Fractal analysis revealed persistent correlations in long-term vegetation fire data in most South and Southeast Asian countries

Abstract

Abstract Vegetation fires are most common in South/Southeast Asian countries (S/SEA). Characterizing the statistical nature of long-term fire datasets can provide valuable information on fire management. Specifically, distinguishing random noise from correlated noise in long-term signals is critical for linking with the underlying processes. Fractal methods can help to assess long-range correlations in long-term timeseries data. This study tested the daily time-series fire data retrieved from the VIIRS satellite (2012–2021) for fractal behavior. Descriptive statistics and popular Detrended Fluctuation Analysis (DFA) were used to assess fire characteristics and persistent versus non-persistent correlations. Results over South Asia (SA) suggested India with the highest mean fire counts (FC) and the least in Bhutan. Fire radiative power (FRP), an indicator of fire intensity, was highest in India and least in Afghanistan. Among Southeast Asia (SEA), Myanmar had the highest mean FC and FRP and least in Timor Leste. The DFA results revealed the fractal nature of FC in different countries. In SA, FC over India, Nepal, Sri Lanka, Afghanistan, and Bhutan showed persistent correlation behavior, whereas anti-persistence in Bangladesh and Pakistan. In addition, FRP showed anti-persistent behavior in Afghanistan, Bangladesh, and Pakistan and a persistent signal for Bhutan, India, Sri Lanka, and Nepal. In contrast to SA, FC and FRP showed persistent behavior in all SEA countries. The persistent or non-persistence nature of the data can help model fire behavior to aid in management and mitigation efforts.