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Abstract Background Anoplophora glabripennis (Motschulsky), commonly known as Asian longhorned beetle (ALB), is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees. In Gansu Province, northwest China, ALB has caused a large number of deaths of a local tree species Populus gansuensis. The damaged area belongs to Gobi desert where every single tree is artificially planted and is extremely difficult to cultivate. Therefore, the monitoring of the ALB infestation at the individual tree level in the landscape is necessary. Moreover, the determination of an abnormal phenotype that can be obtained directly from remote-sensing images to predict the damage degree can greatly reduce the cost of field investigation and management. Methods Multispectral WorldView-2 (WV-2) images and 5 tree physiological factors were collected as experimental materials. One-way ANOVA of the tree’s physiological factors helped in determining the phenotype to predict damage degrees. The original bands of WV-2 and derived vegetation indices were used as reference data to construct the dataset of a prediction model. Variance inflation factor and stepwise regression analyses were used to eliminate collinearity and redundancy. Finally, three machine learning algorithms, i.e., Random Forest (RF), Support Vector Machine (SVM), Classification And Regression Tree (CART), were applied and compared to find the best classifier for predicting the damage stage of individual P. gansuensis. Results The confusion matrix of RF achieved the highest overall classification accuracy (86.2%) and the highest Kappa index value (0.804), indicating the potential of using WV-2 imaging to accurately detect damage stages of individual trees. In addition, the canopy color was found to be positively correlated with P. gansuensis’ damage stages. Conclusions A novel method was developed by combining WV-2 and tree physiological index for semi-automatic classification of three damage stages of P. gansuensis infested with ALB. The canopy color was determined as an abnormal phenotype that could be directly assessed using remote-sensing images at the tree level to predict the damage degree. These tools are highly applicable for driving quick and effective measures to reduce damage to pure poplar forests in Gansu Province, China.

Background: Anoplophora glabripennis (Motschulsky), commonly known as Asian longhorned beetle (ALB), is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees. In Gansu Province, northwest China, ALB has caused a large number of deaths of a local tree species Populus gansuensis. The damaged area belongs to Gobi desert where every single tree is artificially planted and is extremely difficult to cultivate. Therefore, the monitoring of the ALB infestation at the individual tree level in the landscape is necessary. Moreover, the determination of an abnormal phenotype that can be obtained directly from remote-sensing images to predict the damage degree can greatly reduce the cost of field investigation and management. Methods: Multispectral WorldView-2 (WV-2) images and 5 tree physiological factors were collected as experimental materials. One-way ANOVA of the tree's physiological factors helped in determining the phenotype to predict damage degrees. The original bands of WV-2 and derived vegetation indices were used as reference data to construct the dataset of a prediction model. Variance inflation factor and stepwise regression analyses were used to eliminate collinearity and redundancy. Finally, three machine learning algorithms, i.e., Random Forest (RF), Support Vector Machine (SVM), Classification And Regression Tree (CART), were applied and compared to find the best classifier for predicting the damage stage of individual P. gansuensis. Results: The confusion matrix of RF achieved the highest overall classification accuracy (86.2%) and the highest Kappa index value (0.804), indicating the potential of using WV-2 imaging to accurately detect damage stages of individual trees. In addition, the canopy color was found to be positively correlated with P. gansuensis' damage stages. Conclusions: A novel method was developed by combining WV-2 and tree physiological index for semi-automatic classification of three damage stages of P. gansuensis infested with ALB. The canopy color was determined as an abnormal phenotype that could be directly assessed using remote-sensing images at the tree level to predict the damage degree. These tools are highly applicable for driving quick and effective measures to reduce damage to pure poplar forests in Gansu Province, China. [ABSTRACT FROM AUTHOR]

During rapid urbanization, it is necessary to increase soil permeability and soil porosity for reducing urban runoff and waterlogging risk. Woody plants are known to increase soil porosity and preferential flow in soil via living roots growth and dead roots decay. However, the primary results of dead woody plant roots on soil porosity and permeability have been discussed based only on the hypotheses or assumptions of different researchers. In this study, living and dead roots (decayed under natural conditions for more than 5 years) of Gansu poplar trees (Populus gansuensis) were selected. They were selected to compare the influence between living and dead roots on water infiltration rate and soil porosity in a cylindrical container (diameter = 20 cm, height = 66 cm) under laboratory conditions. Results indicated that the steady-state water fluxes at the bottom of the containers without roots (control), with living roots, and with dead roots were 54.75 ± 0.80, 61.31 ± 0.61, and 55.97 ± 0.59 cm d−1, respectively. Both living roots and dead roots increased the water infiltration rates in soil and also increased the water storage capacity of soil. The water storage capacities of soil without roots, with living roots, and with dead roots were 0.279, 0.317, and 0.322 cm3 cm−3, respectively. The results from SEM indicated that smaller pores (30–50 μm) were in living roots and larger pores (100–1000 μm) were in dead roots. The soil permeability was increased by living roots possibly due to the larger channels generated on the surface of the roots; however, water absorbed into the dead roots resulted in greater water storage capacity. [ABSTRACT FROM AUTHOR]

During rapid urbanization, it is necessary to increase soil permeability and soil porosity for reducing urban runoff and waterlogging risk. Woody plants are known to increase soil porosity and preferential flow in soil via living roots growth and dead roots decay. However, the primary results of dead woody plant roots on soil porosity and permeability have been discussed based only on the hypotheses or assumptions of different researchers. In this study, living and dead roots (decayed under natural conditions for more than 5 years) of Gansu poplar trees (Populus gansuensis) were selected. They were selected to compare the influence between living and dead roots on water infiltration rate and soil porosity in a cylindrical container (diameter = 20 cm, height = 66 cm) under laboratory conditions. Results indicated that the steady-state water fluxes at the bottom of the containers without roots (control), with living roots, and with dead roots were 54.75 ± 0.80, 61.31 ± 0.61, and 55.97 ± 0.59 cm d−1, respectively. Both living roots and dead roots increased the water infiltration rates in soil and also increased the water storage capacity of soil. The water storage capacities of soil without roots, with living roots, and with dead roots were 0.279, 0.317, and 0.322 cm3 cm−3, respectively. The results from SEM indicated that smaller pores (30–50 μm) were in living roots and larger pores (100–1000 μm) were in dead roots. The soil permeability was increased by living roots possibly due to the larger channels generated on the surface of the roots; however, water absorbed into the dead roots resulted in greater water storage capacity.