Towards a national mapped classification of terrestrial ecosystems in Mongolia: a pilot study in the Gobi Desert region

Includes bibliographical references.Presented at the Building resilience of Mongolian rangelands: a trans-disciplinary research conference held on June 9-10, 2015 in Ulaanbaatar, Mongolia.In Mongolia, partners from national and aimag governments, academia and NGOs have developed regional conservation plans that balance the government commitment to protection of natural habitats with planned development of mineral resources and related infrastructure. A key input is a mapped classification of major habitat types, or ecosystems, to represent the range of natural habitats and function as a surrogate for biodiversity. We developed a GIS model to map ecosystems across the Mongolian Gobi Desert region by comparing the distribution of plant communities and major vegetation types, taken from field surveys and national maps, with patterns of above-ground biomass, elevation, climate and topography derived from remote sensing. The resulting mapped classification is organized as a hierarchy of 1) biogeographic regions, 2) terrestrial ecosystem types based on vegetation, elevation and geomorphology, and 3) landforms. This provides a first-iteration map to support landscape-level conservation planning and a model framework that can support field surveys and future model revisions, with other applications to land use planning, research, surveys and monitoring. To facilitate that, the GIS results are publicly available either for download or to view and query in a web-based GIS available at: http://s3.amazonaws.com/DevByDesign-Web/MappingAppsVer2/Gobi/index.htm

A scalable big data approach for remotely tracking rangeland conditions

Rangelands, covering half of the global land area, are critically degraded by unsustainable use and climate change. Despite their extensive presence, global assessments of rangeland condition and sustainability are limited. Here we introduce a novel analytical approach that combines satellite big data and statistical modeling to quantify the likelihood of changes in rangeland conditions. These probabilities are then used to assess the effectiveness of management interventions targeting rangeland sustainability. This approach holds global potential, as demonstrated in Mongolia, where the shift to a capitalist economy has led to increased livestock numbers and grazing intensity. From 1986 to 2020, heavy grazing caused a marked decline in Mongolia’s rangeland condition. Our evaluation of diverse management strategies, corroborated by local ground observations, further substantiates our approach. Leveraging globally available yet locally detailed satellite data, our proposed condition tracking approach provides a rapid, cost-effective tool for sustainable rangeland management