Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2025
Authors
Junbin Zhao Holger Lange Christian Wilhelm Mohr Cornelya Klutsch Simon Weldon Jonathan Rizzi Gunnhild Søgaard Hanna Marika Silvennoinen Teresa Gómez de la BárcenaAbstract
Jordrespirasjonsmålinger på Svanhovd og dens modellering
Abstract
Studien undersøker hvordan vegetasjonsdekke (NDVI) og overflaterefleksjon (albedo) varierer gjennom året i norske utmarksområder som er beitet og ubeitet. Utmarkene har stor betydning for beitebruk, biologisk mangfold og karbonlagring, men endringer i landbruk og redusert beitepress påvirker vegetasjonen og kan ha klimakonsekvenser. Analysen bygger på satellittdata fra 18 lokaliteter i perioden 2019–2023. Resultatene viser tydelige sesongmønstre: NDVI er lav om vinteren og høy om sommeren, mens albedo er høy i snødekte perioder og lav når vegetasjon dominerer. Det ble ikke funnet signifikante forskjeller mellom beitede og ubeitede områder samlet sett, selv om enkelte lokaliteter viste små variasjoner. Dette tyder på at sesong og fenologi har større betydning enn beite, og at metodiske begrensninger – særlig grov oppløsning i albedodata – kan maskere lokale effekter. Studien anbefaler bruk av høyoppløselige data og mer avanserte metoder for å bedre forstå klimaeffektene av endret beitebruk.
Authors
Christian Pedersen Svein Olav Krøgli Shivesh Karan Svein Dale Grete Stokstad Diress Tsegaye AlemuAbstract
Over recent decades, farmland and meadow-breeding bird populations in Europe have markedly declined, attributed to factors like agricultural intensification and land abandonment. Parts of the Norwegian Monitoring Programme for Agricultural Landscapes explore the correlation between land use and bird species, aiming to understand how spatial heterogeneity and land use diversity affect the richness, abundance, and distribution of farmland birds. Between 2000 and 2023, we saw declining populations and reduced distributions of several farmland bird species within the monitoring squares. Additionally, we found that both spatial heterogeneity of land use and high land type diversity positively influenced farmland birds. This gives important insight on how to design biodiverse agricultural landscapes. We also examined the impact of agricultural intensity on 25 farmland bird species, using livestock density and pasture size as indicators. Larger pastures generally benefited a wide range of farmland bird species. Different bird species responded variably to livestock numbers, but high livestock density led to a decrease in overall farmland bird abundance. Many countries subsidize sustainable farming to protect biodiversity. We studied Norwegian agri-environmental schemes' impact on farmland and meadow-breeding birds. We found that bird observations rose when these measures were in place but often declined once the support ended. Furthermore, the schemes were geographically limited and relatively few farmers participated. While short-term benefits were evident, long-term effects remain uncertain, highlighting the need for improved conservation strategies. Emphasizing the importance of spatially heterogeneous agricultural landscapes with high land type diversity and natural areas, the study indicates the type of agricultural landscapes we should be aiming for to maintain and restore biodiversity.
Abstract
The study focuses on ecosystem services, historical aspects, and natural diversity. Specifically, it assesses possible proxies for investigating a set of cultural ecosystem services from the Norwegian agricultural landscape. Agricultural areas on the Norwegian land cover map surrounded by a 100 m wide buffer zone were analyzed for recorded historical buildings, cultural heritage sites, red-listed vascular plant species (defined as being at varying degrees at risk of extinction), and red-listed nature types (defined as endangered or vulnerable). The results indicate significant contributions from agricultural landscapes with respect to historical buildings, cultural heritage sites, and red-listed plant species. Regarding red-listed nature types, the contributions were diverse. The ecosystem proxies investigated showed increasing distribution trends with increasing proportions of agricultural landscapes in the spatial units, with a sharp increase with smaller area sizes. However, for cultural heritage sites the trend was different when the proportion of the agricultural landscape was below 25%; it showed a very slow increase. In conclusion, the study highlights the agricultural landscape’s diverse contributions to the investigated ecosystem services in Norway, prompting the need for further research on additional ecosystem services to ensure the continued delivery of environmental and social well-being.
Abstract
Agricultural land abandonment is increasingly affecting rural and low-intensity farming regions across Europe, raising concerns about its impact on biodiversity. While some species may benefit from reduced human disturbance, many species in semi-natural ecosystem types depend on traditional agricultural management to maintain their ecological integrity. This study examines whether abandoned agricultural land in Norway contains semi-natural ecosystems that may hold important remnant populations of red-listed plant species and where continued cessation of farming may further threaten these biodiverse ecosystems. Using spatial data on abandoned farmland, semi-natural ecosystem types and species observations, we identify areas of conservation interest and assess the extent to which these areas support endangered species. In addition, we conducted a time-series analysis of vegetation change using NDVI data (2017–2024) to evaluate whether abandonment led to detectable ecological succession. We also analyzed the spatial distribution of abandonment and its correlation with proximity to active farms to understand regional patterns of abandonment. Our results show that only a small percentage (3.7 %) of the abandoned agricultural land considered in this study overlaps with known semi-natural ecosystem types, yet these areas support a significant number of red-listed plant species. The NDVI analysis revealed generally weak but positive greening trends, suggesting early successional changes that are not yet statistically significant across most habitat types. Our method thus suggests a potential approach to allocate limited management resources to key locations. At present, the amount of semi-natural ecosystems is probably underestimated, however, because of limited and time-consuming mapping activity. These findings emphasize the need for more extensive mapping and targeted conservation efforts and highlight the risks posed by abandonment in biodiversity rich semi-natural ecosystem types.
Abstract
No abstract has been registered
Authors
Anne B. NilsenAbstract
NIBIO produces Green Structure Maps (GSM) for Norway that cover built-up areas, including cabin areas. GSM is a hybrid product based on information from remote sensing data and detailed national vector datasets such as roads, water, buildings, and land use. GSM contains 8 classes: Ground, Shrub, Tree, Grey, Road, Water, Building, and Agriculture. QGIS is excellently suited for visual control of GSM. Based on the size of the dataset (number of polygons), a significant random sample of each class is selected to check whether it is correctly classified. You can organize the map layers into different themes, set up QGIS with multiple map windows showing different themes and zoom levels, and use existing plugins to jump from polygon to polygon and compare with aerial images and code whether the classification is correct or not - quickly and efficiently. More comprehensive statistics can then be calculated, and the results can be compared against the requirements to determine if the GSM meets the standards.
Abstract
No abstract has been registered
Abstract
Many countries have goals to reduce soil sealing of agricultural land to preserve food production capacity. To monitor progress, reliable data are needed to quantify soil sealing and changes over time. We examined the potential of the Imperviousness Classified Change (IMCC) 2015–2018 product provided by the Copernicus Land Monitoring Service (CLMS) to assess soil sealing in agricultural areas in Poland and Norway. We found very high overall accuracy due to the dominance of the area with no change. When we focused on areas classified as change, we found low user accuracy, with over-estimation of soil sealing. The producer accuracy was generally much higher, meaning that real cases of soil sealing were captured. This is better than under-estimation of soil sealing because it highlights areas where sealing may have occurred, allowing the user to carry out further control of this much smaller area, without having to assess the great expanse of unchanged area. We concluded that the datasets provide useful information for Europe. They are standardized and comparable across countries, which can enable comparison of the effects of policies intended to prevent soil sealing. Some distinctions between classes are not reliable, but the general information about increase or decrease is useful.
Abstract
The precise spatially explicit data on land cover and land use changes is one of the essential variables for enhancing the quantification of greenhouse gas emissions and removals, which is relevant for meeting the goal of the European economy and society to become climate-neutral by 2050. The accuracy of the machine learning models trained on remote-sensed data suffers from a lack of reliable training datasets and they are often site-specific. Therefore, in this study, we proposed a method that integrates the bi-temporal analysis of the combination of spectral indices that detects the potential changes, which then serve as reference data for the Random Forest classifier. In addition, we examined the transferability of the pre-trained model over time, which is an important aspect from the operational point of view and may significantly reduce the time required for the preparation of reliable and accurate training data. Two types of vegetation losses were identified: woody coverage converted to non-woody vegetation, and vegetated areas converted to sealed surfaces or bare soil. The vegetation losses were detected annually over the period 2018–2021 with an overall accuracy (OA) above 0.97 and a Kappa coefficient of 0.95 for all time intervals in the study regions in Poland and Norway. Additionally, the pre-trained model’s temporal transferability revealed an improvement of the OA by 5 percentage points and the macroF1-Score value by 12 percentage points compared to the original model.