snow cover mapping nepal

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, [email protected]



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



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.



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.



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).



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.



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



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



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

−

=

+



MODIS Snow Products



MODIS L2 5 minute SwathProduct (MOD10_L2 Snow)

It is a 5 minuteswath product



MODIS Level 3 ProductIn Sinusoidal Projection

It is in 10 deg X 10 deg tiles.

• Horizontal = 36 tiles

• Vertical = 18 tiles



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



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.



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



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



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.]



Terra MODIS (Natural Color)

Cloud is always a major problem in the remote sensing

images which uses visible wavelength regions.



Terra MODIS Snow Cover

Water Body

Snow free land Snow

Cloud/No data



Aqua MODIS (Natural Color)



Aqua MODIS Snow Cover

Water Body

Snow free land Snow

Cloud/No data



Combined MODIS Snow Cover

Water Body

Snow free land Snow

Cloud/No data



Cloud removal by TemporalFilter

It removes cloud pixels by filling with good data fromeither adjacent backward or forward days.

SL CLand Snow Cloud



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



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



Product Improvement

Most of the cloud can be removed by combinationof two satellite and temporal filtering.

Example for the whole HKH area,



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 %



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



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



2. Decadal Snow Cover Change 2000-2010 (Terra Only)

-16 -12 -8 -4 0 4 8 12 16

Percent

Analysis



2. Decadal Snow Cover Change 2002-2010 (Terra+Aqua)

Analysis

-16 -12 -8 -4 0 4 8 12 16

Percent



3. snow cover trend for the eastern, central, and western parts

of HKH region

Analysis



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



5. Seasonal variation of SCA in the HKH region

Analysis

A l i



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



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



Analysis

8. Monthly variation of snow cover for Brahmaputra basin

Analysis

A l iA l i



Analysis

Example. Brahmaputra basin (for one time point)

Meter

( % )

9. Altitude zone-wise snow cover distribution

Analysis

A l iA l i



E.g Brahmaputra river basin

Degree

Analysis

10. Slope zone-wise snow cover area distribution

Analysis

A l iA l i



E.g Brahmaputra river basin

Unitsnow cover percent area

Analysis

11. Aspect zone-wise snow cover area

Analysis

O t t R 1



Output - Report 1

To be released inCOP17, Durban,S. Africa inDecember 2011

coming soon

O t t P 1



http://www.the-cryosphere.net/home.html

Output - Paper 1

O t t P 2





published in IGARSS 2010

Output - Paper 2

O t t P 3



ACCEPTED IN

CURRENT SCIENCE http://cs-test.ias.ac.in/cs/index.php

Output - Paper 3

C h P t l

http://cs-test.ias.ac.in/cs/index.php







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

http://118.91.160.238/snow/

http://118.91.160.238/snow/



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.



Thank you

snow cover mapping nepal

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