Estimation of the occurrence, severity, and volume of heartwood rot using airborne laser scanning and optical satellite data

Rot in commercial timber reduces the value of the wood substantially and estimating the occurrence, severity, and volume of heartwood rot would be a useful tool in decision-making to minimize economic losses. Remotely sensed data has recently been used for mapping rot on a single-tree level, and although the results have been relatively poor, some potential has been shown. This study applied area-based approaches to predict rot occurrence, rot severity, and rot volume , at an area level. Ground reference data were collected from harvester operations in 2019-2021. Predictor variables were calculated from multi-temporal remotely sensed data together with environmental variables. Response variables from the harvester data and predictor variables from remotely sensed data were aggregated to grid cells and to forest stands. Random Forest models were built for the different combinations of response variables and predictor subsets, and validated with both random- and spatial cross-validation. The results showed that it was not possible to estimate rot occurrence and rot severity with the applied modeling procedure (pR(2): 0.00-0.16), without spatially close training data. The better performance of rot volume models (pR(2): 0.12-0.37) was mainly due to the correlation between timber volume and rot volume.

Automated summary

The study applied area-based approaches to predict rot occurrence, rot severity, and rot volume. Random Forest models were built and validated using remotely sensed data and ground reference data. The results showed that rot volume models performed better due to the correlation between timber volume and rot volume.