scholarly journals Combined dust detection algorithm by using MODIS infrared channels over East Asia

2014 ◽  
Vol 141 ◽  
pp. 24-39 ◽  
Author(s):  
Sang Seo Park ◽  
Jhoon Kim ◽  
Jaehwa Lee ◽  
Sukjo Lee ◽  
Jeong Soo Kim ◽  
...  
Keyword(s):  
East Asia ◽  
Detection Algorithm ◽  
Dust Detection

Development of a new over‐water Advanced Very High Resolution Radiometer dust detection algorithm

2006 ◽  
Vol 27 (18) ◽  
pp. 3903-3924 ◽  
Author(s):  
Amato T. Evan ◽  
Andrew K. Heidinger ◽  
Michael J. Pavolonis
Keyword(s):  
High Resolution ◽  
Detection Algorithm ◽  
Dust Detection ◽  
Very High

A simplified Suomi NPP VIIRS dust detection algorithm

2017 ◽  
Vol 164 ◽  
pp. 314-323 ◽  
Author(s):  
Yikun Yang ◽  
Lin Sun ◽  
Jinshan Zhu ◽  
Jing Wei ◽  
Qinghua Su ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Dust Detection

scholarly journals Detecting Low-Intensity Fires in East Asia Using VIIRS Data: An Improved Contextual Algorithm

2021 ◽  
Vol 13 (21) ◽  
pp. 4226
Author(s):  
Ning Zhang ◽  
Lin Sun ◽  
Zhendong Sun ◽  
Yu Qu
Keyword(s):  
Low Temperature ◽  
East Asia ◽  
Infrared Imaging ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Small Scale ◽  
Context Sensitive ◽  
Test Conditions ◽  
Test Algorithm ◽  
Increase In Accuracy

The Visible Infrared Imaging Radiometer Suite (VIIRS) fire detection algorithm mostly relies on thermal infrared channels that possess fixed or context-sensitive thresholds. The main channel used for fire identification is the mid-infrared channel, which has relatively low temperature saturation. Therefore, when the high temperature of a fire in this channel is used for initial screening, the threshold is relatively high. Although screening results are tested at different levels, few small fires will be lost under these strict test conditions. However, crop burning fires often occur in East Asia at a small scale and relatively low temperature, such that their radiative characteristics cannot meet the global threshold. Here, we propose a new weighted fire test algorithm to accurately detect small-scale fires based on differences in the sensitivity of test conditions to fire. This method reduces the problem of small fires being ignored because they do not meet some test conditions. Moreover, the adaptive threshold suitable for small fires is selected by bubble sorting according to the radiation characteristics of small fires. Our results indicate that the improved algorithm is more sensitive to small fires, with accuracies of 53.85% in summer and 73.53% in winter, representing an 18.69% increase in accuracy and a 28.91% decline in error rate.

Development of GK-2A AMI Aerosol Detection Algorithm in the East-Asia Region Using Himawari-8 AHI Data

2019 ◽  
Vol 56 (2) ◽  
pp. 207-223 ◽  
Author(s):  
Joon-Bum Jee ◽  
Kyu-Tae Lee ◽  
Kwon-Ho Lee ◽  
Il-Sung Zo
Keyword(s):  
East Asia ◽  
Detection Algorithm ◽  
East Asia Region

scholarly journals Improved Dust Detection over East Asia Using Geostationary Satellite Data

2021 ◽  
Author(s):  
Yu-Rim Shin ◽  
Eun-Ha Sohn ◽  
Ki-Hong Park ◽  
Geun-Hyeok Ryu ◽  
Soobong Lee ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Asian Dust ◽  
Observation Data ◽  
Threshold Values ◽  
Clear Sky ◽  
East Asian Region ◽  
Data Comparison ◽  
Dust Detection ◽  
Rgb Images ◽  
Dust Dispersion

AbstractThis paper presents an improved algorithm, based on the D*-parameter, for dust detection over the East Asian region using brightness temperature differences (BTDs) between the infrared channels of the Advanced Himawari Imager (AHI) onboard Himawari-8. The developed algorithm defines a dust index in the form of the ratio of BTDs: BTD between the 10.4 μm and 12.4 μm channels (BTD10.4–12.4) to that between the 8.6 μm and 10.4 μm channels (BTD8.6–10.4). To identify dust with this index, threshold values were determined empirically. A masking technique using the BTD8.6–10.4 was utilized in the dust index to mitigate the problem of detecting clear-sky deserts and fog over the ocean as dust. BTD8.6–10.4 was analyzed for dust, clear-sky desert, and fog over the ocean cases during 2017 and 2018 with this method. Fog over the ocean and clear-sky desert were distinguished by the criteria of BTD8.6–10.4 > −1.1 K and BTD8.6–10.4 > −1 K, respectively. Based on these thresholds, the influence of fog over the ocean and clear-sky desert was filtered out. The results showed that the dust area was qualitatively consistent with RGB images and ground observation data. Comparison with the AERONET aerosol optical depth (AOD) demonstrated that the D*-parameter was exponentially proportional to AOD, and the correlation coefficient between them was approximately 0.6. The improved Asian Dust detection algorithm can be applied to the monitoring of dust dispersion and movement and also serve as a quantitative indicator of Asian Dust.

scholarly journals Correction to: Improved Dust Detection over East Asia Using Geostationary Satellite Data

2021 ◽  
Author(s):  
Yu-Rim Shin ◽  
Eun-Ha Sohn ◽  
Ki-Hong Park ◽  
Geun-Hyeok Ryu ◽  
Soobong Lee ◽  
...  
Keyword(s):  
East Asia ◽  
Satellite Data ◽  
Geostationary Satellite ◽  
Dust Detection

scholarly journals Combined Dust Detection Algorithm for Asian Dust Events Over East Asia Using GK2A/AMI: a Case Study in October 2019

2021 ◽  
Author(s):  
Jae-Cheol Jang ◽  
Soobong Lee ◽  
Eun-Ha Sohn ◽  
Yoo-Jeong Noh ◽  
Steven D. Miller
Keyword(s):  
Detection Algorithm ◽  
Low Earth Orbit ◽  
Probability Of Detection ◽  
Dust Storms ◽  
Detection Methods ◽  
False Alarms ◽  
Monitoring Methods ◽  
Dust Detection ◽  
Ground Based Lidar ◽  
Combined Algorithm

AbstractA combined algorithm comprising multiple dust detection methods was developed using infrared (IR) channels onboard the GEOstationary Korea Multi-Purpose SATellite 2A equipped with the Advanced Meteorological Imager (GK2A/AMI). Six cloud tests using brightness temperature difference (BTD) were utilized to reduce errors caused by clouds. For detecting dust storms, three standard BTD tests (i.e., $${BT}_{12.3}-{BT}_{10.5}$$, $${BT}_{8.7}-{BT}_{10.5}$$, and $${BT}_{11.2}-{BT}_{10.5}$$) were combined with the polarized optical depth index (PODI). The combined algorithm normalizes the indices for cloud and dust detection, and adopts weighted combinations of dust tests depending on the observation time (day/night) and surface type (land/sea). The dust detection results were produced as quantitative confidence factors and displayed as false color imagery, applying a dynamic enhancement background reduction algorithm (DEBRA). The combined dust detection algorithm was qualitatively assessed by comparing it with dust RGB imageries and ground-based lidar data. The combined algorithm especially improved the discontinuity in weak dust advection to the sea and considerably reduced false alarms as compared to previous dust monitoring methods. For quantitative validation, we used aerosol optical thickness (AOT) and fine mode fraction (FMF) derived from low Earth orbit (LEO) satellites in daytime. For both severe and weakened dust cases, the probability of detection (POD) ranged from 0.667 to 0.850 and it indicated that the combined algorithm detects more potential dust pixels than other satellites. In particular, the combined algorithm was advantageous in detecting weak dust storms passing over the warm and humid Yellow Sea with low dust height and small AOT.

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

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