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.
2020
Abstract
No abstract has been registered
Authors
Johannes Breidenbach Lars T. Waser Misganu Debella-Gilo Johannes Schumacher Johannes Rahlf Marius Hauglin Stefano Puliti Rasmus AstrupAbstract
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Authors
Johannes BreidenbachAbstract
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Authors
Fatima Heinicke Xiangfu Zhong Karola Manuela Zucknick Johannes Breidenbach Arvind Yegambaram Meenakshi Sundaram Siri Tennebø Flåm Magnus Leithaug Marianne Dalland Simon Rayner Benedicte Alexandra Lie Gregor GilfillanAbstract
No abstract has been registered
Authors
Johannes Breidenbach Lars T. Waser Misganu Debella-Gilo Johannes Schumacher Johannes Rahlf Marius Hauglin Stefano Puliti Rasmus AstrupAbstract
Nation-wide Sentinel-2 mosaics were used with National Forest Inventory (NFI) plot data for modelling and subsequent mapping of spruce-, pine-, and deciduous-dominated forest in Norway at a 16 m × 16 m resolution. The accuracies of the best model ranged between 74% for spruce and 87% for deciduous forest. An overall accuracy of 90% was found on stand level using independent data from more than 42 000 stands. Errors mostly resulting from a forest mask reduced the model accuracies by ∼10%. The produced map was subsequently used to generate model-assisted (MA) and poststratified (PS) estimates of species-specific forest area. At the national level, efficiencies of the estimates increased by 20% to 50% for MA and up to 90% for PS. Greater minimum numbers of observations constrained the use of PS. For MA estimates of municipalities, efficiencies improved by up to a factor of 8 but were sometimes also less than 1. PS estimates were always equally as or more precise than direct and MA estimates but were applicable in fewer municipalities. The tree species prediction map is part of the Norwegian forest resource map and is used, among others, to improve maps of other variables of interest such as timber volume and biomass.
Abstract
No abstract has been registered
Authors
Stefano Puliti Johannes Breidenbach Johannes Schumacher Marius Hauglin Torgeir Ferdinand KlingenbergAbstract
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Abstract
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Abstract
Forest inventories provide predictions of stand means on a routine basis from models with auxiliary variables from remote sensing as predictors and response variables from field data. Many forest inventory sampling designs do not afford a direct estimation of the among-stand variance. As consequence, the confidence interval for a model-based prediction of a stand mean is typically too narrow. We propose a new method to compute (from empirical regression residuals) an among-stand variance under sample designs that stratify sample selections by an auxiliary variable, but otherwise do not allow a direct estimation of this variance. We test the method in simulated sampling from a complex artificial population with an age class structure. Two sampling designs are used (one-per-stratum, and quasi systematic), neither recognize stands. Among-stand estimates of variance obtained with the proposed method underestimated the actual variance by 30-50%, yet 95% confidence intervals for a stand mean achieved a coverage that was either slightly better or at par with the coverage achieved with empirical linear best unbiased estimates obtained under less efficient two-stage designs.