Fri Apr 10, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Li, Huxiong, Isazade, Vahid, Pirasteh, Saied, Serwa, Ahmed, Abdehvand, Zeinab Zaheri, Rezaei, Davoud, Isazade, Esmail, Fang, Zhaoxi, and Shirmohammadi, Mahdieh, 2026. Spatiotemporal analysis of hydrological and environmental changes in lake Urmia (2000─2024) using google earth engine. Earth Science Informatics, 19(4):41, doi:10.1007/s12145-026-02089-8.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2026EScIn..19...41L,
author = {{Li}, Huxiong and {Isazade}, Vahid and {Pirasteh}, Saied and {Serwa}, Ahmed and {Abdehvand}, Zeinab Zaheri and {Rezaei}, Davoud and {Isazade}, Esmail and {Fang}, Zhaoxi and {Shirmohammadi}, Mahdieh},
title = "{Spatiotemporal analysis of hydrological and environmental changes in lake Urmia (2000─2024) using google earth engine}",
journal = {Earth Science Informatics},
keywords = {Aerosol optical depth (AOD), Remote sensing, Google earth engine (GEE), Environmental migration, Salt dust emission hotspots, Lake urmia, Environmental Sciences, Environmental Science and Management, Engineering, Environmental Engineering, Earth Sciences, Physical Geography and Environmental Geoscience},
year = 2026,
month = mar,
volume = {19},
number = {4},
eid = {41},
pages = {41},
abstract = "{Lake Urmia, once one of Iran's largest inland water bodies, has severely
dried up, creating major sources of salt and dust storms that
threaten environmental stability, public health, and regional
demographics. This study utilizes a cloud-based Google Earth
Engine (GEE) platform to analyze remote sensing big data,
defined here as the long-term, multi-sensor processing of
thousands of satellite observations at pixel scale using a
cloud-based environment, to investigate spatiotemporal
environmental degradation associated with hydrological changes
in the Lake Urmia basin from 2000 to 2024. Demographic trends in
population change were examined to provide contextual insight
into regional pressures rather than to imply direct causation.
We processed 6500 MODIS and Landsat (TM/ETM+/OLI) images to
extract Aerosol Optical Depth (AOD), the Normalized Difference
Vegetation Index (NDVI), and the Normalized Difference Water
Index (NDWI). Pixel-wise Linear Fit Regression (LFR) was then
applied to MODIS- and Landsat-derived indices to quantify long-
term trends, with validation using observations from seven
regional meteorological stations. Monthly GRACE satellite data
(2002─2024) were also used to assess changes in total
terrestrial water storage (TWS) using Equivalent Water Height
(EWH) anomalies. Results revealed five persistent hotspots of
salt and dust emissions concentrated along the eastern and
northeastern lake margins. AOD exhibited an overall increase of
approximately 55\%, lake surface area declined by more than 2700
km{\texttwosuperior}, and vegetation cover decreased by nearly
50\%, with associated uncertainty reflected in validation
metrics (r > 0.71 for AOD-dust events, R{\texttwosuperior} â‰
0.66 for NDVI comparisons, and 87\% accuracy for NDWI-based
shoreline detection). Population trends showed spatially
heterogeneous patterns, including declines in cities such as
Tabriz and relative growth in Urmia and Osku, concurrent with
observed environmental degradation. While these patterns suggest
a strong association between hydrological-environmental
stressors and demographic redistribution, they should be
interpreted as correlational rather than evidence of direct
causality. Overall, this integrated hydrological, atmospheric,
and demographic assessment highlights the need for sustainable
water management and ecosystem restoration strategies to
mitigate further environmental degradation and potential human
displacement, in line with global Disaster Risk Reduction
frameworks.}",
doi = {10.1007/s12145-026-02089-8},
adsurl = {https://ui.adsabs.harvard.edu/abs/2026EScIn..19...41L},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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Fri Apr 10, F. Flechtner