- -

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

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

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

Show full item record

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. doi:10.4995/raet.2018.10366

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/114906

Files in this item

Item Metadata

Title: Delimitación de pequeñas marismas interiores mediante índices espectrales y árboles de decisión en el sur de Brasil
Secondary Title: Small inner marsh area delimitation using remote sensing spectral indexes and decision tree method in southern Brazil
Author: Simioni, J. P. D. Guasselli, L. A. Ruiz, L. F. C. Nascimento, V. F. de Oliveira, G.
Issued date:
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, ...[+]


[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 ...[+]
Subjects: Marismas , Sentinel 2A , Teledetección , Método CART , Marshes , Remote sensing , CART method
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Source:
Revista de Teledetección. (issn: 1133-0953 ) (eissn: 1988-8740 )
DOI: 10.4995/raet.2018.10366
Publisher:
Universitat Politècnica de València
Publisher version: https://doi.org/10.4995/raet.2018.10366
Description: Revista oficial de la Asociación Española de Teledetección
Thanks:
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) ...[+]
Type: Artículo

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

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

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 [+]
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

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

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

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

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.

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

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.

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

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

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

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

Environmental Protection Agency. 2001. Functions and Values of Wetlands. Watershed Academy Web. Washington. Retrieved from https://www.epa.gov/wetlandsfunctionsvalues

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

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

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

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

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

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

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

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

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

Jensen, J. R. 2007. Remote sensing of the environment : an earth resource perspective. Pearson Prentice Hall.

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

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

Junk, W. J., Bayley, P. B., Sparks, R. E. 1989. The Flood Pulse Concept in River-Floodplain Systems. International Large River Symposium.

Junk, W. J., Piedade, M. F. 2015. Áreas Úmidas (AUs) Brasileiras: Avanços e Conquistas Recentes. Boletim Ablimno, 41(2), 20-24.

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

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

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

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.

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

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

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

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.

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

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

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.

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

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

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

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

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

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.

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

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.

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

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

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.

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

Richardson, A. J., Wiegand, C. L. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12), 1541-1552.

Rossato, M. S. 2011. Os climas do Rio Grande do Sul: variabilidade, tendências e tipologia. Universidade Federal do Rio Grande do Sul.

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

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

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

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.

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

Silva, R. C. da. 2016. Estudo da dinâmica da fragilidade ambiental na Bacia Hidrográfica do Rio Gravataí, RS. Universidade Federal da Bahia.

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

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.

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.

Teixeira, S. G. 2011. Radar de abertura sintética aplicado ao mapeamento e reconhecimento de zonas úmidas costeiras. Universidade Federal do Pará.

Visser, J. M., Sasser, C. E. 1999. Marsh Vegetation of the Mississippi River Deltaic Plain. Estuaries, 21(4B), 818-828.

Walsh, N., Bhattasali, N., Chay, F. 2014. Mapping Tidal Salt Marshes.

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

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

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.

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

[-]

This item appears in the following Collection(s)

Show full item record