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The potential of vegetation recovery through resprouting of plant tissue from buds after the  removal  of  aboveground biomass  is  a  key  resilience  strategy  for populations under  abrupt environmental change. Resprouting leads to fast regeneration, particularly after the implementation of mechanical mowing as part of active management for promoting open habitats. We investigated whether recovery dynamics of resprouting and the threat of habitat conversion can be predicted by optical and structural stand traits derived from drone imagery in a protected heathland area. We conducted multivariate regression for variable selection and random forest regression for predictive modeling using 50 spectral predictors, textural features and height parameters to quantify Calluna resprouting and grass invasion in before‐mowing images that were related to vegetation recovery in after‐mowing imagery. The study reveals that Calluna resprouting can be explained by significant optical  predictors  of  mainly  green  reflectance  in  parental  individuals.  In  contrast,  grass encroachment  is  identified  by  structural  canopy  properties  that  indicate  before‐mowing  grass interpenetration  as  starting  points  for  after‐mowing  dispersal. We  prove  the  concept  of  trait propagation  through  time providing significant derivates  for a  low‐cost drone system.  It can be utilized  to  build  drone‐based  decision  support  systems  for  evaluating  consequences  and requirements of habitat management practice.

The Regenerative Potential of Managed Calluna Heathlands—Revealing Optical and Structural Traits for Predicting Recovery Dynamics