Comparing Grassland Fire Drivers and Models in Inner Mongolia Using Field and Remote Sensing Data DOI Creative Commons
Heng Zhang,

Yongguang Liang,

Haiyan Ren

et al.

Fire, Journal Year: 2025, Volume and Issue: 8(3), P. 93 - 93

Published: Feb. 25, 2025

Frequent and intense grassland fires represent a significant threat to the stability sustainability of ecosystems. Therefore, understanding driving factors fire occurrence is key formulating effective management policies plans. Based on dataset (manually recorded data, satellite remote sensing data) from 2001 2022, this study uses six models analyze differences in different regions prevention periods area, determine relative importance factors, draw probability map fire. The results show that both types data selected Boosted Regression Trees (BRT) model as optimal for predicting Inner Mongolia Autonomous Region. Meteorological are main Region, topographic socio-economic important factors. number gradually decreased east west, were mainly concentrated northeast middle area. our functioned explore spatio-temporal pattern fire, accurately predict at scales, provide scientific basis rational allocation resources

Language: Английский

Development of an indicator system for solar-induced chlorophyll fluorescence monitoring to enhance early warning of flash drought DOI Creative Commons
Zixuan Qi, Yuchen Ye, Sun Lian

et al.

Agricultural Water Management, Journal Year: 2025, Volume and Issue: 312, P. 109397 - 109397

Published: March 9, 2025

Language: Английский

Citations

1
Mini-Review on Petroleum Molecular Geochemistry: Opportunities with Digitalization, Machine Learning, and Artificial Intelligence DOI
Kaiming Su,

Yaohui Xu,

Qingyong Luo

et al.

Energy & Fuels, Journal Year: 2025, Volume and Issue: unknown

Published: March 9, 2025

Language: Английский

Citations

0
Comparing Grassland Fire Drivers and Models in Inner Mongolia Using Field and Remote Sensing Data DOI Creative Commons
Heng Zhang,

Yongguang Liang,

Haiyan Ren

et al.

Fire, Journal Year: 2025, Volume and Issue: 8(3), P. 93 - 93

Published: Feb. 25, 2025

Frequent and intense grassland fires represent a significant threat to the stability sustainability of ecosystems. Therefore, understanding driving factors fire occurrence is key formulating effective management policies plans. Based on dataset (manually recorded data, satellite remote sensing data) from 2001 2022, this study uses six models analyze differences in different regions prevention periods area, determine relative importance factors, draw probability map fire. The results show that both types data selected Boosted Regression Trees (BRT) model as optimal for predicting Inner Mongolia Autonomous Region. Meteorological are main Region, topographic socio-economic important factors. number gradually decreased east west, were mainly concentrated northeast middle area. our functioned explore spatio-temporal pattern fire, accurately predict at scales, provide scientific basis rational allocation resources

Language: Английский

Citations

0