Remote Sensing of Environment
Remote Sensing of Environment
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DOI：
10.1111/j.0033-0124.1965.00020.x
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通讯作者：
Daniele Marinelli;M. Dalponte;L. Frizzera;Erik Næsset;D. Gianelle;Marie Weiss
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作者：
Daniele Marinelli;M. Dalponte;L. Frizzera;Erik Næsset;D. Gianelle;Marie Weiss
森林干扰对地方和全球尺度的生态系统动态都有重大影响。因此，必须获得关于此类事件的地点、性质和时间的客观信息，以更好地了解其影响，更新森林管理政策和减少干扰的战略。到目前为止，遥感数据已被广泛用于监测林分更替干扰，如风灾和野火。相比之下，较少的努力一直致力于检测非林分替代干扰（NSRD），通常其特征在于缓慢和渐进的时间动态。为了解决这一差距，我们提出了一种方法，用于自动检测SRD和NSRD。所提出的方法可以检测过去和最近的干扰，每月的时间分辨率，在近实时的方式，通过处理新的图像，因为它们被收购。与将时间序列处理为一维（1D）时间轨迹的现有方法不同，该方法通过将图像组织成二维（2D）网格状结构来分析图像序列。这种表示使我们能够利用植物物候的年度周期性来模拟时间序列的年内和年际变化。该方法已被测试的研究领域的小蠹攻击实现用户的准确度和生产者的准确度为0.91 ± 0.08和0.81 ± 0.07（95%的置信区间）的干扰地区，分别。
Forest disturbances have a major impact on ecosystem dynamics both at local and global scales. Accordingly, it is important to acquire objective information about the location, nature and timing of such events to improve the understanding of their impact, update forest management policies and disturbance mitigation strategies. To this date, remotely sensed data have been widely used for the detection of stand replacing disturbances (SRD) such as windthrows and wildfires. In contrast, less effort has been devoted to the detection of non-stand replacing disturbances (NSRD), typically characterized by slower and gradual temporal dynamics. To address this gap, we propose a method for the automated detection of both SRD and NSRD. The proposed method can detect both past and recent disturbances, with a monthly temporal resolution, in a near real-time fashion by processing new images as they are acquired. Differently from existing approaches that handle the time series as a one-dimensional (1D) temporal trajectory, the method analyzes the sequence of images by organizing them in a two-dimensional (2D) grid-like structure. This representation allows us to model both the intra-and inter-annual variations of the time series taking advantage of the annual cyclical nature of the plant phenology. The method has been tested on study areas attacked by bark beetles achieving a user’s accuracy and producer’s accuracy of 0.91 ± 0.08 and 0.81 ± 0.07 (with 95% confidence intervals) for the disturbed areas, respectively.