Claudio Clemente Faria Barbosa

Estimation of chlorophyll-a concentration and the trophic state of the Barra Bonita hydroelectric reservoir using oli/landsat-8 images

Reservoirs are artificial environments built by humans, and the impacts of these environments are not completely known. Retention time and high nutrient availability in the water increases the eutrophic level. Eutrophication is directly correlated to primary productivity by phytoplankton. These organisms have an important role in the environment. However, high concentrations of determined species can lead to public health problems. Species of cyanobacteria produce toxins that in determined concentrations can cause serious diseases in the liver and nervous system, which could lead to death. Phytoplankton has photoactive pigments that can be used to identify these toxins. Thus, remote sensing data is a viable alternative for mapping these pigments, and consequently, the trophic. Chlorophyll-a (Chl-a) is present in all phytoplankton species. Therefore, the aim of this work was to evaluate the performance of images of the sensor Operational Land Imager (OLI) onboard the Landsat-8 satellite in determining Chl-a concentrations and estimating the trophic level in a tropical reservoir. Empirical models were fitted using data from two field surveys conducted in May and October 2014 (Austral Autumn and Austral Spring, respectively). Models were applied in a temporal series of OLI images from May 2013 to October 2014. The estimated Chl-a concentration was used to classify the trophic level from a trophic state index that adopted the concentration of this pigment-like parameter. The models of Chl-a concentration showed reasonable results, but their performance was likely impaired by the atmospheric correction. Consequently, the trophic level classification also did not obtain better results.

Telemetric monitoring system for meteorological and limnological data acquisition

(2006). Telemetric monitoring system for meteorological and limnological data acquisition. SIL Proceedings, 1922-2010: Vol. 29, No. 4, pp. 1747-1750.

Improving the spectral unmixing algorithm to map water turbidity Distributions

In this paper we evaluate the suitability of the spectral unmixing algorithm to map the turbidity in the Curuai floodplain lake and enhance its applicability using autocorrelation modelling. The Spectral Unmixing Model (SMM) was applied to a Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance (MOD09) image, taking in-situ measurements close to the acquisition date. Fraction images of inorganic matter-laden water, dissolved organic matter-laden water, and phytoplankton-laden water were generated by SMM, using 4 MODIS spectral bands (blue, green, red, and near infrared). These endmembers were selected based on the dominance of these components, which affect water turbidity. These fraction images allowed assessing the turbidity distribution in the study area but showing only places with high or low turbidity. The kernel estimation algorithm was then used to verify the spatial correlation among the in-situ measurement data. The occurrence of clusters suggests that there are different spatial water regimes. One spatial regression model was then compiled for each water regime, each of which presented a better turbidity estimation as opposed to the one derived from the Ordinary Least Square (OLS). The methodology applied was hence useful to analyze the spatial distribution of turbidity in the Curuai floodplain lake.

Geospatial analysis of spatiotemporal patterns of pH, total suspended sediment and chlorophyll-a on the Amazon floodplain

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.

Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes

Satellite data provide the only viable means for extensive monitoring of remote and large freshwater systems, such as the Amazon floodplain lakes. However, an accurate atmospheric correction is required to retrieve water constituents based on surface water reflectance ( R W ). In this paper, we assessed three atmospheric correction methods (Second Simulation of a Satellite Signal in the Solar Spectrum (6SV), ACOLITE and Sen2Cor) applied to an image acquired by the MultiSpectral Instrument (MSI) on-board of the European Space Agency’s Sentinel-2A platform using concurrent in-situ measurements over four Amazon floodplain lakes in Brazil. In addition, we evaluated the correction of forest adjacency effects based on the linear spectral unmixing model, and performed a temporal evaluation of atmospheric constituents from Multi-Angle Implementation of Atmospheric Correction (MAIAC) products. The validation of MAIAC aerosol optical depth (AOD) indicated satisfactory retrievals over the Amazon region, with a correlation coefficient (R) of ~0.7 and 0.85 for Terra and Aqua products, respectively. The seasonal distribution of the cloud cover and AOD revealed a contrast between the first and second half of the year in the study area. Furthermore, simulation of top-of-atmosphere (TOA) reflectance showed a critical contribution of atmospheric effects (>50%) to all spectral bands, especially the deep blue (92%–96%) and blue (84%–92%) bands. The atmospheric correction results of the visible bands illustrate the limitation of the methods over dark lakes ( R W < 1%), and better match of the R W shape compared with in-situ measurements over turbid lakes, although the accuracy varied depending on the spectral bands and methods. Particularly above 705 nm, R W was highly affected by Amazon forest adjacency, and the proposed adjacency effect correction minimized the spectral distortions in R W (RMSE < 0.006). Finally, an extensive validation of the methods is required for distinct inland water types and atmospheric conditions.

Analysis of MERIS Reflectance Algorithms for Estimating Chlorophyll-a Concentration in a Brazilian Reservoir

Chlorophyll-a (chl-a) is a central water quality parameter that has been estimated through remote sensing bio-optical models. This work evaluated the performance of three well established reflectance based bio-optical algorithms to retrieve chl-a from in situ hyperspectral remote sensing reflectance datasets collected during three field campaigns in the Funil reservoir (Rio de Janeiro, Brazil). A Monte Carlo simulation was applied for all the algorithms to achieve the best calibration. The Normalized Difference Chlorophyll Index (NDCI) got the lowest error (17.85%). The in situ hyperspectral dataset was used to simulate the Ocean Land Color Instrument (OLCI) spectral bands by applying its spectral response function. Therefore, we evaluated its applicability to monitor water quality in tropical turbid inland waters using algorithms developed for MEdium Resolution Imaging Spectrometer (MERIS) data. The application of OLCI simulated spectral bands to the algorithms generated results similar to the in situ hyperspectral: an error of 17.64% was found for NDCI. Thus, OLCI data will be suitable for inland water quality monitoring using MERIS reflectance based bio-optical algorithms.

Turbidity in the amazon floodplain assessed through a spatial regression model applied to fraction images derived from MODIS/Terra

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.

Reference spectra to classify Amazon water types

Reference spectra extracted from spectral libraries can distinguish different water types in images when associated with limnological information. In this study, we compiled available databases into a single spectral library, using field water reflectance spectra and limnological data collected by different researchers and campaigns in the Amazonian region. By using an iterative clustering procedure based on the combination of reflectance and optically active components (OACs), reference spectra representative of the major Amazonian water types were defined from this library. Differences between the resultant limnological classes were also evaluated by paired t-tests at significance level 0.05. Finally, reference spectra were tested for Spectral Angle Mapper (SAM) classification of waters in Hyperion/Earth Observing-One (EO-1) and Medium Resolution Imaging Spectrometer (MERIS)/Environment Satellite (Envisat) images acquired simultaneously as the field campaigns. Results showed highly variable concentrations of OACs due to the complexity of the Amazonian aquatic environments. Ten classes were defined to represent this complexity, broadly grouped into four limnological characteristics: clear waters with low concentrations of OACs (class 1); black waters rich in dissolved organic carbon (DOC) (class 2); waters with large concentrations of inorganic suspended solids (ISSs) (classes 3–7); and waters dominated by chlorophyll-a (chl-a) (classes 8–10). Using the ten reference spectra, SAM classification of the field water curves produced an overall accuracy of 86% with the highest values observed for classes 3, 4, 6 and 7 and the lowest accuracy for classes 1 and 2. The results of paired t-tests confirmed the class differences based on the concentrations of OACs. SAM classification of the Hyperion and MERIS images using ground truth information resulted in overall classification accuracies of 48% and 67%, respectively, with the highest errors associated with specific portions of the scenes that were not adequately represented in the spectral library.

Water quality changes in floodplain lakes due to the Amazon River flood pulse: Lago Grande de Curuaí (Pará)

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.

Mapping inland water carbon content with Landsat 8 data

ABSTRACT Landsat 8 is the first Earth observation satellite with sufficient radiometric and spatial resolution to allow global mapping of lake CDOM and DOC (coloured dissolved organic matter and dissolved organic carbon, respectively) content. Landsat 8 is a multispectral sensor however, the number of potentially usable band ratios, or more sophisticated indices, is limited. In order to test the suitability of the ratio most commonly used in lake carbon content mapping, the green–red band ratio, we carried out fieldwork in Estonian and Brazilian lakes. Several atmospheric correction methods were also tested in order to use image data where the image-to-image variability due to illumination conditions would be minimal. None of the four atmospheric correction methods tested, produced reflectance spectra that matched well with in situ measured reflectance. Nevertheless, the green–red band ratio calculated from the reflectance data was in correlation with measured CDOM values. In situ data show that there is a strong correlation between CDOM and DOC concentrations in Estonian and Brazilian lakes. Thus, mapping the global CDOM and DOC content from Landsat 8 is plausible but more data from different parts of the world are needed before decisions can be made about the accuracy of such global estimation.