Permafrost estimation model in Upper Indus Basin

Remotely sensed topo-climatic factors, potential incoming solar radiation (PISR), land surface temperature (LST), topographic wetness index (TWI), Surface emissivity, and elevation, and machine learning techniques are used for mapping the spatial distribution of permafrost in the Tso Kar, a sub-basi...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of Earth System Science Ročník 132; číslo 4; s. 156
Hlavní autoři: Pandey, Aayushi, Yadav, Bankim Chandra, Wani, John Mohd, Dimri, A P
Médium: Journal Article
Jazyk:angličtina
Vydáno: New Delhi Springer India 01.12.2023
Springer Nature B.V
Témata:
Climate change
Earth and Environmental Science
Earth Sciences
Emissivity
Image processing
Lakes
Land surface temperature
Learning algorithms
Machine learning
Mapping
Mathematical models
Parameter identification
Parameters
Permafrost
Radiation
Remote sensing
Satellites
Solar radiation
Space Exploration and Astronautics
Space Sciences (including Extraterrestrial Physics
Spatial distribution
Surface temperature
Temperature
Topography
Variables
Watersheds
Wetness index
Alaska
Himalaya Mountains
United States > US
Indus Valley
India
Tso Kar
Permafrost
model
Upper Indus Basin
ISSN:0973-774X, 0253-4126, 0973-774X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Remotely sensed topo-climatic factors, potential incoming solar radiation (PISR), land surface temperature (LST), topographic wetness index (TWI), Surface emissivity, and elevation, and machine learning techniques are used for mapping the spatial distribution of permafrost in the Tso Kar, a sub-basin of Upper Indus Basin (UIB) in Leh, Ladakh (UT). This schematic model is employed to identify remotely sensed parameters which are crucial in assessing permafrost extent over the study region. It is followed by the application and tuning of several machine learning models to deliver an expected accuracy in terms of permafrost classes demarcated over the study region based on literature. Results show that the PISR, LST and TWI are the most significant remotely sensed parameters affecting the permafrost and associated processes. Above 5000 m a.s.l., the proportion of permafrost in the study catchment is higher. Synergistic use of remote sensing image processing and machine learning techniques together provide mapping of permafrost over the region, which is elusive so far.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0973-774X
0253-4126
0973-774X
DOI:10.1007/s12040-023-02176-0