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Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches

机译：监测森林结构，指导森林恢复的适应性管理：遥感方法综述

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With the demand for, and scale of, ecological restoration increasing globally, effectiveness monitoring remains a significant challenge. For forest restoration, structural complexity is a recognised indicator of ecosystem biodiversity and in turn a surrogate for restoration effectiveness. Structural complexity captures the diversity in vegetation elements, from tree height to species composition, and the layering of these elements is critical for dependent organisms which rely upon them for their survival. Traditional methods of measuring structural complexity are costly and time-consuming, resulting in a discrepancy between the scales of 'available' versus 'needed' information. With advancements in both sensors and platforms, there exists an unprecedented opportunity for landscape-level effectiveness monitoring using remote sensing. We here review the key literature on passive (e.g., optical) and active (e.g., LiDAR) sensors and their available platforms (spaceborne to unmanned aerial vehicles) used to capture structural attributes at the tree- and stand-level relevant for effectiveness monitoring. Good cross-validation between remotely sensed and ground truthed data has been shown for many traditional attributes, but remote sensing offers opportunities for assessment of novel or difficult to measure attributes. While there are examples of the application of such technologies in forestry and conservation ecology, there are few reports of remote sensing for monitoring the effectiveness of ecological restoration actions in reversing land degradation. Such monitoring requires baseline data for the restoration site as well as benchmarking the trajectory of remediation against the structural complexity of a reference system.
机译：随着全球的需求和规模，生态恢复不断增加，有效监测仍然是一个重大挑战。对于森林恢复，结构复杂性是生态系统生物多样性的公认指标，并转动替代恢复效果。结构复杂性从树高到物种组成中捕获植被元素的多样性，并且这些元素的分层对于依赖于它们的存活的依赖性生物来说至关重要。测量结构复杂性的传统方法是昂贵且耗时的，导致“可用”的尺度与“需要”信息之间的差异。在传感器和平台中的进步，使用遥感的景观级效果监测存在前所未有的机会。我们在这里审查了被动（例如，光学）和有效（例如，LIDAR）传感器及其可用平台的关键文献（例如，到无人驾驶飞行器），用于捕获树木和稳定级别的结构属性，以获得有效监测。对于许多传统属性，已经显示了远程感测和地面判正数据之间的良好交叉验证，但远程传感为评估小说或难以衡量属性提供机会。虽然存在这些技术在林业和保护生态中的应用的例子，但很少有关于监测逆转土地退化中生态恢复行为的有效性的遥感的报道。这种监视需要恢复现场的基线数据，以及基于参考系统的结构复杂性的修复轨迹。

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