Brief info
The goal of the project is to design landscape networks (LN) by using novel Machine Learning (ML) approaches and to apply the model for study sites in the Romanian Carpathians. When building a landscape network, we aim to consider both delivering services and avoiding landscape disservices for wildlife and people (HWI).
The hypothesis of the project is that protected areas could be better connected through other landscape elements (e.g., isolated trees) to design coherent landscape networks (LN). Landscape permeability as conservation tool.
Objectives
O1. To determine elements of optimal potential connectivity (PC) - connectors (landscape patches, isolated trees, etc.) between the landscape network elements such as Natura 2000 areas, protected areas, etc., by using available ML algorithms and tools, already developed by the team members.
O2. Design suitable LN with ML taking in count also Landscape permeability.
Expected results
1. Mapping the configuration of landscape networks for the entire Carpathian area.
2. Development of theoretical support. ML techniques on algorithms for building LN
Research Team
P.I. Senior Research: Dr. Ileana Stupariu, Faculty of Geography
Post-doc., Dr. Ancuta Fedorca – Transilvania University of Brasov from Faculty of Silviculture and Forest Engineering and the Research and Development Institute
PhD Student Gîlea Alexandru Faculty of Geography
