Small area estimations of proportion of forest and timber volume combining Lidar data and stereo aerial images with terrestrial data

Methods for small area estimations were compared for estimating the proportion of forest and growing stock volume of temperate mixed forests within a district of a member state (canton) in Switzerland. The estimators combine terrestrial data with remotely sensed auxiliary data. By using different model types, different sources of auxiliary data and different methods of processing the auxiliary data, the increase in estimation precision was tested. Using the canopy height derived from remote sensing data, the growing stock volume and the proportion of forest were estimated. The regression models used for the small area estimation provided a coefficient of determination of up to 68% for the timber volume. The proportion of plots correctly classified into forest and non-forest plots ranged between 0.9 and 0.98. Models calibrated over forest area only resulted in a maximal coefficient of determination of 37%. Even though these coefficients indicate a moderate model quality, the use of remote sensing data clearly improved the estimation precision of both the proportion of forest and the growing stock volume. Generally, Lidar data led to slightly higher estimates compared to data from aerial photography. It was possible to reduce the variance of the estimated proportion of forest to nearly one tenth compared with the variance based on the terrestrial measurements alone. Similarly, the variance of the growing stock volume could be reduced to one fourth as compared with the variance based solely on the terrestrial measurements.