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Untangling the Interactions Between Rural Outmigration, Grassland Degradation, and Sustainable Land Use in Mongolia
Project Start Date
03/01/2022
Project End Date
03/01/2025
Region
Solicitation

Team Members:

Person Name Person role on project Affiliation
Qiongyu Huang Principal Investigator Smithsonian Institution , Front Royal , USA
Melissa Songer Collaborator Smithsonian Conservation Biology Institute, Front Royal, US
Peter Leimgruber Collaborator Smithsonian Institution, Front Royal, US
Nicole Motzer Co-Investigator National Socio-Environmental Synthesis Center , Annapolis , USA
Ginger Allington Co-Investigator Natural Resources and the Environment , Cornell University, Ithaca, NY , USA
Tungalag Ulambayar Collaborator Zoological Society Luujin , Ulaanbaatar , Mongolia
Kirk Olson Collaborator Wildlife Conservation Society- Mongolia , Sukhbaatar district , Mongolia
Ochirkhuyag Lkhamjav Collaborator Mongolian Geo-spatial Association, , Mongolia
Abstract

The objectives of the proposal are to: 1. Assess changes in household demographics, livestock management practices, and opportunities of sustainable livelihoods related to rural outmigration; 2. Develop and assess novel algorithms for quantifying fractional grass cover, fractional vegetational functional types, and a synthetic grassland resilience index by leveraging medium-resolution satellite imagery and UAV data, and 3. Use statistical matching method to systematically assess the differences of fractional grass cover and resilience at the district level as a function of changing human and social capital.

We will evaluate changes to rural demographics and livelihoods associated with rural outmigration through a household survey administered across multiple districts in three Mongolian provinces. Survey questions will focus on the rural herding labor force, finances, and associated decisions, and potential impact on opportunities for a sustainable livelihood. We will use Landsat-8 data to model fractional grass cover in two periods between 2013 and 2022. The model will be trained using UAV-derived vegetation functional type information based on object-based segmentation and classification algorithm. We will use the Harmonized Landsat and Sentinel-2 (HLS) data to create a fractional vegetation functional types product circa 2020 and, subsequently, a synthetic grassland resilience index map. The resulting metrics will be compared between regions using a statistical matching method to systematically assess the social drivers of degradation.

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Project Research Area