Predicting net growth rates in boreal forests using Landsat time series and permanent sample plot data

Abstract

Abstract Increasing temperature and changes in water dynamics are bringing uncertainty regarding the future productivity of boreal forests, even in the absence of stand-replacing disturbances. There is accumulating evidence that water deficits caused by warmer summer temperatures are linked to decreases in the growth rate of boreal tree species in some regions. In this context, it is essential to provide forest professionals with a means of monitoring net forest growth rates in undisturbed areas and at the scale of a management unit in order to determine where and when changes in growth are taking place. This is challenging using conventional forest inventory approaches. In this study, we use Landsat time series and data from permanent sample plots (PSP) to develop spatially explicit estimates of annual net basal area growth at a 30-m spatial resolution for a forest management unit in Canada. An ordinary least square regression model was developed using data from 120 PSPs and validated on an independent set of 60 PSPs, with R2 values of 0.61 and 0.58, respectively. Applying the model over a 586 607-ha study area revealed considerable temporal and spatial variability in the predicted growth rates and their evolution through time. There was an overall decline in predicted growth rates over time, with this trend corroborated by the PSP data and attributed to the ageing demographics of the forests in the study area. This variability was related to forest development stage, species composition, and structural attributes derived from light detection and ranging (LiDAR). The information generated by the suggested approach can help to improve yield predictions, optimize rotation lengths, and allow for the identification of target areas where silvicultural interventions aimed at maintaining or enhancing growth could be conducted.