Persistent and intensive wildland dense-fires (DFs) release substantial amounts of airborne pollutants, resulting in a sharp increase in emissions and leading to serious impacts on the environment and human health over extensive geographical areas. It is challenging to thoroughly investigate patterns of fire occurrence and fire distribution for predicting wildfire behaviour, and it is especially difficult to distinguish the characteristics of human-caused and climate-driven fires. Here, we identify and assess dense-fire (DF) from the perspective of spatiotemporally integrated processes using a machine-learning method based on a density-based clustering algorithm with noise constraint ratio. DFs represent collections of fires with homogenous behaviour and therefore allow the study of their internal features, which can reveal fixed patterns of fire occurrence and distribution as well as the evolution of fires over time. We estimated and labelled thousands of fire clusters on the Indochina Peninsula between 2010 and 2020, most of which occurred between December and May. For large-scale DFs, the number of fires contained and amount of atmospheric pollutants emitted were accounted for throughout most of the region, and the time, location and scale of their occurrence each year were relatively stable and predictable. Furthermore, the results of a secondary cluster analysis of fire interactions over the past decade showed two extreme fire events, labelled”north” and”south” groups, whose activities significantly impacted the atmospheric environment of the Indochina Peninsula. Additionally, we predicted their start/end dates and daily emissions. The study also found that the recurrence of high-density fires and the correlation between the DF edge and administrative border suggested a positive anthropogenic influence. To the authors' knowledge, this study is the first to analyze fires in a spatiotemporal Euclidean space by using density-based clustering, with high-density fires as independent subjects to study fire behaviour. The method proposed in this study can provide a reference for wildfire prediction and emission forecasting and fire control work.

持续和密集的荒地密集火灾 (DFs) 释放大量空气污染物，导致排放量急剧增加，并对广泛地理区域的环境和人类健康造成严重影响。彻底调查火灾发生模式和火灾分布以预测野火行为具有挑战性，尤其难以区分人为火灾和气候驱动火灾的特征。在这里，我们使用基于具有噪声约束比的基于密度的聚类算法的机器学习方法从时空集成过程的角度识别和评估密集火灾 (DF)。DF 代表具有同质行为的火灾集合，因此可以研究其内部特征，它可以揭示火灾发生和分布的固定模式以及火灾随时间的演变。我们估计并标记了 2010 年至 2020 年期间印度支那半岛上的数千个火灾集群，其中大部分发生在 12 月至 5 月之间。对于大范围的DFs，火灾的数量和大气污染物的排放量占了整个区域的大部分，并且每年发生的时间、地点和规模都比较稳定和可预测。此外，对过去十年火灾相互作用的二次聚类分析结果显示，两个极端火灾事件，分别标记为“北”和“南”组，其活动显着影响了印度支那半岛的大气环境。此外，我们预测了它们的开始/结束日期和每日排放量。该研究还发现，高密度火灾的再次发生以及DF边缘与行政边界之间的相关性表明存在积极的人为影响。据作者所知，这项研究是第一个使用基于密度的聚类分析时空欧几里得空间中的火灾的研究，其中高密度火灾作为独立的主题来研究火灾行为。本研究提出的方法可为野火预报和排放预报及消防工作提供参考。以高密度火灾作为独立受试者研究火灾行为。本研究提出的方法可为野火预报和排放预报及消防工作提供参考。以高密度火灾作为独立受试者研究火灾行为。本研究提出的方法可为野火预报和排放预报及消防工作提供参考。