Remotely sensed data controlled forest inventory concept

Abstract

Nowadays, the image of the forest in Germany is changing from monoculture areas to very mixed forests, where individual stands are no longer clearly visible. The objective of this study was to examine the use of remotely sensed data at enterprise level for pre-stratification and sample plot allocation in the planning stage of forest inventories in a very heterogeneous forest. On the basis of RapidEye satellite data and object-based image analysis, a stratified segment-based non-permanent sampling design was developed and evaluated against the results of a permanent systematic sampling design. The relative efficiency (RE) was calculated based on variance estimators for simple random sampling and stratified random sampling for the variable timber volume [m3/ha]. By stratification of the sample designs, we achieved an RE of 1.25 for the systematic sampling and 1.34 with the segment-based sampling design. Based on a targeted standard error of 4.6%, the sampling designs were compared with respect to the required sample size. The stratified segment-based sampling design reduced the number of sample plots compared to the systematic sampling design by 28%. Furthermore, it was shown that the possible reduction of sampling plots leads to a cost saving of 21%.