Evaluation of a restoration through drone in the tamaulipeco thornscrub
DOI:
https://doi.org/10.18387/polibotanica.54.5Keywords:
drone, NDVI, RPAS, survival, vigorAbstract
The evaluation of forest restoration projects is important and is usually long-term with intermediate evaluations. To reduce evaluation times in the field, an alternative is the use of remote sensing using unmanned aerial vehicles. The objective was to test the usefulness of a system carried by unmanned aerial vehicles for the spatial monitoring of a forest plantation. The survival and vigor status of each of the plants was evaluated in the field, recording the location, species, survival (V = alive, M = dead) and plant vigor (B = good, R = regular and M = dead), in addition, a flight was made conformed by the UAS / RPAS, Wingtra brand, with a Micasense® Altum® multispectral camera with 6 new generation sensors (blue, green, red, red edge and near infrared and thermal), of This information was calculated by NDVI and the values were extracted according to a 30 cm buffer. It was determined that the average values of the NDVI for dead plants was less than 0.35 and live plants greater than 0.35, where a 57.72 % of global precision was obtained. For vigor, it was determined that for the dead state the average value of the NDVI was less than 0.35, the regular state was in the range of 0.35 to 0.38, and finally, the good state the range of NDVI was greater than 0.38, with an overall precision of 43.79 %. It is concluded that the system transported by unmanned aerial vehicles has the potential to be used to evaluate forest plantations of mixed species of the Tamaulipas thornscrub.
References
Almeida, D. R. A., Broadbent, E. N., Zambrano, A. M. A., Wilkinson, B. E., Ferreira, M. E., Chazdon, R., … Brancalion, P. H. S. (2019). Monitoring the structure of forest restoration plantations with a drone-lidar system. International Journal of Applied Earth Observation and Geoinformation, 79(March), 192–198. https://doi.org/10.1016/j.jag.2019.03.014
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
Barbati, A., Corona, P., & Marchetti, M. (2007). A forest typology for monitoring sustainable forest management: The case of European Forest Types. Plant Biosystems, 141(1), 93–103. https://doi.org/10.1080/11263500601153842
Chemura, A., Mutanga, O., & Dube, T. (2017). Integrating age in the detection and mapping of incongruous patches in coffee (Coffea arabica) plantations using multi-temporal Landsat 8 NDVI anomalies. International Journal of Applied Earth Observation and Geoinformation, 57, 1–13. https://doi.org/10.1016/j.jag.2016.12.007
Chuvieco, E. (1996). Fundamentos de Teledetección Espacial (Tercera ed). Madrid, España: RIAL.
Doi, R. (2020). Assessing the reforestation effects of plantation plots in the Thai savanna based on 45 cm resolution true-color images and machine learning. Environmental Research Letters, 16(1). https://doi.org/10.1088/1748-9326/abcfe3
Estrada Zúñiga, A. C., & Vásquez, J. Ñ. (2021). Detection and identification of high Andean plant communities, Wetlands and Tolar de Puna Seca by means of RGB and NDVI orthophotos in “Unmanned Aerial Systems” drones. Scientia Agropecuaria, 12(3), 291–301. https://doi.org/10.17268/SCI.AGROPECU.2021.032
García, E. (2004). Modificaciones al sistema de clasificación climática de Köppen (Quinta Edi). México D. F.: Instituto de Geografia - UNAM. Retrieved from http://www.igeograf.unam.mx/sigg/utilidades/docs/pdfs/publicaciones/geo_siglo21/serie_lib/modific_al_sis.pdf
Goodbody, T. R. H., Coops, N. C., Marshall, P. L., Tompalski, P., & Crawford, P. (2017). Unmanned aerial systems for precision forest inventory purposes: A review and case study. Forestry Chronicle, 93(1), 71–81. https://doi.org/10.5558/tfc2017-012
Hernández Ramos, A., Cano Pineda, A., Buendía Rodríguez, E., Tamarit Urias, J. C., & Hernández Ramos, J. (2020). Mapeo del peso verde y cobertura arbistiva de Euphorbia antisyphilitica Zucc. con datos espectrales del satelite Sentinel-2. In I. Orona Castillo, A. Amador Machado, J. Puentes Gutiérrez, M. Fortis Hernández, P. Preciado Rangel, & F. J. Ruiz Ortega (Eds.), Memoria de la XXXII Semana Internacional de Agronomía (1ra ed., pp. 1254–1260). Venecia, Durango, México: Universidad Juárez del Estado de Durango, Facultad de Agricultura y Zootecnia.
Katila, P., Colfer, C. J. P., de Jong, W., Galloway, G., Pacheco, P., & Winkel, G. (2019). Sustainable development goals: Their impacts on forests and people. Sustainable Development Goals: Their Impacts on Forests and People, 1–617. https://doi.org/10.1017/9781108765015
Pequeño-Ledezma, M., Alanís-Rodríguez, E., Jiménez-Pérez, J., Aguirre-Calderón, O., González-Tagle, M. A., & Molina Guerra, V. M. (2016). Criterios a considerar para desarrollar proyectos de restauración ecológica. Revista Iberoamericana de Ciencias, 3(2), 94–105. Retrieved from http://reibci.org/publicados/2016/jun/1600108.pdf
Plaza Duarte, H. (2020). Niveles de vigorosidad de plantaciones de teca, (Tectona grandis), mediante índices de vegetación. Universidad Técnica Estatal de Quevedo. Retrieved from https://repositorio.uteq.edu.ec/handle/43000/6288
Reed B., C., Schwartz M., D., & Xiao, X. (2009). Remote sensing phenology: Status and the way forward. In A. Noormets (Ed.), Phenology of ecosystem processes (pp. 231–246). Netherlands: Springer. https://doi.org/10.1007/9781-4419-0026-5_10
van Leeuwen, M., & Nieuwenhuis, M. (2010). Retrieval of forest structural parameters using LiDAR remote sensing. European Journal of Forest Research, 129(4), 749–770. https://doi.org/10.1007/s10342-010-0381-4
Wang, Q., Tenhunen, J., Dinh, N. Q., Reichstein, M., Vesala, T., & Keronen, P. (2004). Similarities in ground- and satellite-based NDVI time series and their relationship to physiological activity of a Scots pine forest in Finland. Remote Sensing of Environment, 93(1–2), 225–237. https://doi.org/10.1016/j.rse.2004.07.006
White, R. A., Bomber, M., Hupy, J. P., & Shortridge, A. (2018). UAS-GEOBIA approach to sapling identification in jack pine barrens after fire. Drones, 2(4), 1–15. https://doi.org/10.3390/drones2040040
Zar, J. H. (2010). Biostatistical analysis (5th ed.). New Jersey: Prentice Hall.
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