Data Acquisition for Forestry Planning by Remote Sensing Based Sample Plot Imputation

Holmström, H. 2001. Data acquisition for forestry planning by remote sensing based sample plot imputation. Doctoral dissertation. ISSN 1401-6230, ISBN 91-576-6086-7. In forestry planning, accurate description of the state of the forests is essential. Advanced planning models often require input data with high resolution, i.e. data at the single-tree level. Field inventory procedures based on sample plot measurements are usually employed. However, such methods are expensive, so cost-efficient alternatives would be attractive. In the work described in this thesis, inventory methods based on imputation of reference sample plot data were evaluated. The reference material consisted of data from previously performed field inventories. The k nearest neighbour (kNN) method was used, in which all variables available at the reference plots were simultaneously estimated for the target areas. The imputations were based on information derived from interpretations of aerial photographs, optical satellite data, radar data (airborne sensor), and existing stand records. To account for differences in the qualities of the different information sources combined in the kNN estimations, distance metrics were defined and applied using regression functions. The utility of various types of forecasted reference material was evaluated. Increasing the length of forecasts of reference sample plot data increased the mean square error (MSE) in stem volume estimates. However, by excluding disturbed plots (due to thinnings) from the reference material, plot data forecasted for up to 25 years could be used without severely decreasing the accuracy of the estimations. Using aerial photo interpretations together with stand records, kNN estimates of stem volume with relative root MSEs (RMSEs) of 14-20% at the stand level were obtained. More accurate estimates were obtained for a northern test site, in comparison with results from southern Sweden. Combining optical satellite data and radar data significantly improved results, giving a RMSE in standwise stem volume estimates of 22%, compared to 30% for the best single-sensor case. Consequences of using kNN estimations in a planning context were evaluated by a cost-plus-loss approach. The total cost of undertaking an inventory was obtained by summing the actual inventory cost and the net present value of expected future losses due to non-optimal decisions caused by erroneous data. Input data obtained by imputation of reference sample plots were compared with traditional field sample plot inventories. Results indicated that the total cost of an inventory could be reduced by 15-50% by integrating different methods; imputation could be applied for some types of stands while more accurate field inventories should be carried out in others. It is not necessarily the most valuable stands that should be inventoried by careful field measurements, but many of those with a treatment impending.