Proceedings of the 18th Mediterranean Electrotechnical Conference MELECON 2016, Limassol, Cyprus, 18-20 April 2016
Implementation of an Automated Snow Monitoring System using MODIS Products in Lebanon Mahdi Saleh
Ghaleb Faour
Islamic University of Lebanon National Center for Remote Sensing Beirut, Lebanon
[email protected]
National Center for Remote Sensing National Council for Scientific Research Beirut, Lebanon
[email protected]
Abstract—Snow Cover Area monitoring is an important factor in studies of global climate change, regional water balance and soil moisture. Recently, the usage of remote sensing techniques has flourished. In fact, remote sensing data provides timely adequate snow cover information for large areas. While the National Center for Remote Sensing in Lebanon (CNRS) has recently established an operational monitoring room for natural resources and natural disasters, this paper presents the implementation of a fully automated snow cover monitoring system based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images. The system uses snow products from EOS Terra, and Aqua satellites to monitor the Snow Cover of Lebanon during the snow season (i.e. November–April). The importance of this project lies in its daily and fully automated process of acquiring, processing, storing and displaying statistics of the snow covered areas in Lebanon. Applying a custom algorithm based on combining Terra and Aqua snow products will reduce cloud contamination. Keywords—Snow Cover; MODIS; MOD10A1; MYD10A1; TERRA; AQUA; Remote Sensing; Automated Snow Cover Monitoring
I.
INTRODUCTION
One of the essential factors for water availability is snow cover. It impacts the radiation budget of the Earth surface, and may lead to natural disasters such as floods that occur after the snow has melted [4]. Remote sensing techniques allow the examination of the physical properties of snow in areas that are difficult to reach and where measurements may be costly and hazardous. The global scope and repeatability of estimations offered by satellite remote sensing permits researchers to monitor the temporal and spatial variability of snow spread. A wide range of studies such as hydrology and water management, climatology and ecosystem uses Snow cover monitoring based on remote sensing techniques [9]. Since 2000, Nasa and European Space Agency (ESA adopted the “Open Data” policy providing free access to intermediate spatial resolution satellite images such as LANDSAT, ASTER, ALI or Hyperion at non-regular dates and small spatial resolution at a daily frequency from MODIS, SPOT VEGETATION, and NOAA. For environmental monitoring, several products based on remote sensing were developed from sensors such as MODIS as well as SPOT Vegetation [3]. Snow detection techniques based on the visible
and infrared bands uses Snow high reflectance in the visible part and low reflectance in the mid-infrared part of the electromagnetic spectrum [10]. On 24 February 2000, operational information gathering from the Terra MODIS started. Terra satellite orbits at 10: 30 A.M. local descending node. Aqua MODIS began on 24 June 2002, at 1:30 P.M. local time ascending node [2]. At regional and global scale, the measurements of the two satellites having multispectral capabilities proved to be successfully used for efficient snow cover monitoring. [1]. The difference between the overpass times for terra and aqua satellites (morning and afternoon), gives the ability to obtain clearer measurements of snow, as clouds may change their position within three hours [8]. According to Reference [2] “MODIS is an Imaging Spectroradiometer that employs a cross-track scan mirror, collecting optics, and a set of detector elements to provide imagery of the Earth’s surface and clouds in 36 discrete, narrow spectral bands from approximately 0.4 to 14.0 ȝm”. The implemented system uses daily snow product tiles obtained from Terra and Aqua satellites, MYD10A1 and MOD10A1 respectively. These products represent tiles of data gridded in the sinusoidal projection, having approximately 1200 x 1200 km in the area [6]. The system downloads the files with the coordinate’s h20v5, h21v5 from NSIDC online archive to cover the study area (Lebanon). This paper presents the implementation of a snow cover automated monitoring system for Lebanese territories. It describes the system design and its workflow. Also, it includes a brief explanation of the algorithm used to reduce cloud contamination. All works performed in this paper are based on a manually conducted study in 2014 at the CNRS (Mario Mhawej, Ghaleb Faour, Abbas Fayad, Amin Shaban, “Towards an enhanced method to map snow cover areas and derive snow-water equivalent in Lebanon”, Journal of Hydrology, 4 April 2014). II.
STUDY REGION
Lebanon’s total area is 10,452 km2, and this relatively small country receives every year an amount between 800 and 1500 mm of precipitation. Precipitation in Lebanon is limited to the winter season because the climate variability is highly affected by the Mediterranean climate. Mount Lebanon, Bekaa Plain, Coastal zone and the Anti-Lebanon represents the
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physiographic regions that form Lebanon. The increase in altitude produces colder winters, more precipitation, and snowfall. Fig. 1 illustrates the country’s topography. Around 25% of the country’s total area is covered by snow each year that lasts on mountain crests for more than four years. The snow cover area contributes to the feeding of 15 river basin systems and more than 2000 springs. Snow also contributes to groundwater recharge via some aquiferous formations and karstic galleries [7]. Fig. 2. Server application GUI
The second block contains a set of image processing scripts implemented using Python Scripting Language (PIL imaging library). These scripts process the downloaded images and calculate the snow covered extent area. The Terra and Aqua MODIS daily snow products are combined to reduce cloud contamination. The system saves the numerical and graphical results in a database implemented by Microsoft SQL Server 2008 R2. The web application (C#, asp.net) provides the users with the ability to download and observe daily and durational statistics (numerical and graphical) for any desired duration. Fig. 3 and Fig.4 show the durational and daily view offered by the web application respectively.
Fig. 1. Zonal Classification of elevations in Lebanon [7] Fig. 3. Statistical view
III.
SYSTEM DESCRIPTION
The system introduces a fully automated process that updates daily the produced snow cover dataset without the need for any user interaction. This dataset provides information about the current status of snow cover characteristics in Lebanon and shall be a useful source for any future climate change studies. Also, results obtained from a single snow cover season can be utilized to distinguish regions with uncommonly long or short Snow Cover Duration, which may affect the economy and the environment. The system includes a server application implemented by using C# Windows Forms Platform, to download the raw MODIS data as “.hdf” files. HDF is a file format that defines data collections in a hierarchical manner. It also embeds directory structure and data descriptions [5]. Fig. 2 shows the GUI of the server application. Fig. 4. Daily view
IV.
CLOUD REDUCTION
The confusion between clouds and snow highly affects the accuracy of the MODIS snow products[2]. The combination process merges the two TERRA and AQUA MODIS snow cover products on a pixel basis. The pixels classified as clouds in the Aqua images are replaced by the Terra pixel value of the same location if the Terra pixel is snow or land. We used several python scripts to combine each tile obtained from Terra and Aqua satellites to minimize cloud cover. The Combination Scripts will check each pixel in Aqua image and set it according to the pixel values of the Terra image based on the following criteria: -
If (Aqua Pixel & Terra Pixel = Snow), New pixel set to Snow.
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If (Aqua Pixel = Cloud) & (Terra Pixel != Cloud), New pixel is set to Terra pixel.
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If (Aqua pixel = Cloud) & (Terra pixel = Cloud), New pixel is set to Cloud.
For the default case, values taken from Terra images are used because they are more accurate than Aqua images [11]. According to this combining criterion a new image will be drawn having the following pixel values: -
The pixels are set to cloud values only if the two images have this pixel as a cloud pixel. This decision will reduce the existence of cloud pixels in the Aqua image.
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The cloud pixels in the old Aqua image will be set to the value of the Terra image pixel if it isn’t a cloud pixel, such as snow or land or water.
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The pixel that has a satellite error or missing value will be set to the other corresponding pixel value if it is has a valid value. This decision will decrease the satellite error pixels in the new image.
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The pixel having a snow value in the two images is set to snow, which ensures the reality of getting snow actual pixels.
For the other type of snow cover images named “Fractional Snow Cover“, we applied the same criteria presented above. The only difference in dealing with the two types (Binary Cover and Fractional Cover) is that the new snow pixel value will take the value of the pixel having the higher snow percentage between the two images. This decision decreases the partial cloud effect within the same pixel. Fig. 5 shows the result of clipping the obtained images by using Lebanon’s shape file. After applying the combination algorithm, we observed the reduction of clouds contamination.
Fig. 5. Combined Snow Cover Daily Tile Image for 01-01-2013
V.
STORING AND DISPLAYING RESULTS
After the completion of the cloud reduction phase, another script is used to calculate the area of snow-covered, land, clouds and all other features in km2. The server application inserts the numerical and graphical results into the database.The database contains a data table for each data type (Snow Cover – Fractional Cover – Snow Albedo – Quality Assessment). Also, the database contains an datatable named “Archive” to store all dates and primary keys for all data records saved in the other tables. The web application query the database for the desired results according to the user’s request. It offers the ability to download and save numerical results as a Microsoft Excel sheet. We hosted the database and the web application locally at the national center for remote sensing, and it is used daily to acquire snow covered area information. VI.
RESULTS AND DISCUSSION
By using the system, we implemented a dataset for Snow covered area in Lebanon for 15 years (2000-2015). The overall error is a result of the collection of error types such as, satellite error, clouds error, and forest masks error. Snow surface area was identified using Landsat (Thematic Mapper Instrument) having high spatial resolution (30 m) and compared to the values generated by the presented system using MODIS instrument (TABLE. I). The comparison shows that the overall accuracy indicates the error is about 10%, which is considered acceptable and applicable.
TABLE I.
ACCURACY EVALUATION
VII. CONCLUSION
Date
SCA (Landsat) km2
FSC (MODIS) km2
Error
December 26, 2004 January 11, 2005
1348 1421
1410 1490
4.59 4.85
January 27, 2005
1785
1742
-2.40
February 12, 2005
2038
1788
-12.26 -39.04
March 16, 2005
735
448
January 30, 2006
1388
1363
-1.80
January 1, 2007
1464
1773
21.10
January 17, 2007
1311
1294
-1.29
March 6, 2007
715
344
-51.88 -42.95
March 22, 2007
582
332
January 20, 2008
829
863
4.10
February 21, 2008
3061
3323
8.55
January 22, 2009
734
665
-9.40
February 7, 2009
492
271
-44.91 -0.82
November 6, 2009
243
241
January 12, 2011
606
642
5.94
February 13, 2011
366
293
-19.94
Overall Error
-10.44
In future improvements, more advanced techniques should be used to lower forest and cloud mask error effect whereas the satellite or sensor error will still exist since it due to the installed imaging device. Results obtained showed clearly the significant decline of snow covered area in 2014 while the results in the previous years were relatively close. The obtained results for 2014 indicates the scarcity of precipitation in that year. However, this decline reflected on the groundwater level and caused real water crisis in the country. Obtained results demonstrate the close relation between the land areas covered by snow and by the ground water level affecting the general situation and people lives.
The presented system is a fully automated and carries daily estimation of Snow Covered Area based on MODIS satellite images. The National Center for Remote Sensing is using this system as a part of its operational room for snow cover monitoring over the Lebanese territories. Dealing with cloud cover is a significant problem in the snow covers’ assessment. We observed that the Aqua snow product has relatively more cloud cover compared to Terra snow product. The overall performance of the applied cloud filtering technique based on combination method is satisfactory. REFERENCES [1]
Claudia Notarnicola, Martial Duguay, Nico Model, Thomas Schellenberger, Anke Tetzlaff, Roberto Monsorno, Armin Costa, Christian Steurer, and Marc Zebisch, “Snow Cover Maps from MODIS Images at 250 m Resolution, Part 1: Algorithm Description”, Remote Sensing Journal, 2013 [2] Dorothy K. Hall and George A. Riggs, “Accuracy assessment of the MODIS snow products”, Wiley InterScience, 2007 [3] Dorothy K. Hall, George A. Riggs, Vincent V. Salomonson, Nicolo DiGirolamo, Klaus J. Bayr, “MODIS Snow-Cover Products,” Remote Sensing of Environment, 2002 [4] Andreas J. Dietz, Christoph Wohner and Claudia Kuenzer, “European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products,” Remote Sensing Journal, 2012 [5] Karl Nyberg, “Parsing Hierarchical Data Format (HDF) Files,” ACM SIGAda Ada Letters, 2010 [6] George A. Riggs, Dorothy K. Hall, Vincent V. Salomonson, “MODIS Snow Products User Guide to Collection 5,” 2006 [7] Mario Mhawej, Ghaleb Faour, Abbas Fayad, Amin Shaban, “Towards an enhanced method to map snow cover areas and derive snow-water equivalent in Lebanon,” Journal of Hydrology, 2014 [8] J. Parajka, L. Holko, Z. Kostka, and G. Bl¨oschl, “MODIS snow cover mapping accuracy in a small mountain catchment – comparison between open and forest sites,” Hydrology and Earth System Sciences, 2012 [9] Anne W. NOLIN,” Recent advances in remote sensing of seasonal snow”, Journal of Glaciology, 2010 [10] Jonathan Munoz, Jose Infante1, Tarendra Lakhankar, Reza Khanbilvardi, Peter Romanov, Nir Krakaue and Al Powell, “Synergistic Use of Remote Sensing for Snow Cover and Snow Water Equivalent Estimation”, British Journal of Environment & Climate Change, 2013 [11] Julienne C. Stroeve, Jason E. Box, Terry Haran,” Evaluation of the MODIS (MOD10A1) daily snow albedo product over the Greenland ice sheet”, Remote Sensing of Environment, 2006
