scholarly journals Volcanic ash detection and retrievals using MODIS data by means of neural networks

2011 ◽  
Vol 4 (12) ◽  
pp. 2619-2631 ◽  
Author(s):  
M. Picchiani ◽  
M. Chini ◽  
S. Corradini ◽  
L. Merucci ◽  
P. Sellitto ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Classification Problem ◽  
Detection Accuracy ◽  
Modis Data ◽  
Cloud Properties ◽  
Mandatory Requirement ◽  
Mt Etna ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Time Basis

Abstract. Volcanic ash clouds detection and retrieval represent a key issue for aviation safety due to the harming effects on aircraft. A lesson learned from the recent Eyjafjallajokull eruption is the need to obtain accurate and reliable retrievals on a real time basis. In this work we have developed a fast and accurate Neural Network (NN) approach to detect and retrieve volcanic ash cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) data in the Thermal InfraRed (TIR) spectral range. Some measurements collected during the 2001, 2002 and 2006 Mt. Etna volcano eruptions have been considered as test cases. The ash detection and retrievals obtained from the Brightness Temperature Difference (BTD) algorithm are used as training for the NN procedure that consists in two separate steps: ash detection and ash mass retrieval. The ash detection is reduced to a classification problem by identifying two classes: "ashy" and "non-ashy" pixels in the MODIS images. Then the ash mass is estimated by means of the NN, replicating the BTD-based model performances. A segmentation procedure has also been tested to remove the false ash pixels detection induced by the presence of high meteorological clouds. The segmentation procedure shows a clear advantage in terms of classification accuracy: the main drawback is the loss of information on ash clouds distal part. The results obtained are very encouraging; indeed the ash detection accuracy is greater than 90%, while a mean RMSE equal to 0.365 t km−2 has been obtained for the ash mass retrieval. Moreover, the NN quickness in results delivering makes the procedure extremely attractive in all the cases when the rapid response time of the system is a mandatory requirement.

scholarly journals Volcanic ash detection and retrievals from MODIS data by means of Neural Networks

2011 ◽  
Vol 4 (3) ◽  
pp. 2567-2598 ◽  
Author(s):  
M. Picchiani ◽  
M. Chini ◽  
S. Corradini ◽  
L. Merucci ◽  
P. Sellitto ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Confusion Matrix ◽  
Classification Problem ◽  
Cloud Detection ◽  
Cloud Properties ◽  
Mandatory Requirement ◽  
Mt Etna ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Time Basis ◽  
Ash Cloud

Abstract. Volcanic ash clouds detection and retrieval represent a key issue for the aviation safety due to the harming effects they can provoke on aircrafts. A lesson learned from the recent Icelandic Eyjafjalla volcano eruption is the need to obtain accurate and reliable retrievals on a real time basis. The current most widely adopted procedures for ash detection and retrieval are based on the Brightness Temperature Difference (BTD) inversion observed at 11 and 12 μm that allows volcanic and meteo clouds discrimination. While ash cloud detection can be readily obtained, a reliable quantitative ash cloud retrieval can be so time consuming to prevent its utilization during the crisis phase. In this work a fast and accurate Neural Network (NN) approach to detect and retrieve volcanic ash cloud properties has been developed using multispectral IR measurements collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) over Mt. Etna volcano during 2001, 2002 and 2006 eruptive events. The procedure consists in two separate steps: the ash detection and ash mass retrieval. The detection is reduced to a classification problem by identifying two classes of "ashy" and "non-ashy" pixels in the MODIS images. Then the ash mass is estimated by means of the NN, replicating the BTD-based model performances. The results obtained from the entire procedure are very encouraging; indeed the confusion matrix for the test set has an accuracy greater than 90 %. Both ash detection and retrieval show a good agreement when compared to the results achieved by the BTD-based procedure. Moreover, the NN procedure is so fast to be extremely attractive in all the cases when the quick response time of the system is a mandatory requirement.

Detection of Russian Fires Using MOPITT and MODIS Data

2005 ◽  
Vol 277-279 ◽  
pp. 816-823
Author(s):  
Sang Hee Lee ◽  
Gi Hyuk Choi ◽  
Hyo Suk Lim ◽  
Joo Hee Lee ◽  
Kwon Ho Lee ◽  
...  
Keyword(s):  
East Asia ◽  
Forest Fires ◽  
Eastern China ◽  
Northeast Asia ◽  
Smoke Aerosol ◽  
Modis Data ◽  
Smoke Pollution ◽  
Moderate Resolution ◽  
Large Fires ◽  
Moderate Resolution Imaging Spectroradiometer

The great fires were detected through the Moderate Resolution Imaging Spectroradiometer (MODIS) observations over Northeast Asia. The large amount of smoke produced near Lake Baikal was transported to East Asia using high Aerosol Optical Thickness (AOT) as seen through the satellite images. The smoke pollution from the Russian forest fires would sometimes reach Korea through Mongolia and eastern China. In May 2003, a number of large fires blazed through eastern Russian, producing a thick, widespread pall of smoke over much of East Asia. This study focuses on the identification of the carbon monoxide (CO) for MOPITT released from MOPITT primarily into East Asia during the Russian Fires. In the wake of the fires, the 700hPa MOPITT retrieved CO concentrations which reached up to 250ppbv. Smoke aerosol retrieval using a separation technique was also applied to the MODIS data observed in 14-22 May 2003. Large AOT, 2.0 ~ 5.0, was observed over Korea on 20 May 2003 due to the influence of the long range transport of smoke aerosol plume from the Russian Fires.

scholarly journals A critical look at spatial scale choices in satellite-based aerosol indirect effect studies

2010 ◽  
Vol 10 (23) ◽  
pp. 11459-11470 ◽  
Author(s):  
B. S. Grandey ◽  
P. Stier
Keyword(s):  
Radiative Forcing ◽  
Spatial Scales ◽  
Cloud Droplet ◽  
Spatial Variations ◽  
Opposite Pattern ◽  
Cloud Properties ◽  
Methodological Errors ◽  
Albedo Effect ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Analysing satellite datasets over large regions may introduce spurious relationships between aerosol and cloud properties due to spatial variations in aerosol type, cloud regime and synoptic regime climatologies. Using MODerate resolution Imaging Spectroradiometer data, we calculate relationships between aerosol optical depth τa derived liquid cloud droplet effective number concentration Ne and liquid cloud droplet effective radius re at different spatial scales. Generally, positive values of dlnNedlnτa are found for ocean regions, whilst negative values occur for many land regions. The spatial distribution of dlnredlnτa shows approximately the opposite pattern, with generally postive values for land regions and negative values for ocean regions. We find that for region sizes larger than 4° × 4°, spurious spatial variations in retrieved cloud and aerosol properties can introduce widespread significant errors to calculations of dlnNedlnτa and dlnredlnτa. For regions on the scale of 60° × 60°, these methodological errors may lead to an overestimate in global cloud albedo effect radiative forcing of order 80% relative to that calculated for regions on the scale of 1° × 1°.

Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data

2008 ◽  
Vol 112 (5) ◽  
pp. 2643-2655 ◽  
Author(s):  
Kamel Soudani ◽  
Guerric le Maire ◽  
Eric Dufrêne ◽  
Christophe François ◽  
Nicolas Delpierre ◽  
...  
Keyword(s):  
Modis Data ◽  
Moderate Resolution ◽  
Temperate Deciduous ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

scholarly journals Understanding the Influence of Crop Residue Burning on PM2.5 and PM10 Concentrations in China from 2013 to 2017 Using MODIS Data

2018 ◽  
Vol 15 (7) ◽  
pp. 1504 ◽  
Author(s):  
Yan Zhuang ◽  
Danlu Chen ◽  
Ruiyuan Li ◽  
Ziyue Chen ◽  
Jun Cai ◽  
...  
Keyword(s):  
Crop Residue ◽  
Public Life ◽  
Temporal Perspective ◽  
Modis Data ◽  
Crop Residue Burning ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Pm2.5 And Pm10 ◽  
Pm2.5 Concentration

In recent years, particulate matter (PM) pollution has increasingly affected public life and health. Therefore, crop residue burning, as a significant source of PM pollution in China, should be effectively controlled. This study attempts to understand variations and characteristics of PM10 and PM2.5 concentrations and discuss correlations between the variation of PM concentrations and crop residue burning using ground observation and Moderate Resolution Imaging Spectroradiometer (MODIS) data. The results revealed that the overall PM concentration in China from 2013 to 2017 was in a downward tendency with regional variations. Correlation analysis demonstrated that the PM10 concentration was more closely related to crop residue burning than the PM2.5 concentration. From a spatial perspective, the strongest correlation between PM concentration and crop residue burning existed in Northeast China (NEC). From a temporal perspective, the strongest correlation usually appeared in autumn for most regions. The total amount of crop residue burning spots in autumn was relatively large, and NEC was the region with the most intense crop residue burning in China. We compared the correlation between PM concentrations and crop residue burning at inter-annual and seasonal scales, and during burning-concentrated periods. We found that correlations between PM concentrations and crop residue burning increased significantly with the narrowing temporal scales and was the strongest during burning-concentrated periods, indicating that intense crop residue burning leads to instant deterioration of PM concentrations. The methodology and findings from this study provide meaningful reference for better understanding the influence of crop residue burning on PM pollution across China.

Forest and Field Fire Search System Using MODIS Data

2007 ◽  
Vol 11 (8) ◽  
pp. 1043-1048 ◽  
Author(s):  
Eiji Nunohiro ◽  
◽  
Kei Katayama ◽  
Kenneth J. Mackin ◽  
Jong Geol Park ◽  
...  
Keyword(s):  
Large Scale ◽  
Image Database ◽  
System Configuration ◽  
Search System ◽  
Modis Data ◽  
Level Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Process Level

Tokyo University of Information Sciences receives MODIS (Moderate Resolution Imaging Spectroradiometer) data from NASA’s Terra and Aqua satellites, and provides the processed data to universities and research institutes as part of the academic frontier project. This paper considers the utilization of MODIS data for a system to search for fire regions in forests and fields. For the search system to be effective, the system must be able to extract the location, range and distribution of fires in forests and fields from a large scale image database quickly with high accuracy. In order to achieve high search response time and to improve the accuracy of the analysis, we propose a forest and field fire search system which implements a) a parallel distributed system configuration using multiple PC clusters, and b) MOD02, MOD03 and MOD09 process levels of MODIS data for input data which provide higher resolution and more accurate readings than the standard MOD14 process level data.

scholarly journals Reconciling Simulated and Observed Views of Clouds: MODIS, ISCCP, and the Limits of Instrument Simulators

2012 ◽  
Vol 25 (13) ◽  
pp. 4699-4720 ◽  
Author(s):  
Robert Pincus ◽  
Steven Platnick ◽  
Steven A. Ackerman ◽  
Richard S. Hemler ◽  
Robert J. Patrick Hofmann
Keyword(s):  
Optical Thickness ◽  
Model Error ◽  
Diagnostic Tools ◽  
Limited Information ◽  
Global Models ◽  
Cloud Properties ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Top Pressure

Abstract The properties of clouds that may be observed by satellite instruments, such as optical thickness and cloud-top pressure, are only loosely related to the way clouds are represented in models of the atmosphere. One way to bridge this gap is through “instrument simulators,” diagnostic tools that map the model representation to synthetic observations so that differences can be interpreted as model error. But simulators may themselves be restricted by limited information or by internal assumptions. This paper considers the extent to which instrument simulators are able to capture essential differences between the Moderate Resolution Imaging Spectroradiometer (MODIS) and the International Satellite Cloud Climatology Project (ISCCP), two similar but independent estimates of cloud properties. The authors review the measurements and algorithms underlying these two cloud climatologies, introduce a MODIS simulator, and detail datasets developed for comparison with global models using ISCCP and MODIS simulators. In nature MODIS observes less midlevel cloudiness than ISCCP, consistent with the different methods used to determine cloud-top pressure; aspects of this difference are reproduced by the simulators. Differences in observed distributions of optical thickness, however, are not captured. The largest differences can be traced to different approaches to partly cloudy pixels, which MODIS excludes and ISCCP treats as homogeneous. These cover roughly 15% of the planet and account for most of the optically thinnest clouds. Instrument simulators cannot reproduce these differences because there is no way to synthesize partly cloudy pixels. Nonetheless, MODIS and ISCCP observations are consistent for all but the optically thinnest clouds, and models can be robustly evaluated using instrument simulators by integrating over the robust subset of observations.

scholarly journals Retrieval of canopy height using moderate-resolution imaging spectroradiometer (MODIS) data

2011 ◽  
Vol 115 (6) ◽  
pp. 1595-1601 ◽  
Author(s):  
Zhuosen Wang ◽  
Crystal B. Schaaf ◽  
Philip Lewis ◽  
Yuri Knyazikhin ◽  
Mitchell A. Schull ◽  
...  
Keyword(s):  
Canopy Height ◽  
Modis Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

scholarly journals Aerosols and Clouds Interactions in an Urban Atmosphere

2020 ◽  
Vol 237 ◽  
pp. 02004
Author(s):  
Indira Gunaseelan ◽  
Vijay Bhaskar
Keyword(s):  
Urban Air Pollution ◽  
Urban Atmosphere ◽  
Annual Variations ◽  
Cloud Properties ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Extensive Collection ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Aerosol Emissions ◽  
Precipitation Reduction

Aerosols create great uncertainties in studying climate change under global warming and atmospheric dynamics. To understand the impacts of aerosols on cloud properties in Madurai, we have analyzed an extensive collection of aerosol and cloud properties, obtained from the Moderate resolution Imaging Spectroradiometer (MODIS) data, over the study site during 2012-2013. Monthly, seasonal and annual variations of aerosols and clouds studied along their interactions and impacts on climate. Considering annual averages for all these parameters, most often the year 2012 was dominated with a higher presence of AOD, COD, CER, CTT, CTP whereas rainfall and CF were found to be dominated in 2013. The presence of higher CF in 2013 may be a cause for the higher rainfall and the lower level of CF in 2012 may be a cause for less rainfall. High aerosol loading in this area is due to biomass burning and urban air pollution which may significantly suppress precipitation. Increased aerosols and the local aerosol emissions may reduce the precipitation efficiency, which is responsible for the precipitation reduction and vice-versa.

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