Wildfires exhibit extensive nonlinear characteristics and threshold effects in response to environmental changes. However, how threshold effects affect wildfire responses and their future changes remains unclear. Here we identified thresholds where wildfire-driver relationships shift and estimated the impact of threshold effects on wildfire dynamics in the 21st century in northern mid- and high latitudes (>30°N). Wildfire-driver thresholds, coregulated by gradient differences in heat and moisture conditions, vegetation productivity, and human activities, effectively explain the spatial patterns and uneven responses of wildfires. Despite the dominance of temperature, precipitation, and socioeconomic status on wildfires, 30.70% of wildfire changes are attributed to their threshold effects, notably in boreal and temperate ecosystems. Climate change will enhance the threshold effects of wildfire drivers, leading to a 47.81 ± 3.08% increase in burned areas by 2030–2100 compared with 2001‒2022. Our findings highlight that enhanced threshold effects of drivers promote future wildfire changes, posing challenges to fire suppression.

Phenology is an important biological indicator for monitoring terrestrial ecosystems and global change. Solar-induced chlorophyll fluorescence (SIF) emitted by chlorophyll has been proven to characterize vegetation photosynthesis and phenology. In this study, we used monthly normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and SIF products to qualitatively compare the effectiveness at detecting the phenological characteristics (SOS (start-of-season), EOS (end-of-season), and LOS (length-of-season)) over China during 2007–2013. The phenological characteristics determined by gross primary productivity (GPP) were applied as the reference to validate the phenological characteristics derived from NDVI, EVI, and SIF. The results demonstrated that the phenological characteristics derived from SIF were more consistent with that of GPP than VIs (NDVI and EVI) when considering all latitude grades, different elevation grades, and different land cover types in China. In the middle- and high-latitude regions, SOS derived from the vegetation indices (SOS_VIs) did not deviate from those from GPP (SOS_GPP) and SIF (SOS_SIF), while in low latitudes, SOS_VIs were about 20 d later than SOS_SIF and SOS_GPP. The VIs (EOS_VIs) had a severe lag behind those of SIF (EOS_SIF) in estimating the EOS at all latitudes. The EOS_SIF had a deviation of fewer than 5 d compared with EOS estimated by GPP (EOS_GPP), whereas the deviation of EOS_VIs from EOS_GPP was about 10–31 d across low to high latitude regions. The biases of SIF and VIs were due to the inconsistency between vegetation photosynthesis and leaf greenness. Also, VIs overestimated the LOS at all latitudes, the difference of LOS between estimated by NDVI and estimated by GPP was as long as 39 d in the high-latitude region. Our study suggests that SIF is suitable for estimating the phenological characteristics of vegetation regardless of different latitudes, elevation grades, and land cover types in China, providing a basis for SIF to study the vegetation phenological characteristics in a regional scope.

Wildfires are among the main factors that disturb terrestrial ecosystems, landscapes, and the environment. Understanding the differences that the socioeconomic factors of different administrative levels have on fire occurrence remains critical to inform the driving function of ignition sources. In this study, we collected socioeconomic and land use data for 21 cities and 81 counties in the study area from 2001 to 2019 and applied a multilevel model to explore the relationship between wildfire occurrence density and the driving factors. We estimated the fixed and random effect of the factors at different levels and built three hierarchical linear models (HLMs) to quantify the impacts of socioeconomic drivers on wildfires. The results showed that the variance among cities contributed to 14.01% of the unexplained variation of random effects at the county level. At the county level, the densities of middle school student populations, gross domestic product (GDP), and impervious surface areas were significantly positively correlated with fire occurrence density. At the city level, GDP and its interaction with county-level factors were significantly negatively correlated with fire occurrence density. This study provides a new method and findings for the research of wildfire occurrence and risk.