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.
2023
Authors
Fride Høistad ScheiAbstract
No abstract has been registered
Abstract
Aim Current global warming is driving changes in biological assemblages by increasing the number of thermophilic species while reducing the number of cold-adapted species, leading to thermophilization of these assemblages. However, there is increasing evidence that thermophilization might not keep pace with global warming, resulting in thermal lags. Here, we quantify the magnitude of thermal lags of plant assemblages in Norway during the last century and assess how their spatio-temporal variation is related to variables associated with temperature-change velocity, topographic heterogeneity, and habitat type. Location Norway. Time period 1905–2007. Major taxa studied Vascular plants. Methods We inferred floristic temperature from 16,351 plant assemblages and calculated the floristic temperature anomaly (difference between floristic temperature and baseline temperature) and thermal lag index (difference between reconstructed floristic temperature and observed climatic temperature) from 1905 until 2007. Using generalized least squares models, we analysed how the variation in observed lags since 1980 is related to temperature-change velocity (measured as magnitude, rate of temperature change, and distance to past analogous thermal conditions), topographic heterogeneity, and habitat type (forest versus non-forest), after accounting for the baseline temperature. Results The floristic temperature anomaly increases overall during the study period. However, thermophilization falls behind temperature change, causing a constantly increasing lag for the same period. The thermal lag index increases most strongly in the period after 1980, when it is best explained by variables related to temperature-change velocity. We also find a higher lag in non-forested areas, while no relationship is detected between the degree of thermal lag and fine-scale topographic heterogeneity. Main conclusions The thermal lag of plant assemblages has increased as global warming outpaces thermophilization responses. The current lag is associated with different dimensions of temperature-change velocity at a broad landscape scale, suggesting specifically that limited migration is an important contributor to the observed lags.
Authors
Fride Høistad ScheiAbstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Stefano Puliti Grant Pearse Peter Surovy Luke Wallace Markus Hollaus Maciej Wielgosz Rasmus AstrupAbstract
The challenge of accurately segmenting individual trees from laser scanning data hinders the assessment of crucial tree parameters necessary for effective forest management, impacting many downstream applications. While dense laser scanning offers detailed 3D representations, automating the segmentation of trees and their structures from point clouds remains difficult. The lack of suitable benchmark datasets and reliance on small datasets have limited method development. The emergence of deep learning models exacerbates the need for standardized benchmarks. Addressing these gaps, the FOR-instance data represent a novel benchmarking dataset to enhance forest measurement using dense airborne laser scanning data, aiding researchers in advancing segmentation methods for forested 3D scenes. In this repository, users will find forest laser scanning point clouds from unamnned aerial vehicle (using Riegl sensors) that are manually segmented according to the individual trees (1130 trees) and semantic classes. The point clouds are subdivided into five data collections representing different forests in Norway, the Czech Republic, Austria, New Zealand, and Australia. These data are meant to be used either for developement of new methods (using the dev data) or for testing of exisitng methods (test data). The data splits are provided in the data_split_metadata.csv file. A full description of the FOR-instance data can be found at http://arxiv.org/abs/2309.01279
Authors
Stefano Puliti Grant Pearse Peter Surovy Luke Wallace Markus Hollaus Maciej Wielgosz Rasmus AstrupAbstract
The FOR-instance dataset (available at this https URL) addresses the challenge of accurate individual tree segmentation from laser scanning data, crucial for understanding forest ecosystems and sustainable management. Despite the growing need for detailed tree data, automating segmentation and tracking scientific progress remains difficult. Existing methodologies often overfit small datasets and lack comparability, limiting their applicability. Amid the progress triggered by the emergence of deep learning methodologies, standardized benchmarking assumes paramount importance in these research domains. This data paper introduces a benchmarking dataset for dense airborne laser scanning data, aimed at advancing instance and semantic segmentation techniques and promoting progress in 3D forest scene segmentation. The FOR-instance dataset comprises five curated and ML-ready UAV-based laser scanning data collections from diverse global locations, representing various forest types. The laser scanning data were manually annotated into individual trees (instances) and different semantic classes (e.g. stem, woody branches, live branches, terrain, low vegetation). The dataset is divided into development and test subsets, enabling method advancement and evaluation, with specific guidelines for utilization. It supports instance and semantic segmentation, offering adaptability to deep learning frameworks and diverse segmentation strategies, while the inclusion of diameter at breast height data expands its utility to the measurement of a classic tree variable. In conclusion, the FOR-instance dataset contributes to filling a gap in the 3D forest research, enhancing the development and benchmarking of segmentation algorithms for dense airborne laser scanning data.
Abstract
No abstract has been registered
Authors
Marta Vergarechea Rasmus Astrup Knut Øistad Carolin Fischer Clemens Blattert Markus Hartikainen Kyle Eyvindson Fulvio Di Fulvio Nicklas Forsell Daniel Burgas Astor Toraño-Caicoya Mikko Mönkkönen Clara Antón FernándezAbstract
No abstract has been registered