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Active PROJECT GRANT Swedish Research Council

Remote sensing-based assessment of tundra’s grazing capacity in northern Fennoscandia, with a focus on biodiversity conservation

49.85M kr SEK

Funder Swedish National Space Agency
Recipient Organization Lund University
Country Sweden
Start Date May 01, 2025
End Date Apr 30, 2029
Duration 1,460 days
Number of Grantees 3
Roles Co-Investigator; Principal Investigator
Data Source Swedish Research Council
Grant ID 2024-00260_SNSB
Grant Description

Tundra is a vital natural resource for traditional Sami culture. Northern Fennoscandian tundra is responding to climate change by rapid shrubification and loss of biodiversity.

Additionally, expansion of land use limits the availability of tundra areas and leads to increasing grazing press on the remained pastures.

Thus, the effective management of tundra grazing lands that maintains vegetation productivity while preserving biodiversity is crucial for the economic stability of indigenous people.The project aims to develop a remote-sensing-based methodology for monitoring key vegetation characteristics: productivity, cover, and species richness and estimating grazing capacity across the Northern Fennoscandian tundra.

Our research will address the following research objectives: 1) based on Sentinel-2 time series data to develop a methodology to model and map vegetation cover, productivity and biodiversity of Northern Fennoscandian grazing lands in the 2017-2024 period applicable for further monitoring; 2) by analyzing the explanatory effect of the model parameters, to reveal the role of different factors on vegetation species richness distribution 3) to identify tundra vegetation diversity hot-spots and the most vulnerable territories that needed an update of the conservational and land-use policies; 4) based on the vegetation cover, productivity and diversity, to estimate the optimal reindeer capacity for the study area.The method utilizes the time series of satellite multispectral, phenology and productivity data, and different machine learning methods.

The project will be carried out in collaboration with the representatives from Sámi communities from Sweden, Norway and Finland, which allow the incorporation of their knowledge and everyday practice into the grazing capacity estimations.

The combination of the proposed approaches is novel and will result in the innovative, spatially explicit monitoring of tundra vegetation cover, productivity and plant diversity and can be used as a national program for monitoring grazing capacity.

Additionally, it may be used internationally for countries with different approaches to regulating reindeer husbandry.The study demonstrates an innovative, interdisciplinary approach to assessing and monitoring Fennoscandian tundra pastures on the base of remote sensing methods.

By the combination of scientific results and indigenous knowledge, the project will create a foundation for better decision-making to promote conflict resolution and support mutually beneficial solutions among indigenous communities, government agencies, and other organisations to mitigate the climate change and biodiversity crisis.

All Grantees

Lund University

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