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
DOI:
https://doi.org/10.17268/sci.agropecu.2021.032Keywords:
Plant community, Remote sensors, Tolar, Bofedal, UAS, UAVAbstract
Remote sensing and geographic information systems are tools that in the last decade have been widely used in the management of natural resources, however, they have presented deficiencies for precision livestock studies due to the quality of spatial resolutions, spectral and temporal. Faced with this limitation, microsensors appear as an alternative in Unmanned Aerial Systems (UAS) that allow obtaining orthophotographs with better resolutions. Considering these advantages, a study was developed to determine the best flight height in the detection and identification of the tolar and bofedal plant communities of the dry puna. For the study, RGB and NDVI photographs were collected with ZENMUSE X3 DJI RGB-NDVI sensors in UAS with flight heights of 25, 50, 75 and 100 m. In the field, tola plants and DIMU cushions were counted in quadrants of 10 m x 10 m (100 m2). The preparation of orthophotographs was carried out in the Pix 4D software and to analyze the information an algorithm was developed with the ability to identify a segmented element (tola Plant and / or DIMU cushion) using Python. The study found that the NDVI range for the identification of tolares of Parastrephia lepidophilla is from 0.20 to 0.45 and for Distichia muscoides bogs is from 0.68 to 0.95; finally, using RGB and NDVI orthophotographs, it was determined that the best flight height to identify the Tola and DIMU segmented species is 25 m followed by 50 m.
References
Acebedo, R. (2011). Sistemas de teledetección activos y pasivos embarcados en sistemas aéreos no tripulados para la monitorización de la tierra Barcelona España.15 pp.
Alve, S., Carvalho, F., Queiroz, D., Terra, N., & Sousa, J. (2013). Determination of nitrogen and Chlorophyll levels in bean plant leaves by using spectral vegetation bands and indices. Revista Ciência Agronômica, 44(3), 464-473.
Azcoiti, J. (2016). Evaluación de aplicaciones para el tratamiento de imágenes mediante correlación Automática (Tesis de Maestría). Universidad Pública de Navarra. España.
Berrio, M., Mosquera, T., & Álzate, D. (2015). Uso de drones para el análisis de imágenes multiespectrales en agricultura de pre uutilización de imágenes multiespectrales obtenidas por vehículos aéreos no tripulados para la determinación de zonas intra-lote con mayor rendimiento y calidad en el trigo (Triticum aestivum). Universidad Nacional de Villamaría, Argentina.
Bethany, M., Lucier, A., & Aryal, J. (2019). Classification of lowland native grassland communities using hyperspectral Unmanned Aircraft System (UAS) imagery in the Tasmanian midlands. Drones, 3(1), 5.
Baranowski, P., Jedryczka, M., Mazurek, W., Skowronska, D., Siedlidka, A., & Kaczmarek, J. (2015). Hyperspectral and Thermal Imagen of Rape (Brassica napus) response to fungal species of the genus Alternaria. PLoS ONE 10(3): e0122913.
Buitrón, C., & Callisaya, J. (2012). Estudio espacial multitemporal de variaciones en superficie observada a través de imágenes satelitales LANDSAT en una región del Parque Nacional Sajama Bolivia, DIACONIA. La Paz, Bolivia.
D’oleira, S., Marzolff, I., Klaus, D., & Ries, J. (2012). Unmanned aerial Veicle (UAV) for Monitoring soil erosion in Moroco. Remote sens., 4, 3390-3416.
Easdale, M., Umaña, F., Rafo, F., Fariña, C., & Bruzzone, O. (2019). Evaluación de pastizales patagónicos con imágenes de satélites y de vehículos aéreos no tripulados. Ecología Austral, 29, 285-445.
Escuer, J. (2007). Análisis multitemporal mediante el uso de imágenes del satélite Landsat. Innovación y formación, 2, 34-36.
Estrada, A., Cárdenas, J., Ñaupari, J., & Zapana, J. (2018). Capacidad de carga de pastos de puna húmeda en un contexto de cambio climático. Revista de Investigaciones Altoandinas, 20(3), 361-368.
Ezenne, G., Jupp, L., Mantel, S., & Tanner, J. L. (2019). Current and potential capabilities of UAS for crop water productivity in precision agriculture. Agricultural Water Management, 218, 158-164.
Lost, F., Heldens, W., Kong, Z., & Lange, E. (2019). Drones: Innovate technology for use in precision pest management. Journal of Economic Entomology, 113(1), 1–25.
Flores, D., Gonzáles, H., Lozano, R., Vazquez, J., & Hernandez, J. (2021). Automated Agave detection and counting using a covolucional Neural Network and Unmanned Aerial Systems. Drones, 5(1), 4.
Flores, E. R.; Ñaupari, J. A.; & Tacuna, R. E. (2014). La economía del cambio climático en el Perú: Ganadería altoandina en la economía del cambio climático en el Perú: Desarrollo, CEPAL.
Grüner, E, Astor, T., & Wachendorf, M. (2019). Biomass Prediction of Heterogeneous Temperate Grasslands Using an SfM Approach Based on UAV Imaging. Agronomy, 9(2), 54.
Hatton, N., Sharda, A., Schapaugh, W., & Merwe, D. (2020). Remote thermal infrared imaging for rapid screening of sudden death syndrome in soyben. Computers and Electronics in Agriculture, 178, 105738.
Hernández, L., Rudge, M., Bartolo, R., & Erskine, P. (2019). Identifying species and monitoring understory from UAS derived data; a literature reviw and future directions. Drones, 3(1), 9.
Holdrige, L. R. (1987). Ecología basada en zonas de vida. Servicio editorial IICA, 3era Edición, San José de Costa Rica. ISBN 92 9039 1316 pp. 2-26.
Ishida, T., Kurihara, J., Viray, F., Namuco, S., Paringit, E, et al. (2018). A novel approach for vegetation classification using UAV-based hyperspectral imaging. Computers and Electronics in Agriculture, 144, 80-85.
Jacsaon, M, Potrillo, Q., Cox, R., Ritchie, G., Johnson, M., et al. (2020). Season classifier, and special resolution impact honey mesquite and yellow bluestem detection using unmanned aerial System. Rangeland Ecology & Management, 73(5), 658-672.
Kharuf, S; Hernandez, L, Orosco, R., & Delagdo, I. (2018). Análisis de imágenes multiespectrales adquiridas con vehículos aéreos no tripulados. Ingeniería Electrónica, Automática y comunicaciones, 39(2), 79-91.
Librán-Embid, F., Klaus, F., Tscharntke, T., & Grass, I. (2020). Unmanned aerial Vehicles for biodiversity friendly agricultural landscape a systematic review. Science of The Total Environment, 732, 139204.
Lin, Z., & Guo, W, (2020). Sorghum panicle detection and counting using unmanned aerial system images and deep learning. Frontiers in Plant Science, 11, 534853.
Malambo, L., Popescu, S., Wei, N., Romey, W., Zhou, T., & Moore, S. (2019). Ad deep learning semantic segmentation-based approach for field level sorghum panicle counting. Remote Sens., 11(24), 2939.
Norasma, C., Fadzilah, M., Roslin, N., Zanariah, Z., Tarmidi, Z., & Candra, S., (2019). Unmanned aerial vehicle applications in agriculture. IOP Conf. Ser.: Mater. Sci. Eng., 506, 012063.
Pulgar, J. (1996). Geografía del Perú, las ocho regiones naturales. Promoción editorial Inca S.A. 10a edición, Lima Perú, ISBN 9972400273. 15-23.
Robb, C., Harday, A., Doonan, J., & Brook, J. (2020). Semi automated field plot segmentation from UAS imagery for experimental agriculture. Frontiers in plant Science, 11, 591886.
Sakaran, S., Quirós, J., & Milkas, P. (2019). Unmanned aerial system and satellite based high resolution imagery for high throughput phenotyping in dry bean. Computers and Electronics in Agriculture, 165, 104965.
Sankey, J., Sankey, T., Li, J., Ravi, S., Wang, G., et al. (2021). Quantifying plant-soil-nutrient dynamics in rangelands: Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland. Remote Sensing of Environment, 253, 112223.
Tapia, M., & Flores, J. (1984). Ecosistema de los andes del sur del Perú y su relación con los pastizales. Capítulo del libro Pastoreo y pastizales en los andes del sur del Perú. Programa colaborativo de apoyo a la investigación en rumiantes menores Cusco, Perú.
Tilahun, T., & Seyoum, W. (2020). Heigh-resolution mapping of tile drainage in agricultural fields using unmanned System (UAS) based radiometric thermal and optical sensors. Hydrology, 8(1), 2.
Xie, Ch., & Yang, C., (2020). A review on plant high throughput traits using UAV based sensors. Computers and Electronics in Agriculture, 178, 105731.
Xu, R., & Paterson, A. (2019). Multispectral imaging and unmanned aerial systems for cotton plant phenotyping. PLoS ONE, 14(2), e0205083.
Zhengxia, Z., Zhenwi, S., Young, G., & Jienping, Y. (2019). Object Detection in 20 Years: A survey. ArXiv, abs/1905.05055.
Zhu, F., Jiewei, L., Honghui, X., & Goodman, E. (2018). Automatic Tobacco plant detection in UAV images via deep Neural Networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(3), 876–887.
Zorogastua, P., Quiroz, R., & Garatuza, J. (2012). Dinámica de los bofedales en el altiplano peruano. Revista Latinoamericana de Recursos Naturales, 8(2), 63-75.
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Andrés C. Estrada Zúñiga, Javier Ñaupari Vásquez
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The authors who publish in this journal accept the following conditions:
a. The authors retain the copyright and assign to the magazine the right of the first publication, with the work registered with the Creative Commons attribution license, which allows third parties to use the published information whenever they mention the authorship of the work and the First publication in this journal.
b. Authors may make other independent and additional contractual arrangements for non-exclusive distribution of the version of the article published in this journal (eg, include it in an institutional repository or publish it in a book) as long as it clearly indicates that the work Was first published in this journal.
c. Authors are encouraged to publish their work on the Internet (for example, on institutional or personal pages) before and during the review and publication process, as it can lead to productive exchanges and a greater and faster dissemination of work Published (see The Effect of Open Access).
