Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches
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Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches Nicolò Camarretta1 · Peter A. Harrison1,3 · Tanya Bailey1,3 · Brad Potts1 · Arko Lucieer2 · Neil Davidson3 · Mark Hunt1 Received: 1 July 2019 / Accepted: 27 September 2019 © Springer Nature B.V. 2019
Abstract 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. Keywords Structural complexity · Structural attributes · Restoration success · Effectiveness monitoring · Remote sensing
* Nicolò Camarretta [email protected] 1
ARC Training Centre for Forest Value and School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia
2
School of Technology, Environment and Design, University of Tasmania, Hobart, TAS, Australia
3
Greening Australia, Mt Nelson, TAS, Australia
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New Forests
Introduction The Anthropocene has heralded an era of widespread land degradation, with over six billion hectares of land damaged or destroyed through human activities (Gibbs and Salmon 2015). To mitigate land degradation, and more broadly the loss of biodiversity through global change (land-use and climate change), there is a growing focus on the restoration of terrestrial ecosystems (Löf et al. 2019), through initiatives such as The Bonn Challenge (Verdone and Seidl 2017). Indeed, the recent United Na
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