A scalable big data approach for remotely tracking rangeland conditions

Abstract

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.

Rangelands in Mongolia suffered a marked decline in grazing conditions between 1986 and 2020, according to an approach which combines satellite big data with statistical modeling to remotely assess rangeland sustainability and management strategies