LiDAR as a tool for remote sensing of moose (Alces alces) forage biomass | LiDAR som verktøy for fjernregisterering av biomasse elg (Alces alces) beite

Moose (Alces alces) play an ecological keystone role in the boreal forest ecosystemand increasingly so during the last decades due to the large population increase. The growingmoose population has a large impact on forage plant species, including commerciallyimportant tree species. Conversely, the quantity and quality of forage feedback on the bodyweight and condition of the moose, which is a key trait for moose managers. To improvemoose management it is central to estimate and monitor “carrying capacity” over time and onrealistic management scales. In forest inventory remote sensing is extensively used withdifferent tools, such as LiDAR (Light Detection and Ranging).This study examined the potential of LiDAR as a tool for remote sensing of mooseforage biomass. The study was conducted on a 735 km2 area, within the counties of Telemarkand Vestfold (N 59o20.285 E 9o39.664) in the south-eastern part of Norway. The field dataused in this study were collected during a moose forage study carried out in August 2007. Thefield data included biomass data for 640 circular (2500 m2) plots. The LiDAR data used inthis study were collected in the years 2008-2010 for multipurpose. Three modelingapproaches were used: One model with only field inventory variables origin from forestinventories (Forest model), one model with only LiDAR derived variables (LiDAR model)and one model combining both forest and LiDAR variables. The aim was to asses if includingLiDAR derived information resulted in better models for moose forage biomass. All modelswere mixed effects regression models.For all combination of tree species and seasons, one or more LiDAR variables wereincluded in the best model. In the model validation the LiDAR + Forest models (r rangingfrom 0.38 to 0.51) generally performed better than the pure Forest models (r ranging from0.35 to 0.49) which again always performed better than the pure LiDAR models (r rangingfrom 0.21 to 0.37). Important LiDAR variables like Understory LiDAR Cover Density(ULCD) and Spacing Index (Spi) replaced forest variables such as cutting class in some of themodel groups. This study concludes that LiDAR can improve the ability to predict mooseforage biomass if variables from traditional forest inventory, such as site index, dominant treespecies, and cutting class, are added. Still, the validation revealed that models had lowgenerality. This study is based on field data with a relatively low spatial precision and with atemporal mismatch between LiDAR and field data sampling. Future studies should sampledata simultaneously and with higher precision to investigate if large scale monitoring ofmoose forage with LiDAR may become an operative tool in management.