2,916 research outputs found

    Sensor-independent LAI/FPAR CDR: reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022

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    Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) are critical biophysical parameters for the characterization of terrestrial ecosystems. Long-term global LAI/FPAR products, such as the moderate resolution imaging spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS), provide the fundamental dataset for accessing vegetation dynamics and studying climate change. However, existing global LAI/FPAR products suffer from several limitations, including spatial–temporal inconsistencies and accuracy issues. Considering these limitations, this study develops a sensor-independent (SI) LAI/FPAR climate data record (CDR) based on Terra-MODIS/Aqua-MODIS/VIIRS LAI/FPAR standard products. The SI LAI/FPAR CDR covers the period from 2000 to 2022, at spatial resolutions of 500 m/5 km/0.05∘, 8 d/bimonthly temporal frequencies and available in sinusoidal and WGS1984 projections. The methodology includes (i) comprehensive analyses of sensor-specific quality assessment variables to select high-quality retrievals, (ii) application of the spatial–temporal tensor (ST-tensor) completion model to extrapolate LAI and FPAR beyond areas with high-quality retrievals, (iii) generation of SI LAI/FPAR CDR in various projections and various spatial and temporal resolutions, and (iv) evaluation of the CDR by direct comparisons with ground data and indirectly through reproducing results of LAI/FPAR trends documented in the literature. This paper provides a comprehensive analysis of each step involved in the generation of the SI LAI/FPAR CDR, as well as evaluation of the ST-tensor completion model. Comparisons of SI LAI (FPAR) CDR with ground truth data suggest an RMSE of 0.84 LAI (0.15 FPAR) units with R2 of 0.72 (0.79), which outperform the standard Terra/Aqua/VIIRS LAI (FPAR) products. The SI LAI/FPAR CDR is characterized by a low time series stability (TSS) value, suggesting a more stable and less noisy dataset than sensor-dependent counterparts. Furthermore, the mean absolute error (MAE) of the CDR is also lower, suggesting that SI LAI/FPAR CDR is comparable in accuracy to high-quality retrievals. LAI/FPAR trend analyses based on the SI LAI/FPAR CDR agree with previous studies, which indirectly provides enhanced capabilities to utilize this CDR for studying vegetation dynamics and climate change. Overall, the integration of multiple satellite data sources and the use of advanced gap filling modeling techniques improve the accuracy of the SI LAI/FPAR CDR, ensuring the reliability of long-term vegetation studies, global carbon cycle modeling, and land policy development for informed decision-making and sustainable environmental management. The SI LAI/FPAR CDR is open access and available under a Creative Commons Attribution 4.0 License at https://doi.org/10.5281/zenodo.8076540 (Pu et al., 2023a).</p

    Optimization of a soil type prediction method based on the deep learning model and vegetation characteristics

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    The structure and composition of forest vegetation plays an important role in different ecosystem functions and services. This study aimed to identifying soil types based on vegetation characteristics using a deep learning model in the High Conservation Value (HCV) area of Central Kalimantan, spanning 632.04 hectares. The data on vegetation were collected using a combination method between line transect and quadratic plots were placed. The development of a deep learning model was based on the results of a vegetation survey and the processing of aerial photos using the Feature Classifier method. The results of applying a deep learning model could provide a relatively accurate and consistent prediction in identifying soil types (Entisols 62%, Spodosols 90%, Ultisols 90% accuracy). The composition of vegetation community in Ultisols was dominated of seedling and tree (closed canopy), meanwhile in Entisols and Spodosols was dominated of seedling and sapling (dominantly open canopy). Ultisols exhibited the highest species richness (57 species), followed by Spodosols (31 species) and Entisols (14 species). Ultisols, Entisols, and Spodosols displayed even species distribution(J' close to 1) without dominance of certain species(D &lt; 0.5). The species diversity index was at a low to moderate level(H' &lt; 3), while the species richness index remained at a very low level(D_mg &gt; 3.5)

    The increasing importance of satellite observations to assess the ocean carbon sink and ocean acidification

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    This is the author accepted manuscript. The final version is available on open access from Elsevier via the DOI in this recordData availability Data will be made available on request.The strong control that the emissions of carbon dioxide (CO2) have over Earth's climate identifies the need for accurate quantification of the emitted CO2 and its redistribution within the Earth system. The ocean annually absorbs more than a quarter of all CO2 emissions and this absorption is fundamentally altering the ocean chemistry. The ocean thus provides a fundamental component and powerful constraint within global carbon assessments used to guide policy action for reducing emissions. These carbon assessments rely heavily on satellite observations, but their inclusion is often invisible or opaque to policy. One reason is that satellite observations are rarely used exclusively, but often in conjunction with other types of observations, thereby complementing and expanding their usability yet losing their visibility. This exploitation of satellite observations led by the satellite and ocean carbon scientific communities is based on exciting developments in satellite science that have broadened the suite of environmental data that can now reliably be observed from space. However, the full potential of satellite observations to expand the scientific knowledge on critical processes such as the atmosphere-ocean exchange of CO2 and ocean acidification, including its impact on ocean health, remains largely unexplored. There is clear potential to begin using these observation-based approaches for directly guiding ocean management and conservation decisions, in particular in regions where in situ data collection is more difficult, and interest in them is growing within the environmental policy communities. We review these developments, identify new opportunities and scientific priorities, and identify that the formation of an international advisory group could accelerate policy relevant advancements within both the ocean carbon and satellite communities. Some barriers to understanding exist but these should not stop the exploitation and the full visibility of satellite observations to policy makers and users, so these observations can fulfil their full potential and recognition for supporting society.European Space Agenc

    Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea

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    ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK

    Trends and variability in methane concentrations over the Southeastern Arabian Peninsula

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    Methane (CH4) is a potent greenhouse gas with an important contribution to global warming. While national and international efforts have been put in place to reduce methane emissions, little is known about its variability, especially in hotspot regions where natural and anthropogenic emissions are compounded. In this study, the current state of CH4 concentrations and their trends over the United Arab Emirates (UAE) and surrounding region are investigated with satellite and reanalysis data. CH4 concentrations have increased over the last 5 years, with a trend in the satellite-derived column values (XCH4) of about 9 ppb/year. A clear annual cycle is detected in XCH4, with an amplitude of up to 75 ppb and peak values in the warmer months. The largest concentrations are found in coastal sites, where sabkhas and landfills are present, and along the Al Hajar mountains, where agricultural activities and microhabitats that may host CH4-producing microbes occur and where advection by the background flow is likely an important contributor. The reanalysis data shows a good agreement with the satellite-derived estimates in terms of the spatial pattern, but the magnitudes are smaller by up to 50 ppb, due to deficiencies in the data assimilated. Surface CH4 concentrations in the reanalysis data account for more than 50% of the corresponding XCH4 values, and exhibit a seasonal cycle with the opposite phase due to uncertainties in the emissions inventory. Our findings provide an overview of the state of CH4 concentration in the UAE and surrounding region, and may aid local authorities to propose the appropriate emission reduction strategies in order to meet the proposed net-zero greenhouse gas emission target by 2050. This study highlights the need for the establishment in the Arabian Peninsula region of a ground-based observational network for greenhouse gas concentrations which is still lacking to date

    Satellite remote sensing of surface winds, waves, and currents: Where are we now?

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    This review paper reports on the state-of-the-art concerning observations of surface winds, waves, and currents from space and their use for scientific research and subsequent applications. The development of observations of sea state parameters from space dates back to the 1970s, with a significant increase in the number and diversity of space missions since the 1990s. Sensors used to monitor the sea-state parameters from space are mainly based on microwave techniques. They are either specifically designed to monitor surface parameters or are used for their abilities to provide opportunistic measurements complementary to their primary purpose. The principles on which is based on the estimation of the sea surface parameters are first described, including the performance and limitations of each method. Numerous examples and references on the use of these observations for scientific and operational applications are then given. The richness and diversity of these applications are linked to the importance of knowledge of the sea state in many fields. Firstly, surface wind, waves, and currents are significant factors influencing exchanges at the air/sea interface, impacting oceanic and atmospheric boundary layers, contributing to sea level rise at the coasts, and interacting with the sea-ice formation or destruction in the polar zones. Secondly, ocean surface currents combined with wind- and wave- induced drift contribute to the transport of heat, salt, and pollutants. Waves and surface currents also impact sediment transport and erosion in coastal areas. For operational applications, observations of surface parameters are necessary on the one hand to constrain the numerical solutions of predictive models (numerical wave, oceanic, or atmospheric models), and on the other hand to validate their results. In turn, these predictive models are used to guarantee safe, efficient, and successful offshore operations, including the commercial shipping and energy sector, as well as tourism and coastal activities. Long-time series of global sea-state observations are also becoming increasingly important to analyze the impact of climate change on our environment. All these aspects are recalled in the article, relating to both historical and contemporary activities in these fields

    Using Machine Learning in Forestry

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    Advanced technology has increased demands and needs for innovative approaches to apply traditional methods more economically, effectively, fast and easily in forestry, as in other disciplines. Especially recently emerging terms such as forestry informatics, precision forestry, smart forestry, Forestry 4.0, climate-intelligent forestry, digital forestry and forestry big data have started to take place on the agenda of the forestry discipline. As a result, significant increases are observed in the number of academic studies in which modern approaches such as machine learning and recently emerged automatic machine learning (AutoML) are integrated into decision-making processes in forestry. This study aims to increase further the comprehensibility of machine learning algorithms in the Turkish language, to make them widespread, and be considered a resource for researchers interested in their use in forestry. Thus, it was aimed to bring a review article to the national literature that reveals both how machine learning has been used in various forestry activities from the past to the present and its potential for use in the future

    Evaluation of mixed microalgae species biorefinery of Desmodesmus sp. And Scenedesmus sp. For bioproducts synthesis

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    Microalgae is known to produce numerous bioactive compounds for instance proteins, fatty acid, polysaccharides, enzymes, sterols, and antioxidants. Due to their valuable biochemical composition, microalgae are regarded as a very intriguing source to produce novel food products and can be utilised to improve the nutritional content of traditional foods. Additionally, microalgae are used as animal feed and additives in the cosmetics, pharmaceutical as well as nutraceutical industries. As compared to other terrestrial plants and other microorganisms, microalgae possess few advantages: (1) rapid growth rate; (2) able to grow in non-arable land and harsh cultivation conditions; (3) low nutritional requirements; (4) high productivity; and (5) reduce emission of carbon dioxide. Despite the large number of microalgae species found in nature, only a few species are identified and commercialized such as Chlorella sp., Spirulina sp. Haematococcus pluvialis, Nannochloropsis sp. and Chlamydomonas reinhardtii, which is one of the major obstacles preventing the full utilisation of microalgae-based technology. This thesis provides information on the overall composition of mixed microalgae species, Desmodesmus sp. and Scenedesmus sp., for instance protein, carbohydrate, lipid, antioxidants, and pigment. This thesis firstly introduces the application of triphasic partitioning (TPP) in the extraction and partitioning of the biomolecules from the microalgae. The latest advancement of technology has evolved from a liquid biphasic flotation (LBF) to TPP. T-butanol and ammonium sulphate are used in TPP to precipitate desired biomolecules from the aqueous solutions with the formation of three layer. TPP is a simple, time- and cost- efficient, as well as scalable process that does not require toxic organic solvents. Lipase is abundantly produced by microbes, bacteria, fungi, yeast, mammals, and plants. Lipase is widely used in the oleochemical, detergent, dairy, leather, cosmetics, paper, cosmetics, and nutraceutical industries. Therefore, this thesis also discusses the possibility of identifying and extracting enzyme lipase from the microalgae using LBF. Several parameters (volume and concentration of solvents, weight of biomass, flotation kinetics and solvent types, etc.) have been investigated to optimize the lipase extraction from LBF. Chlorophyll is the main pigment present in the microalgae. Thus, this work proposes the digital imaging approach to determine the chlorophyll concentration in the microalgae rapidly because the chlorophyll content has a significant impact on microalgae physiological health status as well as identifies the chlorophyll concentration in the production of by-products. Lastly, microalgae oil can be used as the feedstock for biodiesel as well as nutraceutical, pharmaceutical, and health-care products. The challenge in the lipid extraction is the co-extraction of chlorophyll into the oil, which can have serious consequences for downstream processing. Therefore, the removal of the chlorophyll from the microalgae using activated clay or sodium chlorite in the pre-treatment procedure are examined. The research achievements in these works and future opportunities are highlighted in the last chapter of the thesis

    Contrasting seasonal cycling of arsenic in a series of subarctic shield lakes with different morphometric properties

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    The subarctic shield near Yellowknife, Northwest Territories (NWT), is populated with thousands of small lakes (\u3c1.5 km2) and several large lakes. Historic mining activities in the region have left a legacy of environmental impacts and widespread arsenic (As) contamination in both aquatic and terrestrial environments. In particular, several small subarctic lakes near Yellowknife have been previously documented to be contaminated with high levels of As. Subarctic lakes are characterized by seasonal ice-cover that can persist for more than half of the year, yet little is known about the under-ice spatial and seasonal dynamics of As cycling. The objective of this study is to contrast seasonal changes in As cycling within and among a series of lakes with different basin morphologies during ice-cover and into the ice-free seasons. In this study, a combination of data including water profile sampling, sediment cores, snow and ice measurements, and bathymetric mapping were collected in four lakes from November 2020 to October 2021. Continuous monitoring of lake physical properties (dissolved oxygen, temperature, and light) was conducted via data loggers installed at 1 m depth intervals in each lakes’ water column. Detailed profiles of water chemistry were collected monthly at the deepest part of each lake, examining numerous key water chemistry elements with a focus on dissolved and particulate As concentrations. Key results from this study indicated: 1) Distinct seasonal variation in As over the ice-on and open-water periods, 2) The important role of lake mixing regimes in the mobility of As, 3) Field evidence of Fe attenuation of As from the water column. This project contributes important information on the winter cycling of As, which will help to inform our understanding of the chemical recovery of subarctic lakes from As pollution
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