ABSTRACT

Monitoring and classifying disturbed forests can provide information support for not only sustainable forest management but also global carbon sequestration assessments. In this study, we propose an autoencoder-based model for forest disturbance detection, which considers disturbances as anomalous events in forest temporal trajectories. The autoencoder network is established and trained to reconstruct intact forest trajectories. Then, the disturbance detection threshold is derived by Tukey’s method based on the reconstruction error of the intact forest trajectory. The assessment result shows that the model using the NBR time series performs better than the NDVI-based model, with an overall accuracy of 90.3%. The omission and commission errors of disturbed forest are 7% and 12%, respectively. Additionally, the trained NBR-based model is implemented in two test areas, with overall accuracies of 87.2% and 86.1%, indicating the robustness and scalability of the model. Moreover, comparing three common methods, the proposed model performs better, especially for intact forest detection accuracy. This study provides a novel and robust approach with acceptable accuracy for forest disturbance detection, enabling forest disturbance to be identified in regions with limited disturbance reference data.

摘要

监测和分类受干扰的森林不仅可以为可持续森林管理提供信息支持，还可以为全球碳固存评估提供信息支持。在这项研究中，我们提出了一种基于自动编码器的森林干扰检测模型，该模型将干扰视为森林时间轨迹中的异常事件。建立并训练自动编码器网络以重建完整的森林轨迹。然后，基于完整森​​林轨迹的重建误差，通过Tukey方法推导出扰动检测阈值。评估结果表明，使用NBR时间序列的模型性能优于基于NDVI的模型，总体准确率为90.3%。干扰林的遗漏和委托误差分别为7%和12%。此外，训练好的基于 NBR 的模型在两个测试区域实现，总体准确率分别为 87.2% 和 86.1%，表明模型的鲁棒性和可扩展性。此外，比较三种常用方法，所提出的模型性能更好，尤其是在完整森林检测精度方面。该研究为森林干扰检测提供了一种具有可接受精度的新颖而稳健的方法，能够在干扰参考数据有限的地区识别森林干扰。