Modelo de distribución potencial de Leontochir ovallei con datos de sensores remotos

[EN] Predicting the potential distribution of short-lived species with a narrow natural distribution range is a difficult task, especially when there is limited field data. The possible distribution of L. ovallei was modeled using the maximum entropy approach. This species has a very restricted distribution along the hyperarid coastal desert in northern Chile. Our results showed that local and regional environmental factors define its distribution. Changes in altitude and microhabitat related to the landforms are of critical importance at the local scale, whereas cloud cover variations associated with coastal fog was the principal factor determining the presence of L. ovallei at the regional level. This study verified the value of the maximum entropy in understanding the factors that influence the distribution of plant species with restricted distribution ranges.[ES] Predecir la distribución potencial de especies de vida corta con un rango de distribución natural restringido es una tarea compleja, especialmente cuando los datos de campo son limitados. La posible distribución de L. ovallei se modeló utilizando la técnica de máxima entropía. Esta especie tiene una distribución muy restringida a lo largo del desierto costero hiperárido del norte de Chile. Nuestros resultados mostraron que los factores ambientales locales y regionales definen su distribución. Los cambios de altitud y el microhábitat relacionados con la forma del terreno son de importancia crítica a escala local, mientras que las variaciones en la cobertura nubosa asociadas con la niebla costera fueron el principal factor que determinó la presencia de L. ovallei a nivel regional. Este estudio verificó el valor de la técnica de máxima entropía en la comprensión de los factores que influyen en la distribución de las especies de plantas con rangos de distribución restringidos.The Master supported this work in Remote Sensing program, Earth Observation Center Hémera and Centre for Genomics, Ecology and Environment (GEMA), Faculty of Sciences, Universidad Mayor.Payacán, S.; Alfaro, F.; Pérez-Martínez, W.; Briceño-De-Urbaneja, I. (2019). Potential distribution model of Leontochir ovallei using remote sensing data. Revista de Teledetección. 0(54):59-69. https://doi.org/10.4995/raet.2019.12792OJS5969054Austin, M. 2007. Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecological Modellling, 200(1-2), 1-19. https://doi.org/10.1016/j. ecolmodel.2006.07.005Baldwin, R.A. 2009. Use of Maximum Entropy Modeling in Wildlife Research. Entropy, 11(4), 854-866. https://doi.org/10.3390/e11040854.Bartel, R.A., Sexton, J.O. 2009. Monitoring habitat dynamics for rare and endangered species using satellite images and niche-based models. Ecography, 32(5), 888-896. https://doi.org/10.1111/ j.1600-0587.2009.05797.x.Barragán-Barrera, D.C., do Amaral, K.B., ChávezCarreño, P.A., Farías-Curtidor, N., LancherosNeva, R., Botero-Acosta, N., Bueno, P., Moreno, I.B., Bolaños-Jiménez, J., Bouveret, L., Castelblanco-Martínez, D.N., Luksenburg, J.A., Melliger, J., Mesa-Gutiérrez R., de Montgolfier, B., Ramos, E.A., Ridoux, V., Palacios, D.M. 2019. Ecological Niche Modeling of Three Species of Stenella Dolphins in the Caribbean Basin, With Application to the Seaflower Biosphere Reserve. Frontiers in Marine Science, 6(10). https://doi. org/10.3389/fmars.2019.00010.Carvajal, D.E., Loayza, A.P., López, R.P., Toro, P.J., Squeo, F.A. 2014. Growth and early seedling survival of four Atacama Desert shrub species under experimental light and water availability regimes. Revista Chilena de Historia Natural, 87(1), 28. https://doi.org/10.1186/S40693-014- 0028-9.Cereceda, P, Larraín, H., Osses, P., Lázaro, P., García, J.L., Hernández, V. 2000. El factor clima en la floración del desierto en los años "El Niño" 1991 y 1997". Revista de Geografía Norte Grande, 27, 37-52. Accesible at https://repositorio.uc.cl/ bitstream/handle/11534/10433/000313720. pdf?sequence=1&isAllowed=yCONAF. 1997. Corporación Nacional Forestal, Plan de Manejo Parque Nacional Llanos de Challe. Documento de Trabajo Nº 250. Mieres, G. (Ed.) Santiago, 129 pp.Ćorović, J, Popović, M., Cogălniceanu, D., Carretero, M.A., Crnobrnja-Isailović, J. 2018. Distribution of the meadow lizard in Europe and its realized ecological niche model. Journal of Natural History, 52(29-30), 1909-1925. https://doi.org/10.1080/002 22933.2018.1502829.Chavez, P.S. 1996. Image-based atmospheric corrections: Revisited and improved. Photogrammetric Engineering and Remote Sensing, 62(9), 1025-1036.Chávez, R.O., Moreira-Muñoz, A., Galleguillos, M., Olea, M., Aguayo, J., Latín, A., Aguilera-Betti, I., Muñoz, A.A., Manríquez H. 2019. GIMMS NDVI time series reveal the extent, duration, and intensity of "blooming desert" events in the hyper-arid Atacama Desert, Northern Chile. International Journal Applied Earth Observation and Geoinformation, 76, 193-203. https://doi.org/10.1016/j.jag.2018.11.013.Dormann, C.F., Elith, J., Bacher, S., Buchmann, C., Carls, G., Carré, G., Marquéz, J.R., Gruber, B., Lafourcade, B., Leitão, P.J., Münkemüller, T., McClean, C., Osborne, P.E., Reineking, B., Schröder, B., Skidmore, A.K., Zurell, D., Lautenbach, S. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 27-46. https://doi.org/10.1111/j.1600- 0587.2012.07348.x.Errázuriz, A.M., Hanisch, M. 1995. Horizonte 7: Historia y Geografía. Editorial Andrés Bello. https:// books.google.com.br/books?id=fB2TTLzH_5IC (accessed 07 February 2017).Franklin, J. 2010. Ecology, Biodiversity and Conservation. In Mapping Species Distributions: Spatial Inference and Prediction, Frontmatter, pp. I-Viii, Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511810602.Garreaud, R., Rutllant, J.A., Fuenzalida, H. 2002. Coastal Lows along the Subtropical West Coast of South America: Mean Structure and Evolution. Monthly Weather Review, 130, 75-88. https://doi. org/10.1175/1520-0493(2002)1302.0.CO;2.Garreaud, R., Barichivich, J., Christie, D.A. and Maldonado, A. 2008. Interannual variability of the coastal fog at Fray Jorge relict forests in semiarid Chile. Journal of Geophysical Research, 113, G04011. https://doi.org/10.1029/2008JG000709.Giannakopoulos, A., Vasileiou, N.G.C., Gougoulis, D.A., Cripps, P.J., Ioannidi, K.S., Chatzopoulos, D.C., Billinis, C., Mavrogianni, V.S., Petinaki, E., Fthenakis, G.C. 2019. Use of geographical information system and ecological niche modelling for predicting potential space distribution of subclinical mastitis in ewes. Veterinary Microbiology, 228, 119-128. https://doi.org/10.1016/j.vetmic.2018.11.021.González, B.A., Samaniego, H., Marín, J.C., Estades, C.F. 2013. Unveiling Current Guanaco Distribution in Chile Based upon Niche Structure of Phylogeographic Lineages: Andean Puna to Subpolar Forests. PLoS ONE, 8(11), e78894. https://doi.org/10.1371/journal.pone.0078894.Haughian, S.R., Clayden, S.R., Cameron, R. 2018. On the distribution and habitat of Fuscopannaria leucosticta in New Brunswick, Canada. Écoscience, 26(2), 99-112. https://doi.org/10.1080/11956860.2 018.1526997.Hawk, A.M. 2017. Habitat modeling of a rare endemic Trillium Species (Trillium Simile Gleason): a comparison of the methods maxent and domain for modeling rare species-rich habitat. In Biology Department. Vol. Master of Science in Biology (Dissertation). Western Carolina University, USA.Hernández, P.A., Graham, C.H., Master, L.L., Albert, D.L. 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29, 773-785. https://doi.org/10.1111/j.0906- 7590.2006.04700.x.Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jaarvis, A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal Climatology, 25(15), 1965-1978. https://doi.org/10.1002/joc.1276.Jafari, A., Zamani-Ahmadmahmoodi, R., Mirzaei, R. 2018. Persian leopard and wild sheep distribution modeling using the Maxent model in the Tang-eSayad protected area, Iran. Mammalia, 83(1), 84- 96. https://doi.org/10.1515/mammalia-2016-0155.Juliá, C., Montecinos, S., Maldonado, A. 2008. Características climáticas de la Región de Atacama, In Libro Rojo de la Flora Nativa y de Los Sitios Prioritarios para su Conservación: Región de Atacama, Squeo, F.A., Arancio, G., Gutiérrez, J.R., (Eds.), pp. 3: 25-42, Ediciones Universidad de La Serena, La Serena, Chile.Kafley, H., Khadka, M., Sharma, M. 2009. Habitat Evaluation and Suitability Modeling of Rhinoceros unicornis in Chitwan National Park, Nepal: A Geospatial Approach, In XII World Forestry Congress, 18-23 October, Buenos Aires, Argentina.Kottek, M., Grieser, J., Beck, C., Rudolf, B., Rubel, F. 2006. World Map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15(3), 259-263. https://doi.org/10.1127/0941- 2948/2006/0130.Kumar, S., Stohlgren, T.J. 2009. Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. Journal of Ecology and The Natural Environment, 1(4), 94-98. Accesible at https://pdfs. semanticscholar.org/66b8/8f62b73226934f6f7216 efbcf31ac1e7ef61.pdf.Larraín, H., Velásquez, F., Cereceda, P., Espejo, R., Pinto, R., Osses, P., Schemenauer, R.S. 2002. Fog measurements at the site "Falda Verde" north of Chañaral compared with other fog stations of Chile. Atmospheric Research, 64(1-4), 273-284. https://doi.org/10.1016/S0169-8095(02)00098-4.Latorre, C., González, A.L., Quade, J., Fariña, J.M., Pinto, R., Marquet, P.A. 2011. Establishment and formation of fog-dependent Tillandsia landbeckii dunes in the Atacama Desert: Evidence from radiocarbon and stable isotopes. Journal of Geophysical Research, 116, G03033. https://doi.org/10.1029/2010JG001521.Marini, M.Á., Barbet-Massin, M., Martínez, J., Prestes, N.P., Jiguet, F. 2010. Applying ecological niche modelling to plan conservation actions for the Red-spectacled Amazon (Amazona pretrei). Biological Conservation, 143(1), 102-112. https://doi.org/10.1016/j.biocon.2009.09.009.MINSEGPRES (Ministerio Secretaría General de la Presidencia). 2008. Decreto Supremo N° 50/2008. Aprueba y oficializa nómina para el segundo proceso de clasificación de especies según su estado de conservación, Santiago, Chile.Morales, N. 2012. Modelos de distribución de especies: Software Maxent y sus aplicaciones en Conservación. Revista Conservación Ambiental, 2(1), 1-5. Accesible at https://issuu.com/ fundacionecomabi/docs/revista_conservaci__n_ ambiental_mas.Muñoz, C. 1973. Chile: Plantas en extinción. Editorial Universitaria, Santiago, Chile. 248 p.Muñoz-Schick, M., Sierra, T. 2006. Ficha de antecedentes de especie: Leontochir ovallei, in Documento de trabajo, Proceso Nacional de Clasificación de Especies. Comisión Nacional del Medio Ambiente (CONAMA).Phillips, S.J., Dudík, M., Schapire, R.E. 2004. A maximum entropy approach to species distribution modeling. In Proceedings of the Twenty-First International Conference on Machine Learning (ICML), 4-8 July, Banff, Alberta Canada, p.83. https://doi.org/10.1145/1015330.1015412.Phillips, S.B., Aneja, V.P., Kang, D., Arya, S.P. 2006. Modelling and analysis of the atmospheric nitrogen deposition in North Carolina. International Journal of Global Environmental Issues, 6(2-3), 231-252. https://doi.org/10.1504/IJGENVI.2006.010156Pliscoff, P., Fuentes-Castillo, T. 2011. Modelación de la distribución de especies y ecosistemas en el tiempo y en el espacio: una revisión de las nuevas herramientas y enfoques disponible. Revista de Geografía Norte Grande, 48, 61-79. https://doi.org/10.4067/S0718-34022011000100005.QGIS Development Team. 2017. QGIS Geographic Information System, Version 2.18 Brighton. Open Source Geospatial Foundation Project. Accesible at http://www.qgis.org.R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/.Rundel, P.W., Dillon, M.O., Palam, B., Mooney, H.A., Gulmon, S.L., Ehleringer, J.R. 1991. The Phytogeography and Ecology of the Coastal Atacama and Peruvian Deserts. Aliso: A Journal of Systematic and Evolutionary Botany, 13(1/2), 1-49. https://doi.org/10.5642/aliso.19911301.02.Sarma, B., Baruah, P., Tanti B. 2018. Habitat distribution modeling for reintroduction and conservation of Aristolochia indica L. - a threatened medicinal plant in Assam, India. Journal Threatened Taxa, 10(11), 12531-12537. https://doi.org/10.11609/jott.3600.10.11.12531-12537.Sarricolea, P., Herrera-Ossandon, M.J., MeseguerRuiz, Ó. 2017. Climatic regionalisation of continental Chile. Journal of Maps, 13(2), 66-73.https://doi.org/10.1080/17445647.2016.1259592.Shiv, P., Samant, S.S., Lal, M., Ram J. 2019. Population Assessment and Habitat Distribution Modelling of High Value Corylus jacquemontii for in situ Conservation in the State of Himachal Pradesh, India. Proceedings of the Indian National Science Academy, 85(1), 275-289. https://doi.org/10.16943/ ptinsa/2018/49507.Squeo, F.A., Arancio, G., Gutiérrez, J.R. 2008. Libro rojo de la Flora Nativa y de los Sitios Prioritarios para su Conservación: Región de Atacama. Editorial Universidad de La Serena, La Serena, Chile, pp. 25-42.Tang, J., Li, J., Lu, H., Lu, F.,d Lu, B. 2018. Potential distribution of an invasive pest, Euplatypus parallelus, in China as predicted by Maxent. Pest Management Science, 75, 1630-1637. https://doi. org/10.1002/ps.5280.Thapa, A., Wu, R., Hu, Y., Nie, Y., Sing, P.B., Khatiwada, J.R., Yan, L., Gu, X., Wei, F. 2018. Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling. Ecology and Evolution, 8(21), 10542-10554. https://doi.org/10.1002/ece3.4526.Thompson, M.V., Palma, B., Knowles, J.T., Holbrook, N.M. 2003. Multi-annual climate in Parque Nacional Pan de Azúcar, Atacama Desert, Chile. Revista Chilena de Historia Natural, 76(2), 235-254. https://doi.org/10.4067/S0716- 078X2003000200009.Urbina-Cardona, J.N., Flores-Villela, O. 2010. Ecological-Niche Modeling and Prioritization of Conservation-Area Networks for Mexican Herpetofauna. Conservation Biology, 24(4), 1031-1041. https://doi.org/10.1111/j.1523- 1739.2009.01432.x.Wang, W.C., Lo, N.J., Chang, W.I., Huang, K.Y. 2012. Modeling spatial distribution of a rare and endangered plant species (Brainea insignis) in Central Taiwan", in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B7, 241-246. https://doi.org/10.5194/isprsarchivesXXXIX-B7-241-2012.Wilson, A.M., Jetz, W. 2016. Remotely Sensed HighResolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions. PLOS Biology, 14(3), e1002415. https://doi.org/10.1371/ journal.pbio.1002415.Wisz, M.S., Hijmans, R.J., Li, J., Peterson, A.T., Graham, C.H., Guisan, A., NCEAS Predicting Species Distributions Working Group. 2008. Effects of sample size on the performance of species distribution models. Diversity and Distributions, 14(5), 763-773. https://doi.org/10.1111/j.1472- 4642.2008.00482.x.Zimmermann, N.E., Edwards, T.C., Moisen G.G., Frescino, T.S., Blackard, J.A. 2007. Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah. Journal of Applied Ecology, 44(5), 1057-1067. https://doi.org/10.1111%2Fj.1365- 2664.2007.01348.x

Coastal spatial-temporal changes with Landsat 8 and Sentinel2 imagine (2015-2019) in Central Chile; Reñaca Beach, Concón Bay and Algarrobo Bay

[EN] Due to their oceanographic and climatic ecological characteristics, the coastal areas of the Valparaíso Region of Chile are of great importance for the country's tourist, economic, and environmental development. The region's coasts have been affected by extreme events since 2015, with recurrent swells that have increased between 10% and 25% compared to previous years, causing the interruption of the annual dynamic process of the beaches accentuated by anthropic interventions on beaches, wetlands and coastal dunes that promote coastal degradation. This work aims to characterize which part of the beaches and at what time of year there is a higher risk of severe erosion in the immediate areas to the coast and explain its causes based on satellite images of medium resolution satellite images. 128 SDS were extracted with Landsat 8 OLI and Sentinel 2 images with systems SHOREX. Average widths calculated for the series in each of the seasons. Recording the minimum widths in the proximal areas with averages of 12.3 m; 23.6 m and 21.2 m Concón Bay, Reñaca Beach and Algarrobo bay, respectively showing critical or problematic states. The dominant direction of the waves and the geographical disposition of the three beaches seems evident that there is a robust longitudinal transport to the north that generates less sediment accumulation in the proximal areas. The seasonal variation of the beaches distinguishes by more energetic conditions in winter than in summer. But they are also influenced by rapid episodic periods, low atmospheric pressure systems from the north and south, swells and tropical storms. Hence, it is unlikely that the variations in the width of the beach are only due to winter-summer energetic conditions.[ES] Las áreas costeras de la región de Valparaíso de Chile por sus características oceanográficas, climáticas y ecológicas son de gran importancia para el desarrollo turístico, económico y ambiental del país. Las costas de la región se han visto afectadas por eventos extremos desde 2015, con marejadas recurrentes que han aumentado respecto a años anteriores entre el 10% y 25%. Esto ha provocado la interrupción del proceso dinámico anual de las playas, acentuado por las intervenciones antrópicas sobre estos ambientes costeros, humedales y dunas que promueven su degradación. El trabajo que se presenta tiene como objetivo caracterizar en qué parte de las playas y en qué momento del año hay un riesgo más alto de erosión grave en las áreas inmediatas a la costa, así como tratar de explicar sus causas apoyándose en la utilización de imágenes satelitales de resolución media. Se extrajeron 128 SDS con imágenes Landsat 8 OLI y Sentinel 2 con el sistema SHOREX. Se calcularon los valores promedio de anchura de playa para la serie en cada una de las temporadas registrando anchuras mínimas en las zonas proximales de 12,3 m; 23,6 m y 21,2 m para la Bahía de Concón, Playa Reñaca y Bahía de Algarrobo respectivamente, demostrando estados críticos o problemáticos. La dirección dominante del oleaje y la disposición geográfica de las tres playas hace evidente la existencia de un fuerte transporte longitudinal hacia el norte que genera que las áreas proximales presenten menor acumulación de sedimentos. La variación estacional de las playas se caracteriza por condiciones más energéticas en invierno que en verano. No obstante, se ven también influenciadas por periodos episódicos rápidos, sistemas de bajas presiones atmosféricas del norte y del sur, así como marejadas y tormentas tropicales que se suman al moldeamiento de las playas.Este trabajo ha sido financiado por la Agencia Nacional de Investigación y Desarrollo (ANID) a través del FONDEF IDeA I+D 2019 – Desafío País Adaptación al Cambio Climático y Desastres Naturales. Código Proyecto ID19I10361. Agradecimiento a los practicantes de Escuela de Geología de la Universidad Mayor por su colaboración en la toma de datos en campo y procesamiento de datos sedimentarios. Álvaro Millamán, Maximiliano Parrao, Catalina Cerda, Nicolás Rodríguez, a las asistentes, Natalia Medina, Roxana Mansilla. Al IHC Cantabria que nos ha cedido datos de oleaje y marea de la zona.Briceño De Urbaneja, I.; Sánchez-García, E.; Pardo Pascual, JE.; Palomar Vázquez, JM.; Ugalde-Peralta, R.; Aguirre-Galaz, C.; Perez Martinez, W.... (2021). Cambios espacio-temporales costeros con imágenes Landsat 8 y Sentinel 2 (2015-2019) en Chile Central; Playa Reñaca, Bahía de Concón y Bahía de Algarrobo. En Proceedings 3rd Congress in Geomatics Engineering. Editorial Universitat Politècnica de València. 302-310. https://doi.org/10.4995/CiGeo2021.2021.12766OCS30231

CoastSnap Valparaíso Region: An Experience of Citizen Science in Chile

The coastal areas of Chile are undergoing major transformations associated with changes in the frequency and intensity of coastal storms, alterations in the rainfall regime with a megadrought of more than 10 years, and variations in ocean currents, which have generated a series of impacts related to beach erosion, the alteration of coastal wetlands and the effects on coastal cities. Faced with this problem, it is necessary to incorporate multiple monitoring methodologies that are complementary to existing ones, and that promote the incorporation of communities as fundamental axes in data collection. In this paper, we present the CoastSnap initiative implemented in Chile, whose results have been notorious, despite the short implementation time. Up to July 2023, the communities had shared 350 photographs that have allowed for analysis of the variability of the beaches, allowing for the quantification of variations on average up to 45 m wide in some of their beaches

Quantification of Coastal Erosion Rates Using Landsat 5, 7, and 8 and Sentinel-2 Satellite Images from 1986–2022—Case Study: Cartagena Bay, Valparaíso, Chile

Coastal erosion has become one of the many natural hazards affecting Chile’s sandy coastlines. Currently, more than 90% of the sandy coasts of Valparaíso show high erosion rates. Cartagena Bay is one of the coastal areas with the greatest transformations caused by extreme events and anthropogenic activities. Satellite imagery is seen as an invaluable resource for following these coastal changes. This study combines optical satellite imagery, a simulation-derived wave climate, in situ data, the SHOREX system developed in Python, and GIS-based tools such as DSAS to quantify rates of change in the Bay from 1986 to 2022. Satellite-derived shorelines were used to identify erosion hotspot areas in the Bay, differentiating the impact of erosive processes associated with ENSO hydrometeorological phenomena, the 27-F 2010 earthquake, and tidal waves from 2015–2022, which led to major transformations in the morphodynamics of the beach. The results show that the Bay is currently undergoing high erosional processes in 20% of the coastline with values <− 1.5 m/year and 60% with erosion rates ranging from [−0.2 to −1.5 m/year]. Since 2015, these processes have been accentuated, due to increased swells throughout the year

Seasonality analysis of sentinel-1 and ALOS-2/PALSAR-2 backscattered power over salar de Aguas Calientes Sur, Chile

Seasonal changes control the development of salt crust over the Salar de Aguas Calientes Sur located in Andes Highlands, Chile. Precipitations throughout the Altiplanic winter (December to March) and austral winter (June to September) caused ponds to enlarge and surface salt crusts to dissolve driving roughness and dielectric features of the salar surface change over time. A four-year time series backscattering coefficient analysis, obtained by Sentinel 1 and ALOS-2/PALSAR-2 with 10 m of spatial resolution, demonstrated the capability of microwaves to discriminate seasonal patterns illustrated in this paper. Both sensors showed to be sensitive to changes in the surface crust due to weather conditions. Backscattered power gradually increased during the driest months as the rough salt crusts develop and decreased rapidly due to precipitations or flooding events, which lead to a smoothing appearance to radar. The high temporal frequency of acquisition in Sentinel 1 (5-13 scenes/month) allowed the discrimination among climate and annual seasonality and episodic events in the C-band backscatter coefficient. On the other hand, ALOS-2/PALSAR-2 showed subsurface changes at L-band since the salinity of the brine in the soil reduces the penetration depth of backscattered power for shorter wavelengths. Results might be useful to monitor salars with geographic and weather conditions similar to Salar de Aguas Calientes Sur.Fil: Delsouc, Analía Silvana. Universidad de Chile; Chile. Universidad Mayor; ChileFil: Barber, Matias Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: Gallaud, Audrey. Universidad de Chile; Chile. Universidad Mayor; ChileFil: Grings, Francisco Matias. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: Vidal Páez, Paulina. Universidad de Chile; Chile. Universidad Mayor; ChileFil: Pérez Martínez, Waldo. Universidad de Chile; Chile. Universidad Mayor; ChileFil: Briceño De Urbaneja, Idania. Universidad de Chile; Chile. Universidad Mayor; Chil