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Abstract The leaf phenology (i.e. the seasonality of leaf amount and leaf demography) of ecosystems can be characterized through the use of Earth observation data using a variety of different approaches. The most common approach is to derive time series of vegetation indices (VIs) which are related to the temporal evolution of FPAR, LAI and GPP or alternatively used to derive phenology metrics that quantify the growing season. The product presented here shows a map of average ‘amplitude’ (i.e. maximum minus minimum) of annual cycles observed in MODIS‐derived NDVI and EVI from 2000 to 2013 for Meso‐ and South America. It is a robust determination of the amplitude of annual cycles of vegetation greenness derived from a Lomb–Scargle spectral analysis of unevenly spaced data. VI time series pre‐processing was used to eliminate measurement outliers, and the outputs of the spectral analysis were screened for statistically significant annual signals. Amplitude maps provide an indication of net ecosystem phenology since the satellite observations integrate the greenness variations across the plant individuals within each pixel. The average amplitude values can be interpreted as indicating the degree to which the leaf life cycles of individual plants and species are synchronized. Areas without statistically significant annual variations in greenness may still consist of individuals that show a well‐defined annual leaf phenology. In such cases, the timing of the phenology events will vary strongly within the year between individuals. Alternatively, such areas may consist mainly of plants with leaf turnover strategies that maintain a constant canopy of leaves of different ages. Comparison with in situ observations confirms our interpretation of the average amplitude measure. VI amplitude interpreted as leaf life cycle synchrony can support model evaluation by informing on the likely leaf turn over rates and seasonal variation in ecosystem leaf age distribution.

Over the last 20 years, there has been a surge of interest in the use of reflectance data collected using satellites and aerial vehicles to monitor vegetation diversity. One methodological option to monitor these systems involves developing empirical relationships between spectral heterogeneity in space (spectral variation) and plant or habitat diversity. This approach is commonly termed the ‘Spectral Variation Hypothesis’. Although increasingly used, it is controversial and can be unreliable in some contexts. Here, we review the literature and apply three-level meta-analytical models to assess the test results of the hypothesis across studies using several moderating variables relating to the botanical and spectral sampling strategies and the types of sites evaluated. We focus on the literature relating to grasslands, which are less well studied compared to forests and are likely to require separate treatments due to their dynamic phenology and the taxonomic complexity of their canopies on a small scale. Across studies, the results suggest an overall positive relationship between spectral variation and species diversity (mean correlation coefficient = 0.36). However, high levels of both within-study and between-study heterogeneity were found. Whether data was collected at the leaf or canopy level had the most impact on the mean effect size, with leaf-level studies displaying a stronger relationship compared to canopy-level studies. We highlight the challenges facing the synthesis of these kinds of experiments, the lack of studies carried out in arid or tropical systems and the need for scalable, multitemporal assessments to resolve the controversy in this field.

Hyperspectral leaf-level reflectance data may enable the creation of taxonomic inventories and diversity assessments of grasslands, but little is known about the stability of species-specific spectral classes and discrimination models over the course of a growing season. Here, we present a cross-seasonal dataset of seventeen species that are common to a temperate, dry and nutrient-poor calcareous grassland, which spans thirteen sampling dates, a week apart, during the spring and summer months. By using a classification model that incorporated waveband selection (a sparse partial least squares discriminant analysis), most species could be classified, irrespective of the sampling date. However, between 42 and 95% of the available spectral information was required to obtain these results, depending on the date and model run. Feature selection was consistent across time for 70 out of 720 wavebands and reflectance around 1410 nm, representing water features, contributed the most to the discrimination. Model transferability was higher between neighbouring sampling dates and improved after the “green-up” period. Some species were consistently easy to classify, irrespective of time point, when using up to six latent variables, which represented about 99% of the total spectral variance, whereas other species required many latent variables, which represented very small spectral differences. We concluded that it did seem possible to create reliable taxonomic inventories for combinations of certain grassland species, irrespective of sampling date, and that the reason for this could lie in their distinctive morphological and/or biochemical leaf traits. Model transferability, however, was limited across dates and cross-seasonal sampling that captures leaf development would probably be necessary to create a predictive framework for the taxonomic monitoring of grasslands. In addition, most variance in the leaf reflectance within this system was driven by a subset of species and this finding implies challenges for the application of spectral variance in the estimation of biodiversity.

Recent work has shown that leaf traits and spectral properties change through time and/or seasonally as leaves age. Current field and hyperspectral methods used to estimate canopy leaf traits could, therefore, be significantly biased by variation in leaf age. To explore the magnitude of this effect, we used a phenological dataset comprised of leaves of different leaf age groups -developmental, mature, senescent and mixed-age- from canopy and emergent tropical trees in southern Peru. We tested the performance of partial least squares regression models developed from these different age groups when predicting traits for leaves of different ages on both a mass and area basis. Overall, area-based models outperformed mass-based models with a striking improvement in prediction observed for area-based leaf carbon (Carea) estimates. We observed trait-specific age effects in all mass-based models while area-based models displayed age effects in mixed-age leaf groups for Parea and Narea. Spectral coefficients and variable importance in projection (VIPs) also reflected age effects. Both mass- and area-based models for all five leaf traits displayed age/temporal sensitivity when we tested their ability to predict the traits of leaves of other age groups. Importantly, mass-based mature models displayed the worst overall performance when predicting the traits of leaves from other age groups. These results indicate that the widely adopted approach of using fully expanded mature leaves to calibrate models that estimate remotely-sensed tree canopy traits introduces error that can bias results depending on the phenological stage of canopy leaves. To achieve temporally stable models, spectroscopic studies should consider producing area-based estimates as well as calibrating models with leaves of different age groups as they present themselves through the growing season. We discuss the implications of this for surveys of canopies with synchronised and unsynchronised leaf phenology.

Surface water plays a vital role in the spread of infectious diseases. Information on the spatial and temporal dynamics of surface water availability is thus critical to understanding, monitoring and forecasting disease outbreaks. Before the launch of Sentinel-1 Synthetic Aperture Radar (SAR) missions, surface water availability has been captured at various spatial scales through approaches based on optical remote sensing data. A critical drawback of the latter is data loss due to cloud cover, however few studies have quantified this. This study evaluated data loss due to clouds in three Western Ghats (India) districts. These forest-agricultural mosaic landscapes, where water-related diseases are prevalent, experience the Indian monsoon. We compared surface water areas mapped by thresholding 10m Sentinel-1A SAR data with the optical 30m Landsat-derived Joint Research Centre (JRC) Global Surface Water product, currently the only globally available long-term monthly surface water data product. Backscatter thresholds were identified manually, and our Bayesian algorithm found these thresholds were very likely (>97%). While the Sentinel-1 SAR-based and JRC's optical-based approach mapped surface water extent with high overall accuracy (> 98%) when the cloud cover was low, the unmapped surface water area was substantial in the JRC product during the monsoon months. Across the districts, the average cloud cover in the July-August period was 92% or 90% for 2017 and 2018 respectively, resulting in 25% or 23% of the surface water area being unmapped. Also, the more detailed 10m resolution of Sentinel-1A SAR helped detect the many small water features missed by 30m JRC. Thus, for predicting water-related disease risks linked to small water features or monsoon rainfall, Sentinel-1A SAR is more effective. Finally, automatic backscatter thresholding for unvegetated surface water mapping can be effective if threshold values are adapted to regional-specific backscatter spatial and temporal variations. [ABSTRACT FROM AUTHOR]

Leaf aging is a fundamental driver of changes in leaf traits, thereby regulating ecosystem processes and remotely sensed canopy dynamics. We explore leaf reflectance as a tool to monitor leaf age and develop a spectra-based partial least squares regression (PLSR) model to predict age using data from a phenological study of 1099 leaves from 12 lowland Amazonian canopy trees in southern Peru. Results demonstrated monotonic decreases in leaf water (LWC) and phosphorus (Pmass ) contents and an increase in leaf mass per unit area (LMA) with age across trees; leaf nitrogen (Nmass ) and carbon (Cmass ) contents showed monotonic but tree-specific age responses. We observed large age-related variation in leaf spectra across trees. A spectra-based model was more accurate in predicting leaf age (R2  = 0.86; percent root mean square error (%RMSE) = 33) compared with trait-based models using single (R2  = 0.07-0.73; %RMSE = 7-38) and multiple (R2  = 0.76; %RMSE = 28) predictors. Spectra- and trait-based models established a physiochemical basis for the spectral age model. Vegetation indices (VIs) including the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), normalized difference water index (NDWI) and photosynthetic reflectance index (PRI) were all age-dependent. This study highlights the importance of leaf age as a mediator of leaf traits, provides evidence of age-related leaf reflectance changes that have important impacts on VIs used to monitor canopy dynamics and productivity and proposes a new approach to predicting and monitoring leaf age with important implications for remote sensing.

O presente trabalho é fruto de uma investigação sobre o panorama das pesquisas relacionadas à Educação em Astronomia nos programas de Pós-graduação da Amazônia Legal. Os objetivos deste estudo consistiram em mapear as teses e dissertações pertinentes ao tema, identificar os programas de pós-graduação que atuam nessa área, analisar os principais focos temáticos das pesquisas realizadas e compreender as tendências metodológicas adotadas. Assumimos a pesquisa como do tipo Estado da Arte e para realizar o tratamento dos dados nos aportamos na Análise de Conteúdo. Foram consultadas três bibliotecas digitais, resultando em um recorte que engloba 50 pesquisas, sendo 49 dissertações de mestrado e 1 tese de doutorado. Os resultados revelaram um aumento significativo no número de pesquisas a partir de 2015, com os mestrados profissionais sendo responsáveis por 76% das investigações realizadas. Em relação aos focos temáticos das pesquisas, observou-se que as “abordagens didáticas”, a “formação docente” e as “tecnologias” foram os temas mais recorrentes. Este estudo evidencia que um dos desafios da Educação em Astronomia na Amazônia Legal é promover mais pesquisas de doutorado nos programas de pós-graduação, incentivando, assim, a continuidade e aprofundamento das pesquisas desenvolvidas nos mestrados acadêmicos e profissionais.

Measuring nearshore waves remains technically challenging despite wave properties are being used in a variety of applications. With the promise of high-resolution and remotely-sensed measurements of water surfaces in four dimensions (spatially and temporally), stereo-photogrammetry applied to video imagery has grown as a viable solution over the last ten years. However, past deployments have essentially used costly cameras and optics, requiring fixed deployment platforms and hindering the applicability of the method in the field.Focusing on close-range measurements of nearshore waves at break point, this paper presents a detailed evaluation of a field-oriented and cost-effective stereo-video system composed of two GoProTM (Hero 7) cameras capable of collecting 12-megapixel imagery at 24 frames per second. The so-called ‘Stereo-GoPro’ system was deployed in the surf zone during energetic conditions at a macrotidal field site using a custom-assembled mobile tower. Deployed concurrently with stereo-video, a 16-beam LiDAR (Light Detection and Ranging) and two pressure sensors provided independent data to assess stereo-GoPro performance. All three methods were compared with respect to the evolution of the free-surface elevation over 25 min of recording at high tide and the wave parameters derived from spectral analysis. We show that stereo-GoPro allows producing digital elevation models (DEMs) of the water surface over large areas (250 m2) at high spatial resolution (0.2 m grid size), which was unsurpassed by the LiDAR. From instrument inter-comparisons at the location of the pressure transducers, free-surface elevation root-mean square errors of 0.11 m and 0.18 m were obtained respectively for LiDAR and stereo-GoPro. This translated into a maximum relative error of 3.9% and 12.5% on spectral wave parameters for LiDAR and stereo-GoPro, respectively. Optical distortion in imagery, which could not be completely corrected with calibration, was the main source of error. Whilst stereo-video processing workflow remains complex, cost-effective stereo-photogrammetry already opens new opportunities for deriving wave parameters in coastal regions, as well as for various other practical applications. Further tests should try to address specifically challenges associated to variable ambient conditions and acquisition configurations, affecting measurement performance, to guarantee a larger uptake of the technique.

Apresenta parte de pesquisa doutoral desenvolvida na formação de professores que ensinam ciências nos anos iniciais da EJAI. O objetivo principal foi identificar que aprendizagens da docência em EJAI, evidenciadas por meio da biografia educativa, são capazes de indicar outros modos de formar professores para o ensino de ciências. Tratou-se de pesquisa narrativa que assumiu a abordagem processual da pesquisa-formação e contou com a participação de professores da EJAI dos anos iniciais. O estudo evidenciou aprendizagens emergidas em processos anteriores à formação inicial desses professores, como suas vivências estudantis, e as aprendizagens tecidas ao longo de suas trajetórias na EJAI.

This study examines the evolution of wave shapes as they propagate over a beach of varying morphology, information essential for understanding coastal dynamics and supporting coastal management. Our objective was to analyze the relationship between wave shape parameters and the local slope of the beach. To achieve this, we used data from pressure sensors and topographic measurements to evaluate the shape of waves on a cross-shore profile of a low-tide terrace beach. The analysis of wave conditions revealed a pronounced modulation of the tidal signal, which is augmented during storm events. Our findings demonstrate that the asymmetry and skewness parameters are more pronounced in the reflective zone of the beach. Considering these results, it can be concluded that the non-linearity of waves is significantly affected by the beach slope. The parameterization method employed in this study effectively incorporates this factor, offering improved accuracy in comparison to the existing approaches.

In accordance with European Directive 2009/128/CE, France has set up national policies to curb the reliance of agriculture on phytosanitary inputs. The DEPHY network is one such initiative;, which has been designed to drive, support and document the fieldwork of 3,000 farms voluntarily taking part in a collective input reduction programme. This study aimed to describe the achievements of the network’s viticultural sector, as its perennial crop is heavily reliant on pesticide applications. We chose a sample of 343 wine-growing systems across mainland France, analysed records of their vineyard operations from before they joined the network, and compared them to those from the 2017-2020 period. We used the Treatment Frequency Index (TFI) to assess pesticide reliance together with other techno-economical parameters: production costs, workloads, greenhouse gas emissions and disease control. A significant decrease in pesticide use was found over the whole study period, with, for instance, an average TFI reduction of 24 %, which is consistent with national objectives but unmatched outside of the network. We observed an overall shift towards pesticide inputs with lower repercussions on health or the environment, as well as different TFI reduction trajectories based on initial pesticide dependency. We described the alternative practices introduced to the systems, and our results suggest that reductions in pesticide use stem from quite small and minor changes made to the cropping systems. These new practices only slightly lowered the vineyards’ technical and economical performances in specific situations involving an indepth revamping of the systems; e.g., conversion to organic viticulture. To increase pesticide phase-out, further research is needed on redesigning cropping systems, and on how to scale up the network’s results to the national level.

Summary: Background: Accurate prediction of seizures can help to direct resource-intense continuous electroencephalogram (CEEG) monitoring to neonates at high risk of seizures. We aimed to use data from standardised EEG reports to generate seizure prediction models for vulnerable neonates. Methods: In this retrospective cohort study, we included neonates who underwent CEEG during the first 30 days of life at the Children's Hospital of Philadelphia (Philadelphia, PA, USA). The hypoxic ischaemic encephalopathy subgroup included only patients with CEEG data during the first 5 days of life, International Classification of Diseases, revision 10, codes for hypoxic ischaemic encephalopathy, and documented therapeutic hypothermia. In January, 2018, we implemented a novel CEEG reporting system within the electronic medical record (EMR) using common data elements that incorporated standardised terminology. All neonatal CEEG data from Jan 10, 2018, to Feb 15, 2022, were extracted from the EMR using age at the time of CEEG. We developed logistic regression, decision tree, and random forest models of neonatal seizure prediction using EEG features on day 1 to predict seizures on future days. Findings: We evaluated 1117 neonates, including 150 neonates with hypoxic ischaemic encephalopathy, with CEEG data reported using standardised templates between Jan 10, 2018, and Feb 15, 2022. Implementation of a consistent EEG reporting system that documents discrete and standardised EEG variables resulted in more than 95% reporting of key EEG features. Several EEG features were highly correlated, and patients could be clustered on the basis of specific features. However, no simple combination of features adequately predicted seizure risk. We therefore applied computational models to complement clinical identification of neonates at high risk of seizures. Random forest models incorporating background features performed with classification accuracies of up to 90% (95% CI 83–94) for all neonates and 97% (88–99) for neonates with hypoxic ischaemic encephalopathy; recall (sensitivity) of up to 97% (91–100) for all neonates and 100% (100–100) for neonates with hypoxic ischaemic encephalopathy; and precision (positive predictive value) of up to 92% (84–96) in the overall cohort and 97% (80–99) in neonates with hypoxic ischaemic encephalopathy. Interpretation: Using data extracted from the standardised EEG report on the first day of CEEG, we predict the presence or absence of neonatal seizures on subsequent days with classification performances of more than 90%. This information, incorporated into routine care, could guide decisions about the necessity of continuing EEG monitoring beyond the first day, thereby improving the allocation of limited CEEG resources. Additionally, this analysis shows the benefits of standardised clinical data collection, which can drive learning health system approaches to personalised CEEG use. Funding: Children's Hospital of Philadelphia, the Hartwell Foundation, the National Institute of Neurological Disorders and Stroke, and the Wolfson Foundation.

Measurement(s) Beach topography • Nearshore bathymetry Technology Type(s) RTK-GNSS • Terrestrial laser scanning • Digital photogrammetry • Multibeam echosounder Factor Type(s) Elevation Sample Characteristic - Environment shore Sample Characteristic - Location Bretagne Region

Monitoring coastal seabed in very shallow waters (0–5 m) is a challenging methodological issue, even though such data is of major importance to many scientific and technical communities. Over the years, Structure-from-Motion (SfM) photogrammetry has emerged as a flexible and inexpensive method able to provide both a 3D model and high-resolution imagery of the seabed (~cm level). In this study, we propose a low-cost (about USD 1500), adaptable, lightweight and easily dismantled system called POSEIDON (for Platform Operating in Shallow-water Environment for Imaging and 3D reconstructiON). This prototype combines a floating support (typically a bodyboard), two imagery sensors (here, GoPro® cameras) and an accurate positioning system using Real Time Kinematic GNSS. Validation of this method was deployed in a macrotidal zone, comparing on the foreshore the point cloud provided by POSEIDON “SfM bathymetry” and by classical terrestrial SfM survey. Mean deviation was 5.2 cm and standard deviation was 4.6 cm. Such high-resolution SfM bathymetric surveys have a great potential for a wide range of applications: micro-bathymetry, hydrodynamics (bottom roughness), benthic habitats, ecological inventories, archaeology, etc.

Este trabalho objetiva compreender de que maneira a realidade aumentada como ferramenta tecnológica utilizada por professores de Ciências em formação pode favorecer novas percepções sobre o ensino de Ciências, em termos conceituais e sobre o uso de tecnologias digitais em aula. Para tanto, buscamos apoio em Fernandes e Ferreira (2014), Tori (2006); Mattar (2017); Moran (2000), dentre outros. Indagamos: Em que termos a vivência de professores de Ciências em processos de formação pós-graduada, que propicia aprendizagens tecnológicas sobre a realidade aumentada, pode contribuir para novas percepções sobre o ensino de Ciências? Assumimos a abordagem qualitativa na modalidade narrativa (CLANDININ e CONNELLY, 2011), além da Análise Textual Discursiva (ATD) como metodologia analítica do material empírico. Os resultados revelam a potencialidade do uso da realidade aumentada como ferramenta tecnológica em processos de formação, pois possibilita ao professor desenvolver novas/outras percepções sobre o ensino de Ciências, bem como refletir sobre a própria prática que realiza. Nesse sentido, o professor move-se cada vez mais no processo de apropriação das tecnologias digitais disponíveis no contexto social, em busca de aproximar-se das linguagens e códigos de seus alunos com objetivos educacionais. Assim defendemos propostas dessa natureza na formação inicial e continuada de professores.

Esta pesquisa trata do Ensino de Ciências para alunos da Educação de Jovens e Adultos (EJA) a partir de narrativas de licenciandos do extremo norte do Brasil. O objetivo foi analisar as aprendizagens para a docência quanto ao Ensino de Ciências na EJA. Este estudo configura-se em pesquisa narrativa. Como pressuposto teórico discutimos a formação de professores para o Ensino de Ciências na EJA. As narrativas sinalizaram possibilidades para uma formação de professores centrada na fala do professor a partir de suas experiências formadoras e na investigação de sua própria prática. Assim como identificamos premissas para o Ensino de Ciências na EJA na perspectiva de uma alfabetização científica que assuma a trajetória de vida dos adultos e suas necessidades do dia a dia como ponto de partida para explorar conteúdos e conceitos científicos importantes na formação de sujeitos da EJA.

This paper gives attention to analyze the scientific productions about Science and Adult education teaching (EJA) in two digital platforms which has an academic relevance and promote and socialize researches about education. The study has explored published data in the period from 2009 to 2019 in the Coordination for higher Education Staff Development (CAPES) and National Association of post-graduation and researches in education’s platforms (ANPED). Both researches sources are important scientific productions representatives in education in Brazil, specially about initial schooling, including EJA. The productions’ mapping at CAPES and ANPED confirm the necessity and importance about intensifying the development of more researches which discuss more about science teaching at EJA, not only about initial production, they are highlighted in most of the academic researches which were analyzed, but due to its relevance, inclusive, through the critical training in which people are able to identify and analyze the economic development models, politics, social, scientific, technological and environmental which are included.