Benefits of past inventory data as prior information for the current inventory

Abstract

Background: When auxiliary information in the form of airborne laser scanning (ALS) is used to assist in estimating the population parameters of interest, the benefits of prior information from previous inventories are not selfevident. In a simulation study, we compared three different approaches: 1) using only current data, 2) using nonupdated old data and current data in a composite estimator and 3) using updated old data and current data with a Kalman filter. We also tested three different estimators, namely i) Horwitz-Thompson for a case of no auxiliary information, ii) model-assisted estimation and iii) model-based estimation. We compared these methods in terms of bias, precision and accuracy, as estimators utilizing prior information are not guaranteed to be unbiased. Results: The largest standard errors were obtained when neither prior information nor auxiliary information were used. If a growth model was not applied to update the old data, the resulting composite estimators were biased. Largest RMSEs were obtained using non-updated prior information in a composite estimator. Using the ALS data as auxiliary information produced smaller RMSE than using prior information from the old inventory. The smallest RMSEs were obtained when both the auxiliary data and updated old data were used. With growth updating the bias can be substantially reduced, although design-unbiasedness of the estimator cannot be guaranteed. Conclusions: Prior information from old inventory data can be useful also when combined with highly accurate auxiliary information, when both data sources are efficiently used. The benefits obtained from using the old data will increase if the past harvests can be detected without errors from changes in the auxiliary data instead of being predicted with models.

Drone data for decision making in regeneration forests: from raw data to actionable insights1

“…While prior information availability did not affect the drone model, it had a substantial positive effect on the models relying on ALS data. These results are in line with the findings by Kangas et al (2020), who found that models using both ALS explanatory variables and prior information were more accurate than using the two data sources separately. Even though the cost model using ALS+Prior had a similar model fit to the drone models, the respective residuals were not significantly different according to a paired ttest (p-value = 0.6).…”

“…Systematic errors can be particularly problematic in a forest enterprise as they can lead to substantial economic losses in the long term. A promising way to reduce the bias in the estimates using prior inventory information is to use growth models to update prior information to the present date (Kangas et al 2020). However, such growth models are non-existent in Norway, and thus it is currently not possible to update prior information on regeneration forests.…”

“…In Norway, forest managers often have access to prior inventory information derived either from aerial photo interpretation or from field visits. The use of prior information is known to be beneficial for forest inventories based on remotely sensed data (Kangas et al 2020). However, there is no specific understanding of the importance of these data for decision making in the regeneration forest.…”

A century of national forest inventories – informing past, present and future decisions

“…In a simulation study, Kangas et al (2020) show that old measurements on permanent sample plots can constitute valuable source of auxiliary information for augmenting and complementing high-quality airborne laser scanning (ALS) data. The study highlights data-fusion opportunities with model-assisted and model-based estimators.…”

Potential of Bayesian formalism for the fusion and assimilation of sequential forestry data in time and space

“…In the context of estimating forest variables, the findings of Fortin (2020) and Kangas et al (2020) suggest that the growth model should actually be considered as the "change model", as it is used to predict all possible changes in the forest stand during the growing period; therefore, abrupt changes in forest conditions need to be addressed. Notably, both Fortin (2020) and Kangas et al (2020) suggest disturbances (both natural and forestry operations) are the most difficult changes to model. If harvests in the past sample-plot data could be detected, and the difficulties associated with this task were acknowledged, then the use of prior data could be expected to yield more accurate results (Kangas et al, 2020;Fortin 2020).…”