 56.85 for belowground biomass and 127.43 ± 73.49 for carbon stock. The Random Forest based model stood out with an outstanding performance, showing an RMSE of 140.68 ± 98.76 and an R2 of 0.78 ± 0.28, outperforming global models. Additionally, it was evidenced that the spectral indices strengthen the model's ability to explain and predict aerial biomass. It is suggested to explore the use of Lidar images and SAR data to improve accuracy in local studies with higher spatial resolution.MaestríaMagíster en Ingeniería - Ingeniería AmbientalEs importante determinar la biomasa viva y el carbono almacenado en estos ecosistemas para proporcionar una base sólida en la planificación y gestión gubernamental. Este estudio presenta un enfoque innovador ya que utiliza herramientas de teledetección junto con datos recolectados en campo, imágenes Worldview 2 y evalúa dos algoritmos de aprendizaje automático, Random Forest y Support Vector Regression. El caso de estudio en el manglar de Tumaco, Nariño, incluyó el cálculo de la superficie del bosque por medio de una clasificación supervisada, la estimación de la biomasa viva (aboveground y belowground) y el carbono almacenado, y la evaluación de los modelos.Monitoreo, modelación y gestión de recursos naturalesIngeniería.Sede Palmiraxiv, 82 páginasapplication/pdfspaUniversidad Nacional de ColombiaPalmira - Ingeniería y Administración - Maestría en Ingeniería - Ingeniería AmbientalFacultad de Ingeniería y AdministraciónPalmira, Valle del Cauca, ColombiaUniversidad Nacional de Colombia - Sede Palmira620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaSecuestro de carbonoCarbon sequestrationEstimación de las existencias de carbonoCarbon stock assessmentsManglesControl remotoRemote controlBiomasaManglarTeledetecciónReservas de carbonoAprendizaje automáticoBiomassCarbon stocksMachine learningMangroveRemote sensingWorldview-2TumacoNariñoUn nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-NariñoA new approach for estimating living biomass and stored carbon in mangrove forests using remote sensing and machine learning: Tumaco-Nariño case study.Trabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAhmad, A., Gilani, H., & Ahmad, S. R. (2021). Forest aboveground biomass estimation and mapping through high-resolution optical satellite imagery—a literature review. Forests, 12(7), 914. https://doi.org/10.3390/F12070914/S1Alboukadel, K. (2023). RDocumentation. Factoextra. fviz_contrib (1.0.7). https://www.rdocumentation.org/packages/factoextra/versions/1.0.7/topics/fviz_contribAldrich, R. C., Bailey, W. F., & Heller, R. C. (1959). Large Scale 70 mm. Color Photography Techniques and Equipment and Their Application to a Forest Sampling Problem. Photogrammetric Engineering, 25, 747–754.FAO. (2007). The world’s mangroves 1980-2005. In FAO Forestry Paper (Vol. 153).FAO, Yigini, Y., Olmedo, G. F., Reiter, S., Baritz, R., Viatkin, K., & Vargas, R. R. (2018). Soil Organic Carbon Mapping Cookbook. 2nd Edition, Rome. https://fao-gsp.github.io/SOC-Mapping-Cookbook/mappingMethods.html#rfCongalton, R. G., & Green, K. (2008). Assessing the accuracy of remotely sensed data: Principles and practices, second edition. In Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition.Anand, A., Pandey, P. C., Petropoulos, G. P., Pavlides, A., Srivastava, P. K., Sharma, J. K., & Malhi, R. K. M. (2020). Use of hyperion for mangrove forest carbon stock assessment in bhitarkanika forest reserve: A contribution towards blue carbon initiative. Remote Sensing, 12(4). https://doi.org/10.3390/rs12040597Escobar, H. A. T. (2010). Documento síntesis: Caracterización, Diagnóstico y Zonificación de los Manglares en el Departamento de Nariño.Forkuor, G., Benewinde Zoungrana, J. B., Dimobe, K., Ouattara, B., Vadrevu, K. P., & Tondoh, J. E. (2020). Above-ground biomass mapping in West African dryland forest using Sentinel-1 and 2 datasets - A case study. Remote Sensing of Environment, 236(November 2019), 111496. https://doi.org/10.1016/j.rse.2019.111496Gao, B.-C. (1996). NDWI - A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water From Space. REMOTE SENS. ENVIRON, 7212, 257–266.GAFMEX. (2023). Clinómetro Electrónico Ecii D Haglöf. https://gafmex.com/clinometro-electronico-ecii-d-haglof/Gao, Y., Yu, G., Yang, T., Jia, Y., He, N., & Zhuang, J. (2016). New insight into global blue carbon estimation under human activity in land-sea interaction area: A case study of China. Earth-Science Reviews, 159, 36–46. https://doi.org/10.1016/J.EARSCIREV.2016.05.003Donato, D. C., Kauffman, J. B., Murdiyarso, D., Kurnianto, S., Stidham, M., & Kanninen, M. (2011). Mangroves among the most carbon-rich forests in the tropics. Nature Geoscience, 4(5), 293–297. https://doi.org/10.1038/ngeo1123Dimar. (2020). Compilación Oceanográfica de la Cuenca Pacífica Colombiana II (Vol. 9). Editorial Dimar.Digital Globe. (2010). DigitalGlobe Core Imagery Products Guide. 8–11. https://www.satimagingcorp.com/satellite-sensors/worldview-3Darmawan, S., Takeuchi, W., Vetrita, Y., Wikantika, K., & Sari, D. K. (2015). Impact of topography and tidal height on ALOS palsar polarimetric measurements to estimate aboveground biomass of mangrove forest in Indonesia. Journal of Sensors, 2015. https://doi.org/10.1155/2015/641798Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics Surveys, 4, 40–79. https://doi.org/10.1214/09-SS054Awad, M., & Khanna, R. (2015). Efficient Learning Machines. Theories, Concepts, and Applications for Engineers and System Designers. Apress, Berkeley, CA.Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007). Random Forest for classification in ecology. Ecology, 88(11), 2783–2792. https://doi.org/10.1890/07-0539.1Basith, A., & Prastyani, R. (2020). Evaluating ACOMP , FLAASH AND QUAC on WORLDVIEW-3 for Satellite Derived Bathymetry (SDB) in shallow water. 46(3), 151–158.Cantera, J. R., Thomassin, B. A., & Arnaud, P. M. (1999). Faunal zonation and assemblages in the Pacific Colombian mangroves. In Hydrobiologia (Vol. 413).Castellanos-Galindo, G. A., Kluger, L. C., Camargo, M. A., Cantera, J., Mancera Pineda, J. E., Blanco-Libreros, J. F., & Wolff, M. (2021). Mangrove research in Colombia: Temporal trends, geographical coverage and research gaps. Estuarine, Coastal and Shelf Science, 248(October 2019). https://doi.org/10.1016/j.ecss.2020.106799Chave, J., Andalo, C., Brown, S., Cairns, M. A., Chambers, J. Q., Eamus, D., Fölster, H., Fromard, F., Higuchi, N., Kira, T., Lescure, J. P., Nelson, B. W., Ogawa, H., Puig, H., Riéra, B., & Yamakura, T. (2005). Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 145(1), 87–99. https://doi.org/10.1007/S00442-005-0100-XBunting, P., Rosenqvist, A., Hilarides, L., Lucas, R. M., Thomas, N., Tadono, T., Worthington, T. A., Spalding, M., Murray, N. J., & Rebelo, L. M. (2022). Global Mangrove Extent Change 1996–2020: Global Mangrove Watch Version 3.0. Remote Sensing, 14(15). https://doi.org/10.3390/rs14153657Bunting, P. (2020). Class Accuracy (V1.0.0). https://github.com/remotesensinginfo/classaccuracy/releases/tag/qgis3_v1.0.0Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324/METRICSBolivar, J. M., Gutierrez-Velez, V. H., & Sierra, C. A. (2018). Carbon stocks in aboveground biomass for Colombian mangroves with associated uncertainties. Regional Studies in Marine Science, 18, 145–155. https://doi.org/10.1016/j.rsma.2017.12.011Bolar, K. (2019). STAT V.0.1.0 (0.1.0). https://cran.r-project.org/web/packages/STAT/index.htmlBishop, C. (2008). Pattern Recognition and Machine Learning. Springer Verlag. http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop - Pattern Recognition And Machine Learning - Springer 2006.pdfBindu, G., Rajan, P., Jishnu, E. S., & Ajith Joseph, K. (2020). Carbon stock assessment of mangroves using remote sensing and geographic information system. Egyptian Journal of Remote Sensing and Space Science, 23(1), 1–9. https://doi.org/10.1016/j.ejrs.2018.04.006Beeston, M., Cuyvers, L., & Vermilye, J. (2020). Blue Carbon: Mind the Gap. October, 16. https://gallifrey.foundation/wp-content/uploads/2020/10/Blue-Carbon-Mind-the-Gap-V2.2.pdfZhu, Y., Liu, K., Liu, L., Wang, S., & Liu, H. (2015). Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images. Remote Sensing 2015, Vol. 7, Pages 12192-12214, 7(9), 12192–12214. https://doi.org/10.3390/RS70912192Zhu, Y., Liu, K., Liu, L., Myint, S. W., Wang, S., Liu, H., & He, Z. (2017). Exploring the potential of world view-2 red-edge band-based vegetation indices for estimation of mangrove leaf area index with machine learning algorithms. Remote Sensing, 9(10). https://doi.org/10.3390/rs9101060Zhu, Y., Liu, K., Liu, L., Myint, S. W., Wang, S., Cao, J., & Wu, Z. (2020). Estimating and Mapping Mangrove Biomass Dynamic Change Using WorldView-2 Images and Digital Surface Models. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 2123–2134. https://doi.org/10.1109/JSTARS.2020.2989500Zhang, M., Lin, H., Zeng, S., Li, J., Shi, J., & Wang, G. (2013). Impacts of plot location errors on accuracy of mapping and scaling up aboveground forest carbon using sample plot and landsat tm data. IEEE Geoscience and Remote Sensing Letters, 10(6), 1483–1487. https://doi.org/10.1109/LGRS.2013.2260719Zhu, J. J., & Yan, B. (2022). Blue carbon sink function and carbon neutrality potential of mangroves. Science of the Total Environment, 822, 153438. https://doi.org/10.1016/j.scitotenv.2022.153438Wicaksono, P., Danoedoro, P., Hartono, H., Nehren, U., & Ribbe, L. (2011). Preliminary work of mangrove ecosystem carbon stock mapping in small island using remote sensing: above and below ground carbon stock mapping on medium resolution satellite image. Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII, 8174, 81741B. https://doi.org/10.1117/12.897926Toosi, N. B., Soffianian, A. R., Fakheran, S., Pourmanafi, S., Ginzler, C., & Waser, L. T. (2019). Comparing different classification algorithms for monitoring mangrove cover changes in southern Iran. Global Ecology and Conservation, 19, e00662. https://doi.org/10.1016/J.GECCO.2019.E00662Wicaksono, P., Danoedoro, P., Hartono, & Nehren, U. (2016). Mangrove biomass carbon stock mapping of the Karimunjawa Islands using multispectral remote sensing. International Journal of Remote Sensing, 37(1), 26–52. https://doi.org/10.1080/01431161.2015.1117679Yepes, A., Duque, Á., Navarrete, D., & Philips, J. (2011). Protocolo para la estimación nacional y subnacional de biomasa de biomasa-carbono en Colombia. Instituto de Hidrología, Meteorología y Estudios Ambientales-IDEAM. https://www.researchgate.net/publication/269107473_What_is_governance/link/548173090cf22525dcb61443/download%0Ahttp://www.econ.upf.edu/~reynal/Civil wars_12December2010.pdf%0Ahttps://think-asia.org/handle/11540/8282%0Ahttps://www.jstor.org/stable/41857625Yepes, A., Zapata, M., Bolivar, J., Monsalve, A., Espinosa, S. M., Sierra-Correa, P. C., & Sierra, A. (2016). Ecuaciones alométricas de biomasa aérea para la estimación de los contenidos de carbono en manglares del Caribe Colombiano. In Rev. Biol. Trop. (Int. J. Trop. Biol. ISSN (Vol. 64, Issue 2).Zanne, A. E., Lopez-Gonzalez, G., Coomes, D. A., Ilic, J., Jansen, S., Lewis, S. L., Miller, R. B., Swenson, N. G., Wiemann, M. C., & Chave, J. (2009). Global wood density database. Dryad. http://hdl.handle.net/10255/dryad.235Wang, L., Jia, M., Yin, D., & Tian, J. (2019). A review of remote sensing for mangrove forests: 1956–2018. Remote Sensing of Environment, 231(May). https://doi.org/10.1016/j.rse.2019.111223Nellemann, C., Corcoran, E., Duarte, C. M., Valdés, L., De Young, C., Fonseca, L., & Grimsditch, G. (2009). Blue carbon: A Rapid Response Assessment. In Environment. http://www.grida.no/files/publications/blue-carbon/BlueCarbon_screen.pdfNaciones Unidas, & Asamblea General. (2015). Transformar nuestro mundo: la Agenda 2030 para el Desarrollo Sostenible.Nguyen, H. H., Nghia, N. H., Nguyen, H. T. T., Le, A. T., Tran, L. T. N., Duong, L. V. K., Bohm, S., & Furniss, M. J. (2020). Classification Methods for Mapping Mangrove Extents and Drivers of Change in Thanh Hoa Province, Vietnam during 2005-2018. Forest and Society, 4(1), 225–242. https://doi.org/10.24259/FS.V4I1.9295Pham, T. D., Yokoya, N., Bui, D. T., Yoshino, K., & Friess, D. A. (2019). Remote sensing approaches for monitoring mangrove species, structure, and biomass: Opportunities and challenges. Remote Sensing, 11(3), 1–24. https://doi.org/10.3390/rs11030230Nesha, M. K., Hussin, Y. A., van Leeuwen, L. M., & Sulistioadi, Y. B. (2020). Modeling and mapping aboveground biomass of the restored mangroves using ALOS-2 PALSAR-2 in East Kalimantan, Indonesia. International Journal of Applied Earth Observation and Geoinformation, 91, 102158. https://doi.org/10.1016/J.JAG.2020.102158Mutanga, O., Adam, E., & Cho, M. A. (2012). High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. International Journal of Applied Earth Observation and Geoinformation, 18, 399–406. https://doi.org/10.1016/j.jag.2012.03.012Navarrete-Ramírez, S. M., & Rodríguez-Rincón, A. M. (2014). Protocolo IndicadorCondición Tendencia Bosques de Manglar. Indicadores de monitoreo biológico del Subsistema de Áreas Marinas Protegidas (SAMP): Vol. No. 67 (Invemar, GEF, & PNUD (eds.)). Serie de Publicaciones Generales del Invemar.INVEMAR, UNIVALLE, & INCIVA. (2006). Valoración de la biodiversidad marina y costera de Bahía Málaga (Valle del Cauca), como uno de los instrumentos necesarios para que sea considerada un área protegida. http://www.invemar.org.co/redcostera1/invemar/docs/9860IF_BIOMALAGA2007.pdfGross, J., Flores, E. E., & Schwendenmann, L. (2014). Stand structure and aboveground biomass of a Pelliciera rhizophorae mangrove forest, gulf of Monitjo Ramsar Site, Pacific Coast, Panama. Wetlands, 34(1), 55–65. https://doi.org/10.1007/s13157-013-0482-1Gupta, K., Mukhopadhyay, A., Giri, S., Chanda, A., Datta Majumdar, S., Samanta, S., Mitra, D., Samal, R. N., Pattnaik, A. K., & Hazra, S. (2018). An index for discrimination of mangroves from non-mangroves using LANDSAT 8 OLI imagery. MethodsX, 5, 1129–1139. https://doi.org/10.1016/j.mex.2018.09.011Google, & Maxar Technologies. (2021). Maps Data. https://earth.google.com/web/Giri, C., Ochieng, E., Tieszen, L. L., Zhu, Z., Singh, A., Loveland, T., Masek, J., & Duke, N. (2011). Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20(1), 154–159. https://doi.org/10.1111/j.1466-8238.2010.00584.xHastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. https://doi.org/10.1007/978-0-387-84858-7Gonzalez, R. C., & Woods, R. E. (2008). Digital Image Processing Third Edition Library of Congress Cataloging-in-Publication Data on File.Hickey, S. M., Callow, N. J., Phinn, S., Lovelock, C. E., & Duarte, C. M. (2018). Spatial complexities in aboveground carbon stocks of a semi-arid mangrove community: A remote sensing height-biomass-carbon approach. Estuarine, Coastal and Shelf Science, 200, 194–201. https://doi.org/10.1016/j.ecss.2017.11.004Howard, J., Hoyt, S., Isensee, K., Pidgeon, E., & Telszewski, M. (2014). Coastal Blue Carbon: Methods for Assessing Carbon Stocks and Emissions Factors in Mangroves, Tidal Salt Marshes, and Seagrass Meadows. Conservation International, Intergovernmental Oceanographic Commission of UNESCO, International Union for Conservation of Nature. Arlington, Virginia, USA., 1–180. www.ioc.unesco.orgHu, T., Zhang, Y. Y., Su, Y., Zheng, Y., Lin, G., & Guo, Q. (2020). Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data. Remote Sensing 2020, Vol. 12, Page 1690, 12(10), 1690. https://doi.org/10.3390/RS12101690Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. https://doi.org/10.1016/0034-4257(88)90106-XHutchison, J., Manica, A., Swetnam, R., Balmford, A., & Spalding, M. (2014). Predicting global patterns in mangrove forest biomass. Conservation Letters, 7(3), 233–240. https://doi.org/10.1111/conl.12060Ideam. (2010). Leyenda nacional de coberturas de la tierra. Metodología CORINE Land Cover adaptada para Colombia, escala 1:100.000. In Area: Vol. TH-62-04-1 (Issue 257).Thuy, H. L. T., Tan, M. T., Van, T. T. T., Bien, L. B., Ha, N. M., & Nhung, N. T. (2020). Using sentinel image data and plot survey for the assessment of biomass and carbon stock in coastal forests of Thai Binh Province, Vietnam. Applied Ecology and Environmental Research, 18(6), 7499–7514. https://doi.org/10.15666/aeer/1806_74997514Twilley, R. R., Chen, R. H., & Hargis, T. (1992). Carbon sinks in mangroves and their aplications to carbon budget of tropical coastal ecosystems. Water, Air, and Soil Pollution 64:, 64, 265–288. https://doi.org/10.1021/es983796zVu, T. D., Takeuchi, W., & Van, N. A. (2014). Carbon Stock Calculating and Forest Change Assessment Toward REDD+ Activities for The Mangrove Forest in Vietnam. Transactions of the Japan Society for Aeronautical and Space Sciences, Aerospace Technology Japan, 12(ists29), Pn_23-Pn_31. https://doi.org/10.2322/tastj.12.pn_23Wang, G., Zhang, M., Gertner, G. Z., Oyana, T., McRoberts, R. E., & Ge, H. (2011). Uncertainties of mapping aboveground forest carbon due to plot locations using national forest inventory plot and remotely sensed data. Scandinavian Journal of Forest Research, 26(4), 360–373. https://doi.org/10.1080/02827581.2011.564204Takeuchi, W., Darmawan, S., Nakazono, E., Vetrita, Y., Winarso, G., Dien, V. T., Oo, K. S., Wikantika, K., Sari, D. K., Darmawan, S., Nakazono, E., Vetrita, Y., Winarso, G., Dien, V. T., Oo, K. S., Wikantika, K., & Sari, D. K. (2016). Carbon stock calculation and forest change assessment toward REDD+ activities for the mangrove forests in Southeast Asia. Institue of Industrial Science The University of Tokyo, Japan, 2. http://www.asienreisender.de/mangroveforest.htmlKogut, P. (2021). Teledetección: Tipos Y Aplicaciones De Los Sensores Remotos. https://eos.com/es/blog/teledeteccion/Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C., & Hunt, T. (2023). Caret V.6.0-94 (6.0-94). https://cran.r-project.org/web/packages/caret/index.htmlLu, D. (2006). The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing, 27(7), 1297–1328. https://doi.org/10.1080/01431160500486732Khorram, S., Nelson, S. A. C., Koch, F. H., & van der Wiele, C. F. (2012). Remote Sensing. https://doi.org/10.1007/978-1-4614-3103-9Kershaw, J. A., Ducey, M. J., Beers, T. W., & Husch, B. (2016). Sampling Designs in Forest Inventories. In Forest Mensuration (pp. 305–360). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118902028.ch10Kauffman, J. B., & Donato, D. C. (2012). Protocols for the measurement, monitoring and reporting of structure, biomass and carbon stocks in mangrove forests. Working Paper 86. CIFOR, Bogor, Indonesia. Working Paper 86, CIFOR, January, 50.Kauffman, J. B., & Cole, T. G. (2010). Micronesian mangrove forest structure and tree responses to a severe typhoon. Wetlands, 30(6), 1077–1084. https://doi.org/10.1007/S13157-010-0114-Y/METRICSJiang, Z., Huete, A. R., Didan, K., & Miura, T. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112(10), 3833–3845. https://doi.org/10.1016/J.RSE.2008.06.006Jiang, Y., Zhang, L., Yan, M., Qi, J., Fu, T., Fan, S., & Chen, B. (2021). High-Resolution Mangrove Forests Classification with Machine Learning Using Worldview and UAV Hyperspectral Data. Remote Sensing 2021, Vol. 13, Page 1529, 13(8), 1529. https://doi.org/10.3390/RS13081529Jachowski, N. R. A., Quak, M. S. Y., Friess, D. A., Duangnamon, D., Webb, E. L., & Ziegler, A. D. (2013). Mangrove biomass estimation in Southwest Thailand using machine learning. Applied Geography, 45, 311–321. https://doi.org/10.1016/J.APGEOG.2013.09.024Lu, D., Chen, Q., Wang, G., Liu, L., Li, G., & Moran, E. (2016). A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. In International Journal of Digital Earth (Vol. 9, Issue 1, pp. 63–105). https://doi.org/10.1080/17538947.2014.990526Luo, Y. M., Huang, D. T., Liu, P. Z., & Feng, H. M. (2016). An novel random forests and its application to the classification of mangroves remote sensing image. Multimedia Tools and Applications, 75(16), 9707–9722. https://doi.org/10.1007/s11042-015-2906-9MAXAR Technologies. (2020, July). Multispectral reference guide. https://resources.maxar.com/white-papers/multispectral-reference-guideMcCarthy, M. J., & Halls, J. N. (2014). Habitat mapping and change assessment of coastal environments: An examination of worldview-2, quickbird, and ikonos satellite imagery and airborne lidar for mapping barrier island habitats. ISPRS International Journal of Geo-Information, 3(1), 297–325. https://doi.org/10.3390/ijgi3010297McLeod, E., Chmura, G. L., Bouillon, S., Salm, R., Björk, M., Duarte, C. M., Lovelock, C. E., Schlesinger, W. H., & Silliman, B. R. (2011). A blueprint for blue carbon: Toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Frontiers in Ecology and the Environment, 9(10), 552–560. https://doi.org/10.1890/110004Mejía-Rentería, J. C., Castellanos-Galindo, G. A., Cantera-Kintz, J. R., & Hamilton, S. E. (2018). A comparison of Colombian Pacific mangrove extent estimations: Implications for the conservation of a unique Neotropical tidal forest. Estuarine, Coastal and Shelf Science, 212(July), 233–240. https://doi.org/10.1016/j.ecss.2018.07.020Microsoft Bing, & Maxar Technologies. (2022). Bing Maps. https://www.bing.com/maps?cc=es Millennium Ecosystem Assessment. (2005). Ecosystems and Human Well-being : Current State and Trends, Volume 1 (R. Hassan, R. Scholes, & N. Ash (eds.)). Island Press. http://www.millenniumassessment.org//en/products.global.condition.aspxMonsalve, A., & Ramírez, G. (2015). Caracterización de la estructura y contenido de carbono de los bosques de manglar en el área de jurisdicción del Consejo comunitario La Plata, Bahía Málaga, Valle del Cauca (Vol. 57, Issue 4).Mukhtar, E., Raynaldo, A., & Novarino, W. (2021). Carbon stock mapping using mangrove discrimination indices in mandeh bay, west sumatra. AACL Bioflux, 14(1), 430–440.Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. In Remote Sensing of Environment (Vol. 148, pp. 42–57). Elsevier Inc. https://doi.org/10.1016/j.rse.2014.02.015Palacios- penaranda, M. L. (2017). Evaluación del almacenamiento de carbono como servicio ecosistémico en bosques de manglar de la costa pacífica colombiana. [UNIVERSIDAD DEL VALLE]. https://bibliotecadigital.univalle.edu.co/handle/10893/14485Rahman, M. M., Lagomasino, D., Lee, S. K., Fatoyinbo, T., Ahmed, I., & Kanzaki, M. (2019). Improved assessment of mangrove forests in Sundarbans East Wildlife Sanctuary using WorldView 2 and TanDEM-X high resolution imagery. Remote Sensing in Ecology and Conservation, 5(2), 136–149. https://doi.org/10.1002/rse2.105Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95–107. https://doi.org/10.1016/0034-4257(95)00186-7Rovai, A. S., Twilley, R. R., Castañeda-Moya, E., Midway, S. R., Friess, D. A., Trettin, C. C., Bukoski, J. J., Stovall, A. E. L., Pagliosa, P. R., Fonseca, A. L., Mackenzie, R. A., Aslan, A., Sasmito, S. D., Sillanpää, M., Cole, T. G., Purbopuspito, J., Warren, M. W., Murdiyarso, D., Mofu, W., … Riul, P. (2021). Macroecological patterns of forest structure and allometric scaling in mangrove forests. Global Ecology and Biogeography, 30(5), 1000–1013. https://doi.org/10.1111/GEB.13268Rwanga, S. S., Ndambuki, J. M., Rwanga, S. S., & Ndambuki, J. M. (2017). Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS. International Journal of Geosciences, 8(4), 611–622. https://doi.org/10.4236/IJG.2017.84033Saeys, Y., Inza, I., & Larrañaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics (Oxford, England), 23(19), 2507–2517. https://doi.org/10.1093/BIOINFORMATICS/BTM344Quang, N. H., Quinn, C. H., Carrie, R., Stringer, L. C., Hue, L. T. Van, Hackney, C. R., & Tan, D. Van. (2022). Comparisons of regression and machine learning methods for estimating mangrove above-ground biomass using multiple remote sensing data in the red River Estuaries of Vietnam. Remote Sensing Applications: Society and Environment, 26(November 2021), 100725. https://doi.org/10.1016/j.rsase.2022.100725Qiu, P., Wang, D., Zou, X., Yang, X., Xie, G., Xu, S., & Zhong, Z. (2019). Finer Resolution Estimation and Mapping of Mangrove Biomass Using UAV LiDAR and WorldView-2 Data. Forest, 10(817). https://doi.org/10.3390/f10100871QGIS. (2023, February 25). QGIS 3.28. https://www.qgis.org/en/site/Pham, T. D., Xia, J., Thang Ha, N., Tien Bui, D., Nhu Le, N., & Tekeuchi, W. (2019). A review of remote sensing approaches for monitoring blue carbon ecosystems: Mangroves, sea grasses and salt marshes during 2010–2018. Sensors (Switzerland), 19(8). https://doi.org/10.3390/s19081933Pham, T. D., Le, N. N., Ha, N. T., Nguyen, L. V., Xia, J., Yokoya, N., To, T. T., Trinh, H. X., Kieu, L. Q., & Takeuchi, W. (2020). Estimating mangrove above-ground biomass using extreme gradient boosting decision trees algorithm with fused sentinel-2 and ALOS-2 PALSAR-2 data in can Gio biosphere reserve, Vietnam. In Remote Sensing (Vol. 12, Issue 5). https://doi.org/10.3390/rs12050777Perumal, K., & Bhaskaran, R. (2010). Supervised Classification Performance of Multispectral Images. Journal of Computing, 2(2), 124–129. http://arxiv.org/abs/1002.4046Perea Ardila, M. A., & Murillo Sandoval, P. J. (2022). La ganancia de manglar y sus implicaciones en el reservorio de Carbono del Parque Nacional Natural Sanquianga en Colombia. Ecosistemas, 31(3), 2386. https://doi.org/10.7818/ecos.2386Perea Ardila, M. A., Leal Villamil, J., & Oviedo Barrero, F. (2021). Caracterización espectral y monitoreo de bosque de manglar con teledetección en el litoral Pacífico Colombiano: Bajo Baudó, Chocó. LA GRANJA: REVISTA DE CIENCIAS DE LA VIDA, 34(C), 27–44. https://doi.org/http://doi.org/10.17163/lgr.n34.2021.02 EdiciónPerea-Ardila, M. A., Oviedo-Barrero, F., & Leal-Villamil, J. (2019). Cartografía de bosques de manglar mediante imágenes de sensores remotos: estudio de caso Buenaventura, Colombia. Revista de Teledetección, 53, 73.Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., & Thirion, B. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830. http://scikit-learn.sourceforge.net.Palacios-Peñaranda, M. L., Cantera-Kintz, J. R., & Peña-Salamanca, E. J. (2019). Carbon stocks in mangrove forests of the Colombian Pacific. Estuarine, Coastal and Shelf Science, 227(April 2018), 106299. https://doi.org/10.1016/j.ecss.2019.106299Saldarriaga, J. G., Duque, A. J., & Álvarez, E. (2011). Modelos para la estimación de la biomasa y el carbono en diferentes tipos de bosque del choco biogeográfico, Colombia: manglar, guandal y bosques de colina localizados en los Consejos Comunitarios de Bajo Mira y Concosta a partir de los datos colectados e. Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM), Ministerio de Medio Ambiente, Vivienda y Desarrollo Territorial (MAVDT), Fundación Natura, Fundación Gordon y Betty Moore – USAID.Santos, T., & Freire, S. (2015). Testing the Contribution of WorldView-2 Improved Spectral Resolution for Extracting Vegetation Cover in Urban Environments. Canadian Journal of Remote Sensing, 41(6), 505–514. https://doi.org/10.1080/07038992.2015.1110011SciPy Community. (2024). SciPy documentation. SciPy v1.12.0 Manual. https://docs.scipy.org/doc/scipy/index.htmlSelvaraj, J. J., & Gallego-Pérez, B. E. (2023). Estimating mangrove aboveground biomass in the Colombian Pacific coast: A multisensor and machine learning approach. Heliyon, 9, 2405–8440. https://doi.org/10.1016/j.heliyon.2023.e20745Simard, M., Fatoyinbo, L., Smetanka, C., Rivera-Monroy, V. H., Castañeda-Moya, E., Thomas, N., & Van der Stocken, T. (2019). Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nature Geoscience, 12(1), 40–45. https://doi.org/10.1038/s41561-018-0279-1Sinha, S., Jeganathan, C., Sharma, L. K., & Nathawat, M. S. (2015). A review of radar remote sensing for biomass estimation. International Journal of Environmental Science and Technology 2015 12:5, 12(5), 1779–1792. https://doi.org/10.1007/S13762-015-0750-0Simard, M., Rivera-Monroy, V. H., Mancera-Pineda, J. E., Castañeda-Moya, E., & Twilley, R. R. (2008). A systematic method for 3D mapping of mangrove forests based on Shuttle Radar Topography Mission elevation data, ICEsat/GLAS waveforms and field data: Application to Ciénaga Grande de Santa Marta, Colombia. Remote Sensing of Environment, 112(5), 2131–2144. https://doi.org/10.1016/j.rse.2007.10.012Spalding, M. D., & Leal, M. (2021). The State of the World’s Mangroves 2021. Global Mangrove Alliance.Investigación de servicios ecosistémicos derivados de bosques de manglar en el Pacífico Colombiano: Valle del Cauca, Cauca, Nariño, ChocóSistema General de RegalíasEstudiantesGrupos comunitariosInvestigadoresMaestrosMedios de comunicaciónPadres y familiasPersonal de apoyo escolarPúblico generalResponsables políticosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86135/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1144200812.2024.pdf1144200812.2024.pdfapplication/pdf2952513https://repositorio.unal.edu.co/bitstream/unal/86135/2/1144200812.2024.pdf07639ee190f34706854fff07e9a3c39fMD52THUMBNAIL1144200812.2024.pdf.jpg1144200812.2024.pdf.jpgGenerated Thumbnailimage/jpeg5678https://repositorio.unal.edu.co/bitstream/unal/86135/3/1144200812.2024.pdf.jpg354dd9e8f8933982f7330e40600fc4a1MD53unal/86135oai:repositorio.unal.edu.co:unal/861352024-08-24 23:14:20.012Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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 |