Propuesta metodológica para la teledetección de la zona estuarina del humedal del Río Limarí, sitio RAMSAR, Región de Coquimbo, Chile
DOI:
https://doi.org/10.22370/rbmo.2024.59.3.4847Keywords:
Environmental teledetection, estuarine wetland, RAMSAR, decision treeAbstract
Preliminary results of a standardized procedure to delimit the estuarine zones are presented by means of a decision tree on histogram thresholds, taking as the object of study the Limarí River wetland, a RAMSAR site, using the satellite indices NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), NDMI (Normalized Difference Moisture Index), VSSI (Vegetation Soil Salinity Index) and SWI (Salty Water Index); which identified its vegetation, hydrological and sedimentary components, contributing from 85.27% to 38.04% of the pixels in the final solution, in descending order. The results were optimized using a raster filter and vector selection, offering a new tool for the delimitation and discrimination of the lower estuary and the fluvial zone of this wetland.
References
Amani M, B Salehia, S Mahdavia & B Brisco. 2018. Spectral analysis of wetlands using multi-source optical satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing 144: 119-136.
Ansari M & M Akhoondzadeh. 2019. Mapping water salinity using Landsat-8 OLI satellite images (Case study: Karun basin located in Iran). Advances in Space Research 65: 1490-1502. <https://doi.org/10.1016/j.asr.2019.12.007>
Bahadur TK. 2018. NDVI, NDBI & NDWI calculation using Landsat 7, 8. <https://www.linkedin.com/pulse/ndvi-ndbi-ndwi-calculation-using-landsat-7-8-tek-bahadur-kshetri/>
Baker C, R Lawrence, C Montagne & D Patten. 2006. Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision-tree-based models. Wetlands 26(2): 465-474.
Barraza D & P Ponce. 2014. Caracterización ecológica del humedal de Mantagua, Región de Valparaíso, como base para su protección y conservación. Tesis de Ingeniería Ambiental, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, 160 pp.
Bhatnagar S, L Gill, S Regan, O Naughton, P Johnston, S Waldren & B Ghosh. 2020. Mapping vegetation communities inside wetlands using Sentinel-2 imagery in Ireland. International Journal of Applied Earth Observation and Geoinformation 88, 102083. <https://doi.org/10.1016/j.jag.2020.102083>
Chamberlain D, S Phinn & H Possingham. 2020. Remote sensing of mangroves and estuarine communities in Central Queensland, Australia. Remote Sensing 12(1): 197. <https://doi.org/10.3390/rs12010197>
Chenyu Z, C Shenliang, L Peng & L Qinglan. 2022. Spatiotemporal dynamic remote sensing monitoring of typical wetland vegetation in the Current Huanghe River Estuary Reserve. Haiyang Xuebao 44(1): 125-136. <https://doi.org/10.12284/hyxb2022014>
Chi Y, W Zheng, H Shi, J Sun & Z Fu. 2018. Spatial heterogeneity of estuarine wetland ecosystem health influenced by complex natural and anthropogenic factors. Science of the Total Environment 634: 1445-1462.
Chuvieco E. 1995. Fundamentos de la teledetección espacial, 449 pp. Ediciones Rialp, Madrid.
Cohen J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1): 37-46. <https://doi.org/10.1177/001316446002000104>
Dehni A & M Lounis. 2012. Remote sensing techniques for salt affected soil mapping: Application to the Oran Region of Algeria. Procedia Engineering 33: 188-198.
Fernández A. 2007. Estudio de técnicas basadas en la transformada de Wavelet y optimización de sus parámetros para la clasificación por texturas de imágenes digitales. Tesis Doctoral, Departamento de Ingeniería Cartográfica, Geodesia y Fotogrametría, Universidad Politécnica de Valencia, Valencia, 247 pp. <https://riunet.upv.es/bitstream/handle/10251/1955/tesisUPV2573.pdf?sequence=1&isAllowed=y>
Figueroa R, ML Suarez, A Andreu, VH Ruiz & MR Vidal. 2009. Caracterización ecológica de humedales de la zona semiárida en Chile central. Gayana 73(1): 76-94.
Gxokwe S, T Dube & D Mazvimavi. 2020. Multispectral remote sensing of wetlands in semi-arid and arid areas: A review on applications, challenges and possible future research directions. Remote Sensing 12(24): 4190. <https://doi.org/10.3390/rs12244190>
Hassan R, Z Ahmed, MT Islam, R Alam & Z Xie. 2021. Soil salinity detection using salinity indices from Landsat 8 satellite image at Rampal, Bangladesh. Remote Sensing in Earth Systems Sciences 4: 1-12.
Irons JR, JL Dwyer & JA Barsi. 2012. The next Landsat satellite: The Landsat Data Continuity Mission. Remote Sensing of Environment 122: 11-21.
Kaplan G & U Avdan. 2017. Mapping and monitoring wetlands using Sentinel-2 satellite imagery. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 4-4/W4: 271-277. [4th International GeoAdvances Workshop, 14-15 October 2017, Safranbolu, Karabuk, Turkey]
Kaplan G & U Avdan. 2018. Monthly analysis of wetlands dynamics using remote sensing data. International Journal of Geo-Information 7(10): 1-20.
Kolesnyk A & N Khairova. 2022. Justification for the use of Cohen’s Kappa statistic in experimental studies of NLP and text mining. Cybernetics and Systems Analysis 58(2): 280-288. <https://doi.org/10.1007/s10559-022-00460-3>
Landis R & G Koch. 1977. The measurement of observer agreement for categorical data. Biometrics 33(1): 159-174. <https://doi.org/10.2307/2529310>
Mao D, Z Wang, B Du, L Li, Y Tian, M Jia, Y Zeng, K Song, M Jiang & Y Wang. 2020. National wetland mapping in China: A new product resulting from object-based and hierarchical classification of Landsat 8 OLI images. ISPRS Journal of Photogrammetry and Remote Sensing 164: 11-25.
McFeeters SK. 2013. Using the normalized difference water index (NDWI) within a geographic information system to detect swimming pools for Mosquito Abatement: A practical approach. Remote Sensing 5: 3544-3561.
Meruane JA, MC Morales, CA Galleguillos, MA Rivera & H Hosokawa. 2006. Experiencias y resultados de investigaciones sobre el camarón de rio del norte Cryphiops caementarius (Molina 1782) (Decapoda: Palaemonidae): Historia natural y cultivo. Gayana 70(2): 280-292.
Nguyen KN, YA Liou, HP Tran, PP Hoang & TH Nguyen. 2020. Soil salinity assessment by using near infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: A case study in the Tra Vinh Province, Mekong Delta, Vietnam. Progress in Earth and Planetary Science 7(1): 1-16.
Oyola-Lepe N. 2009. Identificación de humedales del norte grande de Chile utilizando técnicas geomáticas en imágenes satelitales Landsat. Tesis de Magister, Facultad de Ciencias Forestales y de la Conservación de la Naturaleza, Universidad de Chile, Santiago, 48 pp. <https://bibliotecadigital.ciren.cl/server/api/core/bitstreams/9f0b5b5e-2df2-423a-bb3c-e5e8e0d7e556/content>
Ozesmi A & M Bauer. 2002. Satellite remote sensing of wetlands. Wetlands Ecology and Management 10: 381-402.
Pahlevan N, JC Roger & Z Ahmad. 2017. Revisiting short-wave-infrared (SWIR) bands for atmospheric correction in coastal waters. Optical Express 25(6): 6015-6035.
Perillo G. 1995. Definitions and geomorphologic classifications of estuarines. Developments in Sedimentology 53: 17-47.
RAMSAR. 2016. Introducción a la convención sobre los humedales, 118 pp. Ramsar Convention Secretariat, Gland.
Rapinel S, L Panhelleux, G Gayet, R Vanacker, B Lemercier, B Laroche, F Chambaud, A Guelmami & L Hubert-Moy. 2023. National wetland mapping using remote-sensing-derived environmental variables, archive field data, and artificial intelligence. Heliyon 9, e13482. <https://doi.org/10.1016/j.heliyon.2023.e13482>
Roy DP, MA Wulder, TR Loveland, CE Woodcock, RG Allen, MC Anderson, D Helder, JR Irons, DM Johnson, R Kennedy, TA Scambos, CB Schaaf, JR Schott, Y Sheng, EF Vermote, AS Belwardo, R Bindschadler, WB Cohen, F Gao, JD Hipple, P Hostert, J Huntington, CO Justice, A Kilic, V Kovalskyy, ZP Lee, L Lymburner, JG Masek, J McCorkel, Y Shuai, R Trezza, J Vogelmann, RH Wynne & Z Zhu. 2014. Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment 145: 154-172.
Sánchez-Espinosa A & C Schröder. 2019. Land use and land cover mapping in wetlands one step closer to the ground: Sentinel-2 versus Landsat 8. Journal of Environmental Management 247: 484-498.
Schultz M, JGPW Clevers, S Carter, J Verbesselt, V Avitabilea, HV Quang & M Herolda. 2016. Performance of vegetation indices from Landsat time series in deforestation monitoring. International Journal of Applied Earth Observation and Geoinformation 52: 318-327.
SHOA. 2018. Tablas de marea de la costa de Chile 2018. Servicio Hidrográfico y Oceanográfico de la Armada de Chile. SHOA PUB 3009: 1-200.
Siles-Ajamil R. 2022. Evaluación de escenarios de gestión mediante un modelo lineal mareal en el estuario del Guadalquivir. Tesis Doctoral, Programa de Doctorado en Dinámica de Flujos Biogeoquímicos y sus Aplicaciones, Universidad de Granada, Granada, 119 pp. <https://digibug.ugr.es/handle/10481/77141>
Slagter B, NE Tsendbazar, A Vollrath & J Reiche. 2020. Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands, South Africa. International Journal of Applied Earth Observation and Geoinformation 86: 1-11.
Stuardo J & C Valdovinos. 1989. Estuarios y lagunas costeras: ecosistemas importantes del Chile central. Ambiente y Desarrollo 1: 107-115.
Tapia-Zurita L. 2018. Análisis de la calidad hídrica del humedal de Mantagua, Región de Valparaíso, y su relación con el entorno social. Tesis de Ingeniería Forestal, Facultad de Ciencias Forestales y de la Conservación de la Naturaleza, Universidad de Chile, Santiago, 58 pp. <https://repositorio.uchile.cl/handle/2250/152402>
Toffanin P. 2019. OpenDroneMap: The missing guide. A practical guide to drone mapping using free and open source software, 264 pp. Independently published, <https://ftp.itc.nl/pub/maathuis/EENSAT_batch1_Course_Materials/EENSAT_week2/photogrammetry/opendronemap-the-missing-guide.pdf>
Turnbull A, M Soto-Berelov & M Coote. 2024. Delineation and classification of wetlands in the Northern Jarrah Forest, Western Australia using remote sensing and machine learning. Wetlands 44: 52. <https://doi.org/10.1007/s13157-024-01806-7>
Verrelst J, Z Malenovský, C Van der Tol, G Camps-Valls, J Gastellu-Etchegorry, P Lewis, P North & J Moreno. 2019. Quantifying vegetation biophysical variables from imaging spectroscopy data: A review on retrieval methods. Survey in Geophysics 40: 589-629. <https://doi.org/10.1007/s10712-018-9478-y>
Waqar R, H Shu, K Javid, S Pervaiz, F Mustafa, D Raza, B Ahmed, A Quddoos, S Al-Ahmadi & W Atef. 2024a. Wetland identification through remote sensing: Insights into wetness, greenness, turbidity, temperature, and changing landscapes. Big Data Research 35: 100416. <https://doi.org/10.1016/j.bdr.2023.100416>
Waqar R, H Shu, I Naz, A Quddoos, A Yaseen, K Gulshad & S Alarifi. 2024b. Machine learning-based wetland vulnerability assessment in the Sindh Province RAMSAR site using remote sensing data. Remote Sensing 16(5): 928. <https://doi.org/10.3390/rs16050928>
WCS. 2019. Chile, país de humedales: 40 mil reservas de vida, 192 pp. Wildlife Conservation Society, Santiago.
Yousefian F, M Sahebi, M Shokri & M Moradi. 2019. A Novel Water Index (SWI) for salty water from Landsat - 8 OLI/TIRS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42-4/W18: 1097-1105. [GeoSpatial Conference 2019 – Joint Conferences of SMPR and GI Research, 12-14 October 2019, Karaj, Iran]
Yue W, J Xu, W Tan & L Xu. 2007. The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM+ data. International Journal of Remote Sensing 28(15): 3205-3226.
Zambra-Ramos R. 2019. Transferencia de masa entre la desembocadura del Río Limarí, bahía Tongoy y bahía Barnes. Región de Coquimbo, Chile. Memoria de Geógrafo, Facultad de Arquitectura y Urbanismo, Universidad de Chile, Santiago, 135 pp. <https://repositorio.uchile.cl/handle/2250/170802>
Zhang J, P Atkinson & MF Goodchild. 2014. Scale in spatial information and analysis, 368 pp. CRC Press, Boca Raton.
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