Prediction of tree biomass in the forest–tundra ecotone using airborne laser scanning

Abstract

The effect of ongoing climate change on sub-arctic and alpine forests has led to increased interest in monitoring potential changes in the forest–tundra ecotone. In addition to climate change, insect damage, browsing pressure by herbivores such as moose and reindeer, as well as anthropogenic impacts will contribute to changes in the forest–tundra ecotone. These changes are difficult to monitor with manual methods because of the complex mosaic pattern of the ecotone. In this study, the possibility to predict maximum tree height, above ground tree biomass and canopy cover with airborne laser scanning (ALS) was therefore tested at a forest–tundra ecotone site near Abisko in northern Sweden (Lat. N 68°20′, Long. E 19°01′, 420-700ma.s.l.). The forest in the area is dominated by mountain birch (Betula pubescens ssp. czerepanovii), which has highly irregular stem and canopy forms. Predictions from two different laser data acquisitions were compared. The first laser data set had 6.1pointsm−2 and was obtained in 2008 with a TopEye MKII scanner carried by a helicopter flown at 500m a.g.l. The second laser data set had 1.4pointsm−2 and was obtained in 2010 with an Optech ALTM Gemini scanner carried by a fixed-wing aircraft flown at 1740m a.g.l. Linear regression models were developed for the predictions using data from 73 sample plots with ten meter radius surveyed in 2009 and 2010. The relative RMSEs obtained for the TopEye and Optech data after leave-one-out cross-validation were, respectively, 8.8% and 9.5% for maximum tree height; 18.7% and 21.2% for above ground tree biomass; and, 16.8% and 18.7% for vertical canopy cover on plot level. The results were clearly improved by introducing a new procedure to compensate for unevenly distributed laser points. In conclusion, ALS has strong potential as a data source to map mountain birch biomass in the forest–tundra ecotone, even when using sparse point density ALS data.