Assessment of snow cover dynamics and the effects of environmental drivers in High Mountain ecosystems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12220%2F25%3A43910865" target="_blank" >RIV/60076658:12220/25:43910865 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0195925525001660?pes=vor&utm_source=clarivate&getft_integrator=clarivate" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0195925525001660?pes=vor&utm_source=clarivate&getft_integrator=clarivate</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.eiar.2025.107969" target="_blank" >10.1016/j.eiar.2025.107969</a>
Alternative languages
Result language
angličtina
Original language name
Assessment of snow cover dynamics and the effects of environmental drivers in High Mountain ecosystems
Original language description
Remote sensing is crucial for monitoring decadal-scale snow cover dynamics in response to climate change across mountainous ecosystems. This study analyzes spatiotemporal snow cover trends and their driving factors using satellite observations, topographic data, and climate variables. MODIS snow product (MOD10A1) data from 2000 to 2022 were used to assess snow cover changes across 69 High Mountain Ecosystems (HME) in the Middle East. We analyzed data from January to April and October to December to capture seasonal snow cover dynamics. A linear regression method detected significant trends in the Normalized Difference Snow Index (NDSI), while a pixel-based Random Forest (RF) regression model assessed environmental drivers influencing NDSI variability. Results show that northern and northwestern humid regions generally experienced increasing NDSI, whereas central and southern arid regions exhibited a decline, highlighting spatial and temporal heterogeneity in snow cover trends. Temperature and precipitation change significantly influenced NDSI patterns, suggesting climate variability plays a critical role in snow cover disturbances. RF analysis identified mean annual temperature, precipitation changes, and mean annual precipitation as the top three drivers of NDSI variability. Future research should focus on the impact of extreme weather events on snow cover. Additionally, refining the methodology with higher-resolution data across diverse climate zones could enhance predictive accuracy.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10511 - Environmental sciences (social aspects to be 5.7)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Environmental Impact Assessment Review
ISSN
0195-9255
e-ISSN
1873-6432
Volume of the periodical
114
Issue of the periodical within the volume
Neuveden
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
1-12
UT code for WoS article
001487481600001
EID of the result in the Scopus database
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