Derivation of more sensitive spectral features from
the satellite data is immensely important for better retrieving land cover
information and change monitoring, such as changes in snow covered area,
forests, and barren lands as some examples from local to the global scale. The
major objectives of this paper are to present the potential of water-resistant
snow index (WSI) for the detection of snow cover changes in the Himalayas,
extant two composite images, biophysical image composite (BIC) and forest cover
composite (FCC) for the detection of changes in barren lands and forested areas
respectively, and two newly designed composite images, water cover composite
(WCC) and urban cover composite (UCC) for the detection of changes in water and
urban areas respectively. This research implemented the image compositing
technique for the detection and visualization of land cover changes (water,
forest, barren, and urban) with respect to local administrative areas where a
significant land cover change occurred from 2001 to 2016. A case study was also
conducted in the Himalayan region to identify snow cover changes from 2001 to
2015 using the WSI. Analysis of the annual variation of the snow cover in the
Himalayas indicated a decreasing trend of the snow cover. Consequently, the
downstream areas are more likely to suffer from snow related hazards such as glacial outbursts, avalanches,
landslides and floods. The changes in snow cover in the Himalayas may
bring significant hydrophysical and livelihood changes in the downstream area
including the Mekong Delta. Therefore, the countries sharing the Himalayan
region should focus on adapting the severe impacts of snow cover changes. The
image compositing approach presented in the research demonstrated promising
performance for the detection and visualization of other land cover changes as
well.
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