Evaluation of the Spatial Distribution of Predictors of Fire Regimes in China from 2003 to 2016

Jiajia Su, Zhihua Liu, Wenjuan Wang, Kewei Jiao, Yue Yu, Kaili Li, Qiushuang Lü, Tamara L. Fletcher

Research output: Contribution to journalArticlepeer-review

Abstract

Wildfire has extensive and profound impacts on forest structure and function. Therefore, it is important to study the spatial and temporal patterns of forest fire regimes and their drivers in order to better understand the feedbacks between climate change, fire disturbance, and forest ecosystems. Based on the Global Fire Atlas dataset, three forest fire regime components (fire occurrence density, burned rate, and median fire size) were extracted for China from 2003 to 2016. Three statistical models (Boosted Regression Tree, Random Forest, and Support Vector Machine) were used to systematically analyze the relationships between patterns of forest fire disturbance and climate, human activities, vegetation, and topography in China, as well as their spatial heterogeneity in different climatic regions. The results indicate that the spatial distribution of forest fires is heterogeneous, and different forest fire regime components are predicted by different factors. At the national level, the distribution of forest fire regimes mainly corresponds to climatic factors, although the relationship between median fire size and predictors is obscure. At the scale of each ecoregion, the main climate predictors of forest fire occurrence density and burned rate change from temperature in the north to temperature and precipitation in the south. Median fire size varies with elevation and temperature in the south. These results demonstrate that the spatial heterogeneity of predictors and scaling effects must be fully considered in the study of forest fire disturbance.

Original languageEnglish
Article number4946
JournalRemote Sensing
Volume15
Issue number20
DOIs
StatePublished - Oct 2023

Keywords

  • Boosted Regression Tree model
  • Random Forest model
  • Support Vector Machine model
  • forest fire

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