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.
2018
Authors
Signe Kynding Borgen Gry Alfredsen Johannes Breidenbach Lise Dalsgaard Gunnhild Søgaard Aaron SmithAbstract
No abstract has been registered
2017
Abstract
The use of digital aerial photogrammetry (DAP) for forest inventory purposes has been widely studied and can produce comparable accuracy compared with airborne laser scanning (ALS) in small, homogeneous areas. However, the accuracy of DAP for large scale applications with heterogeneous terrain and forest vegetation has not yet been reported. In this study we examined the accuracy of timber volume, biomass and basal area prediction models based on DAP and national forest inventory (NFI) data on a large area in central Norway. Two separate point clouds were derived from aerial image acquisitions of 2010 and 2013. Vegetation heights were extracted by subtracting terrain elevation derived from ALS. A large number of NFI sample plots (483) measured between 2010 and 2014 were used as reference data to fit linear models for timber volume, biomass and basal area with height metrics derived from the DAP data as explanatory variables. Variables describing the heterogeneous environmental and image acquisition conditions were calculated and their influence on the model accuracy was tested. The results showed that forest parameter prediction using DAP works well when applied to a large area. The model fits of the timber volume, biomass and basal area models were good with R2 of 0.80, 0.81, 0.81 and RMSEs of 41.43 m3 ha−1 (55% of the mean observed value), 32.49 t ha−1 (47%), 5.19 m2 ha−1 (41%), respectively. Only a small proportion of the variation could be attributed to the heterogeneous conditions. The inclusion of the relative sun inclination led to an improvement of the model RMSEs by 2% of the mean observed values. The relatively low cost and stability across large areas make DAP an attractive source of auxiliary information for large scale forest inventories.
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Kimm J.E. van Hulzen Claus J. Scholz Barbara Franke Stephan Ripke Marieke Klein Andrew McQuillin Edmund J.S. Sonuga-Barke John R. Kelsoe Mikael Landén Ole Andreassen Klaus-Peter Lesch Heike Weber Stephen Faraone Alejandro Arias-Vasquez Andreas Reif Søren Dalsgaard Srdjan Djurovic Omar Gustafsson Morten Mattingsdal Ingrid Melle Gunnar Morken Johannes SchumacherAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Johannes BreidenbachAbstract
No abstract has been registered
Abstract
Small-area estimation is a subject area of growing importance in forest inventories. Modelling the link between a study variable Y and auxiliary variables X— in pursuit of an improved accuracy in estimators—is typically done at the level of a sampling unit. However, for various reasons, it may only be possible to formulate a linking model at the level of an area of interest (AOI). Area-level models and their potential have rarely been explored in forestry. This study demonstrates, with data (Y = stem volume per ha) from four actual inventories aided by aerial laser scanner data (3 cases) or photogrammetric point clouds (1 case), application of three distinct models representing the currency of area-level modelling. The studied AOIs varied in size from forest management units to forest districts, and municipalities. The variance explained by X declined sharply with the average size of an AOI. In comparison with a direct estimate mean of Y in an AOI, all three models achieved practically important reduction in the relative root-mean-squared error of an AOI mean. In terms of the reduction in mean-squared errors, a model with a spatial location effect was overall most attractive. We recommend the pursuit of a spatial model component in area-level modelling as promising within the context of a forest inventory.
Authors
Hans Petersson Johannes Breidenbach David Ellison Sören Holm Anders Muszta Mattias Lundblad Göran R. StåhlAbstract
Many parties to the United Nation's Framework Convention on Climate Change (UNFCCC) base their reporting of change in Land Use, Land-Use Change and Forestry (LULUCF) sector carbon pools on national forest inventories. A strong feature of sample-based inventories is that very detailed measurements can be made at the level of plots. Uncertainty regarding the results stems primarily from the fact that only a sample, and not the entire population, is measured. However, tree biomass on sample plots is not directly measured but rather estimated using regression models based on allometric features such as tree diameter and height. Estimators of model parameters are random variables that exhibit different values depending on which sample is used for estimating model parameters. Although sampling error is strongly influenced by the sample size when the model is applied, modeling error is strongly influenced by the sample size when the model is under development. Thus, there is a trade-off between which sample sizes to use when applying and developing models. This trade-off has not been studied before and is of specific interest for countries developing new national forest inventories and biomass models in the REDD+ context. This study considers a specific sample design and population. This fact should be considered when extrapolating results to other locations and populations.
Authors
Johannes BreidenbachAbstract
No abstract has been registered