Coca crop classification and mapping using spectral, temporal and spatial features from satellite imagery for the Catatumbo region in Colombia - 2019

ilustraciones a color, diagramas, mapas

Autores:: Albarracín Barrera, Camilo Andrés

Tipo de recurso:

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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dc.title.eng.fl_str_mv	Coca crop classification and mapping using spectral, temporal and spatial features from satellite imagery for the Catatumbo region in Colombia - 2019
dc.title.translated.spa.fl_str_mv	Clasificación y mapeo de cultivos de coca utilizando características espectrales, temporales y espaciales a partir de imágenes satelitales para la región del Catatumbo en Colombia - 2019
title	Coca crop classification and mapping using spectral, temporal and spatial features from satellite imagery for the Catatumbo region in Colombia - 2019
spellingShingle	Coca crop classification and mapping using spectral, temporal and spatial features from satellite imagery for the Catatumbo region in Colombia - 2019 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Vigilancia de cultivos Crop monitoring Aerofotografía en control de drogas y narcóticos Photography, aerial in drug and narcotic control Cultivos ilícitos - Mediciones Fotografía multiespectral - Métodos estadísticos Multispectral photogrphy - Statistical methods clasificación de cultivos coca datos multiespectrales espacio-temporal política de drogas XGBoost Planet crop classification multispectral data spatial-temporal drug policy Google Earth Engine
title_short	Coca crop classification and mapping using spectral, temporal and spatial features from satellite imagery for the Catatumbo region in Colombia - 2019
title_full	Coca crop classification and mapping using spectral, temporal and spatial features from satellite imagery for the Catatumbo region in Colombia - 2019
title_fullStr	Coca crop classification and mapping using spectral, temporal and spatial features from satellite imagery for the Catatumbo region in Colombia - 2019
title_full_unstemmed	Coca crop classification and mapping using spectral, temporal and spatial features from satellite imagery for the Catatumbo region in Colombia - 2019
title_sort	Coca crop classification and mapping using spectral, temporal and spatial features from satellite imagery for the Catatumbo region in Colombia - 2019
dc.creator.fl_str_mv	Albarracín Barrera, Camilo Andrés
dc.contributor.advisor.none.fl_str_mv	Bohórquez Castañeda, Martha Patricia
dc.contributor.author.none.fl_str_mv	Albarracín Barrera, Camilo Andrés
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Vigilancia de cultivos Crop monitoring Aerofotografía en control de drogas y narcóticos Photography, aerial in drug and narcotic control Cultivos ilícitos - Mediciones Fotografía multiespectral - Métodos estadísticos Multispectral photogrphy - Statistical methods clasificación de cultivos coca datos multiespectrales espacio-temporal política de drogas XGBoost Planet crop classification multispectral data spatial-temporal drug policy Google Earth Engine
dc.subject.agrovoc.none.fl_str_mv	Vigilancia de cultivos Crop monitoring
dc.subject.lemb.none.fl_str_mv	Aerofotografía en control de drogas y narcóticos Photography, aerial in drug and narcotic control Cultivos ilícitos - Mediciones Fotografía multiespectral - Métodos estadísticos Multispectral photogrphy - Statistical methods
dc.subject.proposal.spa.fl_str_mv	clasificación de cultivos coca datos multiespectrales espacio-temporal política de drogas
dc.subject.proposal.eng.fl_str_mv	XGBoost Planet crop classification multispectral data spatial-temporal drug policy
dc.subject.proposal.none.fl_str_mv	Google Earth Engine
description	ilustraciones a color, diagramas, mapas
publishDate	2023
dc.date.issued.none.fl_str_mv	2023-12-14
dc.date.accessioned.none.fl_str_mv	2024-02-12T21:56:13Z
dc.date.available.none.fl_str_mv	2024-02-12T21:56:13Z
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	Image Text
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dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/85690
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/85690 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	eng
language	eng
dc.relation.references.spa.fl_str_mv	[Abdikan et al., 2023] Abdikan, S., Sekertekin, A., Narin, O. G., Delen, A., and Sanli, F. B. (2023). A comparative analysis of SLR, MLR, ANN, XGBoost and CNN for crop height estimation of sunflower using sentinel-1 and sentinel-2. Advances in Space Research, 71(7):3045–3059. [Aerts et al., 2015] Aerts, S., Haesbroeck, G., and Ruwet, C. (2015). Multivariate coefficients of variation: Comparison and influence functions. Journal of Multivariate Analysis, 142:183–198. [Arbia, 2014] Arbia, G. (2014). A Primer for Spatial Econometrics. Palgrave Macmillan UK. [Aybar et al., 2020] Aybar, C., Wu, Q., Bautista, L., Yali, R., and Barja, A. (2020). rgee: An r package for interacting with google earth engine. Journal of Open Source Software, 5(51):2272. [Bohorquez et al., 2017] Bohorquez, M., Giraldo, R., and Mateu, J. (2017). Multivariate functional random fields: prediction and optimal sampling. Stochastic Environmental Research and Risk Assessment, 31(1):53–70. [Bouhennache et al., 2018] Bouhennache, R., Bouden, T., Taleb-Ahmed, A., and Cheddad, A. (2018). A new spectral index for the extraction of built-up land features from landsat 8 satellite imagery. Geocarto International, 34(14):1531–1551. [Breiman, 2001] Breiman, L. (2001). Random forests. Machine Learning, 45(1):5–32. [Breiman et al., 1984] Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984). Classification And Regression Trees. Routledge. [Ceballos and Lopera, 2009] Ceballos, N. and Lopera, G. (2009). El caso coca nasa. Cuadernos de Investigaci ́on. [Ceccato et al., 2002] Ceccato, P., Gobron, N., Flasse, S., Pinty, B., and Tarantola, S. (2002). Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1. Remote Sensing of Environment, 82(2-3):188–197. [Chen et al., 2004] Chen, D., Stow, D. A., and Gong, P. (2004). Examining the effect of spatial resolution and texture window size on classification accuracy: an urban environment case. International Journal of Remote Sensing, 25(11):2177–2192. [Chen and Guestrin, 2016] Chen, T. and Guestrin, C. (2016). Xgboost: Scalable tree boosting system. CoRR, abs/1603.02754. [El Financiero, 2023] El Financiero (2023). Destruyen casi 18,000 plantas de coca en guatemala. [El Tiempo, 2023] El Tiempo (2023). La coca florece en m ́exico a la sombra de drogas sint ́eticas. [ESA, nda] ESA, E. S. A. (n.d.a). User guides - sentinel-2 msi - overview - sentinel online. https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/overview. Accessed: 2023-07-29 [ESA, ndb] ESA, E. S. A. (n.d.b). User guides - sentinel-2 msi - resolutions - sentinel online. https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/resolutions. Accessed: 2023-07-29. [ESA, ndc] ESA, E. S. A. (n.d.c). User guides - sentinel-2 msi - revisit and coverage - sentinel online. https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/revisit-coverage. Accessed: 2023-07-29. [Farfán and Jaramillo, 2009] Farfán, F. and Jaramillo, A. (2009). Sombrío para el cultivo del café según la nubosidad de la región. Avances Técnicos. [Frampton et al., 2013] Frampton, W. J., Dash, J., Watmough, G., and Milton, E. J. (2013). Evaluating the capabilities of sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 82:83–92. [Friedman et al., 2000] Friedman, J., Hastie, T., and Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The Annals of Statistics, 28(2). [Friedman, 2001] Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5). [Galindo and Fernández, 2010] Galindo, A. and Fernánndez, J. (2010). Plantas de coca en Colombia. discusión crítica sobre la taxonomía de las especies cultivadas del género erythroxylum p. browne (erythroxylaceae). [Gerber et al., 2018] Gerber, F., de Jong, R., Schaepman, M. E., Schaepman-Strub, G., and Furrer, R. (2018). Predicting missing values in spatio-temporal remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 56(5):2841–2853. [Haboudane, 2004] Haboudane, D. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90(3):337–352. [Huang et al., 2022] Huang, L., Liu, Y., Huang, W., Dong, Y., Ma, H., Wu, K., and Guo, A. (2022). Combining random forest and XGBoost methods in detecting early and mid-term winter wheat stripe rust using canopy level hyperspectral measurements. Agriculture, 12(1):74. [Huber et al., 2022] Huber, F., Yushchenko, A., Stratmann, B., and Steinhage, V. (2022). Extreme gradient boosting for yield estimation compared with deep learning approaches. Computers and Electronics in Agriculture, 202:107346. [Huete, 1988] Huete, A. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3):295–309. [InfoBAE, 2022] InfoBAE (2022). Autoridades localizan 25.000 arbustos de hoja de coca en el caribe hondureño [Jensen, 2006] Jensen, J. R. (2006). Remote Sensing of the Environment. Prentice Hall. [Jiang and Shekhar, 2017] Jiang, Z. and Shekhar, S. (2017). Spatial Big Data Science. Springer International Publishing. [Karpatne et al., 2016] Karpatne, A., Jiang, Z., Vatsavai, R. R., Shekhar, S., and Kumar, V. (2016). Monitoring land-cover changes: A machine-learning perspective. IEEE Geoscience and Remote Sensing Magazine, 4(2):8–21. [Louhaichi et al., 2001] Louhaichi, M., Borman, M. M., and Johnson, D. E. (2001). Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International, 16(1):65–70. [Machado et al., 2019] Machado, M. R., Karray, S., and de Sousa, I. T. (2019). LightGBM: an effective decision tree gradient boosting method to predict customer loyalty in the finance industry. In 2019 14th International Conference on Computer Science &amp Education (ICCSE). IEEE. [Matteucci and Morello, 2001] Matteucci, S. and Morello, J. (2001). Aspectos ecológicos del cultivo de coca. 1(8). [McFEETERS, 1996] McFEETERS, S. K. (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7):1425–1432. [Mishra and Mishra, 2012] Mishra, S. and Mishra, D. R. (2012). Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sensing of Environment, 117:394–406. [Mohanaiah et al., 2013] Mohanaiah, P., P.Sathyanarayana, and GuruKumar, L. (2013). Image texture feature extraction using glcm approach. International Journal of Scientific and Research Publications, 3(5). [Mutanga and Kumar, 2019] Mutanga, O. and Kumar, L. (2019). Google earth engine applications. Remote Sensing, 11(5):591. [Nazir et al., 2021] Nazir, A., Ullah, S., Saqib, Z. A., Abbas, A., Ali, A., Iqbal, M. S., Hussain, K., Shakir, M., Shah, M., and Butt, M. U. (2021). Estimation and forecasting of rice yield using phenology-based algorithm and linear regression model on sentinel-II satellite data. Agriculture, 11(10):1026. [Oliver and Webster, 1990] Oliver, M. A. and Webster, R. (1990). Kriging: a method of interpolation for geographical information systems. International journal of geographical information systems, 4(3):313–332. [Park et al., 2021] Park, J., Lee, Y., and Lee, J. (2021). Assessment of machine learning algorithms for land cover classification using remotely sensed data. Sensors and Materials, 33(11):3885. [Park et al., 2018] Park, S., Im, J., Park, S., Yoo, C., Han, H., and Rhee, J. (2018). Classification and mapping of paddy rice by combining landsat and SAR time series data. Remote Sensing, 10(3):447. [PBC, 2022] PBC, P. L. (2022). Combined imagery product specifications. [Piedelobo et al., 2019] Piedelobo, L., Herna ́ndez-Lo ́pez, D., Ballesteros, R., Chakhar, A., Pozo, S. D., Gonza ́lez-Aguilera, D., and Moreno, M. A. (2019). Scalable pixel-based crop classification combining sentinel-2 and landsat-8 data time series: Case study of the duero river basin. Agricultural Systems, 171:36–50. [Rouse et al., 1974] Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W., et al. (1974). Monitoring vegetation systems in the great plains with erts. NASA Spec. Publ, 351(1):309. [Rustowicz, 2017] Rustowicz, R. M. (2017). Crop classification with multi-temporal satellite imagery. [Saini and Ghosh, 2021] Saini, R. and Ghosh, S. K. (2021). Crop classification in a heterogeneous agricultural environment using ensemble classifiers and single-date sentinel-2a imagery. Geocarto International, 36(19):2141–2159. [Serrano, 2014] Serrano, M. (2014). Cultivos il ́ıcitos de coca y bienestar en las regiones productoras: Un ana ́lisis desde el enfoque de capacidades. [Tatsumi et al., 2015] Tatsumi, K., Yamashiki, Y., Torres, M. A. C., and Taipe, C. L. R. (2015). Crop classification of upland fields using random forest of time-series landsat 7 ETM data. Computers and Electronics in Agriculture, 115:171–179. [Tobler, 1970] Tobler, W. R. (1970). A computer movie simulating urban growth in the detroit region. Economic Geography, 46:234. [UNODC, 2023] UNODC (2023). World drug report 2023. [UNODC and MINJUSTICIA, 2010] UNODC and MINJUSTICIA (2010). Caracter ́ısticas agroculturales de los cultivos de coca 2005-2010. [UNODC and MINJUSTICIA, 2012] UNODC and MINJUSTICIA (2012). Estructura econo ́mica de la unidades productores agropecuarias en zonas de influencia de cultivos de coca. [UNODC and MINJUSTICIA, 2019] UNODC and MINJUSTICIA (2019). Monitoreo de cultivos il ́ıcitos 2018. [UNODC and MINJUSTICIA, 2022] UNODC and MINJUSTICIA (2022). Monitoreo de cultivos il ́ıcitos 2021. [Valavi et al., 2018] Valavi, R., Elith, J., Lahoz-Monfort, J. J., and Guillera-Arroita, G. (2018). blockcv: An r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods in Ecology and Evolution, 10(2):225–232. [Valen, 1974] Valen, L. V. (1974). Multivariate structural statistics in natural history. Journal of Theoretical Biology, 45(1):235–247. [Yu and Zhu, 2020] Yu, T. and Zhu, H. (2020). Hyper-parameter optimization: A review of algorithms and applications. [A ́ngel, 2012] A ́ngel, Y. (2012). Metodolog ́ıa para identificar cultivos de coca mediante ana ́lisis de para ́metros red edge y espectroscopia de im ́agenes.
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dc.rights.license.spa.fl_str_mv	Reconocimiento 4.0 Internacional
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dc.format.extent.spa.fl_str_mv	[xiii], 58 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.coverage.temporal.none.fl_str_mv	2019
dc.coverage.country.spa.fl_str_mv	Colombia
dc.coverage.region.spa.fl_str_mv	Región del Catatumbo Norte de Santander
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ciencias - Maestría en Ciencias - Estadística
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias
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
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Bohórquez Castañeda, Martha Patriciaf8beb6c8ebea265d30530d109695eddcAlbarracín Barrera, Camilo Andrés42497d56ac534182b01c8c365e49bd142019ColombiaRegión del CatatumboNorte de Santander2024-02-12T21:56:13Z2024-02-12T21:56:13Z2023-12-14https://repositorio.unal.edu.co/handle/unal/85690Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones a color, diagramas, mapasEl monitoreo de cultivos de coca es esencial para la formulacio ́n de pol ́ıticas pu ́blicas de drogas a nivel global, especialmente con la expansio ́n hacia a pa ́ıses no tradicionales. Como el principal productor de coca ́ına del mundo, Colombia ejemplifica los desaf ́ıos inherentes al monitoreo de este cultivo. El modelo actual de monitoreo, establecido en colaboracio ́n con la Oficina de las Naciones Unidas contra la Droga y el Delito (UNODC), ofrece una estimacio ́n robusta pero sujeta a mejoras en t ́erminos de oportunidad y eficiencia, dado que depende de la interpretaci ́on visual de ima ́genes satelitales anuales. Este trabajo presenta una metodolog ́ıa innovadora que emplea XGBoost con datos multiespectrales y espacio-temporales, principalmente de ima ́genes Sentinel-2. El flujo de trabajo escalable utiliza Google Earth Engine (GEE) para acceder a las im ́agenes satelitales y extraer variables para la clasificacio ́n. Los modelos XGBoost se entrenan para diferenciar entre coca y no coca y se optimizan utilizando un m ́etodo de validaci ́on cruzada espacial. Al aplicarse en dos zonas de Putumayo, Colombia, esta metodolog ́ıa produce una puntuaci ́on Kappa de 0,7512 usando datos de Sentinel-2, superando la puntuaci ́on Kappa de 0,7090 alcanzada en trabajos anteriores. Este avance representa un paso significativo en la precisio ́n de la clasificacio ́n a gran escala de cultivos de coca. Un experimento complementario utilizando imagenes de Planet, de mayor resolucio ́n, en una de las zonas para 2021 produjo una precisi ́on menor pero una mejor delimitacio ́n geom ́etrica, verificada al evaluar la homogeneidad espectral entre pol ́ıgonos clasificados y pol ́ıgonos de referencia. Esta notable mejora en las metodolog ́ıas de clasificacio ́n de cultivos tiene el potencial de fortalecer las operaciones de las fuerzas del orden, perfeccionar las pol ́ıticas de drogas e influir en las relaciones internacionales.Coca crop monitoring is essential for the formulation of public drug policies at the global level, especially with the expansion into non-traditional countries. As the world’s leading cocaine producer, Colombia exemplifies the challenges inherent in monitoring this crop. The current monitoring model, established in collaboration with the United Nations Office on Drugs and Crime (UNODC), offers a robust estimate but is subject to improvement in terms of timeliness and efficiency, as it relies on visual interpretation of annual satellite imagery. This paper presents an innovative methodology that employs XGBoost with multispectral and spatio-temporal data, mainly from Sentinel-2 imagery. The scalable workflow uses Google Earth Engine (GEE) to access satellite imagery and extract variables for classification. The XGBoost models are trained to differentiate between coca and non-coca and optimized using a spatial cross-validation method. When applied in two areas of Putumayo, Colombia, this methodology produced a Kappa score of 0,7512 using Sentinel-2 data, surpassing the Kappa score of 0,7090 achieved in previous work. This advance represents a significant step forward in the accuracy of large-scale coca field classification. A complementary experiment using higher resolution Planet imagery in one of the zones for 2021 produced a lower accuracy but better geometric delineation, verified by assessing spectral homogeneity between classified polygons and reference polygons. This marked improvement in crop classification methodologies has the potential to strengthen law enforcement operations, refine drug policies and influence international relations.MaestríaMagíster en Ciencias - EstadísticaEstadística Espacial[xiii], 58 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasVigilancia de cultivosCrop monitoringAerofotografía en control de drogas y narcóticosPhotography, aerial in drug and narcotic controlCultivos ilícitos - MedicionesFotografía multiespectral - Métodos estadísticosMultispectral photogrphy - Statistical methodsclasificación de cultivoscocadatos multiespectralesespacio-temporalpolítica de drogasXGBoostPlanetcrop classificationmultispectral dataspatial-temporaldrug policyGoogle Earth EngineCoca crop classification and mapping using spectral, temporal and spatial features from satellite imagery for the Catatumbo region in Colombia - 2019Clasificación y mapeo de cultivos de coca utilizando características espectrales, temporales y espaciales a partir de imágenes satelitales para la región del Catatumbo en Colombia - 2019Trabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionImageTexthttp://purl.org/redcol/resource_type/TM[Abdikan et al., 2023] Abdikan, S., Sekertekin, A., Narin, O. G., Delen, A., and Sanli, F. B. (2023). A comparative analysis of SLR, MLR, ANN, XGBoost and CNN for crop height estimation of sunflower using sentinel-1 and sentinel-2. Advances in Space Research, 71(7):3045–3059.[Aerts et al., 2015] Aerts, S., Haesbroeck, G., and Ruwet, C. (2015). Multivariate coefficients of variation: Comparison and influence functions. Journal of Multivariate Analysis, 142:183–198.[Arbia, 2014] Arbia, G. (2014). A Primer for Spatial Econometrics. Palgrave Macmillan UK.[Aybar et al., 2020] Aybar, C., Wu, Q., Bautista, L., Yali, R., and Barja, A. (2020). rgee: An r package for interacting with google earth engine. Journal of Open Source Software, 5(51):2272.[Bohorquez et al., 2017] Bohorquez, M., Giraldo, R., and Mateu, J. (2017). Multivariate functional random fields: prediction and optimal sampling. Stochastic Environmental Research and Risk Assessment, 31(1):53–70.[Bouhennache et al., 2018] Bouhennache, R., Bouden, T., Taleb-Ahmed, A., and Cheddad, A. (2018). A new spectral index for the extraction of built-up land features from landsat 8 satellite imagery. Geocarto International, 34(14):1531–1551.[Breiman, 2001] Breiman, L. (2001). Random forests. Machine Learning, 45(1):5–32.[Breiman et al., 1984] Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984). Classification And Regression Trees. Routledge.[Ceballos and Lopera, 2009] Ceballos, N. and Lopera, G. (2009). El caso coca nasa. Cuadernos de Investigaci ́on.[Ceccato et al., 2002] Ceccato, P., Gobron, N., Flasse, S., Pinty, B., and Tarantola, S. (2002). Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1. Remote Sensing of Environment, 82(2-3):188–197.[Chen et al., 2004] Chen, D., Stow, D. A., and Gong, P. (2004). Examining the effect of spatial resolution and texture window size on classification accuracy: an urban environment case. International Journal of Remote Sensing, 25(11):2177–2192.[Chen and Guestrin, 2016] Chen, T. and Guestrin, C. (2016). Xgboost: Scalable tree boosting system. CoRR, abs/1603.02754.[El Financiero, 2023] El Financiero (2023). Destruyen casi 18,000 plantas de coca en guatemala.[El Tiempo, 2023] El Tiempo (2023). La coca florece en m ́exico a la sombra de drogas sint ́eticas.[ESA, nda] ESA, E. S. A. (n.d.a). User guides - sentinel-2 msi - overview - sentinel online. https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/overview. Accessed: 2023-07-29[ESA, ndb] ESA, E. S. A. (n.d.b). User guides - sentinel-2 msi - resolutions - sentinel online. https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/resolutions. Accessed: 2023-07-29.[ESA, ndc] ESA, E. S. A. (n.d.c). User guides - sentinel-2 msi - revisit and coverage - sentinel online. https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/revisit-coverage. Accessed: 2023-07-29.[Farfán and Jaramillo, 2009] Farfán, F. and Jaramillo, A. (2009). Sombrío para el cultivo del café según la nubosidad de la región. Avances Técnicos.[Frampton et al., 2013] Frampton, W. J., Dash, J., Watmough, G., and Milton, E. J. (2013). Evaluating the capabilities of sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 82:83–92.[Friedman et al., 2000] Friedman, J., Hastie, T., and Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The Annals of Statistics, 28(2).[Friedman, 2001] Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5).[Galindo and Fernández, 2010] Galindo, A. and Fernánndez, J. (2010). Plantas de coca en Colombia. discusión crítica sobre la taxonomía de las especies cultivadas del género erythroxylum p. browne (erythroxylaceae).[Gerber et al., 2018] Gerber, F., de Jong, R., Schaepman, M. E., Schaepman-Strub, G., and Furrer, R. (2018). Predicting missing values in spatio-temporal remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 56(5):2841–2853.[Haboudane, 2004] Haboudane, D. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90(3):337–352.[Huang et al., 2022] Huang, L., Liu, Y., Huang, W., Dong, Y., Ma, H., Wu, K., and Guo, A. (2022). Combining random forest and XGBoost methods in detecting early and mid-term winter wheat stripe rust using canopy level hyperspectral measurements. Agriculture, 12(1):74.[Huber et al., 2022] Huber, F., Yushchenko, A., Stratmann, B., and Steinhage, V. (2022). Extreme gradient boosting for yield estimation compared with deep learning approaches. Computers and Electronics in Agriculture, 202:107346.[Huete, 1988] Huete, A. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3):295–309.[InfoBAE, 2022] InfoBAE (2022). Autoridades localizan 25.000 arbustos de hoja de coca en el caribe hondureño[Jensen, 2006] Jensen, J. R. (2006). Remote Sensing of the Environment. Prentice Hall.[Jiang and Shekhar, 2017] Jiang, Z. and Shekhar, S. (2017). Spatial Big Data Science. Springer International Publishing.[Karpatne et al., 2016] Karpatne, A., Jiang, Z., Vatsavai, R. R., Shekhar, S., and Kumar, V. (2016). Monitoring land-cover changes: A machine-learning perspective. IEEE Geoscience and Remote Sensing Magazine, 4(2):8–21.[Louhaichi et al., 2001] Louhaichi, M., Borman, M. M., and Johnson, D. E. (2001). Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International, 16(1):65–70.[Machado et al., 2019] Machado, M. R., Karray, S., and de Sousa, I. T. (2019). LightGBM: an effective decision tree gradient boosting method to predict customer loyalty in the finance industry. In 2019 14th International Conference on Computer Science &amp Education (ICCSE). IEEE.[Matteucci and Morello, 2001] Matteucci, S. and Morello, J. (2001). Aspectos ecológicos del cultivo de coca. 1(8).[McFEETERS, 1996] McFEETERS, S. K. (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7):1425–1432.[Mishra and Mishra, 2012] Mishra, S. and Mishra, D. R. (2012). Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sensing of Environment, 117:394–406.[Mohanaiah et al., 2013] Mohanaiah, P., P.Sathyanarayana, and GuruKumar, L. (2013). Image texture feature extraction using glcm approach. International Journal of Scientific and Research Publications, 3(5).[Mutanga and Kumar, 2019] Mutanga, O. and Kumar, L. (2019). Google earth engine applications. Remote Sensing, 11(5):591.[Nazir et al., 2021] Nazir, A., Ullah, S., Saqib, Z. A., Abbas, A., Ali, A., Iqbal, M. S., Hussain, K., Shakir, M., Shah, M., and Butt, M. U. (2021). Estimation and forecasting of rice yield using phenology-based algorithm and linear regression model on sentinel-II satellite data. Agriculture, 11(10):1026.[Oliver and Webster, 1990] Oliver, M. A. and Webster, R. (1990). Kriging: a method of interpolation for geographical information systems. International journal of geographical information systems, 4(3):313–332.[Park et al., 2021] Park, J., Lee, Y., and Lee, J. (2021). Assessment of machine learning algorithms for land cover classification using remotely sensed data. Sensors and Materials, 33(11):3885.[Park et al., 2018] Park, S., Im, J., Park, S., Yoo, C., Han, H., and Rhee, J. (2018). Classification and mapping of paddy rice by combining landsat and SAR time series data. Remote Sensing, 10(3):447.[PBC, 2022] PBC, P. L. (2022). Combined imagery product specifications.[Piedelobo et al., 2019] Piedelobo, L., Herna ́ndez-Lo ́pez, D., Ballesteros, R., Chakhar, A., Pozo, S. D., Gonza ́lez-Aguilera, D., and Moreno, M. A. (2019). Scalable pixel-based crop classification combining sentinel-2 and landsat-8 data time series: Case study of the duero river basin. Agricultural Systems, 171:36–50.[Rouse et al., 1974] Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W., et al. (1974). Monitoring vegetation systems in the great plains with erts. NASA Spec. Publ, 351(1):309.[Rustowicz, 2017] Rustowicz, R. M. (2017). Crop classification with multi-temporal satellite imagery.[Saini and Ghosh, 2021] Saini, R. and Ghosh, S. K. (2021). Crop classification in a heterogeneous agricultural environment using ensemble classifiers and single-date sentinel-2a imagery. Geocarto International, 36(19):2141–2159.[Serrano, 2014] Serrano, M. (2014). Cultivos il ́ıcitos de coca y bienestar en las regiones productoras: Un ana ́lisis desde el enfoque de capacidades.[Tatsumi et al., 2015] Tatsumi, K., Yamashiki, Y., Torres, M. A. C., and Taipe, C. L. R. (2015). Crop classification of upland fields using random forest of time-series landsat 7 ETM data. Computers and Electronics in Agriculture, 115:171–179.[Tobler, 1970] Tobler, W. R. (1970). A computer movie simulating urban growth in the detroit region. Economic Geography, 46:234.[UNODC, 2023] UNODC (2023). World drug report 2023.[UNODC and MINJUSTICIA, 2010] UNODC and MINJUSTICIA (2010). Caracter ́ısticas agroculturales de los cultivos de coca 2005-2010.[UNODC and MINJUSTICIA, 2012] UNODC and MINJUSTICIA (2012). Estructura econo ́mica de la unidades productores agropecuarias en zonas de influencia de cultivos de coca.[UNODC and MINJUSTICIA, 2019] UNODC and MINJUSTICIA (2019). Monitoreo de cultivos il ́ıcitos 2018.[UNODC and MINJUSTICIA, 2022] UNODC and MINJUSTICIA (2022). Monitoreo de cultivos il ́ıcitos 2021.[Valavi et al., 2018] Valavi, R., Elith, J., Lahoz-Monfort, J. J., and Guillera-Arroita, G. (2018). blockcv: An r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods in Ecology and Evolution, 10(2):225–232.[Valen, 1974] Valen, L. V. (1974). Multivariate structural statistics in natural history. Journal of Theoretical Biology, 45(1):235–247.[Yu and Zhu, 2020] Yu, T. and Zhu, H. (2020). Hyper-parameter optimization: A review of algorithms and applications.[A ́ngel, 2012] A ́ngel, Y. (2012). Metodolog ́ıa para identificar cultivos de coca mediante ana ́lisis de para ́metros red edge y espectroscopia de im ́agenes.EstudiantesInvestigadoresMaestrosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85690/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53ORIGINAL1020837434.2023.pdf1020837434.2023.pdfTesis de Maestría en Ciencias - Estadísticaapplication/pdf17341027https://repositorio.unal.edu.co/bitstream/unal/85690/4/1020837434.2023.pdf594bbbd046507b178d12d42bed13e511MD54THUMBNAIL1020837434.2023.pdf.jpg1020837434.2023.pdf.jpgGenerated Thumbnailimage/jpeg5427https://repositorio.unal.edu.co/bitstream/unal/85690/5/1020837434.2023.pdf.jpge8c3ee8b1f68f925c00c2b51f9f6b844MD55unal/85690oai:repositorio.unal.edu.co:unal/856902024-02-12 23:03:43.902Repositorio Institucional Universidad Nacional de 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Coca crop classification and mapping using spectral, temporal and spatial features from satellite imagery for the Catatumbo region in Colombia - 2019

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