论文标题
一种全球方法，可识别具有中分辨率卫星图像的封闭式森林之外的树木
A global method to identify trees outside of closed-canopy forests with medium-resolution satellite imagery
论文作者
论文摘要
茂密的封闭式森林之外的散落树木对于碳封存，支持生计，维持生态系统完整性以及气候变化的适应性和缓解非常重要。与封闭式森林内部的树木相反，关于全球尺度上散落的树木的空间范围和分布知之甚少。由于高分辨率卫星图像的成本，全球监测系统依靠中分辨率卫星来监视土地使用。在这里，我们提出了一种全球一致的方法，可以鉴定具有大于三米的冠层直径的树木，具有中分辨率光学和雷达图像。双周无云的锅贴10米的哨兵2光学图像和哨兵1雷达图像用于训练完全卷积的网络，该网络由卷积门控的复发单位层和特征金字塔注意力层组成。在215,000多个Sentinel-1和Sentinel-2像素分布在-60到+60纬度的测试超过了75％的用户和生产商的准确性，可识别出低至中等密度（小于40％）树木覆盖率的公顷树木的树木，并且用户和生产商的准确度更高，而较高的是40％的树木（比40％）。提出的方法将在稀疏和散落的树覆盖物（小于40％）的地区监测树木存在的准确性增加了20％，并且在山区和非常多云的地区的佣金和省略误差减少了近一半。当应用于大型的异质景观时，结果表明了对全球各种景观的高度细节和准确性绘制树木的潜力。该信息对于理解当前的土地覆盖物很重要，可用于检测土地覆盖的变化，例如农林业，生物热点周围的缓冲区以及森林的扩张或侵占。
Scattered trees outside of dense, closed-canopy forests are very important for carbon sequestration, supporting livelihoods, maintaining ecosystem integrity, and climate change adaptation and mitigation. In contrast to trees inside of closed-canopy forests, not much is known about the spatial extent and distribution of scattered trees at a global scale. Due to the cost of high-resolution satellite imagery, global monitoring systems rely on medium-resolution satellites to monitor land use. Here we present a globally consistent method to identify trees with canopy diameters greater than three meters with medium-resolution optical and radar imagery. Biweekly cloud-free, pan-sharpened 10 meter Sentinel-2 optical imagery and Sentinel-1 radar imagery are used to train a fully convolutional network, consisting of a convolutional gated recurrent unit layer and a feature pyramid attention layer. Tested across more than 215,000 Sentinel-1 and Sentinel-2 pixels distributed from -60 to +60 latitude, the proposed model exceeds 75% user's and producer's accuracy identifying trees in hectares with a low to medium density (less than 40%) of tree cover, and 95% user's and producer's accuracy in hectares with dense (greater than 40%) tree cover. The proposed method increases the accuracy of monitoring tree presence in areas with sparse and scattered tree cover (less than 40%) by as much as 20%, and reduces commission and omission error in mountainous and very cloudy regions by nearly half. When applied across large, heterogeneous landscapes, the results demonstrate potential to map trees in high detail and accuracy over diverse landscapes across the globe. This information is important for understanding current land cover and can be used to detect changes in land cover such as agroforestry, buffer zones around biological hotspots, and expansion or encroachment of forests.
