Remote sensing can provide a relatively low-cost approach to large scale assessment of forest productivity but the connection between remote sensing products and scalable field metrics is not well understood. Much of the existing research has focused on homogeneous, single species forests, with limited remote sensing inputs for model calibration or field data to assess accuracy of productivity predictions. Here we develop and evaluate yearly basal area increment models for common species across the northeastern US. Applying these models across the landscape highlights spatial and temporal variability in forest growth patterns.
To determine whether remote sensed datasets could be used to accurately predict forest productivity across heterogeneous forests in the Northeast
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Status - Completed
Start date: 2015-09-01
End date: 2016-12-31