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Vehículos aéreos no tripulados para el monitoreo del estado nutricional y fitosanitario de cultivos forestales

dc.contributor.authorGuevara Bonilla, Mariospa
dc.contributor.authorOrtiz Malavasi, Edgarspa
dc.contributor.authorVillalobos Barquero, Verónicaspa
dc.contributor.authorHernández Cole, Javierspa
dc.date.accessioned2023-01-01 00:00:00
dc.date.accessioned2023-09-19T21:10:40Z
dc.date.available2023-01-01 00:00:00
dc.date.available2023-09-19T21:10:40Z
dc.date.issued2022-01-01
dc.identifier.issn0120-0739
dc.identifier.urihttp://test.repositoriodigital.com:8080/handle/123456789/44516
dc.description.abstractEl uso de vehículos aéreos no tripulados (VANTs) en el monitoreo de plantaciones forestales permite obtener información precisa sobre distintos atributos de los árboles. Este trabajo presenta una revisión crítica del uso potencial de los VANTs para el monitoreo del estado nutricional y fitosanitario de plantaciones forestales. Se realizó una búsqueda bibliográfica en las plataformas Google Scholar, Scopus y Science Direct, utilizando palabras claves como estrés, nutrición y forestería. Se encontraron estudios principalmente en el género Pinus y en el continente asiático, que utilizan drones de ala fija y rotatoria para el monitoreo de plagas y enfermedades. Las experiencias en el monitoreo de deficiencias nutricionales son pocas. El uso futuro de VANTs para el monitoreo de estreses en cultivos forestales parece ir dirigido a la automatización en la toma de datos y a combinación de estos con algoritmos de inteligencia artificial.spa
dc.description.abstractThe use of unmanned aerial vehicles (UAVs) to monitor forest plantations allows obtaining precise information on different tree attributes. This paper presents a critical review of the potential use of UAVs for monitoring the nutritional and phytosanitary status of forest plantations. A bibliographic search was carried out on the Google Scholar, Scopus, and Science Direct platforms, using keywords such as stress, nutrition, and forestry. Studies were found mainly on the genus Pinus and the Asian continent which use fixed and rotary wing drones to monitor pests and diseases. Experiences in monitoring nutritional deficiencies are few. The future use of UAVs for stress monitoring in forest crops seems to be aimed at automating data collection and combining these with artificial intelligence algorithms.eng
dc.format.mimetypeapplication/pdfspa
dc.format.mimetypetext/xmlspa
dc.language.isospaspa
dc.publisherUniversidad Distrital Francisco José de Caldasspa
dc.rightsColombia forestal - 2023spa
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0/spa
dc.sourcehttps://revistas.udistrital.edu.co/index.php/colfor/article/view/19250spa
dc.subjectDronspa
dc.subjectplagas y enfermedades forestalesspa
dc.subjectnutrición forestalspa
dc.subjectplantaciones forestalesspa
dc.subjectmonitoreospa
dc.subjectDroneeng
dc.subjectpest and diseaseseng
dc.subjectforest nutritioneng
dc.subjectpestseng
dc.subjectforestry plantationseng
dc.subjectmonitoringeng
dc.titleVehículos aéreos no tripulados para el monitoreo del estado nutricional y fitosanitario de cultivos forestalesspa
dc.typeArtículo de revistaspa
dc.identifier.doi10.14483/2256201X.19250
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa
dc.type.localJournal articleeng
dc.title.translatedUnmanned Aerial Vehicles to Monitor the Nutritional and Phytosanitary Status of Forest Cropseng
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa
dc.relation.referencesREFERENCIAS Alvarado, A., & Raigosa, J. (2012). Nutrición y fertilización forestal en regiones tropicales. Agronomía Costarricense, 36(1), 113-115. https://www.scielo.sa.cr/scielo.php?script=sci_arttext&pid=S0377-94242012000100009 Anderson, K., & Gaston, K. J. (2013). Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment, 11(3), 138-146. https://doi.org/10.1890/120150 Arriola-Valverde., S, Ferencz-Appel, A., & Rimolo-Donadio, R. (2018). Fotogrametría terrestre con sistemas aéreos autónomos no tripulados. Investiga.Tec, 31, 3475. https://doi.org/10.18845/tm.v29i4.3040 Avtar, R., & Watanabe, T. (Eds.). (2020). Unmanned aerial vehicle: Applications in agriculture and environment. Springer International Publishing. Balasubramaniam, P., & Ananthi, V. P. (2016). Segmentation of nutrient deficiency in incomplete crop images using intuitionistic fuzzy C-means clustering algorithm. Nonlinear Dynamics, 83(1), 849-866. https://doi.org/10.1007/s11071-015-2372-y Banu, T. P., Borlea, G. F., & Banu, C. (2016). The use of drones in forestry. Journal of Environmental Science and Engineering B, 5(11), 557-562. https://doi.org/10.17265/2162-5263/2016.11.007 Barbedo, J. G. A. (2016). A review on the main challenges in automatic plant disease identification based on visible range images. Biosystems Engineering, 144, 52-60. https://doi.org/10.1016/j.biosystemseng.2016.01.017 Barbedo, J. G. A. (2019). A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones, 3(2), 40. https://doi.org/10.3390/drones3020040 Berie, H. T., & Burud, I. (2018). Application of unmanned aerial vehicles in earth resources monitoring: focus on evaluating potentials for forest monitoring in Ethiopia. European Journal of Remote Sensing, 51(1), 326-335. https://doi.org/10.1080/22797254.2018.1432993 Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79-97. https://doi.org/10.1016/j.isprsjprs.2014.02.013 Congalton, R. G., Gu, J., Yadav, K., Thenkabail, P., & Ozdogan, M. (2014). Global land cover mapping: A review and uncertainty analysis. Remote Sensing, 6(12), 12070-12093. https://doi.org/10.3390/rs61212070 Dang-Ngoc, H., & Nguyen-Trung, H. (2019, 17-19 de octubre). Aerial forest fire surveillance-evaluation of forest fire detection model using aerial videos [Conference presentation]. 2019 International Conference on Advanced Technologies for Communications (ATC), Hannoi, Vietnam. https://doi.org/10.1109/atc.2019.8924547 Dash, J., Pont, D., Brownlie, R., Dunningham, A., Watt, M., & Pearse, G. (2016). Remote sensing for precision forestry. New Zealand Journal of Forestry Science, 60(4), 15-24. de Lima Santos, I. C., dos Santos, A., Costa, J. G., Rosa, A. M., Zanuncio, A. J. V., Zanetti, R., Oumar, Z., & Zanuncio, J. C. (2021). Tectona grandis canopy cover predicted by remote sensing. Precision Agriculture, 22(3), 647-659. https://doi.org/10.1007/s11119-020-09748-w Duarte, A., Acevedo-Muñoz, L., Gonçalves, C. I., Mota, L., Sarmento, A., Silva, M., Fabres, S., Borralho, N., & Valente, C. (2020). Detection of longhorned borer attack and assessment in eucalyptus plantations using UAV imagery. Remote Sensing, 12(19), 3153. https://doi.org/10.3390/rs12193153 Fernández-Moya, J., Alvarado, A., San Miguel-Ayanz, A., & Marchamalo-Sacristán, M. (2014). Forest nutrition and fertilization in teak (Tectona grandis Lf) plantations in Central America. New Zealand Journal of Forestry Science, 44, S6. https://doi.org/10.1186/1179-5395-44-s1-s6 Getzin, S., Nuske, R. S., & Wiegand, K. (2014). Using unmanned aerial vehicles (UAV) to quantify spatial gap patterns in forests. Remote Sensing, 6(8), 6988-7004. https://doi.org/10.3390/rs6086988 Jia, L., Chen, X., Zhang, F., Buerkert, A., & Römheld, V. (2004). Use of digital camera to assess nitrogen status of winter wheat in the northern China plain. Journal of Plant Nutrition, 27(3), 441-450. https://doi.org/10.1081/pln-120028872 Khan, A., Gupta, S., & Gupta, S. K. (2020). Multi-hazard disaster studies: Monitoring, detection, recovery, and management, based on emerging technologies and optimal techniques. International Journal of Disaster Risk Reduction, 47, 101642. https://doi.org/10.1016/j.ijdrr.2020.101642 Lee, S., Park, S., Baek, G., Kim, H., & Lee, C. (2019). Detection of damaged pine tree by the pine wilt disease using UAV Image. Korean Journal of Remote Sensing, 35(3), 359-373. Lehmann, J. R. K., Nieberding, F., Prinz, T., & Knoth, C. (2015). Analysis of unmanned aerial system-based CIR images in forestry—A new perspective to monitor pest infestation levels. Forests, 6(3), 594-612. https://doi.org/10.3390/f6030594 Lin, Q., Huang, H., Wang, J., Huang, K., & Liu, Y. (2019). Detection of pine shoot beetle (PSB) stress on pine forests at individual tree level using UAV-based hyperspectral imagery and lidar. Remote Sensing, 11(21), 2540. https://doi.org/10.3390/rs11212540 Megat Mohamed Nazir, M. N., Terhem, R., Norhisham, A. R., Mohd Razali, S., & Meder, R. (2021). Early monitoring of health status of plantation-grown Eucalyptus pellita at large spatial scale via visible spectrum imaging of canopy foliage using unmanned aerial vehicles. Forests, 12(10), 1393. https://doi.org/10.3390/f12101393 Méndez, A., Vélez, J., Scaramuzza, F., & Villaroel, D. (2015). Los drones como herramienta para el monitoreo de cultivos. Revista de la Bolsa de Comercio de Rosario, (1524), 6-10. https://www.bcr.com.ar/sites/default/files/drones.pdf Michez, A., Piégay, H., Lisein, J., Claessens, H., & Lejeune, P. (2016). Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system. Environmental Monitoring and Assessment, 188(3), 146. https://doi.org/10.1007/s10661-015-4996-2 Miraki, M., Sohrabi, H., Fatehi, P., & Kneubuehler, M. (2021). Detection of mistletoe infected trees using UAV high spatial resolution images. Journal of Plant Diseases and Protection, 128, 1679-1689. https://doi.org/10.1007/s41348-021-00502-6 Nauš, J., Prokopová, J., Řebíček, J., & Špundová, M. (2010). SPAD chlorophyll meter reading can be pronouncedly affected by chloroplast movement. Photosynthesis Research, 105(3), 265-271. https://doi.org/10.1007/s11120-010-9587-z Nex, F., y Remondino, F. (2014). UAV for 3D mapping applications: A review. Applied Geomatics, 6, 1-15. https://doi.org/10.1007/s12518-013-0120-x Ortiz-Malavassi, E., Tapia-Arenas, A., Guevara-Bonilla, M., & Hernández-Cole, J. (2020). Viabilidad del uso de los VANTS en el monitoreo de plantaciones forestales. Boletín técnico. https://drive.google.com/drive/u/0/folders/104yhSyE2rMG4hTPs__52V_kvxCZaOZwh Otsu, K., Pla, M., Vayreda, J., & Brotons, L. (2018). Calibrating the severity of forest defoliation by pine processionary moth with Landsat and UAV imagery. Sensors, 18(10), 3278. https://doi.org/10.3390/s18103278 Pádua, L., Vanko, J., Hruška, J., Adão, T., Sousa, J. J., Peres, E., & Morais, R. (2017). UAS, sensors, and data processing in agroforestry: A review towards practical applications. International Journal of Remote Sensing, 38(8-10), 2349-2391. https://doi.org/10.1080/01431161.2017.1297548 Pedrali, L. D., Borges Junior, N., Pereira, R. S., Tramontina, J., Alba, E., & Marchesan, J. (2019). Multispectral remote sensing for determining dry severity levels of pointers in Eucalyptus spp. Scientia Forestalis, 47(122), 224-234. https://doi.org/10.18671/scifor.v47n122.05 Prado Osco, L., Marques Ramos, A. P., Roberto Pereira, D., Akemi Saito Moriya, É, Nobuhiro Imai, N., Takashi Matsubara, E., Estrabis, N., de Souza, M., Marcato Junior, J., & Gonçalves, W. N. (2019). Predicting canopy nitrogen content in citrus-trees using random forest algorithm associated to spectral vegetation indices from UAV-imagery. Remote Sensing, 11(24), 2925. https://doi.org/10.3390/rs11242925 Qin, J., Wang, B., Wu, Y., Lu, Q., & Zhu, H. (2021). Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms. Remote Sensing, 13(2), 162. https://doi.org/10.3390/rs13020162 Quintana, R. (2014). Técnicas avanzadas de análisis para los cultivos en tiempo real. En IICA y PROCISUR (Eds.), Manual de agricultura de precisión (pp. 58-70). IICA. Ramírez-Mesén, C. M. (2019). Uso de un vehículo aéreo no tripulado como alternativa para evaluar el estado nutricional de una plantación de Gmelina arborea Roxb, San Carlos, Costa Rica. https://repositoriotec.tec.ac.cr/bitstream/handle/2238/11154/uso_vehiculo_aereo_no_tripulado.pdf?sequence=1&isAllowed=y Richards, J. A., & Richards, J. A. (1999). Remote sensing digital image analysis. Springer. Sims, N. C., Culvenor, D., Newnham, G., Coops, N. C., & Hopmans, P. (2013). Towards the operational use of satellite hyperspectral image data for mapping nutrient status and fertilizer requirements in Australian plantation forests. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2), 320-328. https://doi.org/10.1109/jstars.2013.2251610 Smigaj, M., Gaulton, R., Barr, S. L., & Suárez, J. C. (2015). UAV-borne thermal imaging for forest health monitoring: detection of disease induced canopy temperature increase. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, XL-3/W3, 349-354. https://doi.org/10.5194/isprsarchives-xl-3-w3-349-2015 Tahir, M. N., Naqvi, S. Z. A., Lan, Y., Zhang, Y., Wang, Y., Afzal, M., Cheema, M. J. M., & Amir, S. (2018). Real time estimation of chlorophyll content based on vegetation indices derived from multispectral UAV in the kinnow orchard. International Journal of Precision Agricultural Aviation, 1(1), 24-31. http://dx.doi.org/10.33440/j.ijpaa.20180101.0001 Tang, L., & Shao, G. (2015). Drone remote sensing for forestry research and practices. Journal of Forestry Research, 26(4), 791-797. https://doi.org/10.1007/s11676-015-0088-y Torresan, C., Berton, A., Carotenuto, F., Di Gennaro, S. F., Gioli, B., Matese, A., Miglietta, F., Vagnoli, C., Zaldei, A., & Wallace, L. (2017). Forestry applications of UAVs in Europe: A review. International Journal of Remote Sensing, 38(8-10), 2427-2447. https://doi.org/10.1080/01431161.2016.1252477 Tsouros, D. C., Bibi, S., & Sarigiannidis, P. G. (2019). A review on UAV-based applications for precision agriculture. Information, 10(11), 349. https://doi.org/10.3390/info10110349 Watt, M. S., Pearse, G. D., Dash, J. P., Melia, N., & Leonardo, E. M. C. (2019). Application of remote sensing technologies to identify impacts of nutritional deficiencies on forests. ISPRS Journal of Photogrammetry and Remote Sensing, 149, 226-241. https://doi.org/10.1016/j.isprsjprs.2019.01.009 Wu, B., Liang, A., Zhang, H., Zhu, T., Zou, Z., Yang, D., Tang, W., Li, J., & Su, J. (2021). Application of conventional UAV-based high-throughput object detection to the early diagnosis of pine wilt disease by deep learning. Forest Ecology and Management, 486, 118986. https://doi.org/10.1016/j.foreco.2021.118986 Yu, R., Luo, Y., Zhou, Q., Zhang, X., Wu, D., & Ren, L. (2021a). Early detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery. Forest Ecology and Management, 497, 119493. https://doi.org/10.1016/j.foreco.2021.119493 Yu, R., Luo, Y., Zhou, Q., Zhang, X., Wu, D., & Ren, L. (2021b). A machine learning algorithm to detect pine wilt disease using UAV-based hyperspectral imagery and LiDAR data at the tree level. International Journal of Applied Earth Observation and Geoinformation, 101, 102363. https://doi.org/10.1016/j.jag.2021.102363 Yu, R., Ren, L., & Luo, Y. (2021c). Early detection of pine wilt disease in Pinus tabuliformis in North China using a field portable spectrometer and UAV-based hyperspectral imagery. Forest Ecosystems, 8(1), 1-19. https://doi.org/10.1186/s40663-021-00328-6 Yu, L., Zhan, Z., Ren, L., Zong, S., Luo, Y., & Huang, H. (2020). Evaluating the potential of WorldView-3 data to classify different shoot damage ratios of Pinus yunnanensis. Forests, 11(4), 417. https://doi.org/10.3390/f11040417 Yuan, Y., & Hu, X. (2016). Random forest and objected-based classification for forest pest extraction from UAV aerial imagery. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 41, 1093. https://doi.org/10.5194/isprsarchives-xli-b1-1093-2016 Zhang, N., Zhang, X., Yang, G., Zhu, C., Huo, L., & Feng, H. (2018). Assessment of defoliation during the Dendrolimus tabulaeformis Tsai et Liu disaster outbreak using UAV-based hyperspectral images. Remote Sensing of Environment, 217, 323-339. https://doi.org/10.1016/j.rse.2018.08.024spa
dc.rights.creativecommonsEsta obra está bajo una licencia internacional Creative Commons Atribución-CompartirIgual 4.0.spa
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dc.relation.citationeditionNúm. 1 , Año 2023 : Enero-juniospa
dc.relation.ispartofjournalColombia forestalspa
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dc.identifier.urlhttps://doi.org/10.14483/2256201X.19250
dc.relation.citationstartpage123
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