Increased salinity and groundwater levels lead to degradation of the Robinia pseudoacacia forest in the Yellow River Delta

Forest degradation is a worldwide problem,although its causes vary due to geographical and climatic differences and man-made causes.In recent years,the Robinia pseudoacacia forest in the Yellow River Delta has suffered severe degradation.The causative mechanisms were investigated in the field over two years,and the results show that increased forest degradation was reflected by increased tree mortality,high leaf and soil sodium salt levels and groundwater depth.Average tree diameters decreased,and leaf chlorophyll and soil microbial contents decreased.Redundancy discriminate analysis(RDA)showed that degradation of the forest was correlated positively with soil salt content,but negatively with groundwater depth.Correlation analysis showed that 0.79%–0.95%soil salt content and above 1.20 m groundwater depth caused the death of R.pseudoacacia trees due to localized anthropogenic economic activities,such as rice farming,that disrupted the original water–salt balance.Measures are recommended to prevent further degradation and restore degraded forests.

Health assessment of plantations based on LiDAR canopy spatial structure parameters

The Yellow River Delta(YRD)has China's largest artificial Robinia pseudoacacia forest,which was planted in the late 1970s and suffered extensive dieback in the 1990s.The health grade of the R.pseudoacacia forest(named canopy vigor grade,CVG)could be achieved by using high-resolution images and canopy vigor indicators(CVIs).However,a previous study showed that there was no significant correlation between CVG and the field-estimated aboveground biomass(AGB)of R.pseudoacacia forest.Therefore,this study aims to construct forest health indicators(FHIs)based on canopy spatial structure parameters extracted from LiDAR.The FHIs included Weibull_α(the scale parameter of the Weibull density function that reflects the shape of the tree canopy),VCI(vertical complexity index),sdCC(the standard deviation of canopy cover),H99(the 99th percentile height)and cvLAD(the coefficient of variation of leaf area density),and could significantly distinguish three forest health grades(FHG)(p<0.05).The FHG was positively correlated with forest AGB(rs=0.51,p=0.004),and the similarity value with CVG was 63.33%.The results of this study confirmed that the FHIs can reflect both canopy vigor and tree productivity,and distinguish forest health status without prior classification information.