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.
2022
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Transpiration makes up the bulk of total evaporation in forested environments yet remains challenging to predict at landscape-to-global scales. We harnessed independent estimates of daily transpiration derived from co-located sap flow and eddy-covariance measurement systems and applied the triple collocation technique to evaluate predictions from big leaf models requiring no calibration. In total, four models in 608 unique configurations were evaluated at 21 forested sites spanning a wide diversity of biophysical attributes and environmental backgrounds. We found that simpler models that neither explicitly represented aerodynamic forcing nor canopy conductance achieved higher accuracy and signal-to-noise levels when optimally configured (rRMSE = 20%; R2 = 0.89). Irrespective of model type, optimal configurations were those making use of key plant functional type dependent parameters, daily LAI, and constraints based on atmospheric moisture demand over soil moisture supply. Our findings have implications for more informed water resource management based on hydrological modeling and remote sensing.
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Using periodic measurements from permanent plots in non-thinned and thinned Norway spruce (Picea abies (L.) H. Karst.) stands in Norway, individual-tree growth models were developed to predict annual diameter increment, height increment, and height to crown base increment. Based on long-term data across a range of thinning regimes and stand conditions, alternative approaches for modeling response to treatment were assessed. Dynamic thinning response functions in the form of multiplicative modifiers that predict no effect at the time of thinning, a rapid increase followed by an early maximum before the effect gradually declines to zero could not be fitted to initially derived baseline models without thinning related predictors. However, alternative approaches were used and found to perform well. Specifically, indicator variables representing varying time periods after thinning were statistically significant and behaved in a robust manner as well as consistent with general expectations. In addition, they improved overall prediction accuracy when incorporated as fixed effects into the baseline models for diameter and height to crown base increment. Further, more simply, including exponentially decreasing multiplicative thinning response functions improved prediction accuracy for height increment and height to crown base increment. Irrespective of studied attribute and modelling approach, improvement in performance of these extended models was relatively limited when compared to the corresponding baseline models and more pronounced in trees from thinned stands. We conclude that the largely varying and often multi-year measurement intervals of the periodic data used in this study likely prevented the development of more sophisticated thinning response functions. However, based on the evaluation of the final models’ overall performance such complex response functions may not to be necessary to reliably predict individual tree growth after thinning for certain conditions or species, which should be further considered in future analyses of similar nature.
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The assessment of forest abiotic damages such as snow breakage is important to ensure compensation to forest owners. Currently, information on the extent of snow breakage is gathered through time-consuming and potentially biased field surveys. In such situations where field surveys are still common practice, unmanned aerial vehicles (UAVs) are increasingly being used to provide a more cost-efficient and objective methods to answer forest information needs. Further, the advent of sophisticated computer vision techniques such as convolutional neural networks (CNNs) offers new ways to analyze image data more efficiently and accurately. We proposed an object detection method to automatically identify trees and classify them according to the damage by snow based on a YOLO CNN architecture. UAV imagery collected across 89 study areas and over the course of the entire year were manually annotated into a total of >55 K single trees classified as healthy, damaged, or dead. The annotated trees, along with the corresponding UAV imagery were used to train a YOLOv5 object detection model. Furthermore, we tested the effect of seasonality, and varying atmospheric and lighting conditions on the model’s performance. Based on an independent test set of data we found that the general model including all of the data (i.e. any seasons, atmospheric conditions, and time of the day) outperformed all other tested scenarios (i.e. precision = 62 %; recall = 61 %). Furthermore, we found that despite the fact that the snow damaged trees represented a minority class (i.e. 16 % of the annotated trees), they were detected with the largest precision (76 %) and recall (78 %). Finally, the general model transferred well across the variation in seasons, atmospheric and illumination conditions, making it suitable for usage for any new UAV image acquisition.
Authors
Benjamin Allen Michele Dalponte Hans Ole Ørka Erik Næsset Stefano Puliti Rasmus Astrup Terje GobakkenAbstract
Numerous species of pathogenic wood decay fungi, including members of the genera Heterobasidion and Armillaria, exist in forests in the northern hemisphere. Detection of these fungi through field surveys is often difficult due to a lack of visual symptoms and is cost-prohibitive for most applications. Remotely sensed data can offer a lower-cost alternative for collecting information about vegetation health. This study used hyperspectral imagery collected from unmanned aerial vehicles (UAVs) to detect the presence of wood decay in Norway spruce (Picea abies L. Karst) at two sites in Norway. UAV-based sensors were tested as they offer flexibility and potential cost advantages for small landowners. Ground reference data regarding pathogenic wood decay were collected by harvest machine operators and field crews after harvest. Support vector machines were used to classify the presence of root, butt, and stem rot infection. Classification accuracies as high as 76% with a kappa value of 0.24 were obtained with 490-band hyperspectral imagery, while 29-band imagery provided a lower classification accuracy (~60%, kappa = 0.13).
Abstract
No abstract has been registered