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Vegetation green aboveground biomass is an important parameter of terrestrial ecosystem process-based models, and is closely related to photosynthetic activity. In this paper, the hyperspectral predictors for estimating green aboveground biomass were evaluated based on field spectral and corresponding biophysical parameter measurements during the growing seasons of 2009 and 2010 in the desert steppe of Inner Mongolia. Results showed that the performance of red-edge reflectance curve area (between normalized reflectance curve and wavelength in 680–780 nm region) was better than that of traditional vegetation indices (VIs) and red-edge position (REP). The model based on red-edge reflectance curve area produced a lower standard error of prediction (26.4 g m−2) compared with the optimal narrow-band ratio vegetation index (RVI) (37.4 g m−2), normalized difference vegetation index (NDVI) (33.5 g m−2), soil-adjusted vegetation index (SAVI) (37.5 g m−2) and modified soil-adjusted vegetation index (MSAVI) (38.6 g m−2). Statistical analyses performed on the slopes and intercepts of the fitted lines between actual and predicted values demonstrated that no statistical differences (P > 0.05) were found in regards to slope = 1 and intercept = 0 for red-edge reflectance curve area and optimal SAVI and MSAVI, whereas the slope and intercept based on optimal RVI were different (P < 0.05) from 1 to 0 respectively, and the slope based on optimal NDVI was different (P < 0.05) from 1. In conclusion, the red-edge reflectance curve area method is a practical and suitable technique for complementing the existing methods on estimation of green aboveground biomass in arid and semi-arid areas. [Copyright &y& Elsevier]

Soil wind erosion is an important ecological environmental problem that is widespread in arid and semi-arid regions. Currently, related studies are mainly focused on spatiotemporal characteristics or analysis of effector mechanisms, and they do not facilitate direct servicing of management decisions. In this paper, we used the Xilingol typical steppe in Inner Mongolia, China, as a study site to develop a decision framework for a comprehensive understanding of soil wind erosion and to promote sustainable management of steppes. In this study, we used the Revised Wind Erosion Equation model to simulate soil wind erosion. We combined this model with linear trend analysis to evaluate the ecological effects of soil wind erosion and wind erosion intensity, and delineated the gravity center migration path. We used the constraint line method to reveal the mechanisms by which climatic factors affected soil wind erosion, achieved the spatial visualization of wind-breaking and sand-fixing service flow, and proposed decision-based regional sustainable development suggestions. The results showed that long-term soil wind erosion will lead to soil coarsening and loss of soil nutrients. Soil wind erosion in the study site showed worsening trends and risks, and as such, ecological environment conservation and management are urgently required. The study framework promotes a clear understanding of the complex relationship of human–earth systems. The study results will aid in the ecological remediation of steppe landscapes and the prevention of desertification and will provide a foundation for win–win management of ecological conservation and economic development in arid and semi-arid regions. • A decision-oriented management framework was developed from the perspective of soil wind erosion prevention and control. • Long-term soil wind erosion will lead to soil coarsening and loss of soil nutrients. • The constraint line method can help to reveal the complex effects of meteorological factors on soil wind erosion. • The study achieved spatial visualization of wind-breaking and sand-fixing service flow in grassland ecosystem. [ABSTRACT FROM AUTHOR]