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Monitoring grassland degradation on a large scale has proven very difficult. Early estimates of the extent of degraded grasslands in dry areas put their number as about 3,050 million hectares (ha), or 94% of all degraded drylands, but relied on subjective assessments. Attempts to base estimates on measurements, e.g. by means of the Normalised Difference Vegetation Index (NDVI) derived from low resolution satellite data, came in for much criticism. Continuing difficulties of this kind prevented the Third Edition of the World Atlas of Desertification from displaying maps of the extent of dryland degradation based on satellite data. So there is an urgent need to devise new methods for measuring grassland degradation using satellite sensors. This project aims to fill this gap by devising such methods. It will experiment with various combinations of optical and radar data with resolutions varying from as much as 30 m (medium resolution) to ≤ 1 m (very high resolution). The research will focus on grasslands in China, which cover 400 million ha, or 42% of national land area, and this enables it to take advantage of data collected by both Chinese and ESA satellites. Its findings will make it feasible to monitor the degradation of grasslands, and drylands generally, in a reliable way, and to tackle the even more challenging task of measuring the combination of degradation and restoration that is required for monitoring progress in achieving the goal of land degradation neutrality, which are included in one of the sustainable development goals. Without reliable measurements of human-induced degradation, catalysed by droughts, it will be impossible to estimate the likely impact of long-term global climate change. Topics and methods: (1) Mapping and dynamic monitoring of grassland types: Referring to the traditional grassland categorizing system, the remote sensing classification system of the study area would be firstly constructed on the basis of analyzing the charicteristics of grassland types in the study area and payloads of China and ESA’s EO satellites. Then methods on mapping and dynamic monitoring of grassland types will be studied by multi-source remote sensing, especially with the utilization of Very High Resoultuion (VHR) datasets. (2) Quantitative estimation of grassland ecological parameters:The estimation and dynamic monitoring technologies of grassland ecological parameters, including grassland vegetation coverage, NPP and grassland biomass, will be developed by means of the combination of multi-scale and multi-source remote sensing and field investigation, and a grassland ecological static and dynamic monitoring technological system is expected to build (3) Degraded Grassland detection and assessment: The regularity of grassland ecological change and its spatio-temporal coupling relationship with climatic factors, soil characteristics and intensity of grazing will be analyzied synthetically. A remote sensing method for detection of degraded grassland which can greatly eliminate the impact of climate fluctuation will be developed, and the degree of grassland degradation will be assessed scientifically by grading the vegetation productivity and soil characteristics within degraded grassland. Availability of funding to run the project: For the European side, the School of Geography has the necessary institutional capacity to run the project. In addition, during the 4 years research, two funding projects, sponsored by National Science and Technology Major Project (No. 21-Y30B02-9001-19/22) and Fundamental Research Funds for the Central Non-Prof.it Research Institution of CAF (CAFYBB2019ZB004), are being undertaking by the Chinese team members. They will support the normal progressing of the Dragon 5 cooperation and the participating of the annual symposium.


PI Europe
Prof.. Alan Grainger, School of Geography, University of Leeds, UK
PI China
Prof.. Zhihai Gao, Chinese Academy of Forestry, CHINA