ABSTRACT Consistent forest loss estimates are important to enforce forest management regulations. In Tunisia, recent evidence has suggested that the deforestation rate is increasing, especially since the 2011’s Revolution. However, no spatially explicit data on the extent of deforestation before and after the Revolution exists. Here, we quantify deforestation in the country for the period 2001–2014 and we propose a novel spatio-temporal pattern-based sequence classification framework for forest loss estimation. To do so, expert knowledge and spatial techniques are applied to identify deforestation drivers. Then, we adopt sequential pattern mining to extract sets of patterns sharing similar spatiotemporal behavior. The sequence miner generates multidimensional-closed sequential patterns at different time granularities. Then, a discriminative filter is employed to decide on patterns to use as relevant classification features. Lastly, the classifier is trained using random forest and shows an improved result.

中文翻译：

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使用基于时空模式的序列分类方法估计森林损失

摘要 一致的森林损失估计对于执行森林管理法规很重要。在突尼斯，最近的证据表明森林砍伐率正在上升，尤其是自 2011 年革命以来。然而，没有关于革命前后森林砍伐程度的空间明确数据。在这里，我们量化了该国 2001 年至 2014 年期间的森林砍伐情况，并提出了一种新的基于时空模式的序列分类框架，用于估计森林损失。为此，应用专家知识和空间技术来确定森林砍伐的驱动因素。然后，我们采用序列模式挖掘来提取共享相似时空行为的模式集。序列挖掘器以不同的时间粒度生成多维闭合序列模式。然后，使用判别过滤器来决定用作相关分类特征的模式。最后，使用随机森林训练分类器并显示改进的结果。