【目的】对受松材线虫病影响的树木进行快速、高效和精确的检测。【方法】利用深度学习技术中的YOLO v4(you only look once version 4)目标检测模型,对高分辨率影像中的松材线虫病变色木进行检测,并与SSD(single shot multibox detect...【目的】对受松材线虫病影响的树木进行快速、高效和精确的检测。【方法】利用深度学习技术中的YOLO v4(you only look once version 4)目标检测模型,对高分辨率影像中的松材线虫病变色木进行检测,并与SSD(single shot multibox detector)模型进行对比。【结果】YOLO v4模型的检测精度较高,精确度(P)为0.961 3,召回率(R)为0.764 9,F1分数为0.851 9。【结论】YOLO v4可准确地识别和定位松材线虫病变色木,且精确度比SSD高。展开更多
Background： Coarse woody debris （CWD） is an important element of forest structure that needs to be considered when managing forests for biodiversity, carbon storage or bioenergy. To manage it effectively dynamics o...Background： Coarse woody debris （CWD） is an important element of forest structure that needs to be considered when managing forests for biodiversity, carbon storage or bioenergy. To manage it effectively dynamics of CWD decomposition should be known. Methods： Using a chronosequence approach, we assessed the decomposition rates of downed CWD of Fagus sylvatica, Picea obies and Pinus sylvestfis, which was sampled from three different years of tree fall and three different initial diameter classes （〉10 - ≤20 cm, 〉20 - ≤40 cm, 〉40 cm）. Samples originating from wind throws in 1999 were collected along a temperature and precipitation gradient. Based on the decay class and associated wood densities, log volumes were converted into CWD mass and C content. Log fragmentation was assessed over one year for log segments of intermediate diameters （〉20 - 40 cm） after 8 and 18 years of decomposition. Results： Significantly higher decomposition constants （k） were found in logs of F. sylvotica （0.054 year^-1） than in P. abies （0.033 year^-1） and P. sylvestris （0.032 year^-1）. However, mass loss of P. sylvestris occurred mainly in sapwood and hence k for the whole wood may be overestimated. Decomposition rates generally decreased with increasing log diameter class except for smaller dimensions in P. obies. About 74 % of the variation in mass remaining could be explained by decomposition time （27 %）, tree species （11%）, diameter （17 %）, the interactive effects between tree species and diameter （4 %） as well as between decomposition time and tree species （3 %） and a random factor （site and tree; 9.5 %）, whereas temperature explained only 2 %. Wood fragmentation may play a more important role than previously thought. Here, between 14 % and 30 % of the decomposition rates （for the first 18 years） were attributable to this process. Carbon （C） density （mgC· cm ^-3）, which was initially highest for F. sylvatico, followed by P. sylvestris and P. obies, decreased with increasing decay stage to similar values for all species. Conclusions： The apparent lack of climate effects on decomposition of logs in the field indicates that regional decomposition models for CWD may be developed on the basis of information on decomposition time, tree species and dimension only. These can then be used to predict C dynamics in CWD as input for C accounting models and for habitat management.展开更多
文摘【目的】对受松材线虫病影响的树木进行快速、高效和精确的检测。【方法】利用深度学习技术中的YOLO v4(you only look once version 4)目标检测模型,对高分辨率影像中的松材线虫病变色木进行检测,并与SSD(single shot multibox detector)模型进行对比。【结果】YOLO v4模型的检测精度较高,精确度(P)为0.961 3,召回率(R)为0.764 9,F1分数为0.851 9。【结论】YOLO v4可准确地识别和定位松材线虫病变色木,且精确度比SSD高。
基金funded by a German Science Foundation grant to Jürgen Bauhus(DFG-BA 2821/4-1)
文摘Background： Coarse woody debris （CWD） is an important element of forest structure that needs to be considered when managing forests for biodiversity, carbon storage or bioenergy. To manage it effectively dynamics of CWD decomposition should be known. Methods： Using a chronosequence approach, we assessed the decomposition rates of downed CWD of Fagus sylvatica, Picea obies and Pinus sylvestfis, which was sampled from three different years of tree fall and three different initial diameter classes （〉10 - ≤20 cm, 〉20 - ≤40 cm, 〉40 cm）. Samples originating from wind throws in 1999 were collected along a temperature and precipitation gradient. Based on the decay class and associated wood densities, log volumes were converted into CWD mass and C content. Log fragmentation was assessed over one year for log segments of intermediate diameters （〉20 - 40 cm） after 8 and 18 years of decomposition. Results： Significantly higher decomposition constants （k） were found in logs of F. sylvotica （0.054 year^-1） than in P. abies （0.033 year^-1） and P. sylvestris （0.032 year^-1）. However, mass loss of P. sylvestris occurred mainly in sapwood and hence k for the whole wood may be overestimated. Decomposition rates generally decreased with increasing log diameter class except for smaller dimensions in P. obies. About 74 % of the variation in mass remaining could be explained by decomposition time （27 %）, tree species （11%）, diameter （17 %）, the interactive effects between tree species and diameter （4 %） as well as between decomposition time and tree species （3 %） and a random factor （site and tree; 9.5 %）, whereas temperature explained only 2 %. Wood fragmentation may play a more important role than previously thought. Here, between 14 % and 30 % of the decomposition rates （for the first 18 years） were attributable to this process. Carbon （C） density （mgC· cm ^-3）, which was initially highest for F. sylvatico, followed by P. sylvestris and P. obies, decreased with increasing decay stage to similar values for all species. Conclusions： The apparent lack of climate effects on decomposition of logs in the field indicates that regional decomposition models for CWD may be developed on the basis of information on decomposition time, tree species and dimension only. These can then be used to predict C dynamics in CWD as input for C accounting models and for habitat management.
