A simulation of image-assisted forest monitoring for national inventories
The efficiency of national forest monitoring efforts can be increased by the judicious incorporation of ancillary data. For instance, a fixed number of ground plots might be used to inform a larger set of annual estimates by observing a smaller proportion of the plots each year while augmenting each annual estimate with ancillary data in order to reduce overall costs while maintaining a desired level of accuracy. Differencing successive geo-rectified remotely sensed images can conceivably provide forest change estimates at a scale and level of accuracy conducive to the improvement of temporally relevant forest attribute estimates. Naturally, the degree of improvement in the desired estimates is highly dependent on the relationships between the spatial-temporal scales of ground plot and remotely sensed observations and the desired spatial-temporal scale of estimation. In this paper, fixed scales of observation for each data source are used to explore the value of three different levels of information available from the remotely sensed image-change estimates. Four populations are simulated and sampled under four sampling error structures. The results show that the image change estimates (ICE) can be used to significantly reduce bias for annual estimates of harvest and mortality and that improved estimation of harvest and mortality can sometimes, but not always, contribute to better estimates of standing volume.