Estimation of Pinus radiata D. DON tree heights in San Juan, Chimborazo, using unmanned aerial vehicles
DOI:
https://doi.org/10.47187/perf.v1i32.276Keywords:
Plantation, Clinometer, RTK, Drones, HeightAbstract
The evolution of technology has made possible its application in the forestry sector. Manned aircraft known as drones have had a growing incidence and drones have become devices with a wide variety of functions with ease of use. The present investigation was developed in a forest plantation located in the San Juan parish, Riobamba canton, province of Chimborazo. We chose 15 trees in the young stand (6 years old) and 15 trees in the adult stand (25 years old) which were randomly selected from the entire research area. The measuring equipment used was a haglof digital clinometer and a Leica D5 distance meter to measure the distance from the observer's point to the tree, a Mavic Air 2 drone and a Trimble RTK station were used to take 5 control points considering the irregularities of the terrain. Finally, the results of the coefficients are not statistically significant, so it cannot be stated with confidence that it has a real effect on the dependent variable (DIFFERENCE OF MEASUREMENTS) based on the data and the significance level selected.
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Cabrera J, Lamelas MT, Montealegre AL. Estimación de variables dasométricas a partir de datos LiDAR PNOA en masas regulares de Pinus halepensis Mill. 2014.
Ferreira Rojas O. Manual de inventarios forestales. Escuela Nacional de Ciencias Forestales; 1990.
Gambetta F, Bermudez. Curso inventarios forestales en bosques secos [Internet]. 1994. Available from: https://repositorio.catie.ac.cr/handle/11554/1056
Suh J, Choi Y. Mapping hazardous mining-induced sinkhole subsidence using unmanned aerial vehicle (drone) photogrammetry. Environ Earth Sci. 2017;76. https://doi.org/10.1007/s12665-017-6458-3
Rouse JW, Haas RH, Deering DW, Schell JA, Harlan JC. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation (E75-10354) [Internet]. 1974. Available from: https://ntrs.nasa.gov/citations/19750020419
Gutiérrez ARH, Duarte MAT, France RG, León RR. El uso de drones en ciencias de la tierra. Reaxión. Revista arbitrada de divulgación científica de la Universidad Tecnológica de León. 2017 Jan 23 [Internet]. Available from: http://reaxion.utleon.edu.mx/Art_El_uso_de_drones_en_ciencias_de_la_tierra.html
Baena S, Boyd DS, Moat J. UAVs in pursuit of plant conservation—Real world experiences. Ecol Inform. 2018;47:2-9. https://doi.org/10.1016/j.ecoinf.2017.11.001
Gago J, Douthe C, Coopman RE, Gallego PP, Ribas-Carbo M, Flexas J, et al. UAVs challenge to assess water stress for sustainable agriculture. Agric Water Manag. 2015;153:9-19. https://doi.org/10.1016/j.agwat.2015.01.020
Colomina I, Molina P. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J Photogramm Remote Sens. 2014;92:79-97. https://doi.org/10.1016/j.isprsjprs.2014.02.013
Rejeb A, Abdollahi A, Rejeb K, Treiblmaier H. Drones in agriculture: A review and bibliometric analysis. Comput Electron Agric. 2022;198:107017. https://doi.org/10.1016/j.compag.2022.107017
Cuerno Rejado C, Garcia Hernandez L, Sanchez Carmona A, Carrio Fernandez A, Sanchez Lopez JL, Campoy Cervera P. EVOLUCIÓN HISTÓRICA DE LOS VEHÍCULOS AÉREOS NO TRIPULADOS HASTA LA ACTUALIDAD. DYNA Ing Ind. 2016;91(1):282-8. https://doi.org/10.6036/7781
Westoby MJ, Brasington J, Glasser NF, Hambrey MJ, Reynolds JM. ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology. 2012;179:300-14. https://doi.org/10.1016/j.geomorph.2012.08.021
Fritz A, Kattenborn T, Koch B. UAV-BASED PHOTOGRAMMETRIC POINT CLOUDS – TREE STEM MAPPING IN OPEN STANDS IN COMPARISON TO TERRESTRIAL LASER SCANNER POINT CLOUDS. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2013;XL-1-W2:141-6. https://doi.org/10.5194/isprsarchives-XL-1-W2-141-2013
Hung C, Bryson M, Sukkarieh S. Multi-class predictive template for tree crown detection. ISPRS J Photogramm Remote Sens. 2012;68:170-83. https://doi.org/10.1016/j.isprsjprs.2012.01.009
Noboa S. Estimación de altura de frailejones (Espeletia pycnophylla) en el volcán Chiles mediante UAV (Carchi – Ecuador) [Master’s thesis]. Universidad Politécnica Salesiana; 2019.
Manfreda S, McCabe M, Miller P, Lucas R, Madrigal V, Mallinis G, et al. On the Use of Unmanned Aerial Systems for Environmental Monitoring [Internet]. 2018. https://doi.org/10.20944/preprints201803.0097.v1
Cunliffe AM, Brazier RE, Anderson K. Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sens Environ. 2016;183:129-43. https://doi.org/10.1016/j.rse.2016.05.019
Moradi S, Bokani A, Hassan J. UAV-based Smart Agriculture: A Review of UAV Sensing and Applications. 2022 32nd International Telecommunication Networks and Applications Conference (ITNAC). 2022:181-4. https://doi.org/10.1109/ITNAC55475.2022.9998411
Reyes-Zurita N, Rodríguez-Ortiz G, Valle JRE-D, Jiménez-Colmenares CL, Rincón-Ramírez JA. Estimación de variables dasométricas en rodales bajo manejo forestal con vehículos aéreos no tripulados. FIGEMPA: Investig Desarro. 2022;13(1), Article 1. https://doi.org/10.29166/revfig.v13i1.3299
White L, Lucieer A, Turner D, Watson C. Evaluación y mitigación de errores para levantamientos LiDAR hipertemporales transportados por UAV del inventario forestal. Actas de Silvilaser, Hobart. 2011.
Queiroz WT. Amostragem em inventário florestal. Belém: Universidade Rural do Amazônia; 2012. 441 p.
Mikita T, Janata P, Surov P. Inventario de rodales forestales basado en fotogrametría combinada aérea y terrestre de corto alcance. Bosques. 2016;7(8):1-14.
Groot A, Cortini F, Wulder MA. Relaciones de atributos de fibra de copa para un inventario forestal mejorado: Progreso y perspectivas. For Chron. 2015;91(3):266-79.
Wallace L, Lucieer A, Turner D, Watson C. Evaluación y mitigación de errores para levantamientos LiDAR hipertemporales transportados por UAV del inventario forestal. Actas de Silvilaser, Hobart. 2011.
Bastian A. Análisis comparativo entre os programas para restituição Fotogramétrica photomodeler y orthowre. III Seminario Internacional sobre Documentación del Patrimonio Arquitectónico con el Uso de Tecnologías Digitales. Joao Pessoa. PB; 2014. p. 119-29.
Iglhaut J, Cabo C, Puliti S, Piermattei L, O'Connor J, Rosette J. Estructura de la fotogrametría de movimiento en la silvicultura: una revisión. Informes For Actuales. 2019;5(3):155-68.
Ablanedo ES, Candler JH, Pérez JRR, Ordoñez C. Precisión de levantamientos de fotogrametría con vehículos aéreos no tripulados (UAV) y SFM en función del número y ubicación de los puntos de control terrestre utilizados. Teledetección. 2018;10(10):2-19.
Figueiredo EO, D'Oliveira MVN, Cerraduras CJ, Papá DA. Estimativa del Volumen de Madeira en Patios de Estocagem de Toras por medio de Cámaras RGB Instaladas en Aeronaves Remotamente Pilotadas (ARP). Bol Cien For.
Rouse JW, Haas RH, Deering DW, Schell JA, Harlan JC. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation (E75-10354) [Internet]. 1974. Available from: https://ntrs.nasa.gov/citations/19750020419
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