FOSTER-An R package for forest structure extrapolation

Abstract

The uptake of technologies such as airborne laser scanning (ALS) and more recently digital aerial photogrammetry (DAP) enable the characterization of 3-dimensional (3D) forest structure. These forest structural attributes are widely applied in the development of modern enhanced forest inventories. As an alternative to extensive ALS or DAP based forest inventories, regional forest attribute maps can be built from relationships between ALS or DAP and wall-to-wall satellite data products. To date, a number of different approaches exist, with varying code implementations using different programming environments and tailored to specific needs. With the motivation for open, simple and modern software, we present FOSTER (Forest Structure Extrapolation in R), a versatile and computationally efficient framework for modeling and imputation of 3D forest attributes. FOSTER derives spectral trends in remote sensing time series, implements a structurally guided sampling approach to sample these often spatially auto correlated datasets, to then allow a modelling approach (currently k-NN imputation) to extrapolate these 3D forest structure measures. The k-NN imputation approach that FOSTER implements has a number of benefits over conventional regression based approaches including lower bias and reduced over fitting. This paper provides an overview of the general framework followed by a demonstration of the performance and outputs of FOSTER. Two ALS-derived variables, the 95th percentile of first returns height (elev_p95) and canopy cover above mean height (cover), were imputed over a research forest in British Columbia, Canada with relative RMSE of 18.5% and 11.4% and relative bias of -0.6% and 1.4% respectively. The processing sequence developed within FOSTER represents an innovative and versatile framework that should be useful to researchers and managers alike looking to make forest management decisions over entire forest estates.

Fig 1. Framework around which FOSTER is designed.

Each colored box represent a processing step in the workflow and the names of the functions are displayed in green.

Fig 2. Study area in Alex Fraser Research Forest (British Columbia, Canada).

Blocks in red represent the ALS coverage and the light grey box is the area where ALS metrics need to be extrapolated. Predictor variables are available across the entire study area, at both references and targets.

Fig 3. Computing times, in minutes, required to process calcIndices, temporalMetrics and predictTrgs with 5 datasets of different sizes on single and multiples threads.

Dashed lines for the 4000 x 4000 pixels in temporalMetrics represent computing times without calculating the Theil–Sen slope.

Fig 4

Scatterplots between observed and predicted elev_p95 (left) and cover (right). The 5 folds of the cross-validation are included in the plots. The solid red lines indicate the best linear regression fit.

Fig 5

Mapped difference between predicted and observed elev_p95 values (left) and associated residuals distribution (right). Observed elev_p95 is extracted directly from wall-to-wall ALS acquisition across the study area.

Fig 6

Mapped difference between predicted and observed cover values (left) and associated residuals distribution (right). Observed cover is extracted directly from wall-to-wall ALS acquisition across the study area.

Fig 7. Scaled importance and mean scaled importance of the predictors for each response variable.