Correction to: Wind and rain are the primary climate factors driving changing phenology of an aerial insectivore

The subtropical vegetation plays an important role in maintaining the structure and function of global ecosystems, and its contribution to the global carbon balance are receiving increasing attention. The fractional vegetation cover (FVC) as an important indicator for monitoring environment change, is widely used to analyze the spatiotemporal pattern of regional and even global vegetation. China is an important distribution area of subtropical vegetation. Therefore, we first used the dimidiate pixel model to extract the subtropical FVC of China during 2001–2018 based on MODIS land surface reflectance data, and then used the linear regression analysis and the variation coefficient to explore its spatiotemporal variations characteristics. Finally, the partial correlation analysis and the partial derivative model were used to analyze the influences and contributions of climate factors on FVC, respectively. The results showed that (1) the subtropical FVC had obvious spatiotemporal heterogeneity; the FVC high-coverage and medium-coverage zones were concentratedly and their combined area accounted for more than 70% of the total study area. (2) The interannual variation in the average subtropical FVC from 2001 to 2018 showed a significant growth trend. (3) In 76.28% of the study area, the regional FVC showed an increasing trend, and the remaining regional FVC showed a decreasing trend. However, the overall fluctuations in the FVC (increasing or decreasing) in the region were relatively stable. (4) The influences of climate factors to the FVC exhibited obvious spatial differences. More than half of all pixels exhibited the influence of the average annual minimum temperature and the annual precipitation had positive on FVC, while the average annual maximum temperature had negative on FVC. (5) The contributions of climate changes to FVC had obvious heterogeneity, and the average annual minimum temperature was the main contribution factor affecting the dynamic variations of FVC.

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Are Climate Factors Driving the Contemporary Wildfire Occurrence in China?

Forests ◽

10.3390/f12040392 ◽

2021 ◽

Vol 12 (4) ◽

pp. 392

Author(s):

Zige Lan ◽

Zhangwen Su ◽

Meng Guo ◽

Ernesto C. Alvarado ◽

Futao Guo ◽

...

Keyword(s):

Human Factors ◽

Large Scale ◽

Fire Occurrence ◽

Climate Factors ◽

Wildfire Occurrence ◽

Prediction And Prevention ◽

Data Resolution ◽

Main Driver ◽

Landscape Factors ◽

Modelling Approach

Understanding the drivers of wildfire occurrence is of great value for fire prevention and management, but due to the variation in research methods, data sources, and data resolution of those studies, it is challenging to conduct a large-scale comprehensive comparative qualitative analysis on the topic. China has diverse vegetation types and topography, and has undergone rapid economic and social development, but experiences a high frequency of wildfires, making it one of the ideal locations for wildfire research. We applied the Random Forests modelling approach to explore the main types of wildfire drivers (climate factors, landscape factors and human factors) in three high wildfire density regions (Northeast (NE), Southwest (SW), and Southeast (SE)) of China. The results indicate that climate factors were the main driver of wildfire occurrence in the three regions. Precipitation and temperature significantly impacted the fire occurrence in the three regions due to the direct influence on the moisture content of forest fuel. However, wind speed had important influence on fire occurrence in the SE and SW. The explanation power of the landscape and human factors varied significantly between regions. Human factors explained 40% of the fire occurrence in the SE but only explained less than 10% of the fire occurrence in the NE and SW. The density of roads was identified as the most important human factor driving fires in all three regions, but railway density had more explanation power on fire occurrence in the SE than in the other regions. The landscape factors showed nearly no influence on fire occurrence in the NE but explained 46.4% and 20.6% in the SE and SW regions, respectively. Amongst landscape factors, elevation had the highest average explanation power on fire occurrence in the three regions, particularly in the SW. In conclusion, this study provides useful insights into targeted fire prediction and prevention, which should be more precise and effective under climate change and socio-economic development.

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