Mapping and Estimating Aboveground Biomass in an Alpine Treeline Ecotone under Model-Based Inference

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Delineation of Geomorphological Woodland Key Habitats Using Airborne Laser Scanning

Forest ecosystems provide a range of services and function as habitats for many species. The concept of woodland key habitats (WKH) is important for biodiversity management in forest planning standards and certification schemes. The main idea of the WKH is to preserve biodiversity hotspots in the forest landscape. Current methods used in delineating WKH rely on costly field inventories. Furthermore, it is well known that the surveyor introduces an error because of the subjective assessment. Remote sensing may reduce this error in a cost-efficient way. The current study develops automated methods using airborne laser scanning (ALS) data to delineate geomorphological WKH, i.e., rock walls and stream gorges. The methods were evaluated based on a complete field inventory of WKH in a 1600 ha area in south-eastern Norway. The delineated WKH showed high detection rates, minor omission errors, but high commissions errors. Combining the delineation into a map of potential WKH suitable to guide field surveyors resulted in detecting all field reference WKH, i.e., a detection rate of 100% and a commission error of 25%. It is concluded that a higher degree of automatization might be possible to improve results and increase the efficiency of WKH inventories

Monitoring tree occupancy and height in the Norwegian alpine treeline using a time series of airborne laser scanner data

The main objective of this study was to demonstrate a method for monitoring tree occupancy and height in the alpine treeline ecotone using a time series of ALS data. We applied data collected in a longitudinal survey, comprising three spatially consistent campaigns from the years 2008, 2012 and 2018, on 25 sites along the Scandinavian Mountain Range (60–69°N). We compared ALS-based estimates of tree occupancy and height with corresponding field-based estimates and provided ALS-based estimates of uncertainty. Cross validation of a longitudinal model for predicting tree occurrence probability from ALS data revealed an overall accuracy of 83 %. ALS data were useful for predicting the height of pioneer trees, despite sparse laser points. Both models needed to account for the time of measurement. ALS-based estimates of tree occupancy were 4.6, 6.7 and 6.0 % for the three measurement occasions, respectively, and corresponding field-based estimates were 4.3, 4.6 and 5.0 %. ALS-based estimates of tree height were 2.2, 2.1 and 2.2 m, respectively, and corresponding field-based height estimates were 2.3, 2.2 and 2.2 m. Overlapping confidence intervals of ALS-based estimates for both variables and for all three measurement occasions indicated no statistically significant changes in either of the studied variables. The proposed method can be used to monitor alpine treeline ecotones and to provide accompanying uncertainty estimates to inform whether changes are significant

Mapping and Estimating Aboveground Biomass in an Alpine Treeline Ecotone under Model-Based Inference

Due to climate change, treelines are moving to higher elevations and latitudes. The estimation of biomass of trees and shrubs advancing into alpine areas is necessary for carbon reporting. Remotely sensed (RS) data have previously been utilised extensively for the estimation of forest variables such as tree height, volume, basal area, and aboveground biomass (AGB) in various forest types. Model-based inference is found to be efficient for the estimation of forest attributes using auxiliary RS data, and this study focused on testing model-based estimations of AGB in the treeline ecotone using an area-based approach. Shrubs (Salix spp., Betula nana) and trees (Betula pubescens ssp. czerepanovii, Sorbus aucuparia, Populus tremula, Pinus sylvestris, Picea abies) with heights up to about five meters constituted the AGB components. The study was carried out in a treeline ecotone in Hol, southern Norway, using field plots and point cloud data obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP). The field data were acquired for two different strata: tall and short vegetation. Two separate models for predicting the AGB were constructed for each stratum based on metrics calculated from ALS and DAP point clouds, respectively. From the stratified predictions, mean AGB was estimated for the entire study area. Despite the prediction models showing a weak fit, as indicated by their R-2-values, the 95% CIs were relatively narrow, indicating adequate precision of the AGB estimates. No significant difference was found between the mean AGB estimates for the ALS and DAP models for either of the strata. Our results imply that RS data from ALS and DAP can be used for the estimation of AGB in treeline ecotones