Publikasjoner

NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.

2019

Sammendrag

Norway has a political goal to minimize the loss of cultural heritage due to removal, destruction or decay. On behalf of the national Directorate for Cultural Heritage, we have developed methods to monitor Cultural Heritage Environments. The complementary set of methods includes (1) landscape mapping through interpretation of aerial photographs, including field control of the map data, (2) qualitative and quantitative initial and repeat landscape photography, (3) field recording of cultural heritage objects including preparatory analysis of public statistical data, and (4) recording of stakeholder attitudes, perceptions and opinions. We applied these methods for the first time to the historical clustered farm settlement of Havrå in Hordaland County, West Norway. The methods are documented in a handbook and can be applied as a toolbox, where different monitoring methods or frequency of repeat recording may be selected, dependent on local situations, e.g., on the landscape character of the area in focus.

Sammendrag

[Forord] Gjennom det nasjonale programmet for systematisk overvåking av jordbrukets kulturlandskap, 3Q, dokumenterer NIBIO hvordan jordbrukslandskapet endres. Et av målene med å overvåke tilstand og endringer i jordbrukslandskapet er å fange opp endringstrender på et så tidlig tidspunkt at disse fortsatt kan påvirkes. Derfor er det også viktig å formidle overvåkingsresultatene og fortelle hvilke endringer som skjer. Dette gjøres gjennom kart og statistikk, presentasjoner og publikasjoner i ulike media og gjennom rapporter. Formidlingen vil imidlertid fremstå noe ulikt i form i de ulike kanalene...

Sammendrag

Repeat photography is an efficient method for documenting long-term landscape changes. So far, the usage of repeat photographs for quantitative analyses is limited to approaches based on manual classification. In this paper, we demonstrate the application of a convolutional neural network (CNN) for the automatic detection and classification of woody regrowth vegetation in repeat landscape photographs. We also tested if the classification results based on the automatic approach can be used for quantifying changes in woody vegetation cover between image pairs. The CNN was trained with 50 × 50 pixel tiles of woody vegetation and non-woody vegetation. We then tested the classifier on 17 pairs of repeat photographs to assess the model performance on unseen data. Results show that the CNN performed well in differentiating woody vegetation from non-woody vegetation (accuracy = 87.7%), but accuracy varied strongly between individual images. The very similar appearance of woody vegetation and herbaceous species in photographs made this a much more challenging task compared to the classification of vegetation as a single class (accuracy = 95.2%). In this regard, image quality was identified as one important factor influencing classification accuracy. Although the automatic classification provided good individual results on most of the 34 test photographs, change statistics based on the automatic approach deviated from actual changes. Nevertheless, the automatic approach was capable of identifying clear trends in increasing or decreasing woody vegetation in repeat photographs. Generally, the use of repeat photography in landscape monitoring represents a significant added value to other quantitative data retrieved from remote sensing and field measurements. Moreover, these photographs are able to raise awareness on landscape change among policy makers and public as well as they provide clear feedback on the effects of land management.

Sammendrag

Over the past few decades, there has been increasing interest in recording landscape change. Monitoring programmes have been established to measure the scope, direction and rate of change, and assess the consequences of changes for multiple interests, such as biodiversity, cultural heritage and recreation. The results can provide feedback for multiple sectors and policy domains. Political interests may change over time, but long-term monitoring demands long-term funding. This requires that monitoring programmes remain relevant and cost-efficient. In this paper, we document experiences from 20 years of the Norwegian Monitoring Programme for Agricultural Landscapes—the ‘3Q Programme’. We explain how data availability and demands for information have changed over time, and how the monitoring programme has been adapted to remain relevant. We also discuss how methods of presentation influence the degree of knowledge transfer to stakeholders, in particular to policy makers.