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Re-constructing and projecting vegetation coverage area variations: A numerical approach based on MRI-ESM2.0 climatic datasets.

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Název: Re-constructing and projecting vegetation coverage area variations: A numerical approach based on MRI-ESM2.0 climatic datasets.
Autoři: Aghelpour, Pouya1 (AUTHOR) p.aghelpour@agr.basu.ac.ir, Sabziparvar, Ali-Akbar1 (AUTHOR) swsabzi@basu.ac.ir, Varshavian, Vahid1 (AUTHOR) v.varshavian@basu.ac.ir
Zdroj: Advances in Space Research. Dec2025, Vol. 76 Issue 11, p6647-6672. 26p.
Témata: *GROUND vegetation cover, *ATMOSPHERIC models, *GEOGRAPHICAL positions, *REMOTE sensing, *METEOROLOGICAL databases, *MACHINE learning, *COMPUTER simulation, *CLIMATE change adaptation
Geografický termín: ZAGROS Mountains (Iran & Iraq), IRAN
Abstrakt: The relationship between climatic fluctuations and vegetation cover in different regions has consistently indicated a strong correlation between these phenomena. Therefore, examining the response of vegetation cover changes to variations in climatic components is considered one of the main focuses of global climate and environmental research. The present study aims to numerically model and predict vegetation coverage area (VCA) based on climatic factors for the mountainous Zagros region in Iran. VCA data, derived from NDVI, were extracted from MODIS sensor imagery on a monthly scale for the period 2000–2024. Meteorological data from CMIP6, including 19 variables representing components such as temperature, precipitation, solar radiation, relative humidity, air pressure, and wind speed, were acquired on a monthly scale from the MRI-ESM2.0 model and integrated across the entire region. Results from the cross correlation test indicated that meteorological variables showed higher correlations with VCA variations at temporal lags, with components such as radiation, temperature, and relative humidity demonstrating the strongest impacts. Estimation using machine learning involved MLP and SVM models, with results indicating satisfactory performance for both models, particularly SVM, in estimating VCA for the period 2000–2014. Subsequently, the extracted model was utilized to reconstruct VCA for the 1950–1999 period and to project VCA for the future (post-2015). For projection, meteorological variables from SSP245 and SSP585 climate scenarios were used as inputs. Initially, projection for the available future period (2015–2024) were validated, demonstrating that the SVM model under the SSP585 climate scenario (R2 = 80.74 % and NRMSE = 0.124) achieved the best alignment with MODIS derived VCA. The VCA was then projected for the near future (2025–2050) and the distant future (2051–2080). In both future periods, results indicated a mild increasing trend in VCA for this region with a relatively stronger increase observed under the SSP585 scenario, especially for the 2051–2080 period. The current numerical approach could serve as a valuable tool for natural resource and agricultural planners in defining environmental strategies for the region's future and holds significant research value for other vegetation-covered regions. [ABSTRACT FROM AUTHOR]
Databáze: Academic Search Index
Popis
Abstrakt:The relationship between climatic fluctuations and vegetation cover in different regions has consistently indicated a strong correlation between these phenomena. Therefore, examining the response of vegetation cover changes to variations in climatic components is considered one of the main focuses of global climate and environmental research. The present study aims to numerically model and predict vegetation coverage area (VCA) based on climatic factors for the mountainous Zagros region in Iran. VCA data, derived from NDVI, were extracted from MODIS sensor imagery on a monthly scale for the period 2000–2024. Meteorological data from CMIP6, including 19 variables representing components such as temperature, precipitation, solar radiation, relative humidity, air pressure, and wind speed, were acquired on a monthly scale from the MRI-ESM2.0 model and integrated across the entire region. Results from the cross correlation test indicated that meteorological variables showed higher correlations with VCA variations at temporal lags, with components such as radiation, temperature, and relative humidity demonstrating the strongest impacts. Estimation using machine learning involved MLP and SVM models, with results indicating satisfactory performance for both models, particularly SVM, in estimating VCA for the period 2000–2014. Subsequently, the extracted model was utilized to reconstruct VCA for the 1950–1999 period and to project VCA for the future (post-2015). For projection, meteorological variables from SSP245 and SSP585 climate scenarios were used as inputs. Initially, projection for the available future period (2015–2024) were validated, demonstrating that the SVM model under the SSP585 climate scenario (R2 = 80.74 % and NRMSE = 0.124) achieved the best alignment with MODIS derived VCA. The VCA was then projected for the near future (2025–2050) and the distant future (2051–2080). In both future periods, results indicated a mild increasing trend in VCA for this region with a relatively stronger increase observed under the SSP585 scenario, especially for the 2051–2080 period. The current numerical approach could serve as a valuable tool for natural resource and agricultural planners in defining environmental strategies for the region's future and holds significant research value for other vegetation-covered regions. [ABSTRACT FROM AUTHOR]
ISSN:02731177
DOI:10.1016/j.asr.2025.09.019