Department of Earth Sciences, National Cheng Kung University, Tainan, 701, Taiwan.
Global Earth Observation and Data Analysis Center, National Cheng Kung University, Tainan, 701, Taiwan.
Sci Rep. 2019 May 13;9(1):7279. doi: 10.1038/s41598-019-43544-5.
Climate variability and man-made impacts have severely damaged forests around the world in recent years, which calls for an urgent need of restoration aiming toward long-term sustainability for the forest environment. This paper proposes a new three-level decision tree (TLDT) approach to map forest, shadowy, bare and low-vegetated lands sequentially by integrating three spectral indices. TLDT requires neither image normalization nor atmospheric correction, and improves on the other methods by introducing more levels of decision tree classification with inputs from the same multispectral imagery. This approach is validated by comparing the results obtained from aerial orthophotos (25 cm) that were acquired at approximately the same time in which the Formosa-2 images (8 m) were being taken. The overall accuracy is as high as 96.8% after excluding the deviations near the boundary of each class caused by the different resolutions. With TLDT, the effectiveness of forest restoration at 30 sites are assessed using all available multispectral Formosat-2 images acquired between 2005 and 2016. The distinction between natural regeneration and regrowth enhanced by restoration efforts were also made by using the existing dataset and TLDT developed in this research. This work supports the use of multitemporal remote sensing imagery as a reliable source of data for assessing the effectiveness of forest restoration on a regular basis. This work also serves as the basis for studying the global trend of forest restoration in the future.
近年来，气候变异性和人为影响严重破坏了世界各地的森林，这迫切需要进行恢复工作，以实现森林环境的长期可持续性。本文提出了一种新的三层决策树（TLDT）方法，通过整合三个光谱指数，依次对森林、阴影、裸地和低植被土地进行映射。TLDT 既不需要图像归一化，也不需要大气校正，通过从同一多光谱图像中引入更多级别的决策树分类输入，改进了其他方法。通过将与获取 Formosa-2 图像（8 米）大致同时获取的航空正射影像（25 厘米）的结果进行比较，验证了该方法的有效性。在排除了由于分辨率不同而导致的每个类边界附近的偏差后，整体精度高达 96.8%。使用 TLDT，评估了 2005 年至 2016 年期间获取的所有可用多光谱 Formosat-2 图像中 30 个地点的森林恢复效果。还利用现有数据集和本研究中开发的 TLDT，对自然再生和恢复努力增强的再生进行了区分。这项工作支持使用多时相遥感图像作为定期评估森林恢复效果的可靠数据源。这项工作还为未来研究全球森林恢复趋势奠定了基础。
