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Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches
New forests ( IF 2.2 ) Pub Date : 2019-10-09 , DOI: 10.1007/s11056-019-09754-5 Nicolò Camarretta , Peter A. Harrison , Tanya Bailey , Brad Potts , Arko Lucieer , Neil Davidson , Mark Hunt
New forests ( IF 2.2 ) Pub Date : 2019-10-09 , DOI: 10.1007/s11056-019-09754-5 Nicolò Camarretta , Peter A. Harrison , Tanya Bailey , Brad Potts , Arko Lucieer , Neil Davidson , Mark Hunt
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.
中文翻译:
监测森林结构,指导森林恢复的适应性管理:遥感方法综述
随着全球对生态恢复的需求及其规模的增长,有效性监测仍然是一项重大挑战。对于森林恢复而言,结构复杂性是公认的生态系统生物多样性指标,反过来又是恢复有效性的替代指标。结构的复杂性捕获了从树高到物种组成的植被要素的多样性,而这些要素的分层对于依赖它们生存的依赖生物至关重要。传统的测量结构复杂性的方法既昂贵又费时,导致“可用”信息量与“所需”信息量之间的差异。随着传感器和平台的发展,存在使用遥感进行景观水平有效性监测的前所未有的机会。在这里,我们回顾了有关无源(例如,光学)和有源(例如,LiDAR)传感器及其可用平台(空载到无人飞行器)的关键文献,这些传感器用于捕获与有效性监控相关的树和林分级别的结构属性。对于许多传统属性,已显示出遥感和地面真实数据之间的良好交叉验证,但是遥感为评估新颖或难以测量的属性提供了机会。尽管有此类技术在林业和保护生态学中的应用实例,但很少有遥感报道用于监测生态恢复行动在扭转土地退化方面的有效性。
更新日期:2019-10-09
中文翻译:
监测森林结构,指导森林恢复的适应性管理:遥感方法综述
随着全球对生态恢复的需求及其规模的增长,有效性监测仍然是一项重大挑战。对于森林恢复而言,结构复杂性是公认的生态系统生物多样性指标,反过来又是恢复有效性的替代指标。结构的复杂性捕获了从树高到物种组成的植被要素的多样性,而这些要素的分层对于依赖它们生存的依赖生物至关重要。传统的测量结构复杂性的方法既昂贵又费时,导致“可用”信息量与“所需”信息量之间的差异。随着传感器和平台的发展,存在使用遥感进行景观水平有效性监测的前所未有的机会。在这里,我们回顾了有关无源(例如,光学)和有源(例如,LiDAR)传感器及其可用平台(空载到无人飞行器)的关键文献,这些传感器用于捕获与有效性监控相关的树和林分级别的结构属性。对于许多传统属性,已显示出遥感和地面真实数据之间的良好交叉验证,但是遥感为评估新颖或难以测量的属性提供了机会。尽管有此类技术在林业和保护生态学中的应用实例,但很少有遥感报道用于监测生态恢复行动在扭转土地退化方面的有效性。