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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.

2007

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Sammendrag

Proper management of wildlife relies on metrics of population development. Typically, the best estimation techniques are too expensive for coarse-scale management. In marine fisheries, catch-per-unit effort is commonly used, but problems may arise due to changes in spatial harvest effort or in habitat use as density changes. Managers in Norway are in the early phases of implementing "seen deer" during harvesting and "spring counts" on farmland as a means of monitoring red deer Cervus elaphus populations. We provide a first evaluation of how suitable these methods are by comparing the results with population estimates obtained using cohort analysis, and by analysing the within-season variation in number of seen deer. "Seen deer" predicted annual increases in populations fairly well. Adjusting for harvesting effort provided less good estimates, due to a proportionally larger increase in effort relative to deer population size as population size increased. The number of seen deer per day decreased rapidly at the beginning of the season, and then levelled off or increased slightly during the rut, especially on farmland. The number of seen deer increased both with the number of harvesters and hours harvested, but at a diminishing rate. The current practice of "spring counts" was not successful in predicting population changes, probably due to a lack of replication. Indeed, date strongly affected the number of deer seen during spring counts. While "seen deer" seems to be a very promising tool for monitoring population size of red deer, there are some limitations to the practice as implemented for moose Alces alces in Scandinavia due to a more complex relationship with harvesting effort. Our study highlights that the large number of hours harvesters observe wildlife can provide a useful tool for population monitoring. However, the use of such indices may vary between species and according to harvest techniques and should thus be assessed with care before implementation

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Sammendrag

The relationships between measures of forest structure as derived from airborne laser scanner data and the variation in quantity of young trees established by natural regeneration in a size-diverse spruce forest were analyzed. A regeneration success rate (RSR) was regressed against 27 different laser-derived explanatory variables. The 27 different models were ranked according to their Akaike information criterion score. Each laser variable was then associated with two categories. These were return and type. Within the return and type categories, the variables were grouped according to if they originated from first or last return echoes and if they were canopy height or canopy density metrics. The results show that the laser variables strongest correlated to the quantity of small trees could be attributed to last return and density metrics.

Sammendrag

Forest health monitoring may be done with remote sensing. Satellite based SAR is one promising technology as it works day and night and with cloud cover, and because it is sensitive to 3D properties. We here apply an interferometry based XDEM approach, where we assumed that an increasing defoliation would cause an increasing X band penetration downwards into the canopy layer, and that the penetration depth is a function of the amount of leaf area index (LAI) penetrated. We had at hand data for a 4 km2 forest area, having an SRTM X and C band SAR data set from 2000; a discrete-return laser scanning data set from 2003; and ground based measurements of some hundred trees and a forest stand map from 2003. We initially adjusted the XDEM and CDEM using elevation data from some agricultural fields nearby the forest using an official, Norwegian DTM data base having a 25mx25m spatial resolution. All further analyses were carried out on a 10mx10m grid. With the laser data we obtained a DTM and a canopy surface model (CSM), where the latter was set to the 75 percentile of the DZ data in each grid cell. The X band penetrated about six m downwards into the canopy layer, which means that for all grid cells having a forest canopy lower than six m, the XDEM was around zero. With an increasing DSM from six m upwards, the DSM could be approximated by the linear function DSM = 6 + 0.91*XDEM, having a RMSE of 4.0 m. The laser data provided the possibility to estimate LAI in every grid cell and at any height in that cell. For every grid cell, an LAI value was estimated for the forest canopy being above the XDEM height, using the method of Solberg et al (2006), where LAI = C * ln(N/Nb), where LAI is effective LAI above a given height; C is a constant calibrated from ground based measurements with the value 2.0, N is the total number of laser pulses; and Nb is the number of laser pulses below the given height. The median LAIaboveX value was 1.42, and 25-75 percentile values being 0.86-2.15. Also, in order to have a more homogeneous data set we redid the analyses using only spruce dominated stands, and excluding all grid cells at stand borders. The latter was set as grid cells that had neighbour grid cells in a neighbour stand. This had however, only a minor influence on the results.