Monitoring Water Siltation Caused by Small-Scale Gold Mining in Amazonian Rivers Using Multi-Satellite Images

The small-scale mining techniques applied all over the Amazon river basin use water from streams, including digging and riverbed suctioning, rarely preventing environmental impacts or recovery of the impacted areas. As a consequence, thousands of tons of inorganic sediment (which can contain mercury) have been discharged directly into the rivers creating sediment plumes that travel hundreds of kilometers downstream with unknown consequences to the water quality and aquatic biota. We hypothesize that because of intensification of mining activities in the Brazilian Amazon, clear water rivers such as the Tapajós and Xingu rivers and its tributaries are becoming permanently turbid waters (so-called white waters in the Amazonian context). To investigate this hypothesis, satellite images have been used to monitor the sediment plume caused by gold mining in Amazonian rivers. Given the threat of intense water siltation of the Amazonian rivers combined with the technological capacity of detecting it from satellite images, the objective of this chapter is to inform the main activities carried out to develop a monitoring system for quantifying water siltation caused by small-scale gold mining (SSGM) in the Amazon rivers using multi-satellite data

Integração de informações de multi-sensores para estimar alterações da rugosidade hidráulica da várzea do Baixo Amazonas em função da remoção da floresta inundável

This paper describes the integration of floodplain cover types mapped from MSS and TM Landsat images and floodplain topographic data from SRTM to estimate changes in the hydraulic roughness caused by floodplain forest removal in the last forty years. Land cover maps representing each decade (1970 and 2000) were overlaid to a floodplain height map following the concept of high and low varzea proposed by Whittmann et al. (2002, 2004). A look up table of hydraulic roughness coefficient based on published data was then used to compute the hydraulic roughness in both decades and assess the percent change and the impact of deforestation on the hydraulic roughness coefficient. Results showed that the integration of theory on vegetation resistance to flow and different types of remote sensing derived data allowed to estimate the spatial distribution of hydraulic roughness in the lower Amazon floodplain and to compute the loss in hydraulic resistance in a reach stretching from Parintins (AM) to Almeirim (PA).Pages: 5193-520

Mapping potential cyanobacterial bloom using Hyperion/EO-1 data in the Patos Lagoon estuary

This paper proposes an approach for using remote sensing data for identification and mapping of cyanobacterial blooms; Methods: It uses two sets of spectra (empirical and theoretical) as reference to classify the areas of cyanobacteria blooms using a Hyperion image acquired over the Patos Lagoon, located in Rio Grande do Sul State, Brazil. To circumvent cyanobacteria misclassification due to suspended inorganic particle (SIP) scattering, pigment band ratios – phycocyanin (650/620 nm) and chlorophyll-a (700/680 nm) - were applied; Results: An area of 22.5 km2 prone to cyanobacterial blooms was mapped into 5 classes with chlorophyll-a concentration varying from 8 to 1,000 μg.L–1 using both, empirical and theoretical spectra; Conclusions: The results corroborate the general spectral features of cyanobacterial blooms and indicated that band ratios operation removed the areas affected by high concentrations of SIP.Este artigo apresenta um método de identificação e mapeamento de florações de cianobatérias utilizando imagem do sensor hiperespectral Hyperion/EO-1; Métodos: O método aplica dois conjuntos de curvas espectrais (uma empírica e outra teórica) como referência para classificar áreas potenciais de ocorrência de cianobactérias na Lagoa dos Patos localizada no Estado do Rio Grande do Sul Rio Grande, Brasil. De modo a evitar que partículas inorgânicas fossem indevidamente classificadas como cianobactérias devido ao espalhamento da radiação por partículas inorgânicas em suspensão (PIS) foi realizada a intersecção das áreas resultantes do cálculo da razão entre as bandas referentes às absorções pela ficocianina (650/620 nm) e clorofila-a (700/680 nm); Resultados: Uma área total de 22,5 km2 potencial na ocorrência de cianobactéria foi identificada e classificada em 5 classes de concentrações de clorofila-a variando entre 8 e 1000 μg.L–1 a partir dos conjuntos de espectros de referência – empírico e teórico – com 88% de similaridade entre eles; Conclusões: Os resultados corroboram as feições espectrais de florações de cianobactérias e indicam que as áreas afetadas pelo efeito de espalhamento pela presença de altas concentrações de PIS foram removidas pela metodologia aplicada

Assessment of Estimated Phycocyanin and Chlorophyll-a Concentration from PRISMA and OLCI in Brazilian Inland Waters: A Comparison between Semi-Analytical and Machine Learning Algorithms

Peer reviewed: TrueThe aim of this work is to test the state-of-the-art of water constituent retrieval algorithms for phycocyanin (PC) and chlorophyll-a (chl-a) concentrations in Brazilian reservoirs from hyperspectral PRISMA images and concurrent in situ data. One near-coincident Sentinel-3 OLCI dataset has also been considered for PC mapping as its high revisit time is a relevant element for mapping cyanobacterial blooms. The testing was first performed on remote sensing reflectance (Rrs), as derived by applying two atmospheric correction methods (6SV, ACOLITE) to Level 1 data and as provided in the corresponding Level 2 products (PRISMA L2C and OLCI L2-WFR). Since PRISMA images were affected by sun glint, the testing of three de-glint models was also performed. The applicability of Semi-Analytical (SA) and Mixture Density Network (MDN) algorithms in enabling PC and chl-a concentration retrieval was then tested over three PRISMA scenes; in the case of PC concentration estimation, a Random Forest (RF) algorithm was further applied. Regarding OLCI, the SA algorithm was tested for PC estimation; notably, only SA was calibrated with site-specific data from the reservoir. The algorithms were applied to the Rrs spectra provided by PRISMA L2C products—and those derived with ACOLITE, in the case of OLCI—as these data showed better agreement with in situ measurements. The SA model provided low median absolute error (MdAE) for PRISMA-derived (MdAE = 3.06 mg.m−3) and OLCI-derived (MdAE = 3.93 mg.m−3) PC concentrations, while it overestimated PRISMA-derived chl-a (MdAE = 42.11 mg.m−3). The RF model for PC applied to PRISMA performed slightly worse than SA (MdAE = 5.21 mg.m−3). The MDN showed a rather different performance, with higher errors for PC (MdAE = 40.94 mg.m−3) and lower error for chl-a (MdAE = 23.21 mg.m−3). The results overall suggest that the model calibrated with site-specific measurements performed better and indicates that SA could be applied to PRISMA and OLCI for remote sensing of PC in Brazilian reservoirs.</jats:p

Mapping of diffuse attenuation coefficient in optically complex waters of amazon floodplain lakes

International audienceThe modeling of underwater light field is essential for the understanding of biogeochemical processes, such as photosynthesis, carbon fluxes, and sediment transports in inland waters. Water-column light attenuation can be quantified by the diffuse attenuation coefficient of the downwelling irradiance (Kd) using semi-analytical algorithms (SAA). However, the accuracy of these algorithms is currently limited in highly turbid environments, such as Amazon Floodplains, due to the SAA parametrization steps. In this study, we assessed an SAA approach for Kd retrieval using a sizeable (n = 239) and diverse dataset (e.g., Kd (490) ranging from almost 0 to up to 30 m-1 with mean values of 5.75 ± 3.94 m-1) in Amazon freshwater ecosystem. The main framework of this study consists of i) re-parametrization of a quasi-analytical algorithm using regional in-situ inherent optical properties (IOPs) and ii) application and validation of SAA for Kd retrieval using in-situ and Sentinel-2/MSI (n = 49) derived from Remote Sensing Reflectance (Rrs). Overall, the performance of the calibrated SAA was satisfactory for both in-situ and satellite Rrs. The validation results with in-situ data achieved a Mean Absolute Percentage Error (MAPE) lower than 22%, Correlation Coefficient (R) > 0.80, Root Mean Square Error (RMSE) lower than 1.7 m-1, and bias between 0.73 and 1.34 for simulated visible bands of Sentinel-2/MSI (490, 560 and 660 nm) (VIS). The results using MSI imagery were similar to those of in-situ, with R > 0.9, MAPE -1, and bias between 0.98 and 1.10 for VIS bands, which illustrate the viability of this methodology for Kd mapping in Amazon Floodplain Lakes. Therefore, this study demonstrates a successful application of satellite remote sensing data for the spatialization of the Kd in the optically complex waters of Amazon Basin, which is essential for the ecological management of the Amazon Floodplain Lakes