The Estimation of Grassland Aboveground Biomass and Analysis of Its Response to Climatic Factors Using a Random Forest Algorithm in Xinjiang, China DOI Creative Commons
Ping Dong, Changqing Jing, Gongxin Wang

et al.

Plants, Journal Year: 2024, Volume and Issue: 13(4), P. 548 - 548

Published: Feb. 17, 2024

Aboveground biomass (AGB) is a key indicator of the physiological status and productivity grasslands, its accurate estimation essential for understanding regional carbon cycles. In this study, we developed suitable AGB model grasslands in Xinjiang based on random forest algorithm, using observation data, remote sensing vegetation indices, meteorological data. We estimated grassland from 2000 to 2022, analyzed spatiotemporal changes, explored response climatic factors. The results showed that (1) was reliable (R2 = 0.55, RMSE 64.33 g·m−2) accurately Xinjiang; (2) spatial distribution high levels northwest low values southeast. growing trend most areas, with share 61.19%. Among these lowland meadows fastest growth, an average annual increment 0.65 g·m−2·a−1; (3) Xinjiang’s climate exhibited characteristics warm humidification, higher correlation precipitation than temperature. Developing models algorithms proves effective approach estimating AGB, providing fundamental data maintaining balance between grass livestock sustainable use conservation resources Xinjiang, China.

Language: Английский

Severe constraints on water vapor diffusion due to the shrinkage of the Aral Sea DOI

Xueyan Qin,

Xiuliang Yuan, Hossein Tabari

et al.

Atmospheric Research, Journal Year: 2025, Volume and Issue: unknown, P. 108008 - 108008

Published: Feb. 1, 2025

Language: Английский

Citations

0
Multidimensional evaluation of satellite-based and reanalysis-based precipitation datasets in the Tibetan Plateau DOI
Yuanyuan Cheng, Xiaolong Zhang,

Kunxin Wang

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133364 - 133364

Published: April 1, 2025

Language: Английский

Citations

0
Assessment of Soil Wind Erosion and Population Exposure Risk in Central Asia’s Terminal Lake Basins DOI Open Access
Wei Yu, Xiaofei Ma, Wei Yan

et al.

Water, Journal Year: 2024, Volume and Issue: 16(13), P. 1911 - 1911

Published: July 4, 2024

In the face of climate change and human activities, Central Asia’s (CA) terminal lake basins (TLBs) are shrinking, leading to deteriorating natural environments serious soil wind erosion (SWE), which threatens regional socio-economic development, health, safety. Limited research on SWE population exposure risk (PER) in these areas prompted this study, applied RWEQ a PER model assess spatiotemporal changes TLBs CA, including Ili River Basin (IRB), Tarim (TRB), Syr Darya (SRB), Amu (ARB), from 2000 2020. We analyzed driving factors used Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) simulate dust event trajectories. The findings 2020 show spatial reduction trend PER, with primary Taklamakan Desert, Aral Sea Basin, Lake Balkhash. Significant was observed along River, near Balkhash, middle lower reaches ARB SRB. Over past 21 years, temporal trends have occurred across basins, decreasing IRB, but increasing TRB, SRB, ARB. Dust movement trajectories indicate that SRB could affect Europe, while TRB impact northern China Japan. Correlations between SWE, NDVI, temperature, precipitation revealed negative correlation suggesting an inhibitory vegetation cover SWE. also varied significantly under different LUCCs, increases cropland, forestland, desert land, decreases grassland wetland. These insights vital for understanding offer theoretical support emergency mitigation arid regions.

Language: Английский

Citations

1
The Estimation of Grassland Aboveground Biomass and Analysis of Its Response to Climatic Factors Using a Random Forest Algorithm in Xinjiang, China DOI Creative Commons
Ping Dong, Changqing Jing, Gongxin Wang

et al.

Plants, Journal Year: 2024, Volume and Issue: 13(4), P. 548 - 548

Published: Feb. 17, 2024

Aboveground biomass (AGB) is a key indicator of the physiological status and productivity grasslands, its accurate estimation essential for understanding regional carbon cycles. In this study, we developed suitable AGB model grasslands in Xinjiang based on random forest algorithm, using observation data, remote sensing vegetation indices, meteorological data. We estimated grassland from 2000 to 2022, analyzed spatiotemporal changes, explored response climatic factors. The results showed that (1) was reliable (R2 = 0.55, RMSE 64.33 g·m−2) accurately Xinjiang; (2) spatial distribution high levels northwest low values southeast. growing trend most areas, with share 61.19%. Among these lowland meadows fastest growth, an average annual increment 0.65 g·m−2·a−1; (3) Xinjiang’s climate exhibited characteristics warm humidification, higher correlation precipitation than temperature. Developing models algorithms proves effective approach estimating AGB, providing fundamental data maintaining balance between grass livestock sustainable use conservation resources Xinjiang, China.

Language: Английский

Citations

0