Monitoring and mapping of snow cover variability using topographically derived NDSI model over north Indian Himalayas during the period 2008–19

The Himalayas is an essential component of the cryosphere due to the large extent of snow or ice cover. The mapping and monitoring of snow cover variability over the Himalayas is the focus of many scientific studies due to the major source of water for Asian countries and equally important for climate change studies. This study describes the analysis of snow cover variability over North Indian Himalayas (NIH) covering Western Himalayas and Karakoram mountain ranges. The snow cover area (SCA) has been analyzed in three different climate zones such as the upper Himalayan zone (UHZ) (Ladakh and Karakoram range), middle Himalayan zone (MHZ) (Great Himalaya and Zanskar), and lower Himalayan zone (LHZ) (Pir Panjal and Shamshbari range) at various elevation levels as well as aspect levels during the past decade (2008–2019). The snow cover maps have been generated for NIH and its climate zones from Moderate Resolution Imaging Spectroradiometer (MODIS) data. 

Glimpse of Study

The global climate change is directly or indirectly impacting the regional climate of the Himalayas and thus, requires more attention to seasonal or inter-annual snow cover variations over the mountainous region. In the present work, the focus is on analyzing the seasonal snow cover variability at different elevations (LHZ, MHZ, and UHZ) and aspect levels (N, NE, SE, S, SW, W, NW) during the last decade (2008–2019). The NIH experienced global warming effects especially at lower and middle Himalayan zones which can be observed as shifting of snowmelt runoff and snow accumulation periods. The experimental outcomes suggest that shifting of this period are generally due to an increase in temperature over the Himalayan mountain range. The seasonal and annual trend analysis has shown that seasonal snow cover is varying across different elevations and geographical extent. The results computed in the present study have some limitations such as lack of incorporating precipitation data and in-situ or field observations. Therefore, the comparative analysis of different model outputs delivers important information regarding the impact of climate change over the snow cover area. This study also provides effective guidance in the prediction of natural hazard analysis and the protection of water resources.

For detailed Study:

Sood, V., Singh, S., Taloor, A.K., Prashar, S. and Kaur, R., 2020. Monitoring and mapping of snow cover variability using topographically derived NDSI model over north Indian Himalayas during the period 2008‒19. Applied Computing and Geosciences. https://doi.org/10.1016/j.acags.2020.100040

Nearest-Neighbor Diffusion-based pan-sharpening using multispectral MODIS and AWiFS

Remote sensing plays a significant role in the monitoring of the undulating the Himalayas. With continuous monitoring, the preservation of natural resources and mitigation of natural hazards is possible. Currently, satellite sensors are not capable enough to deliver the earth's surface image at a very high temporal, spectral, and spatial resolution, simultaneously. Therefore, it is essential to perform the pan-sharpening of spatially high-resolution (HR) panchromatic (PAN) spectral band with low-resolution (LR) multispectral (MS) imagery which must be acquired on the same temporal date from multiple sensors. On the other hand, due to the rugged topography of the Himalayas, topographic effects are generally induced in the form of shadow and affect the spatial information or spectral information. 

Process of Pan-sharpening (Fusion)

For regional or global scale studies, the LR satellite dataset is more preferable and can be merged with the HR dataset with nearest-neighbor diffusion (NND) -based pan-sharpening algorithm. With visual interpretation, it is apparent that NND pan-sharpening with topographic correction offers more reliable information by effectively removing the shadow effects as compared with NND pan-sharpening without topographic correction.

Singh et al. (2020) address the topographic correction is required to be implemented with NND-based pan-sharpening and other classification models. For experimental purposes, AWiFS as HR-PAN data and MODIS as LR-MS data have been used.

  

Reference: Singh, S., Sood, V., Prashar, S. and Kaur, R., 2020. Response of topographic control on nearest-neighbor diffusion-based pan-sharpening using multispectral MODIS and AWiFS satellite dataset. Arabian Journal of Geosciences, 13(14), pp.1-9. 

Link for full study: https://rdcu.be/b5DMK