The Sierra Madre Occidental (SMO) is the longest continuous mountain complex in Mexico and is characterised by high species diversity and a high proportion of endemism. The rate of deforestation is high in Mexico, as in other megadiverse countries, and protection of the country’s biodiversity is a top priority. Quantification of changes in vegetation cover is essential for this purpose. Temporal information is required to enable classification of vegetation cover and change processes. In this study, the disturbances that occurred in the temperate forest of the SMO in the State of Durango (Mexico) during the period 1986–2012 were quantified using Landsat Time Series Stacks (LTSS) and the Vegetation Change Tracker (VCT) algorithm. The results obtained confirmed that land cover changes were detected with high overall accuracy (97.6%). In order to analyze the forest losses corresponding to the only official data available in Mexico, we retrieved land use and vegetation mapping (USV) data from the Mexican National Institute of Statistics and Geography (INEGI). The aridity index was established and fragmentation analysis was carried out in the study area, showing that forest pests and forest fires were the principal disturbance events in the SMO of Durango, and that the climate greatly influenced the occurrence of disturbances. The LTSS-VCT analysis revealed that for the period 1986–2012, about 34% of the temperate forest cover in the SMO in Durango was lost due to different types of disturbance, representing an annual rate of loss of forest cover of 1.3% and affecting 32,840 ha of land per year. The trend analysis of USV data showed very similar changes to those indicated by the LTSS-VCT analysis in terms of loss of temperate forest. However, differences were observed in regards to the absolute values of forest cover and vegetation loss, with analysis of the USV data indicating forest losses of 28% due to disturbances and an annual disturbance rate of 1%, affecting 49,940 ha of land per year. The LTSS-VCT approach proved efficient for mapping data on forest disturbance acquired by a medium spatial resolution (Landsat) sensor in the SMO in the State of Durango, providing satisfactory results and at low cost.

西马德雷山脉（SMO）是墨西哥最长的连续山区，其特点是物种多样性高，地方病比例高。像其他巨型生物多样性国家一样，墨西哥的毁林率很高，保护该国的生物多样性是重中之重。为此目的，量化植被覆盖的变化至关重要。需要时间信息以对植被覆盖度和变化过程进行分类。在这项研究中，使用Landsat时间序列堆栈（LTSS）和植被变化追踪器（VCT）算法对杜兰戈州（墨西哥）SMO温带森林在1986-2012年期间发生的干扰进行了量化。获得的结果证实以较高的总体准确性（97.6％）检测到土地覆被变化。为了分析与墨西哥唯一可用的官方数据相对应的森林损失，我们从墨西哥国家统计和地理研究所（INEGI）检索了土地利用和植被测绘（USV）数据。在研究区建立了干旱指数并进行了破碎化分析，结果表明，杜兰戈SMO的主要干扰事件是森林有害生物和森林火灾，而且气候对干扰的发生有很大影响。LTSS-VCT分析显示，在1986-2012年期间，杜兰戈SMO中约34％的温带森林覆盖率由于不同类型的干扰而丧失，这意味着每年森林覆盖率的损失率为1.3％，每年32,840公顷土地。就温带森林的损失而言，USV数据的趋势分析显示出与LTSS-VCT分析表明的变化非常相似。但是，在森林覆盖率和植被丧失的绝对值方面观察到差异，对USV数据的分析表明，由于干扰而造成的森林损失为28％，年扰动率为1％，每年影响49,940公顷土地。事实证明，LTSS-VCT方法可有效地绘制由杜兰戈州SMO中的中空分辨率（Landsat）传感器获取的森林干扰数据，可提供令人满意的结果且成本低廉。通过对USV数据的分析表明，由于干扰而造成的森林损失为28％，年干扰率为1％，每年影响49,940公顷土地。事实证明，LTSS-VCT方法可有效地绘制由杜兰戈州SMO中的中空分辨率（Landsat）传感器获取的森林干扰数据，可提供令人满意的结果且成本低廉。通过对USV数据的分析表明，由于干扰而造成的森林损失为28％，年干扰率为1％，每年影响49,940公顷土地。事实证明，LTSS-VCT方法可有效地绘制由杜兰戈州SMO中的中空分辨率（Landsat）传感器获取的森林干扰数据，可提供令人满意的结果且成本低廉。