Work

Evaluating vegetation dynamics in the Yangtze river basin in relation to climatological parameters using remote sensing data from 2001 to 2022

Year: 2024

Type: article

Abstract: This study investigates the dynamics of vegetation cover in the Yangtze river basin, separated into three sub-basins: upper basin, middle basin, and lower basin, concerning temperature and precipitation changes.The variations in the normalized difference vegetation index, precipitation, and temperature over 22 years were analyzed annually, seasonally, and monthly using remote sensing data.The relationship between normalized difference vegetation index changes and precipitation and temperature was evaluated using Kendall's correlation.The findings reveal a significant correlation between vegetation, surface temperature, and precipitation in the Yangtze river basin.Additionally, a significant (at p-value = 0.05) downward trend of vegetation coverage in the entire basin was observed for the classes between 0.2 and 0.5 (indicating poor and moderate vegetation).Conversely, a significant (at p-value = 0.05) upward trend in the area covered by denser vegetation (normalized difference vegetation index exceeding 0.5) was observed during the studied period in the Yangtze river basin.The largest vegetation coverage area was observed in middle basin, while the lowest values were seen in upper basin in 2008, middle basin in 2001, and lower basin in 2005.The highest vegetation coverage area in all three basins was recorded in 2021.In general, a significant (at p-value = 0.01) upward trend in vegetation coverage was observed in all three basins between 2001 and 2022.Finally, the results demonstrate that temperature exhibits a stronger correlation with increased vegetation cover in the Yangtze river basin, compared to precipitation.K e y w o r d s: normalized difference vegetation index, land surface temperature, precipitation, vegetation coverage, Yangtze river basin, remote sensing

Source: International Agrophysics

DOI: 10.31545/intagr/192302

Authors: Jiwu Li, Xiaohong Zhang, Iman Rousta, Haraldur Olafsson, Jaromir Krzyszczak

Institutions: Yazd University, University of Iceland, Icelandic Meteorological Office, Institute of Agrophysics, Polish Academy of Sciences

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $647