A near-real-time approach for monitoring forest disturbance using Landsat time series: stochastic continuous change detection
A near-real-time approach for monitoring forest disturbance using Landsat time series: stochastic continuous change detection
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DOI：
10.31223/osf.io/cqhsj
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发表时间：
2020-05
影响因子：
13.5
通讯作者：
Su Ye;J. Rogan;Zhe Zhu;R. Eastman
中科院分区：
文献类型：
--
作者：
Su Ye;J. Rogan;Zhe Zhu;R. Eastman
摘要森林干扰严重影响天然林的生态功能。及时提供有关森林干扰事件的范围、时间和规模的信息，对于有效的干扰管理战略至关重要。然而，我们仍然缺乏准确、接近实时和高性能的遥感工具来监测突然和微妙的森林干扰。提出了一种利用密集的陆地卫星数据时间序列进行随机连续变化检测的新方法--S连续变化检测。S卫星定位系统对“陆地扰动连续监测(COLD)”方法进行了改进，引入了一种称为“状态空间模型”的数学工具，该模型将趋势和季节性视为随机过程，允许以递归方式对卫星观测的时间动态进行建模。定量精度评估是根据分布在美国各地的概率抽样的3782个基于陆地卫星的干扰参考图(30米)进行评估的。验证结果表明，S-CCD法的总体精度(F1最佳得分)为0.793，遗漏误差为20%，委托误差为21%，略高于COLD(0.789)。两个分别与野火和昆虫干扰相关的干扰点被用于基于地图的定性分析。定量和定性分析都表明，对于光谱变化细微/渐变的扰动，S-ccd的漏检误差比COLD要小。此外，S-ccd具有更好的实时监测能力，其完整的递推方式和确认扰动的滞后时间比冷扰动短(126d比166d，50%扰动事件报警)，计算加速比可达~4.4倍。这项研究解决了对森林健康的近实时监测和大规模测绘的需求，并为从密集的基于陆地卫星的时间序列执行变化检测任务提供了一种新的方法。
Abstract Forest disturbances greatly affect the ecological functioning of natural forests. Timely information regarding extent, timing and magnitude of forest disturbance events is crucial for effective disturbance management strategies. Yet, we still lack accurate, near-real-time and high-performance remote sensing tools for monitoring abrupt and subtle forest disturbances. This study presents a new approach called ‘Stochastic Continuous Change Detection (S-CCD)’ using a dense Landsat data time series. S-CCD improves upon the ‘COntinuous monitoring of Land Disturbance (COLD)’ approach by incorporating a mathematical tool called the ‘state space model’, which treats trends and seasonality as stochastic processes, allowing for modeling temporal dynamics of satellite observations in a recursive way. The quantitative accuracy assessment is evaluated based on 3782 Landsat-based disturbance reference plots (30 m) from a probability sampling distributed throughout the Conterminous United States. Validation results show that the overall accuracy (best F1 score) of S-CCD is 0.793 with 20% omission error and 21% commission error, slightly higher than that of COLD (0.789). Two disturbance sites respectively associated with wildfire and insect disturbances are used for qualitative map-based analysis. Both quantitative and qualitative analyses suggest that S-CCD achieves fewer omission errors than COLD for detecting those disturbances with subtle/gradual spectral change. In addition, S-CCD facilitates a better real-time monitoring, benefited by its complete recursive manner and a shorter lag for confirming disturbance than COLD (126 days vs. 166 days for alerting 50% disturbance events), and reached up to ~4.4 times speedup for computation. This research addresses the need for near-real-time monitoring and large-scale mapping of forest health and offers a new approach for operationally performing change detection tasks from dense Landsat-based time series.