In contrast to abrupt changes caused by land cover conversion, subtle changes driven by a shift in the condition, structure, or other biological attributes of land often lead to minimal and slower alterations of the terrestrial surface. Accurate mapping and monitoring of subtle change are crucial for an early warning of long-term gradual change that may eventually result in land cover conversion. Freely accessible moderate-resolution datasets such as the Landsat archive have great potential to characterize subtle change by capturing low-magnitude spectral changes in long-term observations. However, past studies have reported limited success in accurately extracting subtle changes from satellite-based time series analysis. In this study, we introduce a supervised framework named ‘PIDS’ to detect subtle forest disturbance from a comprehensive Landsat data archive by leveraging disturbance-based calibration sites. PIDS consists of four components: (1) Parameter optimization; (2) Index selection; (3) Dynamic stratified monitoring; and (4) Spatial consideration. PIDS was applied to map the early stage of bark beetle infestations (i.e., a lower per-pixel fraction of trees cover that show visual signs of infestation), which are a typical example of subtle change in conifer forests. Landsat Analysis Ready Data were used as the time series inputs for mapping mountain pine beetle and spruce beetle disturbance between 2001 and 2019 in Colorado, USA. PIDS-detection map assessment showed that the overall performance of PIDS (namely ‘F1 score’) was 0.86 for mountain pine beetle and 0.73 for spruce beetle, making a substantial improvement (> 0.3) compared to other approaches/products including COntinuous monitoring of Land Disturbance, LandTrendr, and the National Land Cover Database forest disturbance product. A sub-pixel analysis of tree canopy mortality percentage was performed by linking classified high-resolution (0.3- and 1-m) aerial imagery and 30-m PIDS-detection maps. Results show that PIDS typically detects mountain pine beetle infestation when ≥56% of a Landsat pixel is occupied by red-stage canopy mortality (one year after initial infestation), and spruce beetle infestation when ≥55% is occupied by gray-stage mortality (two years after initial infestation). This study addresses an important methodological goal pertinent to the utility of event-based reference samples for detecting subtle forest change, which could be potentially applied to other types of subtle land change.

与土地覆盖转换引起的突然变化相反，由土地状况、结构或其他生物属性的变化驱动的细微变化通常会导致陆地表面的变化最小且速度较慢。精确制图和监测细微变化对于对可能最终导致土地覆盖转变的长期逐渐变化的早期预警至关重要。可免费访问的中等分辨率数据集（例如 Landsat 档案）具有通过捕获长期观测中的低幅度光谱变化来表征细微变化的巨大潜力。然而，过去的研究报告称，在从基于卫星的时间序列分析中准确提取细微变化方面取得的成功有限。在这项研究中，我们引入了一个名为“PIDS”的监督框架，通过利用基于干扰的校准站点从综合的 Landsat 数据档案中检测微妙的森林干扰。PIDS 由四个部分组成： (1) 参数优化；(2) 指标选择；(3) 动态分层监测；(4) 空间考虑。PIDS 被用于绘制树皮甲虫侵染的早期阶段（即，显示侵染视觉迹象的树木覆盖的每像素比例较低），这是针叶林细微变化的典型例子。Landsat 分析就绪数据被用作时间序列输入，用于绘制 2001 年至 2019 年在美国科罗拉多州的山松甲虫和云杉甲虫干扰图。PIDS-检测图评估表明，PIDS的整体性能（即“F1分数”）对于山松甲虫为0.86，为0。73 对于云杉甲虫，与其他方法/产品（包括持续监测土地扰动、LandTrendr 和国家土地覆盖数据库森林扰动产品）相比，取得了实质性的改进（> 0.3）。通过将分类的高分辨率（0.3 米和 1 米）航空影像和 30 米 PIDS 检测图联系起来，对树冠死亡率百分比进行了亚像素分析。结果表明，当 ≥ 56% 的 Landsat 像素被红色阶段树冠死亡（初始侵染后一年）占据时，PIDS 通常检测到山松甲虫侵染，当 ≥55% 被灰色阶段死亡占据时，云杉甲虫侵染（初次侵染两年后）。这项研究解决了一个重要的方法学目标，该目标与基于事件的参考样本用于检测森林细微变化的效用有关，