Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya.
Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya.
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DOI：
10.1016/j.scitotenv.2024.171161
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发表时间：
2024-02
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影响因子：
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通讯作者：
Tzu-Hsin Karen Chen;M. Kincey;N. J. Rosser;Karen C. Seto
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作者：
Tzu-Hsin Karen Chen;M. Kincey;N. J. Rosser;Karen C. Seto
本文提出了一种基于遥感的方法，以有效地生成多时相滑坡清单，并识别经常性和持续性滑坡。我们使用来自Landsat的免费数据、夜间灯光、数字高程模型和卷积神经网络模型，开发了第一个跨越喜马拉雅山脉的数十年滑坡清单，时间跨度从1992年到2021年。该模型成功圈定了265,000个滑坡，准确识别了该地区83%的人工绘制的滑坡区域和94%的报告的滑坡事件。令人惊讶的是，每年只有14%的滑坡区首次发生，55 - 83%的滑坡区持续发生，3 - 24%的滑坡区再次发生。平均而言，受滑坡影响的像素在恢复前持续4.7年，比大地震事件后的小规模研究结果要短。在恢复的地区中，有50%的地区平均每5年就会出现山体滑坡。事实上，喜马拉雅地区22%的滑坡区域在30年内至少经历了三次滑坡。喜马拉雅地区山体滑坡持续时间的差异非常明显，西印度和尼泊尔的平均恢复时间为6年，而不丹和东印度的平均恢复时间为3年。坡度和高程成为持续和反复发生的滑坡的重要控制因素。道路建设、造林政策、地震和季风活动与喜马拉雅地区滑坡模式的变化有关。
This paper presents a remote sensing-based method to efficiently generate multi-temporal landslide inventories and identify recurrent and persistent landslides. We used free data from Landsat, nighttime lights, digital elevation models, and a convolutional neural network model to develop the first multi-decadal inventory of landslides across the Himalaya, spanning from 1992 to 2021. The model successfully delineated >265,000 landslides, accurately identifying 83 % of manually mapped landslide areas and 94 % of reported landslide events in the region. Surprisingly, only 14 % of landslide areas each year were first occurrences, 55–83 % of landslide areas were persistent and 3–24 % had reactivated. On average, a landslide-affected pixel persisted for 4.7 years before recovery, a duration shorter than findings from small-scale studies following a major earthquake event. Among the recovered areas, 50 % of them experienced recurrent landslides after an average of five years. In fact, 22 % of landslide areas in the Himalaya experienced at least three episodes of landslides within 30 years. Disparities in landslide persistence across the Himalaya were pronounced, with an average recovery time of 6 years for Western India and Nepal, compared to 3 years for Bhutan and Eastern India. Slope and elevation emerged as significant controls of persistent and recurrent landslides. Road construction, afforestation policies, and seismic and monsoon activities were related to changes in landslide patterns in the Himalaya.