Cloud Cover Research Articles

УСВОЕНИЕ ДАННЫХ НАБЛЮДЕНИЙ В СИСТЕМЕ КРАТКОСРОЧНОГО ЧИСЛЕННОГО ПРОГНОЗА ПОГОДЫ COSMO-RU ГИДРОМЕТЦЕНТРА РОССИИ

The paper presents a system of regional data assimilation prepared and functioning at the Hydrometcenter of Russia for the COSMO-Ru short-range weather prediction system. It describes the assimilation methods used: both the nudging and latent heat nudging methods incorporated into the model and the developed surface temperature correction module. An optimal scheme for preparing the analysis fields and the assimilation cycle for obtaining refined initial data is also described. The presented scores of numerical experiments with and without assimilation show that the use of the detailed analysis allows improving the skill of the model very-short-range forecast, including the nowcasting interval, for the fields of temperature, humidity, wind, cloud cover, and precipitation. It is shown that the assimilation of precipitation based on radar data also makes it possible to simulate associated convective phenomena, including severe ones, much more accurately.

Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations

Abstract Convective initiation (CI) nowcasting remains a challenging problem for both numerical weather prediction models and existing nowcasting algorithms. In this study, an object-based probabilistic deep learning model is developed to predict CI based on multichannel infrared GOES-16 satellite observations. The data come from patches surrounding potential CI events identified in Multi-Radar Multi-Sensor Doppler weather radar products over the Great Plains region from June and July 2020 and June 2021. An objective radar-based approach is used to identify these events. The deep learning model significantly outperforms the classical logistic model at lead times up to 1 h, especially on the false alarm ratio. Through case studies, the deep learning model exhibits dependence on the characteristics of clouds and moisture at multiple altitudes. Model explanation further reveals that the contribution of features to model predictions is significantly dependent on the baseline, a reference point against which the prediction is compared. Under a moist baseline, moisture gradients in the lower and middle troposphere contribute most to correct CI forecasts. In contrast, under a clear-sky baseline, correct CI forecasts are dominated by cloud-top features, including cloud-top glaciation, height, and cloud coverage. Our study demonstrates the advantage of using different baselines in further understanding model behavior and gaining scientific insights.

Simulated Climate of TRAPPIST-1e Using MPAS-A and Comparisons with Other GCMs

Dayside convection is one of the most important contributors to a tidally locked planet’s climate. Considering the long-standing challenge of simulating convections, we employ a convection-resolving model known as the Model for Prediction across Scales—Atmosphere and perform a series of simulations with spatial resolution ranging from 960 to 10 km. With TRAPPIST-1e, a potentially habitable exoplanet, as the target, we aim to draw a comparative analysis against the results from the TRAPPIST-1 Habitable Atmosphere Intercomparison project. Regarding the overall climate states, our simulations reaffirm the findings of the previous general circulation model (GCM). Both the extensive substellar cloud cluster and the intricate cloud street feature are successfully reproduced. The influence of varying grid resolution exhibits a remarkably marginal impact across our resolution spectrum, albeit with a slightly heightened sensitivity observed at the nightside. Major differences center around the cloud-related variables, including cloud phase (liquid and ice), amount, and height, in both the grid resolution assessments and GCM intercomparison scenarios. Furthermore, we explore the repercussions on the phase curve and transit spectrum.

Impact of three decades of warming, increased nutrient availability, and increased cloudiness on the fluxes of greenhouse gases and biogenic volatile organic compounds in a subarctic tundra heath.

Climate change is exposing subarctic ecosystems to higher temperatures, increased nutrient availability, and increasing cloud cover. In this study, we assessed how these factors affect the fluxes of greenhouse gases (GHGs) (i.e., methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2)), and biogenic volatile organic compounds (BVOCs) in a subarctic mesic heath subjected to 34 years of climate change related manipulations of temperature, nutrient availability, and light. GHGs were sampled from static chambers and gases analyzed with gas chromatograph. BVOCs were measured using the push-pull method and gases analyzed with chromatography-mass spectrometry. The soil temperature and moisture content in the warmed and shaded plots did not differ significantly from that in the controls during GHG and BVOC measurements. Also, the enclosure temperatures during BVOC measurements in the warmed and shaded plots did not differ significantly from temperatures in the controls. Hence, this allowed for assessment of long-term effects of the climate treatment manipulations without interference of temperature and moisture differences at the time of measurements. Warming enhanced CH4 uptake and the emissions of CO2, N2O, and isoprene. Increased nutrient availability increased the emissions of CO2 and N2O but caused no significant changes in the fluxes of CH4 and BVOCs. Shading (simulating increased cloudiness) enhanced CH4 uptake but caused no significant changes in the fluxes of other gases compared to the controls. The results show that climate warming and increased cloudiness will enhance CH4 sink strength of subarctic mesic heath ecosystems, providing negative climate feedback, while climate warming and enhanced nutrient availability will provide positive climate feedback through increased emissions of CO2 and N2O. Climate warming will also indirectly, through vegetation changes, increase the amount of carbon lost as isoprene from subarctic ecosystems.

Changes in Compound Hot Extremes over the Mid–High Latitudes of Asia and the Underlying Mechanisms

Abstract This study investigates the spatiotemporal variations of the summer frequency of daytime–nighttime compound extreme high-temperature events (FCEHEs) in the mid–high latitudes of Asia (MHA) from 1979 to 2014. Results show that FCEHE has shown an upward trend with fluctuations, especially in Mongolia–Baikal. The descending anomaly caused by the anomalous high pressure over Mongolia–Baikal results in reduced cloud cover, which increases solar radiation reaching the ground, favoring the higher FCEHE. This process is consistent during the daytime and nighttime periods, with relatively limited nighttime solar radiation, potentially compensated by the increased downward longwave radiation to sustain the extreme high temperatures. This benefit process is closely connected with two main factors: the increased sea ice in the Barents Sea during spring and the anomalously warm sea surface temperature (SST) in the Northwest Pacific during summer. The increased sea ice can affect the Eurasia (EU) teleconnection, while the warm SST affects the Pacific-Japan/East Asia–Pacific pattern (PJ/EAP). Subsequently, these factors further modulate the circulation anomalies and then FCEHE. Significance Statement This study provides valuable insights into the spatiotemporal variations and the possible underlying mechanisms for change in the frequency of daytime–nighttime compound extreme high-temperature events (FCEHEs) in the mid–high latitudes of Asia. The spring sea ice anomalies over the Barents Sea and summer sea surface temperature anomalies in the Northwest Pacific affect the local anticyclonic circulation in Mongolia–Baikal through Eurasia (EU) and Pacific-Japan/East Asia–Pacific (PJ/EAP) patterns, respectively. The resulting descending anomaly and reduced cloud cover contribute to interannual variations of FCEHE, which is highly similar during the daytime and nighttime periods. During the nighttime, when the solar radiation is relatively limited, the increased downward longwave radiation may compensate to sustain extreme high temperatures.

Hybrid solar irradiance nowcasting and forecasting with the SCOPE method and convolutional neural networks

We use a full year (2018) of GOES-R satellite data to produce 5-minute resolved information on cloud coverage for 7 Surface Radiation Budget Network (SURFRAD). The remote sensing images are then processed using the Spectral Cloud Optical Property Estimation (SCOPE) method in conjunction with Convolution Neural Networks (CNNs) for nowcasts and 1-hour ahead forecasts. We propose and compare two CNN based models for GHI now-casting with a 1-hour forecast horizon and a time resolution that processes data every 5 minutes: one enhanced with the SCOPE method for cloud estimation and another without it. The inclusion of SCOPE-derived information significantly improves model performance, yielding an average RMSE of 44.7 versus 68.9 W/m2 for the model without SCOPE information. The present work underscores the efficacy of even basic CNNs in interpreting satellite imagery combined with atmospheric models of the atmosphere to estimate ground irradiance accurately. The hybrid SCOPE-CNN model outperforms the basic CNN model that relies solely on 10 longwave channels, indicating the relevance of SCOPE’s physical features in enhancing predictions across various solar micro-climates. Further advancements using Convolutional Long Short-Time Memory (ConvLSTM) schemes lead to the development of a SCOPE-CNN-ConvLSTM model, showcasing significant enhancements over smart persistence across all conditions.

An accurate measurement of high-reflective objects by using 3D structured light

Using three-dimensional structured light to measure high-reflective objects accurately is a big challenge. Some overexposed and blurred areas in the structured light fringe patterns lead to the loss of semantic and texture information on the object surface, further leading to 3D model reconstruction errors. To solve this problem, a lightweight novel network, Deformable Convolutional and Multi-scale Convolutional Network (DcMcNet), is proposed for repairing highly reflectively distorted regions in sinusoidal fringe patterns and the Gray code binary fringe patterns. DcMcNet uses deformable convolution and multi-scale convolution to realize the effective utilization of multiscale global features in repairing highly reflective distorted regions and can adapt well to the different shapes of these regions. In addition, virtual software is used to acquire large-scale and high-fidelity datasets and a diverse loss function is constructed to better optimize DcMcNet. Depth reconstruction experiments of sinusoidal fringe patterns and point cloud reconstruction experiments of Gray code binary fringe patterns show that our method can repair highly reflective distortion regions with high accuracy, and for aeronautical blades, the MAE of the depth map is reduced from 0.1608 to 0.0675, and the point cloud coverage is improved from 76.64% to 98.95%.

Modeling the distribution of the proboscis monkey (Nasalis larvatus) in Sabah (Borneo) based on remotely sensed high-resolution global cloud dynamics

Proboscis monkeys, Nasalis larvatus (Wurmb, 1787), are indigenous to the island of Borneo and are considered one of its most emblematic species. Today the conservation status of this primate is classified as Endangered on the the IUCN Red List and listed under Appendix I of CITES, prohibiting all international commercial trade. In the Malaysian state of Sabah, the species is listed as totally protected and cannot be hunted. Continuing studies suggest that the number of proboscis monkeys has been decreasing in recent years. These studies have identified various factors contributing to this decline and its potential consequences. In order to carry out a thorough assessment of the conservation status of the species it is essential to have a good understanding of the animal`s ecology and habitat requirements and to use research-based approaches. One of such are species distribution models (SDMs) which in recent decades have become widely used tools in ecology by relating species occurrences to environmental data so as to gain ecological insights. In this work we specifically evaluated the effect of environmental parameters such as cloud cover to predict the potential distribution of the proboscis monkey in Sabah. Cloud cover, a seemingly simple atmospheric phenomenon, exerts a profound influence on a wide range of ecological biological processes, yet the assessment of its importance has remained remarkably limited. For modeling purposes the ‘flexsdm’ R (v. 3.3.3) modeling package was employed for testing out the Maximum Entropy (Maxent) algorithm, one of the most widely used SDM modeling methods. Model evaluation gave satisfactory results and the resulting model found a high level of suitability for proboscis monkeys in nearshore areas. A concerning discovery is that perhaps less than 13% of Sabah's area is suitable for proboscis monkey habitats, raising questions about their long-term viability. Cloud cover, particularly average annual cloudiness, is a key environmental factor influencing the distribution of proboscis monkeys in Sabah. The conversion of Borneo's forests to oil palm plantations can negatively impact cloud properties, potentially threatening the monkeys' habitat.

Влияние погодных условий на завязываемость семян рапса озимого в условиях центральной зоны Краснодарского края

The purpose of the research was to study the influence of temperature and air moisture, wind velocity, cloud coverage, and precipitation amount on seed set of winter rapeseed in the central zone of the Krasnodar region. The objects of the research were winter rapeseed cultivars Elvis, Loris, Sarmat, Selegor, and Olivin. The conditions of 2022 during flowering period were favourable. The air temperature varied from 14.3 to 18.3 °C, precipitation amounted 12.4 mm, and they did not prevent pollination. The wind velocity in average by ten-day periods was 5.1 m/sec, cloud coverage (32.2%) and air moisture (61.1%) were op-timal both for self-pollination and for pollen transfer by wind and insects. The seed set in average was 81.3%. The weather conditions of 2023 were less fa-vorable. Lowered air temperatures – from 12.2 to 16.1 °C, frequent precipitations – 40 mm, air velocity from 2.8 to 4.7 m/sec, increased cloud coverage – to 85%, and air moisture to 77.3% negatively influenced self-pollination and prevented pollen transfer by wind and insects, and thereby decreased the realization of a potential productivity by 23.9%. The cultivars Sarmat and Selegor demonstrated the highest seed set both in favorable and unfavorable conditions. The cultivar Elvis was less sensitive to weather changes during flowering. The variation of a trait of seed set in favorable conditions was insignificant (CV = 2.4–7.1%). Changing of a trait of seed set in a pod to highly variable (CV = 11.7–28.3%) in 2023 certifies the genotype selection with high seed set ability is able only in unfavorable for pollination weather con-ditions.

Variations of floral temperature in changing weather conditions.

Floral temperature is a flower characteristic that has the potential to impact the fitness of flowering plants and their pollinators. Likewise, the presence of floral temperature patterns, areas of contrasting temperature across the flower, can have similar impacts on the fitness of both mutualists. It is currently poorly understood how floral temperature changes under the influence of different weather conditions, and how floral traits may moderate these changes. The way that floral temperature changes with weather conditions will impact how stable floral temperatures are over time and their utility to plants and pollinators. The stability of floral temperature cues is likely to facilitate effective plant-pollinator interactions and play a role in the plant's reproductive success. We use thermal imaging to monitor how floral temperatures and temperature patterns of four plant species (Cistus 'snow fire' and 'snow white', Coreopsis verticillata and Geranium psilostemon) change with several weather variables (illumination, temperature; windspeed; cloud cover; humidity and pressure) during times that pollinators are active. All weather variables influenced floral temperature in one or more species. The directionality of these relationships was similar across species. In all species, light conditions (illumination) had the greatest influence on floral temperatures overall. Floral temperature and the extent to which flowers showed contrasting temperature patterns were influenced predominantly by light conditions. However, several weather variables had additional, lesser, influences. Furthermore, differences in floral traits, pigmentation and structure, likely resulted in differences in temperature responses to given conditions between species and different parts of the same flower. However, floral temperatures and contrasting temperature patterns that are sufficiently elevated for detection by pollinators were maintained across most conditions if flowers received moderate illumination. This suggests the presence of elevated floral temperature and contrasting temperature patterns are fairly constant and may have potential to influence plant-pollinator interactions across weather conditions.

A study on spatio-temporal trend of rubber leaf fall phenomenon using planetscope multi-index vegetation imagery in relations to climatological conditions

Background: Rubber plants are one of the most important plantation commodities in Indonesia. However, rubber production has declined due to leaf fall disease caused by the pathogen Pestalotiopsis sp. This study aims to analyze the spatial and temporal distribution of rubber plant leaf fall disease using multi-vegetation indices from PlanetScope imagery, as well as to analyze the influence of climatological conditions on the disease. Methods: The research was conducted at the Sembawa Rubber Research Center Garden, South Sumatra, using PlanetScope imagery data and climatological data in 2017 (before leaf fall) and 2023 (after leaf fall). Finding: Spatially, the 2023 leaf fall occurred in almost the entire garden area with poor to moderate levels. Blocks 2013D, 2012F, and 2009F experienced the most severe levels, with a total defoliated area reaching 396.76 ha. Analysis of monthly variations in vegetation index values revealed a decrease in values during leaf fall due to Pestalotiopsis sp., specifically in February, May, and September 2023. Statistical test results showed significant differences in vegetation index values between 2017 and 2023. Furthermore, based on Spearman's correlation analysis, there was a positive correlation between vegetation index values and humidity, but no significant correlation with rainfall and temperature. Conclusion: This research provides insights into mapping and monitoring rubber leaf fall disease using remote sensing data and climatological factors, which can be used for more effective rubber plantation management. However, the study has some limitations: monthly Planet data for 2017 is not fully available, several Planet image scenes from 2017 still have more than 50% cloud cover, and there may be biases as plants falling into the low health class are included in the high range of vegetation index values. Novelty/Originality of this Study: The study employs multi-vegetation indices derived from high-resolution PlanetScope imagery to detect and assess rubber leaf fall disease (LFD) within a plantation environment. By integrating spatial and temporal analyses with climatological data, the research provides a precise and comprehensive method for monitoring LFD and understanding its environmental determinants, thereby enhancing traditional rubber plantation management practices.

The joint effects of the El Niño-Southern Oscillation and Arctic Oscillation on tropical Indian Ocean heat flux during boreal winter

In this study, the joint effects of the El Niño-Southern Oscillation (ENSO) and Arctic Oscillation (AO) on winter net surface heat flux (Qnet) anomalies over the tropical Indian Ocean (TIO) are investigated for the 1979/1980–2017/2018 period. The results show that, in multisource datasets, the Qnet pattern for El Niño plus positive AO cases is the most robust. The major feature of Qnet is dominated by a significant dipole pattern, with oceanic heat gain anomalies appearing over the southeastern TIO and oceanic heat loss near the western TIO. Analysis of the flux components shows that the Qnet anomalies are mainly contributed by changes in latent heat flux and shortwave radiation (QLHF and QSWR), which are tightly associated with the regional atmospheric and oceanic conditions. In association with positive AO, northerly wind anomalies on the eastern flanks of a low-level anomalous anticyclone in the Arabian Sea enhance the intertropical convergence zone (ITCZ) in the western TIO. Meanwhile, the El Niño-related SST warming occurs in the western TIO. Both enhanced ITCZ and anomalous SST warming favour in situ increased low and middle cloud cover and a reduced QSWR. Furthermore, under El Niño's effect, a low-level anomalous anticyclone located in the southeastern TIO leads to a positive QLHF through decreased near-surface wind speed and increased near-surface air humidity. As a result, a dipole Qnet pattern tends to be observed for the combination of El Niño and positive AO.

Identifying the influence of traffic management on vehicle emissions and the distribution of air dispersion in the Makassar port area

Air pollution in several cities in Indonesia has been increasing significantly over the years. One of the primary triggers is the rise in motor vehicle activity. This increase is due to the improving economic conditions and the decreasing prices of motor vehicles. Vehicles produce various types of pollutants. The pollutants contained in vehicle exhaust gas are carbon monoxide (CO), nitrogen oxide (NOx), sulfur (SOx) and dust (PM). One of the busiest areas for motorized vehicle activity is the Makassar Port Area, which is the largest port in Eastern Indonesia. This area experiences high vehicle activity. The emissions reviewed in this research are CO (carbon monoxide) and NOX (nitrogen oxide) in the Makassar Port Area which is located on Jalan Nusantara, Jalan Sulawesi, Jalan Siswa Army, Dr. Wahidin Jalan Sudirohusodo, Jalan Sangir, Jalan Kalimantan, Jalan Sarappo, Jalan Banda, and Jalan Butung. This research utilizes historical data on traffic volume, wind speed and direction, air humidity, air temperature, air pressure, solar radiation, cloud cover and surface height. Air dispersion analysis was carried out using AERMOD software. The highest emissions obtained were 67,121 μg/m3 for CO and 9,570 μg/m3 for NOx under existing conditions and after implementing traffic management measures, the highest emissions were reduced to 45,737 μg/m3 for CO and 7,217 μg/m3 for NOx. These results conclude that traffic management can reduce air pollution. Air dispersion is not only influenced by vehicle volume but also meteorological factors. This can be seen in the dispersion results. Where, the conditions before and after traffic management showed differences in terms of the distribution of air dispersion

A Review and Evaluation of the State of Art in Image-Based Solar Energy Forecasting: The Methodology and Technology Used

The increasing penetration of solar energy into the grid has led to management difficulties that require high accuracy forecasting systems. New techniques and approaches are emerging worldwide every year to improve the accuracy of solar power forecasting models and reduce uncertainty in predictions. This article aims to evaluate and compare various solar power forecasting methods based on their characteristics and performance using imagery. To achieve this goal, this article presents an updated analysis of diverse research, which is classified in terms of the technologies and methodologies applied. This analysis distinguishes studies that use ground-based sensor measurements, satellite data processing, or all-sky camera images, as well as statistical regression approaches, artificial intelligence, numerical models, image processing, or a combination of these technologies and methods. Key findings include the superior accuracy of hybrid models that integrate multiple data sources and methodologies, and the promising potential of all-sky camera systems for very short-term forecasting due to their ability to capture rapid changes in cloud cover. Additionally, the evaluation of different error metrics highlights the importance of selecting appropriate benchmarks, such as the smart persistence model, to enhance forecast reliability. This review underscores the need for continued innovation and integration of advanced technologies to meet the challenges of solar energy forecasting.

Using SAR Data for Monitoring of Agricultural Crops in the South of the Russian Far East

The use of SAR data to monitoring agricultural crops is a promising area of research designed to complement existing methods and technologies based on the analysis of multispectral images. The main advantages of vegetation indices calculated from SAR data include their sensitivity to the polarimetric properties of the backscatter intensity, its scattering characteristics, and independence from cloud cover. This is especially important for the territory of the south of the Russian Far East, whose monsoon climate provides humid and cloudy weather during the period when crops gain maximum biomass. For arable lands in the Khabarovsk Territory and the Amur Region, a total of 64 Sentinel-1 SAR images were obtained from May to October 2021. For each date, the values of the DpRVI, RVI, VH/VV indices were calculated and time series were constructed for the entire observation period for individual fields (342 fields in total). NDVI time series were constructed from Sentinel-2 multispectral images using a cloud mask. The characteristics of time series extremes were calculated for different types of arable land: soybeans, oats, and fallows. It was shown that for each crop the seasonal curves DpRVI, RVI, VH/VV had a characteristic appearance. It was found that the DpRVI demonstrated the highest stability – the coefficients of variation of the seasonal variation of DpRVI were significantly lower than those for RVI and VH/VV. It was also revealed that the similarity between the curves of these indices remained for regions quite distant from each other - the Khabarovsk Territory and the Amur Region. The main characteristics of the seasonal variation of time series of radar indices were calculated in comparison with NDVI - the magnitude of the maximum, the date of the maximum and the values of the coefficient of variation for these indicators. It was found, firstly, that the values of these indicators in different regions are similar to each other; secondly, the variability of the maximum and the day of the maximum for DpRVI is lower than for RVI and VH/VV; thirdly, the variability of the maximum and the day of the maximum for DpRVI is comparable to NDVI. Thus, time series of radar indices DpRVI, RVI, VH/VV for the main types of agricultural lands in the south of the Far East have distinctive features and can be used in classification problems, yield modeling and crop rotation control.

Improved fusion model for generating hourly fine scale land surface temperature data under all-weather condition

Existing Land Surface Temperature (LST) fusion models encounter some challenges due to missing data, complex weather areas, and rapid land cover changes. To overcome these limitations, we proposed the Integrated SpatioTemporal Fusion Algorithm (ISFAT). ISFAT is developed based on contemporary fusion models but in addition incorporates data from partially contaminated LSTs using the masked weight function. This helps to predict fine-scale LST on prediction date while considering error resulting from landcover changes between the base and prediction date. This algorithm also factors in the calculation of model residuals, which are distributed back to the predicted fine-scale LST using the thin-plate spline function. The fine-scale LST on prediction can thereafter employed for predicting hourly fine-scale LST images by integrating a coarse resolution LST with hourly temporal resolution. Compared to contemporary LST fusion models, ISFAT demonstrates superior performance, with mean average differences of 0.1 K and 0.27 K over SADFAT and STITFM, respectively. Additionally, diurnal LST predictions from ISFAT compare well with air temperatures from automatic weather stations. Notably, on February 18, 2020, ISFAT effectively optimized fine-scale LST for Hong Kong, achieving an RMSE of 3.33 K, despite the limitation of cloud cover in the base date. The newly developed ISFAT could facilitate better LST retrieval over a large spatial coverage under different degrees of cloud contamination.

Study on Dynamic Scanning Trajectory of Large Aerospace Parts Based on 3D Scanning

The aim of manufacturing large aerospace parts for the three-dimensional scanning field demands high precision and efficiency. However, it may be more challenging to meet the full coverage of the measurement problems for large aerospace parts with the scanning range of traditional three-dimensional scanning methods. This paper establishes a dynamic posturing scanning measurement system for large aerospace parts with a six-degree-of-freedom posturing platform and a six-degree-of-freedom industrial robot linkage. It establishes a mathematical model of dynamic three-dimensional scanning posturing. It proposes a platform attitude adjustment strategy based on the field of view angle of a 3D scanner during the adjustment of a six-degree-of-freedom platform. The dynamic scanning path planning is carried out using the three-dimensional spatial decomposition method, and the vector coordinates of the critical points at the edges of the missing areas of the scan are used to re-scan the missing areas to establish the dynamic scanning paths of large aerospace parts. It is experimentally verified that the system can realize the dynamic scanning of complex curved large aerospace parts. The experimental results show that the measurement efficiency is improved by more than 75%, and the point cloud coverage of the scanning reconstruction is improved by 18% for large aerospace components with complex surfaces.

EWMACD Algorithm in Early Detection of Defoliation Caused by Dendrolimus tabulaeformis Tsai et Liu

The persistent increase in forest pest outbreaks requires timely detection methods to monitor the disaster precisely. However, early detection is challenging due to insufficient temporal observation and subtle tree changes. This article proposed a novel framework that collaborates multi-source remote sensing data and uses a change detection algorithm to archive early detection of infestation caused by Dendrolimus tabulaeformis Tsai et Liu (D. tabulaeformis) attacks. First, all available Sentinel-2 images with less than 20% cloud cover were utilized. During periods with long intervals (>16 days) between Sentinel-2 images, Landsat-8 images with less than 20% cloud cover were downscaled to a spatial resolution of 10 m using a deep learning algorithm to meet the requirement for a high temporal frequency of clear observations. Second, the spectral index differences between healthy and infested trees were examined to address the challenge of detecting subtle changes in pest attacks. The Enhanced Vegetation Index (EVI) was selected for early defoliation detection. On this basis, the EWMACD (Exponentially Weighted Moving Average Change Detection) algorithm, which is sensitive to subtle changes, was enhanced to improve the capability of detecting early D. tabulaeformis attacks. The assessment showed that the overall accuracy of the change detection (F1 score) reached 0.86 during the early stage and 0.88 during the late stage. The temporal accuracy (Precision) was 84.1% during the early stage. The accuracy significantly improved compared to using a single remote sensing data source. This study presents a new framework capable of monitoring early forest defoliation caused by D. tabulaeformis attacks and offering opportunities for predicting future outbreaks and implementing preventive measures.

Tracking the Dynamics of Spartina alterniflora with WorldView-2/3 and Sentinel-1/2 Imagery in Zhangjiang Estuary, China

The invasion of Spartina alterniflora (S. alterniflora) has posed serious threats to the sustainability, quality and biodiversity of coastal wetlands. To safeguard coastal ecosystems, China has enacted large-scale S. alterniflora removal projects, which set the goal of effectively controlling S. alterniflora throughout China by 2025. The accurate monitoring of S. alterniflora with remote sensing is urgent and requisite for the scientific eradication, control and management of this invasive plant. In this study, we combined multi-temporal WorldView-2/3 (WV-2/3) and Sentinel-1/2 imagery to monitor the S. alterniflora dynamics before and after the S. alterniflora removal projects in Zhangjiang Estuary. We put forward a new method for S. alterniflora detection with eight-band WV-2/3 imagery. The proposed method first used NDVI to discriminate S. alterniflora from water, mud flats and mangroves based on Ostu thresholding and then used the red-edge, NIR1 and NIR2 bands and support vector machine (SVM) classifier to distinguish S. alterniflora from algae. Due to the contamination of frequent cloud cover and tidal inundation, the long revisit time of high-resolution satellite sensors and the short-term S. alterniflora removal projects, we combined Sentinel-1 SAR time series and Sentinel-2 optical imagery to monitor the S. alterniflora removal project status in 2023. The overall accuracies of the S. alterniflora detection results here are above 90%. Compared with the traditional SVM method, the proposed method achieved significantly higher identification accuracy. The S. alterniflora area was 115.19 hm2 in 2015, 152.40 hm2 in 2017 and 15.29 hm2 in 2023, respectively. The generated S. alterniflora maps clearly show the clonal growth of S. alterniflora in Zhangjiang Estuary from 2015 to 2017, and the large-scale S. alterniflora eradication project has achieved remarkable results with a removal rate of about 90% in the study area. With the continuous implementation of the “Special Action Plan for the Prevention and Control of Spartina alterniflora (2022–2025)” which aims to eliminate more than 90% of S. alterniflora in all provinces in China by 2025, the continual high-spatial resolution monitoring of S. alterniflora is crucial to control secondary invasion and restore coastal wetlands.

The universal federator: A third-party authentication solution to federated cloud, edge, and fog

Cloud, Edge, and Fog computing provide computational services to different end users. A federation among these computing paradigms is beneficial, as it enhances the capability, capacity, coverage, and services of cloud, edge, and fog. An authentication method is needed to realize such a federation among cloud, edge, and fog so that a user belonging to one of these computing paradigms can use the services offered by other computing paradigms in the federation without creating a new account. This paper proposes a standard-compliant universal federator that transparently provides third-party authentication among different protocols, used by cloud, edge, and fog, such as 3GPP EPS-AKA, OpenID Connect (OIDC), and 802.1x. The federator provides transparency by using a controller and modules that act as virtual counterparts of the authentication entities in EPS-AKA, OIDC, and 802.1x. These virtual counterparts play multiple roles, depending upon the involved protocols. We deployed a testbed, published our implementation on GitHub, and tested third-party authentication for 16 scenarios across EPS-AKA, OIDC, and 802.1x. The results show that our federator successfully provides third-party authentication while taking 4.07–51.8% of the total authentication time, which ranges between 1.193–3.825 s for 16 scenarios. Some scenarios involving 802.1x take considerably longer due to the bottleneck caused by the 802.1x switch. We also conducted a security analysis to show that our proposed federator fulfills multiple security requirements.