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Crop phenological information must be reliably acquired earlier in the growing season to benefit agricultural management. Although the popular shape model fitting (SMF) method and its various improved versions (e.g., SMF by the Separate phenological stage, SMF-S) have been successfully applied to after-season crop phenology detection, these existing methods cannot be applied to within-season crop phenology detection. This discrepancy arises due to the fact that, in the within-season scenario, phenological stages can beyond the defined cut-off time. Consequently, enhancing the alignment of the vegetation index (VI) curve segments prior to the cut-off time does not necessarily guarantee accurate within-season phenological detection. To resolve this issue, a new method named s patio t emporal s hape m odel f itting (STSMF) was developed. STSMF does not seek to optimize the local curve matching between the target pixel and the shape model; instead, it determines similar local VI trajectories in the neighboring pixels of previous years. The within-season phenology of the target pixel was thus estimated from the corresponding phenological stage of the determined local VI trajectories. When compared with ground phenology observations, STSMF outperformed the existing SMF and SMF-S which were modified for the within-season scenario ( SMF ws and SMFS ws ) with the smallest mean absolute differences (MAE) between observed phenological stages and their corresponding model estimates. The MAE values averaged over all phenological stages for STSMF, SMFS ws , and SMF ws were 9.8, 12.4, and 27.1 days at winter wheat stations; 8.4, 14.9, and 55.3 days at corn stations; and 7.9, 12.4, and 64.6 days at soybean stations, respectively. Intercomparisons between after-season and within-season regional phenology maps also demonstrated the superior performance of STSMF (e.g., correlation coefficients for STSMF and SMFS ws are 0.89 and 0.80 at the maturity stage of winter wheat). Furthermore, the performance of STSMF was less affected by the detection time and the determination of shape models. In conclusion, the straightforward, effective, and stable nature of STSMF makes it suitable for within-season detection of agronomic phenological stages. [ABSTRACT FROM AUTHOR]

Skin exposome encapsulates all internal and environmental exposures that affect skin health. Of these, photo-pollution refers to the combined effect on human skin of the simultaneous exposure to solar radiation (especially UV) and air pollution. Providing personalised photo-pollution exposure warnings and dose monitoring to an individual through a smartphone app could help in reducing skin ageing and degradation as well as in managing skin conditions (for example Atopic Dermatitis). However, accurate monitoring is challenging without a potentially expensive or cumbersome sensor device. In this work we present an innovative satellite-based air pollutant monitoring software service, ExpoPol, developed by siHealth Ltd. ExpoPol synthesises several inputs including live satellite imagery in real-time into an artificial intelligence (AI) model to provide assessment of the exposure of a smartphone user to relevant air pollutants, such as nitrogen oxides (NOx), poly-aromatic hydrocarbons (PAH) and ozone (O3). When combined with siHealth's patented technology HappySun® for solar radiation monitoring, ExpoPol can effectively provide a sensor-less personal skin photo-pollution dosimetry. By downscaling satellite data using local geographic data, ExpoPol is capable of monitoring pollutants with street-level resolution and global coverage in near real-time. We evaluate the accuracy of ExpoPol against ground-station monitoring data for three pollutants across three continental regions (Europe, Asia, North America) and find R2 values of 0.62, 0.65, 0.74 for PM10, PM2.5, NO2 respectively. ExpoPol is shown to be significantly more accurate than a state-of-the-art global atmospheric forecasting system (CAMS) over the same ground-station dataset and provide data on much finer spatial resolutions. The presented system can support the real-time automatic assessment of the user's skin exposome, anywhere and anytime. This paves the way for the development of mobile applications empowering users and clinicians to make informed decisions about skin health, or assisting dermocosmetic manufacturers in the creation of personalised products for personal care (e.g., skin ageing prevention or hair care). [ABSTRACT FROM AUTHOR]

Near real-time (NRT) crop type mapping plays a crucial role in modeling crop development, managing food supply chains, and supporting sustainable agriculture. The low-latency updates on crop type distribution also help assess the impacts of weather extremes and climate change on agricultural production in a timely fashion, aiding in identification of early risks in food insecurity as well as rapid assessments of the damage. Yet NRT crop type mapping is challenging due to the obstacle in acquiring timely crop type reference labels during the current season for crop mapping model building. Meanwhile, the crop mapping models constructed with historical crop type labels and corresponding satellite imagery may not be applicable to the current season in NRT due to spatiotemporal variability of crop phenology. The difficulty in characterizing crop phenology in NRT remains a significant hurdle in NRT crop type mapping. To tackle these issues, a novel emergence-based thermal phenological framework (EMET) is proposed in this study for field-level NRT crop type mapping. The EMET framework comprises three key components: hybrid deep learning spatiotemporal image fusion, NRT thermal-based crop phenology normalization, and NRT crop type characterization. The hybrid fusion model integrates super-resolution convolutional neural network (SRCNN) and long short-term memory (LSTM) to generate daily satellite observations with a high spatial resolution in NRT. The NRT thermal-based crop phenology normalization innovatively synthesizes within-season crop emergence (WISE) model and thermal time accumulation throughout the growing season, to timely normalize crop phenological progress derived from temporally dense fusion imagery. The NRT normalized fusion time series are then fed into an advanced deep learning classifier, the self-attention based LSTM (SAtLSTM) model, to identify crop types. Results in Illinois and Minnesota of the U.S. Corn Belt suggest that the EMET framework significantly enhances the model scalability with crop phenology normalized in NRT for timely crop mapping. A consistently higher overall accuracy is yielded by the EMET framework throughout the growing season compared to the calendar-based and WISE-based benchmark scenarios. When transferred to different study sites and testing years, EMET maintains an advantage of over 5% in overall accuracy during early- to mid-season. Moreover, EMET reaches an overall accuracy of 85% a month earlier than the benchmarks, and it can accurately characterize crop types with an overall accuracy of 90% as early as in late July. F1 scores for both corn and soybeans also achieve 90% around late July. The EMET framework paves the way for large-scale satellite-based NRT crop type mapping at the field level, which can largely help reduce food market volatility to enhance food security, as well as benefit a variety of agricultural applications to optimize crop management towards more sustainable agricultural production. [ABSTRACT FROM AUTHOR]

Detecting and repairing road defects is crucial for road safety, vehicle maintenance, and enhancing tourism on well-maintained roads. However, monitoring all roads by vehicle incurs high costs. With the widespread use of remote sensing technologies, high-resolution satellite images offer a cost-effective alternative. This study proposes a new technique, SDPH, for automated detection of damaged roads from vast, high-resolution satellite images. In the SDPH technique, satellite images are organized in a pyramid grid file system, allowing deep learning methods to efficiently process them. The images, generated as 256 × 256 dimensions, are stored in a directory with explicit location information. The SDPH technique employs a two-stage object detection models, utilizing classical and modified RCNNv3, YOLOv5, and YOLOv8. Classical RCNNv3, YOLOv5, and YOLOv8 and modified RCNNv3, YOLOv5, and YOLOv8 in the first stage for identifying roads, achieving f1 scores of 0.743, 0.716, 0.710, 0.955, 0.958, and 0.954, respectively. When the YOLOv5, with the highest f1 score, was fed to the second stage; modified RCNNv3, YOLOv5, and YOLOv8 detected road defects, achieving f1 scores of 0.957,0.971 and 0.964 in the second process. When the same CNN model was used for road and road defect detection in the proposed SDPH model, classical RCNNv3, improved RCNNv3, classical YOLOv5, improved YOLOv5, classical YOLOv8, improved RCNNv8 achieved micro f1 scores of 0.752, 0.956, 0.726, 0.969, 0.720 and 0.965, respectively. In addition, these models processed 11, 10, 33, 31, 37, and 36 FPS images by performing both stage operations, respectively. Evaluations on geotiff satellite images from Kayseri Metropolitan Municipality, ranging between 20 and 40 gigabytes, demonstrated the efficiency of the SDPH technique. Notably, the modified YOLOv5 outperformed, detecting paths and defects in 0.032 s with the micro f1 score of 0.969. Fine-tuning on TileCache enhanced f1 scores and reduced computational costs across all models. [ABSTRACT FROM AUTHOR]

The Surface Ocean CO2 Atlas (SOCAT) of CO2 fugacity (fCO2) observations is a key resource supporting annual assessments of CO2 uptake by the ocean and its side effects on the marine ecosystem. SOCAT data are usually released with a lag of up to 1.5 years which hampers timely quantification of recent variations of carbon fluxes between the Earth System components, not only with the ocean. This study uses a statistical ensemble approach to analyze fCO2 with a latency of one month only based on the previous SOCAT release and a series of predictors. Results indicate a modest degradation in a retrospective prediction test for 2021–2022. The generated fCO2 and fluxes for January–August 2023 show a progressive reduction in the Equatorial Pacific source following the La Niña retreat. A breaking‐record decrease in the northeastern Atlantic CO2 sink has been diagnosed on account of the marine heatwave event in June 2023. Plain Language Summary: There is a growing need to monitor carbon emissions and removals over the globe in near real time in order to correctly interpret changes in CO2 concentrations as they unfold. For the oceans, the best information comes from measurements of the surface ocean CO2 fugacity (fCO2) by the international marine carbon research community. So far, this data is mostly available 6 to 18 months behind real time after collection, qualification, harmonization, and processing. Here, we show that a set of biological, chemical, and physical predictors available in near real time, allows the information contained in the "old" fCO2 measurements to be transferred over time. Based on a statistical technique, we combine all these data sources to estimate global monthly maps of fCO2 and of CO2 fluxes at the air‐sea interface within one month behind real time and with good accuracy. Key Points: We demonstrate the capacity of statistical models to generate global maps of fCO2 and air‐sea flux with a latency reduced to one monthA decrease in the CO2 source for January to August 2023 diagnosed in the tropical Pacific coheres with the retreat of the La Niña eventAn unusual northeastern Atlantic sink reduction diagnosed for June 2023 is linked to record heat and exceptionally low winds [ABSTRACT FROM AUTHOR]

Abstract The Surface Ocean CO2 Atlas (SOCAT) of CO2 fugacity (fCO2) observations is a key resource supporting annual assessments of CO2 uptake by the ocean and its side effects on the marine ecosystem. SOCAT data are usually released with a lag of up to 1.5 years which hampers timely quantification of recent variations of carbon fluxes between the Earth System components, not only with the ocean. This study uses a statistical ensemble approach to analyze fCO2 with a latency of one month only based on the previous SOCAT release and a series of predictors. Results indicate a modest degradation in a retrospective prediction test for 2021–2022. The generated fCO2 and fluxes for January–August 2023 show a progressive reduction in the Equatorial Pacific source following the La Niña retreat. A breaking‐record decrease in the northeastern Atlantic CO2 sink has been diagnosed on account of the marine heatwave event in June 2023.

Interference and collisions often occur in the loading process at shipyards. Existing simulation methods focus primarily on resource processes and schedules, and there is a lack of real-time reflection in the complex and highly variable loading process. This study aims to develop a spatial environment incorporating real-time product data, such as hulls, and confirms its effectiveness by simulating various construction scenarios. As a method, a near real-time spatial environment based on broadband laser scanning was established, with the situation of loading heavy cargo assumed when converting an existing ship into an LNG dual-fuel propulsion ship. A case study simulation of near-real-time cargo loading processes was then conducted using Unity 3D to confirm the interference and collision risks within the spatial environment. The results indicated that interference occurred in structures previously not identified in the design data, and a collision occurred during the loading object erection phase. The simulation confirmed that the identification of interference and collision risks during the erection phase highlights the need for a relocation or removal process of potential hazards before erection takes place. An improved erection simulation that integrates near real-time data could effectively prevent interference and collision risks.

Near real-time spatial prediction of earthquake-induced landslides (EQILs) can rapidly forecast the occurrence position of widespread landslides just after a violent earthquake; thus, EQIL prediction is very crucial to the 72-hour ‘golden window’ for survivors. This work focuses on a series of earthquake events from 2008 to 2022 occurring in the Tibetan Plateau, a famous seismically-active zone, and proposes a novel interpretable self-supervised learning (ISeL) method for the near real-time spatial prediction of EQILs. This new method innovatively introduces swap noise at the unsupervised mechanism, which can improve the generalization performance and transferability of the model, and can effectively reduce false alarm and improve accuracy through supervised fine-tuning. An interpretable module is built based on a self-attention mechanism to reveal the importance and contribution of various influencing factors to EQIL spatial distribution. Experimental results demonstrate that the ISeL model is superior to the excellent state-of-the-art machine learning and deep learning methods. Furthermore, according to the interpretable module in the ISeL method, the critical controlling and triggering factors are revealed. The ISeL method can also be applied in other earthquake-frequent regions worldwide because of its good generalization and transferability.

Wildfires have significant impacts on human lives, critical infrastructures, and Earth’s ecosystems. Accurate and timely information on burned area (BA) affected by wildfires is vital to better understand the drivers of wildfire events, as well as its relevance for biogeochemical cycles, climate, and air quality, and to aid wildfire management. Single satellite data have been used to detect the characteristics of wildfires, retrospectively mapping BAs at a variety of spatial resolutions in previous studies. However, due to the trade-off between spatial and temporal resolutions, single-source satellite data are not sufficient to characterize the explicit dynamics of BAs at high resolutions in both space and time. Thus, a two-stage near real-time BA mapping method was developed in this study to take advantage of the high temporal frequency of coarse resolution sensors and the fine spatial resolution of medium resolution sensors in BA mapping by synergizing freely available coarse and medium spatial resolution (MSR) sensors. First, high temporal frequency sensors such as MODIS and VIIRS were used to identify wildfires and potential BAs. Then, multiple MSR sensors such as Sentinel-2A/2B, Landsat OLI, and Resourcesat AWiFS were synthesized for extracting the BAs with more spatial details in near real-time. We applied the method in California, USA, where wildfires occurred in northern and southern parts in 2017. The results showed that the proposed method is promising for BA mapping with an overall accuracy of 0.84 and 0.85 for wildfires in northern and southern California, respectively. Additionally, the proposed method greatly improved the frequency and reduced the latency, with an average interval of 3.5 days (3 days) and latency of 4 days (sub-daily) for wildfires in southern (northern) California. The extracted BAs illustrated accurate spatial details with MSR sensors. Our method can significantly take advantage of multi-source remote-sensing observations to accurately map the BAs of active wildfires in near real-time. More importantly, the method can be applied to other geographic regions where wildfires risk humans and ecosystems.

Winter wheat is one of the main crops in Canada. Near real-time forecasting of within-field variability of yield in winter wheat at the early stages is essential for precision farming. However, the crop yield modelling based on high spatial resolution satellite data is generally affected by the lack of continuous satellite observations, resulting in reducing the generalization ability of the models and increasing the difficulty of near real-time crop yield forecasting at the early stages. In this study, the correlations between Sentinel-2 data (vegetation indices and reflectance) and yield data collected by combine harvester were investigated and a generalized multivariate linear regression (MLR) model was built and tested with data acquired in different years. In addition, three simple unsupervised domain adaptation (DA) methods were adopted for improving the generalization ability of yield prediction. The winter wheat yield prediction using multiple vegetation indices showed higher accuracy than using single vegetation index. The optimum stage for winter wheat yield forecasting varied with different fields when using vegetation indices, while it was consistent when using multispectral reflectance and the optimum stage for winter wheat yield prediction was at the end of flowering stage. This study demonstrated that the simple mean matching (MM) performed better than other DA methods and it was found that "DA then MLR at the optimum stage" performed better than "MLR directly at the early stages" for winter wheat yield forecasting at the early stages. The results indicated that the DA had a great potential in near real-time crop yield forecasting at the early stages. [ABSTRACT FROM AUTHOR]

Featured Application: CMAC software provides reliable and accurate conversion of degraded top-of-atmosphere imagery to surface reflectance. Accomplished in near real-time using only scene statistics, CMAC can reside in-satellite to support low-latency corrected image output to support smallsat's emerging role for intelligence, surveillance, and reconnaissance. High-cadence Earth observation smallsat images offer potential for near real-time global reconnaissance of all sunlit cloud-free locations. However, these data must be corrected to remove light-transmission effects from variable atmospheric aerosol that degrade image interpretability. Although existing methods may work, they require ancillary data that delays image output, impacting their most valuable applications: intelligence, surveillance, and reconnaissance. Closed-form Method for Atmospheric Correction (CMAC) is based on observed atmospheric effects that brighten dark reflectance while darkening bright reflectance. Using only scene statistics in near real-time, CMAC first maps atmospheric effects across each image, then uses the resulting grayscale to reverse the effects to deliver spatially correct surface reflectance for each pixel. CMAC was developed using the European Space Agency's Sentinel-2 imagery. After a rapid calibration that customizes the method for each imaging optical smallsat, CMAC can be applied to atmospherically correct visible through near-infrared bands. To assess CMAC functionality against user-applied state-of-the-art software, Sen2Cor, extensive tests were made of atmospheric correction performance across dark to bright reflectance under a wide range of atmospheric aerosol on multiple images in seven locations. CMAC corrected images faster, with greater accuracy and precision over a range of atmospheric effects more than twice that of Sen2Cor. [ABSTRACT FROM AUTHOR]

Near real-time (NRT) crop phenology detection and forecasting at the sub-field level are important for crop growth monitoring and management in precision agriculture. Previous studies focused mainly on extracting phenological metrics (e.g., the start of season, and end of season) from time-series remote sensing data for a complete growing season. The existing NRT crop phenology detection methods are difficult to implement at the sub-field scale using available high spatial resolution satellite datasets with a low temporal resolution. In addition, these existing approaches can only estimate specific phenological events from the remote sensing perspective, which is different from the commonly used Biologische Bundesanstalt, Bundessortenamt and CHemical Industry (BBCH) scale used in crop phenology. In this study, an NRT phenology framework was proposed to detect and forecast phenology for winter wheat and corn from Sentinel-2 time-series data in Southwestern Ontario, Canada. The framework incorporated both the canopy structure dynamics model (CSDM) and the shape model-fitting approach to capture crop growth of development over time. The framework can be performed in NRT using timely available Sentinel-2 data during the growing season. The day of year (DOY) for each phenological stage and the BBCH scale on a specific date can be obtained. The resultant Root Mean Squared Errors (RMSEs, days) of BBCH scales were less than 2.9 for winter wheat and less than 3.7 for corn. The RMSEs of detected DOY of all the phenological stages were less than 4 days for winter wheat and less than 3.7 days for corn except for the senescence and the end of season (EOS) stage. [ABSTRACT FROM AUTHOR]

Near real-time spatial prediction of earthquake-induced landslides (EQILs) can rapidly forecast the occurrence position of widespread landslides just after a violent earthquake; thus, EQIL prediction is very crucial to the 72-hour 'golden window' for survivors. This work focuses on a series of earthquake events from 2008 to 2022 occurring in the Tibetan Plateau, a famous seismically-active zone, and proposes a novel interpretable self-supervised learning (ISeL) method for the near real-time spatial prediction of EQILs. This new method innovatively introduces swap noise at the unsupervised mechanism, which can improve the generalization performance and transferability of the model, and can effectively reduce false alarm and improve accuracy through supervised fine-tuning. An interpretable module is built based on a self-attention mechanism to reveal the importance and contribution of various influencing factors to EQIL spatial distribution. Experimental results demonstrate that the ISeL model is superior to the excellent state-of-the-art machine learning and deep learning methods. Furthermore, according to the interpretable module in the ISeL method, the critical controlling and triggering factors are revealed. The ISeL method can also be applied in other earthquake-frequent regions worldwide because of its good generalization and transferability. [ABSTRACT FROM AUTHOR]

• VTEC over the sparse observation data area is reasonably extrapolated by the Adjusted Spherical Harmonic (ASH). • Boundary effect of spherical cap is eliminated by the stochastic component estimation with Kriging. • Real-time Multi-GNSS (GPS, GLONASS and BDS-2/3) data streams are used in SHAKING-based solution. Precise positioning based on Global Navigation Satellite System (GNSS) technique requires high accuracy ionospheric total electron content (TEC) correction models to account for the ionospheric path delay errors. We present an adjusted Spherical Harmonics Adding KrigING method (SHAKING) approach for regional ionospheric vertical TEC (VTEC) modeling in real time. In the proposed SHAKING method, the VTEC information over the sparse observation data area is extrapolated by the Adjusted Spherical Harmonic (ASH) function, and the boundary distortion in regional VTEC modeling is corrected by the stochastic VTEC estimated using Kriging interpolation. Using real-time GPS, GLONASS and BDS-2/3 data streams of the Crust Movement Observation Network of China (CMONOC), the SHAKING-based regional ionospheric VTEC maps are re-constructed over China and its boundary regions. Compared to GNSS VTECs derived from the independent stations, the quality of SHAKING solution improves by 13–31% and 6–33% with respect to the ASH-only solution during high and low geomagnetic periods, respectively. Compared to the inverse distance weighting (IDW) generated result, significant quality improved of SHAKING-based VTEC maps is also observed, especially over the edge areas with an improvement of 60–80%. Overall, the proposed SHAKING method exhibits notable advantage over the existing regional VTEC modeling techniques, which can be used for regional TEC modeling and associated high-precision positioning applications. [ABSTRACT FROM AUTHOR]

Near real-time prediction of earthquake-triggered landslides can rapidly forecast the spatial distribution of coseismic landslides just after a great earthquake, and provide effective support for emergency response. However, the prediction of earthquake-triggered landslides has always been a great challenge because of low accuracy and high false alarms. This work proposes a novel fuzzy deep learning (FuDL) model for near real-time earthquake-triggered landslide spatial prediction. Fuzzy learning theory is for the first time employed in earthquake-triggered landslide prediction. The FuDL has high generalization and robustness, effectively improving the accuracy of earthquake-triggered landslide prediction. Eighteen earthquake-triggered landslide inventories worldwide from 2008 to 2022 are employed to conduct ETL prediction. According to the chronological order, 15 earthquake-triggered landslides from 2008 to 2018 are adopted to train the FuDL model, and 3 earthquake-triggered landslides from 2019 to 2022 are utilized for near real-time earthquake-triggered landslide prediction. Furthermore, this work reveals that ground movement, relatively steep and high topography, and strong seismic intensity are critical factors affecting the spatial distribution of earthquake-triggered landslides. In addition, this work conducted a detailed analysis of the distribution patterns of earthquake-triggered landslides on a global scale. • A novel FuDL model is suggested and achieves high accuracy and good generalization. • FuDL model outperforms the state-of-the-art machine learning or deep learning models. • Topography, ground shake, and seismic intensity dominate the distribution of ETLs. [ABSTRACT FROM AUTHOR]

In this article, we present a comprehensive survey on time-sensitive applications implemented in fog computing environments. The goal is to research what applications are being implemented in fog computing architectures and how the temporal requirements of these applications are being addressed. We also carried out a comprehensive analysis of the articles surveyed and separate them into categories, according to a pattern found in them. Our research is important for the area of real-time systems since the concept of systems that respond in real time has presented various understandings and concepts. This variability of concept has been due to the growing requirements for fast data communication and processing. Therefore, we present different concepts of real-time and near real-time systems found in the literature and currently accepted by the academic-scientific community. Finally, we conduct an analytical discussion of the characteristics and proposal of articles.

Forest wildfire has great impacts on both nature and human society. While disrupts the ecosystems, wildfire leads to significant economic loss and poses a threat to local communities. To detect forest wildfire, remote sensing technology has become an essential and powerful tool. Compared with polar-orbiting satellite, the new generation of geostationary satellite provides higher temporal resolution and faster response capability. In this study, we utilize the near real-time data of Himawari-8/9 satellite, to achieve 10-min forest early wildfire detection. A recursive transformer model is proposed in this work. It fuses multi-type and multi-source information for Himawari-8/9 satellite. By leveraging the spectral, temporal and spatial features of fire pixels and considering land cover information, the proposed method reduces interference factors like cloud and terrain, resulting in minute-level and near real-time detection of forest wildfire. In 21 ground truth forest wildfire scenarios and MODIS-based cross-validation dataset, the proposed method achieves better results compared to the JAXA wildfire product, in terms of overall fire detection accuracy, early fire detection rate, omission rate, and real-time performance. Furthermore, the proposed framework effectively lowers the emergency response time for early forest wildfire detection, thereby reducing the loss caused by forest wildfire. [ABSTRACT FROM AUTHOR]

High-cadence Earth observation smallsat images offer potential for near real-time global reconnaissance of all sunlit cloud-free locations. However, these data must be corrected to remove light-transmission effects from variable atmospheric aerosol that degrade image interpretability. Although existing methods may work, they require ancillary data that delays image output, impacting their most valuable applications: intelligence, surveillance, and reconnaissance. Closed-form Method for Atmospheric Correction (CMAC) is based on observed atmospheric effects that brighten dark reflectance while darkening bright reflectance. Using only scene statistics in near real-time, CMAC first maps atmospheric effects across each image, then uses the resulting grayscale to reverse the effects to deliver spatially correct surface reflectance for each pixel. CMAC was developed using the European Space Agency’s Sentinel-2 imagery. After a rapid calibration that customizes the method for each imaging optical smallsat, CMAC can be applied to atmospherically correct visible through near-infrared bands. To assess CMAC functionality against user-applied state-of-the-art software, Sen2Cor, extensive tests were made of atmospheric correction performance across dark to bright reflectance under a wide range of atmospheric aerosol on multiple images in seven locations. CMAC corrected images faster, with greater accuracy and precision over a range of atmospheric effects more than twice that of Sen2Cor.

The constant monitoring of active landslides, particularly those located in the proximity of populated areas or touristic places, is crucial for early warning and risk-management purposes. The commonly used techniques deploy expensive instrumentation that can be hardly afforded, especially by small mountain communities in which landslide events often occur repeatedly. In recent years, the scientific community, as well as the private sector, have devoted growing effort to reducing the costs of monitoring systems. In this work, we present a monitoring network based on single-frequency Global Positioning System (GPS) sensors that have been activated to monitor an active landslide in the Carnic Alps, North-Eastern Italy. The system, which was composed of 12 single-frequency GPS stations, one seismometric station coupled with a single-frequency GPS instrument for real-time monitoring, and one permanent dual-frequency GPS station located in a stable area, provided daily reports of the landslide motion to the local authorities and administration. We show that this system is a valuable, flexible, and cost-effective tool for quick landslide characterization, and has high potential to be used as a landslide early warning system in case of emergency situations. [ABSTRACT FROM AUTHOR]

Continuous monitoring of water bodies is critical for a range of applications including water resource management, natural hazard assessment and climate change analysis. GOES-16 geostationary satellite Advanced Baseline Imager (ABI) imagery has a very fine (per 10 min) temporal resolution, which is essential for frequent monitoring of water body changes. However, GOES-16 ABI imagery has not been applied for water body mapping, as it lacks a green waveband, essential for the construction of water index images used for surface water extraction. Moreover, the spatial resolution of GOES-16 ABI imagery varies from 0.5 km to 2 km, with only the red band having a spatial resolution of 0.5 km. To solve these problems, we constructed a green-like band and applied component substitution (CS), multiresolution analysis (MRA) and geostatistical-based pansharpening to increase the spatial resolution of the original GOES-16 ABI imagery. The results show that the addition of the constructed green-like band increased significantly the accuracy of water body mapping with water indices such as the NDWI and MNDWI. Sharpening the 1 km bands using the 0.5 km red band of GOES-16 ABI imagery further increased the accuracy of water body mapping with greater spatial detail. Notably, experiments performed in this research demonstrated that the order of the two steps (i.e. green-like band construction and multi-spectral image pansharpening), also influences the accuracy of water body mapping. Due to the different ability to preserve spectral information, the strategy of pansharpening-then-construction was more suitable for MRA-based pansharpening, while the opposite was true for CS-based methods, with no significant difference for geostatistical-based methods. An additive wavelet luminance proportion (AWLP) approach in the pansharpening-then-construction strategy was applied to produce 0.5 km time-series water body maps every 10 min within a day for the main part of the Amazon River, as it produced a water body map with the greatest accuracy. The downscaled GOES-16 ABI imagery, with its constructed and sharpened green-like band, has great potential for near real-time fine-scale mapping of surface water bodies.

The 0-day attack is a cyber-attack based on vulnerabilities that have not yet been published. The detection of anomalous traffic generated by such attacks is vital, as it can represent a critical problem, both in a technical and economic sense, for a smart enterprise as for any system largely dependent on technology. To predict this kind of attack, one solution can be to use unsupervised machine learning approaches, as they guarantee the detection of anomalies regardless of their prior knowledge. It is also essential to identify the anomalous and unknown behaviors that occur within a network in near real-time. Three different approaches have been proposed and benchmarked in exactly the same condition: Deep Autoencoding with GMM and Isolation Forest, Deep Autoencoder with Isolation Forest, and Memory Augmented Deep Autoencoder with Isolation Forest. These approaches are thus the result of combining different unsupervised algorithms. The results show that the addition of the Isolation Forest improves the accuracy values and increases the inference time, although this increase does not represent a relevant problematic factor. This paper also explains the features that the various models consider most important for classifying an event as an attack using the explainable artificial intelligence methodology called Shapley Additive Explanations (SHAP). Experiments were conducted on KDD99, NSL-KDD, and CIC-IDS2017 datasets. [ABSTRACT FROM AUTHOR]

As a cutting-edge technique for denoising and feature extraction, singular spectrum analysis (SSA) has been applied successfully for feature mining in hyperspectral images (HSI). However, when applying SSA for in situ feature extraction in HSI, conventional pixel-based 1-D SSA fails to produce satisfactory results, while the band-image-based 2D-SSA is also infeasible especially for the popularly used line-scan mode. To tackle these challenges, in this article, a novel 1.5D-SSA approach is proposed for in situ spectral-spatial feature extraction in HSI, where pixels from a small window are used as spatial information. For each sequentially acquired pixel, similar pixels are located from a window centered at the pixel to form an extended trajectory matrix for feature extraction. Classification results on two well-known benchmark HSI datasets and an actual urban scene dataset have demonstrated that the proposed 1.5D-SSA achieves the superior performance compared with several state-of-the-art spectral and spatial methods. In addition, the near real-time implementation in aligning to the HSI acquisition process can meet the requirement of online image analysis for more efficient feature extraction than the conventional offline workflow.

For applications such as weather, flood, and drought forecasting that need water level estimates as soon as possible, near real-time (NRT) products are most appropriate. Unlike in-situ sensors for water level measurements, which typically deliver NRT data, satellite altimeters deliver physically meaningful observations with substantial delays after acquisition. The new radar altimetry mission, Sentinel-3, is capable of delivering NRT water levels within a few hours of observation. Currently, it remains unexplored how accurate the NRT product is in the context of river monitoring. This study assesses Sentinel-3A/B NRT products in mapping river water level variations globally. Based on a three-year comparison, we find that the water level derived from NRT is almost as good as that from the delayed products (median root-mean-square error (RMSE): 21.5 cm and 23.5 for S3A and S3B), and both products achieve very similar RMSE values (median: 52.5 cm and 59.0 cm for NRT and non-time critical) against in-situ data at 25 locations. This study highlights the usefulness of Sentinel-3 NRT product for river monitoring and forecasting. And we recommend the NRT product if latency is a primary concern.

Background: Closed-loop behavior paradigms enable us to dissect the state-dependent neural circuits underlying behavior in real-time. However, studying context-dependent locomotor perturbations has been challenging due to limitations in molecular tools and techniques for real-time manipulation of spinal cord circuits., New Method: We developed a novel closed-loop optogenetic stimulation paradigm that utilizes DeepLabCut-Live pose estimation to manipulate primary sensory afferent activity at specific phases of the locomotor cycle in mice. A compact DeepLabCut model was trained to track hindlimb kinematics in real-time and integrated into the Bonsai visual programming framework. This allowed an LED to be triggered to photo-stimulate sensory neurons expressing channelrhodopsin at user-defined pose-based criteria, such as during the stance or swing phase., Results: Optogenetic activation of nociceptive TRPV1 + sensory neurons during treadmill locomotion reliably evoked paw withdrawal responses. Photoactivation during stance generated a brief withdrawal, while stimulation during swing elicited a prolonged response likely engaging stumbling corrective reflexes., Comparison With Existing Methods: This new method allows for high spatiotemporal precision in manipulating spinal circuits based on the phase of the locomotor cycle. Unlike previous approaches, this closed-loop system can control for the state-dependent nature of sensorimotor responses during locomotion., Conclusions: Integrating DeepLabCut-Live with optogenetics provides a powerful new approach to dissect the context-dependent role of sensory feedback and spinal interneurons in modulating locomotion. This technique opens new avenues for uncovering the neural substrates of state-dependent behaviors and has broad applicability for studies of real-time closed-loop manipulation based on pose estimation., Competing Interests: Declaration of interests The authors declare no competing interest.

In this article, we present a comprehensive survey on time-sensitive applications implemented in fog computing environments. The goal is to research what applications are being implemented in fog computing architectures and how the temporal requirements of these applications are being addressed. We also carried out a comprehensive analysis of the articles surveyed and separate them into categories, according to a pattern found in them. Our research is important for the area of real-time systems since the concept of systems that respond in real time has presented various understandings and concepts. This variability of concept has been due to the growing requirements for fast data communication and processing. Therefore, we present different concepts of real-time and near real-time systems found in the literature and currently accepted by the academic-scientific community. Finally, we conduct an analytical discussion of the characteristics and proposal of articles. [ABSTRACT FROM AUTHOR]

Large networks of fixed or mobile sensors based on Internet of Things technologies generate a large amount of data, that requires specific methods for preprocessing, averaging, interpolation and graphic representation. In this, bidimensional scalar data are taken into account; we consider the issues of window averaging, interpolation, and convex hull definition. The considered scenario is a fully automated, near real-time acquisition and visualisation system. The aim of this work is to compare the quality and the computational cost of various interpolation methods, while highlighting the possible risks behind the need to introduce experts' choices in some of them. In this, some algorithmic optimisations and comparisons are presented, based on both simulated and real data. We present some software solutions for the problems of window averaging and buffering of the convex hull and some tests on interpolation methods like Inverse Distance Weighting and Natural Neighbours to determine which is the most appropriate for our real-time measurements. [ABSTRACT FROM AUTHOR]

Near real-time (NRT) monitoring of land disturbances holds great importance for delivering emergency aid, mitigating negative social and ecological impacts, and distributing resources for disaster recovery. Many past NRT techniques were built upon examining the overall change magnitude of a spectral anomaly with a predefined threshold, namely the unsupervised approach. However, their lack of fully considering spectral change direction, change date, and pre-disturbance conditions often led to low detection sensitivity and high commission errors, especially when only a few satellite observations were available at the early disturbance stage, eventually resulting in a longer lag to produce a reliable disturbance map. For this study, we developed a novel supervised machine learning approach guided by historical disturbance datasets to accelerate land disturbance monitoring. This new approach consisted of two phases. For the first phase, the supervised approach applied retrospective analysis on historical Harmonized Landsat Sentinel-2 (HLS) datasets from 2015 to 2021, combined with several open disturbance products. The disturbance model was constructed for each condition of consecutive anomaly number, with the aim of enhancing the specificity for delineating early-stage disturbance regions. Then, these stage-based models were applied for an NRT scenario to predict disturbance probabilities with 2022 HLS images incrementally on a weekly basis. To demonstrate the capability of this new approach, we developed an operational NRT system incorporating both the unsupervised and supervised approach. Latency and accuracy were evaluated against 3000 samples that were randomly selected from the five most influential disturbance events of the United States in 2022, based on labels and disturbance dates interpreted from daily PlanetScope images. The evaluation showed that the supervised approach required 15 days (since the start of the disturbance event) to reach the plateau of its F 1 curve (where most disturbance pixels are detected with high confidence), seven days earlier with roughly 0.2 F 1 score improvement compared to the unsupervised approach (0.733 vs. 0.546 F 1 score). Further analysis showed the improvement was mainly due to the substantial decrease in commission errors (17.7% vs 44.4%). The latency component analysis illustrated that the supervised approach only took an average of 4.1 days to yield the first disturbance alert at its fastest daily updating speed, owing to its decreased sensitivity lag. This finding highlighted the importance of using past knowledge and machine learning to reduce detection delays for an NRT monitoring task. • Early disturbance signals were extracted and modeled from historical datasets. • Retrospective models were applied for supervised near real time monitoring. • Evaluated based on five impactful US disturbance events in 2022. • Achieve a seven day earlier to reach accuracy plateau with 0.2 F 1 improvement. • Averagely requires 4.1 days to yield the first alert at its fastest updating. [ABSTRACT FROM AUTHOR]

Abstract Background Information about airborne pollen concentrations is required by a range of end users, particularly from the health sector who use both observations and forecasts to diagnose and treat allergic patients. Manual methods are the standard for such measurements but, despite the range of pollen taxa that can be identified, these techniques suffer from a range of drawbacks. This includes being available at low temporal resolution (usually daily averages) and with a delay (usually 3–9 days from the measurement). Recent technological developments have made possible automatic pollen measurements, which are available at high temporal resolution and in real time, although currently only scattered in a few locations across Europe. Materials & Methods To promote the development of an extensive network across Europe and to ensure that this network will respond to end user needs, a stakeholder workshop was organised under the auspices of the EUMETNET AutoPollen Programme. Participants discussed requirements for the groups they represented, ranging from the need for information at various spatial scales, at high temporal resolution, and for targeted services to be developed. Results The provision of real‐time information is likely to lead to a notable decrease in the direct and indirect health costs associated with allergy in Europe, currently estimated between €50–150 billion/year.1 Discussion & Conclusion A European measurement network to meet end user requirements would thus more than pay for itself in terms of potential annual savings and provide significant impetus to research across a range of disciplines from climate science and public health to agriculture and environmental management.

Background: Information about airborne pollen concentrations is required by a range of end users, particularly from the health sector who use both observations and forecasts to diagnose and treat allergic patients. Manual methods are the standard for such measurements but, despite the range of pollen taxa that can be identified, these techniques suffer from a range of drawbacks. This includes being available at low temporal resolution (usually daily averages) and with a delay (usually 3–9 days from the measurement). Recent technological developments have made possible automatic pollen measurements, which are available at high temporal resolution and in real time, although currently only scattered in a few locations across Europe. Materials & Methods: To promote the development of an extensive network across Europe and to ensure that this network will respond to end user needs, a stakeholder workshop was organised under the auspices of the EUMETNET AutoPollen Programme. Participants discussed requirements for the groups they represented, ranging from the need for information at various spatial scales, at high temporal resolution, and for targeted services to be developed. Results: The provision of real‐time information is likely to lead to a notable decrease in the direct and indirect health costs associated with allergy in Europe, currently estimated between €50–150 billion/year.1 Discussion & Conclusion: A European measurement network to meet end user requirements would thus more than pay for itself in terms of potential annual savings and provide significant impetus to research across a range of disciplines from climate science and public health to agriculture and environmental management. [ABSTRACT FROM AUTHOR]

The constant monitoring of active landslides, particularly those located in the proximity of populated areas or touristic places, is crucial for early warning and risk-management purposes. The commonly used techniques deploy expensive instrumentation that can be hardly afforded, especially by small mountain communities in which landslide events often occur repeatedly. In recent years, the scientific community, as well as the private sector, have devoted growing effort to reducing the costs of monitoring systems. In this work, we present a monitoring network based on single-frequency Global Positioning System (GPS) sensors that have been activated to monitor an active landslide in the Carnic Alps, North-Eastern Italy. The system, which was composed of 12 single-frequency GPS stations, one seismometric station coupled with a single-frequency GPS instrument for real-time monitoring, and one permanent dual-frequency GPS station located in a stable area, provided daily reports of the landslide motion to the local authorities and administration. We show that this system is a valuable, flexible, and cost-effective tool for quick landslide characterization, and has high potential to be used as a landslide early warning system in case of emergency situations.

Satellite-based precipitation products provide critical precipitation sources with high resolution for hydrologic forecasting. This study aims to assess the suitability of the near-real-time "Early" run and "Late" run of the Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (IMERG) products (IMERG-E and IMERG-L), and the post-real-time "Final" run IMERG product (IMERG-F) and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) 3B42RT in flood simulation, particularly flood events, at hourly scale over a humid region of China. A statistical assessment of the accuracy of selected satellite-based precipitation products shows IMERG-F outperforms the others, and the performance of TMPA 3B42RT is generally better than that of IMERG-E and IMERG-L. Furthermore, IMERG-F performs the best in hourly streamflow simulation, as well as in flood event simulation, with a mean Nash-Sutcliffe efficiency coefficient of 0.71, while IMERG-E, IMERG-L and TMPA 3B42RT present overall acceptable performance. The simulated peak flows tend to be smaller than the observations, and the simulated time lags of occurrence are changeable. This study finds that IMERG and Tropical Rainfall Measurement Mission (TRMM) are suitable for hydrological simulations at hourly scale, and IMERG-E is an appropriate replacement for TMPA 3B42RT in this case. [ABSTRACT FROM AUTHOR]

The 0-day attack is a cyber-attack based on vulnerabilities that have not yet been published. The detection of anomalous traffic generated by such attacks is vital, as it can represent a critical problem, both in a technical and economic sense, for a smart enterprise as for any system largely dependent on technology. To predict this kind of attack, one solution can be to use unsupervised machine learning approaches, as they guarantee the detection of anomalies regardless of their prior knowledge. It is also essential to identify the anomalous and unknown behaviors that occur within a network in near real-time. Three different approaches have been proposed and benchmarked in exactly the same condition: Deep Autoencoding with GMM and Isolation Forest, Deep Autoencoder with Isolation Forest, and Memory Augmented Deep Autoencoder with Isolation Forest. These approaches are thus the result of combining different unsupervised algorithms. The results show that the addition of the Isolation Forest improves the accuracy values and increases the inference time, although this increase does not represent a relevant problematic factor. This paper also explains the features that the various models consider most important for classifying an event as an attack using the explainable artificial intelligence methodology called Shapley Additive Explanations (SHAP). Experiments were conducted on KDD99, NSL-KDD, and CIC-IDS2017 datasets.

The commitment by over 100 governments covering over 90% of the world’s forests at the COP26 in Glasgow to end deforestation by 2030 requires more effective forest monitoring systems. The near real-time (NRT) change detection of forest cover loss enables forest landowners, government agencies and local communities to monitor natural and anthropogenic disturbances in a much timelier fashion than the thematic maps that are released every year. NRT deforestation alerts enable the establishment of more up-to-date forest inventories and rapid responses to unlicensed logging. The Copernicus Sentinel-2 satellites provide operational Earth observation (EO) data from multi-spectral optical/near-infrared wavelengths every five days at a global scale and at 10 m resolution. The amount of acquired data requires cloud computing or high-performance computing for ongoing monitoring systems and an automated system for processing, analyzing and delivering the information promptly. Here, we present a Sentinel-2-based NRT change detection system, assess its performance over two study sites, Manantlán in Mexico and Cartagena del Chairá in Colombia, and evaluate the forest changes that occurred in 2018. An independent validation with very high-resolution PlanetScope (~3 m) and RapidEye (~5 m) data suggests that the proposed NRT change detection system can accurately detect forest cover loss (> 87%), other vegetation loss (> 76%) and other vegetation gain (> 71%). Furthermore, the proposed NRT change detection system is designed to be attuned using in situ data. Therefore, it is scalable to larger regions, entire countries and even continents.

This study evaluated and intercompared seven near-real-time (NRT) versions of satellite-based precipitation products (SPPs) with latencies of less than one day, including GSMaP-NRT, GSMaP-Gauge-NRT, GSMaP-NOW, IMERG-Early, IMERG-Late, TMPA 3B42RT, and PERSIANN-CCS for wet seasons from 2008 to 2019 in a typical middle–high latitude temperate monsoon climate basin, namely, the Nierji Basin in China, in four aspects: flood sub-seasons, rainfall intensities, precipitation events, and hydrological utility. Our evaluation shows that the cell-scale and area-scale intercomparison ranks of NRT SPPs are similar in these four aspects. The performances of SPPs at the areal scale, at the event scale, and with light magnitude are better than those at the cell scale, at the daily scale, and with heavy magnitude, respectively. Most SPPs are similar in terms of their Pearson Correlation Coefficient (CC). The main difference between SPPs is in terms of their root-mean-square error (RMSE). The worse performances of TMPA 3B42RT are mainly caused by the poor performances during main flood seasons. The worst performances of PERSIANN-CCS are primarily reflected by the lowest CC and the underestimation of precipitation. Though GSMaP-NOW has the highest RMSE and overestimates precipitation, it can reflect the precipitation variation, as indicated by the relatively high CC. The differences among SPPs are more significant in pre-flood seasons and less significant in post-flood seasons. These results can provide valuable guidelines for the selection, correction, and application of NRT SPPs and contribute to improved insight into NRT-SPP retrieval algorithms.

This paper presents a near real-time automatic sub-pixel registration method of high-resolution panchromatic (PAN) and multispectral (MS) images using a graphics processing unit (GPU). In the first step, the method uses differential geo-registration to enable accurate geographic registration of PAN and MS images. Differential geo-registration normalizes PAN and MS images to the same direction and scale. There are also some residual misalignments due to the geometrical configuration of the acquisition instruments. These residual misalignments mean the PAN and MS images still have deviations after differential geo-registration. The second step is to use differential rectification with tiny facet primitive to eliminate possible residual misalignments. Differential rectification corrects the relative internal geometric distortion between PAN and MS images. The computational burden of these two steps is large, and traditional central processing unit (CPU) processing takes a long time. Due to the natural parallelism of the differential methods, these two steps are very suitable for mapping to a GPU for processing, to achieve near real-time processing while ensuring processing accuracy. This paper used GaoFen-6, GaoFen-7, ZiYuan3-02 and SuperView-1 satellite data to conduct an experiment. The experiment showed that our method’s processing accuracy is within 0.5 pixels. The automatic processing time of this method is about 2.5 s for 1 GB output data in the NVIDIA GeForce RTX 2080Ti, which can meet the near real-time processing requirements for most satellites. The method in this paper can quickly achieve high-precision registration of PAN and MS images. It is suitable for different scenes and different sensors. It is extremely robust to registration errors between PAN and MS.

Information is a critical resource in disaster response scenarios. Data regarding the geographic extent, severity, and socioeconomic impacts of a disaster event can help guide emergency responders and relief operations, particularly when delivered within hours of data acquisition. Information from remote observations provides a valuable tool for assessing conditions “on the ground” more quickly and efficiently. Here, we evaluate the social value of a near real-time flood impact system using a disaster response case study, and quantify the Value of Information (VOI) of satellite-based observations for rapid response using a hypothetical flooding disaster in Bangkok, Thailand. MODIS imagery from NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) system is used to produce operational estimates of inundation depths and economic damages. These rapid Earth observations are coupled with a decision-analytical model to inform decisions on emergency vehicle routing. Emergency response times from vehicles routed using flood damage data are compared with baseline routes without the benefit of advance information on road conditions. Our results illustrate how the application of near real-time Earth observations can improve the response time and reduce potential encounters with flood hazards when compared with baseline routing strategies. Results indicate a potential significant economic benefit (i.e., millions of dollars) from applying near real-time Earth observations for improved flood disaster response and management.

An automated tool for pre-operational mapping of floods and inland waters using Sentinel-1 data is presented. The acronym AUTOWADE (AUTOmatic Water Areas DEtector) is used to denote it. The tool provides the end user (Italian Department of Civil Protection) with a continuous, near real-time (NRT) monitoring of the extent of inland water surfaces (floodwater and permanent water). It implements the following operations: downloading of Sentinel-1 products; preprocessing of the products and storage of the resulting geocoded and calibrated data; generation of the intermediate products, such as the exclusion mask; application of a floodwater/permanent water mapping algorithm; generation of the output layer, i.e., a map of floodwater/permanent water; delivery of the output layer to the end user. The open floodwater/permanent water mapping algorithm implemented in AUTOWADE is based on a new approach, denoted as buffer-from-edge (BFE), which combines different techniques, such as clustering, edge filtering, automatic thresholding and region growing. AUTOWADE copes also with the typical presence of gaps in the flood maps caused by undetected flooded vegetation. An attempt to partially fill these gaps by analyzing vegetated areas adjacent to open water is performed by another algorithm implemented in the tool, based on the fuzzy logic. The BFE approach has been validated offline using maps produced by the Copernicus Emergency Management Service. Validation has given good results with a F1-score larger than 0.87 and a kappa coefficient larger than 0.80. The algorithm to detect flooded vegetation has been visually compared with optical data and aerial photos; its capability to fill some of the gaps present in flood maps has been confirmed.

Abstract River ice breakup and resulting flood risk is a nearly annual concern for communities along the five major rivers draining to the James and Hudson Bay Coasts in Ontario (Moose, Albany, Attawapiskat, Winisk and Severn Rivers). Ice breakup within this region has historically been monitored using flight reconnaissance supplemented by assessment of hydrometric data. More recently, remote sensed imagery have been used to monitor near real-time ice breakup and flood risk. However, the near real-time remotely sensed breakup information was found to have limited utility in the absence of a broader spatial and temporal understanding of breakup progression. The primary purpose of this study was to develop a method for generating a dataset of breakup dates. A secondary objective was to calculate statistics from this dataset that can be used to provide context to operational near real-time imagery analysis and improve understanding of ice processes in the study area. An automated method for detecting river ice breakup dates from 2000 to 2017 using MODIS imagery was developed. This method uses a threshold-based technique that aims to maximize river coverage and minimize effects of cloud obstruction. Image processing was completed in the high-performance Google Earth Engine application which enabled iterative classification and model calibration. The breakup date dataset was used to calculate statistics on breakup timing, duration, annual variability and breakup order. An assessment of patterns within these data is discussed, relationships between breakup timing, duration and highwater years is explored and the operational utility of these statistics is described. The classification compared well with Water Survey of Canada derived breakup dates with mean bias ranging from −2.0 days to 6.7 days and mean absolute error of 3.4 days to 6.9 days across the rivers. Latitude and distance upstream were found to be primary controls on breakup timing with drainage network configuration and reach morphology also having an influence. No relationships between highwater years and calculated breakup statistics were found. It is recommended that future studies use the dataset developed in this study in combination with hydrometric and remotely sensed data to improve prediction of highwater and understanding of breakup processes within these rivers. Highlights • Derive ice breakup dates from MODIS leveraging the rapid processing of GEE • Calculate range of breakup dates, duration, annual variability and sequence • Latitudinal and downstream breakup trends are observed with notable exceptions. • Demonstrate utility of results for informing operational flood monitoring [ABSTRACT FROM AUTHOR]

Abstract Near real-time monitoring systems of forest disturbance are essential to reduce illegal logging and global deforestation, but the assessment of their performance varies greatly and rarely adheres to recommended assessment practices. Current assessment protocols recommended in the published literature focus on sample-based estimation of area and accuracy of features in remote sensing-based maps, which is less relevant for assessing the performance of near real-time monitoring systems. Rather than area bias and accuracy of mapped features, the objective of near real-time monitoring is fast detection of forest disturbance events. Here we present a new assessment framework as well as two new algorithms for near real-time monitoring of tropical forest disturbance. The first algorithm (NRT-CCDC) fits time series models based on MODIS data to predict future observations. Detections of forest disturbance are labeled as "low-probability", "high-probability", and "confirmed" based on the number of consecutive observations that deviate substantially from the prediction. The second algorithm (Fusion2) predicts MODIS observations at a high temporal frequency by building a model based on a time series of Landsat observations. Fusion2 utilizes daily MODIS observations from both Terra and Aqua to achieve rapid detection of disturbance events. A framework for assessing the performance of near real-time monitoring is presented that focuses on the timing and minimum detectable size of forest disturbance events while still being based on probability sampling and design-based inference. Central to the framework is a new metric we refer to as the "alert-lag relationship", which characterizes the frequency of omitted disturbance events as a function of the time lag between the dates of disturbance and detection. When applied to three different near-real monitoring systems representing three levels of operational readiness (Fusion2, NRT-CCDC and Terra-i), we found that Fusion2 achieved an alert accuracy (event-based agreement) of 50% after 82 days and NRT-CCDC achieved 50% after 126 days. Terra-i never achieved an alert accuracy of 50% but achieved 30% after 109 days. In terms of event size, all three systems showed better performance in detecting large events compared to small events. The smallest event detected by the three systems was 6.5 ha. Conventional accuracy assessment (pixel-based agreement) showed that the user's accuracy, which characterizes commission errors, of the disturbance class for Fusion2, NRT-CCDC, and Terra-i were 87%, 79%, and 30% respectively. The two more complex systems, Fusion2 and NRT-CCDC were consistently more accurate than the operational Terra-i system. In general, the higher the cost of implementing the system corresponded to a higher accuracy of the detection system. A primary objective moving forward is to make the NRT-CCDC and Fusion2 more operational over larger areas. Highlights • Two new algorithm for near real-time monitoring of tropical forest disturbance. • New algorithms utilize daily MODIS data for faster detection of deforestation. • New assessment framework quantifies accuracy as a function of the lag time. • Performance of new algorithms and an operational system is assessed and compared. • The more complex algorithms are more reliable than the operational system. [ABSTRACT FROM AUTHOR]

Efficient nursery irrigation is a key factor for the production of high-quality forestry planting stock. Conventional timer-based irrigation systems used by most forestry nurseries have some drawbacks that, if not properly managed, can lead to over-irrigation and water wastage, leaching of nutrients and contribute to increased incidences of pests and/or diseases. A practical method to accurately measure media water content and control irrigation more carefully could prevent some of these issues. To evaluate the potential use of an automated irrigation system, commercially available Decagon EC-5 dielectric soil water content sensors were laboratory-calibrated against the gravimetric method using coir/pine bark/vermiculite, coir/perlite and pine bark media to measure water content of nursery seedling containers. Using Eucalyptus dunnii seedlings raised in polystyrene seedling containers to test the irrigation system, a datalogger was programmed to control irrigation for low (0.22-0.26 m3 m−3), medium (0.26-0.32 m3 m−3) and high (0.32-0.41 m3 m−3) watering treatments. The measurements were displayed in near real-time using a web-based system, which allowed for system checking and from which data could be downloaded. The Decagon EC-5 sensors were successfully calibrated for all media (R2 > 0.97) and provided a better estimation of media water content than the manufacturer-supplied calibration equation better-suited for soils. The automated irrigation system effectively maintained the specified media water content for E. dunnii seedlings. The high watering treatment seedlings had the highest growth rates, although this treatment also had increased leaching of nutrients. By contrast, the low watering treatment seedlings showed the lowest growth rates, although these seedlings seemed to be more robust and tolerant of water stress. This study showed that the automated irrigation system, based on measurements of the media water content, can be successfully used in scheduling irrigation for Eucalyptus seedlings, leading to potential water savings. [ABSTRACT FROM AUTHOR]

Dynamic management (DM) is a novel approach to spatial management that aligns scales of environmental variability, animal movement and human uses. While static approaches to spatial management rely on one‐time assessments of biological, environmental, economic, and/or social conditions, dynamic approaches repeatedly assess conditions to produce regularly updated management recommendations. Owing to this complexity, particularly regarding operational challenges, examples of applied DM are rare. To implement DM, scientific methodologies are operationalized into tools, i.e., self‐contained workflows that run automatically at a prescribed temporal frequency (e.g., daily, weekly, monthly).Here we present a start‐to‐finish framework for operationalizing DM tools, consisting of four stages: Acquisition, Prediction, Dissemination, and Automation. We illustrate this operationalization framework using an applied DM tool as a case study.Our DM tool operates in near real‐time and was designed to maximize target catch and minimize bycatch of non‐target and protected species in a US‐based commercial fishery. It is important to quantify the sensitivity of DM tools to missing data, because dissemination streams for observed (i.e., remotely sensed or directly sampled) data can experience delays or gaps. To address this issue, we perform a detailed example sensitivity analysis using our case study tool.Synthesis and applications. Dynamic management (DM) tools are emerging as viable management solutions to accommodate the biological, environmental, economic, and social variability in our fundamentally dynamic world. Our four‐stage operationalization framework and case study can facilitate the implementation of DM tools for a wide array of resource and disturbance management objectives. Dynamic management (DM) tools are emerging as viable management solutions to accommodate the biological, environmental, economic, and social variability in our fundamentally dynamic world. Our four‐stage operationalization framework and case study can facilitate the implementation of DM tools for a wide array of resource and disturbance management objectives. [ABSTRACT FROM AUTHOR]

A humid tropical forest disturbance alert using Sentinel-1 radar data is presented for the Congo Basin. Radar satellite signals can penetrate through clouds, allowing Sentinel-1 to provide gap-free observations for the tropics consistently every 6–12 days at 10 m spatial scale. In the densely cloud covered Congo Basin, this represents a major advantage for the rapid detection of small-scale forest disturbances such as subsistence agriculture and selective logging. Alerts were detected with latest available Sentinel-1 images and results are presented from January 2019 to July 2020. We mapped 4 million disturbance events during this period, totalling 1.4 million ha with nearly 80% of events smaller than 0.5 ha. Monthly distribution of alert totals varied widely across the Congo Basin countries and can be linked to regional differences in wet and dry season cycles, with more forest disturbances in the dry season. Results indicated high user’s and producer’s accuracies and the rapid confirmation of alerts within a few weeks. Our disturbance alerts provide confident detection of events larger than or equal to 0.2 ha but do not include smaller events, which suggests that disturbance rates in the Congo Basin are even higher than presented in this study. The new alert product can help to better study the forest dynamics in the Congo Basin with improved spatial and temporal detail and near real-time detections, and highlights the value of dense Sentinel-1 time series data for large-area tropical forest monitoring. The research contributes to the Global Forest Watch initiative in providing timely and accurate information to support a wide range of stakeholders in sustainable forest management and law enforcement. The alerts are available via the https://www.globalforestwatch.org and http://radd-alert.wur.nl .