Changes of plant species diversity and biomass with reclaimed marshes restoration

Wetland restoration had been implemented for more than two decades in Sanjiang Plain,Northeast China.To assess the restoration effi ciency of wetland vegetation,we investigated plants composition of community,plant species diversity and aboveground biomass of restored sites in a chronosequence of restoration(1,2,3,6,8,11,15 and 25 years)in the Sanjiang Nature Reserve.As comparison,we also investigated the same factors in a cropland and a natural marsh adjacent to the restored sites.The results showed that wetland plant species can invade quickly after croplands were abandoned when there were suitable hydrology conditions.On the early stage of the restoration,weeds were main plant species in the restored sites.Wetland species appeared at the same time but diff ered from the dominant species from the adjacent natural marshes.Common native wetland species could dominance the community after 3-year restoration.Species richness and diversity increased on the early stage,and then decreased to the similar level of the natural marsh with the extension of restoration.Plant biomass could restore easier than the species composition and diversity.Our results indicated that plant species composition and diversity of abandoned reclaimed wetlands can restore gradually by natural succession in Sanjiang Plain.However,25-year restoration site had similarity index of only 56%with the natural marsh,which revealed that two decades are not enough for complete restoration of vegetation.

Improving forest burn severity estimations with partial least squares regression and orthogonal signal correction methods in Daxing'an Mountains,China

Several indices and simple empirical models and ratios of single band from pre-and post-fire Landsat images have been developed to estimate and/or map burn severity.However,these models and indices are usually site-,time-and vegetation-dependent and their applications are limited.The Daxing'an Mountains range has the largest forested area in China and is prone to wildfires.Whether or not the existing models can effectively characterize the burn severity over a large region is unclear.In this study,we used the orthogonal signal correction method based on partial least squares regression(PLSR)to select those variables that better interpret the variance of burn severity.A new index and other commonly used indices were used to construct a new,multivariate PLSR model which was compared with the popular single variable models,according to three assessment indices:relative root mean square error(RMSE%),relative bias(R E%)and Nash–Sutcliffe efficiency(NSE%).The results indicate that the multivariate PLSR model performed better than the other single variable models with higher NSE%(68.2%vs.67.8%)and less RE%(3.7%vs.-8.7%),while achieving almost the same R MSE%.We also discuss the spectral characteristics of the four selected variables for constructing the multivariate PLSR model and their correlation with the field burn severity data.The new model developed from this study should help to better understand the patterns of forest burn severity and assist in vegetation restoration efforts in the region.