Near real-time satellite-derived flood maps are invaluable to river forecasters and decision-makers for disaster monitoring and relief efforts. With support from the JPSS (Joint Polar Satellite ...System) Proving Ground and Risk Reduction (PGRR) Program, flood detection software has been developed using Suomi-NPP/VIIRS (Suomi National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite) imagery to automatically generate near real-time flood maps for National Weather Service (NWS) River Forecast Centers (RFC) in the USA. The software, which is called VIIRS NOAA GMU Flood Version 1.0 (hereafter referred to as VNG Flood V1.0), consists of a series of algorithms that include water detection, cloud shadow removal, terrain shadow removal, minor flood detection, water fraction retrieval, and floodwater determination. The software is designed for flood detection in any land region between 80°S and 80°N, and it has been running routinely with direct broadcast SNPP/VIIRS data at the Space Science and Engineering Center at the University of Wisconsin-Madison (UW/SSEC) and the Geographic Information Network of Alaska at the University of Alaska-Fairbanks (UAF/GINA) since 2014. Near real-time flood maps are distributed via the Unidata Local Data Manager (LDM), reviewed by river forecasters in AWIPS-II (the second generation of the Advanced Weather Interactive Processing System) and applied in flood operations. Initial feedback from operational forecasters on the product accuracy and performance has been largely positive. The software capability has also been extended to areas outside of the USA via a case-driven mode to detect major floods all over the world. Offline evaluation efforts include the visual inspection of over 10,000 VIIRS false-color composite images, an inter-comparison with MODIS automatic flood products and a quantitative validation using Landsat imagery. The steady performance from the 3-year routine process and the promising evaluation results indicate that VNG Flood V1.0 has a high feasibility for flood detection at the product level.
•A comprehensive introduction to the SNPP/VIIRS flood detection software is presented.•Flood detection considers vegetation/bare soil and snow/ice backgrounds.•Extensive applications and evaluation of the near real-time flood products are discussed.
This study presents a novel approach for the detection of contrails in satellite imagery using a convolutional neural network (CNN). Contrails are important to monitor because their contribution to ...climate change is uncertain and complex. Contrails are found to have a net warming effect because the clouds prevent terrestrial (longwave) radiation from escaping the atmosphere. Globally, this warming effect is greater than the cooling effect the clouds have in the reduction of solar (shortwave) radiation reaching the surface during the daytime. The detection of contrails in satellite imagery is challenging due to their similarity to natural clouds. In this study, a certain type of CNN, U-Net, is used to perform image segmentation in satellite imagery to detect contrails. U-Net can accurately detect contrails with an overall probability of detection of 0.51, a false alarm ratio of 0.46 and a F1 score of 0.52. These results demonstrate the effectiveness of using a U-Net for the detection of contrails in satellite imagery and could be applied to large-scale monitoring of contrail formation to measure their impact on climate change.
Sea ice leads, or fractures account for a small proportion of the Arctic Ocean surface area, but play a critical role in the energy and moisture exchanges between the ocean and atmosphere. As the sea ...ice area and volume in the Arctic has declined over the past few decades, changes in sea ice leads have not been studied as extensively. A recently developed approach uses artificial intelligence (AI) and satellite thermal infrared window data to build a twenty-year archive of sea ice lead detects with Moderate Resolution Imaging Spectroradiometer (MODIS) and later, an archive from Visible Infrared Imaging Radiometer Suite (VIIRS). The results are now available and show significant improvement over previously published methods. The AI method results have higher detection rates and a high level detection agreement between MODIS and VIIRS. Analysis over the winter season from 2002–2003 through to the 2021–2022 archive reveals lead detections have a small decreasing trend in lead area that can be attributed to increasing cloud cover in the Arctic. This work reveals that leads are becoming increasingly difficult to detect rather than less likely to occur. Although the trend is small and on the same order of magnitude as the uncertainty, leads are likely increasing at a rate of 3700 km2 per year with a range of uncertainty of 3500 km2 after the impact of cloud cover changes are removed.
Despite accounting for a small fraction of the surface area in the Arctic, long and narrow sea ice fractures, known as “leads”, play a critical role in the energy flux between the ocean and ...atmosphere. As the volume of sea ice in the Arctic has declined over the past few decades, it is increasingly important to monitor the corresponding changes in sea ice leads. A novel approach has been developed using artificial intelligence (AI) to detect sea ice leads using satellite thermal infrared window data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS). In this new approach, a particular type of convolutional neural network, a U-Net, replaces a series of conventional image processing tests from our legacy algorithm. Results show the new approach has a high detection accuracy with F1 Scores on the order of 0.7. Compared to the legacy algorithm, the new algorithm shows improvement, with more true positives, fewer false positives, fewer false negatives, and better agreement between satellite instruments.
Sea ice leads (fractures) play a critical role in the exchange of mass and energy between the ocean and atmosphere in the polar regions. The thinning of Arctic sea ice over the last few decades will ...likely result in changes in lead distributions, so monitoring their characteristics is increasingly important. Here we present a methodology to detect and characterize sea ice leads using satellite imager thermal infrared window channels. A thermal contrast method is first used to identify possible sea ice lead pixels, then a number of geometric and image analysis tests are applied to build a subset of positively identified leads. Finally, characteristics such as width, length and orientation are derived. This methodology is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) observations for the months of January through April over the period of 2003 to 2018. The algorithm results are compared to other satellite estimates of lead distribution. Lead coverage maps and statistics over the Arctic illustrate spatial and temporal lead patterns.
Biomass burning patterns over the Maritime Continent of Southeast Asia are examined using a new active fire detection product based on application of the Wildfire Automated Biomass Burning Algorithm ...(WF_ABBA) to data from the imagers on the MTSAT geostationary satellites operated by the Japanese space agency JAXA. Data from MTSAT-1R and MTSAT-2 covering 34months from September 2008 to July 2011 are examined for a study region consisting of Indonesia, Malaysia, and nearby environs. The spatial and temporal distributions of fires detected in the MTSAT WF_ABBA product are described and compared with active fire observations from MODIS MOD14 data. Land cover distributions for the two instruments are examined using a new 250m land cover product from the National University of Singapore. The two products show broadly similar patterns of fire activity, land cover distribution of fires, and pixel fire radiative power (FRP). However, the MTSAT WF_ABBA data differ from MOD14 in important ways. Relative to MODIS, the MTSAT WF_ABBA product has lower overall detection efficiency, but more fires detected due to more frequent looks, a greater relative fraction of fires in forest and a lower relative fraction of fires in open areas, and significantly higher single-pixel retrieved FRP. The differences in land cover distribution and FRP between the MTSAT and MODIS products are shown to be qualitatively consistent with expectations based on pixel size and diurnal sampling. The MTSAT WF_ABBA data are used to calculate coverage-corrected diurnal cycles of fire for different regions within the study area. These diurnal cycles are preliminary but demonstrate that the fraction of diurnal fire activity sampled by the two MODIS sensors varies significantly by region and vegetation type. Based on the results from comparison of the two fire products, a series of steps is outlined to account for some of the systematic biases in each of these satellite products in order to produce a successful merged fire detection product.
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► A new satellite fire product for Asia from the MTSAT geostationary satellites exists. ► Compared to MODIS MOD14, this product shows similar broad-scale patterns. ► Differences between the fire products relate to diurnal sampling and pixel size. ► MODIS fire detection efficiency varies strongly across the swath. ► Fire radiative power retrievals are strongly dependent on pixel size.
Recently, global biomass-burning research has grown from what was primarily a climate field to include a vibrant air quality observation and forecasting community. While new fire monitoring systems ...are based on fundamental Earth Systems Science (ESS) research, adaptation to the forecasting problem requires special procedures and simplifications. In a reciprocal manner, results from the air quality research community have contributed scientifically to basic ESS. To help exploit research and data products in climate, ESS, meteorology and air quality biomass burning communities, the joint Navy, NASA, NOAA, and University Fire Locating and Modeling of Burning Emissions (FLAMBE) program was formed in 1999. Based upon the operational NOAA/NESDIS Wild-Fire Automated Biomass Burning Algorithm (WF_ABBA) and the near real time University of Maryland/NASA MODIS fire products coupled to the operational Navy Aerosol Analysis and Prediction System (NAAPS) transport model, FLAMBE is a combined ESS and operational system to study the nature of smoke particle emissions and transport at the synoptic to continental scales. In this paper, we give an overview of the FLAMBE system and present fundamental metrics on emission and transport patterns of smoke. We also provide examples on regional smoke transport mechanisms and demonstrate that MODIS optical depth data assimilation provides significant variance reduction against observations. Using FLAMBE as a context, throughout the paper we discuss observability issues surrounding the biomass burning system and the subsequent propagation of error. Current indications are that regional particle emissions estimates still have integer factors of uncertainty.
Objective
To identify factors associated with regional recurrence after lymph node dissection (LND) for squamous cell carcinoma (SCC) to determine which patients might benefit from adjuvant therapy.
...Patients and Methods
Men who underwent LND for penile SCC from 1977 to 2014 were identified from an institutional database. Kaplan–Meier curves estimated recurrence‐free survival (RFS) calculated from the date of LND. Cox regression models evaluated the association between RFS and patient and tumour characteristics.
Results
In all, 182 men who underwent LND for penile SCC were identified. The median patient age was 62 years and the median follow‐up was 4.2 years. After LND 34 men had regional recurrence, of which 24 developed isolated regional recurrences without distant metastasis. The median RFS was 5.7 months, and the 3‐year RFS rate was 70%. On univariate analysis, lymphovascular invasion, clinical and pathological nodal stage, pathological inguinal laterality, pelvic nodal involvement, lymph node density ≥5.2%, ≥3 pathologically involved lymph nodes, and extranodal extension (ENE) were associated with worse RFS (all P < 0.05). On multivariate analysis, clinical N3 disease adjusted hazard ratio (AHR) 3.53, 95% confidence interval (CI) 1.68–7.45; P = 0.001), ≥3 pathologically involved lymph nodes (AHR 3.78, 95% CI 2.12–6.65; P < 0.001), and ENE (AHR 3.32, 95% CI 1.93–5.76; P < 0.001) were associated with worse RFS. The 3‐year RFS for patients with cN0, cN1, cN2, and cN3 disease was 91.7%, 64.5%, 54.7%, and 38.3%, respectively. For men with ≥3 involved nodes, the 3‐year RFS was 17% vs 82.4% in men with <3 involved nodes. The 3‐year RFS was 29.7% in men with ENE and 85.7% in men without ENE.
Conclusion
The presence of clinical N3 disease, ≥3 pathologically involved lymph nodes, and ENE was associated with worse RFS. As regional recurrence portends a dismal prognosis with few salvage options, adjuvant therapies should be developed for men with the aforementioned adverse factors.
The first high-quality clinical trial to support ultrahypofractionated whole-breast irradiation (ultra-HF-WBI) for invasive early-stage breast cancer (ESBC) was published in April 2020, coinciding ...with the beginning of the COVID-19 pandemic. We analyzed adoption of ultra-HF-WBI for ductal carcinoma in situ (DCIS) and ESBC at our institution after primary trial publication.
We evaluated radiation fractionation prescriptions for all patients with DCIS or ESBC treated with WBI from March 2020 to May 2021 at our main campus and regional campuses. Demographic and clinical characteristics were extracted from the electronic medical record. Treating physician characteristics were collected from licensure data. Hierarchical logistic regression models identified factors correlated with adoption of ultra-HF-WBI (26 Gy in 5 daily factions UK-FAST-FORWARD or 28.5 Gy in 5 weekly fractions UK-FAST).
Of 665 included patients, the median age was 61.5 years, and 478 patients (71.9%) had invasive, hormone-receptor-positive breast cancer. Twenty-one physicians treated the included patients. In total, 249 patients (37.4%) received ultra-HF-WBI, increasing from 4.3% (2 of 46) in March-April 2020 to a high of 45.5% (45 of 99) in July-August 2020 (P < .001). Patient factors associated with increased use of ultra-HF-WBI included older age (≥50 years old), low-grade WBI without inclusion of the low axilla, no radiation boost, and farther travel distance (P < .03). Physician variation accounted for 21.7% of variance in the outcome, with rate of use of ultra-HF-WBI by the treating physicians ranging from 0% to 75.6%. No measured physician characteristics were associated with use of ultra-HF-WBI.
Adoption of ultra-HF-WBI at our institution increased substantially after the publication of randomized evidence supporting its use. Ultra-HF-WBI was preferentially used in patients with lower risk disease, suggesting careful selection for this new approach while long-term data are maturing. Substantial physician-level variation may reflect a lack of consensus on the evidentiary standards required to change practice.
Our purpose was to develop a clinically intuitive and easily understandable scoring method using statistical metrics to visually determine the quality of a radiation treatment plan.
Data from 111 ...patients with head and neck cancer were used to establish a percentile-based scoring system for treatment plan quality evaluation on both a plan-by-plan and objective-by-objective basis. The percentile scores for each clinical objective and the overall treatment plan score were then visualized using a daisy plot. To validate our scoring method, 6 physicians were recruited to assess 60 plans, each using a scoring table consisting of a 5-point Likert scale (with scores ≥3 considered passing). Spearman correlation analysis was conducted to assess the association between increasing treatment plan percentile rank and physician rating, with Likert scores of 1 and 2 representing clinically unacceptable plans, scores of 3 and 4 representing plans needing minor edits, and a score of 5 representing clinically acceptable plans. Receiver operating characteristic curve analysis was used to assess the scoring system's ability to quantify plan quality.
Of the 60 plans scored by the physicians, 8 were deemed as clinically acceptable; these plans had an 89.0th ± 14.5 percentile value using our scoring system. The plans needing minor edits or deemed unacceptable had more variation, with scores falling in the 62.6nd ± 25.1 percentile and 35.6th ± 25.7 percentile, respectively. The estimated Spearman correlation coefficient between the physician score and treatment plan percentile was 0.53 (P < .001), indicating a moderate but statistically significant correlation. Receiver operating characteristic curve analysis demonstrated discernment between acceptable and unacceptable plan quality, with an area under the curve of 0.76.
Our scoring system correlates with physician ratings while providing intuitive visual feedback for identifying good treatment plan quality, thereby indicating its utility in the quality assurance process.
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