Project Period: January 2021-December 2024

Funding Source/Donor Organization: National Natural Science Foundation of China

Executing Agency: Chinese Academy of Forestry

Contact: Zhu Yuanjun: zhuyuanjun@caf.ac.cn

Brief Introduction:

In this project, Inner Mongolia Steppe of China and Patagonia Steppe in Argentina, two typical steppe in the world are selected for current assessment of degraded states using belt transect survey, composite indicators and machine learning methods, spatio-temporal vegetation change will be detected with different non-parametric algorithms based on multi-temporal satellite images, and contribution rates of natural (climate) and human activities (grazing) to steppe degradation will be quantified using RS and climatological dataset. Based on the assessment results, 3 degraded states (light, moderate and severe) from 3 steppe types (meadow steppe, typical steppe and desert steppe) along precipitation gradient will be setup to study vegetation-soil feedback mechanism during degraded steppe restoration and rehabilitation with different steppe management practices (grazing management) and artificial improvement measures (fertilizer and seeding), accordingly, the optimal integrated measure paradigm will be determined for different steppe types and degraded states. The research results will provide theoretical basis and technical support for restoration and rehabilitation of degraded steppe.