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Serviço Público 
Federal Ministério 
da Educação 
Fundação Universidade Federal de Mato Grosso do 
Sul 
Programa de Pós-Graduação em Recursos Naturais 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
THE AVULSIVE TAQUARI RIVER IN THE BRAZILIAN PANTANAL 
WETLAND: HOW LANDSCAPE CHANGES AFFECT LAND USE AND 
ECONOMIC ACTIVITIES 
 
 
 
 
 
 
 
Rômullo Oliveira Louzada 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Campo Grande, MS 
Julho/2020 
 
Fundação Universidade Federal de Mato Grosso do Sul 
Faculdade de Engenharias, Arquitetura e Urbanismo e 
Geografia 
Programa de Pós Graduação em Recursos Naturais 
 
 
 
 
Rômullo Oliveira Louzada 
 
 
 
THE AVULSIVE TAQUARI RIVER IN THE BRAZILIAN PANTANAL 
WETLAND: HOW LANDSCAPE CHANGES AFFECT LAND USE AND 
ECONOMIC ACTIVITIES 
 
Dissertação apresentada visando à obtenção do grau de 
Mestre no Programa de Pós-Graduação em Recursos 
Naturais da Universidade Federal do Mato Grosso do 
Sul, área de concentração: Análise Integrada de 
Geossistemas. 
 
Orientador: Prof. Dr. Ivan Bergier 
Co-orientador: Prof. Dr. Mario Luis Assine 
 
Aprovado em: 
 
 
 
Banca Examinadora 
 
 
 
Prof. Dr. Ivan Bergier 
Orientador 
 
 
 
Prof. Dr. Mario Luis Assine 
Co-orientador 
 
 
 
 
 
 
Prof. Dr. Michael Matthew McGlue Prof. Dr. Fábio de Oliveira Roque 
 
 
Campo Grande, MS 
Julho/2020
1 
Agradecimentos 
 
 
Obrigado a Deus, à doutrina espirita e aos meus guias protetores, incansáveis trabalhadores 
que me guiaram até aqui. Deus, te agradeço mais uma vez por ser corumbaense, 
pantaneiro, apaixonado pela natureza e um defensor incansável dessa linda terra. Taquari, 
obrigado por cruzar meu destino. Sinto que casei com você, e como todo casamento, isso 
dá trabalho. Meu lugar nesse mundo é te cuidar, meu amado rio. 
Meus pais Vanyr e Pedro, exemplos de vida e conduta, progenitores de amor supremo. 
Dedico esta conquista a vocês, que também não desistiram de mim. Não tenho palavras 
para expressar meu orgulho e admiração por vocês. Minha irmã Laila. Você que é minha 
inspiração de amor e caridade ao próximo. Mesmo distante, sinto me afagado pelos seus 
mais puros sentimentos. Minha afilhada e amada Clara, junto ao meu amado cunhado 
Marcio, obrigado. Minha prima-irmã Ariane, obrigado por você estar em minha vida, junto 
a Edmilson e filhinho(a), que já amo. Vovozinhas, obrigado! Isso também é para vocês. 
Julio César, Tia Zica e Vovô Plinio, vocês fazem muita falta mas sei que ficariam 
orgulhosos. 
Minha esposa Iasmine, companheira incansável, exemplo de doação, força e sensibilidade. 
Saiba que isso seria impossivel sem você. Não esqueço do dia em que me apoiou na volta 
ao MS sem questionar. Nosso amor é o meu principio vital, o que me faz mover, portanto, 
obrigado. Deus colocou você em minha vida sabendo de um propósito maior. Não menos 
importante, obrigado ao ponto de ônibus. 
À minha segunda familia Lima, meus amados: Augusto, Aparecida, Dudu, César, Ana e 
Mariana; obrigado por vocês estarem nessa caminhada. De algum modo vocês encheiram 
minha bola e me deram força. 
Padrinho Humberto, que simplesmente transformou minha vida. Primeiro por me 
apresentar o Pantanal, depois por dividir um sanduíche e me aceitar como filho, incentivar 
a fazer o concurso no IMASUL e continuar sempre estudando. Tia Didi, minha madrinha 
implacável na busca pelo conhecimento. Essa conquista tem um pedacinho de você. Tia 
Vera, obrigado pelo apoio, amor e carinho, igualmente à Gauquinho e meu amado afilhado, 
Pedro. 
Meu amigo Frederico Wagner, obrigado por me ouvir e apoiar desde Lavras. Essa 
conquista é também é sua. Paulo Henrique (PH), obrigado por me dar guarita, atenção e 
cerveja, tudo isso seria mais dificil sem você. 
Ao IMASUL, que me proporcionou conhecimento, experiências e muito trabalho. Nesse 
sentido, agradeço especialmente a certas pessoas: Diego Brito e Brigido, por me salvarem 
2 
da onça pintada, Sr. Adauto, Luiz Mário, Vilalva, Ariane, Borginho, Maria Célia, Roberta 
e Regina Cavalcanti, companheiros memoráveis dos tempos de fiscalização. Não menos 
importante, agradeços às meninas do EIA (Ana Luiza, Luciana e Rosana) por me darem 
força e incentivo, além do meu querido chefe Delson Sandim. 
Prof. Ivan Bergier, hoje meu amigo Ivan, que confiou sua orientação sem me conhecer, 
logo no primeiro dia de aula. Nunca mais vou esquecer daquilo, pois foi ali que tudo 
mudou. Ivan, você me mostou um novo mundo, cheio de possibilidades, em que a ciência é 
feita com responsabilidade e eficiência, em prol da sociedade e do uso sustentável dos 
recursos naturais. Obrigado pelo alto nível das exigências e não espero nada mais do que 
isso no Doutorado. Você fez o que um pai deve fazer para um filho, ensinar. 
Ao meu co-orientador Prof. Mario Assine, cuja referência técnica é incontestável, além de 
ser um revisor cirúrgico. É uma pena ainda não nos conhecermos pessoalmente, mas isso 
só reforça meu agradecimento e admiração pela sua pessoa. 
Obrigado à UFMS por me aceitar no PGRN, especialmente ao Prof. Antônio Paranhos na 
primeira correção de um trabalho técnico. Professores e colegas, obrigado a todos, em 
especial ao Prof. Alexandre Vansconcelos pela dedicação nos ensinamentos quanto aos 
métodos de pesquisa. 
Às demais pessoas que me ajudaram nesse processo, são elas: André Borges (IMASUL, 
facilitador e incentivador), Édipo (IMASUL, fera no Spring), Sr. Amaral (pescador 
profisional e exímio condutor de barco), Francisco (consultor técnico e incentivador), 
Luciene (PRGN) e Família Borgatto (Faz. Palmeiras, pantaneiros de coração); meu 
especial agradecimento. 
3 
Sumário 
Agradecimentos ............................................................................................................................. 1 
Resumo .......................................................................................................................................... 5 
Abstract ......................................................................................................................................... 6 
Figure list ...................................................................................................................................... 7 
Table list ........................................................................................................................................ 8 
1. Introdução ................................................................................................................................. 9 
1.1. Objetivo ............................................................................................................................. 11 
1.1.1. Objetivos específicos .................................................................................................. 11 
1.2. Organização da dissertação ............................................................................................... 11 
Referências .................................................................................................................................. 12 
2. Landscape changes in avulsive ryver systems: case study of Taquari River on Brazilian 
Pantanal wetlands .............................................................................................................. 15 
2.1. Introduction ....................................................................................................................... 15 
2.2. Materials and Methods ...................................................................................................... 17 
2.2.1. Study Area .................................................................................................................. 17 
2.2.2. Methodology ............................................................................................................... 18 
2.2.2.1. Tests of landscape mapping methods ..............................................................19 
2.2.2.2. Ground and orbital truths validation ................................................................ 21 
2.2.2.3. Ancillary data .................................................................................................. 22 
2.2.2.4. Temporal analysis of landscape changes ......................................................... 23 
2.3. Results ............................................................................................................................... 23 
2.3.1. Defining the best wetland mapping algorithm and satellite bands ............................. 23 
2.3.1. Mapping of mosaicked/rectified Landsat data timeseries ........................................... 26 
2.4. Discussion .......................................................................................................................... 30 
2.5. Concluding remarks ........................................................................................................... 33 
References ................................................................................................................................... 34 
3. The avulsive Taquari River in the Brazilian Pantanal wetland: how landscape changes affect 
land use and economic activities ....................................................................................... 41 
3.1. Introduction ....................................................................................................................... 41 
3.2. Material and Methods ........................................................................................................ 43 
3.2.1. Study Area .................................................................................................................. 43 
3.2.2. Classification of Landsat images ............................................................................... 45 
3.2.2.1. Data acquisition and preprocessing ................................................................. 45 
3.2.2.2. Image segmentation and classification ............................................................ 44 
3.2.3. Ancillary data ............................................................................................................. 43 
3.2.4. Multitemporal and multi-criteria analysis ................................................................... 46 
3.3. Results ............................................................................................................................... 46 
4 
3.3.1. Geomorphological zonation ........................................................................................ 46 
3.3.2. Decadal mappings of the active lobe .......................................................................... 48 
3.3.3. Temporal landscape changes in the active lobe .......................................................... 50 
3.3.4. Spatiotemporal landscape dynamics in the active lobe .............................................. 52 
3.4. Discussions ........................................................................................................................ 54 
3.5. Conclusion ......................................................................................................................... 58 
References ................................................................................................................................... 59 
4. Conclusões .............................................................................................................................. 66 
Anexos......................................................................................................................................... 68 
Capítulo 2 .................................................................................................................................... 68 
Capítulo 3 .................................................................................................................................... 81 
 
5 
Resumo 
 
O Pantanal é o maior e uma das mais importantes áreas úmidas do mundo, caracterizado pela 
beleza cênica, com uma grande diversidade de fauna e flora. Suas planícies são reguladas por 
ciclos de inundação e abastecidas por rios do planalto, cuja aporte de sedimentos e 
assoreamento, especialmente no rio Taquari, vem intensificando os processos de avulsão dos 
rios. No geral, a mudança do curso afeta a paisagem por modificações nos padrões de 
umidade, sedimentos e nutrientes. A avaliação dessas mudanças nas escalas de tempo é 
facilitada por meio de mapeamentos temáticos baseados em sensores orbitais e técnicas de 
SIG, além de índice de vegetação e modelo de elevação. No rio Taquari, a avulsão completa 
ocorrida no Zé da Costa no final dos anos 90, bem como a avulsão em processo na região 
Caronal, que atualmente é o principal canal do rio, está impulsionando a terrestrialização de 
paisagens. Os resultados indicaram que as classes temáticas no Zé da Costa passaram por um 
processo de terrestrialização, originalmente por macrófitas, passando por brejos até a 
vegetação terrestre. Esse comportamento da paisagem tem semelhanças com o rio que se 
forma no Caronal, em resposta ao assoreamento e splay progradation; no entanto, a 
velocidade de sucessão é menor devido à grande área de acomodação de sedimentos no 
Paiaguás, formada por um lago raso e dominado por macrófitas, e permanentemente 
conectado à planície de inundação do rio Paraguai. Com base na relação entre tempo e área 
em outros processos completos de avulsão, o rio Taquari pode concluir sua recanalização 
entre 2058 e 2095, enquanto isso, o ecossistema foi severamente alterado, promovendo 
migrações de residentes e perdas economicas de fazendeiros locais do Pantanal. Nesse 
sentido, a retenção de sedimentos na parte superior e a integração das comunidades afetadas 
na parte inferior devem ser objeto de políticas públicas. 
 
Palavras-chave: Assoreamento, avulsão fluvial, impactos socioeconômicos, mapeamento de 
paisagens, Pantanal. 
 
 
6 
Abstract 
 
The Pantanal is the largest and one of the most important wetland in the globe, featured by 
scenic beauty with a high diversity of fauna and flora. Its lowlands are regulated by flood 
cycles and supplied by rivers from the plateau, whose sediment input and aggradation, 
especially in the Taquari River, has intensified the processes of river avulsion. In general, the 
changing of the course, affect the landscape by modifications in the humidity, sediment and 
nutrient patterns. The evaluation of these changes in time scales is facilitated through thematic 
mappings based on orbital sensors and GIS techniques, in addition to vegetation index and 
elevation model. On the Taquari River, the complete avulsion that occurred in the Zé da Costa 
in the late 1990s, as well as the avulsion that was taking place in the Caronal region, which is 
currently the main channel of the river, it has driving the terrestrialization on landscapes. The 
results indicated that the thematic classes in Zé da Costa underwent a terrestrialization 
process, originally by macrophytes, passing through swamps to terrestrial vegetation. This 
behavior of the landscape has similarities with the river being formed in Caronal, and are 
responses to aggradation and splay progradation, however, the succession speed is lower due 
to the large sediment accommodation area in Paiaguás, formed by a shallow lake, dominated 
by macrophytes and permanently connected to the Paraguay River floodplain. Based on the 
relation of time and area on other complete avulsion processes, the Taquari River may 
complete the rechannelization between 2058 and 2095, meanwhile the ecosystem has been 
severely altered, promoting migrations and economic losses for local farmers from Pantanal. 
In this sense, the retention of sediments in the upper part and integration of the affected 
communitiesin the lower part must be the object of public policies. 
 
Keywords: Aggradation, Fluvial avulsion, Landscape mapping, Pantanal wetland, 
Socioeconomic impacts. 
 
7 
Figure list 
 
Figure 1: Location of the avulsion area, ground and orbital truths, and studied drainage 
polygons ............................................................................................................................ 18 
Figure 2: Flowchart of the methodology used for mapping assessments ................................... 19 
Figure 3: Ground truths obtained on August 2018 (A-D) and on October 2019 (E) .................. 22 
Figure 4: Classification tests from Landsat-8 OLI by criteria of lowest, intermediate and highest 
accuracies .......................................................................................................................... 25 
Figure 5: Temporal mappings overlaid to abandoned (A), actual (B) and distal (C) drainage 
polygons in the Zé da Costa region ................................................................................... 27 
Figure 6: Timeseries of percentage changes of landscapes and NDVI for the abandoned 
drainage polygon (region A in Figure 5)........................................................................... 28 
Figure 7: Timeseries of percentage changes of landscapes and NDVI for the abandoned 
drainage polygon (region B in Figure 5) ........................................................................... 28 
Figure 8: Timeseries of percentage changes of landscapes and NDVI for the abandoned 
drainage polygon (region C in Figure 5) ........................................................................... 29 
Figure 9: Relation between R and NDVI values for all years (A) and R evolution over time for 
polygons of the abandoned (B), actual (C) and distal (D) drainages ................................ 32 
Figure 10: The Taquari drainage basin, showing the upper bedrock and lower alluvial reach .. 43 
Figure 11: Morphologic zonation of the active depositional lobe of the Taquari River ............. 47 
Figure 12: Lobed landforms on the active lobe of the Taquari River megafan were mapped 
using NDVI and DEM data ............................................................................................... 48 
Figure 13: Decadal mappings of major landscapes in the active lobe of the Taquari River ....... 49 
Figure 14: Sankey diagrams and R indexes for intra-depositional lobes A, B and C of the 
Taquari River megafan. ..................................................................................................... 51 
Figure 15: Spatiotemporal mapping of permanent (constant) and transition (changing) 
landscapes from 1988 to 2019........................................................................................... 52 
Figure 16: Box plots of altitude (DEM) and relative distribution of spatiotemporal classes in the 
lobed landforms at the active lobe of the Taquari River megafan. ................................... 53 
Figure S1: segmented mosaic of the rectified 2018 Landsat 8 false color composition R4G5B3 
parameterized as: a) 5, 10, b) 10, 20, c) 25, 50, and d) 50, 100 ........................................ 68 
Figure S2: AD used for visual interpretation for tests group B .................................................. 69 
Figure S3: Averages and confidence intervals of omission and commission errors by class of 
landscape ........................................................................................................................... 70 
Figure S4: Tests of mappings with replicates and their respective OA and K values for the 
avulsive river system site in the Zé da Costa avulsion on 2018 ....................................... 70 
Figure S5: Mapping results of the mosaicked/rectified Landsat data timeseries (1985-2019) ... 79 
Figure S6: Temporal Landsat data on Google EarthTM from Zé da Costa region evidencing an 
older upstream crevasse (white arrow) in the Taquari River ............................................ 79 
Figure S7: Photographic registers in August of 2018 and October 2019 scenario of Taquari 
River on point at 500m upstream of “Zé da Costa” avulsion ........................................... 80 
Figure S8: High-resolution image of 2013 from Google EarthTM evidencing the growing of the 
bar that might block permanently the older channel of the Taquari River in the “Caronal” 
avulsion ............................................................................................................................. 80 
Figure S9: Mosaic and geotagged photos obtained during fieldworks in 2018/2020 ................. 81 
Figure S10: Ancillary data represented by DEM and temporals NDVI from study area ........... 83 
Figure S11: Box plot of DEM data partitioned by classes of permanent landscapes ................. 84 
Figure S12: Longitudinal profiles of Taquari River on distinct phases of the avulsion ............. 86 
Figure S13: Lobes of the Caronal (C) and Zé da Costa (B, B3) depicted by field photos .......... 87 
Figure S14: Landsat images from Google earth in 1989 and 2019 overlaid by geotagged 
pictures collected on field from 2020................................................................................ 88 
 
8 
 
Table list 
 
Table 1: Landsat timeseries used in this study ............................................................................ 18 
Table 2: Ground and orbital truths per landscape ....................................................................... 22 
Table 3: Tukey´s post-hoc probabilities (p) for the interactions between algorithms and band 
combinations using two-way ANOVA ............................................................................. 24 
Table 4: Landsat timeseries used in this study ............................................................................ 44 
Table 5: Size and time duration of full avulsions in Pantanal and other regions ........................ 57 
Table S1: Radiometric normalization for of Landsat 8 (2018) approximately 1.4 and 2 
megapixels respectively for scenes 226_073 and 227_073 .............................................. 68 
Table S2: Description of realized classifications (in replicates) ................................................. 68 
Table S3: Average confusion of training samples of pixels in tests and their respective 
replicates ........................................................................................................................... 69 
Table S4: Confusion matrix of classification tests ...................................................................... 70 
Table S5: Statistical moments of ancillary data for each mapped landscape (n = 100) of the 
replicated test 3 ................................................................................................................. 76 
Table S6: Radiometric normalization for mosaicking Landsat data scenes ............................... 77 
Table S7: Radiometric normalization of Landsat 5 and 8 for mosaicking scenes 227_72 and 
227_73 ............................................................................................................................... 81 
Table S8: Radiometric normalization of Landsat mosaics ......................................................... 82 
Table S9: Changes of landscape types measured at region A ..................................................... 84 
Table S10: Changes of landscape types measured at region B ................................................... 84 
Table S11: Changes of landscape types measured at region C ................................................... 85 
Table S12: Permanent (constant) areas in the modern depositional lobes .................................. 85 
Table S13: Transitional areas in the modern depositional lobes................................................. 86 
9 
1. IntroduçãoAs áreas úmidas são ecossistemas de transição entre a terra e a água, com provisões 
essenciais de serviços, incluindo controle de inundações, regulação climática, sequestro de 
carbono e recarga de aqüíferos (Evans et al., 2010), oferecendo também condições vitais de 
habitat para diversas espécies de flora e fauna (Junk et al. al., 2014; Lane et al., 2014; 
Mahdavi et al., 2018). O Pantanal é a maior área úmida do mundo, com cerca de 140.000 km2 
(Nunes da Cunha e Junk, 2011). Uma das características marcantes do Pantanal são as 
inundações recorrentes devido à baixa capacidade de drenagem dos sistemas fluviais (Alho e 
Sabino, 2011), intercaladas com as estações secas (Alho, 2005). Essa alternância sazonal entre 
as fases aquática e terrestre, bem como a variabilidade interanual e decadal das chuvas de 
verão, promove mudanças estruturais e funcionais no ecossistema do Pantanal (Bergier e 
Resende, 2010; Bergier et al., 2018). O dinamismo recorrente da paisagem do Pantanal 
também se deve à contribuição de sedimentos das terras altas que são transportadas pelos 
cursos de água para a planície, alterando o regime de vazão dos rios, especialmente o rio 
Taquari (Galdino et al., 2005; Nunes da Cunha e Junk, 2011). 
A expansão agrícola nas bacias hidrográficas desde a década de 1970 foi baseada no 
desmatamento para implantação de pastagens e culturas, culminando no aumento dos 
processos erosivos pela ação das chuvas (Galdino et al., 2003; Bergier, 2013; Roque et al. 
2016). Por esse motivo, cerca de 22,5 milhões/ton/ano de sedimentos são carreados para o 
Pantanal, no qual o rio Taquari (Assine et al., 2015a) transporta 72% do total de sólidos em 
suspensão. Após os planaltos nas áreas de contribuição, o rio Taquari chega às terras baixas 
do Pantanal, começando pelo cinturão meandrante, depois pelos lobos distributários na porção 
distal (Assine, 2005). A densidade de canais ativos e paleocanais (vazantes e corixos) reforça 
o padrão de distribuição dos fluxos e sedimentos das águas superficiais, moldando o grande 
megaleque do Taquari com cerca de 50.000 km2 (Assine, 2005; Zani e Assine, 2012). 
O assoreamento vem restringindo a capacidade de transporte do rio, formando barras 
de deposição ao longo do canal e diminuindo sua profundidade média (Assine, 2009). Nos 
meses de inundação, o nível do curso se expande lateralmente, excedendo o limite de diques 
marginais, que ocasionalmente podem ser rompidos (crevasse), resultando em processos de 
avulsão quando o canal muda de curso (Assine, 2005; Assine et al., 2015a,b; Roque et al., 
2016). No Pantanal, as avulsões são popularmente chamadas de arrombados (Assine, 2009), e 
um conhecido foi o 'Zé da Costa' ocorrido no final dos anos 80, responsável pela mudança 
permanente no curso e na foz do rio Taquari, a cerca de 30 km para o norte (Assine, 2005). 
Desde o final dos anos 90, o lobo deposicional mais importante do rio Taquari vem 
10 
evoluindo da crevasse localizada na região Caronal (Makaske et al., 2012). Essa bifurcação 
do canal parental segue o modelo clássico de avulsão proposto por Smith et al. (1989), e 
empiricamente observado no Zé da Costa por Assine (2005), cujas fases seqüenciais começam 
com crevasse, crevasse splay ou splay progradation e finalmente a mudança de canal. Nesse 
processo de reconstrução, uma extensa área foi permanentemente inundada na região de 
Paiaguás (Assine et al., 2015a), modificando o pulso sazonal de inundação (Junk et al., 1989) 
e promovendo a conexão com o sistema de troncos do rio Paraguai a montante (Assine et al., 
2015a). A ausência de pulsos de inundação devido à inundação perene tem afetado os 
moradores locais como pescadores (Resende, 2008) e criação tradicional de gado nas fazendas 
(Guerreiro et al., 2019), ambos chamados de pantaneiros. Enquanto os últimos têm suas terras 
agrícolas inundadas (Lourival et al., 2008), os primeiros foram forçados a migrar para áreas 
suburbanas (Curado, 2004). 
Rios avulsivos têm influenciado a dinâmica da paisagem, especialmente no moderno 
lobo de deposição (Louzada et al., 2020), por insumos de sedimentos, nutrientes e água (Alho, 
2008). Esses fatores, juntamente com a elevação da superfície (Zani et al., 2012), regulam os 
processos de sucessão ecológica (Henry e Amoros, 1995; Pott e Silva, 2015), tornando a 
paisagem um mosaico complexo de formas de relevo. 
A avaliação temporal de mudanças ambientais em áreas úmidas complexas, como o 
Pantanal, é importante para entender a formação de padrões e, portanto, fazer previsões. 
Nesse sentido, o mapeamento através de imagens de sensoriamento remoto (Gallant, 2015; 
Mahdavi et al., 2018), com o auxílio de técnicas de análise geoespacial em plataformas SIG 
promove subsídios para uma interpretação mais precisa da dinâmica das áreas úmidas 
(Martínez-López et al., 2014 ; Feyissa et al., 2019). Essa constelação de ferramentas digitais 
também se mostrou útil para melhorar a qualidade das discussões sobre mudanças ambientais 
no megaleque do Taquari, oferecendo condições adequadas para o desenvolvimento de 
políticas públicas para os pantaneiros. 
 
 
 
 
 
 
 
 
 
11 
1.1. Objetivo 
A presente dissertação tem o objetivo de avaliar a dinâmica da paisagem decadal 
devido às avulsões fluviais no moderno lobo deposicional do rio Taquari nos pantanais do 
Pantanal. 
1.1.1. Objetivos específicos 
• Através de distintas combinações RGB de imagens Landsat 8 OLI do ano de 
2018 para a região do Zé da Costa, avaliar e comparar as acurácias de 
algoritmos classificadores de paisagens baseados em pixel (Maxver e Maxver-
ICM) e orientado a objeto (segmentação e classificador Bhattacharya), por 
meio do índice Kappa e coeficiente de exatidão global, respectivamente. 
• Avaliar a influência do conjunto de variáveis auxiliares Normalized 
Difference Vegetation Index (NDVI), Modelo Linear de Mistura Espectral 
(MLME) e Modelo Digital de Elevação (MDE) nos resultados estatísticos. 
• Com base nos resultados anteriores, definir e selecionar o melhor 
classificador para ser replicado às imagens Landsat 5 TM e Landsat 8 OLI 
entre os anos de 1985 e 2019. 
• Avaliar a dinâmica espaço-temporal das classes temáticas em escala decadal 
para todo o lobo deposicional do Rio Taquari através de operações booleanas 
em ambiente SIG. 
• Apresentar orientações para a produção de políticas públicas para atender às 
populações de pessoas afetadas por avulsoes no lobo ativo do Rio Taquari 
1.2. Organização da dissertação 
A dissertação está organizada em dois capítulos. O Capítulo 1 já foi publicado 
(Louzada et al., 2020) e fornece a base para a aplicação de um método sistemático para a 
melhor classificação/mapeamento de dados multiespectrais históricos de Landsat-5 (TM) e 8 
(OLI) em áreas complexas de áreas úmidas como o Pantanal. Também explora a dinâmica 
temporal das classes mapeadas na avulsão do Zé da Costa. O capítulo 2 compreende um 
manuscrito não publicado que aplica a metodologia anterior a todo o lobo ativo do rio 
Taquari. O manuscrito explora a dinâmica espaço-temporal, com o objetivo de aumentar o 
conhecimento real da reconstrução de canais e paisagens em larga escala (restauração) do rio 
Taquari. 
12 
Referências 
 
ALHO, C. The pantanal. In: (Ed.). LH Fraser, PA Keddy, The world’s largest wetlands: 
ecology and conservation, Cambridge University Press, New York, 2005. p.203-271. 
 
ALHO, C.; SABINO, J. A conservation agenda for the Pantanal's biodiversity. Brazilian 
Journal of Biology, v. 71, n. 1, p. 327-335, 2011. 
 
ALHO, C. J. R. Biodiversity of the Pantanal: response to seasonal flooding regime and to 
environmental degradation. Brazilian Journal of Biology, v. 68, n. 4, p. 957-966, 2008. 
 
ASSINE, M. L. River avulsions on the Taquari megafan, Pantanal wetland, Brazil. 
Geomorphology, v. 70, n. 3-4, p. 357-371, 2005. 
 
______. Taquari: um rio mutante. Anais 2º Simpósio de Geotecnologias noPantanal, 
Corumbá, p. 7-11, 2009. 
 
ASSINE, M. L. et al. Avulsive rivers in the hydrology of the pantanal wetland. In: (Ed.). 
Dynamics of the Pantanal wetland in South America: Springer, 2015a. p.83-110. 
 
ASSINE, M. L. et al. The Quaternary alluvial systems tract of the Pantanal Basin, Brazil. 
Brazilian Journal of Geology, v. 45, n. 3, p. 475-489, Sep 2015b. 
 
BERGIER, I. Effects of highland land-use over lowlands of the Brazilian Pantanal. Science of 
the Total Environment, v. 463, p. 1060-1066, 2013. 
 
BERGIER, I. et al. Amazon rainforest modulation of water security in the Pantanal wetland. 
Science of the Total Environment, v. 619, p. 1116-1125, 2018. 
 
BERGIER, I.; RESENDE, E. D. Dinâmica de cheias no Pantanal do rio Paraguai de 1900 a 
2009. Anais do, v. 3, 2010. 
 
CURADO, F. Considerações sócio-econômicas e ambientais relacionadas ao" arrombados" na 
planície do rio Taquari, MS. Embrapa Pantanal-Documentos (INFOTECA-E), 2004. 
 
EVANS, T. L. et al. Using ALOS/PALSAR and RADARSAT-2 to Map Land Cover and 
Seasonal Inundation in the Brazilian Pantanal. Ieee Journal of Selected Topics in Applied 
Earth Observations and Remote Sensing, v. 3, n. 4, p. 560-575, Dec, 2010. 
 
FEYISSA, M. E. et al. Integrated remote sensing–GIS analysis of urban wetland potential for 
crop farming: a case study of Nekemte district, western Ethiopia. Environmental Earth 
Sciences, v. 78, n. 5, p. 153, 2019. 
 
GALDINO, S. et al. Erosão na Bacia do Alto Taquari. 2003. 
 
GALDINO, S. et al. Impactos ambientais e socioeconômicos na Bacia do Rio Taquari-
Pantanal. Corumbá: Embrapa Pantanal., 2005. 
 
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n. 8, p. 10938-10950, Aug, 2015. 
 
13 
GUERREIRO, R. L. et al. The soda lakes of Nhecolândia: a conservation opportunity for the 
Pantanal wetlands. Perspectives in ecology conservation, v. 17, n. 1, p. 9-18, 2019. 
 
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publication of fisheries and aquatic sciences, v. 106, n. 1, p. 110-127, 1989. 
 
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research, sustainable management, and protection. Aquatic Conservation: Marine and 
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imagery and a hybrid approach. Remote Sensing, v. 6, n. 12, p. 12187-12216, 2014. 
 
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River on Brazilian Pantanal wetlands. Science of the Total Environment, v. 723, p. 138067, 
2020. 
 
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GIScience & remote sensing, v. 55, n. 5, p. 623-658, 2018. 
 
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megafan, Pantanal, south‐western Brazil. Earth Surface Processes and Landforms, v. 37, n. 
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Biology, v. 30, n. 5, p. 1131-1134, 2016. 
 
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14 
 
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method development and application to the Taquari megafan, Pantanal (Brazil). 
Geomorphology, v. 161, p. 82-92, 2012. 
15 
2. Landscape changes in avulsive ryver systems: case study of Taquari River on 
Brazilian Pantanal wetlands 
2.1. Introduction 
Tropical wetlands are well-known providers of energy, food and water, and are 
considered nature’s supermarket (Mitsch and Gosselink, 2015) because of their ability to 
sustain rich biodiversity. Wetlands also encompass transitional ecosystems between land and 
water providing many environmental services such as water purification, flood control, 
climate regulation, carbon sequestration, aquifer recharge, and niches and habitats for many 
species (Junk et al., 2014). The Pantanal is an important natural region encompassing tropical 
wetlands in South America where biodiversity is still conserved, including large mammals 
(Alho and Sabino, 2011; Assine, 2015a). In geological terms, it is characterized as an active 
sedimentary basin in a depression with a vast, low topographic gradient plain inundated 
during austral summers and autumns (Assine, 2003). The Pantanal flood can be easily 
characterized by the water level dynamics of the Paraguay River and other alluvial river 
systems (Assine et al., 2015a). 
 Since the introduction of government incentives for settlers’ occupation during mid-
1960s, intensification of the land use in the surrounding uplands replaced pristine vegetation 
with exotic pastures and croplands (Galdino et al., 2005; Junk and Nunes da Cunha, 2005; 
Silva and Carlini, 2015; Roque et al., 2016; Padovani, 2017). Moreover, unsustainable land 
use combined with intense rainfalls increased the erodibility of exposed soils (Galdino et al., 
2005) that culminated in more water availability in the plains (Bergier, 2013) and more 
avulsions due to higher rates of river channel aggradation (Assine et al., 2015a). 
Anthropogenic (allogenic) activities (Assine and Soares, 2004) accelerated the rate of 
avulsions and aggradation, particularly in the Taquari River (Assine, 2005; Assine et al., 
2015b), affecting habitats and landscapes (Nunes da Cunha and Junk, 2011) notably on the 
scale of decades (Bergier et al., 2018; Schulz et al., 2019). In the low course of the Taquari 
River, the discrete average terrain slope (~0.36 m/km) induces sand depositions and creates 
conditions for the formation of crevasses on levees, and causes the distribution of water to 
floodbasins during flood events (Assine and Soares, 2004; Assine, 2005). This characteristic 
is very common in the active distributary fan lobe, forming avulsion belts, including the 
important Zé da Costa avulsion in the lower Taquari course by the 1990’s (Assine, 2005). 
Given the ecological and socioeconomical relevance of the Pantanal, as well as other 
worldwide wetlands, its mapping and monitoring are crucial for the comprehension and 
sustainable management of natural resources (Baker et al., 2006; Mahdavi et al., 2018). 
16 
However, in situ monitoring is costly due to the prevailing difficulties in terms of access 
(Gallant, 2015). In this respect, the expansion of the orbital sensor constellation,and the 
advances in remote sensing data analyses and techniques, have enabled the generation of 
highly useful information at a reasonable time-frequency and lower costs (e.g., Evans and 
Costa, 2013; Junk et al., 2014; Silio-Calzada et al., 2017). Additional advances in mapping 
validation can be achieved via high spatial resolution images from Google EarthTM, 
complementing the ground truth points (Adade et al., 2017; Zhai et al., 2017; Chen et al., 
2018). 
Optical remote sensing of the Landsat series provides the longest and consistent 
temporal record of space-borne land surface monitoring systems (Roy et al., 2014). The big 
data is responsible for providing basis to several studies in Earth System Sciences, 
particularly land cover type and land cover changes (Williams et al., 2006; Ridwan et al., 
2018). In addition, Landsat imagery is also widely used for mapping wetlands (Gallant, 2015). 
Several studies have demonstrated the importance of adequate spectral bands and 
classification algorithms for the reliable mapping from orbital images (Dronova, 2015). 
Nonetheless, transitional environments like wetlands make mapping challenging due to the 
mixture of spectral signatures among targets (Adams and Gillespie, 2006; Amani et al., 2017). 
For instance, Guo et al. (2017) showed that medium-resolution images, within a range of 4-30 
m, can be useful to evaluate wetlands and understand their dynamics. 
The level of map detail is important to ensure consistency of information for 
recognition and interpretation (Lillesand et al., 2014). Furthermore, the choice of the most 
appropriate spectral bands and classifier algorithms influences the mapping accuracy 
(Congalton and Green, 2008). Ozesmi and Bauer (2002) suggested that bands 5 (Short-wave 
Infrared 1, SWIR-1), 4 (Near Infrared, NIR) and 3 (Red) of the Landsat 5 Thematic Mapper 
were the best combination for wetland mapping. SWIR-2 (band 7 in Landsat series 5 to 8 and 
MODIS MCD43A4) has also been determined to be a valuable spectral band to discriminate 
wetland soils, rocks, and inundated lands (Demattê et al., 2017; Wolski et al., 2017; Ridwan et 
al., 2018). 
Pixel-based and object-oriented algorithms can be useful for mapping wetland 
landscapes (Mahdavi et al., 2018). Mapping uncertainties are assessed typically via ground 
truth validation and statistical analyses using parameters such as the overall accuracy 
coefficient (OA) and the Kappa index (K) (Duro et al., 2012; Sanhouse-García et al., 2016; 
Berhane et al., 2018). 
Ancillary data such as normalized difference of vegetation index (NDVI), digital 
elevation models (DEMs), and linear spectral mixture models (LSSMs) have also been used 
17 
to improve the mapping quality (Dronova, 2015). Considering the influence of water on 
multispectral images of wetland landscapes (Gallant, 2015), ancillary data subsidizes the 
distinction of spectral targets from multifaceted landscapes. For example, Amani et al. (2018) 
used NDVI to improve UK wetlands mapping, whereas DEMs are useful for separating 
targets at different altitudes (Franklin et al., 2018). Alternatively, LSMMs have the capacity to 
segregate spectral mixtures inside a pixel and produce pure endmember images, for example, 
for vegetation, soil, and water (Cui et al., 2013). 
Considering the latest knowledge, this study seeks to identify the best methodological 
setting for mapping historical Landsat data with the aim of identifying the landscape changes 
in susceptible areas due to river avulsions. We demonstrated the applicability of our new 
methodological framework by studying the region of the “Zé da Costa” in the Taquari River, 
the largest megafan of the Pantanal wetlands. 
2.2. Materials and Methods 
2.2.1. Study Area 
The study area (S18º30'; S19º30' and W56º45'; W57º30') is located in the municipality 
of Corumbá, west of Mato Grosso do Sul state in Central-western Brazil, in the active lobe of 
the fluvial Taquari megafan (Assine, 2005) (Figure 1). The study polygon spans an area of 
approximately 1,785.52 km2 and is delimited by: 1) the Taquari riverbed in the crevasse “Zé 
da Costa”, where an avulsion began in the 1980s (Assine, 2005; Jongman, 2005), 2) the 
abandoned river mouth at Porto da Manga in the south and 3) the new confluence with the 
Paraguay-Mirim River. Abandoned, actual and distal drainages were designed according to 
temporal mappings (Section 3.2). The distal drainage is the area inundated by floods of 
Paraguay River. 
18 
 
Figure 1: Location of the avulsion area, ground and orbital truths, and studied drainage 
polygons. 
2.2.2. Methodology 
We obtained the scenes 226_73 and 227_73 of Landsat 5 and 8 level-2 products 
(Guide, 2018) from USGS (https://earthexplorer.usgs.gov) for producing mosaics that span 
the whole area of study. The scenes were chosen within the years 1985 and 2019 and between 
June to November (lower cloud cover) (Table 1). 
Table 1: Landsat timeseries used in this study. 
Sensor 
Scene 
226_73 227_73 
Landsat-5 TM 
(Bands 2, 3, 4 
and 5) 
1985/11/08, 1986/09/08, 
1987/08/10, 1988/09/29, 
1990/08/02, 1991/08/05, 
1994/07/12, 1995/09/17, 
1996/08/18, 1999/08/11, 
2004/09/09, 2005/07/10, 
2006/07/13, 2007/09/02, 
2008/08/19, 2009/09/07, 
2010/10/12, 2011/09/13 
1985/11/15, 1986/09/15, 
1987/08/01, 1988/09/20, 
1990/08/09, 1991/07/27, 
1994/07/19, 1995/09/08, 
1996/08/25, 1999/08/18, 
2004/08/31, 2005/07/01, 
2006/07/04, 2007/09/09, 
2008/08/26, 2009/09/14, 
2010/10/19, 2011/09/04 
Landsat-8 OLI 
(Bands 3, 4, 5, 
6 and 7) 
2013/07/16, 2014/09/05, 
2015/08/07, 2016/07/24, 
2017/10/15, 2018/08/31*, 
2019/09/26 
2013/07/07, 2014/08/27, 
2015/07/29, 2016/07/31, 
2017/10/06, 2018/09/07*, 
2019/09/10 
* Used for the mapping tests. 
19 
We then investigated the best methodological approach for mapping wetland 
landscapes by using Landsat-8 OLI data acquired in 2018 (Table 1) with the bands 3 (Green), 
4 (Red), 5 (NIR), 6 (SWIR 1), and 7 (SWIR 2). According to Amani et al. (2018), NIR bands 
are the most powerful bands for distinguishing wetland landscapes, followed by red, SWIR 
and green band. The latter bands are useful for assessing plant vigor and isolated types of 
vegetation (Amani et al., 2018). Once the best methodological approach for mapping wetland 
landscapes is defined, it can be applied to the entire set of historical images with the most 
appropriate bands. 
2.2.2.1 Tests of landscape mapping methods 
We used Landsat-8 OLI from 2018 (see Table 1) to test and validate a method 
consisting of radiometric normalization of mosaics (to ease the supervised mapping), spectral 
band combinations, and pixel-based and object-oriented mapping with the aid of ancillary 
data. The scheme of the experimental test is shown in the flowchart above (Figure 2). 
 
 
Figure 2: Flowchart of the methodology used for mapping assessments. Ancillary data are 
normalized difference vegetation index (NDVI), digital elevation model (DEM) and linear 
spectral mixture model (LSMM). Indicators are overall accuracy (OA) and Kappa index (K). 
 
Most of the image processing steps were carried out using the GIS Spring v. 5.5.5 
20 
(Câmara et al., 1996), whereas scenes mosaicking and validation were performed using 
ArcGis v. 10.4.1 (Desktop, 2011). The radiometric normalization was necessary because the 
scenes were acquired on dates with different atmospheric conditions, solar illumination, and 
spectral responses of the targets (Lillesand et al., 2014). We employed the mean and variance 
uniformity (UMV) method, which consists of rectifying the statistical moments of a target 
image (�̅�𝑡, s𝑡
2) based on the statistical moments of a reference image (�̅�𝑟 , s𝑟
2) (Leonardi et al., 
2003). The formulations were developed by Santos et al. (2010) for application in the GIS 
Spring (http://www.dpi.inpe.br/spring/) and are highlighted below: 
 
Iret= Gain*It + Offset (1) 
Gain = √
s𝑟
2
s𝑡
2 (2) 
Offset = �̅�𝑟 – (Gain*�̅�𝑡) (3) 
where Iret is the rectified image of the target image It. Results of radiometric normalization are 
shown in Supplementary Material (Table S1). 
After the scenes were normalized, we obtained a radiometrically rectified mosaic 
image which can be used for performing object-oriented and pixel-based supervised 
classifications and quality-checking the mapping with different band combinations. 
The object-oriented algorithm involves the creation of a segmented image, which 
consists of pixels grouped by similar characteristics in terms of tone and texture by 
polygonising regions with higher homogeneity (Assad and Sano, 1998; Costa and Cesar Jr., 
2000). In this study, we used the ‘Region Growth’ algorithm available in Spring, which is 
based on a priori parameterization of grey level similarity and the maximum number (area) of 
pixels per polygon (Moreira, 2011). 
With the aid of false color RGB composition with contrast setting (Novo, 2008), 
sixteen combinations of a priori parameterization of similarity (5 to 150) and area (10 to 300) 
were assessed for band compositions 345, 456, and 347 by trial and error (Robertson et al., 
2015; Berhane et al., 2018). The details of the segmentation tests were made available in the 
Supplementary Material (Figure S1). We selected values of similarity 10 and area 20 as 
optimal parameters that guarantee sufficient polygons for post-classification re-mapping 
whenever needed. 
We used the Bhattacharya's distance classification algorithm to measure the statistical 
differences between pairs of spectral classes (Mather and Koch, 2011) using the segmented 
image. Here we have adopted a 99.9% acceptance threshold. 
For the pixel-based algorithm, we chose the Maximum Likelihood method (MaxVer). 
21 
According to Mather and Tso (2016), MaxVer is a statistical approach for recognizing pixel 
patterns by applying inference formulae. A 100% acceptance threshold was assigned, which 
means that the algorithm classified all pixels in the image. 
The MaxVer Iterated Conditional Models (MaxVer-ICM) classifier uses the previous 
method a priori and assesses the dependence of pixels on their neighbors by considering a 
certain threshold of acceptance (Santos et al., 2010). To attain the classification of all pixels in 
the image and to allow the comparison with MaxVer under equivalent conditions, we opted to 
dismiss thresholds of acceptance criteria. 
The post-classification is a Spring tool that reclassifies isolated misclassified pixels, 
termed as the ‘salt and pepper’ effect (Lillesand et al., 2014), via standardization of thematic 
classes to improve the accuracy of the obtained results (Duro et al., 2012; Dronova, 2015; 
Franklin et al., 2018). The procedure was applied to both supervised classifications schemes 
with a weighting value of 2 and a threshold value of 5. 
2.2.2.2 Ground and orbital truths validation 
The validation consists of decreasing the uncertainty level of land cover classifications 
obtained from orbital sensors using ground truths and/or high spatial resolution imagery 
(Brogaard and Ólafsdóttir, 1997; Justice et al., 2000; Wang et al., 2016). Ground truths serve 
as a feedback for rechecking the supervised samples used by the classification algorithm, and 
were obtained in from a technical report (Louzada and Brito, 2018) of the Mato Grosso do Sul 
Environmental Institute (IMASUL) that provides geotagged pictures acquired on August 2018 
(and more recently on October 2019). Twenty-four geotagged ground truths were considered 
in this study (Figure 1) and a few of them are shown in Figure 3. 
Terrain elevation plays an important role in the spatial distribution of floods in the 
Pantanal wetlands, where a few decimeters usually separate dry, wet and permanently flooded 
habitats (Junk et al., 2006). Thus, in a gradient from aquatic to terrestrial habitats, it is 
possible to distinguish five major landscapes as Open Water (OW), Aquatic Vegetation (AV), 
Wet Soil/Pasture (WS), Dry Soil/Pasture (DS), and Terrestrial Vegetation (TV). The 
terrestrial habitats includes forests, riparian forests and savannas (Evans et al., 2014), while 
the aquatic habitats are permanent aquatic ecosystems, which may or may not include aquatic 
vegetation (Nunes da Cunha and Junk, 2011). The Dry or Wet habitats are intermediary and 
intermittent ecotones, which are occasionally or frequently influenced by floods (Silva et al., 
2016). 
 
22 
Figure 3: Ground truths obtained on August 2018 (A-D) and on October 2019 (E). A) Dry 
soil/pasture closer to terrestrial vegetation, B) Dry soil/pasture and terrestrial vegetation in the 
background, C) Aquatic vegetation, D) Open water and terrestrial vegetation at the margins, 
and E) Wet soil/pasture. 
 
Orbital truths were obtained through the ArcGIS tool “Create Random Points”, 
overlaid with Google EarthTM (at high spatial resolutions that mimic aerial photographs). The 
randomization of selected orbital points for validation is a strategy that minimizes user´s 
interference (Brogaard and Ólafsdóttir, 1997). 
Five hundred random points were selected and then plotted with a minimum distance 
of 30 meters, according to the spatial resolution of the Landsat-8 OLI sensor. This criterion 
followed Congalton and Green (2008), where large or complex areas need between 75 and 
100 accuracy evaluation samples per class. The number of points of each of the ground and 
orbital truths are shown in Table 2. 
Table 2: Ground and orbital truths per landscape. Truths are shown in Figure 1. 
 
Points 
Aquatic 
vegetation 
Dry soil / 
pasture 
Open 
water 
Terrestrial 
vegetation 
Wet soil / 
pasture 
Total 
Ground truths 4 2 8 8 2 24 
Orbital truths 48 64 19 137 232 500 
Total 52 66 27 250 234 524 
2.2.2.3 Ancillary data 
Wetland landscapes are highly dynamic in terms of soil-water-vegetation interactions, 
making them very difficult to map and classify through remote sensing (Bourgeau-Chavez et 
al., 2009; Gallant, 2015). It has been proven that auxiliary variables such as vegetation indices 
(e.g., NDVI) can improve mapping results from multispectral images (Rouse Jr et al., 1973). 
Moreover, DEMs and LSMMs can also be used as proxies to maximize the accuracy of 
multispectral mappings (Baker et al., 2006; Boyden et al., 2013; Cui et al., 2013). 
DEMs were obtained from TOPODATA (http://www.dsr.inpe.br/topodata/), which is 
an improved version of the SRTM data acquired in the year 2000 (Landau and Guimarães, 
2011; Valeriano and Rossetti, 2012). For LSSMs, we chose three endmembers (‘pure’ pixels): 
vegetation, soil, and water, following the methodology proposed by Shimabukuro et al. 
(1998) for mapping an area in the northern region of the Taquari River megafan. According to 
A B C D E
23 
Shimabukuro and Ponzoni (2017), the classical LSSM is calculated as 
 
ri = a*vegetationi + b*soili + c*wateri + ei (4) 
where ri is the pixel spectral response in band i, ei is the error in band i, and a, b and c are 
relative proportions of each endmember fraction in band i. The endmember (fraction) images 
were converted to 8-bit radiometric resolution with values between 0 (dark) to 255 (bright) 
(Schowengerdt, 2006). 
2.2.2.4 Temporal analysis of landscape changes 
The best mapping method obtained for the 2018 image was then applied to the 
remaining imagery dataset. Before mapping, each mosaic (image/band) was radiometrically 
rectified using Eqs. 1 to 3 with reference to the scene 226_73 for each year. The mappings 
were used to generate percentage (relative area) data for each of the three drainage polygons 
shown in Figure 1 to quantify their respective temporal landscape changes. 
The statistical moments of NDVI were also calculated for each polygon in each year. 
Dynamical studies using mappings ofLandsat 5 and 8 data with NDVI data can be useful to 
understand river avulsion phenomena and their long-term impacts on the landscapes. In this 
case, the intermediate positive value of NDVI (0.5) can be valuable as a threshold based on 
earlier investigations of Taquari megafan (Mioto et al., 2012; Miranda et al., 2018). On the 
other hand, we defined and studied a new index R calculated as the dry/wet ratio: 
 
R = (DS + TV)/(AV + OW + WS) (5) 
Changes in R for each year as a function of environmental changes due to river avulsion and 
other factors were investigated. 
2.3. Results 
2.3.1. Defining the best wetland mapping algorithm and satellite bands 
The ancillary data and analyses of the training samples are available in Section 2, 
whereas the results of the supervised mappings are listed in Section 3 of the Supplementary 
Material. Three tests are presented in Figure 4, that are representative of the worst (A), the 
intermediate (B) and the best (C) results based on K and OA values. Table 3 presents Tukey´s 
post-hoc probabilities of a two-way ANOVA comparing the interactions between algorithms 
24 
and multispectral band combinations. 
Table 3: Tukey´s post-hoc probabilities (p) for the interactions between algorithms and band 
combinations using two-way ANOVA. * Significant at p < 0.05. 
 
Interactions p 
MaxVer-453 MaxVer-654 0.3264 
MaxVer-453 MaxVer-743 0.0138* 
MaxVer-453 MaxVer-ICM-453 1.000 
MaxVer-453 Bhattacharya-453 0.8921 
MaxVer-654 MaxVer-743 0.3264 
MaxVer-654 MaxVer-ICM-654 0.9996 
MaxVer-654 Bhattacharya-654 0.9821 
MaxVer-743 MaxVer-ICM-743 0.9821 
MaxVer-743 Bhattacharya-743 0.5052 
MaxVer-ICM-453 MaxVer-ICM-654 0.1989 
MaxVer-ICM-453 MaxVer-ICM-743 0.0050* 
MaxVer-ICM-453 Bhattacharya-453 0.8921 
MaxVer-ICM-654 MaxVer-ICM-743 0.1989 
MaxVer-ICM-654 Bhattacharya-654 0.9996 
MaxVer-ICM-743 Bhattacharya-743 0.8921 
Bhattacharya-453 Bhattacharya-654 0.0232* 
Bhattacharya-453 Bhattacharya-743 0.0004* 
Bhattacharya-654 Bhattacharya-743 0.0683 
 
Band 453 with object-oriented algorithm achieved better mapping results, 
demonstrating that bands in Red, NIR and Green spectra are the best choice for mapping 
landscapes at avulsive sites in wetlands with object-oriented classifiers. 
The worst mapping performances were observed in those using band 743 (Figure S4 
and Table S4). On the other hand, the best results with band 453 indicate that the band in the 
visible Green is better than SWIR suggested by Ozesmi and Bauer (2002) and Amani et al. 
(2018). Indeed, SWIR bands have been found more useful in differentiating water from 
minerals (Demattê et al., 2017; Wolski et al., 2017; Ridwan et al., 2018). 
Additional features obtained via NDVI and DEM data were valuable for improving the 
quality of sampled pixels in the supervised classifications on avulsive river systems of the 
study area (see Table S5). 
Based on the obtained results, the best method to map wetland landscapes (Figure 4C) 
requires spectral bands in Green, Red and NIR of Landsat series. Although both pixel-based 
and object-oriented classifiers presented satisfactory results, we decided to use the object-
oriented classifiers with ancillary data approach due to better performances (see Table S3). 
25 
 
Figure 4: Classification tests from Landsat-8 OLI by criteria of lowest, intermediate and highest accuracies. A) Bhattacharya with RGB743 
composition, B) Maxver-ICM with RGB654 composition, and C) replicate of Bhattacharya with RGB453 composition.
26 
2.3.2 Mapping of mosaicked/rectified Landsat data timeseries 
Radiometrically rectified mosaics from bands 453 of the entire Landsat 5 and 8 dataset 
(See Table S6 for more information on the image normalization procedure) were selected for 
producing the maps using the object-oriented algorithm with segmentation, and classification 
by Bhattacharya algorithm with post-classification. All mapping results are available in 
Section 6 of the Supplementary Material. Mappings obtained for 1985, 2014 and 2019 are 
shown in Figure 5. The Wet soil/pasture WS landscape in 1985 map (Figure 5A) was used to 
define the threshold between the actual and the abandoned drainages of the Taquari River in 
the NE-SW direction. The AV class in the 2014 map was useful to define the floodplain area 
of the Paraguay River (Figure 5B). The three polygons of study, namely abandoned, actual 
and distal drainages presented in Figures 1 and 5, are derived from these thresholds. LSSM 
Water from ancillary data in classification tests (Fig. S2) was also useful in identifying water 
influence in the area. 
The temporal study of landscape changes for the abandoned, actual, and distal 
drainages polygons are presented in Figures 6, 7 and 8, respectively, with the timeseries of 
NDVI means and ±1SD (standard deviations). 
 
 
27 
 
Figure 5: Temporal mappings overlaid to abandoned (A), actual (B) and distal (C) drainage polygons in the Zé da Costa region.
28 
 
 
Figure 6: Timeseries of percentage changes of landscapes and NDVI for the abandoned 
drainage polygon (region A in Figure 5). 
 
 
Figure 7: Timeseries of percentage changes of landscapes and NDVI for the actual drainage 
polygon (region B in Figure 5). 
1984
1985
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Aquatic vegetation Dry soil/pasture Open water
Terrestrial vegetation Wet soil/pasture
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
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Aquatic vegetation Dry soil/pasture Open water
Terrestrial vegetation Wet soil/pasture
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29 
 
 
Figure 8: Timeseries of percentage changes of landscapes and NDVI for the distal drainage 
polygon (region C in Figure 5). 
 
The percentage of AV at the abandoned drainage was 18.48% in 1985 that decreased 
to 3.56% in 2019. In terms of the actual drainage, this class linearly (R2 = 0.8) dropped from 
58.93% in 1985 to 0.97% in 2019 likely due to a crevasse upstream from Zé da Costa at the 
Taquari River that was formed before 1985, which increased water availability for an 
expansion of AV. The temporal evolution of this crevasse is shown in Supplementary 
Material (Figure S6). The landscape AV and OW in the distal drainage polygon did not reveal 
any clear trends, possibly due to the influence of interdecadal variation of the Paraguay River 
floods. 
The landscape DS increased in both abandoned and actual drainages. In the abandoned 
polygon, this increase started in 1990 (15.86%) after the translocation of the river channel, 
slightly growing to 18.58% by 2019. In the actual drainage, the increase of DS was 
outstanding from 2.91% after 2017 to 28.25% in 2019. These accelerated increases in DS 
likely resulted from land use (deforestation and fires). Like AV, DS areas on the distal 
drainage did not show any clear trends. 
Alternatively, OW landscapes decreased in the abandoned and actual drainage 
polygons. Both polygons suffered water losses as a function of the Caronal avulsion on 
Taquari River after the year 2000 (Assine et al., 2015a, b; see also Figure 1 and Figure S8 in 
the Supporting Material). The distal drainage areas, however, were more influenced by the 
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
1985
1986
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Aquatic vegetation Dry soil/pasture Open water
Terrestrial vegetation Wet soil/pasture
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30 
 
Paraguay River floods which are easily notable in 1988 (OW 20.02%) and 2014 (OW 
41.92%). 
TV linearly increased over time with R2 = 0.31. New sources of humidity and 
nutrients following river avulsion might be responsible for this observation, as noted by 
Cronk and Frennessy (2016). 
WS highlights any noticeable patterns in the three polygons. This landscape is well 
recognized as a variable, known as seasonal aquatic/terrestrial phase of the annual flood pulse 
(Nunes da Cunha and Junk, 2011). Nonetheless, the variations in WS percentage were 
negatively correlated with TV in the abandoned drainage polygon (r = -0.92), as well as with 
AV in actual (r = -0.65) and distal (r = -0.88) drainages. 
Average NDVI values for each polygon ranged around 0.5, except for the low values 
in 2014 in the distal drainage and the high values during the 2011-2017 period in the 
abandoned and actual drainages. The changes in NDVI over time were slightly unclear for all 
polygons. 
2.4. Discussion 
In complex wetlands like the Pantanal, typical objects and features may be difficult to 
define (Dronova, 2015). In this respect, a priori knowledge on defining spectral signatures 
from available targets is more important than selecting the best image-processing algorithm. 
In general, wetlands like Pantanal are characterized by myriad water and vegetation 
landscapes, both sensitive to IR and visible wavelengths. These interactions often challenge 
any remote sensing mapping performed using orbital multispectral sensors when compared to 
those of non-wetlands landscapes (Gallant, 2015; Guo et al., 2017; Mahdavi et al., 2018). 
Therefore, we argue that a few ground truths, many random orbital truths, and ancillary data 
should be combined to improve the supervised multispectral image classifications. 
Land use on the plateaus around the Pantanal wetlands has been altering the 
landscapes in the river megafans of lowlands (Galdino et al., 2003; Junk and Nunes da Cunha, 
2005; Bergier, 2013; Schulz et al., 2019). The acceleration of aggradation in the Taquari 
River bed has been attributed to human-induced erosions from deforested areas in the uplands 
(Galdino et al., 2003; Jongman, 2005). This sedimentary process is more intense downstream 
from the intersection point that separates the modern lobe of sedimentation from the confined 
meandering belt (Zani et al., 2012). In the lower Taquari River, aggradation weakens the 
levees triggering the crevassing processes (Assine et al., 2015a). In the Zé da Costa area, this 
system was dynamical until the end of 1990’s. Since 2000, the Caronal avulsion has been 
31 
 
contributing to water flux reduction (Assine, 2005; Assine et al., 2015a). This flow was totally 
interrupted in 2019 on Zé da Costa (Figure S7) due to the combination of a pronounced 
drought, water evaporation/infiltration along the way and the river blocking resulting from the 
sustained formation of a definite sand bar (Figure S8). 
During the evolution of avulsion, landforms, such as channels, floodouts, shallow 
lakes, and paleochannels are inter-related (Lisenby et al., 2019). According to Smith et al. 
(1989), landforms such as levees, open drainage basins, and closed drainage basins maintain 
their relationship with vegetation through water saturation and nutrient levels. Distinct flows 
redistribute the discharge of river water and sediments across the plain affecting plant 
communities (van Asselen et al., 2017). An upstream crevasse of the Zé da Costa avulsion 
(Fig. S6) had diverted water and fine sediments of Taquari River to a depression region 
(backswamp) much earlier resulting in a predominant AV and OW. Some parts of the 
channels network in this intermediate aquatic phase are characterized by shallow lakes (see 
Gradziński et al., 2003). As a result, the adaptation to shallow inundation enhances the 
competitiveness of AV, which was also observed by Ralph et al. (2011). On the other hand, 
during the processes of the more permanent Zé da Costa avulsion, new channels were steadily 
constructed, re-confining the water into myriad interlaced channels, reducing the AV, OW 
and WS sites in the actual drainage polygon (Figure 6). 
In contrast, in the abandoned drainage polygon (Figure 7), an increase in TV and DS 
was observed, likely due to vegetation succession (Rusnák et al., 2019). The recession of 
humid environments can be related to the avulsion that changed the water course and resulted 
in the expansion of vegetation in a wet-to-dry ecological succession (Lo et al., 2019). 
According to Huges et al. (2019), the natural floodplain processes (e.g., channel migration, 
and avulsion), are commonly associated to local and regional shifts in the wetlands, including 
plants communities, changes in vegetation productivity and successional changes. In this 
respect, the interannual balance of landscapes in avulsive river systems is largely determined 
by the rate of avulsions. 
Through the mapping timeseries, we showed that AV area decreased whereas DS and 
TV increased. OW was the most variable landscape followed by DS. The former has a strong 
dependence on the floods of the Paraguay River (Alho, 2005). During or after inundations, 
lentic environments often create conditions for AV (Pott et al., 2011). The decrease in water 
availability could change AV into WS landscapes in the distal drainage (see comparative 
mappings of 1988-1990, 1999-2004 and 2016-2017 in Fig. S5), or trigger a process of wet-to-
dry ecological succession in the actual drainage (mappings within 1985-2015) (Coutinho et 
al., 2018; Ivory et al., 2019). DS landscapes showed a tendency to increase over the decades. 
32 
 
The rectilinear features in the landscapes serve as an evidence of increasing land use and land 
cover changes (such as those caused due to deforestation and forest fires); from 2007 
onwards, these corroborate the observations made by Pott and Silva (2015). 
The avulsion on alluvial fans offer patchy migration of sediments and nutrients, 
resulting in a mosaic of vegetation (Ward et al., 2002). We verified the changes in the 
vegetation mosaic in the actual drainage polygon because of pre-avulsion, crevasse and 
crevasse splay stages (Smith et al., 1989), and also due to channel migration and the recent 
water flux interruption from the Caronal avulsion. To analyze these complex processes in our 
study area, we explored the values of mean NDVI and the ratio between dry and wet 
landscapes (R index, see Eq. 5) for each polygon. The plots between R and NDVI, and 
variation of R over time are shown in Figure 9. By rule, for R > 1, drier landscapes (TV and 
DS) showed relative predominance. 
 
 
Figure 9: Relation between R and NDVI values for all years (A) and R evolution over time 
for polygons of the abandoned (B), actual (C) and distal (D) drainages. 
 
Figure 9A indicates that R has greater potential to differentiate between dry and wet 
environments when compared to NDVI, suggesting the importance and relevance of accurate 
mapping of wetlands for long-term studies. Over the years, the index R showed a positive 
increase for the abandoned and actual drainages (Figures 9B and 9C), whereas, for the distal 
0
1
2
3
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D
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/W
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ra
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Abandoned drainage
Actual drainage
Distal drainage
A
33 
 
drainage, as expected, no evident trend was observed (Figure 9D). This reinforces that gaining 
a better understanding of the complex avulsive river systems in Pantanal and other analogous 
wetlands requires more attention on the careful selection of orbital sensor data and mapping 
methods used for deriving and studying the dry/wet ration index timeseries. NDVI is useful 
for seasonal studies and as ancillary data for supervised classifications of rectified orbital 
multispectral images. 
Paleochannels in the megafans of avulsive river systems are relicts of ancient and 
natural environmental changes (e.g., Zani and Assine, 2011). Nevertheless, changes in the 
summer rainfall intensity may increase the rate of avulsions in the Pantanal wetlands (Bergier 
et al., 2018), whereas the duration of the dry period might be prolonged because of climate 
change (Marengo et al., 2015; de Oliveira et al., 2019). Altogether, land use and climate 
changes may favor ecological instabilities, which are herein studied and reported, reflecting in 
more successional processes (Amoros et al., 2000; Lo et al., 2019) in the long-term. Drought 
stress has been contributing to an increase in the frequency of man-made fires (Souza et al., 
2019), usually triggered in grasslands and spreading to woodlands (Arruda et al., 2016). 
2.5. Concluding remarks 
Despite the complexity of wetland mapping with multispectral Landsat imagery, the 
substitution of SWIR band by green band in the classification improved overall mapping 
accuracies. Object-oriented classifiers with image segmentation also aided in the mapping 
because of the relative ease of pixel sampling during supervised classifications (Blaschke, 
2010). 
Some authors have emphasized that moderate resolutions on traditional techniques of 
classification are unable to improve the accuracy (Lane et al., 2014; Gallant, 2015; Mahdavi 
et al., 2018). Indeed, a pixel size of 30 m can be a barrier on non-dominant landscapes, where 
canopies usually contain small pockets of water and floating vegetation (Klemas, 2013). 
These limitations can be addressed through field knowledge, which enables better mapping 
outputs and allows the study of the evolution of ever-changing environments (like avulsive 
river systems in the wetlands). All ground truths that guided the discrimination of landscapes, 
also contained features of ancient landforms, mainly in the abandoned course of Taquari 
River (such as the old riverbed siltation covered by herbaceous plants, paleo levees, small 
areas of a late riparian forest, and abandoned floodplains). 
The timeseries of the mapped Landsat rectified data revealed that the avulsion at Zé da 
Costa region on Taquari River is an important product/process of landforms and landscape 
34 
 
changes like other avulsive river systems. Remarkably, our study has shown that the processes 
taking place in Zé da Costa region are likely “self-similar” to the processes taking place in the 
Caronal region, both accelerated by human activities. The Zé da Costa region has been 
transformed into a relatively drier place, affected solely by the floods of the Paraguay River, 
due to recent water flow interruption of Taquari River in the Caronal region. As a result, the 
Zé da Costa region might be more susceptible to droughts and fires, despite the new land 
formation for cattle breeding. 
Finally, our study provides a new and useful framework for studying the evolution of 
avulsive rivers in wetlands affected by land use and climate change. Greater understanding of 
the dry/wet ratio changes in these threatened environments will help to manage, protect and 
conserve them. 
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