Weighting alternative estimates when using multi-source auxiliary data for forest inventory

Abstract

Tuominen, S. 1999. Weighting alternative estimates when using multisource auxiliary data for forest inventory. Silva Fennica 33(1): 41-50.Five auxiliary data sources (Landsat TM, IRS-IC, digitized aerial photographs, visual photo-interpretation and old forest compartment information) applying three study areas and three estimators, two-phase sampling with stratification, the k nearest neighbors and regression estimator, were examined. Auxiliary data were given for a high number of sample plots, which are here called first phase sample plots. The plots were distributed using a systematic grid over the study areas. Some of the plots were then measured in the field for the necessary ground truth. Each auxiliary data source in combination with field sample information was applied to produce a specific estimator for five forest stand characteristics: mean diameter, mean height, age, basal area, and volume of the growing stock. When five auxiliary data sources were used, each stand characteristic and each first phase sample plot were supplied with five alternative estimates with three alternative estimators. Mean square errors were then calculated for each alternative estimator using the cross validation method. The final estimates were produced by weighting alternative estimates inversely according to the mean square errors related to the corresponding estimator. The result was better than the final estimate of any of the single estimators. The improvement over the best single estimate, as measured in mean square error, was 16.9 % on average for all five forest stand characteristics. The improvement was fairly equal for all five forest stand characteristics. Only minor differences among the accuracies of the three alternative estimators were recorded.

A Variance Estimator for Constrained Estimates of Change in Relative Categorical Frequencies

“…Without it the IPF procedure will generate change matrices that are inconsistent with the temporal correlation of state estimated from the permanent samples. The generic nature of the estimator hold promise for wider applications to other situations with mixed data types in multi source samples (Katila and Tomppo, 2001;Poso et al, 1999). The generalized variance function provides a convenient computational short-cut and offers additional insight to the relationship between sampling variances of state and change in mixed designs.…”

Simultaneous Estimations of Forest Parameters using Aerial Photograph Interpreted Data and the k Nearest Neighbour Method

“…Nilsson 1997) and sometimes even higher. Poso et al (1999) used satellite data together with aerial photograph and old stand information, and obtained stem volume estimates for compartments (1 ± 2 ha in size) with errors of about 38%. In a study from New Zealand, Tomppo et al (1999) used satellite data together with stand record information, which resulted in moderately large errors [RMSE ¾100 m 3 ha ¼1 (24%) for compartments of 30 ha size].…”

Combining remote sensing, data from earlier inventories, and geostatistical interpolation in multisource forest inventory

“…According to studies by Poso et al (1999) and Tuominen and Poso (2001), combining several different data sources produced more accurate estimates than any of the data sources used separately. Furthermore, weighting the k-nn estimates from different data sources with the inverse values of their mean square errors (MSE), when calculating the final plot estimates, has been used successfully for improving the accuracy of the estimates (Poso et al 1999;Tuominen and Poso 2001).…”

“…Furthermore, weighting the k-nn estimates from different data sources with the inverse values of their mean square errors (MSE), when calculating the final plot estimates, has been used successfully for improving the accuracy of the estimates (Poso et al 1999;Tuominen and Poso 2001). In this study, weighting the auxiliary data sources with the inverse values of their MSEs was not applied within the k-nn estimation because the differences between the MSEs of various auxiliary data sources were quite small.…”