Скачать с ютуб Statistical training in ecological modeling | Dr. Reza Belaghi в хорошем качестве

Statistical training in ecological modeling | Dr. Reza Belaghi

Instagram:   / resdinet   Facebook:   / resdinet   X: https://x.com/DE_Research_ Website: https://www.resdinet.eu ************************************************************ The third RESDiNET Workshop in the use of remote sensing data in forest disturbance ecology, Oct 8-10, 2024, Skara, Sweden. ************************************************************ Dr. Reza Belaghi, researcher at the Department of Energy and Technology in SLU, demonstrates how generalized linear models can be used to analyze environmental factors of forest damage risks. You will learn about specifying generalized linear models including conditions and assumptions, selecting an appropriate linear model for a given problem, carrying out an analysis based on a generalized linear model in the statistical software R, interpreting and evaluating results correctly and drawing reasonable conclusions, and clearly and concisely communicating results and conclusions. For more information, please contact Dr. Reza Belaghi statistics [at] slu.se. ************************************************************ RESDiNET is an international research project funded by the European Research Executive Agency, Horizon Europe. The project enhances networking activities between research institutions in Widening country (IFE SAS) and top-class counterparts at the EU level (Finnish Geospatial Research Institute, The University of Eastern Finland, and Swedish University of Agricultural Sciences). The project builds on networking for excellence through knowledge transfer and exchange of best practices between involved institutions. The project establishes an initial network and the development of a new joint research project in novel RST applications in FDE. Rigorous analyses of severe insect-induced disturbances using novel RST is carried out in test areas representing different forest and climate types: mountain forests in Slovakia and boreal forests in Finland and Sweden. The project integrates in situ UAV and drone-acquired remotely sensed data, existing multitemporal geospatial information, and field data, particularly data on bark beetle population density, visible infestation symptoms linked to outbreak phases, and tree physiology parameters measured using electronic dendrometers or sapflow meters. The combined dataset is used to develop new tools for landscape-level early bark beetle attack identification and for designing bark beetle infestation risk assessment models. We draw on the latest advances in drone technologies and image analytical tools, including deep Convolutional Neural Networks-based machine learning techniques and Artificial Intelligence algorithms.