The Norwegian area frame survey of land cover and outfield land resources (AR18X18), completed in 2014, provided unbiased statistics of land cover in Norway. The article reports the new statistics, discusses implications of the data set, and provides potential value in terms of research, management, and monitoring. A gridded sampling design for 1081 primary statistical units of 0.9 km² at 18 km intervals was implemented in the survey. The plots were mapped in situ, aided by aerial photos, and all areas were coded following a vegetation type system. The results provide new insights into the cover and distribution of vegetation and land cover types. The statistic for mire and wetlands, which previously covered 5.8%, has since been corrected to 8.9%. The survey results can be used for environmental and agricultural management, and the data can be stratified for regional analyses. The survey data can also serve as training data for remote sensing and distribution modelling. Finally, the survey data can be used to calibrate vegetation perturbations in climate change research that focuses on atmospheric–vegetation feedback. The survey documented novel land cover statistics and revealed that the national cover of wetlands had previously been underestimated.

Systematic sampling is more precise than simple random sampling when spatial autocorrelation is present and the sampling effort is equal, but there is no unbiased method to estimate the variance from a systematic sample. The objective of this paper is to assess selected variance estimation methods and evaluate the influence of spatial structure on the results. These methods are treated as models and a complete enumeration of Norway was used as the modeling environment. The paper demonstrates that the advantage of systematic sampling is closely related to autocorrelation in the material, but also that the improvement is influenced by periodicity and drift in the variables. Variance estimation by stratification with the smallest possible strata gave the best overall results but may underestimate the variance when spatial autocorrelation is absent. Treating the sample as a simple random sample is a safe and conservative alternative when spatial autocorrelation is absent or unknown

Inspired by the success of deep learning techniques in dense-label prediction and the increasing availability of high precision airborne light detection and ranging (LiDAR) data, we present a research process that compares a collection of well-proven semantic segmentation architectures based on the deep learning approach. Our investigation concludes with the proposition of some novel deep learning architectures for generating detailed land resource maps by employing a semantic segmentation approach. The contribution of our work is threefold. (1) First, we implement the multiclass version of the intersection-over-union (IoU) loss function that contributes to handling highly imbalanced datasets and preventing overfitting. (2) Thereafter, we propose a novel deep learning architecture integrating the deep atrous network architecture with the stochastic depth approach for speeding up the learning process, and impose a regularization effect. (3) Finally, we introduce an early fusion deep layer that combines image-based and LiDAR-derived features. In a benchmark study carried out using the Follo 2014 LiDAR data and the NIBIO AR5 land resources dataset, we compare our proposals to other deep learning architectures. A quantitative comparison shows that our best proposal provides more than 5% relative improvement in terms of mean intersection-over-union over the atrous network, providing a basis for a more frequent and improved use of LiDAR data for automatic land cover segmentation.

Policy mixes (i.e. the total structure of policy processes, strategies, and instruments) are complex constructs that can quickly become incoherent, inconsistent, and incomprehensive. This is amplified when the policy mix strives to meet multiple objectives simultaneously, such as in the case of large carnivore policy mixes. Building on Rogge and Reichardt's analytical framework for the analysis of policy mixes, we compare the policy mixes of Norway, Sweden, Finland, the Netherlands, Germany (specifically Saxony and Bavaria), and Spain (specifically Castilla y León). The study shows that the large carnivore policy mixes in the case countries show signs of lacking vertical and horizontal coherence in the design of policy processes, weak consistency between objectives and designated policy instruments, and, as a consequence, lacking comprehensiveness. We conclude that creating consistent, coherent, and comprehensive policy mixes that build on multiple objectives requires stepping away from sectorized policy development, toward a holistic, systemic approach, strong collaborative structures across policy boundaries and regions, the inclusion of diverse stakeholders, and constant care and attention to address all objectives simultaneously rather than in isolation.