2019
Authors
Finn-Arne HaugenAbstract
No abstract has been registered
Abstract
Aim: Many countries lack informative, high‐resolution, wall‐to‐wall vegetation or land cover maps. Such maps are useful for land use and nature management, and for input to regional climate and hydrological models. Land cover maps based on remote sensing data typically lack the required ecological information, whereas traditional field‐based mapping is too expensive to be carried out over large areas. In this study, we therefore explore the extent to which distribution modelling (DM) methods are useful for predicting the current distribution of vegetation types (VT) on a national scale. Location: Mainland Norway, covering ca. 324,000 km2. Methods: We used presence/absence data for 31 different VTs, mapped wall‐to‐wall in an area frame survey with 1081 rectangular plots of 0.9 km2. Distribution models for each VT were obtained by logistic generalised linear modelling, using stepwise forward selection with an F‐ratio test. A total of 116 explanatory variables, recorded in 100 m × 100 m grid cells, were used. The 31 models were evaluated by applying the AUC criterion to an independent evaluation dataset. Results: Twenty‐one of the 31 models had AUC values higher than 0.8. The highest AUC value (0.989) was obtained for Poor/rich broadleaf deciduous forest, whereas the lowest AUC (0.671) was obtained for Lichen and heather spruce forest. Overall, we found that rare VTs are predicted better than common ones, and coastal VTs are predicted better than inland ones. Conclusions: Our study establishes DM as a viable tool for spatial prediction of aggregated species‐based entities such as VTs on a regional scale and at a fine (100 m) spatial resolution, provided relevant predictor variables are available. We discuss the potential uses of distribution models in utilizing large‐scale international vegetation surveys. We also argue that predictions from such models may improve parameterisation of vegetation distribution in earth system models.
Abstract
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Abstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Jana Spulerova Alexandra Kruse Paola Branduini Csaba Centeri Sebastian Eiter Viviana Ferrario Bénédicte Gaillard Fausto Gusmeroli Suzan Jurgens Drago Kladnik Hans Renes Michael Roth Giovanni Sala Hanne Sickel Maurizia Sigura Dagmar Štefunková Kari Stensgaard Peter Strasser Cosmin Marius Ivascu Kinga ÖllererAbstract
Hay-making structures are part of the agricultural landscape of meadows and pastures. Hay meadows are still used and found all over Europe, but their distribution patterns as well as their characteristics and regional features depend on geographical area, climate, culture, and intensity of agriculture. Intensively used hay meadows are the most dominant, using heavy machinery to store hay mostly as rounded or square bales. Traditional hay-making structures represent structures or constructions, used to quickly dry freshly cut fodder and to protect it from humidity. The ‘ancient’ forms of traditional hay-making structures are becoming a relic, due to mechanisation and the use of new technologies. Both the need for drying hay and the traditional methods for doing so were similar across Europe. Our study of hay-making structures focuses on their current state, their development and history, current use and cultural values in various European countries. Regarding the construction and use of hay-making structures, we have distinguished three different types, which correlate to natural and regional conditions: (1) temporary hay racks of various shapes; (2) hay barracks, a special type of shelters for storing hay and (3) different types of permanent construction and buildings for drying and storing hay. Hay-making structures have been mostly preserved in connection with traditional agricultural landscapes, and particularly in the more remote regions or where associated with strong cultural identity.
Abstract
This paper describes the development and utility of the Norwegian forest resources map (SR16). SR16 is developed using photogrammetric point cloud data with ground plots from the Norwegian National Forest Inventory (NFI). First, an existing forest mask was updated with object-based image analysis methods. Evaluation against the NFI forest definitions showed Cohen's kappa of 0.80 and accuracy of 0.91 in the lowlands and a kappa of 0.73 and an accuracy of 0.96 in the mountains. Within the updated forest mask, a 16×16 m raster map was developed with Lorey's height, volume, biomass, and tree species as attributes (SR16-raster). All attributes were predicted with generalized linear models that explained about 70% of the observed variation and had relative RMSEs of about 50%. SR16-raster was segmented into stand-like polygons that are relatively homogenous in respect to tree species, volume, site index, and Lorey's height (SR16-vector). When SR16 was utilized in a combination with the NFI plots and a model-assisted estimator, the precision was on average 2–3 times higher than estimates based on field data only. In conclusion, SR16 is useful for improved estimates from the Norwegian NFI at various scales. The mapped products may be useful as additional information in Forest Management Inventories.
2018
Abstract
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.
Authors
Kjersti Bakkebø FjellstadAbstract
No abstract has been registered
Authors
Kjersti Bakkebø FjellstadAbstract
No abstract has been registered