Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches
Camarretta, N and Harrison, PA and Bailey, T and Potts, B and Lucieer, A and Davidson, N and Hunt, M, Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches, New Forests pp. 1-24. ISSN 0169-4286 (2019) [Refereed Article]
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.