Larch caterpillars are widely distributed in the Great Xing’an boreal forests;however,the relationship between caterpillar defoliation dynamics and climatic factors is poorly understood.The aims of this study are to ...Larch caterpillars are widely distributed in the Great Xing’an boreal forests;however,the relationship between caterpillar defoliation dynamics and climatic factors is poorly understood.The aims of this study are to investigate the primary weather conditions that might influence forest defoliation and to identify the most important life stage of the larch caterpillar at which forest defoliation might be mitigated by incorporating more inhibitory influences from climatic factors.The life cycle of the larch caterpillar was partitioned into four stages and multiple linear regression and mixed effect models were combined with a relative weight analysis approach to evaluate the importance and influence of meteorological variables on defoliation dynamics.The results show that warmer temperatures in growing seasons and overwintering periods can increase the defoliation area,while rainy and humid growing seasons decrease the defoliation area.Total precipitation during the early instar larval period had the greatest power to explain the variance in defoliation dynamics and had a very strong inhibitory effect,followed by the accumulative temperatures of the late instar larval period which had a positive impact,and precipitation during the middle instar larval period which had a negative impact.Weather conditions during the early instar larval period had the greatest influence on the area defoliated and accounted for 40%of the explained variance.This study demonstrates that climatic warming and drying will increase the risk of larch caterpillar outbreaks in the Great Xing’an Mountains.展开更多
Pine wilt disease caused by the pinewood nematode Bursaphelenchus xylophilus has led to the death of a large number of pine trees in China.This destructive disease has the characteristics of bring wide-spread,fast ons...Pine wilt disease caused by the pinewood nematode Bursaphelenchus xylophilus has led to the death of a large number of pine trees in China.This destructive disease has the characteristics of bring wide-spread,fast onset,and long incubation time.Most importantly,in China,the fatality rate in pines is as high as 100%.The key to reducing this mortality is how to quickly find the infected trees.We proposed a method of automatically identifying infected trees by a convolution neural network and bounding box tool.This method rapidly locates the infected area by classifying and recognizing remote sensing images obtained by high resolution earth observation Satellite.The recognition accuracy of the test data set was 99.4%,and the remote sensing image combined with convolution neural network algorithm can identify and determine the distribution of the infected trees.It can provide strong technical support for the prevention and control of pine wilt disease.展开更多
基金The work was supported by the National Key R&D Program of China(2017YFA0604403).
文摘Larch caterpillars are widely distributed in the Great Xing’an boreal forests;however,the relationship between caterpillar defoliation dynamics and climatic factors is poorly understood.The aims of this study are to investigate the primary weather conditions that might influence forest defoliation and to identify the most important life stage of the larch caterpillar at which forest defoliation might be mitigated by incorporating more inhibitory influences from climatic factors.The life cycle of the larch caterpillar was partitioned into four stages and multiple linear regression and mixed effect models were combined with a relative weight analysis approach to evaluate the importance and influence of meteorological variables on defoliation dynamics.The results show that warmer temperatures in growing seasons and overwintering periods can increase the defoliation area,while rainy and humid growing seasons decrease the defoliation area.Total precipitation during the early instar larval period had the greatest power to explain the variance in defoliation dynamics and had a very strong inhibitory effect,followed by the accumulative temperatures of the late instar larval period which had a positive impact,and precipitation during the middle instar larval period which had a negative impact.Weather conditions during the early instar larval period had the greatest influence on the area defoliated and accounted for 40%of the explained variance.This study demonstrates that climatic warming and drying will increase the risk of larch caterpillar outbreaks in the Great Xing’an Mountains.
基金supported by the National Science and Technology Major Project of China’s High Resolution Earth Observation System(21-Y30B02-9001-19/22)the Heilongjiang Provincial Natural Science Foundation of China(YQ2020C018)。
文摘Pine wilt disease caused by the pinewood nematode Bursaphelenchus xylophilus has led to the death of a large number of pine trees in China.This destructive disease has the characteristics of bring wide-spread,fast onset,and long incubation time.Most importantly,in China,the fatality rate in pines is as high as 100%.The key to reducing this mortality is how to quickly find the infected trees.We proposed a method of automatically identifying infected trees by a convolution neural network and bounding box tool.This method rapidly locates the infected area by classifying and recognizing remote sensing images obtained by high resolution earth observation Satellite.The recognition accuracy of the test data set was 99.4%,and the remote sensing image combined with convolution neural network algorithm can identify and determine the distribution of the infected trees.It can provide strong technical support for the prevention and control of pine wilt disease.
