Using delta values of multi-temporal first-return small footprint airborne laser scanner data to predict change of tree biomass in mountain spruce forests.

摘要

The mountain forests are likely to undergo a pronounced growth in biomass stocks because of increased mean temperatures and changed precipitation as a result of a changing climate. The biomass increase is both attributed to an upward advance of the current tree line and also an increased growth of the current forests. In order to monitor changes in such forests, which often are found in remote areas and are structurally diverse, methods based on remote sensing can be more efficient compared to traditional field based inventories. Traditional field based methods can be very costly both because of high transportation costs to get the field crew on site, but also because of the high number of field plots necessary to capture the biomass variations. This study explored the capability of multi-temporal airborne laser scanner data to estimate the change of total aboveground tree biomass in the mountain forest. More specifically we used the observed difference between first return height percentiles and density variables obtained from airborne laser scanner data on two points in time (2005=T1 and 2008=T2) to model the biomass change observed in field during the same time period by means of an ordinary least squares model. Our data covered four growth seasons and the mean change of biomass during this period was 17.5 t/ha with an initial mean biomass of 135.1 t/ha. Results showed that the differences between laser height percentiles at T1 and T2 were most correlated to the observed biomass change. The model fit as expressed by R~2 was 0.87, and the model root mean square error was 4.4 t/ha which corresponds to 25 % of the observed change in biomass.