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BackgroundSchistosomiasis, a chronic parasitic disease, remains a public health issue in tropical and subtropical regions, especially in low and moderate-income countries lacking assured access to safe water and proper sanitation. A national prevalence survey carried out by the Brazilian Ministry of Health from 2011 to 2015 found a decrease in human infection rates to 1%, with 19 out of 26 states still classified as endemic areas. There is a risk of schistosomiasis reemerging as a public health concern in low-endemic regions. This study proposes an integrated landscape-based approach to aid surveillance and control strategies for schistosomiasis in low-endemic areas.Methodology/principal findingsIn the Middle Paranapanema river basin, specific landscapes linked to schistosomiasis were identified using a comprehensive methodology. This approach merged remote sensing, environmental, socioeconomic, epidemiological, and malacological data. A team of experts identified ten distinct landscape categories associated with varying levels of schistosomiasis transmission potential. These categories were used to train a supervised classification machine learning algorithm, resulting in a 92.5% overall accuracy and a 6.5% classification error. Evaluation revealed that 74.6% of collected snails from water collections in five key municipalities within the basin belonged to landscape types with higher potential for S. mansoni infection. Landscape connectivity metrics were also analysed.Conclusions/significanceThis study highlights the role of integrated landscape-based analyses in informing strategies for eliminating schistosomiasis. The methodology has produced new schistosomiasis risk maps covering the entire basin. The region's low endemicity can be partly explained by the limited connectivity among grouped landscape-units more prone to triggering schistosomiasis transmission. Nevertheless, changes in social, economic, and environmental landscapes, especially those linked to the rising pace of incomplete urbanization processes in the region, have the potential to increase risk of schistosomiasis transmission. This study will help target interventions to bring the region closer to schistosomiasis elimination.

Water colour remote sensing is a valuable tool for assessing bio-optical and biogeochemical parameters across the vast extent of the Amazon River Continuum (ARC). However, accurate retrieval depends on selecting the best atmospheric correction (AC). Four AC processors (Acolite, Polymer, C2RCC, OC-SMART) were evaluated against in situ remote sensing reflectance (Rrs) measurements. K-means classification identified four optical water types (OWTs) that are affected by the ARC. Two OWTs showed seasonal differences in the Lower Amazon River, influenced by the increase in suspended sediment concentration with river discharge. The other OWTs in the Amazon River Plume are dominated by phytoplankton or by a mixture of optically significant constituents. The Quality Water Index Polynomial method used to assess the quality of in situ and orbital Rrs had a high failure rate when the Apparent Visible Wavelength was >580 nm for in situ Rrs. OC-SMART Rrs products showed better spectral quality compared to Rrs derived from other AC processors evaluated in this study. These results improve our understanding of remotely sensing very turbid waters, such as those in the Amazon River Continuum.

The increase in the spatial resolution of satellite imagery and the greater distribution of data have enabled the use of remote sensing for urban studies. However, there is still a lack of databases and cartographic publications for cities in the Brazilian Amazon. Thus, the following questions guided this work: what is the state-of-the-art in remote sensing to identify urban patterns in Brazil? Do these urban patterns equally cover all Brazilian regions? How to identify urban patterns in different contexts, such as the Amazon urban region? To this end, we conducted a review of publications that have identified urban patterns in Brazilian cities and put forward an identification model. For the most part, we observed that the works found analyzed small areas in São Paulo cities, using visual interpretation in private access and high spatial resolution imagery. To cover this gap, we propose a framework to identify Urban and Socio-Environmental Patterns based on remote sensing data, Voluntary Geographic Information data and census data. Although the framework can be applied in all regions of Brazil, the focus of the classification model is on Amazonian cities.

Resumo Ocorreu um ciclone na costa sul do Brasil entre 30/06/2020 e 01/07/2020, movendo-se do continente em direção ao mar. O sistema foi registrado em modelos numéricos e observações in-situ (boia e navio) desde o dia em que se formou até se dissipar. O objetivo deste trabalho é utilizar os dados coletados durante essa condição ambiental extrema em uma comparação estatística com o modelo atmosférico operacional Global Forecast System (GFS) e uma implementação local do modelo de ondas WAVEWATCH III (WW3), que utiliza o GFS como um dos inputs, para entender as limitações desses modelos. Inicialmente, os resultados mostraram que o sistema próximo à costa sul do Brasil era um ciclone explosivo forte de acordo com a classificação de Sanders e Gyakum (1980). Descobrimos que os modelos GFS e WW3 apresentaram maiores erros em comparação com os dados da boia durante o período de 48 horas de condições meteorológicas explosivas, enquanto para o modelo GFS, os maiores erros ao redor do navio ocorreram após esse período. Os erros do WW3 podem ser explicados pela diferença entre a profundidade da boia e o ponto da grade do WW3, e os erros do GFS nessa localização.

The rising trend in sea surface temperature presents a threat to tropical coral reefs, causing increased mass bleaching and mortality events. Brazilian reefs have been characterized as less susceptible to thermal stress, where the assessment of bleaching events based on temperature anomalies often overpredicts its actual occurrences in comparison with other regions of the global ocean. With coral reefs acclimatized to local environmental conditions, larval connectivity presents a potential role in introducing organisms adapted to different temperature conditions. In the present study, we evaluated the connectivity patterns of Brazilian coral reefs, verifying its potential influence on the variation of thermal stress tolerance supported by these communities. Connectivity was estimated based on 27-year larval dispersion simulations, from 1993 to 2019, among 180 reef sites distributed in seven ecoregions on the Brazilian continental margin. Simulations were performed using a biophysical model coupling ocean currents data and life history traits of Mussismilia hispida, a widespread stony-coral type species and one of the major endemic reef builders in the South Atlantic. The potential influence of larval connectivity on thermal stress tolerance was evaluated considering the probability of connections between each reef site, their respective bleaching thresholds, and the accumulation of sea surface temperature anomalies over 12-week periods. The results indicated that connectivity-influenced bleaching estimations were significantly closer to observations reported in the literature when compared to estimations without connectivity (p-value < 0.05). These findings present an evidence that larval connectivity may display a relevant role in the adaptation of Brazilian corals to changes in seawater temperature, leading to a potential variation of ± 0.3°C in local thermal stress thresholds. Different connectivity patterns assessed during the occurrence of El Niño–Southern Oscillation events were also observed in association with the migrations of the South Equatorial Current bifurcation. These conditions led to the disruption of connections between the Eastern and Northeastern reef sites during El Niño, and between the Northeastern and Amazon reef sites during La Niña. Ultimately, it is expected that those findings may contribute to the management of Brazilian coral reefs regarding changes in dispersal pathways and thermal stress tolerance given future climate change scenarios.

The Abrolhos Bank harbors the richest coral reef ecosystem in the South Atlantic Ocean. It exhibits unique geomorphologic structures, is localized in shallow depths, and is divided into two reef regions with an inner arc close to the coast (3–20 m depth) and an outer deeper arc (5–30 m depth). This study aims to describe some bio-optical properties of the Abrolhos Bank waters and to evaluate the performance of the inversion Hyperspectral Optimization Processing Exemplar (HOPE) model, developed to retrieve optical properties in shallow waters, in the region. To this end, measurements at 75 stations during two field campaigns conducted during the 2013 and 2016 wet seasons were analyzed, and the HOPE model was applied to both in situ remote sensing reflectance (Rrs) spectra and PRecursore IperSpettrale della Missione Applicativa (PRISMA) imagery. Significant differences in optical and biological properties were found between the two arcs. The empirical relationships between chlorophyll-a concentration (Chl-a) and absorption coefficient of phytoplankton at 440 nm (aphy(440)) diverged from Bricaud’s models, suggesting differences in phytoplankton diversity and cell size. In both arcs, total non-water absorption coefficient at 440 nm (aT-w(440)) was dominated by colored dissolved organic matter (CDOM) by ∼60%. Absorption coefficient by CDOM (acdom) presented a higher variability within the outer arc, with the lowest contribution from non-algal particles (NAPs), and the spectral slopes of aCDOM resembled those of the inner arc. The spectral slopes of the NAP absorption coefficient suggested a dominance by organic rather than mineral particles that probably originated from biological production. The HOPE model applied to in situ Rrs performed satisfactorily for depth in the Abrolhos Bank waters, although retrievals of aphy(440), CDOM plus NAP (adg(440)) and aT-w(440) were underestimated with a relative bias of −27.9%, −32.1% and −45.8%, respectively. The HOPE model retrievals from the PRISMA image exhibited low aphy(440) values over the whole scene and the highest adg(440) values in the Caravelas river plume. Very shallow depths (≤3 m), bottom substrate reflectance used as input in the HOPE model, model parametrization associated with the water complexity in the study site, and uncertainties associated to Rrs measurements used as input might be responsible for differences found when comparing HOPE retrievals with in situ measurements.

Nanosatellites and CubeSats were first developed for educational purposes. However, their low cost and short development cycle made nanosatellite constellations an affordable option for observing the Earth by remote sensing, increasing the frequency of high-resolution imagery, which is fundamental for studying and monitoring dynamic processes. In this sense, although still incipient, nanosatellite applications and proposed Earth observation missions are steadily growing in number and scientific fields. There are several initiatives from universities, space agencies and private companies to launch new nanosatellite missions. These initiatives are actively investigating new technologies to improve image quality and studying ways to increase acquisition frequency through the launch of larger constellations. So far, the private sector is leading the development of new missions, with proposals ranging from 12 to more than one thousand nanosatellite constellations. Furthermore, new nanosatellite missions have been proposed to tackle specific applications, such as natural disasters, or to test improvements on nanosatellite spatial, temporal and radiometric resolution. The unprecedented combination of high spatial and temporal resolution from nanosatellite constellations associated with improvement efforts in sensor quality is promising and may represent a trend to replace the era of large satellites for smaller and cheaper nanosatellites. This article first reports on the development and new nanosatellite missions of space agencies, universities and private companies. Then a systematic review of published articles using the most successful private constellation (PlanetScope and Doves) is presented and the principal papers are discussed.

Hepatitis-A virus is a worldwide healthcare problem, mainly affecting countries with poor sanitary and socioeconomic conditions. This communication evaluates the spatiotemporal variability of the disease’s socioepidemiological profile in one of the endemic Brazilian regions (Pará State) prior to (2008-2013) and after (2014-2017) the launch of the national public vaccination programme. Hepatitis-A epidemiological reports concerning Pará State - Brazil - were used for this study including municipalitylevel data of the disease’s reported positive notification cases (PNCs). The analyses involved socioepidemiological profiling and space-time scan statistics. A total of 5500 PNCs were reported in the study period. On average, PNCs decreased over time throughout the state, with strongest drops after 2015. The PNCs were specific for gender, race/ethnic origin and age group. The predominant gender and race/ethnic groups was male and brown, respectively. While children were the most susceptible age group prior to 2015, there was a shift towards older ages (young and adults) in later years. Those found to be the most affected by the disease, as shown by space-time scan statistics, were people in densely populated municipalities with unsatisfactory sanitary conditions and also less well covered by the public vaccination programme. Despite drops in the number of hepatitis-A PNCs, thanks to the national vaccination programme, the disease still persists in Pará State and elsewhere in Brazil. The present study reinforces the need of continuous prevention and control strategies for effective control and erradication of hepatitis-A.

Abstract Hepatitis‐A is a waterborne infectious disease transmitted by the eponymous hepatitis‐A virus (HAV). Due to the disease's sociodemographic and environmental characteristics, this study applied public census and remote sensing data to assess risk factors for hepatitis‐A transmission. Municipality‐level data were obtained for the state of Pará, Brazil. Generalized linear and nonlinear models were evaluated as alternative predictors for hepatitis‐A transmission in Pará. The Histogram Gradient Boost (HGB) regression model was deemed the best choice (RMSE= 2.36, and higher R2 = 0.95) among the tested models. Partial dependence analysis and permutation feature importance analysis were used to investigate the partial dependence and the relative importance values of the independent variables in the disease transmission prediction model. Results indicated a complex relationship between the disease transmission and the sociodemographic and environmental characteristics of the study area. Population size, lack of sanitation, urban clustering, year of notification, insufficient public vaccination programs, household proximity to open‐air dumpsites and storm‐drains, and lack of access to healthcare facilities and hospitals were sociodemographic parameters related to HAV transmission. Turbidity and precipitation were the environmental parameters closest related to disease transmission. Based on HGB model, a hepatitis‐A risk map was built for Pará state. The obtained risk map can be thought of as an auxiliary tool for public health strategies. This study reinforces the need to incorporate remote sensing data in epidemiological modelling and surveillance plans for the development of early prevention strategies for hepatitis‐A.

Tropical forests provide essential environmental services to human well-being. In the world, Brazil has the largest continuous area of these forests. However, in the state of Maranhão, in the eastern Amazon, only 24% of the original forest cover remains. We integrated and analyzed active fires, burned area, land use and land cover, rainfall, and surface temperature datasets to understand forest fragmentation and forest fire dynamics from a remote sensing approach. We found that forest cover in the Maranhão Amazon region had a net reduction of 31,302 km2 between 1985 and 2017, with 63% of losses occurring in forest core areas. Forest edges extent was reduced by 38%, while the size of isolated forest patches increased by 239%. Forest fires impacted, on average, around 1031 ± 695 km2 year−1 of forest edges between 2003 and 2017, the equivalent of 60% of the total burned forest in this period. Our results demonstrated that forest fragmentation is an important factor controlling temporal and spatial variability of forest fires in the eastern Amazon region. Thus, both directly and indirectly, forest fragmentation can compromise biodiversity and carbon stocks in this Amazon region.

Remote sensing data for space-time characterization of wind fields in extensive oceanic areas have been shown to be increasingly useful. Orbital sensors, such as radar scatterometers, provide data on ocean surface wind speed and direction with spatial and temporal resolutions suitable for multiple applications and air-sea studies. Even considering the relevant role of orbital scatterometers to estimate ocean surface wind vectors on a regional and global scale, the products must be validated regionally. Six different ocean surface wind datasets, including advanced scatterometer (ASCAT-A and ASCAT-B products) estimates, numerical modelling simulations (BRAMS), reanalysis (ERA5), and a blended product (CCMP), were compared statistically with in situ measurements obtained by anemometers installed in fifteen moored buoys in the Brazilian margin (8 buoys in oceanic and 7 in shelf waters) to analyze which dataset best represents the wind field in this region. The operational ASCAT wind products presented the lowest differences in wind speed and direction from the in situ data (0.77 ms−1 1.04 m·s−1, Rspd 42.4°). The results here presented can be used for climatological studies and for the estimation of the potential wind power generation in the Brazilian margin, especially considering the lack of availability or representativeness of regional data for this type of application.

Abstract Most measurements of respiration rates in large tropical rivers do not account for the influence of river flow conditions on microbial activity. We developed a ship‐board spinning incubation system for measuring O2 drawdown under different rotation velocities and deployed the system along the lower Amazon River during four hydrologic periods. Average respiration rates in incubation chambers rotated at 0.22 m s−1 and 0.66 m s−1 were 1.4 and 2.4 times higher than stationary chambers, respectively. On average, depth‐integrated respiration rates in chambers spun at 0.22 m s−1 and 0.66 m s−1 accounted for 64% ± 22% and 104% ± 36% of CO2 outgassing rates, respectively, in mainstem sites. Continuous measurements of in situ pCO2 were also made along with cross‐channel profiles of river velocity. A positive correlation between river velocity and pCO2 was observed along the lower river (r2 = 0.67–0.96) and throughout a tidal cycle.

Optical water types (OWTs) were identified from an in situ dataset of concomitant biogeochemical and optical parameters acquired in the Amazon River and its tributaries, in the Lower Amazon region, at different hydrological conditions from 2014 to 2017. A seasonal bio-optical characterization was performed. The k-means classification was applied to the in situ normalized reflectance spectra (rn(λ)), allowing the identification of four OWTs. An optical index method was also applied to the rn(λ) defining the thresholds of the OWTs. Next, level-3 Sentinel-3 Ocean and Land Color Instrument images representative of the seasonal discharge conditions were classified using the identified in situ OWTs as reference. The differences between Amazon River and clearwater tributary OWTs were dependent on the hydrological dynamics of the Amazon River, also showing a strong seasonal variability. Each OWT was associated with a specific bio-optical and biogeochemical environment assessed from the corresponding absorption coefficient values of colored dissolved organic matter (aCDOM) and particulate matter (ap), chlorophyll-a and suspended particulate matter (SPM) concentrations, and aCDOM/ap ratio. The rising water season presented a unique OWT with high SPM concentration and high relative contribution of ap to total absorption compared to the other OWTs. This bio-optical characterization of Lower Amazon River waters represents a first step for developing remote sensing inversion models adjusted to the optical complexity of this region.

Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and inter-annual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided.

Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remote sensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remote sensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remote sensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remote sensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remote sensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application.

Monitoring chlorophyll-a (chl-a) concentrations is important for the management of water quality, because it is a good indicator of the eutrophication level in an aquatic system. Thus, our main purpose was to develop an alternative technique to monitor chl-a in time and space through remote sensing techniques. However, one of the limitations of remote sensing is the resolution. To achieve a high temporal resolution and medium space resolution, we used the Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m reflectance product, MOD09GA, and limnological parameters from the Itumbiara Reservoir. With these data, an empirical (O14a) and semi-empirical (O14b) algorithm were developed. Algorithms were cross-calibrated and validated using three datasets: one for each campaign and a third consisting of a combination of the two individual campaigns. Algorithm O14a produced the best validation with a root mean square error (RMSE) of 30.4%, whereas O14b produced an RMSE of 32.41% using the mixed dataset calibration. O14a was applied to MOD09GA to build a time series for the reservoir for the year of 2009. The time-series analysis revealed that there were occurrences of algal blooms in the summer that were likely related to the additional input of nutrients caused by rainfall runoff. During the winter, however, the few observed algal blooms events were related to periods of atmospheric meteorological variations that represented an enhanced external influence on the processes of mixing and stratification of the water column. Finally, the use of remote sensing techniques can be an important tool for policy makers, environmental managers and the scientific community with which to monitor water quality.

Irrigated agriculture is a key activity for the long-term survival of human-environmental systems and the assessment of agricultural sustainability has been gaining increasing relevance. In spite of several proposals developed, there is not a holistic approach that can be generally applied to assess sustainability of irrigated agricultural areas. In this paper we present a framework and associated indicators for the assessment of sustainability of irrigated agricultural systems in different contexts and locations. The framework covers four main sustainability dimensions: environmental integrity, economic resilience and profitability, social wellbeing and good governance. This approach was tested in 10 agricultural areas in eight different countries that represent a wide variety of situations in terms of agricultural development, environmental conditions, socio-economic settings and political contexts, but that share the fact that water use is a critical aspect for agricultural development. The obtained results illustrate the usefulness of the proposed framework to obtain a holistic picture of sustainability in irrigated agriculture areas, even in situations of poor data availability. It is also an excellent starting point for the construction of roadmaps towards more sustainable agricultural systems.

The main objective of this work was to characterize the water properties of the Itumbaira, GO, hydroelectrically reservoir based on limnological and radiometric parameters. Two field campaigns were organized, the first during rainy (May) and the second in the wet season (September). In these field campaigns, the following limnological parameters were analyzed: chlorophyll-a, dissolved organic and inorganic carbon, total suspended matter, water temperature, pH and turbidity. The radiometric data were taken using a FieldSpec Hand Held portable spectroradiometer. Also the absorption coefficient of the optically active components (OAC) of the water were measured in laboratory. The limnological data was interpolated using Ordinary Kriging algorithm and the radiometric data were transformed in remote sensing reflectance. The absorption coefficient was used to build a triangular diagram designed to verify the relative contribution of the OAC in water. The results show that there was a distinct spatial variation in the limnology of the area affected by the dam and the one under the river confluences. Due to the low chlorophyll-a concentration in water, the spectral response was strongly influenced by inorganic suspended particles. The triangulation of the absorption coefficient revealed that the water is dominated by the inorganic particulate matter (from 65 to 80%). These results set the Itumbiara's water as case 2 water.

The Eastern Lagoon of New Caledonia (ELNC) is a semi-enclosed system surrounded by an extensive coral reef barrier. The system has been suffering impacts from climate variability and anthropogenic activities, including mining exploitation. Satellite monitoring is thus an essential tool to detect such changes. The present study aimed to assess the bio-optical variability of the ELNC and examine the applicability of ocean colour algorithms, using in situ bio-optical and radiometric data, collected during the March 2014 CALIOPE 2 cruise. The chlorophyll a concentration (Chla) varied from 0.13–0.72 mg·m−3, and the coastal stations were spectrally dominated by non-algal particles (NAP) and coloured dissolved organic matter (CDOM) (>80% of the total non-water absorption at 443 nm), due to the contribution of allochthonous sources. The phytoplankton specific absorption was generally lower (mean, 0.049 m2·mg Chla−1) than typical values observed for the corresponding Chla range, as well as the spectral slopes of the absorption of CDOM plus NAP (adg) (mean, 0.016 nm−1) and of the particle backscattering coefficient (bbp) (mean, 0.07 nm−1). The remote sensing reflectance obtained using two in-water approaches and modelled from Inherent Optical Properties (IOPs) showed less than 20% relative percent differences (RPD). Chla estimates were highly biased for the empirical (OC4 and OC3) and semi-analytical (GSM, QAA, GIOP, LMI) algorithms, especially at the coastal stations. Excluding these stations, the GSM01 yielded the best retrievals with 35–40% RPD. adg(443) was well retrieved by all algorithms with ~18% RPD, and bbp(443) with ~40% RPD. Turbidity algorithms also performed reasonably well (30% RPD), showing the capacity and usefulness of the derived products to monitor the water quality of the ELNC, provided accurate atmospheric correction of the satellite data. Regionally tuned algorithms may potentially improve the Chla retrievals, but better parameterization schemes that consider the spatiotemporal variability of the specific IOPs are still needed.

Remote sensing techniques offer useful tools for estimating forest biomass to large extent, thereby contributing to the monitoring of land use and landcover dynamics and the effectiveness of environmental policies. The main goal of this study was to investigate the potential use of discrete return light detection and ranging (lidar) data to produce accurate aboveground biomass (AGB) maps of mangrove forests. AGB was estimated in 34 small plots scatted over a 50 km2 mangrove forest in Rio de Janeiro, Brazil. Plot AGB was computed using either species-specific or non-species-specific allometric models. A total of 26 descriptive lidar metrics were extracted from the normalized height of the lidar point cloud data, and various model forms (random forest and partial least squares regression with backward selection of predictors (Auto-PLS)) were tested to predict the recorded AGB. The models developed using species-specific allometric models were distinctly more accurate (R2(calibration) = 0.89, R2(validation) = 0.80, root-mean-square error (RMSE, calibration) = 11.20 t·ha−1, and RMSE(validation) = 14.80 t·ha−1). The use of non-species-specific allometric models yielded large errors on a landscape scale (+14% or −18% bias depending on the allometry considered), indicating that using poor quality training data not only results in low precision but inaccuracy at all scales. It was concluded that under suitable sampling pattern and provided that accurate field data are used, discrete return lidar can accurately estimate and map the AGB in mangrove forests. Conversely this study underlines the potential bias affecting the estimates of AGB in other forested landscapes where only non-species-specific allometric equations are available.

Comparisons between in situ measurements of surface chlorophyll-a concentration (CHL) and ocean color remote sensing estimates were conducted during an oceanographic cruise on the Brazilian Southeastern continental shelf and slope, Southwestern South Atlantic. In situ values were based on fluorometry, above-water radiometry and lidar fluorosensor. Three empirical algorithms were used to estimate CHL from radiometric measurements: Ocean Chlorophyll 3 bands (OC3MRAD), Ocean Chlorophyll 4 bands (OC4v4RAD), and Ocean Chlorophyll 2 bands (OC2v4RAD). The satellite estimates of CHL were derived from data collected by the MODerate-resolution Imaging Spectroradiometer (MODIS) with a nominal 1.1 km resolution at nadir. Three algorithms were used to estimate chlorophyll concentrations from MODIS data: one empirical - OC3MSAT, and two semi-analytical - Garver, Siegel, Maritorena version 01 (GSM01SAT), and CarderSAT. In the present work, MODIS, lidar and in situ above-water radiometry and fluorometry are briefly described and the estimated values of chlorophyll retrieved by these techniques are compared. The chlorophyll concentration in the study area was in the range 0.01 to 0.2 mg·m-3. In general, the empirical algorithms applied to the in situ radiometric and satellite data showed a tendency to overestimate CHL with a mean difference between estimated and measured values of as much as 0.17 mg/m3 (OC2v4RAD). The semi-analytical GSM01 algorithm applied to MODIS data performed better (rmse 0.28, rmse-L 0.08, mean diff. -0.01 mg/m3) than the Carder and the empirical OC3M algorithms (rmse 1.14 and 0.36, rmse-L 0.34 and 0.11, mean diff. 0.17 and 0.02 mg/m3, respectively). We find that rmsd values between MODIS relative to the in situ radiometric measurements are < 26%, i.e., there is a trend towards overestimation of RRS by MODIS for the stations considered in this work. Other authors have already reported over and under estimation of MODIS remotely sensed reflectance due to several errors in the bio-optical algorithm performance, in the satellite sensor calibration, and in the atmospheric-correction algorithm.

Chlorophyll concentrations (Chl a) data obtained from the Sea Viewing Wide Field of View Sensor (SeaWIFS) ocean color monthly images, Sea Surface Temperature (SST) pathfinder data obtained from the Advanced Very High Resolution Radiometer (AVHRR) sensors, and lobster (Panulirus argus) captures at the Cuban shelf were examined in order to analyze their spatial and temporal variability. A cross-correlation analysis was made between the standardized anomalies of the environmental variables (Chl a and SST) and the standardized anomalies of lobster captures for each fishery zones for the period between 1997 and 2005. For the deep waters adjacent to the fishing zones it was not observed a clear Chl a seasonality and on average the lowest values occurred south of the Island. It is with the three years lag that Chl a had the greatest numbers of significant correlation coefficients for almost all fishing zones. However, the cross-correlation coefficients with SST showed higher values with 1,5 year lag at all zones. Since the two environmental variables obtained by satellite sensors (SST and Chl a) influence the lobsters mainly during the planktonic life cycle, the cross-correlation with lobster captures begin to show significant indexes with lags of 1.5 years or more.Dados de captura da lagosta Panulirus argus na plataforma cubana foram comparados com concentrações de clorofila (Chl a) e valores de Temperatura de Superfície do Mar (TSM) obtidos pelos sensores Sea Viewing Wide Field of view Sensor (SeaWIFS) e Advanced Very High Resolution Radiometer (AVHRR), respectivamente. Uma análise de correlação cruzada foi realizada entre as anomalias padronizadas das variáveis ambientais (Chl a e TSM) e as anomalias padronizadas de capturas da lagosta para cada zona de pesca no período 1997-2005. Para as águas profundas adjacentes às zonas de pesca não foi observada uma sazonalidade evidente da Chl a. De forma geral, os menores valores de Chl a ocorreram ao sul da Ilha. Na maioria das zonas de pesca, a captura da lagosta apresentou os maiores coeficientes de correlação com valores de Chl a com defasagem de dois e três anos. Já em relação à análise com dados de TSM, os coeficientes de correlação cruzada apresentaram valores significativos apenas a partir de uma defasagem de 1,5 anos para praticamente todas as zonas de pesca. Neste estudo confirma-se que, em águas cubanas, correlações cruzadas significativas entre estas duas variáveis ambientais medidas por satélite e as capturas da lagosta espinhosa ocorrem principalmente durante o ciclo de vida planctônico desta espécie.

The spatial and temporal variability of sea surface temperature (SST) for the Cuban shelf waters was obtained, and the relationship of these with lobster catches in the period from January/1997-December/2004 was analyzed. The data from this environmental variable sensor were obtained from Advanced Very High Resolution Radiometer (AVHRR). The periodicity of the images and data capture by fishing was monthly. Ocean waters and shelf have a seasonal pattern to the SST, reaching the highest in August (29.5 ° C) and the lowest in February (26 ° C). The extreme values recorded were recorded in August 1998 with anomalies of 1.9 ° C and -0.9 ° C in February 2001. During the winter period (Nov-Mar) it was possible to find a general pattern of water circulation in the area by observing the images; during the summer (May-October) this is not observed. Low correlation between SST anomalies and the catch of lobsters by fishing was observed. The best correlation coefficients (0.48) were found to the west of the island, with four years of time lag.

Se obtiene la variabilidad espacial y temporal de la temperatura superficial del mar (TSM) para las aguas de la plataforma cubana, así como la relación de estas con las capturas de langostas, en el período comprendido entre Enero/1997-Diciembre/2004. Los datos de de esta variable ambiental son obtenidos del sensor “Advanced Very High Resolution Radiometer” (AVHRR). La periodicidad de las imágenes y de los datos de captura por zonas de pesca es mensual. Las aguas oceánicas y de la plataforma tienen un comportamiento estacional para la TSM, alcanzando las máximas en Agosto (29,5°C) y las mínimas en Febrero (26°C). Los valores extremos registrados se datan en Agosto del 1998 con anomalías de 1,9°C y de -0,9°C en Febrero del 2001. Durante el período invernal (Nov-Abr) es posible encontrar un padrón general de circulación de las aguas para el área mediante la observación de las imágenes; en verano (May-Oct) no se observa. Se obtiene una baja correlación entre las anomalías de TSM y las capturas de langostas por zonas de pesca. Los mejores coeficientes de correlación (0,48) se hallaron para la zona occidental de la isla, con 4 años de desfase.

O Máquina de Suporte Vetorial (MSV) é um grupo teórico de algoritmos de aprendizagem de máquina e recentemente se tornou uma ferramenta efetiva para o reconhecimento de padrões. O objetivo deste trabalho foi o de comparar esse novo classificador contra os classificadores estatísticos tradicionais e avaliar sua acurácia. A área selecionada para realizar esse experimento foi a área de influência do reservatório hidrelétrico de Itumbiara (GO). As classes selecionadas foram obtidas pelo sistema de classificação de cobertura da FAO. Amostras de treinamento foram coletadas para cada classe e os algoritmos de classificação foram então aplicados. O coeficiente Kappa foi utilizado para avaliar os classificadores. Os resultados mostraram que para o algoritmo MSV a acurácia foi de 71% com um coeficiente Kappa de 0,64. Para o algoritmo de máxima verossimilhança a acurácia foi de 49% com Kappa de 0,36. De acordo com esses resultados, para a classificação da área de estudo selecionada, o algoritmo MSV apresentou melhor resultado na separação das classes propostas pela FAO.

Resumo Imagens ópticas e no infravermelho-próximo adquiridas por sensores remotos em condição de reflexão especular bidirecional entre o sol, a superfície do mar e o sensor (condição de sunglint) podem ser utilizadas para identificar filmes, feições, processos oceanográficos e meteorológicos atuantes sobre a superfície marinha. Imagens do sensor orbital Moderate Resolution Imaging Spectroradiometer (MODIS) em condição de sunglint na plataforma continental sudeste brasileira foram processadas e analisadas, permitindo identificar feições como a borda Oeste da Corrente do Brasil, meandramentos e vórtices de mesoescala, ondas internas solitárias, rolos atmosféricos e filmes superficiais de origem biogênica e mineral. Uma correção atmosférica mais apropriada para detecção de feições no sunglint é aprensentada, assim como, detalhes sobre a geometria de aquisição. A dependência geométrica do sunglint torna sua ocorrência mais favorável entre outubro e fevereiro na região de estudo quando ocorrem zênites solares mais propícios. Os mecanismos e processos que permitem identificar estas feições no sunglint foram descritos e ilustrados. A partir deste entendimento, as complexas feições identificadas podem ser interpretadas e aplicadas na oceanografia operacional e detecção de vazamentos de óleo na superfície do mar, bem como, para melhorar nosso entendimento sobre os processos físicos e biológicos do meio marinho.