Classificação de área úmidas potenciais usando Random Forest no Google Earh Engine em Unidades Geomorfológicas – Rio Grande do Sul, Brasil
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As áreas úmidas são ecossistemas importantes e valiosos na paisagem do extremo sul do Brasil. No entanto, eles estão entre os ecossistemas ameaçados pelas pressões humanas e pelas mudanças climáticas. O uso de técnicas de sensoriamento remoto, classificação supervisionada e algoritmos de aprendizado de máquina oferece uma oportunidade promissora para mapear e monitorar áreas úmidas. O objetivo deste trabalho é desenvolver um método para mapear Áreas Úmidas Potenciais (AP) a partir da integração de imagens de diferentes satélites, sistemas de sensores, feições espectrais, topográficas, climatológicas e hidrológicas, disponibilizadas na plataforma Google Earth Engine. A classificação supervisionada e o mapeamento foram realizados para as unidades geomorfológicas Planície Costeira (PC) e Depressão Central (DC) considerando duas classes: Áreas Úmidas Potenciais, que engloba todos os tipos de áreas úmidas, e Áreas Não Úmidas, para as demais classes. A classificação, individual para cada unidade geomorfológica, permitiu resultados acurados frente a bases oficiais. As APs foram mapeadas com acurácia global superior a 88% e acurácia do consumidor e produtor superior a 81% na PC e DC. Esses resultados demonstram que a metodologia proposta possibilitou a identificação do aumento de 22% a 24% em áreas úmidas potenciais nas regiões geomorfológicas estudadas.
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References
AHMAD, S. K.; HOSSAIN F.; ELDARDIRY, H.; PAVELSKY, T. M. A Fusion Approach for Water Area Classification Using Visible, Near Infrared and Synthetic Aperture Radar for South Asian Conditions. Ieee Transactions On Geoscience And Remote Sensing, [S.L.], v. 58, n. 4, p. 2471-2480, abr. 2020. Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/tgrs.2019.2950705.
ALIKHANI, S.; NUMMI, P.; OJALA, A. Urban Wetlands: a review on ecological and cultural values. Water, [S.L.], v. 13, n. 22, p. 3301, 22 nov. 2021. MDPI AG. DOI: 10.3390/w13223301.
AMANI, M. Wetland inventory system for NL using satellite data (what has been done and what is next step). In: Proc. Real Time Water Qual. Monit. Workshop. 2018. DOI: 10.13140/RG.2.2.23031.27044.
AMANI, M; MAHDAVI, S.; AFSHAR, M.; BRISCO, B.; HUANG, W.; MIRZADEH, S. M. J.; WHITE, L.; BANKS, S.; MONTGOMERY, J.; HOPKINSON, C. Canadian Wetland Inventory using Google Earth Engine: the first map and preliminary results. Remote Sensing, [S.L.], v. 11, n. 7, p. 842, 8 abr. 2019. MDPI AG. DOI: 10.3390/rs11070842.
ASHOK, A.; RANI, H. P.; JAYAKUMAR, K. V. Monitoring of dynamic wetland changes using NDVI and NDWI based landsat imagery. Remote Sensing Applications: Society and Environment, v. 23, p. 100547, 2021. DOI: 10.1016/j.rsase.2021.100547.
ASOKAN, A.; ANITHA, J. Change detection techniques for remote sensing applications: A survey. Earth Science Informatics, v. 12, p. 143-160, 2019. DOI: 10.1007/s12145-019-00380-5.
AYANLADE, A.; PROSKE, U. Assessing Wetland Degradation and Loss of Ecosystem Services in the Niger Delta, Nigeria. Marine and Freshwater Research, v. 67, n. 6, p. 828-836, 2015. DOI: 10.1071/MF15066.
BAN, Y.; YOUSIF, O. Change Detection Techniques: A Review. In: Ban, Y. (eds) Multitemporal Remote Sensing. Remote Sensing and Digital Image Processing, vol 20, p. 19-43, 2016. Springer, Cham. DOI: 10.1007/978-3-319-47037-5_2.
BERTHIER, L.; GUZMOVA, L.; LAROCHE, B.; LEHMANN, S.; SQUIVIDENT, H.; MARTIN, M.; CHENU, J.; THIRY, E.; LEMERCIER, B.; BARDY, M.; MÉROT, P.; WALTER, C. Spatial prediction of potential wetlands at the French national scale based on hydroecoregions stratification and inference modelling. In: EGU General Assembly Conference Abstracts. 2014. p. 12780.
BOURGEAU-CHAVEZ, L.; ENDRES, S.; BATTAGLIA, M.; MILLER, M.; BANDA, E.; LAUBACH, Z.; HIGMAN, P.; CHOW-FRASER, P.; MARCACCIO, J. Development of a Bi-National Great Lakes Coastal Wetland and Land Use Map Using Three-Season PALSAR and Landsat Imagery. Remote Sensing, [S.L.], v. 7, n. 7, p. 8655-8682, 9 jul. 2015. MDPI AG. DOI:10.3390/rs70708655.
BREIMAN, L. Random forests. Machine learning, v. 45, p. 5-32, 2001.
BRINSON, M. M.; CHRISTIAN, R. R. Assessing functions of wetlands and the need for reference. Biologia Ambientale, v. 24, n. 1, p. 307-318, 2010.
BRINSON, M. M. A hydrogeomorphic classification for wetlands, reference wetlands, and functional indices. Vicksburg: US Army Engineers Waterways Experiment Station, 1993. 175p.
CERON, C.; MELESSE, A.; PRICE, R.; DESSU, S.; KANDEL, H. Operational Actual Wetland Evapotranspiration Estimation for South Florida Using MODIS Imagery. Remote Sensing, [S.L.], v. 7, n. 4, p. 3613-3632, 26 mar. 2015. MDPI AG. DOI: 10.3390/rs70403613.
CHANDRASEKAR, K.; SAI, M. V. R. S; ROY, P. S.; DWEVEDI, R. S. Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product. International Journal Of Remote Sensing, [S.L.], v. 31, n. 15, p. 3987-4005, 10 ago. 2010. Informa UK Limited. DOI: 10.1080/01431160802575653.
DASILVA, M. D.; BRUCE, D; HESP, P. A.; SILVA, G. M. da. A New Application of the Disturbance Index for Fire Severity in Coastal Dunes. Remote Sensing, [S.L.], v. 13, n. 23, p. 4739, 23 nov. 2021. MDPI AG.. DOI: 10.3390/rs13234739.
DURAND, P.; GASCUEL-ODOUX, C.; KAO, C.; MEROT, P. Une typologie hydrologique des petites zones humides ripariennes. Etude et gestion des sols, v. 7, n. 3, p. 207-218, 2000.
FARR, T. G.; ROSEN, P. A.; CARO, E.; CRIPPEN, R.; DUREN, R.; HENSLEY, S.; KOBRICK, M.; PALLER, M., RODRIGUEZ, E., ROTH, L., SEAL, D., SHAFFER, S., SHIMADA, J., UMLAND, J., WERNER, M; OSKIN, M.; BURBANK, D.; ALSDORF, D. E. The shuttle radar topography mission. Reviews of geophysics, v. 45, n. 2, 2007.DOI: 10.1029/2005RG000183.
FRANKLIN, S. E.; SKERIES, E. M.; STEFANUK, M. A.; AHMED, O. S. Wetland classification using Radarsat-2 SAR quad-polarization and Landsat-8 OLI spectral response data: A case study in the Hudson Bay Lowlands Ecoregion. International Journal of Remote Sensing, v. 39, n. 6, p. 1615-1627, 2018. DOI: 10.1080/01431161.2017.1410295
GALLANT, A. The Challenges of Remote Monitoring of Wetlands. Remote Sensing, [S.L.], v. 7, n. 8, p. 10938-10950, 24 ago. 2015. MDPI AG. DOI: 10.3390/rs70810938.
GAO, B. C. Normalized difference water index for remote sensing of vegetation liquid water from space. In: Imaging spectrometry. SPIE, 1995. p. 225-236.
GOKCE, D. Wetlands Management: Assessing Risk and Sustainable Solutions. BoD–Books on Demand, 2019. DOI: 10.5772/intechopen.76596
GOMES, C. S.; JÚNIOR, A. P. M. Sistemas de classificação de áreas úmidas no Brasil e no mundo: panorama atual e importância de critérios hidrogeomorfológicos. Geo UERJ, n. 33, p. 34519, 2018. DOI: 10.12957/geouerj.2018.34519.
GORELICK, N.; HANCHER, M.; DIXON, M.; ILYUSHCHENKO, S.; THAU, D.; MOORE, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, v. 202, p. 18-27, 2017. DOI: 10.1016/j.rse.2017.06.031.
GUADAGNIN, D. L.; MALTCHIK, L. Habitat and landscape factors associated with neotropical waterbird occurrence and richness in wetland fragments. Biodiversity And Conservation, [S.L.], v. 16, n. 4, p. 1231-1244, 27 out. 2006. Springer Science and Business Media LLC. DOI: 10.1007/s10531-006-9127-5.
GUADAGNIN, D. L; MALTCHIK, L.; FONSECA, C. R. Species–area relationship of Neotropical waterbird assemblages in remnant wetlands: looking at the mechanisms. Diversity And Distributions, [S.L.], v. 15, n. 2, p. 319-327, 9 fev. 2009. Wiley. DOI: 10.1111/j.1472-4642.2008.00533.x
GUASSELLI, L. A.; SIMIONI, J. P. D.; LAURENT, F. Mapeamento e classificação de áreas úmidas usando Topographic Wetness Index (TWI) a partir de modelos digitais de elevação, na bacia hidrográfica do Rio Gravataí: Rio Grande do Sul, Brasil. Revista Brasileira de Geomorfologia. Vol. 21, n. 3 (jul./set. 2020), p. 639-659, 2020. DOI: 10.20502/rbg.v21i3.1714
GXOKWE, S.; DUBE, T.; MAZVIMAVI, D. Leveraging Google Earth Engine platform to characterize and map small seasonal wetlands in the semi-arid environments of South Africa. Science Of The Total Environment, [S.L.], v. 803, p. 150139, jan. 2022. Elsevier BV. DOI: 10.1016/j.scitotenv.2021.150139.
HALABISKY, M. Improved wetland identification for conservation and regulatory priorities. University of Washington, 2019.
HALABISKY, M.; MILLER, D.; STERWART, A. J.; LORIGAN, D.; BRASEL, T.; MOSKAL, L. The Wetland Intrinsic Potential tool: Mapping wetland intrinsic potential through machine learning of multi-scale remote sensing proxies of wetland indicators. EGUsphere, p. 1-19, 2022. DOI: 10.5194/egusphere-2022-665.
HAN, X.; PAN, J.; DEVLIN, A. T. Remote sensing study of wetlands in the Pearl River Delta during 1995–2015 with the support vector machine method. Frontiers Of Earth Science, [S.L.], v. 12, n. 3, p. 521-531, 30 out. 2017. Springer Science and Business Media LLC. DOI: 10.1007/s11707-017-0672-x.
HIRD, J.; DELANCEY, E.; MCDERMID, G.; KARIYEVA, J. Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping. Remote Sensing, [S.L.], v. 9, n. 12, p. 1315, 14 dez. 2017. MDPI AG. DOI: 10.3390/rs9121315.
HU, S.; NIU, Z.; CHEN, Y.; LI, L.; ZHANG, H. Global wetlands: potential distribution, wetland loss, and status. Science Of The Total Environment, [S.L.], v. 586, p. 319-327, maio 2017. Elsevier BV. DOI:10.1016/j.scitotenv.2017.02.001.
HUANG, W.; DEVRIES, B.; HUANG, C.; JONES, J.; LANG, M.; CREED,I. Automated extraction of inland surface water extent from Sentinel-1 data. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. p. 2259-2262. DOI: 10.1109/IGARSS.2017.8127439.
HUETE, A.; DIDAN, K.; MIURA, T.; RODRIGUEZ, E. P.; GAO, X.; FERREIRA, L. G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing Of Environment, [S.L.], v. 83, n. 1-2, p. 195-213, nov. 2002. Elsevier BV. DOI: 10.1016/s0034-4257(02)00096-2.
JAMALI, A.; MAHDIANPARI, M.; BRISCO, B.; GRANGER, J.; MOHAMMADI, F.; SALEHI, B. Deep Forest classifier for wetland mapping using the combination of Sentinel-1 and Sentinel-2 data. GIScience & Remote Sensing, UK, v. 58, n. 7, p. 1072–1089, set. 2021. DOI: 10.1080/15481603.2021.1965399.
KAPLAN, G.; AVDAN, U. Evaluating the utilization of the red edge and radar bands from sentinel sensors for wetland classification. Catena, [S.L.], v. 178, p. 109-119, jul. 2019. Elsevier BV. DOI: 10.1016/j.catena.2019.03.011.
KELLEY, L. C.; PITCHER, L.; BACON, C. 2018. Using Google Earth Engine to Map Complex Shade-Grown Coffee Landscapes in Northern Nicaragua. Remote Sensing, [S.L.], v. 10, n. 6, p. 952, 14 jun. 2018. MDPI AG. DOI: 10.3390/rs10060952.
LONG, X.; X. LI; H. LIN; M. ZHANG. Mapping the vegetation distribution and dynamics of a wetland using adaptive-stacking and Google Earth Engine based on multi-source remote sensing data. International Journal of Applied Earth Observation and Geoinformation, v. 102, p. 102453, out. 2021. Elsevier BV. DOI: 10.1016/j.jag.2021.102453.
MABWOGA, S. O.; THUKRAL, A. K. Characterization of change in the Harike wetland, a Ramsar site in India, using landsat satellite data. SpringerPlus, v. 3, n. 1, p. 1-11, 2014. DOI: 10.1186/2193-1801-3-576.
MAHDIANPARI, M.; JAFARZADEH, H.; GRANGER, J. E.; MOHAMMADIMANESH, F.; BRISCO, B.; SALEHI, B.; HOMAYOUNI, S.; WENG, Q. A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in newfoundland. Giscience & Remote Sensing, [S.L.], v. 57, n. 8, p. 1102-1124, 16 nov. 2020. Informa UK Limited. DOI: 10.1080/15481603.2020.1846948.
MAHDIANPARI, M.; SALEHI, B.; MOHAMMADIMANESH, F.; BRISCO, B.; HOMAYOUNI, S.; GILL, E.; DELANCEY, E. R.; BOURGEAU-CHAVEZ, L. Big Data for a Big Country: the first generation of canadian wetland inventory map at a spatial resolution of 10-m using sentinel-1 and sentinel-2 data on the google earth engine cloud computing platform. Canadian Journal Of Remote Sensing, [S.L.], v. 46, n. 1, p. 15-33, 2 jan. 2020. Informa UK Limited. DOI: 10.1080/07038992.2019.1711366.
MAHDIANPARI, M.; SALEHI, B.; MOHAMMADIMANESH, F.; HOMAYOUNI, S.; GILL, E. The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform. Remote Sensing, [S.L.], v. 11, n. 1, p. 43, 28 dez. 2018. MDPI AG. DOI: 10.3390/rs11010043.
MAO, D.; WANG, Z.; DU, B.; LI, Lin; TIAN, Y.; JIA, M.; ZENG, Y.; SONG, K.; JIANG, M.; WANG, Y. 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, [S.L.], v. 164, p. 11-25, jun. 2020. Elsevier BV. DOI: 10.1016/j.isprsjprs.2020.03.020.
MAXA, M.; BOLSTAD, P. Mapping northern wetlands with high resolution satellite images and LiDAR. Wetlands, v. 29, p. 248-260, 2009. DOI: https://doi.org/10.1672/08-91.1
MCCARTHY, M. J.; MERTON, E. J.; MULLER-KARGER, F. E. Improved coastal wetland mapping using very-high 2-meter spatial resolution imagery. International Journal Of Applied Earth Observation And Geoinformation, [S.L.], v. 40, p. 11-18, ago. 2015. Elsevier BV. DOI: 10.1016/j.jag.2015.03.011
MCFEETERS, S. K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International journal of remote sensing, v. 17, n. 7, p. 1425-1432, 1996. DOI: 10.1080/01431169608948714.
MEDDE, E. T.; BERTHIER, L.; BARDY, M.; CHENU, J. P.; GUZMOVA, L.; LAROCHE, B. Enveloppes des milieux potentiellement humides de la France métropolitaine. Programme de modélisation des milieux potentiellement humides de France. Ministere d’Ecologie, du Développement Durable et de l’Energie, Rennes, France, 2014.
MEROT, P.; HUBERT-MOY, L.; GASCUEL-ODOUX, C.; CLEMENT, B.; DURAND, P.; BAUDRY, J.; THENAIL, C. A Method for Improving the Management of Controversial Wetland. Environmental Management, [S.L.], v. 37, n. 2, p. 258-270, 4 nov. 2005. Springer Science and Business Media LLC. DOI: 10.1007/s00267-004-0391-4
MOOMAW, W. R.; CHMURA, G. L.; DAVIES, G. T.; FINLAYSON, C. M.; MIDDLETON, B. A.; NATALI, S. M.; SUTTON-GRIER, A. E. Wetlands In a Changing Climate: science, policy and management. Wetlands, [S.L.], v. 38, n. 2, p. 183-205, abr. 2018. Springer Science and Business Media LLC. DOI: 10.1007/s13157-018-1023-8.
MOREIRA, A. A.; FASSONI-ANDRADE, A. C.; RUHOFF, A. L.; PAIVA, R. C. D. REMOTE SENSING OF WATER BALANCE IN PANTANAL. Raega - O Espaço Geográfico em Análise, [S.L.], v. 46, n. 3, p. 20, 28 ago. 2019. Universidade Federal do Paraná. DOI: 10.5380/raega.v46i3.67096.
PEKEL, J. F.; COTTAM, A.; GORELICK, N.; BELWARD, A. S. High-resolution mapping of global surface water and its long-term changes. Nature, [S.L.], v. 540, n. 7633, p. 418-422, 7 dez. 2016. Springer Science and Business Media LLC. DOI:10.1038/nature20584.
PETIT, S.; STASOLLA, M.; WYARD, C.; SWINNEN, G.; NEYT, X.; HALLOT, E. A New Earth Observation Service Based on Sentinel-1 and Sentinel-2 Time Series for the Monitoring of Redevelopment Sites in Wallonia, Belgium. Land, [S.L.], v. 11, n. 3, p. 360, 1 mar. 2022. MDPI AG. DOI: 10.3390/land11030360.
RAPINEL, S.; FABRE, E.; DUFOUR, S.; ARVOR, D.; MONY, C.; HUBERT-MOY, L. Mapping potential, existing and efficient wetlands using free remote sensing data. Journal Of Environmental Management, [S.L.], v. 247, p. 829-839, out. 2019. Elsevier BV. DOI: 10.1016/j.jenvman.2019.06.098.
RIBEIRO, S.; MOREIRA, L. F. B.; OVERBECK, G. E.; MALTCHIK, L. Protected Areas of the Pampa biome presented land use incompatible with conservation purposes. Journal Of Land Use Science, [S.L.], v. 16, n. 3, p. 260-272, 4 maio 2021. Informa UK Limited. DOI: 10.1080/1747423X.2021.1934134.
RIO GRANDE DO SUL. Secretary of Planning, Governance and Management. Department of Government Planning Socioeconomic Atlas of Rio Grande do Sul/Rio Grande do Sul. 6. Ed. – Porto Alegre, 2021. 203 p.: il.
ROLON, A. S.; ROCHA, O.; MALTCHIK, L. Diversidade de macrófitas aquáticas do Parque Nacional da Lagoa do Peixe. Neotropical Biology And Conservation, [S.L.], v. 6, n. 1, p. 5-12, 11 maio 2011. Pensoft Publishers. DOI: 10.4013/nbc.2011.61.02.
ROUSE, J. W.; HAAS, R. H.; SCHELL, J. A.; DEERING, D. W. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ, v. 351, n. 1, p. 309, 1973.
RUBEC, C. The Canadian Wetland Classification System. The Wetland Book, [S.L.], p. 1577-1581, 2018. Springer Netherlands. DOI: 10.1007/978-90-481-9659-3_340.
RUIZ, L. F. C.; GUASSELLI, L .A.; SIMIONI, J. P. D.; BELLOLI, T. F.; FERNANDES, P. C. B. Object-based classification of vegetation species in a subtropical wetland using Sentinel-1 and Sentinel-2A images. Science Of Remote Sensing, [S.L.], v. 3, p. 100017, jun. 2021. Elsevier BV.DOI: 10.1016/j.srs.2021.100017.
SALINAS, J. B. G;. EGGERTH, M. K. P;. MILLER, M. E; MEZA, R. R. B; CHACALTANA, J. T. A;. ACUÑA, J. R;. BARROSO, G. F. Wetland mapping with multitemporal Sentinel Radar remote sensing In the southeast region of Brazil. In: 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS). IEEE, 2020. p. 669-674. DOI: 10.1109/LAGIRS48042.2020.9165593.
SCHÄFER, A.; LANZER, R.; SCUR, L. Atlas socioambiental dos municípios de Cidreira, Balneário Pinhal, Palmares do Sul. EDUCS, Caxias do Sul, 2013, v., p. 14-22.
SCHUH, M. H.; GUADAGNIN, D. L. Habitat and landscape factors associated with the nestedness of waterbird assemblages and wetland habitats in South Brazil. Austral Ecology, [S.L.], v. 43, n. 8, p. 989-999, 25 jul. 2018. Wiley. DOI:10.1111/aec.12648.
SCOTT, D. A.; JONES, T. A. Classification and inventory of wetlands: A global overview. Vegetatio, v. 118, n. 1-2, p. 3-16, 1995.
SEMENIUK, C. A.; SEMENIUK, V. A comprehensive classification of inland wetlands of Western Australia using the geomorphic-hydrologic approach. Journal of the Royal Society of Western Australia, v. 94, n. 3, p. 449, 2011.
SEMENIUK, C. A.; SEMENIUK, V. A geomorphic approach to global classification for inland wetlands. Vegetatio, [S.L.], v. 118, n. 1-2, p. 103-124, jun. 1995. Springer Science and Business Media LLC. . DOI: 10.1007/BF00045193.
SIMIONI, J. P.; GUASSELLI, L. A.; OLIVEIRA, G.; MATAVELI, G. A. V.; SANTOS, T. V Remote Sensing-Based Method to Assess Water Level Fluctuations in Wetlands in Southern Brazil. Geohazards, [S.L.], v. 1, n. 1, p. 20-30, 12 maio 2020. MDPI AG. DOI:10.3390/geohazards1010003.
SIMIONI, J. P. D; GUASSELLI, L. A.; DE OLIVEIRA, G. G. A.; RUIZ, L. F. C.; OLIVEIRA, G. A comparison of data mining techniques and multi-sensor analysis for inland marshes delineation. Wetlands Ecology And Management, [S.L.], v. 28, n. 4, p. 577-594, 8 jun. 2020. Springer Science and Business Media LLC. DOI: 10.1007/s11273-020-09731-2
SMARDON, R. Wetlands and Sustainability. Water, [S.L.], v. 6, n. 12, p. 3724-3726, 28 nov. 2014. MDPI AG. DOI: 10.3390/w6123724.
SMITH R, D.; AMMANN A.; BARTOLDUS, C.; BRINSON, M. M. An approach for assessing wetland functions using hydrogeomorphic classification, reference wetlands, and functional indices. Publisher, U.S. Army Engineer Waterways Experiment Station, 1995.
SOUZA, C. M.; SHIMBO, J. Z.; ROSA, M. R.; PARENTE, L. L.; ALENCAR, A. A.; RUDORFF, B. F. T.; HASENACK, H.; MATSUMOTO, M.; FERREIRA, L. G.; SOUZA-FILHO, P. W. M. Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine. Remote Sensing, [S.L.], v. 12, n. 17, p. 2735, 25 ago. 2020. MDPI AG. DOI: 10.3390/rs12172735.
STATE SECRETARIAT FOR THE ENVIRONMENT OF RIO GRANDE DO SUL (SEMA). FEPAM Digital Cartographic Base, Wetlands, 1:250,000. DIGITAL LIBRARY, 2013. Available at <http://www.fepam.rs.gov.br/biblioteca/geo/bases_geo.asp>
STATE SECRETARIAT FOR THE ENVIRONMENT OF RIO GRANDE DO SUL (SEMA). SEMA Digital Cartographic Database, Hydrography, Wetlands, 2018, Scale 1: 25,000. Available at <http://ww2.fepam.rs.gov.br/bcrs25/>.
STEYAERT, P.; BARZMAN, M.; BILLAUD, J.; BRIVES, H.; HUBERT, B.; OLLIVIER, G.; ROCHE, B. The role of knowledge and research in facilitating social learning among stakeholders in natural resources management in the French Atlantic coastal wetlands. Environmental Science & Policy, [S.L.], v. 10, n. 6, p. 537-550, out. 2007. Elsevier BV. DOI: 10.1016/j.envsci.2007.01.012.
SUERTEGARAY, D. M. A.; GUASSELLI, L .A. Landscapes (images and representations) of Rio Grande do Sul. In Rio Grande do Sul: landscapes and territories in transformation. Org. Verdum, R.; Basso, L.A.; 2ed.Porto Alegre: UFRGS Editora, 2012, v. , p. 27-38, 360p.
TASSI, A.; VIZZARI, M. Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms. Remote Sensing, [S.L.], v. 12, n. 22, p. 3776, 17 nov. 2020. MDPI AG. DOI: 10.3390/rs12223776.
TINER, R. W. Wetland Indicators: A Guide to Wetland Identification, Delineation, Classification, and Mapping. Boca Raton: CRC Press LLC,1999. 418 p. DOI: 10.1201/9781420048612.
TINER, R. W.; LANG, M. W.; KLEMAS, V. V. Remote Sensing of Wetlands: Applications and Advances; CRC Press: Boca Raton, FL, USA, 2015. DOI: 10.1201/b18210.
TOMAZELLI, L. J.; VILLWOCK, J. A. O Cenozóico do Rio grande do Sul: Geologia da Planície Costeira. Holz, M & DeRos, LF. Geologia do Rio Grande do Sul, 2000.
U. S. GEOLOGICAL SURVEY/NASA - USGS 2018 - MOD16A2 v006. Available in: https://lpdaac.usgs.gov/products/mod16a2v006/.
VALENTI, V. L.; CARCELEN, E. C.; LANGE, K.; RUSSO, N. J.; CHAPMAN, B. Leveraging Google Earth Engine User Interface for Semiautomated Wetland Classification in the Great Lakes Basin at 10 m With Optical and Radar Geospatial Datasets. Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing, [S.L.], v. 13, p. 6008-6018, 2020. Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/JSTARS.2020.3023901.
VILLWOCK, J. A.; TOMAZELLI, L. J. TÉCNICAS, Notas. Geologia costeira do Rio Grande do sul. Notas técnicas, v. 8, p. 1-45, 1995.
VILLWOCK, J. A.; TOMAZELLI, L. J.; LOSS, E.L.; DENHARDT, E. A.; HORN FILHO, N. O.; BACHI, F. A.; DENHARDT, B. A. Geology of the Rio Grande do Sul province. In: Rabassa, J. (ed.), International Symposium on Sea Level Changes and Quaternary Shorelines, São Paulo. Quaternary of South America and Antarctic Peninsula. Balkema: Rotterdam, v.4, p79-97, 1986.
VOLK, M.; HOCTOR, T. S.; NETTLES, B. B.; HILSENBECK, R.; PUTZ, F. E.; OETTING, J. Florida land use and land cover change in the past 100 years. Florida's Climate: Changes, Variations, & Impacts, 2017. DOI: 10.17125/fci2017.ch02.
WU, Q.; LANE, C. R.; LI, X.; ZHAO, K.; ZHOU, Y.; CLINTON, N.; DEVRIES, B.; GOLDEN, H. E.; LANG, M. W. Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine. Remote Sensing Of Environment, [S.L.], v. 228, p. 1-13, jul. 2019. Elsevier BV. DOI: 10.1016/j.rse.2019.04.015.
WU, Y.; XI, Yi; FENG, M.; PENG, S. Wetlands Cool Land Surface Temperature in Tropical Regions but Warm in Boreal Regions. Remote Sensing, [S.L.], v. 13, n. 8, p. 1439, 8 abr. 2021. MDPI AG. DOI: 10.3390/rs13081439.
XU, H. A study on information extraction of water body with the modified normalized difference water index (MNDWI). JOURNAL OF REMOTE SENSING-BEIJING-, v. 9, n. 5, p. 595, 2005.
ZANOTTA, D, ZORTEA, M, FERREIRA, M, P. Processamento de imagens de satélite. Edição 1. São Paulo: Oficina de Textos, 2019.
ZOOBOTANICAL FOUNDATION OF RIO GRANDE DO SUL (FZB). Mapping of wetlands,
Digital files for use in GIS - RS digital cartographic base 1:250.000, 2013. Available in <http://www.fepam.rs.gov.br/biblioteca/geo/bases_geo.asp>.
ZOU, Y.; WANG, L.; XUE, Z; E, M.; JIANG, M.; LU, X.; YANG, S.; SHEN, X.; LIU, Z.; SUN, G. Impacts of Agricultural and Reclamation Practices on Wetlands in the Amur River Basin, Northeastern China. Wetlands, [S.L.], v. 38, n. 2, p. 383-389, 22 nov. 2017. Springer Science and Business Media LLC. DOI: 10.1007/s13157-017-0975-4.