Spatiotemporal Analysis of the Causes and Effects of Wildfire by Landsat Imagery and in situ Data: Case studies of Taiwan and California, USA

Records from the Forestry Bureau of Taiwan show that dozens of wildfire events occur every year in Taiwan. Furthermore, it is known that with climate change-induced extreme weather events, e.g., heatwaves and droughts, occurring more frequently, the wildfire occurrences are consequently increasing. Although the scale of wildfires in Taiwan is much smaller than in other places around the world, the potential harm caused by Taiwan’s wildfires is worth investigating due to the potential health issue and the safety concerns of wildfires.

Unfortunately, little has been done on the issue of wildfires in Taiwan. One of the difficulties of wildfire research in Taiwan can be attributed to the lack of forest areas data due to the limited number of observation stations. As a result, satellite data with more information on forest areas are used in this investigation to supplement the missing data and to complete the time series and variables. Multi-dimensional Complementary Ensemble Empirical Mode Decomposition (MCEEMD) is applied to identify the spatiotemporal distribution of variables, the meteorological factors affecting wildfires, and the wildfire influences on the vegetation of forests detected by satellite image time-series data. In the meantime, a time-frequency tool, Complementary Ensemble Empirical Mode Decomposition (CEEMD), is conducted to evaluate the trend and the variability of the time series of wildfire occurrences.

Wildfires can be lightning-caused and anthropogenic-caused. Therefore, to verify the intrinsic correlation between meteorological variables and wildfire occurrences, a scale- and time-dependent correlation approach, time-dependent intrinsic correlation (TDIC), is used. On the other hand, to estimate the impacts of wildfire, which may include air pollution, water quality, and health issues, time-dependent intrinsic cross-correlation (TDICC) is applied by considering the time lag effect.

This study aims at quantifying the time-lag correlation between wildfires and their potential effects on air pollutions, water quality, and health issues. Furthermore, the high-risk areas of wildfires in Taiwan are also identified. Meanwhile, the classical wildfire study case, California, USA, will be studied as a comparison case due to its large-scale wildfire, unique climate, and wildfire research. The results can serve as a reference for the Taiwan government to make decisions on management strategies referring to wildfire occurrences and livelihood problems.