Análisis del nivel de deforestación en la isla de TierraBomba mediante el uso de imágenes NIR e índicesespectrales

Autores:
Chanchí Golondrino, Gabriel Elías
Ospina Alarcón, Manuel Alejandro
Saba, Manuel
Tipo de recurso:
Article of journal
Fecha de publicación:
2026
Institución:
Universidad Pedagógica y Tecnológica de Colombia
Repositorio:
RiUPTC: Repositorio Institucional UPTC
Idioma:
spa
OAI Identifier:
oai:repositorio.uptc.edu.co:001/21897
Acceso en línea:
https://doi.org/10.19053/uptc.01217488.v17.n1.2026.20767
Palabra clave:
Deforestation
Vegetation detection
NDVI
Remote sensing
Deforestación
Detección de vegetación
Sensado remoto
NDVI
Rights
openAccess
License
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
id REPOUPTC2_674b15c8bbd565ec8e4927d5d9194579
oai_identifier_str oai:repositorio.uptc.edu.co:001/21897
network_acronym_str REPOUPTC2
network_name_str RiUPTC: Repositorio Institucional UPTC
repository_id_str
dc.title.es-ES.fl_str_mv Análisis del nivel de deforestación en la isla de TierraBomba mediante el uso de imágenes NIR e índicesespectrales
title Análisis del nivel de deforestación en la isla de TierraBomba mediante el uso de imágenes NIR e índicesespectrales
spellingShingle Análisis del nivel de deforestación en la isla de TierraBomba mediante el uso de imágenes NIR e índicesespectrales
Deforestation
Vegetation detection
NDVI
Remote sensing
Deforestación
Detección de vegetación
Sensado remoto
NDVI
title_short Análisis del nivel de deforestación en la isla de TierraBomba mediante el uso de imágenes NIR e índicesespectrales
title_full Análisis del nivel de deforestación en la isla de TierraBomba mediante el uso de imágenes NIR e índicesespectrales
title_fullStr Análisis del nivel de deforestación en la isla de TierraBomba mediante el uso de imágenes NIR e índicesespectrales
title_full_unstemmed Análisis del nivel de deforestación en la isla de TierraBomba mediante el uso de imágenes NIR e índicesespectrales
title_sort Análisis del nivel de deforestación en la isla de TierraBomba mediante el uso de imágenes NIR e índicesespectrales
dc.creator.fl_str_mv Chanchí Golondrino, Gabriel Elías
Ospina Alarcón, Manuel Alejandro
Saba, Manuel
dc.contributor.author.spa.fl_str_mv Chanchí Golondrino, Gabriel Elías
Ospina Alarcón, Manuel Alejandro
Saba, Manuel
dc.subject.none.fl_str_mv Deforestation
Vegetation detection
NDVI
Remote sensing
Deforestación
Detección de vegetación
Sensado remoto
NDVI
topic Deforestation
Vegetation detection
NDVI
Remote sensing
Deforestación
Detección de vegetación
Sensado remoto
NDVI
publishDate 2026
dc.date.accessioned.none.fl_str_mv 2026-10-17 23:11:30
dc.date.available.none.fl_str_mv 2026-10-17 23:11:30
dc.date.issued.none.fl_str_mv 2026-10-17
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.local.eng.fl_str_mv Journal article
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.coarversion.spa.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
format http://purl.org/coar/resource_type/c_6501
status_str publishedVersion
dc.identifier.issn.none.fl_str_mv 0121-7488
dc.identifier.doi.none.fl_str_mv 10.19053/uptc.01217488.v17.n1.2026.20767
dc.identifier.eissn.none.fl_str_mv 2462-7658
dc.identifier.url.none.fl_str_mv https://doi.org/10.19053/uptc.01217488.v17.n1.2026.20767
identifier_str_mv 0121-7488
10.19053/uptc.01217488.v17.n1.2026.20767
2462-7658
url https://doi.org/10.19053/uptc.01217488.v17.n1.2026.20767
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.references.spa.fl_str_mv L. K. Torres Gil, D. Valdelamar Martínez y M. Saba, “The Widespread Use of Remote Sensing in Asbestos, Vegetation, Oil and Gas, and Geology Applications”, Atmosphere, vol. 14, no. 1, p. 172, 2023.
J. Awange y J. Kiema, “Fundamentals of Remote Sensing”, pp. 115–123, 2019.
B. Zhang et al., “Remotely sensed big data: evolution in model development for information extraction”, Proceedings of the IEEE, vol. 107, no. 12, pp. 2294–2301, 2019.
J. Zhang, M. Feng y Y.Wang, “Automatic Segmentation of Remote Sensing Images on Water Bodies Based on Image Enhancement”, Traitement du Signal, vol. 37, no. 6, pp. 1037–1043, 2020.
M. I. Abdulraheem, W. Zhang, S. Li, A. J. Moshayedi, A. A. Farooque y J. Hu, “Advancement of Remote Sensing for Soil Measurements and Applications: A Comprehensive Review”, Sustainability, vol. 15, no. 21, p. 15444, 2023.
E. Schnebele, B. F. Tanyu, G. Cervone y N. Waters, “Review of remote sensing methodologies for pavement management and assessment”, European Transport Research Review, vol. 7, no. 2, p. 7, 2015.
S. A. Jaywant y K. M. Arif, “Remote Sensing Techniques for Water Quality Monitoring: A Review”, Sensors, vol. 24, no. 24, p. 8041, 2024.
Z.Wang, Y. Ma, Y. Zhang y J. Shang, “Review of Remote Sensing Applications in Grassland Monitoring”, Remote Sensing, vol. 14, no. 12, p. 2903, 2022. [9] Q. Li, “Application of Unmanned Aerial Vehicle Remote Sensing in Engineering Measurement”, Remote Sensing, vol. 9, no. 2, p. 42, 2020.
R. Navalgund,V. Jayaraman y P. S. Roy, “Remote sensing applications: An overview”, Current Science, vol. 93, pp. 1747–1766, 2007.
L. Fonseca, L. Namikawa, E. Castejon, L. Carvalho, C. Pinho y A. Pagamisse, “Image Fusion for Remote Sensing Applications”, en Image Fusion and Its Applications, InTech, 2011.
M. A. Kalitov, “About the Application of Multispectral Visualization in Earth Remote Sensing”, Vestnik NovSU, no. 1, pp. 95–107, 2024.
L. Gomez-Chova, D. Tuia, G. Moser y G. Camps-Valls, “Multimodal Classification of Remote Sensing Images: A Review and Future Directions”, Proceedings of the IEEE, vol. 103, no. 9, pp. 1560–1584, 2015.
O. Gendaram y A. Damdinsuren, “Comparison of Spectral Signatures in Hyperspectral and Multispectral Data”, 2021.
B. Lu, Y. He y P. D. Dao, “Comparing the Performance of Multispectral and Hyperspectral Images for Estimating Vegetation Properties”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 6, pp. 1784–1797, 2019.
X. Feng, L. He, Q. Cheng, X. Long y Y. Yuan, “Hyperspectral and Multispectral Remote Sensing Image Fusion Based on Endmember Spatial Information”, Remote Sensing, vol. 12, no. 6, p. 1009, 2020.
T. J. Vandal et al., “Spectral Synthesis for Geostationary Satellite-to-Satellite Translation”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–11, 2022.
B. Delaney et al., “Material Sensing with Spatial and Spectral Resolution Based on an Integrated Near- Infrared Spectral Sensor and a CMOS Camera”, Sensors, vol. 25, no. 11, p. 3295, 2025.
L. K. Atencia Payares et al., “Assessing Plant Water Status and Physiological Behaviour Using Multispectral Images fromUAVinMerlotVineyards in Central Spain”, Remote Sensing, vol. 17, no. 13, p. 2273, 2025.
Y. Zhou et al., “Diagnosis of winter-wheat water stress based on UAV-borne multispectral image texture and vegetation indices”, Agricultural Water Management, vol. 256, p. 107076, 2021.
L. Zhang, H. Zhang, Y. Niu y W. Han, “Mapping Maize Water Stress Based on UAV Multispectral Remote Sensing”, Remote Sensing, vol. 11, no. 6, p. 605, 2019.
C. Cillero Castro et al., “An UAV and Satellite Multispectral Data Approach to Monitor Water Quality in Small Reservoirs”, Remote Sensing, vol. 12, no. 9, p. 1514, 2020.
M. Ajaoud et al., “Bridging Sustainability and Environmental Impact Assessment: Multi-Scale Bioindication and Remote Sensing for Pollution Monitoring in Agroecosystems”, Sustainability, vol. 17, no. 9, p. 4115, 2025.
Y. Xiao et al., “UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms”, Remote Sensing, vol. 14, no. 14, p. 3272, 2022.
X. Lei et al., “An Ensemble Machine Learning Model to Estimate Urban Water Quality Parameters Using Unmanned Aerial Vehicle Multispectral Imagery”, Remote Sensing, vol. 16, no. 12, p. 2246, 2024.
M. Li et al., “Monitoring of vegetation chlorophyll content in photovoltaic areas using UAV-mounted multispectral imaging”, Frontiers in Plant Science, vol. 16, 2025.
M. Zhou et al., “Robust Bathymetric Mapping in Shallow Waters”, Remote Sensing, vol. 17, no. 17, p. 3066, 2025.
F. L. Macedo et al., “Assessment of Vegetation Indices Derived from UAV Imagery for Weed Detection in Vineyards”, Remote Sensing, vol. 17, no. 11, p. 1899, 2025.
M. D. M. Manessa et al., “Validation of Satellite-Derived Green Canopy Cover in Rubber Plantations Using UAV and Ground Observations”, Forests, vol. 16, no. 5, p. 717, 2025.
P. Wagle et al., “Modeling time series of vegetation indices in tallgrass prairie”, Ecological Informatics, vol. 84, p. 102917, 2024.
B. Gao, “NDWI—A normalized difference water index”, Remote Sensing of Environment, vol. 58, no. 3, pp. 257–266, 1996.
P. Lemenkova, “Analysis of Landsat NDVI Time Series for Detecting Degradation of Vegetation”, 2018.
R. M. M. Abdusamea, “The Importance of NDVI and the Use of ArcGIS to Create NDVI Maps”, 2024.
A. T. Tabildina et al., “The Basis of the Algorithm for Calculating NDVI”, Journal of Geography and Environmental Management, vol. 52, no. 1, 2019.
C. Del Cairo Hurtado et al., “Identificación coparticipativa de impactos del cambio climático”, Boletín Científico CIOH, vol. 42, no. 2, 2023.
G. Plaza-Cardenas, “Evaluación del impacto social y ambiental por la erosión costera”, Universidad de Manizales, 2017.
C. Yu et al., “Optimization and Simulation of Flat Area Sites Based on Remote Sensing Precipitation”, Water Resources Research, vol. 61, no. 6, 2025.
S. Kumar et al., “An Agenda for Land Data Assimilation Priorities”, Journal of Advances in Modeling Earth Systems, vol. 14, no. 11, 2022.
H. Huang et al., “Monitoring southwest drought of China using HJ-1A/B and Landsat data”, 2012.
H. Sabah Jaber et al., “Classification of Hyperspectral Remote Sensing for Production Minerals Mapping”, International Journal of Engineering and Technology, vol. 7, no. 4.20, pp. 480–484, 2018.
A. T. Harris, “Spectral mapping tools from the earth sciences applied to spectral microscopy data”, Cytometry Part A, vol. 69A, no. 8, pp. 872–879, 2006.
dc.relation.citationendpage.none.fl_str_mv 82
dc.relation.citationissue.spa.fl_str_mv 1
dc.relation.citationstartpage.none.fl_str_mv 74
dc.relation.citationvolume.spa.fl_str_mv 17
dc.relation.ispartofjournal.spa.fl_str_mv Ciencia en Desarrollo
dc.relation.bitstream.none.fl_str_mv https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/download/20767/17341
dc.relation.citationedition.spa.fl_str_mv Núm. 1 , Año 2026 : Vol. 17 Núm. 1 (2026): Vol. 17 Núm. 1 (2026): Vol. 17 Núm. 1 (2026): Vol 17, Núm.1 (2026): Enero - Junio
dc.rights.uri.spa.fl_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Pedagógica y Tecnológica de Colombia
dc.source.spa.fl_str_mv https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/20767
institution Universidad Pedagógica y Tecnológica de Colombia
bitstream.url.fl_str_mv https://repositorio.uptc.edu.co/bitstreams/ead9897e-987d-4d61-98ad-741b638aea51/download
bitstream.checksum.fl_str_mv 5b282cd974ef2ee7f836e3abde017dbb
bitstream.checksumAlgorithm.fl_str_mv MD5
repository.name.fl_str_mv Repositorio Institucional UPTC
repository.mail.fl_str_mv bdigital@metabiblioteca.com
_version_ 1881674022324797440
spelling Chanchí Golondrino, Gabriel ElíasOspina Alarcón, Manuel AlejandroSaba, Manuel2026-10-17 23:11:302026-10-17 23:11:302026-10-170121-748810.19053/uptc.01217488.v17.n1.2026.207672462-7658https://doi.org/10.19053/uptc.01217488.v17.n1.2026.20767application/pdfspaUniversidad Pedagógica y Tecnológica de Colombiahttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.esinfo:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/20767DeforestationVegetation detectionNDVIRemote sensingDeforestaciónDetección de vegetaciónSensado remotoNDVIAnálisis del nivel de deforestación en la isla de TierraBomba mediante el uso de imágenes NIR e índicesespectralesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/coar/version/c_970fb48d4fbd8a85L. K. Torres Gil, D. Valdelamar Martínez y M. Saba, “The Widespread Use of Remote Sensing in Asbestos, Vegetation, Oil and Gas, and Geology Applications”, Atmosphere, vol. 14, no. 1, p. 172, 2023.J. Awange y J. Kiema, “Fundamentals of Remote Sensing”, pp. 115–123, 2019.B. Zhang et al., “Remotely sensed big data: evolution in model development for information extraction”, Proceedings of the IEEE, vol. 107, no. 12, pp. 2294–2301, 2019.J. Zhang, M. Feng y Y.Wang, “Automatic Segmentation of Remote Sensing Images on Water Bodies Based on Image Enhancement”, Traitement du Signal, vol. 37, no. 6, pp. 1037–1043, 2020.M. I. Abdulraheem, W. Zhang, S. Li, A. J. Moshayedi, A. A. Farooque y J. Hu, “Advancement of Remote Sensing for Soil Measurements and Applications: A Comprehensive Review”, Sustainability, vol. 15, no. 21, p. 15444, 2023.E. Schnebele, B. F. Tanyu, G. Cervone y N. Waters, “Review of remote sensing methodologies for pavement management and assessment”, European Transport Research Review, vol. 7, no. 2, p. 7, 2015.S. A. Jaywant y K. M. Arif, “Remote Sensing Techniques for Water Quality Monitoring: A Review”, Sensors, vol. 24, no. 24, p. 8041, 2024.Z.Wang, Y. Ma, Y. Zhang y J. Shang, “Review of Remote Sensing Applications in Grassland Monitoring”, Remote Sensing, vol. 14, no. 12, p. 2903, 2022. [9] Q. Li, “Application of Unmanned Aerial Vehicle Remote Sensing in Engineering Measurement”, Remote Sensing, vol. 9, no. 2, p. 42, 2020.R. Navalgund,V. Jayaraman y P. S. Roy, “Remote sensing applications: An overview”, Current Science, vol. 93, pp. 1747–1766, 2007.L. Fonseca, L. Namikawa, E. Castejon, L. Carvalho, C. Pinho y A. Pagamisse, “Image Fusion for Remote Sensing Applications”, en Image Fusion and Its Applications, InTech, 2011.M. A. Kalitov, “About the Application of Multispectral Visualization in Earth Remote Sensing”, Vestnik NovSU, no. 1, pp. 95–107, 2024.L. Gomez-Chova, D. Tuia, G. Moser y G. Camps-Valls, “Multimodal Classification of Remote Sensing Images: A Review and Future Directions”, Proceedings of the IEEE, vol. 103, no. 9, pp. 1560–1584, 2015.O. Gendaram y A. Damdinsuren, “Comparison of Spectral Signatures in Hyperspectral and Multispectral Data”, 2021.B. Lu, Y. He y P. D. Dao, “Comparing the Performance of Multispectral and Hyperspectral Images for Estimating Vegetation Properties”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 6, pp. 1784–1797, 2019.X. Feng, L. He, Q. Cheng, X. Long y Y. Yuan, “Hyperspectral and Multispectral Remote Sensing Image Fusion Based on Endmember Spatial Information”, Remote Sensing, vol. 12, no. 6, p. 1009, 2020.T. J. Vandal et al., “Spectral Synthesis for Geostationary Satellite-to-Satellite Translation”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–11, 2022.B. Delaney et al., “Material Sensing with Spatial and Spectral Resolution Based on an Integrated Near- Infrared Spectral Sensor and a CMOS Camera”, Sensors, vol. 25, no. 11, p. 3295, 2025.L. K. Atencia Payares et al., “Assessing Plant Water Status and Physiological Behaviour Using Multispectral Images fromUAVinMerlotVineyards in Central Spain”, Remote Sensing, vol. 17, no. 13, p. 2273, 2025.Y. Zhou et al., “Diagnosis of winter-wheat water stress based on UAV-borne multispectral image texture and vegetation indices”, Agricultural Water Management, vol. 256, p. 107076, 2021.L. Zhang, H. Zhang, Y. Niu y W. Han, “Mapping Maize Water Stress Based on UAV Multispectral Remote Sensing”, Remote Sensing, vol. 11, no. 6, p. 605, 2019.C. Cillero Castro et al., “An UAV and Satellite Multispectral Data Approach to Monitor Water Quality in Small Reservoirs”, Remote Sensing, vol. 12, no. 9, p. 1514, 2020.M. Ajaoud et al., “Bridging Sustainability and Environmental Impact Assessment: Multi-Scale Bioindication and Remote Sensing for Pollution Monitoring in Agroecosystems”, Sustainability, vol. 17, no. 9, p. 4115, 2025.Y. Xiao et al., “UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms”, Remote Sensing, vol. 14, no. 14, p. 3272, 2022.X. Lei et al., “An Ensemble Machine Learning Model to Estimate Urban Water Quality Parameters Using Unmanned Aerial Vehicle Multispectral Imagery”, Remote Sensing, vol. 16, no. 12, p. 2246, 2024.M. Li et al., “Monitoring of vegetation chlorophyll content in photovoltaic areas using UAV-mounted multispectral imaging”, Frontiers in Plant Science, vol. 16, 2025.M. Zhou et al., “Robust Bathymetric Mapping in Shallow Waters”, Remote Sensing, vol. 17, no. 17, p. 3066, 2025.F. L. Macedo et al., “Assessment of Vegetation Indices Derived from UAV Imagery for Weed Detection in Vineyards”, Remote Sensing, vol. 17, no. 11, p. 1899, 2025.M. D. M. Manessa et al., “Validation of Satellite-Derived Green Canopy Cover in Rubber Plantations Using UAV and Ground Observations”, Forests, vol. 16, no. 5, p. 717, 2025.P. Wagle et al., “Modeling time series of vegetation indices in tallgrass prairie”, Ecological Informatics, vol. 84, p. 102917, 2024.B. Gao, “NDWI—A normalized difference water index”, Remote Sensing of Environment, vol. 58, no. 3, pp. 257–266, 1996.P. Lemenkova, “Analysis of Landsat NDVI Time Series for Detecting Degradation of Vegetation”, 2018.R. M. M. Abdusamea, “The Importance of NDVI and the Use of ArcGIS to Create NDVI Maps”, 2024.A. T. Tabildina et al., “The Basis of the Algorithm for Calculating NDVI”, Journal of Geography and Environmental Management, vol. 52, no. 1, 2019.C. Del Cairo Hurtado et al., “Identificación coparticipativa de impactos del cambio climático”, Boletín Científico CIOH, vol. 42, no. 2, 2023.G. Plaza-Cardenas, “Evaluación del impacto social y ambiental por la erosión costera”, Universidad de Manizales, 2017.C. Yu et al., “Optimization and Simulation of Flat Area Sites Based on Remote Sensing Precipitation”, Water Resources Research, vol. 61, no. 6, 2025.S. Kumar et al., “An Agenda for Land Data Assimilation Priorities”, Journal of Advances in Modeling Earth Systems, vol. 14, no. 11, 2022.H. Huang et al., “Monitoring southwest drought of China using HJ-1A/B and Landsat data”, 2012.H. Sabah Jaber et al., “Classification of Hyperspectral Remote Sensing for Production Minerals Mapping”, International Journal of Engineering and Technology, vol. 7, no. 4.20, pp. 480–484, 2018.A. T. Harris, “Spectral mapping tools from the earth sciences applied to spectral microscopy data”, Cytometry Part A, vol. 69A, no. 8, pp. 872–879, 2006.8217417Ciencia en Desarrollohttps://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/download/20767/17341Núm. 1 , Año 2026 : Vol. 17 Núm. 1 (2026): Vol. 17 Núm. 1 (2026): Vol. 17 Núm. 1 (2026): Vol 17, Núm.1 (2026): Enero - JunioPublicationOREORE.xmltext/xml2796https://repositorio.uptc.edu.co/bitstreams/ead9897e-987d-4d61-98ad-741b638aea51/download5b282cd974ef2ee7f836e3abde017dbbMD51falseAnonymousREAD001/21897oai:repositorio.uptc.edu.co:001/218972026-10-30 07:04:14.745https://creativecommons.org/licenses/by-nc-nd/4.0/deed.esmetadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCbdigital@metabiblioteca.com

Publicaciones similares