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Water stock monitoring is a major issue for society on a local and global scale. Sentinel-1&2 satellites provide frequent acquisitions to track water surface dynamics, proxy variables to enable water surface volume monitoring. How do we combine such observations along time for each sensor? What advantages and disadvantages of single-date, monthly or time-windowed estimations? In this context, we analysed the impact of merging information through different types and lengths of time-windows. Satellite observations were processed separately on optical (Sentinel-2) and radar (Sentinel-1) water detectors at 10 m resolution. The analysis has been applied at two scales. First, validating with 26 large scenes (110 × 110 km) in different climatic zones in France, time-windows yielded an improvement on radar detection (F1-score improved from 0.72 to 0.8 for 30 days on average logic) while optical performances remained stable (F1-score 0.89). Second, validating reservoir area estimations with 29 instrumented reservoirs (20–1250 ha), time-windows presented in all cases an improvement on both optical and radar error for any window length (5–30 days). The mean relative absolute error in optical area detection improved from 16.9% on single measurements to 12.9% using 15 days time-windows, and from 22.15% to 15.1% in radar detection). Regarding reservoir filling rates, we identified an increased negative bias for both sensors when the reservoir is nearly full. This work helped to compare accuracies of separate optical and radar capabilities, where optical statistically outperforms radar at both local and large scale to the detriment of less frequent measurements. Furthermore, we propose a geomorphological indicator of reservoirs to predict the quality of radar area monitoring (R2 = 0.58). In conclusion, we suggest the use of time-windows on operational water mapping or reservoir monitoring systems, using 10–20 days time-windows with average logic, providing more frequent and faster information to water managers in periods of crisis (e.g., water shortage) compared to monthly estimations.
Santiago Peña-Luque; Sylvain Ferrant; Mauricio C. R. Cordeiro; Thomas Ledauphin; Jerome Maxant; Jean-Michel Martinez. Sentinel-1&2 Multitemporal Water Surface Detection Accuracies, Evaluated at Regional and Reservoirs Level. Remote Sensing 2021, 13, 3279 .
AMA StyleSantiago Peña-Luque, Sylvain Ferrant, Mauricio C. R. Cordeiro, Thomas Ledauphin, Jerome Maxant, Jean-Michel Martinez. Sentinel-1&2 Multitemporal Water Surface Detection Accuracies, Evaluated at Regional and Reservoirs Level. Remote Sensing. 2021; 13 (16):3279.
Chicago/Turabian StyleSantiago Peña-Luque; Sylvain Ferrant; Mauricio C. R. Cordeiro; Thomas Ledauphin; Jerome Maxant; Jean-Michel Martinez. 2021. "Sentinel-1&2 Multitemporal Water Surface Detection Accuracies, Evaluated at Regional and Reservoirs Level." Remote Sensing 13, no. 16: 3279.
Monitoring suspended sediments through remote sensing data in black-water rivers is a challenge. Herein, remote sensing reflectance (Rrs) from in situ measurements and Sentinel-2 Multi-Spectral Instrument (MSI) images were used to estimate the suspended sediment concentration (SSC) in the largest black-water river of the Amazon basin. The Negro River exhibits extremely low Rrs values (−1 at visible and near-infrared bands) due to the elevated absorption of coloured dissolved organic matter (aCDOM at 440 nm > 7 m−1) caused by the high amount of dissolved organic carbon (DOC > 7 mg L−1) and low SSC (−1). Interannual variability of Rrs is primarily controlled by the input of suspended sediments from the Branco River, which is a clear water river that governs the changes in the apparent optical properties of the Negro River, even at distances that are greater than 90 km from its mouth. Better results were obtained using the Sentinel-2 MSI Red band (Band 4 at 665 nm) in order to estimate the SSC, with an R2 value greater than 0.85 and an error less than 20% in the adjusted models. The magnitudes of water reflectance in the Sentinel-2 MSI Red band were consistent with in situ Rrs measurements, indicating the large spatial variability of the lower SSC values (0 to 15 mg L−1) in a complex anabranching reach of the Negro River. The in situ and satellite data analysed in this study indicates sedimentation processes in the lower Negro River near the Amazon River. The results suggest that the radiometric characteristics of sensors, like sentinel-2 MSI, are suitable for monitoring the suspended sediment concentration in large tropical black-water rivers.
Rogério Marinho; Tristan Harmel; Jean-Michel Martinez; Naziano Filizola Junior. Spatiotemporal Dynamics of Suspended Sediments in the Negro River, Amazon Basin, from In Situ and Sentinel-2 Remote Sensing Data. ISPRS International Journal of Geo-Information 2021, 10, 86 .
AMA StyleRogério Marinho, Tristan Harmel, Jean-Michel Martinez, Naziano Filizola Junior. Spatiotemporal Dynamics of Suspended Sediments in the Negro River, Amazon Basin, from In Situ and Sentinel-2 Remote Sensing Data. ISPRS International Journal of Geo-Information. 2021; 10 (2):86.
Chicago/Turabian StyleRogério Marinho; Tristan Harmel; Jean-Michel Martinez; Naziano Filizola Junior. 2021. "Spatiotemporal Dynamics of Suspended Sediments in the Negro River, Amazon Basin, from In Situ and Sentinel-2 Remote Sensing Data." ISPRS International Journal of Geo-Information 10, no. 2: 86.
The Tropical Atlantic is facing a massive proliferation of Sargassum since 2011, with severe environmental and socioeconomic impacts. As a contribution to this proliferation, an increase in nutrient inputs from the tropical rivers, in response to climate and land use changes or increasing urbanization, has been often suggested and widely reported in the scientific and public literature. Here we discuss whether changes in river nutrient inputs could contribute to Sargassum proliferation in the recent years or drive its seasonal cycle. Using long-term in situ and satellite measurements of discharge, dissolved and particulate nutrients of the three world largest rivers (Amazon, Orinoco, Congo), we do not find clear evidences that nutrient fluxes may have massively increased over the last 15 years. Moreover, focusing on the "Sargassum year 2017", we estimate that along the year only 10% of the Sargassum biomass occurred in regions under river plume influence. While deforestation and pollution are a reality of great concern, our results corroborate recent findings that hydrological changes are not the first order drivers of Sargassum proliferation. Besides, satellite observations suggest that the major Atlantic river plumes suffered a decrease of phytoplankton biomass in the last two decades. Reconciling these observations requires a better understanding of the nutrient sources that sustain Sargassum and phytoplankton growth in the region.
Julien Jouanno; Jean-Sébastien Moquet; Léo Berline; Marie-Hélène Radenac; William Santini; Thomas Changeux; Thierry Thibaut; Witold Podlejski; Frédéric Ménard; Jean-Michel Martinez; Olivier Aumont; Julio Sheinbaum; Naziano Filizola; Guy Dieudonne Mounkandi N'Kaya. Evolution of the riverine nutrient export to the Tropical Atlantic over the last 15 years: is there a link with Sargassum proliferation? Environmental Research Letters 2021, 16, 034042 .
AMA StyleJulien Jouanno, Jean-Sébastien Moquet, Léo Berline, Marie-Hélène Radenac, William Santini, Thomas Changeux, Thierry Thibaut, Witold Podlejski, Frédéric Ménard, Jean-Michel Martinez, Olivier Aumont, Julio Sheinbaum, Naziano Filizola, Guy Dieudonne Mounkandi N'Kaya. Evolution of the riverine nutrient export to the Tropical Atlantic over the last 15 years: is there a link with Sargassum proliferation? Environmental Research Letters. 2021; 16 (3):034042.
Chicago/Turabian StyleJulien Jouanno; Jean-Sébastien Moquet; Léo Berline; Marie-Hélène Radenac; William Santini; Thomas Changeux; Thierry Thibaut; Witold Podlejski; Frédéric Ménard; Jean-Michel Martinez; Olivier Aumont; Julio Sheinbaum; Naziano Filizola; Guy Dieudonne Mounkandi N'Kaya. 2021. "Evolution of the riverine nutrient export to the Tropical Atlantic over the last 15 years: is there a link with Sargassum proliferation?" Environmental Research Letters 16, no. 3: 034042.
Continuous monitoring of water surfaces is essential for water resource management. This study presents a nonparametric unsupervised automatic algorithm for the identification of inland water pixels from multispectral satellite data using multidimensional clustering and a high-performance subsampling approach for large scenes. Clustering analysis is a technique that is used to identify similar samples in a multidimensional data space. The spectral information and derived indices were used to characterize each scene pixel individually. A machine learning approach with random subsampling and generalization through a Naïve Bayes classifier was also proposed to make the application of complex algorithms to large scenes feasible. Accuracy was evaluated using an independent dataset that provides water bodies in 15 Sentinel-2 images over France acquired in different seasons and that covers a large range of water bodies and water colour types. The validation dataset covers a water surface of more than 1200 km2 (approximately 12 million pixels) including over 80,000 water bodies outlined using a semiautomatic active learning method, which were manually revised. The classification results were compared to the water pixel classification using three of the major Level 2A processors (MAJA, Sen2Cor and FMask) and two of the most common thresholding techniques: Otsu and Canny-edge. An input mask was used to remove coastal waters, clouds, shadows and snow pixels. Water pixels were identified automatically from the clustering process without the need for ancillary or pretrained data. Combinations using up to three water indices (Modified Normalized Difference Water Index-MNDWI, Normalized Difference Water Index-NDWI and Multiband Water Index-MBWI) and two reflectance bands (B8 and B12) were tested in the algorithm, and the best combination was NDWI-B12. Of all the methods, our method achieved the highest mean kappa score, 0.874, across all tested scenes, with a per-scene kappa ranging from 0.608 to 0.980, and the lowest mean standard deviation of 0.091. Standard Otsu's thresholding had the worst performance due to the lack of a bimodal histogram, and the Canny-edge variation achieved an overall kappa of 0.718 when used with the MNDWI. For water masks provided by generic processors, FMask outperformed MAJA and Sen2Cor and obtained an overall kappa of 0.764. In-depth analysis shows a quick drop in performance for all of the methods in identifying water bodies with a surface area below 0.5 ha, but the proposed approach outperformed the second best method by 34% in this size class.
Maurício C.R. Cordeiro; Jean-Michel Martinez; Santiago Peña-Luque. Automatic water detection from multidimensional hierarchical clustering for Sentinel-2 images and a comparison with Level 2A processors. Remote Sensing of Environment 2020, 253, 112209 .
AMA StyleMaurício C.R. Cordeiro, Jean-Michel Martinez, Santiago Peña-Luque. Automatic water detection from multidimensional hierarchical clustering for Sentinel-2 images and a comparison with Level 2A processors. Remote Sensing of Environment. 2020; 253 ():112209.
Chicago/Turabian StyleMaurício C.R. Cordeiro; Jean-Michel Martinez; Santiago Peña-Luque. 2020. "Automatic water detection from multidimensional hierarchical clustering for Sentinel-2 images and a comparison with Level 2A processors." Remote Sensing of Environment 253, no. : 112209.
Data provided by spatial sensors combined with remote sensing techniques and analysis of the optical properties of waters allow the mapping of the suspended sediment concentration (SSC) in aquatic bodies. For this, estimation models require data with the lowest possible amount of atmospheric artifacts. In this study we compared the water remote sensing reflectance (Rrs) of the Santo Antônio Hydroelectric Power Plant reservoir in Porto Velho-RO, Brazil, after applying three different atmospheric corrections algorithms in Sentinel-2/MSI imagery products. The atmospheric corrected reflectances of the MODIS sensor were also used for reference. SSC was calculated with models based on the red and near-infrared (NIR) bands over three distinct regions of the reservoir. Reflectance data showed significant variations for Sentinel-2, bands 4 and 8a, and MODIS, bands RED and IR, when different atmospheric correction algorithms were used. SSC maps and estimates were produced to show sediment load variation as a function of hydrological regime. The analyzes showed that the SSC estimates done with Sentinel-2 / MSI satellite images using GRS (Glint Remove Sentinel) atmospheric correction presented an average difference of 27.3% and were the closest to the in situ measurements. SSC estimates from MODIS products were around 34.6% different from estimates made using the GRS atmospheric correction applied to Sentinel-2 / MSI products.
D. R. A. e Santos; J. M. Martinez; T. Harmel; H. D. Borges; H. Roig. EVALUATION OF SENTINEL-2/MSI IMAGERY PRODUCTS LEVEL-2A OBTAINED BY THREE DIFFERENT ATMOSPHERIC CORRECTIONS FOR MONITORING SUSPENDED SEDIMENTS CONCENTRATION IN MADEIRA RIVER, BRAZIL. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2020, XLII-3/W12, 243 -248.
AMA StyleD. R. A. e Santos, J. M. Martinez, T. Harmel, H. D. Borges, H. Roig. EVALUATION OF SENTINEL-2/MSI IMAGERY PRODUCTS LEVEL-2A OBTAINED BY THREE DIFFERENT ATMOSPHERIC CORRECTIONS FOR MONITORING SUSPENDED SEDIMENTS CONCENTRATION IN MADEIRA RIVER, BRAZIL. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2020; XLII-3/W12 ():243-248.
Chicago/Turabian StyleD. R. A. e Santos; J. M. Martinez; T. Harmel; H. D. Borges; H. Roig. 2020. "EVALUATION OF SENTINEL-2/MSI IMAGERY PRODUCTS LEVEL-2A OBTAINED BY THREE DIFFERENT ATMOSPHERIC CORRECTIONS FOR MONITORING SUSPENDED SEDIMENTS CONCENTRATION IN MADEIRA RIVER, BRAZIL." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12, no. : 243-248.
In this paper, we quantify the CO2 and N2O emissions from denitrification over the Amazonian wetlands. The study concerns the entire Amazonian wetland ecosystem with a specific focus on three floodplain (FP) locations: the Branco FP, the Madeira FP and the FP alongside the Amazon River. We adapted a simple denitrification model to the case of tropical wetlands and forced it by open water surface extent products from the Soil Moisture and Ocean Salinity (SMOS) satellite. A priori model parameters were provided by in situ observations and gauging stations from the HYBAM Observatory. Our results show that the denitrification and the trace gas emissions present a strong cyclic pattern linked to the inundation processes that can be divided into three distinct phases: activation, stabilization and deactivation. We quantify the average yearly denitrification and associated emissions of CO2 and N2O over the entire watershed at 17.8 kgN ha−1 yr−1, 0.37 gC-CO2 m−2 yr−1 and 0.18 gN-N2O m−2 yr−1 respectively for the period 2011–2015. When compared to local observations, it was found that the CO2 emissions accounted for 0.01 % of the integrated ecosystem, which emphasizes the fact that minor changes to the land cover may induce strong impacts on the Amazonian carbon budget. Our results are consistent with the state of the art of global nitrogen models with a positive bias of 28 %. When compared to other wetlands in different pedoclimatic environments we found that the Amazonian wetlands have similar emissions of N2O with the Congo tropical wetlands and lower emissions than the temperate and tropical anthropogenic wetlands of the Garonne (France), the Rhine (Europe) and south-eastern Asia rice paddies. In summary our paper shows that a data-model-based approach can be successfully applied to quantify N2O and CO2 fluxes associated with denitrification over the Amazon basin. In the future, the use of higher-resolution remote sensing products from sensor fusion or new sensors like the Surface Water and Ocean Topography (SWOT) mission will permit the transposition of the approach to other large-scale watersheds in tropical environments.
Jérémy Guilhen; Ahmad Al Bitar; Sabine Sauvage; Marie Parrens; Jean-Michel Martinez; Gwenael Abril; Patricia Moreira-Turcq; José-Miguel Sánchez-Pérez. Denitrification and associated nitrous oxide and carbon dioxide emissions from the Amazonian wetlands. Biogeosciences 2020, 17, 4297 -4311.
AMA StyleJérémy Guilhen, Ahmad Al Bitar, Sabine Sauvage, Marie Parrens, Jean-Michel Martinez, Gwenael Abril, Patricia Moreira-Turcq, José-Miguel Sánchez-Pérez. Denitrification and associated nitrous oxide and carbon dioxide emissions from the Amazonian wetlands. Biogeosciences. 2020; 17 (16):4297-4311.
Chicago/Turabian StyleJérémy Guilhen; Ahmad Al Bitar; Sabine Sauvage; Marie Parrens; Jean-Michel Martinez; Gwenael Abril; Patricia Moreira-Turcq; José-Miguel Sánchez-Pérez. 2020. "Denitrification and associated nitrous oxide and carbon dioxide emissions from the Amazonian wetlands." Biogeosciences 17, no. 16: 4297-4311.
The recent and continuous development of unmanned aerial vehicles (UAV) and small cameras with different spectral resolutions and imaging systems promotes new remote sensing platforms that can supply ultra-high spatial and temporal resolution, filling the gap between ground-based surveys and orbital sensors. This work aimed to monitor siltation in two large rural and urban reservoirs by recording water color variations within a savanna biome in the central region of Brazil using a low cost and very light unmanned platform. Airborne surveys were conducted using a Parrot Sequoia camera (~0.15 kg) onboard a DJI Phantom 4 UAV (~1.4 kg) during dry and rainy seasons over inlet areas of both reservoirs. Field measurements of total suspended solids (TSS) and water clarity were made jointly with the airborne survey campaigns. Field hyperspectral radiometry data were also collected during two field surveys. Bio-optical models for TSS were tested for all spectral bands of the Sequoia camera. The near-infrared single band was found to perform the best (R2: 0.94; RMSE: 7.8 mg L−1) for a 0–180 mg L−1 TSS range and was used to produce time series of TSS concentration maps of the study areas. This flexible platform enabled monitoring of the increase of TSS concentration at a ~13 cm spatial resolution in urban and rural drainages in the rainy season. Aerial surveys allowed us to map TSS load fluctuations in a 1 week period during which no satellite images were available due to continuous cloud coverage in the rainy season. This work demonstrates that a low-cost configuration allows dense TSS monitoring at the inlet areas of reservoirs and thus enables mapping of the sources of sediment inputs, supporting the definition of mitigation plans to limit the siltation process.
Diogo Olivetti; Henrique Roig; Jean-Michel Martinez; Henrique Borges; Alexandre Ferreira; Raphael Casari; Leandro Salles; Edio Malta. Low-Cost Unmanned Aerial Multispectral Imagery for Siltation Monitoring in Reservoirs. Remote Sensing 2020, 12, 1855 .
AMA StyleDiogo Olivetti, Henrique Roig, Jean-Michel Martinez, Henrique Borges, Alexandre Ferreira, Raphael Casari, Leandro Salles, Edio Malta. Low-Cost Unmanned Aerial Multispectral Imagery for Siltation Monitoring in Reservoirs. Remote Sensing. 2020; 12 (11):1855.
Chicago/Turabian StyleDiogo Olivetti; Henrique Roig; Jean-Michel Martinez; Henrique Borges; Alexandre Ferreira; Raphael Casari; Leandro Salles; Edio Malta. 2020. "Low-Cost Unmanned Aerial Multispectral Imagery for Siltation Monitoring in Reservoirs." Remote Sensing 12, no. 11: 1855.
In this paper, we quantify CO2 and N2O emissions from denitrification over the Amazonian wetlands. The study concerns the entire Amazonian wetland ecosystem with a specific focus on three focal locations: the Branco Floodplain, the Madeira Floodplain and the floodplains alongside the Amazon River. We adapted a simple denitrification model to the case of tropical wetlands and forced it by open water surface extent products from the Soil Moisture and Ocean Salinity (SMOS) satellite. A priori model parameters were provided by in situ observations and gauging stations from the HyBAm observatory. Our results show that the denitrification and emissions present a strong cyclic pattern linked to the inundation processes that can be divided into three distinct phases: activation – stabilization – deactivation. We quantify the average yearly denitrification and associated emissions of CO2 and N2O over the entire watershed at 17.8 kgN/ha/yr, 0.37 gC/m2/yr and 0.18 gN/m2/yr respectively. When compared to local observations, it was found that the CO2 emissions accounted for 0.01 % of the integrated ecosystem, which emphasis the fact that minor changes to the land cover may induce strong impacts to the Amazonian carbon budget. Our results are quite consistent with the state of the art global nitrogen models with a positive bias of 28 %. When compared to other wetlands in different pedo-climatic environments we found that the Amazonian wetlands have close emissions of N2O to the tropical Congo wetlands and lower emissions than the tropical and temperate anthropogenic wetlands of the Garonne river, the Rhine river, and south-eastern Asia rice paddies. In summary our paper shows that a data driven approach can be successfully applied to quantify N2O and CO2 fluxes associated with denitrification over the Amazon basin. In the future, the use of higher resolution remote sensing product from sensor fusion or new sensors like the SWOT mission will permit the transposition to other large scale watersheds in tropical environment.
Jérémy Guilhen; Ahmad Al Bitar; Sabine Sauvage; Marie Parrens; Jean-Michel Martinez; Gwenael Abril; Patricia Moreira-Turcq; José-Miguel Sanchez-Pérez. Denitrification, carbon and nitrogen emissions over the Amazonian wetlands. 2020, 2020, 1 -22.
AMA StyleJérémy Guilhen, Ahmad Al Bitar, Sabine Sauvage, Marie Parrens, Jean-Michel Martinez, Gwenael Abril, Patricia Moreira-Turcq, José-Miguel Sanchez-Pérez. Denitrification, carbon and nitrogen emissions over the Amazonian wetlands. . 2020; 2020 ():1-22.
Chicago/Turabian StyleJérémy Guilhen; Ahmad Al Bitar; Sabine Sauvage; Marie Parrens; Jean-Michel Martinez; Gwenael Abril; Patricia Moreira-Turcq; José-Miguel Sanchez-Pérez. 2020. "Denitrification, carbon and nitrogen emissions over the Amazonian wetlands." 2020, no. : 1-22.
Because increasing climatic variability and anthropic pressures have affected the sediment dynamics of large tropical rivers, long-term sediment concentration series have become crucial for understanding the related socioeconomic and environmental impacts. For operational and cost rationalization purposes, index concentrations are often sampled in the flow and used as a surrogate of the cross-sectional average concentration. However, in large rivers where suspended sands are responsible for vertical concentration gradients, this index method can induce large uncertainties in the matter fluxes. Assuming that physical laws describing the suspension of grains in turbulent flow are valid for large rivers, a simple formulation is derived to model the ratio (α) between the depth-averaged and index concentrations. The model is validated using an exceptional dataset (1330 water samples, 249 concentration profiles, 88 particle size distributions and 494 discharge measurements) that was collected between 2010 and 2017 in the Amazonian foreland. The α prediction requires the estimation of the Rouse number (P), which summarizes the balance between the suspended particle settling and the turbulent lift, weighted by the ratio of sediment to eddy diffusivity (β). Two particle size groups, fine sediments and sand, were considered to evaluate P. Discrepancies were observed between the evaluated and measured P, which were attributed to biases related to the settling and shear velocities estimations, but also to diffusivity ratios β≠1. An empirical expression taking these biases into account was then formulated to predict accurate estimates of β, then P (ΔP=±0.03) and finally α. The proposed model is a powerful tool for optimizing the concentration sampling. It allows for detailed uncertainty analysis on the average concentration derived from an index method. Finally, this model could likely be coupled with remote sensing and hydrological modeling to serve as a step toward the development of an integrated approach for assessing sediment fluxes in poorly monitored basins.
William Santini; Benoît Camenen; Jérôme Le Coz; Philippe Vauchel; Jean-Loup Guyot; Waldo Lavado; Jorge Carranza; Marco A. Paredes; Jhonatan J. Pérez Arévalo; Nore Arévalo; Raul Espinoza Villar; Frédéric Julien; Jean-Michel Martinez. An index concentration method for suspended load monitoring in large rivers of the Amazonian foreland. Earth Surface Dynamics 2019, 7, 515 -536.
AMA StyleWilliam Santini, Benoît Camenen, Jérôme Le Coz, Philippe Vauchel, Jean-Loup Guyot, Waldo Lavado, Jorge Carranza, Marco A. Paredes, Jhonatan J. Pérez Arévalo, Nore Arévalo, Raul Espinoza Villar, Frédéric Julien, Jean-Michel Martinez. An index concentration method for suspended load monitoring in large rivers of the Amazonian foreland. Earth Surface Dynamics. 2019; 7 (2):515-536.
Chicago/Turabian StyleWilliam Santini; Benoît Camenen; Jérôme Le Coz; Philippe Vauchel; Jean-Loup Guyot; Waldo Lavado; Jorge Carranza; Marco A. Paredes; Jhonatan J. Pérez Arévalo; Nore Arévalo; Raul Espinoza Villar; Frédéric Julien; Jean-Michel Martinez. 2019. "An index concentration method for suspended load monitoring in large rivers of the Amazonian foreland." Earth Surface Dynamics 7, no. 2: 515-536.
The Madeira River is the second largest Amazon tributary, contributing up to 50% of the Amazon River’s sediment load. The Madeira has significant hydropower potential, which has started to be used by the Madeira Hydroelectric Complex (MHC), with two large dams along the middle stretch of the river. In this study, fine suspended sediment concentration (FSC) data were assessed downstream of the MHC at the Porto Velho gauging station and at the outlet of each tributary (Beni and Mamoré Rivers, upstream from the MHC), from 2003 to 2017. When comparing the pre-MHC (2003–2008) and post-MHC (2015–2017) periods, a 36% decrease in FSC was observed in the Beni River during the peak months of sediment load (December–March). At Porto Velho, a reduction of 30% was found, which responds to the Upper Madeira Basin and hydroelectric regulation. Concerning water discharge, no significant change occurred, indicating that a lower peak FSC cannot be explained by changes in the peak discharge months. However, lower FSCs are associated with a downward break in the overall time series registered at the outlet of the major sediment supplier—the Beni River—during 2010.
Irma Ayes Rivera; Elisa Armijos Cardenas; Raúl Espinoza-Villar; Jhan Carlo Espinoza; Jorge Molina-Carpio; José Max Ayala; Omar Gutierrez-Cori; Jean-Michel Martinez; Naziano Filizola. Decline of Fine Suspended Sediments in the Madeira River Basin (2003–2017). Water 2019, 11, 514 .
AMA StyleIrma Ayes Rivera, Elisa Armijos Cardenas, Raúl Espinoza-Villar, Jhan Carlo Espinoza, Jorge Molina-Carpio, José Max Ayala, Omar Gutierrez-Cori, Jean-Michel Martinez, Naziano Filizola. Decline of Fine Suspended Sediments in the Madeira River Basin (2003–2017). Water. 2019; 11 (3):514.
Chicago/Turabian StyleIrma Ayes Rivera; Elisa Armijos Cardenas; Raúl Espinoza-Villar; Jhan Carlo Espinoza; Jorge Molina-Carpio; José Max Ayala; Omar Gutierrez-Cori; Jean-Michel Martinez; Naziano Filizola. 2019. "Decline of Fine Suspended Sediments in the Madeira River Basin (2003–2017)." Water 11, no. 3: 514.
In this study, we used moderate resolution imaging spectroradiometer (MODIS) satellite images to quantify the sedimentation processes in a cascade of six hydropower dams along a 700-km transect in the Paranapanema River in Brazil. Turbidity field measurement acquired over 10 years were used to calibrate a turbidity retrieval algorithm based on MODIS surface reflectance products. An independent field dataset was used to validate the remote sensing estimates showing fine accuracy (RMSE of 9.5 NTU, r = 0.75, N = 138). By processing 13 years of MODIS images since 2000, we showed that satellite data can provide robust turbidity monitoring over the entire transect and can identify extreme sediment discharge events occurring on daily to annual scales. We retrieved the decrease in the water turbidity as a function of distance within each reservoir that is related to sedimentation processes. The remote sensing-retrieved turbidity decrease within the reservoirs ranged from 2 to 62% making possible to infer the reservoir type and operation (storage versus run-of-river reservoirs). The reduction in turbidity assessed from space presented a good relationship with conventional sediment trapping efficiency calculations, demonstrating the potential use of this technology for monitoring the intensity of sedimentation processes within reservoirs and at large scale.
Rita De Cássia Condé; Jean-Michel Martinez; Marco Aurélio Pessotto; Raúl Villar; Gérard Cochonneau; Raoul Henry; Walszon Lopes; Marcos Nogueira. Indirect Assessment of Sedimentation in Hydropower Dams Using MODIS Remote Sensing Images. Remote Sensing 2019, 11, 314 .
AMA StyleRita De Cássia Condé, Jean-Michel Martinez, Marco Aurélio Pessotto, Raúl Villar, Gérard Cochonneau, Raoul Henry, Walszon Lopes, Marcos Nogueira. Indirect Assessment of Sedimentation in Hydropower Dams Using MODIS Remote Sensing Images. Remote Sensing. 2019; 11 (3):314.
Chicago/Turabian StyleRita De Cássia Condé; Jean-Michel Martinez; Marco Aurélio Pessotto; Raúl Villar; Gérard Cochonneau; Raoul Henry; Walszon Lopes; Marcos Nogueira. 2019. "Indirect Assessment of Sedimentation in Hydropower Dams Using MODIS Remote Sensing Images." Remote Sensing 11, no. 3: 314.
The aim of this study was to derive the Surface Suspended Sediment Concentration (SSSC) and river sediment discharge (for the period 2000-2016) along the Orinoco River reaches using MODIS remote sensing data. A single SSC retrieval algorithm was calibrated using HYBAM long term monitoring program data (n = 183). The comparison of satellite and field measurements showed a RMSE of 32%, corresponding to a mean absolute error of 21 mg l−1. These results supported the use of MODIS sensors to monitor the SSSC at six virtual stations in the middle and lower reaches of the Orinoco River. The highest mean SSSC were found downstream of the Meta River (101 mg l−1) and of the Apure River (99 mg l−1), which provide the major sediment input to the Orinoco main stream. The lowest mean SSSC was found upstream of the Meta River (42 mg l−1). In the lower reach, at Ciudad Bolivar station we assessed a mean SSSC of 88 mg l−1. MODIS-derived and ground-derived assessments of the annual sediment discharge show very fine agreement, within −2%. Coupling satellite-derived SSSC estimates and river discharge monitoring data, we calculated the Orinoco River sediment discharge at Ciudad Bolivar station between 2001 and 2016. For a mean Orinoco River discharge of 33,320 m3.s−1, the sediment loads varied from 75 to 103 Mt.yr−1, with a mean value of 89 Mt.yr−1 (RSD of 9%) corresponding to a sediment yield of 91–125 t.km−2yr−1 with no positive or negative trends over the observed period.
Marjorie Gallay; Jean-Michel Martinez; Abrahan Mora; Bartolo Castellano; Santiago Yépez; Gérard Cochonneau; Juan A. Alfonso; Juan Manuel Carrera; José Luis López; Alain Laraque. Assessing Orinoco river sediment discharge trend using MODIS satellite images. Journal of South American Earth Sciences 2019, 91, 320 -331.
AMA StyleMarjorie Gallay, Jean-Michel Martinez, Abrahan Mora, Bartolo Castellano, Santiago Yépez, Gérard Cochonneau, Juan A. Alfonso, Juan Manuel Carrera, José Luis López, Alain Laraque. Assessing Orinoco river sediment discharge trend using MODIS satellite images. Journal of South American Earth Sciences. 2019; 91 ():320-331.
Chicago/Turabian StyleMarjorie Gallay; Jean-Michel Martinez; Abrahan Mora; Bartolo Castellano; Santiago Yépez; Gérard Cochonneau; Juan A. Alfonso; Juan Manuel Carrera; José Luis López; Alain Laraque. 2019. "Assessing Orinoco river sediment discharge trend using MODIS satellite images." Journal of South American Earth Sciences 91, no. : 320-331.
Because increasing climatic variability and anthropic pressures have affected the sediment dynamics of large tropical rivers, long-term sediment concentration series have become crucial for understanding the related socio-economic and environmental impacts. For operational and cost rationalization purposes, index concentrations are often sampled in the flow and used as a surrogate of the cross-sectional average concentration. However, in large rivers where suspended sands are responsible for vertical concentration gradients, this index method can induce large uncertainties in the matter fluxes. Assuming that physical laws describing the suspension of grains in turbulent flow are valid for large rivers, a simple formulation is derived to model the ratio (α) between index and average concentrations. The model is validated using an exceptional dataset (1330 water samples, 249 concentration profiles, 88 particle size distributions (PSDs) and 494 discharge measurements) that was collected between 2010 and 2017 in the Amazonian foreland. The α prediction requires the estimation of the Rouse number (P), which summarizes the balance between the suspended particle settling and the turbulent lift, weighted by the ratio of sediment to eddy diffusivity (β). Two particle size groups, washload and sand, were considered to evaluate P. Discrepancies were observed between the evaluated and measured P, that were attributed to biases related to the settling and shear velocities estimations, but also to diffusivity ratios β ≠ 1. An empirical expression taking into account these biases was then formulated to predict accurate estimates of β, then P (∆P = ±0.03) and finally α. The proposed model is a powerful tool for optimizing the concentration sampling. It allows for detailed uncertainty analysis on the average concentration derived from an index method. Finally, this model can be coupled with remote sensing and hydrological modeling to serve as a step toward the development of an integrated approach for assessing sediment fluxes in poorly monitored basins.
William Santini; Benoît Camenen; Jérôme Le Coz; Philippe Vauchel; Jean-Loup Guyot; Waldo Lavado; Jorge Carranza; Marco A. Paredes; Jhonatan J. Pérez Arévalo; Nore Arévalo; Raul Espinoza Villar; Frédéric Julien; Jean-Michel Martinez. An index concentration method for suspended load monitoring in large rivers of the Amazonian foreland. 2019, 2019, 1 -36.
AMA StyleWilliam Santini, Benoît Camenen, Jérôme Le Coz, Philippe Vauchel, Jean-Loup Guyot, Waldo Lavado, Jorge Carranza, Marco A. Paredes, Jhonatan J. Pérez Arévalo, Nore Arévalo, Raul Espinoza Villar, Frédéric Julien, Jean-Michel Martinez. An index concentration method for suspended load monitoring in large rivers of the Amazonian foreland. . 2019; 2019 ():1-36.
Chicago/Turabian StyleWilliam Santini; Benoît Camenen; Jérôme Le Coz; Philippe Vauchel; Jean-Loup Guyot; Waldo Lavado; Jorge Carranza; Marco A. Paredes; Jhonatan J. Pérez Arévalo; Nore Arévalo; Raul Espinoza Villar; Frédéric Julien; Jean-Michel Martinez. 2019. "An index concentration method for suspended load monitoring in large rivers of the Amazonian foreland." 2019, no. : 1-36.
The study of small reservoirs with low suspended sediment concentration (CSS) is still a challenge for remote sensing. In this work we estimate CSS from the optical properties of water and orbital imagery. Campaigns were carried out at selected dates according to the calendar of sensor passages, rainfall seasonality and hydrograph of the reservoir for the collection of surface water samples and field spectroradiometry. The calibration between CSS and spectral behavior generated CSS estimation models from MODIS and Landsat 8 data, allowing investigation of their temporal and spatial behavior. The MODIS model generated a time series of CSS from 2000 to 2017, presenting R2 = 0.8105 and RMSE% = 39.91%. The Landsat 8 model allowed the spatial analysis of CSS, with R2 = 0.8352 and RMSE% = 15.12%. The combination of the proposed models allowed the temporal and spatial analysis of the CSS and its relationships with the rainfall regime and the quota variation of the Descoberto reservoir (DF). The results showed that the use of orbital data complements the CSS information obtained by the traditional methods of collecting and analyzing water quality in low CSS reservoirs.
Giancarlo Brugnara Chelotti; Jean Michel Martinez; Henrique Llacer Roig; Diogo Olivietti. Space-Temporal analysis of suspended sediment in low concentration reservoir by remote sensing. RBRH 2019, 24, 1 .
AMA StyleGiancarlo Brugnara Chelotti, Jean Michel Martinez, Henrique Llacer Roig, Diogo Olivietti. Space-Temporal analysis of suspended sediment in low concentration reservoir by remote sensing. RBRH. 2019; 24 ():1.
Chicago/Turabian StyleGiancarlo Brugnara Chelotti; Jean Michel Martinez; Henrique Llacer Roig; Diogo Olivietti. 2019. "Space-Temporal analysis of suspended sediment in low concentration reservoir by remote sensing." RBRH 24, no. : 1.
The French critical zone initiative, called OZCAR (Observatoires de la Zone Critique–Application et Recherche or Critical Zone Observatories–Application and Research) is a National Research Infrastructure (RI). OZCAR-RI is a network of instrumented sites, bringing together 21 pre-existing research observatories monitoring different compartments of the zone situated between “the rock and the sky,” the Earth’s skin or critical zone (CZ), over the long term. These observatories are regionally based and have specific initial scientific questions, monitoring strategies, databases, and modeling activities. The diversity of OZCAR-RI observatories and sites is well representative of the heterogeneity of the CZ and of the scientific communities studying it. Despite this diversity, all OZCAR-RI sites share a main overarching mandate, which is to monitor, understand, and predict (“earthcast”) the fluxes of water and matter of the Earth’s near surface and how they will change in response to the “new climatic regime.” The vision for OZCAR strategic development aims at designing an open infrastructure, building a national CZ community able to share a systemic representation of the CZ , and educating a new generation of scientists more apt to tackle the wicked problem of the Anthropocene. OZCAR articulates around: (i) a set of common scientific questions and cross-cutting scientific activities using the wealth of OZCAR-RI observatories, (ii) an ambitious instrumental development program, and (iii) a better interaction between data and models to integrate the different time and spatial scales. Internationally, OZCAR-RI aims at strengthening the CZ community by providing a model of organization for pre-existing observatories and by offering CZ instrumented sites. OZCAR is one of two French mirrors of the European Strategy Forum on Research Infrastructure (eLTER-ESFRI) project. Copyright © 2018. . Copyright © by the Soil Science Society of America, Inc.
J. Gaillardet; I. Braud; F. Hankard; Sandrine Anquetin; O. Bour; N. Dorfliger; J.R. De Dreuzy; Sylvie Galle; C. Galy; Sébastien Gogo; Laurence Gourcy; F. Habets; F. Laggoun; L. Longuevergne; T. Le Borgne; F. Naaim-Bouvet; Guillaume NORD; Vincent Simonneaux; D. Six; T. Tallec; C. Valentin; G. Abril; P. Allemand; A. Arènes; Bruno Arfib; Luc Arnaud; N. Arnaud; P. Arnaud; S. Audry; V. Bailly Comte; C. Batiot; A. Battais; H. Bellot; Eric Bernard; C. Bertrand; Hélène Bessiere; S. Binet; J. Bodin; X. Bodin; Laurie Boithias; J. Bouchez; B. Boudevillain; I. Bouzou Moussa; F. Branger; J. J. Braun; P. Brunet; B. Caceres; D. Calmels; B. Cappelaere; H. Celle-Jeanton; F. Chabaux; Konstantinos Chalikakis; Cédric Champollion; Y. Copard; C. Cotel; P. Davy; P. Deline; G. Delrieu; Jerome Demarty; C. Dessert; M. Dumont; C. Emblanch; J. Ezzahar; M. Estèves; Vincent Favier; M. Faucheux; N. Filizola; P. Flammarion; P. Floury; O. Fovet; Matthieu Fournier; A. J. Francez; L. Gandois; C. Gascuel; E. Gayer; C. Genthon; M. F. Gérard; D. Gilbert; I. Gouttevin; M. Grippa; G. Gruau; A. Jardani; L. Jeanneau; J. L. Join; Hervé Jourde; Fatima Karbou; D. Labat; Y. Lagadeuc; E. Lajeunesse; R. Lastennet; W. Lavado; E. Lawin; T. Lebel; C. Le Bouteiller; C. Legout; Y. Lejeune; E. Le Meur; Nicolas Le Moigne; J. Lions; Antoine Lucas; J. P. Malet; C. Marais-Sicre; J. C. Maréchal; C. Marlin; P. Martin; Jean Martins; Jean-Michel Martinez; N. Massei; A. Mauclerc; Naomi Mazzilli; J. Molénat; P. Moreira-Turcq; E. Mougin; S. Morin; J. Ndam Ngoupayou; G. Panthou; Christophe Peugeot; G. Picard; M. C. Pierret; G. Porel; A. Probst; J. L. Probst; Antoine Rabatel; D. Raclot; L. Ravanel; Fayçal Rejiba; P. René; O. Ribolzi; J. Riotte; Agnès Rivière; Henri Robain; Laurent Ruiz; J. M. Sanchez-Perez; W. Santini; S. Sauvage; P. Schoeneich; J. L. Seidel; M. Sekhar; O. Sengtaheuanghoung; N. Silvera; M. Steinmann; A. Soruco; G. Tallec; Emmanuel Thibert; D. Valdes Lao; C. Vincent; D. Viville; P. Wagnon; R. Zitouna. OZCAR: The French Network of Critical Zone Observatories. Vadose Zone Journal 2018, 17, 180067 .
AMA StyleJ. Gaillardet, I. Braud, F. Hankard, Sandrine Anquetin, O. Bour, N. Dorfliger, J.R. De Dreuzy, Sylvie Galle, C. Galy, Sébastien Gogo, Laurence Gourcy, F. Habets, F. Laggoun, L. Longuevergne, T. Le Borgne, F. Naaim-Bouvet, Guillaume NORD, Vincent Simonneaux, D. Six, T. Tallec, C. Valentin, G. Abril, P. Allemand, A. Arènes, Bruno Arfib, Luc Arnaud, N. Arnaud, P. Arnaud, S. Audry, V. Bailly Comte, C. Batiot, A. Battais, H. Bellot, Eric Bernard, C. Bertrand, Hélène Bessiere, S. Binet, J. Bodin, X. Bodin, Laurie Boithias, J. Bouchez, B. Boudevillain, I. Bouzou Moussa, F. Branger, J. J. Braun, P. Brunet, B. Caceres, D. Calmels, B. Cappelaere, H. Celle-Jeanton, F. Chabaux, Konstantinos Chalikakis, Cédric Champollion, Y. Copard, C. Cotel, P. Davy, P. Deline, G. Delrieu, Jerome Demarty, C. Dessert, M. Dumont, C. Emblanch, J. Ezzahar, M. Estèves, Vincent Favier, M. Faucheux, N. Filizola, P. Flammarion, P. Floury, O. Fovet, Matthieu Fournier, A. J. Francez, L. Gandois, C. Gascuel, E. Gayer, C. Genthon, M. F. Gérard, D. Gilbert, I. Gouttevin, M. Grippa, G. Gruau, A. Jardani, L. Jeanneau, J. L. Join, Hervé Jourde, Fatima Karbou, D. Labat, Y. Lagadeuc, E. Lajeunesse, R. Lastennet, W. Lavado, E. Lawin, T. Lebel, C. Le Bouteiller, C. Legout, Y. Lejeune, E. Le Meur, Nicolas Le Moigne, J. Lions, Antoine Lucas, J. P. Malet, C. Marais-Sicre, J. C. Maréchal, C. Marlin, P. Martin, Jean Martins, Jean-Michel Martinez, N. Massei, A. Mauclerc, Naomi Mazzilli, J. Molénat, P. Moreira-Turcq, E. Mougin, S. Morin, J. Ndam Ngoupayou, G. Panthou, Christophe Peugeot, G. Picard, M. C. Pierret, G. Porel, A. Probst, J. L. Probst, Antoine Rabatel, D. Raclot, L. Ravanel, Fayçal Rejiba, P. René, O. Ribolzi, J. Riotte, Agnès Rivière, Henri Robain, Laurent Ruiz, J. M. Sanchez-Perez, W. Santini, S. Sauvage, P. Schoeneich, J. L. Seidel, M. Sekhar, O. Sengtaheuanghoung, N. Silvera, M. Steinmann, A. Soruco, G. Tallec, Emmanuel Thibert, D. Valdes Lao, C. Vincent, D. Viville, P. Wagnon, R. Zitouna. OZCAR: The French Network of Critical Zone Observatories. Vadose Zone Journal. 2018; 17 (1):180067.
Chicago/Turabian StyleJ. Gaillardet; I. Braud; F. Hankard; Sandrine Anquetin; O. Bour; N. Dorfliger; J.R. De Dreuzy; Sylvie Galle; C. Galy; Sébastien Gogo; Laurence Gourcy; F. Habets; F. Laggoun; L. Longuevergne; T. Le Borgne; F. Naaim-Bouvet; Guillaume NORD; Vincent Simonneaux; D. Six; T. Tallec; C. Valentin; G. Abril; P. Allemand; A. Arènes; Bruno Arfib; Luc Arnaud; N. Arnaud; P. Arnaud; S. Audry; V. Bailly Comte; C. Batiot; A. Battais; H. Bellot; Eric Bernard; C. Bertrand; Hélène Bessiere; S. Binet; J. Bodin; X. Bodin; Laurie Boithias; J. Bouchez; B. Boudevillain; I. Bouzou Moussa; F. Branger; J. J. Braun; P. Brunet; B. Caceres; D. Calmels; B. Cappelaere; H. Celle-Jeanton; F. Chabaux; Konstantinos Chalikakis; Cédric Champollion; Y. Copard; C. Cotel; P. Davy; P. Deline; G. Delrieu; Jerome Demarty; C. Dessert; M. Dumont; C. Emblanch; J. Ezzahar; M. Estèves; Vincent Favier; M. Faucheux; N. Filizola; P. Flammarion; P. Floury; O. Fovet; Matthieu Fournier; A. J. Francez; L. Gandois; C. Gascuel; E. Gayer; C. Genthon; M. F. Gérard; D. Gilbert; I. Gouttevin; M. Grippa; G. Gruau; A. Jardani; L. Jeanneau; J. L. Join; Hervé Jourde; Fatima Karbou; D. Labat; Y. Lagadeuc; E. Lajeunesse; R. Lastennet; W. Lavado; E. Lawin; T. Lebel; C. Le Bouteiller; C. Legout; Y. Lejeune; E. Le Meur; Nicolas Le Moigne; J. Lions; Antoine Lucas; J. P. Malet; C. Marais-Sicre; J. C. Maréchal; C. Marlin; P. Martin; Jean Martins; Jean-Michel Martinez; N. Massei; A. Mauclerc; Naomi Mazzilli; J. Molénat; P. Moreira-Turcq; E. Mougin; S. Morin; J. Ndam Ngoupayou; G. Panthou; Christophe Peugeot; G. Picard; M. C. Pierret; G. Porel; A. Probst; J. L. Probst; Antoine Rabatel; D. Raclot; L. Ravanel; Fayçal Rejiba; P. René; O. Ribolzi; J. Riotte; Agnès Rivière; Henri Robain; Laurent Ruiz; J. M. Sanchez-Perez; W. Santini; S. Sauvage; P. Schoeneich; J. L. Seidel; M. Sekhar; O. Sengtaheuanghoung; N. Silvera; M. Steinmann; A. Soruco; G. Tallec; Emmanuel Thibert; D. Valdes Lao; C. Vincent; D. Viville; P. Wagnon; R. Zitouna. 2018. "OZCAR: The French Network of Critical Zone Observatories." Vadose Zone Journal 17, no. 1: 180067.
A robust method for characterizing the mineralogy of suspended sediment in continental rivers is introduced. It encompasses 3 steps: the filtration of a few milliliters of water, measurements of X-ray energy dispersive spectra using Scanning Electron Microscopy (SEM), and robust machine learning tools of classification. The method is applied to suspended particles collected from various Amazonian rivers. A total of more than 204,000 particles were analyzed by SEM-EDXS (Energy Dispersive X-ray Spectroscopy), i.e. about 15,700 particles per sampling station, which lead to the identification of 15 distinct groups of mineralogical phases. The size distribution of particles collected on the filters was derived from the SEM micrographs taken in the backscattered electron imaging mode and analyzed with ImageJ freeware. The determination of the main mineralogical groups composing the bulk sediment associated with physical parameters such as particle size distribution or aspect ratio allows a precise characterization of the load of the terrigenous particles in rivers or lakes. In the case of the Amazonian rivers investigated, the results show that the identified mineralogies are consistent with previous studies as well as between the different samples collected. The method enabled the evolution of grain size distribution from fine to coarse material to be described in the water column. Implications about hydrodynamic sorting of mineral particles in the water column are also briefly discussed. The proposed method appears well suited for intensive routine monitoring of suspended sediment in river systems.
Sylvain Pinet; Bruno Lartiges; Jean-Michel Martinez; Sylvain Ouillon. A SEM-based method to determine the mineralogical composition and the particle size distribution of suspended sediment. International Journal of Sediment Research 2018, 34, 85 -94.
AMA StyleSylvain Pinet, Bruno Lartiges, Jean-Michel Martinez, Sylvain Ouillon. A SEM-based method to determine the mineralogical composition and the particle size distribution of suspended sediment. International Journal of Sediment Research. 2018; 34 (2):85-94.
Chicago/Turabian StyleSylvain Pinet; Bruno Lartiges; Jean-Michel Martinez; Sylvain Ouillon. 2018. "A SEM-based method to determine the mineralogical composition and the particle size distribution of suspended sediment." International Journal of Sediment Research 34, no. 2: 85-94.
We analyzed two contrasting catchments located along the world´s largest unspoiled tropical rainforests impacted by mining in the northeastern coast of South America. We used: (i) mining, agricultural and urbanized areas to compare the land use evolution with suspended sediments and sediment yields, (ii) field monthly river suspended sediments in the 2 catchments (2004‐2015: n=154), (iii) MODIS remote sensing water color technique in the Maroni basin to complete (n=387) and extend field suspended sediment sampling from 2000‐2015, (iv) hydroclimatic statistical analysis conditions and sediment concentrations to identify the long term trends, the abrupt changes in time series and to analyze if the environmental and anthropogenic factors control sediment yields regional variations. No significant long‐term changes were observed in precipitation or water discharge with the Mann‐Kendall test. However, the mean suspended sediment concentration has increased significantly (239%) in the Maroni River with a breakpoint in 2009 and decreased (33%) in the Oyapock River (breakpoint in 2008). These differences are explained by the larger percentage of deforestation because of mining activities in the Maroni (0.37%) than in the Oyapock (0.06%) catchment. In the Maroni River, the increasing sediment yield trend (2000‐2015) coincide significantly (r2=0.97; p<0.0001) with the increase of 400% of mining areas, whereas no significant relationship with the runoff was found. In the Oyapock River, the runoff explains the sediment yield decreasing trend (r2=0.82; p<0.0001) and no relationship with the land use change was found.
Marjorie Gallay; Jean-Michel Martinez; Sébastien Allo; Abrahan Mora; Gérard Cochonneau; Antoine Gardel; Jean‐Claude Doudou; Max Sarrazin; Franck Chow‐Toun; Alain Laraque. Impact of land degradation from mining activities on the sediment fluxes in two large rivers of French Guiana. Land Degradation & Development 2018, 29, 4323 -4336.
AMA StyleMarjorie Gallay, Jean-Michel Martinez, Sébastien Allo, Abrahan Mora, Gérard Cochonneau, Antoine Gardel, Jean‐Claude Doudou, Max Sarrazin, Franck Chow‐Toun, Alain Laraque. Impact of land degradation from mining activities on the sediment fluxes in two large rivers of French Guiana. Land Degradation & Development. 2018; 29 (12):4323-4336.
Chicago/Turabian StyleMarjorie Gallay; Jean-Michel Martinez; Sébastien Allo; Abrahan Mora; Gérard Cochonneau; Antoine Gardel; Jean‐Claude Doudou; Max Sarrazin; Franck Chow‐Toun; Alain Laraque. 2018. "Impact of land degradation from mining activities on the sediment fluxes in two large rivers of French Guiana." Land Degradation & Development 29, no. 12: 4323-4336.
This study focuses on the confluence of two major rivers of the world, the Solimões River (white waters) and Negro River (black waters). Surface suspended sediment samples (SSC) and spectroradiometer taken along transverse profiles at 500 m intervals over a distance of 10 km, as well as satellite images (MODIS) during the hydrological year, were used to follow suspended sediment variability. In January and February, the confluence is dominated by white waters from the Solimões River in the two banks, and in June and July in the right bank by black waters from the Negro River and in the left bank by clear waters from the Solimões River. We found that indirect tools, such as reflectance obtained by spectrometer or MODIS images, can be used to determine surface suspended sediments in a contrasting zone.
Thiago Marinho; Naziano Filizola; Jean-Michel Martinez; Elisa Armijos; André Nascimento. Suspended Sediment Variability at the Solimões and Negro Confluence between May 2013 and February 2014. Geosciences 2018, 8, 265 .
AMA StyleThiago Marinho, Naziano Filizola, Jean-Michel Martinez, Elisa Armijos, André Nascimento. Suspended Sediment Variability at the Solimões and Negro Confluence between May 2013 and February 2014. Geosciences. 2018; 8 (7):265.
Chicago/Turabian StyleThiago Marinho; Naziano Filizola; Jean-Michel Martinez; Elisa Armijos; André Nascimento. 2018. "Suspended Sediment Variability at the Solimões and Negro Confluence between May 2013 and February 2014." Geosciences 8, no. 7: 265.
A. Ovando; Jean-Michel Martinez; J. Tomasella; Daniel Andres Rodriguez; C. von Randow. Multi-temporal flood mapping and satellite altimetry used to evaluate the flood dynamics of the Bolivian Amazon wetlands. International Journal of Applied Earth Observation and Geoinformation 2018, 69, 27 -40.
AMA StyleA. Ovando, Jean-Michel Martinez, J. Tomasella, Daniel Andres Rodriguez, C. von Randow. Multi-temporal flood mapping and satellite altimetry used to evaluate the flood dynamics of the Bolivian Amazon wetlands. International Journal of Applied Earth Observation and Geoinformation. 2018; 69 ():27-40.
Chicago/Turabian StyleA. Ovando; Jean-Michel Martinez; J. Tomasella; Daniel Andres Rodriguez; C. von Randow. 2018. "Multi-temporal flood mapping and satellite altimetry used to evaluate the flood dynamics of the Bolivian Amazon wetlands." International Journal of Applied Earth Observation and Geoinformation 69, no. : 27-40.
Here, we demonstrate how a combination of three multivariate statistic techniques can identify key environmental factors affecting the seasonal and spatial variability of chlorophyll-a (Chl-a) in a productive tropical estuarine-lagoon system. Remote estimation of Chl-a was carried out using a NIR-Red model based on MODIS bands, which is highly consistent with the in situ measurement of Chl-a with root mean square error (RMSE) of 15.24 mg m−3 and 13.43 mg m−3 for two independent datasets used for the model’s calibration and validation, respectively. Our findings suggest that the river discharges and hydraulic residence time of the lagoons promote a stronger effect on the spatial variability of Chl-a in the coastal lagoons, while wind, solar radiation and temperature have a secondary importance. The results also indicate a slight seasonal variability of Chl-a in Mundaú lagoon, which are different the from Manguaba lagoon. The multivariate approach was able to fully understand the relative importance of key environmental factors on the spatiotemporal variability of Chl-a of the aquatic ecosystem, providing a powerful tool for reducing dimensionality and analyzing large amounts of satellite-derived Chl-a data.
Regina Camara Lins; Jean-Michel Martinez; David Da Motta Marques; José Almir Cirilo; Paulo Ricardo Petter Medeiros; Carlos Ruberto Fragoso Júnior. A Multivariate Analysis Framework to Detect Key Environmental Factors Affecting Spatiotemporal Variability of Chlorophyll-a in a Tropical Productive Estuarine-Lagoon System. Remote Sensing 2018, 10, 853 .
AMA StyleRegina Camara Lins, Jean-Michel Martinez, David Da Motta Marques, José Almir Cirilo, Paulo Ricardo Petter Medeiros, Carlos Ruberto Fragoso Júnior. A Multivariate Analysis Framework to Detect Key Environmental Factors Affecting Spatiotemporal Variability of Chlorophyll-a in a Tropical Productive Estuarine-Lagoon System. Remote Sensing. 2018; 10 (6):853.
Chicago/Turabian StyleRegina Camara Lins; Jean-Michel Martinez; David Da Motta Marques; José Almir Cirilo; Paulo Ricardo Petter Medeiros; Carlos Ruberto Fragoso Júnior. 2018. "A Multivariate Analysis Framework to Detect Key Environmental Factors Affecting Spatiotemporal Variability of Chlorophyll-a in a Tropical Productive Estuarine-Lagoon System." Remote Sensing 10, no. 6: 853.