Improving the Accuracy of Vegetation Index Retrieval for Biomass by Combining Ground-UAV Hyperspectral Data-A New Method for Inner Mongolia Typical Grasslands

Grassland biomass is an important parameter of grassland ecosystems.The complexity of the grassland canopy vegetation spectrum makes the long-term assessment of grassland growth a challenge.Few studies have explored the original spectral information of typical grasslands in Inner Mongolia and examined the influence of spectral information on aboveground biomass(AGB)estimation.In order to improve the accuracy of vegetation index inversion of grassland AGB,this study combined ground and Unmanned Aerial Vehicle(UAV)remote sensing technology and screened sensitive bands through ground hyperspectral data transformation and correlation analysis.The narrow band vegetation indices were calculated,and ground and airborne hyperspectral inversion models were established.Finally,the accuracy of the model was verified.The results showed that:(1)The vegetation indices constructed based on the ASD FieldSpec 4 and the UAV were significantly correlated with the dry and fresh weight of AGB.(2)The comparison between measured R^(2) with the prediction R^(2) indicated that the accuracy of the model was the best when using the Soil-Adjusted Vegetation Index(SAVI)as the independent variable in the analysis of AGB(fresh weight/dry weight)and four narrow-band vegetation indices.The SAVI vegetation index showed better applicability for biomass monitoring in typical grassland areas of Inner Mongolia.(3)The obtained ground and airborne hyperspectral data with the optimal vegetation index suggested that the dry weight of AGB has the best fitting effect with airborne hyperspectral data,where y=17.962e^(4.672x),the fitting R^(2) was 0.542,the prediction R^(2)was 0.424,and RMSE and REE were 57.03 and 0.65,respectively.Therefore,established vegetation indices by screening sensitive bands through hyperspectral feature analysis can significantly improve the inversion accuracy of typical grassland biomass in Inner Mongolia.Compared with ground monitoring,airborne hyperspectral monitoring better reflects the inversion of actual surface biomass.It provides a reliable modeling framework for grassland AGB monitoring and scientific and technological support for grazing management.

Environmental filtering does not necessarily prevent trait divergence: a case study of the Xilin River Basin in Inner Mongolia, China

Aims Understanding how environmental factors and human activity drive plant community assembly remains a major challenge in community ecology.Two opposing processes,namely determinis-tic environmental filtering and nondeterministic elements such as interspecies competition,can be quantified by analyzing trait distri-butions in the community-assembly process.Methods We examined the trait-mediated effects of environmental filtering and stochastic process and the distribution over time of nine traits related to vegetative growth,regenerative phase,dispersal capability,decom-position and interspecific competition in plant communities along a degradation gradient in the Xilin River Basin,Inner Mongolia,China.We analyzed the turnover of environmental trait filtering and the diver-gence/convergence of different traits along the degradation gradient.Important Findings Our results showed the following.(i)The patterns of trait distribu-tion and filtering were strongly dependent upon the degradation gradient and trait types.Most traits were filtered intensely in degraded grasslands.(ii)Plants with two different strategies showed contrasting trait-distribution patterns.The traits that were related to biological matter cycling showed divergent pat-terns in highly degraded grasslands,while convergent patterns along the overall gradient were demonstrated in traits associated with other plant strategies.This suggests that the coexistence of multiple‘biological matter cycling-related niches’might be a basic structuring pattern of plant communities in our study area.(iii)The simultaneous occurrence of strong filtering and diver-gence revealed that environmental filtering does not necessar-ily prevent competition,and that different traits show different signatures.