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Year 2017, Volume: 18 Issue: 1, 238 - 246, 31.03.2017
https://doi.org/10.18038/aubtda.273972

Abstract

References

  • Ustin, S.L., Remote sensing of environment: State of the science and new directions. Remote Sensing of Natural Resources Management and Environmental Monitoring, 2004.
  • Gurung, D.R., et al., Monitoring of seasonal snow cover in Bhutan using remote sensing technique. Current Science, 2011. 101(10): p. 1364-1370.
  • Butt, M.J., Characteristics of snow cover in the Hindukush, Karakoram and Himalaya region using Landsat satellite data. Hydrological Processes, 2012. 26(24): p. 3689-3698.
  • Butt, M.J., Exploitation of Landsat data for snow zonation mapping in the Hindukush, Karakoram and Himalaya (HKH) region of Pakistan. Hydrological Sciences Journal-Journal Des Sciences Hydrologiques, 2013. 58(5): p. 1088-1096.
  • Choi, H. and R. Bindschadler, Cloud detection in Landsat imagery of ice sheets using shadow matching technique and automatic normalized difference snow index threshold value decision. Remote Sensing of Environment, 2004. 91(2): p. 237-242.
  • Stueve, K.M., et al., Snow-covered Landsat time series stacks improve automated disturbance mapping accuracy in forested landscapes. Remote Sensing of Environment, 2011. 115(12): p. 3203-3219.
  • Negi, H.S., et al., Monitoring and evaluation of seasonal snow cover in Kashmir valley using remote sensing, GIS and ancillary data. Journal of Earth System Science, 2009. 118(6): p. 711-720.
  • Lan, Y.C., et al., Snow Cover Monitoring by Remote Sensing and Snowmelt Runoff Calculation in the Upper Huanghe River Basin. Chinese Geographical Science, 2002. 12(2): p. 120-125.
  • McFeeters, S.K., The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 1996. 17(7): p. 1425-1432.

ALGORITHM FOR SNOW MONITORING USING REMOTE SENSING DATA

Year 2017, Volume: 18 Issue: 1, 238 - 246, 31.03.2017
https://doi.org/10.18038/aubtda.273972

Abstract

Snow cover is an important part of the
Earth`s climate system so its continuous monitoring is necessary to map snow
cover in high resolution. Satellite remote sensing is a science that
successfully can monitor land cover and land cover changes. Although indexes
such as Normalized Difference Snow Index (NDSI) has quite good accuracy,
sometimes topography shadow, water bodies and clouds can be easily misplaced as
snow. Using Landsat TM, Landsat +ETM and Landsat TIRS/OLI satellite images, the
NDSI was modified for more accurate snow mapping. In this paper, elimination of
the misplaced water bodies was made using the high reflectance of the snow in
the 0.45 – 0.52 µm wavelength. Afterwards, the modified NDSI (MNDSI) was used
for estimating snow cover through the years on the one of the highest mountains
in Republic of Macedonia. The results from this study shows that the MNDSI
accuracy is higher than the NDSI`s, totally eliminating the misplaced water
bodies, and partly the one caused from topography and clouds. Also, it was
noticed that the snow cover in the study area has not been changed drastically
through the years. For future studies, the MNDSI should be validated on
different study areas with different characteristics.  

References

  • Ustin, S.L., Remote sensing of environment: State of the science and new directions. Remote Sensing of Natural Resources Management and Environmental Monitoring, 2004.
  • Gurung, D.R., et al., Monitoring of seasonal snow cover in Bhutan using remote sensing technique. Current Science, 2011. 101(10): p. 1364-1370.
  • Butt, M.J., Characteristics of snow cover in the Hindukush, Karakoram and Himalaya region using Landsat satellite data. Hydrological Processes, 2012. 26(24): p. 3689-3698.
  • Butt, M.J., Exploitation of Landsat data for snow zonation mapping in the Hindukush, Karakoram and Himalaya (HKH) region of Pakistan. Hydrological Sciences Journal-Journal Des Sciences Hydrologiques, 2013. 58(5): p. 1088-1096.
  • Choi, H. and R. Bindschadler, Cloud detection in Landsat imagery of ice sheets using shadow matching technique and automatic normalized difference snow index threshold value decision. Remote Sensing of Environment, 2004. 91(2): p. 237-242.
  • Stueve, K.M., et al., Snow-covered Landsat time series stacks improve automated disturbance mapping accuracy in forested landscapes. Remote Sensing of Environment, 2011. 115(12): p. 3203-3219.
  • Negi, H.S., et al., Monitoring and evaluation of seasonal snow cover in Kashmir valley using remote sensing, GIS and ancillary data. Journal of Earth System Science, 2009. 118(6): p. 711-720.
  • Lan, Y.C., et al., Snow Cover Monitoring by Remote Sensing and Snowmelt Runoff Calculation in the Upper Huanghe River Basin. Chinese Geographical Science, 2002. 12(2): p. 120-125.
  • McFeeters, S.K., The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 1996. 17(7): p. 1425-1432.
There are 9 citations in total.

Details

Subjects Engineering
Journal Section Articles
Authors

Uğur Avdan 0000-0001-7873-9874

Gordana Kaplan

Publication Date March 31, 2017
Published in Issue Year 2017 Volume: 18 Issue: 1

Cite

APA Avdan, U., & Kaplan, G. (2017). ALGORITHM FOR SNOW MONITORING USING REMOTE SENSING DATA. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering, 18(1), 238-246. https://doi.org/10.18038/aubtda.273972
AMA Avdan U, Kaplan G. ALGORITHM FOR SNOW MONITORING USING REMOTE SENSING DATA. AUJST-A. March 2017;18(1):238-246. doi:10.18038/aubtda.273972
Chicago Avdan, Uğur, and Gordana Kaplan. “ALGORITHM FOR SNOW MONITORING USING REMOTE SENSING DATA”. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering 18, no. 1 (March 2017): 238-46. https://doi.org/10.18038/aubtda.273972.
EndNote Avdan U, Kaplan G (March 1, 2017) ALGORITHM FOR SNOW MONITORING USING REMOTE SENSING DATA. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering 18 1 238–246.
IEEE U. Avdan and G. Kaplan, “ALGORITHM FOR SNOW MONITORING USING REMOTE SENSING DATA”, AUJST-A, vol. 18, no. 1, pp. 238–246, 2017, doi: 10.18038/aubtda.273972.
ISNAD Avdan, Uğur - Kaplan, Gordana. “ALGORITHM FOR SNOW MONITORING USING REMOTE SENSING DATA”. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering 18/1 (March 2017), 238-246. https://doi.org/10.18038/aubtda.273972.
JAMA Avdan U, Kaplan G. ALGORITHM FOR SNOW MONITORING USING REMOTE SENSING DATA. AUJST-A. 2017;18:238–246.
MLA Avdan, Uğur and Gordana Kaplan. “ALGORITHM FOR SNOW MONITORING USING REMOTE SENSING DATA”. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering, vol. 18, no. 1, 2017, pp. 238-46, doi:10.18038/aubtda.273972.
Vancouver Avdan U, Kaplan G. ALGORITHM FOR SNOW MONITORING USING REMOTE SENSING DATA. AUJST-A. 2017;18(1):238-46.