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.
2019
Abstract
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.
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Valentina Gallina Silvia Torresan Alex Zabeo Jonathan Rizzi Sandro Carniel Mauro Sclavo Lisa Pizzol Antonio Marcomini Andrea CrittoAbstract
Coastal erosion is an issue of major concern for coastal managers and is expected to increase in magnitude and severity due to global climate change. This paper analyzes the potential consequences of climate change on coastal erosion (e.g., impacts on beaches, wetlands and protected areas) by applying a Regional Risk Assessment (RRA) methodology to the North Adriatic (NA) coast of Italy. The approach employs hazard scenarios from a multi-model chain in order to project the spatial and temporal patterns of relevant coastal erosion stressors (i.e., increases in mean sea-level, changes in wave height and variations in the sediment mobility at the sea bottom) under the A1B climate change scenario. Site-specific environmental and socio-economic indicators (e.g., vegetation cover, geomorphology, population) and hazard metrics are then aggregated by means of Multi-Criteria Decision Analysis (MCDA) with the aim to provide an example of exposure, susceptibility, risk and damage maps for the NA region. Among seasonal exposure maps winter and autumn depict the worse situation in 2070–2100, and locally around the Po river delta. Risk maps highlight that the receptors at higher risk are beaches, wetlands and river mouths. The work presents the results of the RRA tested in the NA region, discussing how spatial risk mapping can be used to establish relative priorities for intervention, to identify hot-spot areas and to provide a basis for the definition of coastal adaptation and management strategies.
Authors
Jonathan RizziAbstract
No abstract has been registered
Authors
Juraj Parajka Nejc Bezak John Burkhart Bjarki Hauksson Ladislav Holko Yeshewa Hundecha Michal Jenicek Pavel Krajčí Walter Mangini Peter Molnar Philippe Riboust Jonathan Rizzi Aynur Sensoy Guillaume Thirel Alberto ViglioneAbstract
This study evaluates MODIS snow cover characteristics for large number of snowmelt runoff events in 145 catchments from 9 countries in Europe. The analysis is based on open discharge daily time series from the Global Runoff Data Center database and daily MODIS snow cover data. Runoff events are identified by a base flow separation approach. The MODIS snow cover characteristics are derived from Terra 500 m observations (MOD10A1 dataset, V005) in the period 2000–2015 and include snow cover area, cloud coverage, regional snowline elevation (RSLE) and its changes during the snowmelt runoff events. The snowmelt events are identified by using estimated RSLE changes during a runoff event. The results indicate that in the majority of catchments there are between 3 and 6 snowmelt runoff events per year. The mean duration between the start and peak of snowmelt runoff events is about 3 days and the proportion of snowmelt events in all runoff events tends to increase with the maximum elevation of catchments. Clouds limit the estimation of snow cover area and RSLE, particularly for dates of runoff peaks. In most of the catchments, the median of cloud coverage during runoff peaks is larger than 80%. The mean minimum RSLE, which represents the conditions at the beginning of snowmelt events, is situated approximately at the mean catchment elevation. It means that snowmelt events do not start only during maximum snow cover conditions, but also after this maximum. The mean RSLE during snowmelt peaks is on average 170 m lower than at the start of the snowmelt events, but there is a large regional variability.
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Authors
Finn-Arne HaugenAbstract
No abstract has been registered
Abstract
The extent of land lease is increasing in many countries, including Norway. This paper develops a von Thünen type model of optimal land plots to lease from a farm’s center. For a single farm setting, the optimality principle is that land is leased as long as the expected marginal value of leasing a tract of land is greater than or equal to the expected marginal costs of leasing the land. The single farm model setting captures land lease at the extensive margin, i.e., under absence of competition for leasing land. Land lease at the intensive margin, i.e., when there is competition for leasing farm fields, is more interesting. We distinguish between two cases. In the first case, continued farm operations do not depend on being able to lease more land. Then we show that optimal land lease results when the expected profits for each farm of leasing its least profitable field is equal among farms competing for the same farm field. This also corresponds to an economically efficient allocation of leased land. Our second case at the intensive margin is more complicated. Here, farm survival depends on attracting acreage of leased land to allow for investment in cost saving technology. We show that the resulting allocation of leased land corresponds to the solution of a game involving bidding for land in order to prevent other farmers from getting land, which in turn leads to farmer exit and therefore increases the future supply of land available at the land lease market. In the first round of the game, winners of the land lease auction pay more for the leased land than they would have done in the absence of preventive bidding. The model framework is applicable for other settings where locking out competitors are parts of agents’ strategy space.
Authors
Christian PedersenAbstract
No abstract has been registered