Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2022
Sammendrag
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Sammendrag
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Sammendrag
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Forfattere
Fride Høistad ScheiSammendrag
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Forfattere
Fride Høistad ScheiSammendrag
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Forfattere
Anne Linn HykkerudSammendrag
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Forfattere
Belachew Gizachew ZelekeSammendrag
We present an innovative value chain on upscaling and commercial production of carbonized bio-briquettes from agro-industrial waste (mainly a sugarcane bagasse), that aims at substituting a forest-based charcoal for household consumption and thus reduce deforestation. We demonstrate the three main pillars of the value-chain: (1). Empowering and capacity building of members of the cooperatives (mainly women), through developing technical skills, using and maintaining technologies and tools, ergonomics and safety, businesses and marketing. (2). Innovative locally built biowaste to biofuel conversion technologies. This are technologies for raw material (biowaste) preparation (transport, drying and storage), locally developing carbonization kilns of high efficiency and commercial volume, biochar production, selection of bio-based binders, local fabrication of briquetting machines, production of briquettes, drying and storage of briquettes. This section demonstrates (using videos and pictures) on how a daily briquettes production of 3-tons is achieved, with briquette qualities comparable to that of wood-based charcoal. We also demonstrate production of custom-made cookstoves for briquettes by modifying existing local cookstoves. Further, we demonstrate the amount of avoided deforestation through such innovative local approaches. (3). Business and market development: This aims at bringing green-jobs to villages in sustainable supply, distribution, and sales of clean locally produced bio-briquettes. The program enables capacity building of members of the cooperatives in business and marketing; building partnership with key market segments and cooperation with private sector such as distributors, consumers, lenders and banks. The complete value-chain is a result of a successful development and partnership program (2018-2021) supported by the government of Norway that involved Kenyan national institutions, local community cooperatives and international partners.
Sammendrag
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Forfattere
Emily Lines Matthew Allen Carlos Cabo Kim Calders Amandine Debus Stuart Grieve Milto Miltiadou Adam Noach Harry JF Owen Stefano PulitiSammendrag
With the rise in high resolution remote sensing technologies there has been an explosion in the amount of data available for forest monitoring, and an accompanying growth in artificial intelligence applications to automatically derive forest properties of interest from these datasets. Many studies use their own data at small spatio-temporal scales, and demonstrate an application of an existing or adapted data science method for a particular task. This approach often involves intensive and time-consuming data collection and processing, but generates results restricted to specific ecosystems and sensor types. There is a lack of widespread acknowledgement of how the types and structures of data used affects performance and accuracy of analysis algorithms. To accelerate progress in the field more efficiently, benchmarking datasets upon which methods can be tested and compared are sorely needed.Here, we discuss how lack of standardisation impacts confidence in estimation of key forest properties, and how considerations of data collection need to be accounted for in assessing method performance. We present pragmatic requirements and considerations for the creation of rigorous, useful benchmarking datasets for forest monitoring applications, and discuss how tools from modern data science can improve use of existing data. We list a set of example large-scale datasets that could contribute to benchmarking, and present a vision for how community-driven, representative benchmarking initiatives could benefit the field.
Sammendrag
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.