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Response of enhanced vegetation index changes to latent/sensible heat flux and precipitation over Pakistan using remote sensing.
Tariq, Salman; Nawaz, Hasan; Ul-Haq, Zia; Mehmood, Usman.
Afiliação
  • Tariq S; Department of Space Science, University of the Punjab, Lahore, Pakistan. salmantariq_pu@yahoo.com.
  • Nawaz H; Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan. salmantariq_pu@yahoo.com.
  • Ul-Haq Z; Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan.
  • Mehmood U; Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan.
Environ Sci Pollut Res Int ; 29(43): 65565-65584, 2022 Sep.
Article em En | MEDLINE | ID: mdl-35488154
ABSTRACT
For a sustainable development and ecological integrity, it is of worth importance to monitor land use/ land cover (LULC) changes and related land-atmosphere fluxes. To serve this purpose, we have used moderate resolution imaging spectroradiometer (MODIS) retrieved-enhanced vegetation index (EVI), MERRA-2 re-analysis surface heat fluxes (latent heat flux, sensible heat flux and specific humidity), TRMM rainfall data, and OMI retrieved aerosol index (AI) over Pakistan during 2000 to 2021. High EVI (0.66) is observed in May 2021 as compared to May 2000 over Muzaffarabad, Srinagar, north and northwest of Khyber Pakhtunkhwa, east of Punjab and along the Indus River in Sindh. The highest increase in vegetative area is observed in Baluchistan (~ 366%), followed by Manavadar (~ 60%), Khyber Pakhtunkhwa (~ 41%), Sindh (~ 37%), and Punjab (~ 20%) whereas Gilgit-Baltistan and Jammu and Kashmir show reduction in vegetative area by 21% and 11% respectively. The coefficient of determination (R2) is found to be highest between rainfall and latent heat flux (R2 = 0.59) followed by rainfall and specific humidity (R2 = 0.35), and rainfall and sensible heat flux (R2 = 0.06). The latent heat flux shows increasing trend at the rate of 0.003 Wm-2 winter-1, 0.0065 Wm-2 pre-monsoon-1 and 0.0272 Wm-2 post-monsoon-1 during 1980-2021 whereas sensible heat flux shows decreasing trend at the rate of 0.00056 Wm-2 winter-1, 0.00249 Wm-2 pre-monsoon-1 and 0.0037 Wm-2 post-monsoon-1 during 1980-2021. Specific humidity depicts increasing trend at the rate of 0.0002 Wm-2 winter-1, 0.0038 Wm-2 pre-monsoon-1 and decreasing trend at the rate of 0.0080 Wm-2 post-monsoon-1 during 1980-2021. The interannual variations in AI show highest AI of 2.28 in 2021 with maximum positive percentage anomaly of 28.06% during 2007.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Monitoramento Ambiental / Tecnologia de Sensoriamento Remoto / Temperatura Alta Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Monitoramento Ambiental / Tecnologia de Sensoriamento Remoto / Temperatura Alta Idioma: En Ano de publicação: 2022 Tipo de documento: Article