A remote-sensing based method for detecting lying deadwood in forests and peatlands

Lying deadwood varies strongly across forest and peatland types and management regimes, and these differences have important consequences for ecological processes, carbon cycling and biodiversity. Different management regimes influence deadwood habitat availability, environmental conditions around logs and decay environments that are further shaped by contact with peat or mineral soil. Despite these clear contrasts, systematic large-scale comparisons of lying deadwood across these management contexts remain limited. Remote sensing provides a way to assess these patterns consistently across large areas. High resolution lidar and multispectral data can capture features related to log size, and distribution. Combining field measurements with airborne or satellite observations will make it possible to identify lying woods, map their distribution and quantify how their abundance and composition differ across management regimes. This approach can also support repeated monitoring by revealing changes in deadwood following harvesting, restoration or natural disturbances. Remote sensing methods work largely well for identifying standing dead wood but not for lying dead wood. To fill this gap, the study will create a remote sensing method that links field based measurements of lying deadwood with remote sensing indicators. Our study objectives are to describe how management drives deadwood availability in both peatlands and forests and to demonstrate how remote sensing can provide accurate mapping of lying wood across diverse management regimes.