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The Madeira River presents higher sediment flux in relation to others Amazonian rivers, contributing approximately 45 % of the sediment load to the entire Amazon Basin, that is also the region with the largest untapped hydroelectric potential. The implementation of Jirau and Santo Antonio Dams on Madeira River in Brazil created large run-of-the-river hydroelectric complex, and assessment of silting became an important issue to estimate the actual impact on sediment connectivity and on operation of the reservoir. This paper presents data of the morphology and stratigraphy of a portion of Jirau Reservoir, between the Jirau Rapids and the levees close to the Jirau Dam, in the period of February 8th to February 15th, 2022. The data were obtained by Multibeam Echo Sounder and High-Resolution Seismic Reflection. The results obtained by analysing reservoir bed morphology helped identify regions prone to siltation. The run-of-the-river Jirau Reservoir seems to have had minimal sediment retention since start of operations, as observed in seismic profiles. The suspended sediment concentration map of the study area collected for the year 2021, obtained within a parallel study based on satellite images, showed that the distribution on the surface of the reservoir correlates with the geophysical results, supporting our findings and suggesting the need for joint utilization of geophysical and remote sensing techniques.

Monitoring sediment transport in a run-of-river hydropower dam was investigated to detect and quantify the siltation processes using Sentinel-2/MSI satellite images. We developed a method allowing us to assess the fate of sediment discharge in reservoirs and to map the locations of eroding and silting stream cross sections. This monitoring was achieved by retrieving the seasonal and interannual variation in suspended sediment concentration (SPM) longitudinally and by developing indices to detect local deviations from the overall SPM trend. A comparison between Sentinel-2 reflectance, processed for atmospheric and glint correction, and field measurements showed fine accuracy for the b8a/b4 band ratio (RMSE = 0.23 sr-1, N = 50) as well as a very good SPM retrieval performance for a wide range of concentrations [10–926 mg/L] with an RMSLE of 0.23 mg/L. SPM trend analysis demonstrated that significant (p

Abstract Background Malaria is transmitted by different Anopheles species. In Brazil, the disease is concentrated in the Amazon region. Rivers play an important role in the life cycle of malaria since the vector reproduces in aquatic environments. The waters of the rivers in the Amazon have distinct chemical characteristics, which affect the colour of the water and therefore, the study analysed whether the colour of the waters of the rivers have an on influence the distribution of malaria. The goal of the study was to correlate the different colourations of the water (black, white and mixed water) and the malaria incidence in 50 municipalities of the Amazonas state, Brazil, and then test hypotheses about the characteristics of the colour of the rivers and disease incidence. Methods This study was conducted for a period of seventeen years (2003–2019) in 50 municipalities in the state of Amazonas, Brazil. A conditionally Gaussian dynamic linear model was developed to analyse the association of malaria incidence and three types of river colour: white, black and mixed. Results The analyses indicate that the distribution of malaria is related to the colouration of the rivers. The results showed that places located near black-water rivers have a higher malaria incidence when compared to places on the banks of white-water rivers. Conclusions Historically, the hydrological regime has played an important role in the dynamics of malaria in the Amazon, but little is known about the relationship between river colours and the incidence of the disease. This research was carried out in a region with hydrographic characteristics that were heterogeneous enough to allow an analysis that contrasted different colours of the rivers and covered almost the whole of the state of Amazonas. The results help to identify the places with the highest risk of malaria transmission and it is believed that they will be able to contribute to more precise planning of actions aimed at controlling the disease in the region.

Dissolved organic matter (DOM) is a crucial component of continental aquatic ecosystems. It plays a vital role in the carbon cycle by serving as a significant source and reservoir of carbon in water. DOM provides energy and nutrients to organisms, affecting primary productivity, organic composition, and the food chain. This study presents empirical bio-optical models for estimating the absorption of colored dissolved organic matter (aCDOM) in the Negro River using in situ remote sensing reflectance (Rrs) data. Physical–chemical data (TSS, DOC, and POC) and optical data (aCDOM and Rrs) were collected from the Negro River, its tributaries, and lakes and empirical relationships between aCDOM at 440 nm, single band, and the ratio bands of Rrs were assessed. The analysis of spectral slope shows no statistically significant correlations with DOC concentration or aCDOM absorption coefficient. However, strong relationships were observed between DOC and aCDOM (R2 = 0.72), aCDOM and Rrs at 650 nm (R2 > 0.80 and RMSE < 1.75 m−1), as well as aCDOM and the green/red band ratio (R2 > 0.80 and RMSE < 2.30 m−1). aCDOM displayed large spatial and temporal variations, varying from 1.9 up to 20.1 m−1, with higher values in rivers of the upper course of the Negro basin and lower values in rivers with total solids suspended > 10 mg·L−1. Environmental factors that influence the production of dissolved organic matter include soil type, dense forest cover, high precipitation, and low erosion rates. This study demonstrated that aCDOM can serve as an indicator of DOC, and Rrs can serve as an indicator of aCDOM in the Negro basin. Our findings offer a starting point for future research on the optical properties of Amazonian black-water rivers.

The Madeira is one of the major tributaries of the Amazon River and is characterized by a large alluvial floodplain throughout the stream continuum. This study aims to better assess the hydrological functioning of the Madeira Basin over its alluvial floodplains at both local and global scales. We used the semi-distributed hydrological Soil and Water Assessment Tool (SWAT) model to simulate water discharge at a daily time step and water resources for each hydrological compartment. A new hydraulic module for water routing was implemented in the SWAT model considering the floodplain either as a simple reservoir or as a continuum where the water can flow along with the floodplain network. Both water surface estimated by L-band passive microwaves (SWAF data) and digital elevation model—shuttle radar topography mission (DEM–SRTM data) were used to delineate the floodplain, as inputs for the model. On the global scale, the amount of water stored in the Madeira floodplain is between 810 ± 230 km3 per year when the floodplains are delimited with SWAF and 1,300 ± 350 km3 per year with the DEM floodplain delineation between 2008 and 2018. Spatial altimetry (Jason 2-3) data were also applied to alluvial areas to validate the water height dynamic in floodplains at a local scale. Results show that more than 60% of the alluvial validation points display a correlation above 0.40 ± 0.02 regardless of the floodplain delineation. This study permits us to better characterize the spatio-temporal storage dynamics of the Madeira floodplains at both local and global scales, and it underlines the importance of a precise floodplain delineation, before computing biogeochemical fluxes and sediment yield.

La gestion des ressources en eau nécessite l’utilisation de données hydrométriques provenant principalement de stations in situ. Malgré les efforts déployés pour la mise en place et le maintien d’un réseau de stations (hydrométriques ou météorologiques), les gestionnaires de ressources en eau font face à de nombreux problèmes (dégradation des équipements lors des crues, vandalisme, inaccessibilité, moyens financiers pour la maintenance …). Les données satellitaires peuvent enrichir le suivi des ressources en eau, quels que soient les objectifs de ce suivi (gestion stratégique des ressources en eau, gestion des ouvrages, prévision des crues et étiages …). Ces données présentent l’avantage de couvrir à pas-de-temps régulier le monde entier, y compris des territoires difficiles d’accès. A travers plusieurs projets, différents partenaires scientifiques et industriels (IRD, CNES, CLS, CNRS, CENEAU …) ont contribué à évaluer et à valoriser l’usage des données satellitaires pour la gestion des ressources en eau à l’échelle d’un bassin-versant (e.g. l’Amazone) ou d’un territoire (l’Ouganda). L’objectif est de délivrer des indicateurs hydrologiques issus de l’association de modèles hydrologiques, de données in-situ et de données satellitaires. Cet article présente les possibilités et les limites actuelles de l’utilisation des données satellitaires afin d’optimiser le suivi des ressources en eau.

This work aimed to assess the potential of unmanned aerial vehicle (UAV) multi- and hyper-spectral platforms to estimate chlorophyll-a (Chl-a) and cyanobacteria in experimental fishponds in Brazil. In addition to spectral resolutions, the tested platforms differ in the price, payload, imaging system, and processing. Hyperspectral airborne surveys were conducted using a push-broom system 276-band Headwall Nano-Hyperspec camera onboard a DJI Matrice 600 UAV. Multispectral airborne surveys were conducted using a global shutter-frame 4-band Parrot Sequoia camera onboard a DJI Phantom 4 UAV. Water quality field measurements were acquired using a portable fluorometer and laboratory analysis. The concentration ranged from 14.3 to 290.7 µg/L and from 0 to 112.5 µg/L for Chl-a and cyanobacteria, respectively. Forty-one Chl-a and cyanobacteria bio-optical retrieval models were tested. The UAV hyperspectral image achieved robust Chl-a and cyanobacteria assessments, with RMSE values of 32.8 and 12.1 µg/L, respectively. Multispectral images achieved Chl-a and cyanobacteria retrieval with RMSE values of 47.6 and 35.1 µg/L, respectively, efficiently mapping the broad Chl-a concentration classes. Hyperspectral platforms are ideal for the robust monitoring of Chl-a and CyanoHABs; however, the integrated platform has a high cost. More accessible multispectral platforms may represent a trade-off between the mapping efficiency and the deployment costs, provided that the multispectral cameras offer narrow spectral bands in the 660–690 nm and 700–730 nm ranges for Chl-a and in the 600–625 nm and 700–730 nm spectral ranges for cyanobacteria.

Abstract In the basin of Mekong, over 70 million people rely on unimproved surface water for their domestic requirements. Surface water is often contaminated with fecal matter and yet little information exists on the underlying mechanisms of fecal contamination in tropical conditions at large watershed scales. Our objectives were to (1) investigate the seasonality of fecal contamination using Escherichia coli as fecal indicator bacteria (FIB), and (2) establish links between the fecal contamination in stream water and its controlling factors (hydrology and land use). We present the results of (1) a sampling campaign at the outlet of 19 catchments across Lao PDR, in both the dry and the rainy seasons of 2016, and (2) a 10-day interval monitoring conducted in 2017 and 2018 at three point locations of three rivers (Nam Ou, Nam Suang, and Mekong) in northern Lao PDR. Our results show the presence of fecal contamination at most of the sampled sites, with a seasonality characterized by higher and extreme E. coli concentrations occurring during the rainy season. The highest E. coli concentrations, strongly correlated with total suspended sediment concentrations, were measured in catchments dominated by unstocked forest areas, especially in mountainous northern Lao PDR and in Vientiane province.

The dynamics of suspended sediment production and transport in large rivers are essential geomorphologicalprocesses that can influence biodiversity. The aim of this work was to analyze the spatio-temporal variation of suspendedsediment transport in the lower Negro River using Sentinel-2 images. The suspended sediment concentration (SSC) wasestimated from the reflectance of band 4 of the MSI sensor onboard the Sentinel-2A and Sentinel-2B. The results indicate thatthe hydrological regime controls the temporal variability of SSC, being higher in the low water period and lower duringflooding, with a mean concentration of 5.28 mg L-1 for the period 2015–2019. Analysis of the spatial variation of SSC inSentinel-2 images indicated a zone of active sediment deposition located downstream of the islands of the AnavilhanasArchipelago. Geology, forest cover, and hydrological variability are the main factors controlling the low sediment yield of thebasin, with an estimated suspended solid flux of 5.76×106 ton∙year-1 and a specific yield of 8 ton∙km-2∙year-1. Despite the lowerquantity of images between February and April, due to high cloud cover, this study showed that Sentinel-2 images could beused to monitor the temporal and spatial dynamics of suspended sediment transport in Amazonian blackwater rivers.

Water and sediment discharges can change rapidly, and low-frequency measurement devices might not be sufficient to elucidate existing dynamics. As such, above-water radiometry might enhance monitoring of suspended particulate matter (SPM) dynamics in inland waters. However, it has been barely applied for continuous monitoring, especially under partially cloudy sky conditions. In this study, an in situ, high-frequency (30 s timestep), above-water radiometric dataset, collected over 18 days in a tropical reservoir, is analyzed for the purpose of continuous monitoring of SPM concentration. Different modalities to retrieve reflectance spectra, as well as SPM inversion algorithms, were applied and evaluated. We propose a sequence of processing that achieved an average unsigned percent difference (UPD) of 10.4% during cloudy conditions and 4.6% during clear-sky conditions for Rrs (665 nm), compared to the respective UPD values of 88.23% and 13.17% when using a simple calculation approach. SPM retrieval methods were also evaluated and, depending on the methods used, we show that the coefficient of variation (CV) of the SPM concentration varied from 69.5% down to 2.7% when using a semi-analytical approach. As such, the proposed processing approach is effective at reducing unwanted variability in the resulting SPM concentration assessed from above-water radiometry, and our work paves the way towards the use of this noninvasive technique for high-frequency monitoring of SPM concentrations in streams and lakes.

Climate change, increasing population and changes in land use are all rapidly driving the need to be able to better understand surface water dynamics. The targets set by the United Nations under Sustainable Development Goal 6 in relation to freshwater ecosystems also make accurate surface water monitoring increasingly vital. However, the last decades have seen a steady decline in in situ hydrological monitoring and the availability of the growing volume of environmental data from free and open satellite systems is increasingly being recognized as an essential tool for largescale monitoring of water resources. The scientific literature holds many promising studies on satellite-based surface-water mapping, but a systematic evaluation has been lacking. Therefore, a round robin exercise was organized to conduct an intercomparison of 14 different satellite-based approaches for monitoring inland surface dynamics with Sentinel-1, Sentinel-2, and Landsat 8 imagery. The objective was to achieve a better understanding of the pros and cons of different sensors and models for surface water detection and monitoring. Results indicate that, while using a single sensor approach (applying either optical or radar satellite data) can provide comprehensive results for very specific localities, a dual sensor approach (combining data from both optical and radar satellites) is the most effective way to undertake largescale national and regional surface water mapping across bioclimatic gradients.

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.

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.

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 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.

In the northwestern Amazon basin, the low water period favors flood recession farming on large emerging fertile surfaces on the river banks and in the inundations plains. The variability of hydrological extremes and of the structure of the flood recession cycle are analyzed in the fluviometric Tamshiyacu station on the Amazonas River, as they are considered as important for the crop quality. Rice cropping which is a profitable activity in this region is neither related to the duration of low-waters, nor to the speed of the rising water. But this work show that since 1985, low water levels have decreased while the duration of the low water period has increased. Moreover, there has been an acceleration of the speed of the water rising.

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.

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.

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.

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.

The Sahelian region is characterized by significant variations in precipitation, impacting water quantity and quality. Suspended particulate matter (SPM) dynamics has a significant impact on inland water ecology and water resource management. In-situ data in this region are scarce and, consequently, the environmental factors triggering SPM variability are yet to be understood. This study addresses these issues using remote sensing optical data. Turbidity and SPM of the Agoufou Lake in Sahelian Mali were measured from October 2014 to present, providing a large range of `values (SPM ranging from 106 to 4178 mg/L). These data are compared to satellite reflectance from Landsat (ETM+, OLI) and MODIS (MOD09GQ, MYD09GQ). For each of these sensors, a spectral band in the near infrared region is found to be well suited to retrieve turbidity and SPM, up to very high values (R2 = 0.70) seldom addressed by remote sensing studies. The satellite estimates are then employed to assess the SPM dynamics in the main lakes and ponds of the Gourma region and its links to environmental and anthropogenic factors. The main SPM seasonal peak is observed in the rainy season (June to September) in relation to precipitation and sediment transport. A second important peak occurs during the dry season, highlighting the importance of resuspension mechanisms in maintaining high values of SPM. Three different periods are observed: first, a relatively low winds period in the early dry season, when SPM decreases rapidly due to deposition; then, a period of wind-driven resuspension in January‒March; and lastly, an SPM deposition period in April–May, when the monsoon replaces the winter trade wind. Overall, a significant increase of 27% in SPM values is observed between 2000 and 2016 in the Agoufou Lake. The significant spatio-temporal variability in SPM revealed by this study highlights the importance of high resolution optical sensors for continuous monitoring of water quality in these poorly instrumented regions.

A floresta amazônica, com cerca de 5,4 milhões de km2, abriga uma das maiores biodiversidades do planeta a qual vem sendo ameaçada em decorrência do crescente aumento da ocupação humana, que introduz fortes alterações na fitofisionomia da floresta. A identificação e monitoração dessas alterações, por meio de sensoriamento remoto, requerem o uso de imagens de radar devido ao fato de as ondas eletromagnéticas das micro-ondas penetrarem as nuvens, que na maior parte do ano recobrem essa região. Neste estudo avaliou-se a potencialidade do uso de imagens de radar ALOS/PALSAR banda L, nas polarizações HH e HV e resolução espacial de 12,5m, com o objetivo de se executar um mapeamento das áreas desmatadas por meio de um processo de classificação semi-automática de uma série temporal de dados de três anos. O classificador baseia-se numa regra de decisão que possibilita reduzir a multi-dimensionalidade dos dados da série temporal de seis imagens para um espaço bidimensional formado por duas variáveis independentes: média do sinal retroespalhado e o cálculo de mudança temporal (Absolut Change). A média fornece informações sobre o tipo de cobertura do solo e a mudança temporal estima a intensidade de mudanças em uma série temporal. Foram estudadas as regiões em torno dos municípios de Machadinho d'Oeste em (RO), Confresa (MT) e Medicilândia (PA), municípios que estão na área de influência do arco de desmatamento. A comparação dos resultados da classificação semi-automática foi efetuada com os mapeamentos realizados a partir de imagens de radar aerotransportado SAR/R99 do SIPAM com resolução espacial de 6m. Em Machadinho d'Oeste o nível de concordância foi de 86,4% estimando um total de áreas desmatadas de 919 km2, em Confresa a concordância foi de 84,4% com total de áreas desmatadas de 2.311 km2 e em Medicilândia a concordância foi de 83% com total de 731 km2 de áreas desmatadas. Contudo, a precisão dos limites de classes definidos pela classificação semi-automática mostrou-se ser bem maior do que os resultados obtidos da classificação manual realizada pelo SIPAM, o que pôde ser comprovado com observações de imagens SPOT de 2,5m de resolução espacial.