Optimal plot design in a multipurpose forest inventory

Background： We explore the factors affecting the optimal plot design （size and type as well as the subsample tree selection strategies within a plot） and their relative importance in defining the optimal plot design in amultipurpose forest inventory. The factors include time used to lay out the plot and to make the tree measurements within the plot, the between-plot variation of each of the variables of interest in the area, and the measurement and model errors for the different variables. Methods： We simulate different plot types and sizes and subsample tree selection strategies on measuredtest areas from North Lapland. The plot types used are fixed-radius, concentric and relascope plots. Weselect the optimal type and size first at plot level using a cost-plus-loss approach and then at cluster level byminimizing the weighted standard error with fixed budget. Results： As relascope plots are ve~/efficient at the plot level for volume and basal area, and fixed-radius plots for stems per ha, the optimal plot type strongly depends on the relative importance of these variables. The concentric plot seems to be a good compromise between these two in many cases. The subsample tree selection strategy was more important in selecting optimal plot than many other factors. In cluster level, the most important factor is the transfer time between plots. Conclusions： While the optimal radius of plots and other parameters were sensitive to the measurement times and other cost factors, the concentric plot type was optimal in almost all studied cases. Subsample tree measurement strategies need further studies, as they were an important cost factor. However, their importance to the precision was not as clear.

Predicting stand age in managed forests using National Forest Inventory field data and airborne laser scanning

Background: The aim of this study was to construct a nationwide stand age model by using National Forest Inventory(NFI) data and nationwide airborne laser scanning(ALS) data. In plantation forestry, age is usually known.While this is not the case in boreal managed forests, age is still seldom predicted in forest management inventories.Measuring age accurately in situ is also very laborious. On the other hand, tree age is one of the accurately measured sample tree attributes in NFI field data. Many countries also have a nationwide coverage of airborne laser scanning(ALS) data. In this study, we merged these data sources and constructed a nationwide, area-based model for stand age.Results: While constructing the model, we omitted old forests from the data, since the correlation between ALS height metrics and stand age diminished at stands with age > 100 years. Additionally, the effect of growth conditions was considerable, so we also utilized different geographical and NFI variables such as site fertility and soil type in the modeling. The resultant nationwide model for the stand age of managed forests yielded a root mean square error(RMSE) of about 14 years. The model could be improved further by additional forest structure variables, but such information may not be available in practice.Conclusions: The results showed that the prediction of stand age by ALS, geographical and NFI information was challenging, but stil possible with moderate success. This study is an example of the joint use of NFI and nationwide ALS data and re-use of NFI data in research.

Comparison of the local pivotal method and systematic sampling for national forest inventories

Background:The local pivotal method(LPM)utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories(NFIs).Its performance compared to simple random sampling(SRS)and LPM with geographical coordinates has produced promising results in simulation studies.In this simulation study we compared all these sampling methods to systematic sampling.The LPM samples were selected solely using the coordinates(LPMxy)or,in addition to that,auxiliary remote sensing-based forest variables(RS variables).We utilized field measurement data(NFI-field)and Multi-Source NFI(MS-NFI)maps as target data,and independent MS-NFI maps as auxiliary data.The designs were compared using relative efficiency(RE);a ratio of mean squared errors of the reference sampling design against the studied design.Applying a method in NFI also requires a proven estimator for the variance.Therefore,three different variance estimators were evaluated against the empirical variance of replications:1)an estimator corresponding to SRS;2)a Grafström-Schelin estimator repurposed for LPM;and 3)a Matérn estimator applied in the Finnish NFI for systematic sampling design.Results:The LPMxy was nearly comparable with the systematic design for the most target variables.The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18,according to the studied target variable.The SRS estimator for variance was expectedly the most biased and conservative estimator.Similarly,the Grafström-Schelin estimator gave overestimates in the case of LPMxy.When the RS variables were utilized as auxiliary data,the Grafström-Schelin estimates tended to underestimate the empirical variance.In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally.Conclusions:LPM optimized for a specific variable tended to be more efficient than systematic sampling,but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables.The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling.Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.

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

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.