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Abstract: State and local governments in China have implemented a series of grassland protection policies to address the problem of grassland degradation. In 2003, Ningxia was the first province to implement a province-wide grazing ban. The effect of this ban is contentious at all levels of government and has become a topic of public concern. Grassland desertification is the most direct indicator of the effect of the grazing ban. We selected 14 counties and cities in north-central Ningxia as the study area. A desertification classification and grading system for Ningxia's grassland was then designed based on fieldwork and expert review. Using the Spectral Mixture Analysis (SMA) and decision-tree methods, we interpreted Landsat TM/ETM+ images of the study area during four years: 1993, 2000, 2006 and 2011. The following results were obtained: from 1993 to 2011, the area of desertified grassland in north-central Ningxia decreased gradually from 8702km2 in 1993 to 7485km2 in 2011, a decrease of 13.98%; the degree of desertification gradually decreased from 3573km2 of severely desertified grassland in 1993 to 1450km2 in 2011, a decrease of 59.41%; desertified grassland vegetation was restored rapidly during 2000–2006 and 2006–2011, reducing the total area of desertified grassland annually by 1.87 and 0.61%, respectively; finally, the area of severely desertified grassland decreased annually by 5.78 and 6.28% during 2000–2006 and 2006–2011, respectively. These results show that the region-wide grazing ban, together with other ecological engineering measures, has helped reverse desertification and promote the restoration of grassland vegetation. [Copyright &y& Elsevier]

Shrub encroachment, characterized by the proliferation of shrubs into grasslands, is a challenge faced by grasslands worldwide that significantly impacts livestock production and ecosystem functions. Rapid and accurate estimation of shrub dominance is important for understanding changes in plant community structures and formulating grassland management policies. However, the limited spatial resolution of commonly used satellite imagery poses a challenge when estimating shrub dominance at the landscape scale. The rapid development of Unoccupied Aerial Vehicles (UAVs) has opened up new opportunities for cross-scale observations of shrub encroachment in grasslands by effectively bridging the scale gap between ground sampling and satellite image pixels while reducing the required groundwork. This study utilized ground reference data, UAV data (RGB, hyperspectral, and LiDAR), and satellite data (Sentinel-1 and Sentinel-2) to estimate shrub and total above-ground biomass (AGB) in temperate grasslands to map the shrub dominance. First, UAV data were applied at the plot scale for the classification of shrub and herbaceous vegetation using the maximum entropy model (MaxEnt), estimation of shrub AGB by employing the vegetation index weighted canopy volume model (CVM VI), and estimation of herbaceous AGB based on the partial least squares regression (PLSR). Second, UAV AGB mapping results were upscaled as samples at the landscape scale and integrated with satellite imagery to establish the shrub and total AGB models using the extreme gradient boosting (XGBoost). Finally, shrub dominance, represented as shrub AGB/total AGB, was mapped across the study area. We found that at the plot scale, the MaxEnt model achieved an overall accuracy of 0.990 for object-based classification. The CVM VI combined with canopy height model and narrow-band vegetation index achieved the highest accuracy for estimating shrub AGB (R2 = 0.821, RMSE = 30.1 g). The PLSR combined with features derived from all UAV data achieved the highest accuracy for estimating herbaceous AGB (R2 = 0.856, RMSE = 9.1 g/m2). At the landscape scale, the XGBoost achieved high accuracy for estimating both the shrub AGB (R2 = 0.719, RMSE = 4.2 g/m2) and total AGB (R2 = 0.961, RMSE = 5.0 g/m2). The high-precision mapping results further facilitate the generation of shrub dominance maps at a landscape scale. This study presents a more accurate and efficient framework for mapping shrub AGB, total AGB, and shrub dominance using multi-scale remote sensing data, which offers new approaches for large-scale grassland AGB mapping and monitoring of shrub encroachment in grasslands. • Multisensory UAV data were evaluated for shrub and herbaceous AGB estimation at the plot scale. • SAR and multispectral satellite data were used to map AGB and shrub dominance at the landscape scale. • Our multi-scale framework outperformed the traditional AGB estimation method. • Assessing shrub dominance is important for understanding shrub encroachment. [ABSTRACT FROM AUTHOR]