Airborne laser scanning (ALS)-based mapping campaigns are expanding in numbers throughout the world. Lands are scanned for the purposes of topography mapping and forestry. Yet, as much of wildlife lives in forests, the data hold accurate information about the structure of wildlife habitats. This is valuable information, because vegetation structure is a key component of habitat suitability.
In this thesis, ALS data were used to analyze habitat use and behavior of moose. The ALS data were integrated into locations of GPS-collared moose. As a consequence, patterns in their habitat use were seen from the ALS point clouds. The types of forests moose used during different seasons, different times of day, or when under thermal stress, were examined in detail. Lastly, ALS data were used to identify moose browsing damages.
The results revealed the usefulness of ALS in wildlife ecology research. It was shown that habitats used during different seasons are significantly different from one another in terms of forest structure, which links to the type of food used during each season and where it exists. Also, the effect of temperature on moose habitat use was revealed: high summer temperatures made moose utilize thermal shelters under high and dense canopies. Views were also gained about the role of forest structure for calving females, who gave birth in open areas (mires) but moved to forests with dense shrub layers shortly after calving: cover and food for the growing calf and the lactating female. Finally, it was shown that differences in forest structure caused by intense moose browsing can be detected from ALS data.
Information about vegetation structure is valuable additional data for wildlife research and can easily be integrated with the existing methods. This thesis gives good examples of how to do this. The approach is applicable to other species as well.
The aims of the study were to identify factors related to temporal and spatial variation in forest soil CO2 efflux(Fs), compare measurement chambers, and to test effects of a climate change experiment. The study was based on four-year measurements in upland Scots pine forests.
Momentary plot averages of Fs ranged from 0.04 to 1.12 gCO2m−2 h−1 and annual estimates for the forested area from1750 to 2050 gCO2 m−2. Soil temperature was a dominant predictor of the temporal variation in Fs (R2=76–82%). A temperature and degree days model predicted Fs of independent data within 15% on the average but underestimated it during the peak efflux period (July–August), possibly because of seasonal pattern in growth of roots and mycorrhiza. A comparison sub-study indicated that the reliability of the measurement chambers was not related to the principle i.e. non-steady-state through-flow, non-steady-state non-through-flow or steady-state through-flow.
Spatial variability of Fs within 400 m2 plots in four stands was large; coefficients of variation (CV) ranged from 0.10 to 0.80, with growing season averages of 0.22–0.36. A positive spatial autocorrelation was found at short distances (3–8 m). In data from several stands, thickness of the humus layer explained 28% of the variation in Fs, and with the distance to the closest trees it explained 40%. Fs also correlated with root mass of the humus layer. Between-plot differences in Fs were small.
In the climate change experiment, CO2 enrichment and air warming consistently, but not always significantly,increased Fs in whole-tree chambers. Their combined effect was additive, with no interaction; i.e. +23–37% (elevated CO2), +27–43% (elevated temperature), and +35–59% (combined treatment), depending on year. Air warming was a significant factor in the 4-year data according to ANOVA. Temperature sensitivity of Fs under the warming, however, decreased in the second year.