Applicability Assessment of GFED4 and GFED5 on Forest Fires in Chinese Mainland and Its Fire-Scale Patterns Change

The GFED (Global Fire Emissions Database) series products are widely used in global fire research, yet their applicability in mainland China remains insufficiently evaluated. Additionally, large fires and small fires are rarely studied separately. This study first evaluates GFED4’s applicability for monitoring forest fire burned areas in Chinese mainland (2001–2015) through multi-temporal (annual, seasonal, and monthly) and multi-spatial (national, regional, provincial, and 0.25° grid) analyses, using Pearson correlation (CC), root mean square error (RMSE), and mean error (ME) alongside official statistical data. Then, the forest fire-burned areas of small fires were extracted based on the difference between GFED4 and GFED5. The results show that GFED4 exhibits strong consistency at the national level and in key fire-prone regions such as Northeast, North, and Central South China, especially during high-fire years and in spring. However, systematic overestimation occurs in the Northwest, while underestimation or seasonal bias is observed in parts of East and Southwest China. The results show a clear decline in large-fire burned area, but a significant increase in small fires, particularly in Northeast, Central South, and East China. Spatial analysis indicates small fires exhibit strong clustering (Moran’s I = 0.270, p < 0.01), whereas large fires are spatially dispersed. The study concludes that GFED4 is reliable for monitoring large fires in forested zones but should be applied cautiously in non-forested and small-fire-dominated regions.

Authors

Institutions

Publication Details

Journal
Remote Sensing
Published
2025-10-16
DOI
https://doi.org/10.3390/rs17203461
Citations
1
Primary Topic
Fire effects on ecosystems
Type
article
Field-Weighted Citation Impact
1.11
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Applicability Assessment of GFED4 and GFED5 on Forest Fires in Chinese Mainland and Its Fire-Scale Patterns Change

Hao Meng, Shenglei Zhang, Zhenhua Di, Meixia Xie et al.
1 citations
Remote Sensing
Fire effects on ecosystems
1.11
article

Applicability Assessment of GFED4 and GFED5 on Forest Fires in Chinese Mainland and Its Fire-Scale Patterns Change

Hao Meng, Shenglei Zhang, Zhenhua Di, Meixia Xie, Xurui Wang, Xinling Tian
article en
1 citations

Abstract

The GFED (Global Fire Emissions Database) series products are widely used in global fire research, yet their applicability in mainland China remains insufficiently evaluated. Additionally, large fires and small fires are rarely studied separately. This study first evaluates GFED4’s applicability for monitoring forest fire burned areas in Chinese mainland (2001–2015) through multi-temporal (annual, seasonal, and monthly) and multi-spatial (national, regional, provincial, and 0.25° grid) analyses, using Pearson correlation (CC), root mean square error (RMSE), and mean error (ME) alongside official statistical data. Then, the forest fire-burned areas of small fires were extracted based on the difference between GFED4 and GFED5. The results show that GFED4 exhibits strong consistency at the national level and in key fire-prone regions such as Northeast, North, and Central South China, especially during high-fire years and in spring. However, systematic overestimation occurs in the Northwest, while underestimation or seasonal bias is observed in parts of East and Southwest China. The results show a clear decline in large-fire burned area, but a significant increase in small fires, particularly in Northeast, Central South, and East China. Spatial analysis indicates small fires exhibit strong clustering (Moran’s I = 0.270, p < 0.01), whereas large fires are spatially dispersed. The study concludes that GFED4 is reliable for monitoring large fires in forested zones but should be applied cautiously in non-forested and small-fire-dominated regions.

Remote SensingVol. 17(20)
Chinese Academy of Sciences (CN), Beijing Normal University (CN), Aerospace Information Research Institute (CN)
Openalex Percentile: Top 19%
Fire effects on ecosystems
1.11
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.