Modelado de la distribución de especies de fauna silvestre mediante aeronaves no tripuladas
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El uso de drones y software libre en Modelos de Distribución de Especies (MDE) representa una alternativa innovadora para el estudio de la fauna silvestre. Este artículo revisa el estado del arte sobre su integración, identificando tendencias, desafíos y oportunidades.
La metodología siguió las directrices PRISMA, con búsqueda en bases como Scopus, Web of Science y Google Scholar. Se seleccionaron artículos publicados en la última década que aborden el uso de drones en monitoreo faunístico y el empleo de herramientas libres en análisis espacial.
Los resultados muestran un aumento notable en el uso de drones y fotogrametría para obtener datos geoespaciales precisos, lo que mejora la identificación de hábitats y patrones de distribución. Herramientas como QGIS, R y MaxEnt permiten procesar estos datos sin costos de licencia, fomentando la accesibilidad y la reproducibilidad científica.
No obstante, persisten desafíos en la estandarización metodológica, la fusión de datos heterogéneos y la detección limitada de ciertas especies. La variabilidad en la calidad de las imágenes y las condiciones ambientales también afecta los resultados.
En conclusión, la combinación de drones y software libre ofrece beneficios claros: mejora la eficiencia, aumenta la precisión de los modelos y reduce costos. Sin embargo, se requiere mayor estandarización y validación técnica para optimizar su aplicación en ecología y conservación.
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Referencias
A. Jiménez-Valverde, P. Acevedo, A. M. Barbosa, J. M. Lobo, and R. Real, “Maximum entropy modeling of species geographic distributions: A review of recent developments and applications,” Ecosphere, vol. 12, no. 1, p. e03320, 2021.
E. Gutiérrez and I. Trejo, “Application of different types of data in the distribution modeling of tree species in mexico,” Colombia Forestal, vol. 26, no. 1, pp. 48–63, 2023.
M. Mo and K. Bonatakis, “Approaching wildlife with drones: using scientific literature to identify factors to consider for minimising disturbance,” Australian Zoologist, vol. 42, no. 1, pp. 1–29, 2022.
Y. Huang et al., “Realized ecological forecast through an interactive ecological platform for assimilating data (ecopad, v1.0) into models,” Geoscientific Model Development, vol. 12, no. 3, pp. 1119–1137, 2019.
R. G. Mateo et al., “Hierarchical species distribution models in support of vegetation conservation at the landscape scale,” Journal of Vegetation Science, vol. 30, no. 2, pp. 386–396, 2019.
R. Valavi, J. Elith, J. J. Lahoz-Monfort, and G. Guillera-Arroita, “Modelling species presence-only data with random forests,” Ecography, vol. 44, no. 12, pp. 1731–1742, 2021.
N. Millner, A. M. Cunliffe, M. Mulero-Pázmány, B. Newport, C. Sandbrook, and S. Wich, “Exploring the opportunities and risks of aerial monitoring for biodiversity conservation,” Global Social Challenges Journal, vol. 2, pp. 2–23, 2023.
D. Zurell, S. A. Fritz, A. Rönnfeldt, and M. J. Steinbauer, “Predicting extinctions with species distribution models,” Cambridge Prisms: Extinction, vol. 1, 2023.
M. Rashid, B. S. Bari, Y. Yusup, M. A. Kamaruddin, and N. Khan, “A comprehensive review of crop yield prediction using machine learning approaches,” IEEE Access, vol. 9, pp. 63 406–63 439, 2021.
D. Boral and S. Moktan, “Modelling current and future potential distribution of medicinal orchids in darjeeling eastern himalaya,” Plant Ecology, vol. 225, no. 3, pp. 213–226, 2024.
J. Elith and J. R. Leathwick, “Species distribution models: Ecological explanation and prediction across space and time,” Annual Review of Ecology, Evolution, and Systematics, vol. 40, pp. 677–697, 2009.
A. Guisan, T. C. Edwards, and T. Hastie, “Generalized linear and generalized additive models in studies of species distributions,” Ecological Modelling, vol. 157, no. 2–3, pp. 89–100, 2002.
S. B. Phillips, V. P. Aneja, D. Kang, and S. P. Arya, “Modelling and analysis of the atmospheric nitrogen deposition in north carolina,” International Journal of Global Environmental Issues, pp. 231–252, 2006.
S. Mohanty, S. Mustak, D. Singh, T. Van Hoang, M. Mondal, and C. T. Wang, “Vulnerability and risk assessment mapping of bhitarkanika national park, odisha, india using machine-based embedded decision support system,” Frontiers in Environmental Science, vol. 11, pp. 1–19, 2023.
C. Waldock et al., “A quantitative review of abundance-based species distribution models,” Ecography, 2022.
F. E. Rodríguez-Manzo and L. Juárez González, “Exploración cualitativa sobre el ruido ambiental urbano en la ciudad de méxico,” Estudios Demográficos y Urbanos, vol. 35, no. 3, pp. 803–838, 2020.
J. Elith, S. J. Phillips, T. Hastie, M. Dudík, Y. E. Chee, and C. J. Yates, “A statistical explanation of maxent for ecologists,” Diversity and Distributions, vol. 17, no. 1, pp. 43–57, 2011.
Y. Zhang, Y. Feng, S. Wang, H. Li, and J. Chen, “Predicting the spatiotemporal dynamics of invasive species using ensemble machine learning and remote sensing data,” Ecological Informatics, vol. 71, p. 101756, 2022.
F. Fischer, É. Joetzjer, I. Maréchaux, I. F. Sun, and J. Chave, “Transferability of an individual- and trait-based forest dynamics model: A testcase across the tropics,” bioRxiv, 2021.
V. F. Frans, T. H. Oliver, T. H. August, N. J. B. Isaac, and L. V. Dicks, “Integrated sdm database: Enhancing the relevance and utility of species distribution models in conservation management,” Methods in Ecology and Evolution, vol. 13, no. 1, pp. 243–261, 2022.
H. L. Kopsco, R. L. Smith, and S. J. Halsey, “A scoping review of species distribution modeling methods for tick vectors,” Frontiers in Ecology and Evolution, 2022.
A. Hua et al., “Protecting endangered megafauna through ai analysis of drone images in a low-connectivity setting: a case study from namibia,” PeerJ, vol. 10, 2022.
C. Guillaumot, B. Danis, T. Saucéde, G. Guillou, B. David, and T. Saucéde, “Species distribution modelling of the southern ocean benthos: A review on methods, cautions and solutions,” Antarctic Science, vol. 33, no. 4, pp. 389–406, 2021.
J. Aguirre-Gutiérrez, F. Bisanzio, C. Capinha, and H. M. Pereira, “Mapping the gaps in alien species richness across contrasting european regions,” Diversity and Distributions, vol. 28, no. 5, pp. 946–958, 2022.
A. Iqbal, M. S. Mondal, W. Veerbeek, M. S. A. Khan, and H. Hakvoort, “Effectiveness of uav-based dtm and satellite-based sdms for local-level flood modeling in jamuna floodplain,” Journal of Flood Risk Management, vol. 16, no. 4, pp. 1–19, 2023.
D. Phiri, M. Simwanda, S. Salekin, V. R. Nyirenda, Y. Murayama, and M. Ranagalage, “Sentinel-2 data for land cover/use mapping: A review,” Remote Sensing, vol. 12, no. 14, 2020.
J. C. Padró, F. J. Muñoz, J. Planas, and X. Pons, “Comparison of four uav georeferencing methods for environmental monitoring purposes focusing on the combined use with airborne and satellite remote sensing platforms,” International Journal of Applied Earth Observation and Geoinformation, vol. 75, pp. 130–140, 2019.
M. D. Meza Castro, “El recurso de información y comunicación visual: imagen. apuntes en torno a las ciencias de la información y bibliotecología,” e-Ciencias de la Información, vol. 8, no. 2, 2018.
J. J. López and M. Mulero-Pázmány, “Drones for conservation in protected areas: Present and future,” Drones, vol. 3, no. 1, 2019.
PRISMA Group, “Prisma 2020 flow diagram,” https://www.prisma-statement.org, 2020, accessed: 2024.
X. Huang, L. Wang, and Y. Li, “Integrating remote sensing and geospatial modeling for forest management: A review of current approaches and future trends,” Remote Sensing, vol. 14, no. 18, p. 4567, 2022.
M. M. Quamar, B. Al-Ramadan, K. Khan, M. Shafiullah, and S. El Ferik, “Advancements and applications of drone-integrated geographic information system technology—a review,” Remote Sensing, vol. 15, no. 20, p. 5039, 2023.
J. Detka, H. Coyle, M. Gomez, and G. S. Gilbert, “A drone-powered deep learning methodology for high precision remote sensing in california’s coastal shrubs,” Drones, vol. 7, no. 7, 2023.
J. C. Hernández Hernández, “La tecnología y la conservación de especies: el poder de las cámaras-trampa,” Revista Digital Universitaria, vol. 24, no. 4, 2023.
C. Merow et al., “What do we gain from simplicity versus complexity in species distribution models?” Ecography, vol. 37, no. 12, pp. 1267–1281, 2014.
D. Zurell, J. Franklin, C. König, and V. Bahn, “The machine learning-based species distribution models: Are they good for real-world predictions?” Diversity and Distributions, vol. 26, no. 8, pp. 852–864, 2020.
B. Deneu, M. Servajean, P. Bonnet, C. Botella, F. Munoz, and A. Joly, “Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment,” PLoS Computational Biology, vol. 17, no. 4, 2021.
A. Anand, M. K. Pandey, P. K. Srivastava, A. Gupta, and M. L. Khan, “Integrating multi-sensors data for species distribution mapping using deep learning and envelope models,” Remote Sensing, vol. 13, no. 16, 2021.
B. Bansod, R. Singh, R. Thakur, and G. Singhal, “A comparision between satellite based and drone based remote sensing technology to achieve sustainable development: A review,” Journal of Agriculture and Environment for International Development (JAEID), 2017.
M. P. Corcoran, M. L. Hobi, V. A. Barber, and A. Mathys, “Applications and implications of unmanned aerial vehicles (uavs) in ecology,” Methods in Ecology and Evolution, vol. 12, no. 9, pp. 1564–1577, 2021.
S. Ecke et al., “Uav-based forest health monitoring: A systematic review,” Remote Sensing, vol. 14, no. 13, 2022.
H. R. Gomes, M. Zortea, L. M. Carvalho, N. P. Ferreira, and R. A. da Silva, “Use of uav-based remote sensing for plant species distribution mapping in a tropical savanna,” Remote Sensing, vol. 14, no. 18, p. 2938, 2022.
B. S. Krishnan et al., “Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys,” Scientific Reports, vol. 13, no. 1, 2023.
G. M. Mori et al., “Predictive modelling of current and future potential distribution of the spectacled bear (tremarctos ornatus) in amazonas, northeast peru,” Animals, vol. 10, no. 10, pp. 1–21, 2020.
X. Zhu, Y. Zhao, W. J. Platt, and H. Liu, “Deep learning applications in species distribution models using remotely sensed data: A review,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 203, pp. 1–15, 2023.
D. Rocchini, C. Ricotta, G. Bacaro, and S. Schmidtlein, “High-resolution mapping of plant diversity using uavbased spectral metrics,” Ecological Informatics, vol. 73, p. 102014, 2023.
K. Witt and R. R. Dunn, “Open-source tools in ecology: Bridging the gap between accessibility and scientific rigor,” Frontiers in Ecology and Evolution, vol. 10, p. 881845, 2022.
W. Turner, I. Costello, and A. Soria-Barreto, “Low-cost tools for biodiversity monitoring in under-resourced regions: Opportunities and challenges,” Biodiversity and Conservation, vol. 30, no. 10, pp. 2649–2666, 2021.
J. Torres, R. Cárdenas, and K. Rojas, “Open-source tools for drone image analysis in ecological monitoring: A case study in andean ecosystems,” Ecological Informatics, vol. 75, p. 102067, 2023.
J. Fernández-López and F. J. Ariza, “Assessing the performance of open-access environmental data for species distribution modeling using uav imagery,” Land, vol. 12, no. 10, p. 1869, 2023.