Remote Sensing Based Assessment of Glacier Resources in Parts of Ladakh Mountain Range, a Trans-Himalayan Region

2021 ◽  
pp. 85-100
Author(s):  
Riyaz Ahmad Mir
Keyword(s):  
Remote Sensing ◽  
Himalayan Region ◽  
Mountain Range

scholarly journals Genomic Insight Into 21 Hypervariable Molecular Markers in The Population of Lower Himalayan Geographical Province Himachal Pradesh, India

2021 ◽  
Author(s):  
Arun Sharma ◽  
Ankit Srivast ◽  
Vivek Sahajpal ◽  
R Kumawat ◽  
Shivani Dixit ◽  
...  
Keyword(s):  
Molecular Markers ◽  
Central India ◽  
Himachal Pradesh ◽  
Genomic Diversity ◽  
Himalayan Region ◽  
North India ◽  
Mountain Range ◽  
Genetic Affinity ◽  
Discrimination Power ◽  
North West

Abstract A natural geographical barrier between Tibetan plateau and south Asian countries, the Himalayan Mountain range, serves as a natural barrier between these regions for genetic flow. This has lead to vast genomic divergence among the population residing in Indian Himalayan region..This study was designed with an aim to decipher the genomic diversity and molecular characterization of 21 hypervariable molecular markers in the population of geographical province of Himachal Pradesh in the lower Himalayan region. Randomly selected 401 unrelated individuals native of the lower Himalayan geographical region were included for this study. 21 hypervariable molecular markers included in the PowerPlex® 21 system were amplified and genotyped. A total of 246 alleles and 12.3 (SE 0.927) private alleles per locus were observed. Population differentiation analysis revealed that the studied population showed a genetic affinity with the population of North India, North-west India, Central India, and Uttar Pradesh rather than the population of East India, South India, East Asia, and West Asia. Heterozygosity at the studied loci was found in the range of 0.686 to 0.920. The unite discrimination power (PD) and exclusion power (PE) was found to be 1 and 0.999999998073765 respectively. The unite value of matching probability and typical paternity index was found to be 9.33x10-26 and 5.05x108 respectively for the studied population. All the tested loci were found in Hardy Weinberg Equilibrium (HWE) expectations. Overall the studied population exhibited a great extent of genomic diversity and had a greater genetic affinity with Indo-European speakers rather than Dravidian and Tibeto-Burman speakers.

scholarly journals HYPERSPECTRAL REMOTE SENSING FOR TEMPERATE HORTICULTURE FRUIT CROPS IN NORTHERN-WESTERN HIMALAYAN REGION: A REVIEW

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 333-338
Author(s):  
P. Upadhyay ◽  
D. Uniyal ◽  
M. P. S. Bisht
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Himalayan Region ◽  
Specific Class ◽  
Fruit Crops ◽  
Spectral Library ◽  
Indian States ◽  
The North ◽  
Hyper Spectral ◽  
North Western

<p><strong>Abstract.</strong> The North-Western Indian States and the North-Eastern Indian States of Indian Himalayan Region (IHR) are rich of various temperate horticulture fruits such as the Apple, Pear, Peach, Plum, Apricot, Sweet Cherry and Sour Cherry. These horticulture fruits are majorly grown in North-western region comprising of Jammu and Kashmir (J&amp;amp;K), Himachal Pradesh (H.P.) and Uttarakhand (U.K.). These states of IHR share the same type of geographical and climatic condition and having nearly common flora and fauna. Out of the various horticulture temperate fruit crops apple and apricot have the potential to make a positive impact on economy of these states. Hyper-spectral remote sensing due to its capability of identifying the small variations within a particular feature (or land cover) is an important tool for discriminating or mapping the specific land cover among the various existing classes. Contrary to multispectral remote sensing, it is not only capable of mapping the vegetation class among the various classes in the land but also has the potential to discriminate within the different classes of vegetation as well as diseases identification within a class. This specific class level discrimination of vegetation is an important tool for mapping. In hyper-spectral remote sensing this variation is observed through the possible discrimination of spectral signatures of various vegetation classes. Thus, due to its fine spectral bands this type of remote sensing data has the potential to map the horticulture crops. However, the processing of hyper-spectral data always require the in-situ measurements or existing spectral library. Such a type of spectral library is never generated for the horticulture crops of IHR. This can be further useful for identifying the disease affected crops and input for developing model for estimation of biophysical and biochemical parameters. Therefore, in this study, a need for the development of spectral library for temperate horticulture crop has been highlighted. Further, a methodology for the processing of hyperspectral data has also be proposed.</p>

scholarly journals Assimilation of Snowmelt Runoff Model (SRM) Using Satellite Remote Sensing Data in Budhi Gandaki River Basin, Nepal

2020 ◽  
Vol 12 (12) ◽  
pp. 1951 ◽  
Author(s):  
Til Prasad Pangali Sharma ◽  
Jiahua Zhang ◽  
Narendra Raj Khanal ◽  
Foyez Ahmed Prodhan ◽  
Basanta Paudel ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
River Basin ◽  
Satellite Remote Sensing ◽  
River Discharge ◽  
Remote Sensing Data ◽  
Himalayan Region ◽  
Snowmelt Runoff ◽  
Sensing Data ◽  
Runoff Model

The Himalayan region, a major source of fresh water, is recognized as a water tower of the world. Many perennial rivers originate from Nepal Himalaya, located in the central part of the Himalayan region. Snowmelt water is essential freshwater for living, whereas it poses flood disaster potential, which is a major challenge for sustainable development. Climate change also largely affects snowmelt hydrology. Therefore, river discharge measurement requires crucial attention in the face of climate change, particularly in the Himalayan region. The snowmelt runoff model (SRM) is a frequently used method to measure river discharge in snow-fed mountain river basins. This study attempts to investigate snowmelt contribution in the overall discharge of the Budhi Gandaki River Basin (BGRB) using satellite remote sensing data products through the application of the SRM model. The model outputs were validated based on station measured river discharge data. The results show that SRM performed well in the study basin with a coefficient of determination (R2) >0.880. Moreover, this study found that the moderate resolution imaging spectroradiometer (MODIS) snow cover data and European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological datasets are highly applicable to the SRM in the Himalayan region. The study also shows that snow days have slightly decreased in the last three years, hence snowmelt contribution in overall discharge has decreased slightly in the study area. Finally, this study concludes that MOD10A2 and ECMWF precipitation and two-meter temperature products are highly applicable to measure snowmelt and associated discharge through SRM in the BGRB. Moreover, it also helps with proper freshwater planning, efficient use of winter water flow, and mitigating and preventive measures for the flood disaster.

Sign in / Sign up

Export Citation Format

Share Document