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A global method to identify trees outside of closed-canopy forests with medium-resolution satellite imagery
International Journal of Remote Sensing ( IF 3.4 ) Pub Date : 2020-12-20 , DOI: 10.1080/01431161.2020.1841324
John Brandt 1 , Fred Stolle 1
Affiliation  

ABSTRACT 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 and land use change. However, detecting and monitoring scattered trees with an open canopy using medium-resolution satellites is difficult because individual trees often cover a smaller footprint than the satellites’ resolution. Additionally, the variable background land uses and canopy shapes of trees cause a high variability in their spectral signatures. Here we present a globally consistent method to identify trees with canopy diameters greater than 3 m with medium-resolution optical and radar imagery. Biweekly cloud-free, pan-sharpened 10 metres 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 thousand 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 ( ) of tree cover, and 95% user’s and producer’s accuracy in hectares with dense ( ) tree cover. In comparison with common remote-sensing classification methods, the proposed method increases the accuracy of monitoring tree presence in areas with sparse and scattered tree cover ( ) 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 consistent 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.

中文翻译:

一种利用中分辨率卫星图像识别封闭林冠外树木的全球方法

摘要 在密闭的树冠林外散落的树木对于固碳、支持生计、维持生态系统完整性以及适应和减缓气候变化非常重要。与封闭树冠林内的树木相比,对全球范围内零星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测土地利用和土地利用变化。然而,使用中等分辨率卫星检测和监测带有开放树冠的分散树木是很困难的,因为单个树木的覆盖范围通常比卫星分辨率小。此外,可变的背景土地用途和树木的冠层形状导致其光谱特征的高度可变性。在这里,我们提出了一种全球一致的方法,用中等分辨率的光学和雷达图像识别冠层直径大于 3 m 的树木。每两周一次的无云、全锐化 10 米 Sentinel-2 光学图像和 Sentinel-1 雷达图像用于训练完全卷积网络,该网络由卷积门控循环单元层和特征金字塔注意层组成。对分布在 – 60 到 +60 纬度的超过 215,000 个 Sentinel-1 和 Sentinel-2 像素进行了测试,所提出的模型超过了 75% 的用户和生产者的准确度,可以识别具有中低密度 ( ) 树木覆盖的公顷数的树木,并且95% 的用户和生产者的准确度(以公顷为单位),有密集的 ( ) 树木覆盖。与常用的遥感分类方法相比,所提出的方法将树木覆盖稀疏和分散区域的监测精度提高了 20%,并将山区和多云地区的委托和遗漏误差减少了近一半。当应用于大型异质景观时,结果证明了在全球不同景观中以高细节和一致的准确性绘制树木地图的潜力。此信息对于了解当前的土地覆盖非常重要,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。结果证明了在全球不同景观中以高度详细和一致的准确性绘制树木地图的潜力。此信息对于了解当前的土地覆盖非常重要,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。结果证明了在全球不同景观中以高度详细和一致的准确性绘制树木地图的潜力。此信息对于了解当前的土地覆盖非常重要,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。
更新日期:2020-12-20
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