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3.11 IUKWC Workshop Freshwater EO - Arindam Chowdhury - Jun17

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Freshwater Lake Mapping and its Volumetric Estimation in the Glaciated Valley of Chhombu in Sikkim Himalayas Using High-Resolution Optical (Sentinel-2 MSI) Imagery.
Arindam Chowdhury North Eastern Hill University, India)

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3.11 IUKWC Workshop Freshwater EO - Arindam Chowdhury - Jun17

  1. 1. FRESHWATER LAKE MAPPING AND ITS VOLUMETRIC ESTIMATION IN THE GLACIATED VALLEY OF CHHOMBU IN SIKKIM HIMALAYAS USING HIGH RESOLUTION OPTICAL (SENTINEL-2 MSI) IMAGERY 1Department of Geography, North-Eastern Hill University, Shillong 2Centre for the Study of Regional Development, Jawaharlal Nehru University, New Delhi India *EMAIL ID: arindam.iirs2015@gmail.com *Arindam Chowdhury1; Prof. Sunil Kumar De1; Manasi Debnath1; Prof. Milap Chand Sharma2 IUKWC Workshop University of Stirling, UK
  2. 2. IMPORTANCE OF THE STUDY • Recent glacier fluctuation in Sikkim Himalaya has given rise to the formation of numerous freshwater cryospheric lakes and hazards originated from the lakes (e.g. GLOF) may affect the downstream region of Sikkim devastatingly. • Inaccessibility of Himalayan high altitudinal region promote the application of high resolution optical satellite imagery (Sentinel-2 MSI) for monitoring cryospheric lakes which may reduce the risk of such hazards. • Shadow effect in mountainous region, depth of lake water, sediment flux, proximity to the glacier enhanced the need of proper lake boundary delineation.
  3. 3. STUDY REGION: CHHOMBU CATCHMENT • Extension: 27°55’ to 28°04’N and 88°37’ to 88°50’E. • Extremely cold winters and mild summers, but dry all year round. • According to Köppen-Geiger climate classification, the lower reaches of the catchment is characterised by temperate climate (Cwb); whereas high mountainous region in the north-east and south-east resemble by the combination of tundra climate (ET) and Cold Semi-arid climate (Bsk).
  4. 4. OBJECTIVES • To test the suitability of sentinel-2 multispectral imager’s (MSI) data for freshwater glacial lake mapping with best-suited image transformation methods • To estimate the area and volume of the selected lakes
  5. 5. DATABASE: Satellite Sensors Acquired date Repeat coverage interval (days) Band Number Spatial Resolution (m) #Sentinel 2A MSI 2016-OCT-18 10 B2 - Blue 10 B3 - Green 10 B4 - Red 10 B8 - NIR 10 B11 - SWIR 20* Google Earth - - - 1.65 to 2.62 SRTM DEM - - - 30 Source: ESA Sentinel-2 Pre-Operations Hub (https://scihub.copernicus.eu/) SRTM DEM from USGS. Note: • *The spatial resolution of Band 11 (SWIR) is increased from 20 m to 10 m through resampling method. • #The performance of the satellite data is best during the cloud-, snow- and ice-free season. • The Sentinel-2 (MSI) Level-1C product is composed of 100 km * 100 km tiles in the UTM/WGS84 projection and provides the TOA reflectance. • The use of Top-Of-Atmospheric (TOA) spectral reflectance values are more useful than DN (Digital Number) values of RS data for the detection and discrimination of snow-ice and water from other objects.
  6. 6. (a) Ten-metre False Colour Map (RGB) (b) 10-m Green Band 3 Datasets: (c) 10-m NIR Band 8 (d) 10-m SWIR Band 11
  7. 7. • MCA (Multi Criteria Analysis) is one of the most common geospatial tools that has been designed to facilitate feature extraction (viz. Water bodies, Lake Ice, Snow, Shadow etc.) and aid in decision making.
  8. 8. SPECTRAL WATER INDICES: Various scholars have used different band rationing indices to extract water bearing pixels. • NDWI1 (MCFEETERS, 1996): This is designed to: (1) maximize the reflectance of the water body in green band; (2) minimize the reflectance of water body in the NIR band 𝑁𝐷𝑊𝐼1 10𝑚 = Ρ3 𝐺𝑟𝑒𝑒𝑛 − Ρ8 (𝑁𝐼𝑅) Ρ3 𝐺𝑟𝑒𝑒𝑛 + Ρ8 (𝑁𝐼𝑅) -- Equation 1 • NDWI2 (ROGERS & KEARNEY, 2004): Red and SWIR bands (bands 4 and 11 respectively) to produce NDWI2. Therefore, NDWI2 for sentinel-2 is calculated as: 𝑁𝐷𝑊𝐼2 10𝑚 = Ρ4 𝑅𝑒𝑑 − Ρ11 (𝑆𝑊𝐼𝑅) Ρ4 𝑅𝑒𝑑 + Ρ11 (𝑆𝑊𝐼𝑅) -- Equation 2
  9. 9. • NDWI3 (OUMA & TATEISHI, 2006): For Sentinel-2, the NIR band has the spatial resolution of 10 m, while the SWIR band (Band 11) has the spatial resolution of 20 m. Therefore, the spatial resolution of Band 11 is increased from 20 m to 10 m. Therefore, NDWI3 for sentinel-2 is calculated as: 𝑁𝐷𝑊𝐼3 10𝑚 = ρ11 𝑆𝑊𝐼𝑅 − ρ8 (𝑁𝐼𝑅) ρ11 𝑆𝑊𝐼𝑅 + ρ8 (𝑁𝐼𝑅) -- Equation 3 Where, ρ11 is the TOA reflectance of the Band 11 (the SWIR band) and ρ8 is the TOA reflectance of the Band 8 (the NIR band) of Sentinel-2 MSI.
  10. 10. SPECTRAL SNOW INDEX: • NDSI 10m helps to separate different water forms and glacial debris. Because snow has maximum absorption in short wave infrared portion and maximum reflectance in visible region (Green band). The 10m NDSI is calculated as: 𝑁𝐷𝑆𝐼10𝑚 = Ρ3 𝐺𝑟𝑒𝑒𝑛 − Ρ11 (𝑆𝑊𝐼𝑅) Ρ3 𝐺𝑟𝑒𝑒𝑛 + Ρ11 (𝑆𝑊𝐼𝑅) -- Equation 4 Where ρ11 refers to the TOA reflectance of band 11 at 10 m, which is produced by downscaling the original 20m band 11. • NDSIPCA 10m: It is processed by the original green band and the PCA-sharpened 10 m resampled SWIR band. PCA is an approach based on the component substitution for spectral transformation of the original data. • 𝑁𝐷𝑆𝐼10𝑚 = Ρ3 𝐺𝑟𝑒𝑒𝑛 − Ρ11 (𝑆𝑊𝐼𝑅) Ρ3 𝐺𝑟𝑒𝑒𝑛 + Ρ11 (𝑆𝑊𝐼𝑅) --Equation 5PCA
  11. 11. SPECTRAL VEGETATION INDEX: • NDVI 10m ratio for SENTINEL-2 is outlined as: 𝑁𝐷𝑉𝐼10𝑚 = Ρ8 (𝑁𝐼𝑅)−Ρ4 𝑅𝑒𝑑 Ρ8 𝑁𝐼𝑅 +Ρ4 𝑅𝑒𝑑 --Equation 6
  12. 12. COMPARISON OF WATER EXTRACTION RESULTS OF THE SPECTRAL INDICES
  13. 13. A. B. C. D. RGB NDWI 1 NDWI 3NDWI 2
  14. 14. G. E. F.NDSI NDSI PCA NDVI
  15. 15. Class Index NDWI1 NDWI2 NDWI3 NDSI NDSIPCA NDVI Water Bearing Pixels Lake No. 1 0.66 – 0.81 0.88 – 0.94 (-0.84) – (-0.74) 0.94 – 0.99 0.74 – 0.94 (-0.6) – (-0.45) Lake No. 2 0.68 - 0.79 0.92 - 0.98 (-0.9) - (-0.67) 0.94 – 0.99 0.81 – 0.97 (-0.65) – (-0.55) Lake No. 3 0.4 – 0.6 0.94 – 0.99 (-0.99)- (-0.86) 0.94 – 0.99 0.79 – 0.98 (-0.55) – (-0.35) Lake No. 4 0.23 – 0.61 0.7 – 0.91 (-0.78)-(-0.67) 0.8 – 0.95 0.5 - 0.89 (-0.35) – (-0.2) Lake No. 5 0.4 – 0.47 0.93 – 0.99 (-0.96)-(-0.85) 0.94 – 0.99 0.85 – 0.97 (-0.45) – (-0.4) Lake Ice 0.06 – 0.39 - - - - (-0.35) – (-0.05) Lake water Shadow 0.23 – 0.42 - - - - (-0.4) – (-0.2)
  16. 16. • NDWI1 values of different lake water varied differently in the selected environment and indices value of lake water ranging from 0.23 to 0.81. Lake ice has been separated by NDWI1 values ranging between 0.06-0.39 in the selected glaciated region. NDWI1 also good to identify shadow and shadow on lake water whose pixel values ranging between 0.23-0.42. • Out of 3 different NDWI indices, the NDWI3 appropriately delineate the lakes shoreline boundary but could not able to differentiate water characteristics further. • Likewise NDWI1, the other index entitled NDVI is also good to separate lake water, lake ice and shadow on water. • On the other hand, NDSI does not help to identify lake ice by its index pixel values. But 4 lakes (except Cho Lhamu Lake no. 4) out of 5 have been identified appropriately. • NDSIPCA calculating after performing the PCA sharpening of 10m - SWIR band increased the range of pixel values but could not able to differentiate objects further. For example, this indices can’t identify the ice on lakes. RESULTS & ANALYSIS:
  17. 17. LAKE ICE PIXELS
  18. 18. RGB NDWI 1 NDWI 3NDWI 2
  19. 19. NDSI NDSI PCA NDVI
  20. 20. LAKE WATER PIXELS
  21. 21. RGB NDWI 1 NDWI 3NDWI 2
  22. 22. NDSI NDSI PCA NDVI
  23. 23. VOLUMETRIC ESTIMATION OF WATER STORED IN THE SELECTED GLACIAL LAKES
  24. 24. Volume-Area-Depth Relationship • The following empirical models have been used to estimate the approximate volume of selected glacial lakes. EMPIRICAL FORMULAE SOURCE Volume = 0.104 * (Area)^1.42 (Huggel, Kääb, Haeberli, Teysseire, & Paul, 2002) Depth= -11.32+53.21*(Area)^0.3 Volume= Area * Depth (Chaohai & Sharma, 1988) Volume= 0.191* (Area)^1.375 (Bahr et al, 1997) Volume= 0.2055* (Area)^1.36 (Chen & Ohmura, 1990) Volume= 0.28* (Area)^1.375 (Arendt et al, 2006) 0.000 0.020 0.040 0.060 0.080 0.100 0.120 Volume By Bahr (Cubic km) Volume By Chaohai (Cubic km) Volume By Huggel (Cubic km) Volume By Chen (Cubic km) Volume By Arendt (Cubic km) GLACIALLAKEVOLUMECUBICKM KhangChung_Lake Gurodongmar_LC_R Gurudongmar_Lake Cho_Lhamu_Lake Gurodongmar_LC_L Fig. Resulting volumes (km3) for the Major Glacial Lakes in the Catchment
  25. 25. • Kangchung lake (5) which is a pro glacial lake of Tista Khangse glacier. It has a larger shore length and area of 1.74 km2 and depth of 51.47m (by Chaohai) followed by Lake no. 3, 1, 4 and 2. • The Pro-glacial lake no. 5, 2, and 3 are very dynamic in nature respect to the glacier fluctuations. • Furthermore, Gurudongmar Lake Complex (Lake no. 1, 2 & 3) share a huge volume of water (0.1626 cubic km) and due to chain effect of these lakes, therefore these lakes are needed to be monitored regularly. • Further in-situ measurements are needed to validate the lake depth and volume. ANALYSIS:
  26. 26. CONCLUSIONS AND FUTURE RECOMMENDATIONS: • The newly-launched sentinel-2 can provide fine spatial resolution multispectral imagery at a fine temporal resolution, which makes it an important dataset for water bodies’ mapping at the global scale. • Different radiometric image enhancement techniques have already proved to delineate the Freshwater glacial lakes in the High altitude of Chhombu chu catchment in Sikkim Himalayas. • In concise, the whole study has thrown light on the critical signature of the proglacial lakes on the northern side of Kangchengayo-Pauhunri massif for which a regular monitoring is required in the era of rapid climate change.
  27. 27. THANK YOU SO MUCH ALL THE HONOURABLE DELEGATES & ORGANISERS FROM INDIA AND UK *Arindam Chowdhury1; Prof. Sunil Kumar De1; Manasi Debnath1; Prof. Milap Chand Sharma2 1Department of Geography, North-Eastern Hill University, Shillong 2Centre for the Study of Regional Development, Jawaharlal Nehru University, New Delhi India *Email id: arindam.iirs2015@gmail.com

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