Spatio-Temporal Spectral Analysis of High Mountains Wetlands and Their Relationship to Climate Variability
DOI:
https://doi.org/10.18172/cig.6410Keywords:
ONI index, SPEI index, NDVI index, NDWI index, wetlandsAbstract
The high-altitude wetlands of the Andes in South America are unique ecosystems, characterized by their hyper-humidity and close connection with groundwater discharge or snowmelt. These environments exhibit relatively stagnant waters or low circulation, which favors the proliferation of vegetation that can be monitored through satellite imagery. The objective of this study is to analyze, through the use of remote sensors, the spatiotemporal variation associated with climate cycles in two high-Andean wetlands located in arid environments, where access and working conditions are limited. To achieve this, two case studies were taken: one involving wetlands associated with groundwater discharge, and another supported by river courses with contributions from rainfall and snowmelt.
The areal variation was linked to climate cycles, for which the spectral indices Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were calculated for representative years, selected according to the climate indices Standardized Precipitation-Evapotranspiration Index (SPEI) for dry and wet periods, Oceanic Niño Index (ONI), and data from available meteorological stations for the period 1980-2022. The results showed a variation in the extent of the wetlands between El Niño and La Niña periods. During wet periods (El Niño), the wetlands reached their largest extents, while in dry periods (La Niña), a reduction in wetland area of approximately 30% was quantified. This indicates a rapid hydrological response of the environment to climate changes, associated with contributions from shallow groundwater flow. Consequently, it is likely that in the future, the impact of atmospheric cycles, intensified by climate change, will lead to a critical decrease in the surface area of these wetlands. Monitoring the evolution of high-Andean wetlands in arid conditions is essential to characterize their response to climate cycles. Extrapolating these analyses to other wetlands in similar environments will enable future research on a broader regional scale, facilitating a comprehensive approach to their behavior in the face of climate variations. Understanding these fragile ecosystems is key to implementing effective conservation and management measures, especially in the face of growing pressure from global climate change.
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References
Adauto, M., Bram, L.W., 2015. Identificación de humedales alto andinos integrando imágenes Landsat Aster GDEM con árbol de decisión sobre la cabecera de las cuencas de pisco y pampas en Huancavelica-Perú. Anais XVII Simpósio Brasileiro de Sensoriamento Remoto - SBSR, João Pessoa-PB, Brasil, 25 a 29 de abril de 2015, INPE.
Alatorre, L.C., Beguería, S., Vicente-Serrano, S.M., 2010. Análisis de la evolución espacio-temporal del NDVI sobre áreas vegetadas y zonas de riesgo de erosión en el Pirineo Central. Pirineos 165, 7-27. https://doi.org/10.3989/Pirineos.2010.165001 DOI: https://doi.org/10.3989/Pirineos.2010.165001
Arana-Ruedas, D.P.R., Moggiano, N., 2023. ENSO Influence on Agricultural Drought Identified by SPEI Assessment in the Peruvian Tropical Andes, Mantaro Valley. Manglar 20(2), 157-167. DOI: https://doi.org/10.57188/manglar.2023.018
Ashok, A., Ponnamma Rani, H., Jayakumar K.V., 2021 Monitoring of dynamic wetland changes using NDVI and NDWI based landsat imagery, Remote Sensing Applications: Society and Environment V 23. https://doi.org/10.1016/j.rsase.2021.100547 DOI: https://doi.org/10.1016/j.rsase.2021.100547
Balbarini, S., Comes, D., Langer, K., 2017. Estudio comparativo de índices de vegetación derivado de imágenes satelitales de mediana resolución y sensores terrestres: su aplicación en la viticultura de precisión. Boletín de Estudios Geográficos 108, 9-32. https://bdigital.uncu.edu.ar/10249
Brendel, A., Bohn, V.Y., Piccolo, M.C., 2017. Variabilidad de la precipitación y su relación con los rendimientos agrícolas en una región semiárida de la llanura pampeana (Argentina). Estudios Geográficos 78, 282, 7-29. https://doi.org/10.3989/estgeogr.201701 DOI: https://doi.org/10.3989/estgeogr.201701
Buono, G., Oesterheld M., Nakamatsu V., Paruelo J.M., 2010. Spatial and temporal variation of primary production of Patagonian wet meadows. Journal of Arid Environments 74(10), 1257–1261. https://doi.org/10.1016/j.jaridenv.2010.05.026 DOI: https://doi.org/10.1016/j.jaridenv.2010.05.026
Cancelliere, A., Mauro, G.D., Bonaccorso, B., Rossi, G. 2007. Drought forecasting using the standardized precipitation index. Water Resources Management 21, 801-819. https://doi.org/10.1007/s11269-006-9062-y DOI: https://doi.org/10.1007/s11269-006-9062-y
Calvi, C., Carol, E., Fennell, L., Naipauer, M., 2024. Assessment of hydrological systems associated with groundwater discharges in arid high mountain environments of the Argentine Andes. Groundwater for Sustainable Development 101218. https://doi.org/10.1016/j.gsd.2024.101218 DOI: https://doi.org/10.1016/j.gsd.2024.101218
Chen, P.Y., Popovich, P.M., 2002. Correlation: Parametric and Nonparametric Measures, vol. 139. Sage. DOI: https://doi.org/10.4135/9781412983808
Chen, J., Jönsson, P., Tamura, M., Gu, Z., Matsushita, B., Eklundh, L., 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sensing of Environment 91, 332-344. https://doi.org/10.1016/j.rse.2004.03.014 DOI: https://doi.org/10.1016/j.rse.2004.03.014
Chuvieco, E., 1991. Fundamentos de teledetección espacial. Estudios Geográficos 52(203), 371.
Cohen, J., Cohen, P., West, S.G., Aiken, L.S., 2003. Applied Multiple Regression/correlation Analysis for the Behavioral Sciences, third ed. Lawrence Erlbaum Associates, Mahwah, NJ. https://doi.org/10.4324/9780203774441 DOI: https://doi.org/10.4324/9780203774441
Davey, M.K., Brookshaw, A., Ineson, S., 2014. The probability of the impact of ENSO on precipitation and near-surface temperature. Climate Risk Management 1, 5-24. http://doi.org/10.1016/j.crm.2013.12.002 DOI: https://doi.org/10.1016/j.crm.2013.12.002
Gaiolini, M., Acosta, R., Carol, E., Colombani, N., 2025. Assessing the effects of ENSO-induced climate variability on shallow coastal groundwater reserves of north Patagonia, Argentina. Groundwater for Sustainable Development, 29. https://doi.org/10.1016/j.gsd.2025.101427 DOI: https://doi.org/10.1016/j.gsd.2025.101427
Gaitán, J., Ciano, N., Oliva, G., Bran, D., Butti, L., Cariac, G., Caruso, C., Opazo, W., Ferrante, D., Echevarria, D., Buono, G., Fantozzi, A., Guirado, E., Maestre, F., 2021. La variación temporal del índice NDVI predice los cambios temporales de la cobertura vegetal en las tierras secas de la Patagonia argentina. Ecosistemas 30(3), 2229. https://doi.org/10.7818/ECOS.2229 DOI: https://doi.org/10.7818/ECOS.2229
Gallant, A.L., 2015. The challenges of remote monitoring of wetlands. Remote Sensing 7(8), 10938-10950. https://doi.org /10.3390/rs70810938 DOI: https://doi.org/10.3390/rs70810938
Garreaud, R., Vuille, M., Compagnucci, R., Marengo, J., 2009. Present-day South American climate. Palaeogeography, Palaeoclimatology, Palaeoecology 281(3-4), 180-195. https://doi.org/10.1016/j.palaeo.2007.10.032 DOI: https://doi.org/10.1016/j.palaeo.2007.10.032
Jensen, J.R., 2007. Remote Sensing of the Environment: An Earth Resource Perspective. Pearson Prentice Hall.
Ling, M., Han, H., Hu, X., Xia, Q., Guo, X., 2023. Drought characteristics and causes during summer maize growth period on Huang-Huai-Hai Plain based on daily scale SPEI. Agricultural Water Management 280, 108198. https://doi.org /10.1016/j.agwat.2023.108198 DOI: https://doi.org/10.1016/j.agwat.2023.108198
Llanes-Cárdenas, O., Gaxiola-Hernández, A., Estrella-Gastelum, R.D., Norzagaray-Campos, M., Troyo-Diéguez, E., Pérez-González, E., de J. Pellegrini Cervantes, M., 2018. Variability and factors of influence of extreme wet and dry events in Northern Mexico. Atmosphere 9(4), 122. https://doi.org/10.3390/atmos9040122 DOI: https://doi.org/10.3390/atmos9040122
Mahdavi, S., Salehi, B., Granger, J., Amani, M., Brisco, B., Huang, W., 2018. Remote sensing for wetland classification: A comprehensive review. GIScience & Remote Sensing 55(5), 623-658. https://doi.org/10.1080/15481603.2017.1419602 DOI: https://doi.org/10.1080/15481603.2017.1419602
Mallmann, C.L., Prado, D.D.A., Pereira Filho, W., 2015. Índice de vegetação por diferença normalizada para caracterização da dinâmica florestal no parque estadual Quarta Colônia, estado do Rio Grande do Sul-Brasil. Revista Brasileira de Geografia Física, Recife 8(5), 1454-1469. https://doi.org/10.5935/1984-2295.20150080 DOI: https://doi.org/10.5935/1984-2295.20150080
Marcosig, I.P., Liaudat, D.T., 2021. Análisis de la dinámica de dos mallines de altura en Vallecitos, Cordón del Plata, Mendoza, Argentina en el periodo 2002-2019. Acta Geológica Lilloana 1-24. http://doi.org/10.30550/j.agl/2021.33.1/2021-02-18 DOI: https://doi.org/10.30550/j.agl/2021.33.1/2021-02-18
Mazzarino, M., Finn, J.T., 2016. An NDVI analysis of vegetation trends in an Andean watershed. Wetlands Ecology and Management 24, 623-640. https://doi.org/10.1007/s11273-016-9492-0 DOI: https://doi.org/10.1007/s11273-016-9492-0
McFeeters, S.K.,1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 17(7), 1425-1432. https://doi.org/10.1080/01431169608948714 DOI: https://doi.org/10.1080/01431169608948714
Mejía, J.F., González, J.D., Albarrán, A., 2022. Influencia de la variabilidad climática en los pantanos altoandinos de la microcuenca Miguaguó Andes venezolanos. Revista de Ciencias Ambientales 56 (2), 38-62. https://doi.org/10.15359/rca.56/2.3 DOI: https://doi.org/10.15359/rca.56-2.3
Meza Aliaga, M., Díaz Villalobos, Y., 2014. Efectos de la variabilidad climática sobre las fluctuaciones del nivel de las aguas y actividad ganadera en humedales altoandinos. Interciencia 39(9), 651-658.
Otto, M., Scherer, D., Richters, J., 2011. Hydrological differentiation and spatial distribution of high-altitude wetlands in a semi-arid Andean region derived from satellite data. Hydrology and Earth System Sciences 15, 1713-1727. https://doi.org/10.5194/hess-15-1713-2011 DOI: https://doi.org/10.5194/hess-15-1713-2011
Ozesmi, S.L., Bauer, M.E., 2002. Satellite remote sensing of wetlands. Wetlands ecology and management 10, 381-402. https://doi.org/10.1023/A:1020908432489 DOI: https://doi.org/10.1023/A:1020908432489
Pauca-Tanco, A., Ramos-Mamani, C., Luque-Fernández, C.R., Talavera-Delgado, C., Villasante-Benavides, J.F., Quispe-Turpo, J P., Villegas-Paredes, L., 2020. Análisis espacio temporal y climático del humedal altoandino de Chalhuanca (Perú) durante el periodo 1986-2016. Revista de Teledetección 55, 105-118. https://doi.org/10.4995/raet.2020.13325 DOI: https://doi.org/10.4995/raet.2020.13325
Peñalba, O.C., Rivera, J.A., 2016. Precipitation response to El Niño/La Niña events in Southern South America – emphasis in regional drought occurrences. Advances in Geosciences 42, 1–14. https://doi.org/10.5194/adgeo-42-1-2016 DOI: https://doi.org/10.5194/adgeo-42-1-2016
Riaño, D., Moreno Ruiz, J.A., Isidoro, D., Ustin, S.L., 2007. Global spatial patterns and temporal trends of burned area between 1981 and 2000 using NOAA-NASA Pathfinder. Global Change Biology 13: 40-50. https://doi.org/10.1111/j.1365-2486.2006.01268.x DOI: https://doi.org/10.1111/j.1365-2486.2006.01268.x
Rivera, J.A., Peñalba, O.C., Villalba, R., 2014. ENSO-related precipitation variability in the Andes of subtropical Argentina: relationships with large-scale atmospheric circulation. International Journal of Climatology 34(12), 3476-3492.
Rouse, J.W., Jr., Haas, R.H., Schell, J.A., Deering, D.W., 1973. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation (No. NASA-CR-132982). https://ntrs.nasa.gov/citations/19750020419?utm
Scordo, F., Piccolo, M.C., Perillo, G.M.E., 2018. Aplicación del índice de precipitación evapotranspiración estandarizada (SPEI) para identificar períodos húmedos y secos en la Patagonia andina y extra andina argentina. Geosciences = Geociências 37(2), 423-436. https://doi.org/10.5016/geociencias.v37i2.12241 DOI: https://doi.org/10.5016/geociencias.v37i2.12241
Tahsin, S., Medeiros, S.C., Singh, A., 2018. Assessing the resilience of coastal wetlands to extreme hydrologic events using vegetation indices: A review. Remote Sensing 10(9), 1390. https://doi.org/10.3390/rs10091390 DOI: https://doi.org/10.3390/rs10091390
Vicente-Serrano, S.M., Beguería, S., López-Moreno, J.I., 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of Climate 23(7), 1696-1718. https://doi.org/10.1175/2009JCLI2909.1 DOI: https://doi.org/10.1175/2009JCLI2909.1
Vicente-Serrano, S., Lasanta, T., Romo, A. 2005. Analysis of Spatial and Temporal Evolution of Vegetation Cover in the Spanish Central Pyrenees: Role of Human Management. Environmental management 34, 802-18. https://doi.org/10.1007/s00267-003-0022-5 DOI: https://doi.org/10.1007/s00267-003-0022-5
Wang, K., Li, Q., Yang, Y., Zeng M., Li, P., Zhang, J., 2015. Analysis of spatio-temporal evolution of droughts in Luanhe River Basin using different drought indices. Water Science and Engineering 8 (4), 282-290. https://doi.org/10.1016/j.wse.2015.11.004 DOI: https://doi.org/10.1016/j.wse.2015.11.004
Wilson, N.R., Norman, L.M., 2018. Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI). International Journal of Remote Sensing 39(10), 3243-3274. https://doi.org/10.3989/estgeogr.20170110.1080/01431161.2018.1437297 DOI: https://doi.org/10.1080/01431161.2018.1437297
Xue, J., Su, B., 2017. Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors 2017(1353691), 1–17. https://doi.org/10.1155/2017/1353691 DOI: https://doi.org/10.1155/2017/1353691
Xiao, X., Boles, S., Frolking, S., Salas, W., Moore III, B., Li, C., He, L., Zhao, R., 2002. Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using vegetation sensor data. International Journal of Remote Sensing 23(15), 3009-3022. https://doi.org/10.1080/01431160110107734 DOI: https://doi.org/10.1080/01431160110107734
Zhu, Z., Woodcock, C.E., 2014. Automated mapping of land surface phenology using MODIS time series. Remote Sensing of Environment 152, 166-177. https://doi.org/10.1016/j.rse.2014.06.013 DOI: https://doi.org/10.1016/j.rse.2014.01.011
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Copyright (c) 2025 Carolina Calvi, Edoardo Melendi, Eleonora Carol

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