Dust Storm monitoring 10 November 2015, Mexico City, Mexico Near Real Time Dust Storm Monitoring and Impact Assessment A Remote Sensing & GIS application

Dust Storm monitoring

10 November 2015, Mexico City, Mexico

Near Real Time Dust Storm Monitoring

and Impact AssessmentA Remote Sensing & GIS application

MARZIALETTI Pablo FUSILLI Lorenzo LANEVE Giovanni

MYGEOSS First Call For Innovative Apps in the environmental and social domains

The dust storm formation mechanisms are very complex.

They are related to the local weather system, short-term precipitation, soil moisture, and extent of deforestation, long-term increased drought, land use/land coverage changes, as well as other human activities (LI 2009).

Near Real Time Dust Storm Monitoring and Impact Assessment


DUST STORM monitoring

This natural phenomenon, raises a lot of dust from desert surfaces and decreases visibility to less than 1 km. In recent years the temporal frequency of occurrences and their spatial extents has been dramatically increased (SAMADI et al, 2014).In particular, desert areas are considered vulnerable to

climate change and face a unique set of environmental challenges. Rising environmental degradation coupled with the increasing frequency and intensity of extreme weather events, take an enormous toll on socioeconomic life, human development, and air quality across entire regions.

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Ground based and Remote Sensing measurements are the two main methods currently adopted for monitoring these type of events.

The Earth Observation Satellite Images Application Laboratory (EOSIAL) - of the University of Rome “La Sapienza” - has been working on this task in relation to automate satellite image processing chains, including remote sensing and GIS technologies.




Mineral dust and smoke particles can directly alter solar and Earth radiation in both visible and infrared (IR) spectral regions through scattering and absorption processes. Due to specific optical properties of dust and smoke particles , satellite observed radiances carry the spectral signatures of dust and smoke particles, that are different from molecular, cloud and land surface. Based on these differences, various detection schemes have been developed to distinguish dust from others features.

absorption

scattering

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Dust monitoring systems

http://oiswww.eumetsat.org/IPPS/html/MSG/RGB/DUST/

EUMETSAT RGB Composites Dust

NASA Earth Observatory

Dust, Smoke and Haze: Natural Hazards

http://earthobservatory.nasa.gov/NaturalHazards/category.php?cat_id=7

Barcelona Dust Forecast Center

http://dust.aemet.es/forecast/

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Meteosat Second Generation (MSG) satellite monitors a quarter of the Earth and its atmosphere from a fixed position in geostationary orbit at 0º longitude, 35800 km above the Gulf of Guinea off the west coast of equatorial Africa.

MSG carries the instrument SEVIRI (Spinning Enhanced Visible and Infrared Instrument). Which provides measurements in 12 spectral channels every 15 minutes.




BRIGHTNESS TEMPERATURE The equivalent brightness temperatures (BT) of a satellite observation is defined as the temperature of a black body which emits the same amount of radiation as observed. The product is computed for thermal channels 4, 7, 9 and 10 (IR 3.9, IR 8.7, IR 10.8 and IR 12.0 respectively)

Reference System: WGS 1984 - Datum: WGS 1984

Spatial Resolution: 3.5 km / Refresh rate: Daily, every 15 minutes

Output Data Format: Raster Geotiff BT Measurement Unit: Kelvin (K)

RAD Measurement Unit: mWm-2 sr-1 (cm-1)-1AOI: Algeria

Input Files

The radiances (RAD) of a satellite observation, is defined as the upwelling radiation at the top of the atmosphere. The product is computed for optical channels 1, 2 and 3 (VIS 0.6, VIS 0.8 and NIR 1.6 respectively). 0

RADIANCE OPEN STREET MAP

OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. Map data is collected from scratch by volunteers performing systematic ground surveys.OSM data used for Dust Storm app , is provided by EOSIAL Lab. FTP, and previously filtered by “highways” object type.

Reference System: WGS 1984Datum: WGS 1984

Output Data Format: Shapefilesubdivided by provinces

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Datasets Compression




The algorithm is based on spectral thresholds. The technique uses channels differences (in terms of Brightness Temperature) to define the best spectral thresholds to discriminate the dust-contaminated pixels and remove other features, as desert sand, land, clouds etc.

2nd Mask

3rd Mask

4th Mask

5th Mask

1st Mask

RGB False Colorcomposition according to EUMETSAT

R: BT10-BT9 G: BT9-BT7 B: BT9

RGB Real Color

Dust Mask

The first condition removes cloudy pixels from the image: 1st Mask 285 K < BTch04Following conditions identify pixels of dust over somearid/semi-arid surfaces: 2nd Mask 30 K < BTch04 - BTch09 (Severity High) 3rd Mask 25 K < BTch04 - BTch09 < 30 K (Severity Moderate) 4th Mask 0 K < BTch07 - BTch09 5th Mask BTch09 - BTch10 < 2.5 K

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Polygonize

OSM Intersection

LogFile & Metadata creation

07:00 07:15 07:30 07:45 08:00 08:15 08:30 08:45

Original Satelliteimage

Imageprocessing

Featureextraction




case : Iraq - 19 April 2012Comparison between MSG, MODIS & Algorithm product

Natural RGB composition and False colors RGB composition according

to EUMETSAT

Natural MODIS RGB composition of the same dust

storm

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Natural RGB composition of the same dust storm as acquired by MODIS

Algorithm productsanimation

(09:00 – 11:00)

False colors RGB composition according to EUMETSAT

Natural RGB composition of 3rd, 2nd, 1st MSG channels, the green area depicts the

dust storm discriminated by the algorithm

case : Algeria – 3 September 2015Comparison between MODIS, MSG images (UTC 10:00) &

Algorithm product

case: Dust storm occurred on September, 30 2015 over Algeria. Comparison between Forecast and Algorithm product (UTC 12:00).

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case : Lebanon - 8 September 2015Two women have died in Lebanon and hundreds have been treated for respiratory problems as a massive sandstorm blankets much of the Middle East.

Lebanon's Health Ministry said the number of cases of choking and shortness of breath caused by the sandstorm had risen to 750. The storm is forecast to abate from Wednesday night.

It has strongly impacted dozens of Lebanon's informal camps hosting more than 1.1 million Syrian refugees. (source: CCTV News)

Syrian reugee camp near theLebanese city of Baalbek

After hitting parts of Syria on Monday (7-Sep) , the dust spread to larger areas of the country on Tuesday (8-Sep) morning. Parts of Lebanon, Israel, Turkey and Cyprus were also affected. Lebanon's state news agency said two people died - one an elderly man found on a roof. (source: www.bbc.com)




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Dust Monitoring System Architecture

Software requirements for the NRT application: PythonPython modules: os, gdal, shutil, numpy, gdalconst, ogr, sys, fnmatch, time, urllib, osr, logging, watchdog, subprocess SyncBackFree Geoserver GeonetworkApache TomcatGeobatch JavaZip/Unzip

Software requirements for a standalone version: PythonPython modules: os, gdal, shutil, numpy, gdalconst, ogr, sys, fnmatch, time, urllib, osr, logging, watchdog, subprocess Zip/Unzip

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Outputs. Accessibility: Geonetwork Catalogue (http://eosial.psm.uniroma1.it:8082/geonetwork)

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http://eosial.psm.uniroma1.it:8081/geoserver/DUSTSTORM/wms? http://eosial.psm.uniroma1.it:8081/geoserver/DUST_OSM_IMPACT/wms?

The WMS can be accessed by any other Web Based Catalogue (i.e.: http://geo.ices.dk/geonetwork , http://www.fao.org/geonetwork), as well as Desktop GIS (QGIS, GVSIG, ArcGIS , etc.)

Outputs : WMS accessIntroduction

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ftp://eosial.psm.uniroma1.it/ALG/MSG/output_LogFiles/ username: mygeoss password: duststorm

Outputs : Log Files for each Input Image

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LogFile_ALG_YYYYMMDDHHMM_DONE ► product done, with detailed stats.LogFile_ALG_YYYYMMDDHHMM_NOT_DONE (absence) ► product not done because absence of dust.LogFile_ALG_YYYYMMDDHHMM_NOT_DONE (threshold) ► product not done because area covered by dust is

under the minimum threshold

Kms. affected by Highway type




Research in progressIntroduction

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Research in progressIntroduction

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Future developments

QGIS plugin Mobile app

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LogFiles to standard .xml CAP files

on demand Web application

The Common Alerting Protocol (CAP) is a

standard message format designed for All-Media, All-Hazard,

communications

Web Based application to manage: historic datasets, thresholds, minimum dust area, and output products (mask

level)

QGIS plugin including : WMS Dust and OSM access Attribute table of affected

features Barcelona Dust Forecast

Center forecasting

Android app with access to info of events near actual GPS location and forecast

• This service will provide accessibility to valuable information derived from complex satellite image processing and GIS procedures.

• The implementation of the mentioned algorithm inside a complete automated near real time process, provides an option to track and assess the impact of this type of event.

• The detection service, providing maps with high temporal resolution from geostationary MSG imagery (every 15 minutes), provides complementary data to climate monitoring and management agencies devoted to early warning activities.

• An historical database providing detailed dust events could help the scientific community to improve circulation prediction models.

• At the same time, this database (with others devoted to deforestation monitoring, drought analysis, land uses changes, etc.) could help to understand better the correlation between this type of event and its causes.

• Added value products could help to locate potential health effects in the communities affected, and identify roads and cities impacted recurrently.




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Some bibliographic references

Zhao, T.-P.; Ackerman, S.; Guo, W. Dust and Smoke Detection for Multi-Channel Imagers. Remote Sens. 2010, 2, 2347-2368. John J. Qu, Xianjun Hao, Menas Kafatos, and Lingli Wang, Asian Dust Storm Monitoring Combining Terra and Aqua MODIS SRB Measurements, IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 3, NO. 4, OCTOBER 2006. Ginoux, P., D. Garbuzov, and N. C. Hsu (2010), Identification of anthropogenic and natural dust sources using Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue level 2 data, J. Geophys. Res., 115, D05204, doi:10.1029/2009JD012398. Yoram J. Kaufman, Arnon Karnieli, and Didier Tanré. Detection of Dust Over Deserts Using Satellite Data in the Solar Wavelengths. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 38, NO. 1, JANUARY 2000. Martínez, M.A., J. Ruiz, M. Velázquez, E. Cuevas, G. Cuevas, Use of MSG/Seviri in the WMO Sand and Dust Storm Warning Advisory and Assessment System (SDS WAS) for Europe, North Africa and Middle East, The 2008 EUMETSAT Meteorological Satellite Conference in Darmstadt, Germany, 8-12 September 2008. Jamil Amanollahi, Shahram Kboodvandpour, Ahmad Makmom Abdullah and Parinaz Rashidi. Effect of the influence of heat and moisture changes of desert area around the Euphrates on the recent dust storms in Iran using Landsat satellite images processing. International Journal of the Physical Sciences Vol. 7(5), pp. 827 - 833, 30 January, 2012. Genkova I., Velden C., Shapiro M., Hsu H., Dunion J., Stettner D., “Saharan dust motion extraction from MSG-SEVIRI”. University of Wisconsin – Madison. Space Science and Engineering Center. Cooperative Institute for Meteorological Satellite Studies. Romano F., Ricciardelli E., Cimini D., Di Paola F., Viggiano M.,“Dust Detection and Optical Depth Retrieval Using MSG-SEVIRI Data“. Institute of Methodologies for Environmental Analysis. Atmosphere 2013, 4, 35-47; doi:10.3390/atmos4010035. Klüser L., Schepanski K., Holzer-Popp T.,”Bitemporal Mineral Dust Detection from MSG-SEVIRI Observations“. Klüser L., Schepanski K., “Remote sensing of mineral dust over land with MSG infrared channels: A new Bitemporal Mineral Dust Index”. Remote Sensing of Environment 113 (2009) 1853–1867. Geobatch 1.3.0 Documentation (http://geobatch.geo-solutions.it)




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Dust Storm monitoring 10 November 2015, Mexico City, Mexico Near Real Time Dust Storm Monitoring and Impact Assessment A Remote Sensing & GIS application