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8/3/2019 Snow Cover Mapping Nepal
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International Centre for Integrated Mountain Development
Kathmandu, Nepal
Remote Sensing
Based Monitoringand Assessment of
Snow Cover
National Training Course on"Remote Sensing based Monitoring and Assessment ofCryosphere - Snow and Glaciers”
ICIMOD, Nepal17 October, 2011
Khun San Aung, kaung@icimod.org
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Outline1. Introduction
2. MODIS1. MODIS Spectral Band
2. MODIS Snow Products
3. Methods & Tools
1. Snow Cover Monitoring System at ICIMOD
2. Combining MODIS snow products
3. Cloud Removal by Temporal Filtering4. Cloud Removal by Spatial Filtering
5. Cloud Removal by Temporal Analysis
6. Estimating Snow Cover Area
4. Analysis & Output
1. Trend Analysis
2. Decadal Change Analysis
3. Seasonal variation, Monthly variation,
4. Inter-annual variation, Intra-annual variation
5. Altitude-wise, slope-wise, aspect-wise snow cover variation
5. Cryosphere Portal
6. Conclusion
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Introduction
• The term “kryos” after which “cryosphere” is used to collectively describethe portions of the Earth’s surface where water is in a solid form (sea ice,
lake ice, river ice, snow cover, glaciers, ice caps and ice sheets, andpermafrost).
• Snow cover has the largest areal extent of any component of thecryosphere (mean maximum areal extent of approximately 47 million km2).
• Most of the Earth’s snow covered area (SCA) is located in the Northern
Hemisphere and temporal variability is dominated by the seasonal cycle;46.5 million km2 in January to 3.8 million km2 in August
It is an integral part of the global climate system with important linkages andfeedbacks generated through its influence on surface energy and moisture
fluxes, clouds, precipitation, hydrology, and atmospheric and oceaniccirculation.
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Introduction (continued)
• The cryosphere plays a significant role in global climate and in climatemodel response to global change. It is also a major source for river systemin mountain and water for people.
• The recent changes in hydrological regimes in major river system due toalteration of SCA as a result of global warming have become a seriousconcern. This is expected to have direct consequences on water
availability situation which will have influence across different eco-systemservices.
In the backdrop of climate change, it is vital to have an accurate and long-term database established on snow-extent variability to understand through
modeling, the influence of climate change on water availability scenario.
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Introduction- Cryosphere work in ICIMOD
• There was a need in monitoring of cryosphere with use of EO andRS tool in a regional framework. ICIMOD has taken up the role andto function as the regional cryosphere data hub.
• 2009-2010, “Too much too little water” - project (funded by the Sida -
Swedish International Development Agency).• April-2009, “Regional Consultative Workshop on Remote Sensing of
Cryosphere”.
• A customized methodology has been developed to for snow &glacier mapping & monitoring.
• A regional snow & glacier database covering 10 major river basinshas been established in ICIMOD.
• A capacity building training has been conducted (in ICIMOD, YouthForum, October 2010, in Pakistan April 2011).
• 2011- current, Cryosphere studies and Capacity Building project(funded by Norwegian government).
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MODIS (Moderate Resolution Imaging
Spectroradiometer)
• MODIS is an instrument onboard of Terra and Aqua Satellite
• Orbit: 705 km, sun-synchronous, near-polar,circular, 10:30 a.m. descending node (Terra) or
1:30 p.m. ascending node (Aqua)
• Swath Dimensions: 2330 km (cross track) by 10 km(along track at nadir)
• Temporal Resolution: Views the entire surface ofthe Earth every one to two days. It’s high temporalresolution enables to monitor the dynamic of snowcover in both regional and global scale.
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MODIS Spectral Bands
Band Bandwidth Spatial Resolution Primary Use
1 620 - 670 250 m Land/Cloud/AerosolsBoundaries
2 841 - 876 250 m
3 459 - 479 500 m Land/Cloud/AerosolsProperties
4 545 - 565 500 m
5 1230 - 1250 500 m
6 1628 - 1652 500 m
7 2105 - 2155 500 m
8-36405 nm -
14.385 µm 1 km
Atmospheric, Temperature,
Cloud, Ozone, Water Vapour,Ocean Color,Biogeochemistry
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Spectral Reflectance forDifferent Land Covers
0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.40.0
0.2
0.4
0.6
0.8
1.0
MODIS
band2
MODIS
band1
MODIS
band6
R e f l e c t a n c e
Wavelength (µm)
Fine snowConiferGreen grassInceptisol soilBasaltThick cloud
MODIS
band4
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NDSI (Normalized DifferenceSnow Index)
It is the normalized value of the difference of reflectancesobserved in a visible such as MODIS band 4 (0.545-0.565m) band and a short-wave infrared band MODIS band 6
(1.628-1.652 m).
There are many different standard products (cryoshpere,land, atmosphere, etc.) for MODIS.
NSIDC (National Snow & Ice Data Center) has beenproducing snow products in different levels.
4 6
4 6
b b NDSI
b b
−
=
+
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MODIS Snow Products
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MODIS L2 5 minute SwathProduct (MOD10_L2 Snow)
It is a 5 minuteswath product
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MODIS Level 3 ProductIn Sinusoidal Projection
It is in 10 deg X 10 deg tiles.
• Horizontal = 36 tiles
• Vertical = 18 tiles
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MOD10A2 (500m)8-day composite snow product
Maximum SnowExtent Coded Integer
Values
SampleValue
Explanation
0 data missing
1 no decision
11 night
25 no snow
37 lake
39 ocean
50 cloud100 lake ice
200 snow
254detectorsaturated
255 fill
Sample Tile no. h25v06Layer
1. Maximum Snow Extent Layer2. 8-day Snow cover
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Method & Tools
The following slides explains the detail method that wehave adopted to improve the MODIS snow coverproducts.
MODIS Snow Tool for processing and analysis ofMODIS snow data has been developed as itbecomes needed.
The tool has been designed to be able to
handle/process multiple time-series data.
The usage of the tool will be demonstrated/discussedduring the hands on exercises.
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Snow Cover Monitoring System atICIMOD
ICIMOD Server
•Cryosphere Portal
• DVD
CataloguingSnow Products Web
daily
Spatial-temporal Filtering
Internet
Quick Look Generation System
MODIS Snow Products DB
Dissemination Applications
• Hydrological Modelling
Improved Snow Cover Products
Snow Cover DB in Sub-basin Level
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Image Processing Flow
MODIS Snow Products
Mosaicking &Reprojection
Temporal Filtering
Terra Snow Aqua Snow
Combined Snow Product
Spatial Filtering
Major rivers basin/sub-basin/
watershed/catchment level
Temporal Analysis
Improved Snow Product
MODIS Snow Tool
Extracting Snow Cover Area
Analysis
MODIS Reprojection Tool
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Combining Terra & Aqua
• It combines Terra and Aqua dataset in orderto get the most information by taking gooddata from both satellites.
• Cloud pixels/missing data are removed.
good data [snow, land, water, etc.]
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Terra MODIS (Natural Color)
Cloud is always a major problem in the remote sensing
images which uses visible wavelength regions.
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Terra MODIS Snow Cover
Water Body
Snow free land Snow
Cloud/No data
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Aqua MODIS (Natural Color)
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Aqua MODIS Snow Cover
Water Body
Snow free land Snow
Cloud/No data
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Combined MODIS Snow Cover
Water Body
Snow free land Snow
Cloud/No data
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Cloud removal by TemporalFilter
It removes cloud pixels by filling with good data fromeither adjacent backward or forward days.
SL CLand Snow Cloud
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Cloud removal by Spatial Filter
It removes cloud pixels by filling with majority ofgood data from surrounding pixels.
Example of using a 7X7 spatial window size
It can remove scattered cloud pixels and the edge of the
big cloud
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Cloud removal by TemporalAnalysis
It is similar to temporal filtering but it removescloud pixels by filling with the data from adjacentday only when the adjacent backward and
forward days have the same class.Examples,
day1-day2-day3
land-cloud-land land-land-landsnow-cloud-snow snow-snow-snow
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Product Improvement
Most of the cloud can be removed by combinationof two satellite and temporal filtering.
Example for the whole HKH area,
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Estimating Snow Cover Area
A systematic distortion occurs while transforming into GeographicCoordinate System.The area is exaggerated in the higher latitude and is greater than it’s original
values.For area calculation, the image is has to be reprojected into a projection thatpreserve area.
Snow Cover AreaIn this method, the area of each and every pixel for different latitudes isreprojected into the ideal sphere (using the WGS_1984) and the total snowcover area is estimated by summation of all snow pixel area.
Error (500m spatial resolution) = -0.0022 to -0.0023 %
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Area of a snow pixel/cell
The area of a snow pixel, at location (x,y), can be calculated by
Where;A(x,y) = Area of a pixel (square meter)dx(x,y) = longitudinal distance of pixel in East-West direction (meter)dy(x,y) = latitudinal distance of pixel in North-South direction (meter)
The longitudinal and latitudinal distances of a pixel at different latitudes can be calculated by
Where;
φ = latitude in radian (lat * Π / 180) (positive for northern hemisphere) (radian)C(φ) = Circumference of parallel at latitude φ (meter)Polar Circumference = 39,940,653 (meter)dx = Spatial resolution/cell size in East-West direction given by image (in degree for GCS)dy = Spatial resolution/cell size in North-South direction given by image (in degree for
GCS)
dy
dx
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Analysis
a. Time-series of SnowCover Area for Hindu-Kush-Himalaya Regions
b. Annual snow cover forHKH region
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
15
16
17
18
19
20
21
Year
S n o w c o v e r a r e a ( % )
1. Trend Analysis
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2. Decadal Snow Cover Change 2000-2010 (Terra Only)
-16 -12 -8 -4 0 4 8 12 16
Percent
Analysis
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2. Decadal Snow Cover Change 2002-2010 (Terra+Aqua)
Analysis
-16 -12 -8 -4 0 4 8 12 16
Percent
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3. snow cover trend for the eastern, central, and western parts
of HKH region
Analysis
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0
5
10
15
20
25
30
35
40
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
S n o w c o v e r a r e a ( %
)
Months
2002 2003 2004 2005 2006 2007 2008 2009 2010
4. Monthly variation of snow cover for HKH region
Analysis
A l i
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5. Seasonal variation of SCA in the HKH region
Analysis
A l i
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10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
S n o w c o v e r a r e a
( % )
Year
<1000
1000-1500
1500-2000
2000-2500
2500-3000
3000-3500
3500-4000
4000-4500
4500-5000
5000-5500
5500-6000
6000-6500
6500-7000
7000-7500
7500-8000
>8000
6. Altitude zone-wise snow cover variation for HKH region
Analysis
A l i
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7. Interannual variation in snow cover area for the10 major river
basins
20
40
60
80
100
120
140
160
180
200
220
240
260
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
S n o w c o v e r a r e a x 1 0 0 0 ( s
q . k m )
Year
Amu Darya Brahmaputra Ganges Indus Irrawaddy
Mekong Salween Tarim Yangtze Yellow River
Analysis
A l iA l i
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Analysis
8. Monthly variation of snow cover for Brahmaputra basin
Analysis
A l iA l i
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Analysis
Example. Brahmaputra basin (for one time point)
Meter
( % )
9. Altitude zone-wise snow cover distribution
Analysis
A l iA l i
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E.g Brahmaputra river basin
Degree
Analysis
10. Slope zone-wise snow cover area distribution
Analysis
A l iA l i
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E.g Brahmaputra river basin
Unitsnow cover percent area
Analysis
11. Aspect zone-wise snow cover area
Analysis
O t t R 1
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Output - Report 1
To be released inCOP17, Durban,S. Africa inDecember 2011
coming soon
O t t P 1
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http://www.the-cryosphere.net/home.html
Output - Paper 1
O t t P 2
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published in IGARSS 2010
Output - Paper 2
O t t P 3
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ACCEPTED IN
CURRENT SCIENCE http://cs-test.ias.ac.in/cs/index.php
Output - Paper 3
C h P t l
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Cryosphere Portal
• As a cryoshpere data hub, an online snow coverdatabase has been established in ICIMOD.
• Online Snow Cover Database is currently made
accessible to public at http://118.91.160.238/snow/#
• Later, this cryosphere portal will be incorporatedinto SERVIR science application.
• Demo for browsing Cryosphere portal will bedone during the hands on section.
C l i
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Conclusion
• Remote sensing is an essential tool for snow covermonitoring and assessment in both regional andglobal scale.
• The high temporal resolution of MODIS enables us tomonitor the dynamic snow cover in every one or twodays.
• MODIS snow product is the only standard productwhich is available free of cost.
• In addition to MODIS snow products, it is highlysuggested to construct long term historical snow coverdata from other satellite for assessment of snow coverarea changes for climate change analysis.
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Thank you