A new study led by researchers from the Xinjiang Institute of Ecology and Geography of the Chinese Academy of Sciences has revealed significant increases in global vegetation growth under different climate scenarios. The findings were published in Global Change Biology.
The researchers developed an innovative modeling framework called Grid-by-Grid; Multi-Algorithms; Optimal Combination (GGMAOC) to analyze future vegetation changes with greater accuracy. This approach tackles key challenges in predicting global greening patterns by evaluating multiple algorithms at each individual grid cells.
Using GGMAOC, the team analyzed projected changes in Leaf Area Index (LAI)—a key metric of vegetation density—across the globe and in four critical subregions. Their results suggest that high-latitude regions in the Northern Hemisphere could experience greening rates up to 2.25 times faster by the year 2100 compared to the baseline period from 1982 to 2014.
Among the various algorithms tested, the Random Forest method demonstrated particularly strong performance, especially in models for the Northern Hemisphere. The study attributes the global increase in vegetation primarily to climate change, which appears to be fostering more favorable conditions for plant growth across diverse regions.
These insights are crucial for informing ecosystem management, conservation efforts, and climate adaptation strategies, providing a scientific basis for addressing the ongoing environmental transformations driven by global warming.
Researchers of the Institute of Geographic Sciences and Natural Resources Research found that the water background affects the canopy reflectance anisotropy when the leaf area index is less than 4.0.
WEI Shanshan and Prof. FANG Hongliang from the Institute of Geographic Sciences and Natural Resources Research proposed a new method to tackle with the above issues. They found that the CI accuracy is affected by the different configurations of BRDF models and SZA values.
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