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Given the importance of dissolved organic matter (DOM) in the carbon cycling of aquatic ecosystems, information on its seasonal variability is crucial. In this study we assess the use of optical absorption indices available in the literature based on in situ data to both characterize the seasonal variability of DOM in a highly complex environment and for application in large-scale studies using remote sensing data. The study area comprises four lakes located in the Mamirauá Sustainable Development Reserve (MSDR). Samples for the determination of colored dissolved organic matter (CDOM) and measurements of remote sensing reflectance (Rrs) were acquired in situ. The Rrs was used to simulate the response of the visible bands of the Sentinel-2 MultiSpectral Instrument (MSI), which was used in the proposed models. Differences between lakes were tested using the CDOM indices. The results highlight the role of the flood pulse in the DOM dynamics at the floodplain lakes. The validation results show that the use of the absorption coefficient of CDOM (aCDOM) as a proxy of the spectral slope between 275 and 295 nm (S275–295) during rising water is worthwhile, demonstrating its potential application to Sentinel-2 MSI imagery data for studying DOM dynamics on the large scale.

Terrain elevation is essential for land management, navigation, and earth science applications. Remote sensing advancements have led to an increase in the availability of a range of digital elevation models with global to quasi-global land coverage. However, the generation of these models in water bodies requires specialized approaches, such as the delimitation of the shorelines (isobaths) of lakes over time. Therefore, the processing costs are high in complex areas with many lakes. Currently, there is no systematic topographic mapping of lakes and channels in large and complex floodplains using remote sensing data. We present here the first high-resolution topographic mapping (30 m) of the non-forested portion of the middle-lower Amazon floodplain using a new method based on in-situ Amazon river water levels and a flood-frequency map derived from the Landsat Global Surface Water Dataset. Validation using locally derived bathymetry showed a root mean square error (RMSE) of 0.89 m for floodplain elevation and a good representation of spatial patterns with Pearson's correlation coefficient of 0.77. Our approach for improving topographic representation in open water areas is an alternative to SRTM3 DEM or MERIT DEM, which represents these areas as a flat surface. We also generated the Amazon River bathymetry using nautical charts from the Brazilian Navy (average RMSE of 7.5 m and bias of 5 m), and floodplain depths maps corresponding to the high- and low-water periods of the river flood wave. The results show that the storage volume in the open-water floodplain varies 104.3 km3 on average each year (from 11.9 km3 in low-water to 116.2 km3 in high-water). The method can be applied to any temporarily flooded area to provide the often missing underwater digital topographic data required for hydrological, ecological, and geomorphological studies. The data set developed in this study can be found at https://doi.org/ 10.17632/vn599y9szb.1 .

This article analyzes the process of forest cover depletion over the last 40 years at three landscapes distributed along the Amazon floodplain. To this end, we created multi-temporal forest cover maps based on time series of Landsat images, and then analyzed the forest cover dynamics through landscape metrics. Based on landscape analyzes and bibliographic information, we assessed the degree of forest depletion of each landscape and made inferences regarding the main drives of forest changes and their impacts on ecosystem integrity. Results show the existence of an east-to-west gradient of forest depletion that varies in time and space along the floodplain of the Solimoes/Amazonas River, and provides evidence that it is a response to the history of human occupation and public policies. The most degraded landscapes are located on the eastern region, where forest depletion degree indicates substantial damage to biodiversity and ecosystem services. The study increases the scarce knowledge about the dynamics of the floodplain forest over the last decades, allows a deeper understanding of the human influence on the floodplain ecosystem, and supports further studies on the impacts of forest loss and fragmentation on biodiversity, ecosystem services and human well-being in the Amazon floodplain.

River meander migration is a process that maintains biodiverse riparian ecosystems by producing highly sinuous rivers, and oxbow lakes. However, although the floodplains support communities with fish and other practices in the region, meandering rivers can directly affect the life of local communities. For example, erosion of river banks promotes the loss of land on community shores, while sedimentation increases the distance from house to the river. Therefore, communities living along the Jurua River, one of the most sinuous rivers on Earth, are vulnerable to long-term meander migration. In this study, the river meander migration was detected by using Landsat 5-8 data from 1984 to 2020. A per-pixel Water Surface Change Detection Algorithm (WSCDA) was developed to classify regions subject to erosion and sedimentation processes by applying temporal regressions on the water index, called Modified Normalized Difference Water Index (mNDWI). The WSCDA classified the meander migration with omission and commission errors lower than 13.44% and 7.08%, respectively. Then, the number of riparian communities was mapped using high spatial resolution SPOT images. A total of 369 communities with no road access were identified, the majority of which living in stable regions (58.8%), followed by sedimentation (26.02%) and erosion (15.18%) areas. Furthermore, we identified that larger communities (>20 houses) tend to live in more stable locations (70%) compared to smaller communities (1–10 houses) with 55.6%. A theoretical model was proposed to illustrate the main impacts of meander migration on the communities, related to Inundation, Mobility Change, and Food Security. This is the first study exploring the relationship between meander migration and riverine communities at watershed-level, and the results support the identification of vulnerable communities to improve local planning and floodplain conservation.

Terrain elevation data are essential for land management, navigation, and earth science applications. Digital elevation models (DEMs) can be created for land as well as underwater surfaces, and remote sensing advancements have led to the increase in the availability of a range of DEMs over the land. However, the generation of underwater DEMs usually requires the shorelines delineation of the water body, and in regions with many lakes, such approach have high processing costs. Currently, there is no systematic mapping of lakes and channels bathymetry of large and complex wetlands using remote sensing data.We present here the first high-resolution topographic mapping (30 m) of the central Amazon floodplain (~1100 km extension of the Amazon River) using a new method based on water surface elevations and a flood-frequency map derived from Landsat images. Validation using field bathymetric surveys presented a Root Mean Square Error (RMSE) of 1.30 m in floodplain elevations and Pearson’s correlation coefficient of 0.73. These results indicate adequate spatial representation over a large complex floodplain geomorphology and important improvements relative to the SRTM (RMSE of 3.55 and Pearson’s coefficient of 0.22). The method can be applied to temporarily flooded regions, with the advantage of not requiring lake delimitation. Finally, this method provides synergism with the forthcoming satellite SWOT mission for advancements in hydrological, ecological and geomorphological studies of floodplain as the projected increase in availability of surface water elevation data will enhance its applicability and yield unprecedented opportunities to create new datasets of floodplain DEMs and lakes storage volumes.

Remote sensing imagery are fundamental to increasing the knowledge about sediment dynamics in the middle-lower Amazon floodplains. Moreover, they can help to understand both how climate change and how land use and land cover changes impact the sediment exchange between the Amazon River and floodplain lakes in this important and complex ecosystem. This study investigates the suitability of Landsat-8 and Sentinel-2 spectral characteristics in retrieving total (TSS) and inorganic (TSI) suspended sediments on a set of Amazon floodplain lakes in the middle-lower Amazon basin using in situ Remote Sensing Reflectance (Rrs) measurements to simulate Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI (Multispectral Instrument) bands and to calibrate/validate several TSS and TSI empirical algorithms. The calibration was based on the Monte Carlo Simulation carried out for the following datasets: (1) All-Dataset, consisting of all the data acquired during four field campaigns at five lakes spread over the lower Amazon floodplain (n = 94); (2) Campaign-Dataset including samples acquired in a specific hydrograph phase (season) in all lakes. As sample size varied from one season to the other, n varied from 18 to 31; (3) Lake-Dataset including samples acquired in all seasons at a given lake with n also varying from 17 to 67 for each lake. The calibrated models were, then, applied to OLI and MSI scenes acquired in August 2017. The performance of three atmospheric correction algorithms was also assessed for both OLI (6S, ACOLITE, and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of glint correction on atmosphere-corrected image performance was assessed against in situ glint-corrected Rrs measurements. After glint correction, the L8SR and 6S atmospheric correction performed better with the OLI and MSI sensors, respectively (Mean Absolute Percentage Error (MAPE) = 16.68% and 14.38%) considering the entire set of bands. However, for a given single band, different methods have different performances. The validated TSI and TSS satellite estimates showed that both in situ TSI and TSS algorithms provided reliable estimates, having the best results for the green OLI band (561 nm) and MSI red-edge band (705 nm) (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI models provided similar errors, which support the use of both sensors as a virtual constellation for the TSS and TSI estimate over an Amazon floodplain. These results demonstrate the applicability of the calibration/validation techniques developed for the empirical modeling of suspended sediments in lower Amazon floodplain lakes using medium-resolution sensors.

The Amazon várzeas are an important component of the Amazon biome, but anthropic and climatic impacts have been leading to forest loss and interruption of essential ecosystem functions and services. The objectives of this study were to evaluate the capability of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm to characterize changes in várzeaforest cover in the Lower Amazon, and to analyze the potential of spectral and temporal attributes to classify forest loss as either natural or anthropogenic. We used a time series of 37 Landsat TM and ETM+ images acquired between 1984 and 2009. We used the LandTrendr algorithm to detect forest cover change and the attributes of "start year", "magnitude", and "duration" of the changes, as well as "NDVI at the end of series". Detection was restricted to areas identified as having forest cover at the start and/or end of the time series. We used the Support Vector Machine (SVM) algorithm to classify the extracted attributes, differentiating between anthropogenic and natural forest loss. Detection reliability was consistently high for change events along the Amazon River channel, but variable for changes within the floodplain. Spectral-temporal trajectories faithfully represented the nature of changes in floodplain forest cover, corroborating field observations. We estimated anthropogenic forest losses to be larger (1.071 ha) than natural losses (884 ha), with a global classification accuracy of 94%. We conclude that the LandTrendr algorithm is a reliable tool for studies of forest dynamics throughout the floodplain. RESUMOAs várzeas amazônicas são um importante componente do bioma Amazônico, mas impactos antrópicos e climáticos têm levado à perda florestal e à interrupção de processos e serviços ecossistêmicos. O presente estudo teve como objetivos avaliar a aplicabilidade do algoritmo Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) na detecção de mudanças na cobertura florestal de várzea no Baixo Amazonas, e analisar o potencial de atributos espectrais e temporais na classificação das perdas florestais em antrópicas ou naturais. Utilizamos uma série temporal de 37 imagens Landsat TM e ETM+, adquiridas entre 1984 e 2009. Aplicamos o algoritmo LandTrendr para detectar mudanças na cobertura florestal e extrair os atributos de "duração", "magnitude" e "ano de início" das mudanças, além de "NDVI ao final da série". A detecção se restringiu a áreas identificadas como cobertura florestal no início e/ou final da série. Os atributos derivados da série temporal foram classificados pelo algoritmo Support Vector Machine (SVM), diferenciando as perdas florestais antrópicas e naturais. A confiabilidade da detecção dos eventos de mudança foi consistentemente alta ao longo do rio Amazonas, e mais variável no interior da várzea. As trajetórias espectrais-temporais representaram fielmente os eventos de mudança na cobertura florestal, com base em averiguações em campo. A perda da cobertura florestal por causas antrópicas foi maior (1.071 ha) do que por causas naturais (884 ha), com exatidão global de classificação de 94%. Concluímos que o algoritmo LandTrendr é uma ferramenta confiável para aplicação em estudos de dinâmica da cobertura florestal de várzea.

This paper examines water properties from lakes, (depression lakes, sensu Junk et al., 2012), channels (scroll lakes with high connectivity, sensu Junk et al., 2012) and paleo-channels (scroll lakes with low connectivity-sensu Junk et al., 2012, locally called ressacas) located in Mamirauá Sustainable Development Reserve, in Central Amazon floodplain, Amazonas, Brazil. We analysed surface temperature, conductivity, pH, dissolved oxygen, turbidity, transparency, suspended inorganic and organic matter, chlorophyll-a, pheophytin, total nitrogen, total phosphorus, organic and inorganic carbon in 2009 high water phase, 2009 and 2010 low water phases. Multivariate statistical analyses of 24 aquatic systems (6 ressacas, 12 lakes and 6 channels, 142 samples) were applied to the variables in order to: 1) quantify differences among aquatic system types; 2) assess how those differences are affected in the different phases of the hydrological year. First, we analysed the entire set of variables to test for differences among phases of the hydrological year and types of aquatic systems using a PERMANOVA two-way crossed design. The results showed that the all measured limnological variables are distinct regarding both factors: types of aquatic systems and hydrological phases. In general, the magnitude and amplitude of all variables were higher in the low water phase than in the high water phase, except for water transparency in all aquatic system’s types. PERMANOVA showed that the differences between aquatic system’s types and hydrological phases of all variables were highly significant for both main factors (type and phase) and for the type x phase interaction. Limnological patterns of Amazon floodplain aquatic systems are highly dynamic, dependent on the surrounding environment, flood pulse, main river input and system type. These patterns show how undisturbed systems respond to natural variability in such a diverse environment, and how distinct are those aquatic systems, especially during the low water phase. Aquatic systems in Mamirauá floodplain represent limnological patterns of almost undisturbed areas and can be used as future reference for comparison with disturbed areas, such as those of the Lower Amazon, and as a baseline for studies on the effects of anthropogenic influences and climate change and on Amazon aquatic ecosystem. Resumo Esse trabalho investiga as propriedades da água de lagos (lagos de depressão, sensu Junk et al., 2012), canais (scroll lakes com alta conectividade, sensu Junk et al., 2012) e paleo canais (scroll lakes com baixa conectividade - sensu Junk et al., 2012, localmente chamados de ressaca) localizados na Reserva de Desenvolvimento Sustentável Mamirauá (RDSM), na planície de inundação da Amazônia Central, Amazonas, Brasil. Analisaram-se temperatura, condutividade, pH, oxigênio dissolvido, turbidez, e transparência da água na superfície e coletaram-se amostras de água na superfície para a determinação de material orgânico e inorgânico em suspensão, clorofila-a, feofitina, nitrogênio e fósforo total, carbono orgânico e inorgânico dissolvido, em duas fases da hidrógrafa, água alta em 2009, e água baixa em 2009 e 2010. Análises de estatística multivariada de 24 sistemas aquáticos (6 ressacas, 12 lagos e 6 canais, 142 amostras) foram realizadas para: 1) quantificar as diferenças entre os tipos de sistemas aquáticos; 2) determinar como essas diferenças são afetadas nas diferentes fases do ano hidrológico. Primeiramente foram analisadas todas as variáveis para testar as diferenças entre as fases do ano hidrológico e dos tipos de sistemas aquáticos utilizando o teste pareado cruzado PERMANOVA. Os resultados mostraram que todas as variáveis limnológicas medidas são distintas em relação a ambos os fatores: tipos de sistemas aquáticos e fases da hidrógrafa. Em geral, a magnitude e amplitude de todas as variáveis foram maiores na fase seca do que na cheia, com exceção da transparência da água em todos os tipos de sistemas aquáticos. Os resultados da PERMANOVA mostraram que as diferenças entre os tipos de sistemas aquáticos e as fases da hidrógrafa para todas as variáveis foram altamente significativas para ambos os fatores (tipo e fase) e ainda para a interação tipo x fase. Os padrões limnológicos dos sistemas aquáticos da planície de inundação Amazônica são altamente dinâmicos, dependente do ambiente ao redor, do pulso de inundação, do rio principal e do tipo de sistema. Os sistemas aquáticos da planície de inundação de Mamirauá representam os padrões limnológicos de áreas não perturbadas e podem servir como futura referência para comparação com as propriedades dos sistemas aquáticos de áreas perturbadas, como as do Baixo Amazonas. Podem ser também utilizados como linha de base para estudos dos impactos antropogênicos e das mudanças climáticas sobre os ecossistemas aquáticos Amazônicos.

This work presents ongoing efforts and preliminary results for building a dataset that represents the first and most comprehensive bio-optical information available on Brazilian inland waters to support the development of remote sensing algorithms for monitoring aquatic systems. From 2012 to 2014 optical and limnological data was gathered along thirteen field campaigns in five Brazilian reservoirs, in an irrigation and domestic water supply reservoir located in semi-arid northeast of the country and in Amazonian floodplain lakes, thus covering the diversity of Brazilian inland waters. At each site 20 stations, on average, were sampled to acquire profiles of the following optical variables: absorption, attenuation, scattering, and backscattering coefficients and radiances/irradiances spectra above and in-water. Alongside these measurements, water samples were collected for determining concentrations of chlorophyll-a (Chl-a), Total Suspended Solid (TSS), Total Dissolved Carbon (TDC) and its organic/inorganic fractions, CDOM absorption, phytoplankton specific absorption [aph*] and Non-Algal Particles absorption [aNAP*]. Preliminary results show that Chl-a concentrations ranged from 0.6 to 243μg/L in reservoirs and 0.90 to 92μg/L in Amazonian lakes, while TSS concentrations ranged from 0.3 to 31mg/L in reservoirs and 0.5 to 162mg/L in Amazonian lakes. In situ beam attenuation coefficients ranged from 1.4 to 16m-1 in reservoirs and 12.5 to 38m-1 in Amazonian lakes, while diffuse attenuation coefficients of downwelling irradiance over the Photosynthetically Active Radiation (Kd(PAR)) extended from 0.35 to 4.5m-1 in reservoirs and 1.69 to 13.30m-1 in Amazonian lakes. Our research group is building this dataset anticipating future demands for algorithm validation regarding OLI/Landsat8 data and ESA Sentinel missions to be launched as of 2015.

The objective of this study is to assess the influence of the Amazon River seasonal dynamic on floodplain lake morphology. The study area includes the Amazon River floodplain reach encompassed by the Madeira and Tapajos River confluences. Products from the Global Rain Forest Mapping (GRFM) and Moderate Resolution Imaging Spectroradiometer (MODIS) were used to derive variables such as lake size, shape and number. The main steps in the research were: data base implementation, legend definition, image processing (merge, segmentation, classification and edition), morphological mapping and quantitative assessment. Four classes of lacustrine morphology were defined in this study: circular/elliptical, elongated, composite, and dendritic. The result showed that 1) the lake class increased 18.38% from the low- to high-water period; 2) there was a reduction in the total number of lakes from low to high water; 3) the most common lake type was the circular/elliptical; and 4) better results were obtained integrating SAR and optical sensors.

The Amazon River floodplain is subject to large seasonal variations in water level and flood extent, due to the large size and low relief of the basin, and the large amount of precipitation in the region. Synthetic Aperture Radar (SAR) data can be used to map flooded area in these wetlands, given its ability to provide continuous information without being heavily affected by cloud cover. As part of JAXA's Kyoto & Carbon Initiative, extensive wide-swath, multi-temporal SAR coverage of the Amazon basin has been obtained using the ScanSAR mode of ALOS PALSAR. This study presents a method for monitoring flood extent variation using ALOS ScanSAR images, tested at the Curuai Lake floodplain, in the lower Amazon River, Brazil. Twelve ScanSAR scenes were acquired between 2006 and 2010, including seven during the 2007 hydrological year. Water level records, field photographs, optical images (Landsat-5/TM and MODIS/Terra and Aqua) and topographic data were used as auxiliary information. A data mining algorithm allowed the implementation of a hierarchical, object-based classification algorithm, able to map land cover types and flooding status in the study area for all available dates. Land cover based on the entire time series (classification levels 1 and 2) had overall accuracies of 90% and 83%, respectively. Level 3 classifications (one map per image date) were validated only for the lowest and highest water stages, with overall accuracies of 76% and 78%, respectively. Total flood extent (Level 4) was mapped with 84% and 94% accuracies, for the low and high water stages, respectively. Regression models were fitted between mapped flooded area and water levels at the Curuai gauge to predict flood extent. A polynomial model had R2 = 0.95 (p

This article analyzes the process of forest fragmentation of a floodplain landscape of the Lower Amazon over a 30-year period and its implications for the biodiversity and the provision of ecosystem services to the riverine population. To this end, we created a multi-temporal forest cover map based on Landsat images, and then analyzed the fragmentation dynamics through landscape metrics. From the analyses of the landscape and bibliographic information, we made inferences regarding the potential impacts of fragmentation on the biodiversity of trees, birds, mammals and insects. Subsequently, we used data on the local populations’ environmental perception to assess whether the inferred impacts on biodiversity are perceived by these populations and whether the ecosystem services related to the biodiversity of the addressed groups are compromised. The results show a 70% reduction of the forest habitat as well as important changes in the landscape structure that constitute a high degree of forest fragmentation. The perceived landscape alterations indicate that there is great potential for compromise of the biodiversity of trees, birds, mammals and insects. The field interviews corroborate the inferred impacts on biodiversity and indicate that the ecosystem services of the local communities have been compromised. More than 95% of the communities report a decreased variety and/or abundance of animal and plant species, 46% report a decrease in agricultural productivity, and 19% confirm a higher incidence of pests during the last 30 years. The present study provides evidence of an accelerated process of degradation of the floodplain forests of the Lower Amazon and indicate substantial compromise of the ecosystem services provision to the riverine population in recent decades, including reductions of food resources (animals and plants), fire wood, raw material and medicine, as well as lower agricultural productivity due to probable lack of pollination, impoverishment of the soil and an increase of pests.

The floodplain forests bordering the Amazon River have outstanding ecological, economic, and social importance for the region. However, the original distribution of these forests is not well known, since they have suffered severe degradation since the 16th century. The previously published vegetation map of the Amazon River floodplain (Hess et al., 2003), based on data acquired in 1996, shows enormous difference in vegetation cover classes between the regions upstream and downstream of the city of Manaus. The upper floodplain is mostly covered by forests, while the lower floodplain is predominantly occupied by grasses and shrubs. This study assesses deforestation in the Lower Amazon floodplain over a ~ 30 year period by producing and comparing a historical vegetation map based on MSS/Landsat images acquired in the late 1970s with a recent vegetation map produced from TM/Landsat images obtained in 2008. The maps were generated through the following steps: 1) normalization and mosaicking of images for each decade; 2) application of a linear mixing model transformation to produce vegetation, soil and shade fraction-images; and 3) object-oriented image analysis and classification. For both maps, the following classes were mapped: floodplain forest, non-forest floodplain vegetation, bare soil and open water. The two maps were combined using object-level Boolean operations to identify time transitions among the mapped classes, resulting in a map of the land cover change occurred over ~ 30 years. Ground information collected at 168 ground points was used to build confusion matrices and calculate Kappa indices of agreement. A survey strategy combining field observations and interviews allowed the collection of information about both recent and historical land cover for validation purposes. Kappa values (0.77, 0.75 and 0.75) indicated the good quality of the maps, and the error estimates were used to adjust the estimated deforested area to a value of 3457 km 2 ± 1062 km 2 (95% CI) of floodplain deforestation over the ~ 30 years.

We applied spatial data analysis and geostatistical procedures to pH, total suspended sediment and chlorophyll-a concentration data gathered on an Amazon floodplain lake. Variographic analysis and ordinary kriging interpolation were used to identify and describe spatiotemporal patterns of variability in these parameters, which are relevant to understand the dynamics of water circulation on the floodplain lake. In spite of the complexity of the processes underlying the spatiotemporal patterns, this approach demonstrated that the heterogeneity in the seasonal water composition is forced not only by the Amazon River flood pulse, but also by the lake bottom topography and the wind intensity.

The potential of Hyperion images acquired on September 2001 (receding flood period of the Amazon River) and June 2005 (high flood) was investigated for reflectance characterization of selected Amazon floodplain waterbodies using a linear spectral mixture model. The results show the ability of Hyperion to measure adequately the major variation in water reflectance spectral features in response to the annual flood pulse of the Amazon River. Mixture model fraction values were correlated with measured inorganic suspended solids (ISS) but not with chlorophyll (Chl) in the high flood period. Inspection of the fractions across the two images revealed variation in water composition. Small changes in ISS-and Chl-bearing water fractions between the images indicated relatively stable spectral conditions for low (Tapajos River and Lake Juruparipucu) and high (Amazon River) turbidity waterbodies. Large changes indicated reflectance variation in some lakes when the water receded due to algal blooms (Lake Curumu) and sediment resuspension in shallow regions (Lake Aritapera). Although not all water constituents were modelled adequately for quantification purposes, spectral mixture modelling is still an interesting approach for spectral-temporal reflectance characterization of Amazonian floodplains with hyperspectral data.

Made available in DSpace on 2018-12-11T16:59:47Z (GMT). No. of bitstreams: 0 Previous issue date: 2016-03-01 This study answered the following questions: 1) Is polarimetric C-band SAR (PolSAR) more efficient than dual-polarization (dual-pol) C-band SAR for mapping várzea floodplain vegetation types, when using images of a single hydrological period? 2) Are single-season C-band PolSAR images more accurate for mapping várzea vegetation types than dual-season dual-pol C-band SAR images? 3) What are the most efficient polarimetric descriptors for mapping várzea vegetation types? We applied the Random Forests algorithm to classify dual-pol SAR images and polarimetric descriptors derived from two full-polarimetric Radarsat-2 C-band images acquired during the low and high water seasons of Lago Grande de Curuai floodplain, lower Amazon, Brazil. We used the Kappa index of agreement (κ), Allocation Disagreement (AD) and Quantity Disagreement (QD), and Producer's and User's accuracy measurements to assess the classification results. Our results showed that single-season full-polarimetric C-band data can yield more accurate classifications than single-season dual-pol C-band SAR imagery and similar accuracies to dual-season dual-pol C-band SAR classifications. Still, dual-season PolSAR achieved the highest accuracies, showing that seasonality is paramount for obtaining high accuracies in wetland land cover classification, regardless of SAR image type. On average, single-season classifications of low-water periods were less accurate than high-water classifications, likely due to plant phenology and flooding conditions. Classifications using model-based polarimetric decompositions (such as Freeman-Durden, Yamaguchi and van Zyl) produced the highest accuracies (κ greater than 0.8; AD ranging from 7.5% to 2.5%; QD ranging from 15% to 12%), while eigenvector-based decompositions such as Touzi and Cloude-Pottier had the worst accuracies (κ ranging from 0.5 to 0.7; AD greater than 10%; QD smaller than 10%). Vegetation types with dense canopies (Shrubs, Floodable Forests and Emergent Macrophytes), whose classification is challenging using C-band, were accurately classified using dual-season full-polarimetric SAR data, with Producer's and User's accuracies between 80% and 90%. We conclude that full polarimetric C-band imagery can yield very accurate classifications of várzea vegetation (κ ~0.8, AD ~3% and QD ~10%) and can be used as an operational tool for forested wetland mapping. Divisão de Sensoriamento Remoto Instituto Nacional de Pesquisas Espaciais (INPE), Avenida dos Astronautas 1758 Instituto de Geociências e Ciências Exatas UNESP - Univ. Estadual Paulista Departamento de Geografia Ecosystem Dynamics Observatory, Avenida 24A, 1515 Instituto de Geociências e Ciências Exatas UNESP - Univ. Estadual Paulista Departamento de Geografia Ecosystem Dynamics Observatory, Avenida 24A, 1515

To assess seasonal changes in phytoplanktonic chlorophyll distributions in Amazon floodplain lakes, a linear mixing model was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data acquired at four river stages: rising (April), high (June), decreasing (September), and low (November). The study area is located in a floodplain reach from Parintins (Amazonas) to near Almeirim (Para). A three-end-member mixing model designed to uncouple three fractions [high suspended inorganic matter (ip), low inorganic suspended matter (w), and high chlorophyll a (Chl)] was tested in Lake Curuai (1.5°S 55.43°W) based on field sampling done almost concurrently with satellite overpasses. During high water, phytoplankton patches are confined to lakes closer to terra firme under the influence of clear water inflow, whereas during the low and decreasing water stages, the patches are more evenly distributed over the floodplain.

O presente trabalho demonstra a aplicabilidade de imagens de sensoriamento remoto e de métodos de processamento de imagens digitais para definição de locais adequados à instalação de sistemas telemétricos de monitoramento de variáveis ambientais em sistemas aquáticos, localizados em regiões de difícil acesso. A técnica consiste essencialmente da aplicação de operações Booleanas entre mapas da pluma do Rio Amazonas e de zonas inundadas do Lago de Curuai em diferentes etapas do ciclo hidrológico. A localização exata para o sistema de monitoramento telemétrico será vital para o desenvolvimento de modelos de troca de gases traço entre a planície de inundação Amazônica e a atmosfera.

Wetlands of the Amazon River basin are globally significant sources of atmospheric methane. Satellite remote sensing (passive and active microwave) of the temporally varying extent of inundation and vegetation was combined with field measurements to calculate regional rates of methane emission for Amazonian wetlands. Monthly inundation areas for the fringing floodplains of the mainstem Solimoes/Amazon River were derived from analysis of the 37GHz polarization difference observed by the Scanning Multichannel Microwave Radiometer from 1979 to 1987. L-band synthetic aperture radar data (Japanese Earth Resources Satellite-1) were used to determine inundation and wetland vegetation for the Amazon basin (o500m elevation) at high (May-June 1996) and low water (October 1995). An extensive set of measurements of methane emission is available from the literature for the fringing floodplains of the central Amazon, segregated into open water, flooded forest and floating macrophyte habitats. Uncertainties in the regional emission rates were determined by Monte Carlo error analyses that combined error estimates for the measurements of emission and for calculations of inundation and habitat areas. The mainstem Solimoes/Amazon flood- plain (54-701W) emitted methane at a mean annual rate of 1.3TgCyr � 1 , with a standard deviation (SD) of the mean of 0.3TgCyr � 1 ; 67% of this range in uncertainty is owed to the range in rates of methane emission and 33% is owed to uncertainty in the areal estimates of inundation and vegetative cover. Methane emission from a 1.77 million square kilometers area in the central basin had a mean of 6.8TgCyr � 1 with a SD of 1.3TgCyr � 1 . If extrapolated to the whole basin below the 500m contour, approximately 22TgCyr � 1 is emitted; this mean flux has a greenhouse warming potential of about 0.5PgC as CO2. Improvement of these regional estimates will require many more field measurements of methane emission, further examination of remotely sensed data for types of wetlands not represented in the central basin, and process-based models of methane production and emission.

Nonlinear analysis of Amazonian River systems suggests that water level variability is due to Brownian-like motions BH. Dynamical properties of the water level is probably a response of rainfall and runoff smoothing by the stored water in the floodplain; additional inputs of water results in attenuated perturbations of the water level. This implies that noise is a self-affine fractal with scale invariance, and in accordance with surrogate and correlation dimension analysis, which refused the hypothesis of low dimensional chaos for water level dynamics. Opposing to the chaotic approach, we present a fractal model formed by a periodic signal and a BH process that mimics original water level variability. Dynamical exponents are remarkably similar between real and modeled data when BH influence is about 1% the amplitude of the periodic signal. In comparison to natural systems, a hydroelectric reservoir produces different exponents, due to the control of the water level, as demonstrated for the Tucurui dam in the Tocantins River. The Amazon floodplain at Obidos and Curuai exhibits a fortnight cycle most probably due to bore tide influence in the river mouth.

Made available in DSpace on 2015-10-21T20:17:01Z (GMT). No. of bitstreams: 0 Previous issue date: 2015-02-01 Instituto Nacional de Pesquisas Espaciais (INPE) Instituto de Desenvolvimento Sustentavel Mamiraua (IDSM - OS/MCTI) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Remote sensing studies of vegetation cover and hydrologic dynamics in Amazonian wetlands have been mostly limited temporally or spatially, and the distribution and spatial configuration of Amazonian varzea habitats remains poorly known. This study uses multitemporal PALSAR L-band radar imagery combined with object-based image analysis, data mining techniques and field data to derive vegetation structure and inundation patterns and characterize major vegetation types in varzea forests of the Mamiraua Sustainable Development Reserve. Our results show that the combination of vegetation cover and inundation extent information can be a good indicator of the complex gradient of habitats along the floodplain. The intersection between vegetation and flood duration classes showed a wider range of combinations than suggested from field based studies. Chavascal areas-chacaracterized as a dense and species-poor shrub/tree community developing in old depressions, abandoned channels, and shallow lakes-had shorter inundation periods than the usually recognized hydroperiod of 180-240 days of flooding, while low varzea-a diverse community that have fewest and smallest species, and highest individual density and that tolerate 120-180 days of flooding every year-was distributed between flood duration ranges that were higher than reported by the literature. Forest communities growing at sites that were never mapped as flooded could indicate areas that only flood during extreme hydrological events, for short periods of time. Our results emphasize the potential contribution of SAR remote sensing to the monitoring and management of wetland environments, providing not only accurate information on spatial landscape configuration and vegetation distribution, but also important insights on the ecohydrological processes that ultimately determine the distribution of complex floodplain habitat mosaics. Instituto de Desenvolvimento Sustentável Mamirauá, Estrada do Bexiga, 2584, Bairro Fonte Boa, Tefé, AM 69470-000, Brasil Instituto Nacional de Pesquisas Espaciais - Divisão de Sensoriamento Remoto, Coordenação Geral de Observacaoes da Terra Instituto Nacional de Pesquisas da Amazônia - Coordenaçãoo de Dinâmica Ambiental, Instituto Nacional de Pesquisas da Amazônia, Avenida André Araujo, 2936 – Aleixo, Manaus, AM 69060-000, Brazil Departamento de Geografia, Instituto de Geociências e Ciências Exatas de Rio Claro, UNESP - Universidade Estadual Paulista Ju´lio de Mesquita Filho, Avenida 24-A, 1515 - Jardim Bela Vista, Rio Claro, SP 13506-900, Brazil Universidade Estadual Paulista - Departamento de Geografia, Instituto de Geociênncias e Ciências Exatas de Rio Claro, Brasil FAPESP: 2010/11269-2

This paper presents the methodology used to build a Landsat Thematic Mapper (TM) digital mosaic for the Amazon River mainstem floodplain. Twenty-nine almost cloud-free TM Landsat scenes covering a period from 1985 to 1995 were selected from the National Institute for Space Research (INPE) archive. Most of the scenes were acquired from July to September, a period that begins with high waters and ends with receding water up to about the beginning of the low waters. Radiometric rectification was applied to the images to reduce variability of environmental conditions during Landsat TM data acquisition. The radiometric rectification applied had a good performance for bands 3, 5, and 7, for most of the scenes. For bands 1 and 2 the radiometric rectification was limited, especially for scenes with intense haze. Nevertheless, the overall performance of radiometric normalization allowed the production of a uniform dataset for the entire Brazilian Amazon River mainstem floodplain.

Inundation patterns in Amazon River floodplains are revealed by analysis of the 37 GHz polarization difference observed by the Scanning Multichannel Microwave Radiometer on the Nimbus-7 satellite. Flooded area is estimated at monthly intervals for January 1979 through August 1987 using mixing models that account for the major landscape units with distinctive microwave emission characteristics. Results are presented separately for 12 longitudinal reaches along the Amazon River main stem in Brazil as well as for three major tributaries (the Jurua, Purus and Madeira rivers). The total area along the Amazon River main stem that was flooded (including both floodplain and open water) varied between 19000 and 91 000km2. The correlation between flooded area and river stage is used to develop a predictive relationship and reconstruct regional inundation patterns in the floodplain of the Amazon River main stem over the past 94 years of stage records (1903-1996). The mean flooded area along the Amazon River during t...

A methodology is described for identifying and mapping floodplain habitats in a reach of the Amazon mainstream. A linear mixing approach was used to determine the fraction of three pure endmembers. This method was tested for two radiometrically rectified Landsat Thematic Mapper (TM) scenes and the proportions of endmembers were used to identify the following classes: (1) clear/mixed water; (2) turbid water; (3) flooded non-forest; (4) flooded forest; (5) human settlements and (6) aquatic vegetation. The results were compared to visually interpreted Landsat TM images.

The Amazon River floodplain is an important source of atmospheric CO 2 and CH 4 . Aquatic herbaceous vegetation (macrophytes) have been shown to contribute significantly to floodplain net primary productivity (NPP) and methane emission in the region. Their fast growth rates under both flooded and dry conditions make herbaceous vegetation the most variable element in the Amazon floodplain NPP budget, and the most susceptible to environmental changes. The present study combines multitemporal Radarsat-1 and MODIS images to monitor spatial and temporal changes in herbaceous vegetation cover in the Amazon floodplain. Radarsat-1 images were acquired from Dec/2003 to Oct/2005, and MODIS daily surface reflectance products were acquired for the two cloud-free dates closest to each Radarsat-1 acquisition. An object-based, hierarchical algorithm was developed using the temporal SAR information to discriminate Permanent Open Water (OW), Floodplain (FP) and Upland (UL) classes at Level 1, and then subdivide the FP class into Woody Vegetation (WV) and Possible Macrophytes (PM) at Level 2. At Level 3, optical and SAR information were combined to discriminate actual herbaceous cover at each date. The resulting maps had accuracies ranging from 80% to 90% for Level 1 and 2 classifications, and from 60% to 70% for Level 3 classifications, with kappa values ranging between 0.7 and 0.9 for Levels 1 and 2 and between 0.5 and 0.6 for Level 3. All study sites had noticeable variations in the extent of herbaceous cover throughout the hydrological year, with maximum areas up to four times larger than minimum areas. The proposed classification method was able to capture the spatial pattern of macrophyte growth and development in the studied area, and the multitemporal information was essential for both separating vegetation cover types and assessing monthly variation in herbaceous cover extent.

Macrophyte net primary productivity (NPP) is a significant but understudied component of the carbon budget in large Amazonian floodplains. Annual NPP is determined by the interaction between stem elongation (vertical growth) and plant cover changes (horizontal expansion), each affected differently by flood duration and amplitude. Therefore, hydrological changes as predicted for the Amazon basin could result in significant changes in annual macrophyte NPP. This study investigates the responses of macrophyte horizontal expansion and vertical growth to flooding variability, and its possible effects on the contribution of macrophytes to the carbon budget of Amazonian floodplains. Monthly macrophyte cover was estimated using satellite imagery for the 2003-2004 and 2004-2005 hydrological years, and biomass was measured in situ between 2003 and 2004. Regression models between macrophyte variables and river-stage data were used to build a semiempirical model of macrophyte NPP as a function of water level. Historical river-stage records (1970-2011) were used to simulate variations in NPP, as a function of annual flooding. Vertical growth varied by a factor of ca. 2 over the simulated years, whereas minimum and maximum annual cover varied by ca. 3.5 and 1.5, respectively. Results suggest that these processes act in opposite directions to determine macrophyte NPP, with larger sensitivity to changes in vertical growth, and thus maximum flooding levels. Years with uncommonly large flooding amplitude resulted in the highest NPP values, as both horizontal expansion and vertical growth were enhanced under these conditions. Over the simulated period, annual NPP varied by ca. 1.5 (1.06-1.63 TgC yr(-1) ). A small increasing trend in flooding amplitude, and by extension NPP, was observed for the studied period. Variability in growth rates caused by local biotic and abiotic factors, and the lack of knowledge on macrophyte physiological responses to extreme hydrological conditions remain the major sources of uncertainty.

AIM: This paper describes an experiment carried out to evaluate in vivo fluorescence (IVF) as an alternative method for chlorophyll-a estimation in optically complex aquatic environment (Amazon floodplain lakes) METHODS: The experiment consisted of collecting in situ measurements at 26 sampling stations distributed throughout Curuai floodplain lakes. For each sampling station the following parameters were measured: temperature, turbidity, depth, Secchi depth, chlorophyll-a (Chl-a) concentration, total suspended solids (TSS) and dissolved organic carbon (DOC), concurrently with several transects of IVF. Two methods were tested for quantifying the fluorescence measurement to be used as input for the chlorophyll-a estimates: instantaneous IFV and average IVF. Global and regional models were tested and assessed by analyzing optically active components (Chl-a, DOC and TSS) of the water. RESULTS: Regardless of fluorescence estimating method, the results indicate that it was not possible to fit a global model for estimating Chl-a from IVF for all the lakes in the Curuai floodplain. Regional models provided contrasting results according to the concentration of optically active components. The best results were observed for aquatic systems with a single dominant component homogenously distributed throughout the lake. The results highlight the influence of the ratios Chl-a/TSS, Chl-a/DOC and Phaephytin/Chl-a in the relationship between IVF and chlorophyll concentration. CONCLUSIONS: It was not possible to develop a global model to account for the entire region of Curuai floodplain. The search for regional models provided insights on the main factors affecting the relationship between IVF and Chl-a concentration. Nevertheless this work reinforces the great potential of fluorometry technique, since even with a small number of samples it was possible to set a good model in the main lake of the Curuai floodplain. In spite the fact that this is not an accurate method, it is very useful for assessing the chlorophyll spatial distribution with relatively low cost. These possibilities are very interesting in the execution of field missions in the Amazon region.

The objective of this paper is to exemplify the use of remote sensing to assess methane emissions from reservoirs. Man‐made reservoirs seem to behave as flood plain environments that are known to make a significant contribution to tropospheric methane (CH4). Methane emissions from the flood plain, however, are variable: open water environments produce lower fluxes than those covered by floating aquatic vegetation. TM/Landsat images were used to identify aquatic habitats resulting from the construction of the three largest reservoirs in the Amazon region: Tucurui, Balbina and Samuel. These habitats are: open water, floating aquatic vegetation, and transition zone. The area covered by each of these environments was computed for each of the reservoirs. Data on the methane emission for these various environments were not available. Published data were then used to compute their likely contribution to the methane flux, assuming that the reservoir's environments were similar to those on which the data ...

[1] The inundation status of the Amazon floodplain affects biogenic gas production and evasion. We analyzed spatial variability of dissolved CO2 concentration and gas evasion in a large floodplain lake in the lower reach of the Amazon River in four hydrological phases. We calculated surficial CO2 concentrations from measurements of pH, dissolved inorganic carbon, temperature, and conductivity and used meteorological data to calculate gas transfer coefficients to estimate CO2 evasion. Gas transfer coefficients that take into account both wind and heating and cooling at the lake's surface are on the order of 10 cm hr−1, approximately four times higher than values previously used in regional estimates of gas evasion from lakes on the Amazon floodplain. Supersaturation of CO2 occurred throughout the lake and was higher in the littoral zone and in regions receiving Amazon River inflows. CO2 concentration was reduced in regions with phytoplankton blooms. The range of CO2 concentrations was least at low water, 47 μM to 233 μM, and largest at high water, 1 μM to 656 μM; the average annual value was 125 μM. We estimate mean (±standard deviation) fluxes from open-water in L. Curuai to the atmosphere of 44 ± 15, 348 ± 13, 371 ± 23, and 364 ± 20 mmol CO2 m−2 d−1 during receding, low, rising, and high water, respectively. The error associated with these values reflects, for each hydrological phase, the spatial variation in CO2 concentration in L. Curuai, a likely range in atmospheric CO2 levels and temporal variations in gas transfer coefficient within 10-day periods.

Long-term environmental time series of continuously collected data are fundamental to identify and classify pulses and determine their role in aquatic systems. This paper presents in situ daily mean chlorophyll-a concentration time series, key information for the current understanding of carbon fluxes in and out of the Amazonian floodplain system. This paper also investigates how seasonal fluctuations in water level affect the relationship between chlorophyll-a concentration and some of its controlling limnological (water level, water surface temperature, pH and turbidity) and meteorological (wind intensity, relative humidity and short wave radiation) variables provided by an automatic monitoring system (Integrated System for Environmental Monitoring-SIMA) deployed at Curai Lake. The data are collected in preprogrammed time interval (1 h) and are transmitted by satellite in quasi-real time for any user in a range of 2500 km from the acquisition point. We used Pearson correlation to determine the quantitative relationship between chlorophyll-a time series and others environmental parameters. Fourier power spectrum analyses were applied to identify periods of high variability in chlorophyll-a time series and wavelet power spectrum analyses helped to characterize their time-frequency structure. To further investigate the relationship between chlorophyll-a and the statistically significant variable highlighted by Pearson's correlation, the set of time series was submitted to cross wavelet analysis. The time series of chlorophyll-a shows two high peaks (47 μg L−1 and 53.30 μg L−1) of concentration during a year: first during the rising water and second during the low water level. A small peak was observed during the high water level (10 μg L−1). For the most part of rising, high and receding water level, the chlorophyll-a concentration is often low (from 2.20 μg L−1 to 9.10 μg L−1). chlorophyll-a concentration displays periodicities ranging from 2–60 days, with a coherence of approximately 1 in phase with water level during the rising and low water period. Water level dynamics and turbidity explain 68% of the chlorophyll-a time series variability.

AIM: This paper examines the spatial and temporal variation of limnological characteristics of floodplain lakes in the Solimões and Japurá confluence, an undisturbed region - the Mamirauá Sustainable Development Reserve (MSDR); METHODS: We analyzed surface temperature, conductivity, pH, dissolved oxygen, turbidity and transparency, and surface water samples were collected for determination of suspended inorganic and organic matter, chlorophyll-a, pheophytin, total nitrogen, total phosphorus, organic and inorganic carbon, in two phases of the hydrograph stage, 2009 high water phase, 2008, 2009 and 2010 low water phases; RESULTS: The results showed that the studied water bodies have high variability in all measured variables: a) between hydrograph phases; b) among main rivers; and c) between opposite margins of Japurá River; CONCLUSIONS: This shows the remarkable influence of the flood pulse and the primary water source on the limnology of this system. The monitoring of physical and chemical limnological variables in Mamirauá will serve as future reference for comparison with disturbed areas, such as the Lower Amazon, and as a baseline for modeling the effects of climate change and anthropogenic influences on Amazon aquatic ecosystem. OBJETIVO: Esse trabalho investiga a variação temporal e espacial de algumas características limnológicas dos lagos da planície de inundação da região de confluência dos Rios Japurá e Solimões, onde está localizada a Reserva de Desenvolvimento Sustentável Mamirauá (RDSM); MÉTODOS: Analisaram-se temperatura, condutividade, pH, oxigênio dissolvido, turbidez e transparência da água em três profundidades (superfície, Secchi e no limite da zona fótica) e coletadas amostras de água na superfície para a determinação de material orgânico e inorgânico em suspensão, clorofila-a, feofitina, nitrogênio e fósforo total, carbono orgânico e inorgânico dissolvido, em duas fases da hidrógrafa, água alta em 2009, e água baixa em 2008, 2009 e 2010; RESULTADOS: Os resultados mostraram que as variáveis medidas possuem uma alta variabilidade nos corpos d'água na região de estudo: a) entre as fases da hidrógrafas; b) entre os rios principais; e c) entre as margens opostas do Rio Japurá, mostrando a importância do pulso de inundação na variação dos parâmetros físicos e químicos e ainda a em relação ao rio principal de alimentação; CONCLUSÕES: O monitoramento dos parâmetros físicos e químicos em Mamirauá servirá como futura referência para comparação com outras regiões menos preservadas, como o Baixo Amazonas, e ainda como linha de base para modelos sobre efeitos das mudanças climáticas e influências antropogênicas no ecossistema aquático Amazônico.

The objective of this paper was to estimate turbidity in the Curuai floodplain during the high water level period. Spatial regression models were developed by using fraction images derived from a linear spectral mixture model applied to a Moderate Resolution Imaging Spectroradiometer/Terra image and turbidity in situ data. As the turbidity in situ data showed spatial autocorrelation, they were divided into four spatial regimes (clusters). Thus, a spatial regression model was developed for each spatial regime. Through the Akaike information criterion, it was verified which spatial regime showed the best fit in the spatial regression model. The best fit was presented by the spatial regime 4 (R 2 = 0.80,p < 0.05). Then, the spatial regression model developed for the spatial regime 4 was applied to all floodplain lakes. The spatial regression models show potential for assessing the water turbidity in aquatic systems by considering a spatial dependence between samples.

The Amazonian floodplains harbor highly diverse wetland forests, with angiosperms adapted to survive extreme floods and droughts. About 14% of the Amazon Basin is covered by floodplains, which are fundamental to river productivity, biogeochemical cycling and trophic flow, and have been subject to human occupation since Pre-Colombian times. The botanical knowledge about these forests is still incomplete, and current forest degradation rates are much higher than the rate of new botanical surveys. Herein we report the results of three years of botanical surveys in floodplain forests of the Central Amazon. This checklist contains 432 tree species comprising 193 genera and 57 families. The most represented families are Fabaceae, Myrtaceae, Lauraceae, Sapotaceae, Annonaceae, and Moraceae representing 53% of the identified species. This checklist also documents the occurrence of approximately 236 species that have been rarely recorded as occurring in white-water floodplain forests.

Assurance of water quality for human consumption is essential for public health policies. In the Amazon floodplain, the seasonal water level variation causes periodic flooding of marginal areas that are usually used for settlements, agriculture and livestock. Therefore, the exchange of materials between the terrestrial and aquatic ecosystem affects the proportion of suspended and dissolved components in water and its physical-chemical characteristics, and consequently the quality of the water used by local people. Following this approach, the aim of this study is to evaluate changes in water quality in Lago Grande de Curuaí floodplain, Óbidos, Pará in response to the flood pulse, during one hydrological year from 2003 to 2004, based on water use classes (according to National Water Agency 357/2005 resolution) using chlorophyll-a and dissolved oxygen concentration as parameters and the eutrophication index. Ordinary kriging was applied to interpolate chlorophyll-a and dissolved oxygen and to predict values at non sampled locations. Each location was then classified according to water use acceptable parameters and to Carlson Trophic State Index modified by Toledo to map lake water classes and trophic status. The result showed that Lago Grande de Curuaí floodplain is a supereutrophic system, with levels of dissolved oxygen and chlorophyll-a not suitable for human supply during the receding water phase. These areas are located near the riverine communities, which can cause health problems due to the presence of potentially toxic algae. Therefore, monitoring water quality in Amazon lakes is essential to ensure the availability has appropriate quality for human and animal supplies.

Observations of turbidity provide quantitative information about water quality. However, the number of available in situ measurements for water quality determination is usually limited in time and space. Here, we present an analysis of the temporal and spatial variability of the turbidity of an Amazon floodplain lake using two approaches: (1) wavelet analysis of a turbidity time series measured by an automatic monitoring system, which should be improved/simplified, and (2) turbidity samples measured in different locations and then interpolated using an ordinary Kriging algorithm. The spatial and temporal variability of turbidity are clearly related to the Amazon River flood pulses in the floodplain. When the water level in the floodplain is rising or receding, the exchange between the Amazon River and the floodplain is the major driving force in turbidity variability. At high-water levels, turbidity variability is controlled by Lake Bathymetry. When the water level is low, wind action and Lake Morphometry are the main causes of turbidity variability. The combined use of temporal and spatial data shows a good potential for better understanding of the turbidity behaviour in a complex aquatic system such as the Amazon floodplain.