- -

Delimitación de pequeñas marismas interiores mediante índices espectrales y árboles de decisión en el sur de Brasil

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Delimitación de pequeñas marismas interiores mediante índices espectrales y árboles de decisión en el sur de Brasil

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Simioni, J. P. D. es_ES
dc.contributor.author Guasselli, L. A. es_ES
dc.contributor.author Ruiz, L. F. C. es_ES
dc.contributor.author Nascimento, V. F. es_ES
dc.contributor.author de Oliveira, G. es_ES
dc.date.accessioned 2019-01-08T13:04:09Z
dc.date.available 2019-01-08T13:04:09Z
dc.date.issued 2018-12-26
dc.identifier.issn 1133-0953
dc.identifier.uri http://hdl.handle.net/10251/114906
dc.description Revista oficial de la Asociación Española de Teledetección
dc.description.abstract [EN] Vast small inner marsh (SIM) areas have been lost in the past few decades through the conversion to agricultural, urban and industrial lands. The remaining marshes face several threats such as drainage for agriculture, construction of roads and port facilities, waste disposal, among others. This study integrates 17 remote sensing spectral indexes and decision tree (DT) method to map SIM areas using Sentinel 2A images from Summer and Winter seasons. Our results showed that remote sensing indexes, although not developed specifically for wetland delimitation, presented satisfactory results in order to classify these ecosystems. The indexes that showed to be more useful for marshes classification by DT techniques in the study area were NDTI, BI, NDPI and BI_2, with 25.9%, 17.7%, 11.1% and 0.8%, respectively. In general, the Proportion Correct (PC) found was 95.9% and 77.9% for the Summer and Winter images respectively. We hypothetize that this significant PC variation is related to the rice-planting period in the Summer and/or to the water level oscillation period in the Winter. For future studies, we recommend the use of active remote sensors (e.g., radar) and soil maps in addition to the remote sensing spectral indexes in order to obtain better results in the delimitation of small inner marsh areas. es_ES
dc.description.abstract [ES] En las últimas décadas se han perdido grandes áreas de pequeñas marismas interiores (SIM) a través de la conversión a tierras agrícolas, urbanas e industriales. Las marismas restantes enfrentan varias amenazas, como el drenaje para la agricultura, la construcción de carreteras e instalaciones portuarias, la eliminación de residuos, entre otras. Este estudio integra 17 índices espectrales de teledetección y un método basado en árboles de decisión (DT) para cartografiar áreas de pequeñas marismas interiores utilizando imágenes del satélite Sentinel 2A de verano e invierno. Los resultados muestran que los índices de teledetección, aunque no han sido desarrollados específicamente para la delimitación de marismas, presentan resultados satisfactorios para clasificar estos ecosistemas. Los índices que demostraron ser más útiles para la clasificación de marismas mediante técnicas de DT en el área de estudio fueron el NDTI, BI, NDPI y BI_2, con 25.9%, 17.7%, 11.1% y 0.8%, respectivamente. En general, la proporción correcta encontrada fue de 95.9% y 77.9% para las imágenes de verano e invierno, respectivamente. Nuestra hipótesis es que esta variación significativa de la proporción correcta está relacionada con el período de siembra del arroz en verano y/o con el período de oscilación del nivel del agua en invierno. Para futuras investigaciones, recomendamos el uso de sensores remotos activos (por ejemplo, radar) y mapas de suelo además de los índices espectrales de teledetección para obtener mejores resultados en la delimitación de pequeñas áreas de marismas interiores. es_ES
dc.description.sponsorship João Paulo Delapasse Simioni thanks the CAPES agency for providing a doctoral fellowship. The au-thors acknowledge the Center for Remote Sensing and Meteorology (CEPSRM) at the Federal University of Rio Grande do Sul (UFRGS) for the support provided for this research. es_ES
dc.language Inglés es_ES
dc.publisher Universitat Politècnica de València
dc.relation.ispartof Revista de Teledetección
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Marismas es_ES
dc.subject Sentinel 2A es_ES
dc.subject Teledetección es_ES
dc.subject Método CART es_ES
dc.subject Marshes es_ES
dc.subject Remote sensing es_ES
dc.subject CART method es_ES
dc.title Delimitación de pequeñas marismas interiores mediante índices espectrales y árboles de decisión en el sur de Brasil es_ES
dc.title.alternative Small inner marsh area delimitation using remote sensing spectral indexes and decision tree method in southern Brazil es_ES
dc.type Artículo es_ES
dc.date.updated 2019-01-08T12:03:18Z
dc.identifier.doi 10.4995/raet.2018.10366
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Simioni, JPD.; Guasselli, LA.; Ruiz, LFC.; Nascimento, VF.; De Oliveira, G. (2018). Delimitación de pequeñas marismas interiores mediante índices espectrales y árboles de decisión en el sur de Brasil. Revista de Teledetección. (52):55-66. https://doi.org/10.4995/raet.2018.10366 es_ES
dc.description.accrualMethod SWORD es_ES
dc.relation.publisherversion https://doi.org/10.4995/raet.2018.10366 es_ES
dc.description.upvformatpinicio 55 es_ES
dc.description.upvformatpfin 66 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.issue 52
dc.identifier.eissn 1988-8740
dc.contributor.funder Coordenaçao de Aperfeiçoamento de Pessoal de Nível Superior, Brasil
dc.description.references Artigas, F. J., Yang, J. 2006. Spectral discrimination of marsh vegetation types in the New Jersey Meadowlands, USA. Wetlands, 26(1), 271. https:// doi.org/10.1672/0277-5212(2006)26[271:sdomvt]2. 0.co;2 es_ES
dc.description.references Belloli, T. F. 2016. Environmental Impacts Due to Rice, Large Banhado Environmental Protection Area - RS. Federal University of Rio Grande do Sul. Retrieved from https://www.lume.ufrgs.br/bitstream/ handle/10183/158968/001023034.pdf?sequence=1 es_ES
dc.description.references Belluco, E., Camuffo, M., Ferrari, S., Modenese, L., Silvestri, S., Marani, A., Marani, M. 2006. Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing. Remote Sensing of Environment, 105(1), 54-67. https://doi.org/10.1016/j.rse.2006.06.006 es_ES
dc.description.references Canadian Wetland Inventory Technical Group. 2016. Canada Wetland Inventory (Data Model). Stonewall. Retrieved from http://www.ducks.ca/assets/2017/01/ CWIDMv7_01_E.pdf es_ES
dc.description.references Clevers, J. G. P. W., Leeuwen, H. J. C. Van, Sensing, R., Verhoef, W. 1989. Estimanting apar by means of vegetation indeces: a sensitivity analysis. XXIX ISPRS Congress Technical Commission VII: Interpretation of Photographic and Remote Sensing Data, 691-698. es_ES
dc.description.references Congalton, R. G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35-46. https:// doi.org/10.1016/0034-4257(91)90048-B es_ES
dc.description.references Deering, D. W. 1975. Measuring forage production of grazing units from Landsat MSS data. Proceedings of 10th International Symposium on Remote Sensing of Environment, 1975, 1169-1178. es_ES
dc.description.references Delegido, J., Verrelst, J., Alonso, L., Moreno, J. 2011. Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 11(7), 7063-7081. https://doi.org/10.3390/s110707063 es_ES
dc.description.references Di Vittorio, C. A., Georgakakos, A. P. 2018. Land cover classification and wetland inundation mapping using MODIS. Remote Sensing of Environment, 204, 1-17. https://doi.org/10.1016/j.rse.2017.11.001 es_ES
dc.description.references Dong, Z., Wang, Z., Liu, D., Song, K., Li, L., Jia, M., Ding, Z. 2014. Mapping Wetland Areas Using Landsat-Derived NDVI and LSWI: A Case Study of West Songnen Plain, Northeast China. Journal of the Indian Society of Remote Sensing, 42(3), 569-576. https://doi.org/10.1007/s12524-013-0357-1 es_ES
dc.description.references Dvorett, D., Davis, C., Papeş, M. 2016. Mapping and Hydrologic Attribution of Temporary Wetlands Using Recurrent Landsat Imagery. Wetlands, 36(3), 431- 443. https://doi.org/10.1007/s13157-016-0752-9 es_ES
dc.description.references Environmental Protection Agency. 2001. Functions and Values of Wetlands. Watershed Academy Web. Washington. Retrieved from https://www.epa.gov/wetlandsfunctionsvalues es_ES
dc.description.references Escadafal, R. 1989. Remote sensing of arid soil surface color with Landsat thematic mapper. Advances in Space Research, 9(1), 159-163. https://doi.org/10.1016/0273-1177(89)90481-X es_ES
dc.description.references Etchelar, C. B. 2017. Erosive Processes in Wetlands. Rio Grande do Sul Federal University. Retrieved from https://www.lume.ufrgs.br/bitstream/ handle/10183/171041/001054625.pdf?sequence=1 es_ES
dc.description.references Fariña, J. M., He, Q., Silliman, B. R., Bertness, M. D. 2017. Biogeography of salt marsh plant zonation on the Pacific coast of South America. Journal of Biogeography, 12, 238-247. https://doi.org/10.1111/ jbi.13109 es_ES
dc.description.references Fluet-Chouinard, E., Lehner, B., Rebelo, L. M., Papa, F., Hamilton, S. K. 2015. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sensing of Environment, 158, 348-361. https://doi.org/10.1016/j.rse.2014.10.015 es_ES
dc.description.references Friedl, M.A. M. A., Brodley, C. E. C. E. 1997. Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, 61(3), 399- 409. https://doi.org/10.1016/S0034-4257(97)00049-7 es_ES
dc.description.references Gao, B. C. 1996. NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257- 266. https://doi.org/10.1016/S0034-4257(96)00067-3 es_ES
dc.description.references Gedan, K. B., Crain, C. M., Bertness, M. D. 2009. Smallmammal herbivore control of secondary succession in New-England tidal marshes. Ecology, 90(2), 430- 440. https://doi.org/10.1890/08-0417.1 es_ES
dc.description.references Gitelson, A. A., Kaufman, Y. J., Merzlyak, M. N. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289-298. https://doi.org/10.1016/S0034-4257(96)00072-7 es_ES
dc.description.references Huete, A. R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295- 309. https://doi.org/10.1016/0034-4257(88)90106-X es_ES
dc.description.references Jensen, J. R. 2007. Remote sensing of the environment : an earth resource perspective. Pearson Prentice Hall. es_ES
dc.description.references Judd, C., Steinberg, S., Shaughnessy, F., Crawford, G. 2007. Mapping salt marsh vegetation using aerial hyperspectral imagery and linear unmixing in Humboldt Bay, California. Wetlands, 27(4), 1144-1152. https://doi.org/10.1672/0277- 5212(2007)27[1144:msmvua]2.0.co;2 es_ES
dc.description.references Junk. 2013. Definição e Classificação das Áreas Úmidas (AUs) Brasileiras : Base Científica para uma Nova Política de Proteção e Manejo Sustentável Prefácio : Lista dos autores e suas instituições : Centro de Pesquisa Do Pantanal, Brazil es_ES
dc.description.references Junk, W. J., Bayley, P. B., Sparks, R. E. 1989. The Flood Pulse Concept in River-Floodplain Systems. International Large River Symposium. es_ES
dc.description.references Junk, W. J., Piedade, M. F. 2015. Áreas Úmidas (AUs) Brasileiras: Avanços e Conquistas Recentes. Boletim Ablimno, 41(2), 20-24. es_ES
dc.description.references Junk, W. J., Piedade, M. T. F., Lourival, R., Wittmann, F., Kandus, P., Lacerda, L. D., Agostinho, A. A. 2014. Brazilian wetlands: Their definition, delineation, and classification for research, sustainable management, and protection. Aquatic Conservation: Marine and Freshwater Ecosystems, 24(1), 5-22. https://doi. org/10.1002/aqc.2386 es_ES
dc.description.references Kandus, P., Minotti, P., Malvárez, A. I. 2008. Distribution of wetlands in Argentina estimated from soil charts. Acta Scientiarum - Biological Sciences, 30(4), 403-409. https://doi.org/10.4025/actascibiolsci.v30i4.5870 es_ES
dc.description.references Kaplan, G., Avdan, U. 2017. Mapping and Monitoring Wetlands Using SENTINEL 2 Satellite Imagery. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV, 271-277. https:// doi.org/10.5194/isprs-annals-IV-4-W4-271-2017 es_ES
dc.description.references Kaplan, G., Avdan, U. 2017. Wetland Mapping Using Sentinel 1 SAR Data. In Suha Ozden, R. Cengiz Akbulak, Cuneyt Erenoglu, Oznur Karaca, Faize Saris, & Mustafa Avcioglu (Eds.), International Symposium on GIS Applications in Geography & Geosciences. es_ES
dc.description.references Kaufman, Y., Tanre, D. 1992. 1992. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, 30(2). https://doi.org/10.1109/36.134076 es_ES
dc.description.references Kulawardhana, R. W., Thenkabail, P. S., Vithanage, J., Biradar, C., Islam, M. A. a, Gunasinghe, S., Alankara, R. 2007. Evaluation of the wetland mapping methods using Landsat ETM+ and SRTM data. Journal of Spatial Hydrology, 7(2), 62-96. https://doi. org/10.1017/CBO9780511806049 es_ES
dc.description.references Lacaux, J. P., Tourre, Y. M., Vignolles, C., Ndione, J. A., Lafaye, M. 2007. Classification of ponds from highspatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal. Remote Sensing of Environment, 106(1), 66-74. https://doi.org/10.1016/j. rse.2006.07.012 es_ES
dc.description.references Leite, M. G., Guasselli, L. A. 2013. Spatio-temporal dynamics of aquatic macrophytes in Banhado Grande, Gravataí River basin,. Para Onde!?, 7(1), 17-24. es_ES
dc.description.references Liu, L., Liu, Y. H., Liu, C. X., Wang, Z., Dong, J., Zhu, G. F., Huang, X. 2013. Potential effect and accumulation of veterinary antibiotics in Phragmites australis under hydroponic conditions. Ecological Engineering, 53, 138-143. https://doi.org/10.1016/j. ecoleng.2012.12.033 es_ES
dc.description.references Mahdavi, S., Salehi, B., Amani, M., Granger, J. E., Brisco, B., Huang, W., Hanson, A. 2017. ObjectBased Classification of Wetlands in Newfoundland and Labrador Using Multi-Temporal PolSAR Data. Canadian Journal of Remote Sensing, 43(5), 432-450. https://doi.org/10.1080/07038992.2017.1342206 es_ES
dc.description.references Maltchik, L., Rolon, A. S., Guadagnin, D. L., Stenert, C. 2004. Wetlands of Rio Grande do Sul, Brazil: a classification with emphasis on plant communities. Acta Limnol. Bras, 16(2), 137-151. es_ES
dc.description.references Mao, R., Ye, S.-Y., Zhang, X.-H. 2018. SoilAggregate-Associated Organic Carbon Along Vegetation Zones in Tidal Salt Marshes in the Liaohe Delta. CLEAN - Soil, Air, Water, 1-7. https://doi.org/10.1002/clen.201800049 es_ES
dc.description.references 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 es_ES
dc.description.references Mcowen, C. J., Weatherdon, L. V, Bochove, J.-W. Van, Sullivan, E., Blyth, S., Zockler, C., Fletcher, S. 2017. A global map of saltmarshes. Biodiversity Data Journal, 5(5), e11764. https://doi.org/10.3897/BDJ.5.e11764 es_ES
dc.description.references Miranda, C. de S., Paranho Filho, A. C., Pott, A. 2018. Changes in vegetation cover of the Pantanal wetland detected by vegetation index: a strategy for conservation. Biota Neotropica, 18(1), 1-6. https://doi.org/10.1590/1676-0611-bn-2016-0297 es_ES
dc.description.references Mondal, I., Bandyopadhyay, J. 2014. Coastal Wetland Modeling Using Geoinformatics Technology of Namkhana Island, South 24 Parganas, WB, India. Open Access Library Journal, 975, 1-17. https://doi.org/10.4236/oalib.1100975 es_ES
dc.description.references Nielsen, S. 1994. Geomorfologia da bacia do rio GravataíRS. In Bacia do rio Gravataí-RS: informações básicas para a gestão territorial (pp. 1-18). Porto Alegre: Proteger. es_ES
dc.description.references Nunes da Cunha, C., Piedade, M. T. F., Junk, W. J. 2015. Classificação e Delineamento das Áreas Úmidas Brasileiras e de seus Macrohabitats. EdUFMT (Vol. 1). Cuiaba. https://doi.org/10.1017/CBO9781107415324.004 es_ES
dc.description.references Pearson, R. L., Miller, L. D. 1972. Remote Mapping of Standing Crop Biomass for Estimation of the Productivity of the Shortgrass Prairie. Remote Sensing of Environment, 8, 1355-1365. es_ES
dc.description.references Pontius, R. G., Millones, M. 2011. Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407-4429. https://doi.org/10.1080/01431161.2011.552923 es_ES
dc.description.references Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., Sorooshian, S. 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119-126. https://doi.org/10.1016/0034-4257(94)90134-1 es_ES
dc.description.references Ramos, R. A., Pasqualetto, A. I., Balbueno, R. A., Quadros, E. L. L. de, Neves, D. D. das. 2014. Mapeamento e diagnóstico de áreas úmidas no Rio Grande do Sul, com o uso de ferramentas de geoprocessamento. In Anais do Simposio de Áreas Protegidas (pp. 17-21). Viçosa. es_ES
dc.description.references Ramsar. 2002. A Framework for Wetland Inventory. 8th Meeting of the Conference of the Contracting Parties to the Convention on Wetlands. Valencia. Retrieved from http://archive.ramsar.org/pdf/inventoryframework-2002.pdf es_ES
dc.description.references Richardson, A. J., Wiegand, C. L. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12), 1541-1552. es_ES
dc.description.references Rossato, M. S. 2011. Os climas do Rio Grande do Sul: variabilidade, tendências e tipologia. Universidade Federal do Rio Grande do Sul. es_ES
dc.description.references Rouse, J. W., Hass, R. H., Schell, J. A., Deering, D. W. 1973. Monitoring vegetation systems in the great plains with ERTS. Third Earth Resources Technology Satellite (ERTS) Symposium, 1, 309-317. https://doi.org/citeulike-article-id:12009708 es_ES
dc.description.references Ruiz, L. F. C., Caten, A. ten, Dalmolin, R. S. D. 2014. Árvore de decisão e a densidade mínima de amostras no mapeamento da cobertura da terra. Ciência Rural, 44(6), 1001-1007. https://doi.org/10.1590/S0103-84782014000600008 es_ES
dc.description.references Sakané, N., Alvarez, M., Becker, M., Böhme, B., Handa, C., Kamiri, H. W., Langensiepen, M., Menz, G., Misana, S., Mogha, N. G., Möseler, B. M., Mwita, E. J., Oyieke, H. A., Van Wijk, M. T. 2011. Classification, characterisation, and use of small wetlands in East Africa. Wetlands, 31, 1103. https://doi.org/10.1007/s13157-011-0221-4 es_ES
dc.description.references Sharma, A., Panigrahy, S., Singh, T. S., Patel, J. G., Tanwar, H. 2014. Wetland Information System Using Remote Sensing and GIS in Himachal Pradesh , India. Asian Journal of Geoinformatics, 14(4), 13-22. es_ES
dc.description.references Sharpe, P. J., Kneipp, G., Forget, A. 2016. Comparison of Alternative Approaches for Wetlands Mapping: A Case Study from three U.S. National Parks. Wetlands, 36(3), 547-556. https://doi.org/10.1007/s13157-016-0764-5 es_ES
dc.description.references Silva, R. C. da. 2016. Estudo da dinâmica da fragilidade ambiental na Bacia Hidrográfica do Rio Gravataí, RS. Universidade Federal da Bahia. es_ES
dc.description.references Simioni, J. P. D., Guasselli, L. A., Etchelar, C. B. 2017. Connectivity among Wetlands of EPA of Banhado Grande, RS Conetividade entre Áreas Úmidas, APA do Banhado Grande, RS. Brazilian Journal of Water Resources, 22(15). https://doi.org/10.1590/2318-0331.011716096 es_ES
dc.description.references Stefano, L. de. 2003. WWF ' s Water and Wetland Index Summary of Water Framework Directive results. WWF European Living Waters Programme c/o. San Francisco. es_ES
dc.description.references Subramaniam, S., Saxena, M. 2011. Automated algorithm for extraction of wetlands from IRS resourcesat LISS III data. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (pp. 193-198). Bhopal. es_ES
dc.description.references Teixeira, S. G. 2011. Radar de abertura sintética aplicado ao mapeamento e reconhecimento de zonas úmidas costeiras. Universidade Federal do Pará. es_ES
dc.description.references Visser, J. M., Sasser, C. E. 1999. Marsh Vegetation of the Mississippi River Deltaic Plain. Estuaries, 21(4B), 818-828. es_ES
dc.description.references Walsh, N., Bhattasali, N., Chay, F. 2014. Mapping Tidal Salt Marshes. es_ES
dc.description.references White, D. C., Lewis, M. M., Green, G., Gotch, T. B. 2016. A generalizable NDVI-based wetland delineation indicator for remote monitoring of groundwater flows in the Australian Great Artesian Basin. Ecological Indicators, 60, 1309-1320. https://doi.org/10.1016/j.ecolind.2015.01.032 es_ES
dc.description.references Xu, H. 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025-3033. https://doi.org/10.1080/01431160600589179 es_ES
dc.description.references Yan, D., Wünnemann, B., Hu, Y., Frenzel, P., Zhang, Y., Chen, K. 2017. Wetland evolution in the Qinghai Lake area, China, in response to hydrodynamic and eolian processes during the past 1100 years. Quaternary Science Reviews, 162, 42-59. es_ES
dc.description.references Zhou, Q., Jing, Z., Jiang, S. 2003. Remote sensing image fusion for different spectral and spatial resolutions with bilinear resampling wavelet transform. In Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems 2, 1206-1213. Shanghai: IEEE. https://doi.org/10.1109/ITSC.2003.1252676 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem