Landslides are destructive natural hazards that cause substantial loss of life and impact on natural and built environments. Landslide frequencies are important inputs for hazard assessments. However, dating landslides in remote areas is often challenging. We propose a novel landslide dating technique based on Segmented WAvelet-DEnoising and stepwise linear fitting (SWADE), using the Landsat archive (1985–2017). SWADE employs the principle that vegetation is often removed by landsliding in vegetated areas, causing a temporal decrease in normalized difference vegetation index (NDVI). The applicability of SWADE and two previously published methods for landslide dating, harmonic modelling and LandTrendr, are evaluated using 66 known landslides in the Buckinghorse River area, northeastern British Columbia, Canada. SWADE identifies sudden changes of NDVI values in the time series and this may result in one or more probable landslide occurrence dates. The most-probable date range identified by SWADE detects 52% of the landslides within a maximum error of 1 year, and 62% of the landslides within a maximum error of 2 years. Comparatively, these numbers increase to 68% and 80% when including the two most-probable landslide date ranges, respectively. Harmonic modelling detects 79% of the landslides with a maximum error of 1 year, and 82% of the landslides with a maximum error of 2 years, but requires expert judgement and a well-developed seasonal vegetation cycle in contrast to SWADE. LandTrendr, originally developed for mapping deforestation, only detects 42% of landslides within a maximum error of 2 years. SWADE provides a promising fully automatic method for landslide dating, which can contribute to constructing landslide frequency-magnitude distributions in remote areas.

山体滑坡是破坏性的自然灾害，会造成大量生命损失并对自然和建筑环境造成影响。滑坡频率是灾害评估的重要输入。然而，在偏远地区确定滑坡的年代往往具有挑战性。我们使用 Landsat 档案（1985-2017）提出了一种基于分段小波去噪和逐步线性拟合 (SWADE) 的新型滑坡测年技术。SWADE 采用的原理是植被经常被植被区的山体滑坡移除，导致归一化植被指数 (NDVI) 随时间下降。使用加拿大不列颠哥伦比亚省东北部 Buckinghorse 河地区的 66 处已知滑坡，评估了 SWADE 和两种先前发布的滑坡年代测定方法、谐波建模和 LandTrendr 的适用性。SWADE 识别时间序列中 NDVI 值的突然变化，这可能导致一个或多个可能的滑坡发生日期。SWADE 确定的最可能日期范围在 1 年的最大误差内检测到 52% 的滑坡，在 2 年的最大误差内检测到 62% 的滑坡。相比之下，当包括两个最可能的滑坡日期范围时，这些数字分别增加到 68% 和 80%。谐波建模检测到 79% 的滑坡最大误差为 1 年，82% 的滑坡最大误差为 2 年，但与 SWADE 相比需要专家判断和发育良好的季节性植被周期。LandTrendr 最初是为绘制森林砍伐图而开发的，在 2 年的最大误差范围内只能检测到 42% 的山体滑坡。