AI Article Synopsis

  • The study investigates how vegetation in Bangladesh changes over time in response to climate factors, using data from MODIS satellite imagery collected between 2001 and 2020.
  • Key vegetation indices, NDVI and EVI, are analyzed, revealing that EVI has a stronger connection to hydroclimatic factors, especially evapotranspiration (ET).
  • Findings indicate that EVI is more effective for tracking vegetation changes, while NDVI shows limited correlation with precipitation and a weaker relationship with ET.

Article Abstract

Bangladesh, known for its remarkable ecological diversity, is faced with the pressing challenges of contemporary climate change. It is crucial to understand how vegetation dynamics respond to different climatic factors. Hence, this study aimed to investigate the spatio-temporal variations of vegetation and their interconnectedness with a range of hydroclimatic factors. The majority of the dataset used in this study relies on MODIS satellite imagery. The Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), precipitation (PPT), evapotranspiration (ET), and land surface temperature (LST) data from the years 2001 to 2020 have been obtained from Google Earth Engine (GEE). In this study, the temporal variations of the NDVI, EVI, PPT, ET, and LST have been investigated. The findings of the Mann-Kendall trend test indicate noticeable trends in both the NDVI and the EVI. Sen's slope value for NDVI and EVI is 0.00424/year and 0.00256/year, respectively. Compared to NDVI, EVI has shown a stronger connection with hydroclimatic factors. In particular, EVI exhibits a better relationship with ET, as indicated by a r value of 0.37 and a P-value of 6.81 × 10, whereas NDVI exhibits a r value of 0.17 and a P-value of 2.96 × 10. Furthermore, ET can explain 17% of the fluctuation in NDVI, and no correlation between NDVI and PPT has been found. The results clarify the significant relationship between the EVI and hydroclimatic factors and highlight the efficiency of the EVI for detecting vegetation changes.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10391951PMC
http://dx.doi.org/10.1016/j.heliyon.2023.e18412DOI Listing

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