Historical forest fire risk databases are vital for evaluating the effectiveness of past forest management approaches,enhancing forest fire warnings and emergency response capabilities,and accurately budgeting potenti...Historical forest fire risk databases are vital for evaluating the effectiveness of past forest management approaches,enhancing forest fire warnings and emergency response capabilities,and accurately budgeting potential carbon emissions resulting from fires.However,due to the unavailability of spatial information technology,such databases are extremely difficult to build reliably and completely in the non-satellite era.This study presented an improved forest fire risk reconstruction framework that integrates a deep learning-based time series prediction model and spatial interpolation to address the challenge in Sichuan Province,southwestern China.First,the forest fire danger index(FFDI)was improved by supplementing slope and aspect information.We compared the performances of three time series models,namely,the autoregressive integrated moving average(ARIMA),Prophet and long short-term memory(LSTM)in predicting the modified forest fire danger index(MFFDI).The bestperforming model was used to retrace the MFFDI of individual stations from 1941 to 1970.Following this,the Anusplin spatial interpolation method was used to map the distributions of the MFFDI at five-year intervals,which were then subjected to weighted overlay with the distance-to-river layer to generate forest fire risk maps for reconstructing the forest fire danger database.The results revealed LSTM as the most accurate in fitting and predicting the historical MFFDI,with a fitting determination coefficient(R^2)of 0.709,mean square error(MSE)of0.047,and validation R^2 and MSE of 0.508 and 0.11,respectively.Independent validation of the predicted forest fire risk maps indicated that 5 out of 7 historical forest fire events were located in forest fire-prone areas,which is higher than the results determined from the original FFDI(2 out of 7).This proves the effectiveness of the improved MFFDI and indicates a high level of reliability of the historical forest fire risk reconstruction method proposed in this study.展开更多
[Objectives]The paper was to investigate the distribution characteristics of soil total nitrogen in low-efficiency forest land in the northern windy desert area of Jingbian County.[Methods]The distribution of soil tot...[Objectives]The paper was to investigate the distribution characteristics of soil total nitrogen in low-efficiency forest land in the northern windy desert area of Jingbian County.[Methods]The distribution of soil total nitrogen in the 0-40 cm soil layer of 5 towns in the northern windy desert area of Jingbian County was studied through field sampling and laboratory detection.[Results]The average soil total nitrogen contents of Hongdunjie Town,Haizetan Town,Huanghaojie Town,Ningtiaoliang Town,Dongkeng Town and windy desert area in the 0-20 cm soil layer were 0.259,0.224,0.242,0.248,0.431 and 0.275 g/kg,respectively.The soil total nitrogen content in Dongkeng Town was higher than those in other towns,while there was little difference among other regions.The average total nitrogen contents in the 20-40 cm soil layer were 0.239,0.285,0.113,0.262,0.349 and 0.241 g/kg,respectively.The soil total nitrogen content in Huanghaojie Town was slightly lower than those in other towns,while that in Dongkeng town was higher.The variation coefficient of soil total nitrogen content in the survey area was greater than 30%,and there was great difference in spatial distribution.With the increase of soil depth,there was little change in soil total nitrogen content,namely the soil total nitrogen contents in 5 towns and windy desert area were not statistically different in the 0-40 cm soil layer.According to the nutrient grading standard of the second national soil survey,the soil total nitrogen content in the survey area was in the deficiency grade.[Conclusions]The research will provide a scientific guidance for the healthy and sustainable development of vegetation in arid regions.展开更多
The construction of artificial shelter forests(ASFs)has resulted in substantial ecological,economic,and societal benefits to the Chinese Loess Plateau(CLP).However,the health and benefits of ASFs are being increasingl...The construction of artificial shelter forests(ASFs)has resulted in substantial ecological,economic,and societal benefits to the Chinese Loess Plateau(CLP).However,the health and benefits of ASFs are being increasingly threatened by the formation of low-efficiency artificial shelter forests(LEASFs).In this study,LEASFs are systematically analyzed in terms of their status,formation mechanisms,and developmental obstacles.The key restoration techniques and schemes were summarized to improve the quality and efficiency of LEASFs.LEASFs are formed by relatively complex mechanisms,but they arise mainly due to poor habitat conditions,improper tree species selections,mismatch between stands and habitat,extensive forest management measures,and human interferences.The restoration and improvement of LEASFs are hindered by water deficits,mismatch between stands and habitat,single management purpose,and low efficiency.LEASFs are becoming more complex due to their wide range,the challenges associated with their restoration,and insufficient technological measures for their restoration.The key techniques of the quality and efficiency improvement of LEASFs include basic forest tending methods,near-natural restoration,multifunction-oriented improvement,and systematic restoration.An understanding on the formation mechanisms of LEASFs and a scientific approach toward their restoration are urgently needed and critical for the ecological protection and high-quality development of LEASFs on the CLP.Based on these analyses,we recommend strengthening the monitoring and supervision of LEASFs,considering the bearing capacity of regional water resources,implementing multiple restoration techniques,promoting multifunction-oriented ecological development,and exploring new management concepts to achieve the sustainable development of ASFs on the CLP.展开更多
We propose a novel machine learning approach to reconstruct meshless surface wind speed fields,i.e.,to reconstruct the surface wind speed at any location,based on meteorological background fields and geographical info...We propose a novel machine learning approach to reconstruct meshless surface wind speed fields,i.e.,to reconstruct the surface wind speed at any location,based on meteorological background fields and geographical information.The random forest method is selected to develop the machine learning data reconstruction model(MLDRM-RF)for wind speeds over Beijing from 2015-19.We use temporal,geospatial attribute and meteorological background field features as inputs.The wind speed field can be reconstructed at any station in the region not used in the training process to cross-validate model performance.The evaluation considers the spatial distribution of and seasonal variations in the root mean squared error(RMSE)of the reconstructed wind speed field across Beijing.The average RMSE is 1.09 m s^(−1),considerably smaller than the result(1.29 m s^(−1))obtained with inverse distance weighting(IDW)interpolation.Finally,we extract the important feature permutations by the method of mean decrease in impurity(MDI)and discuss the reasonableness of the model prediction results.MLDRM-RF is a reasonable approach with excellent potential for the improved reconstruction of historical surface wind speed fields with arbitrary grid resolutions.Such a model is needed in many wind applications,such as wind energy and aviation safety assessments.展开更多
[Objectives]To explore the distribution characteristics of soil organic carbon in degraded forest land in the sandstorm area of Jingbian County,Shaanxi Province.[Methods]The distribution characteristics and abundance ...[Objectives]To explore the distribution characteristics of soil organic carbon in degraded forest land in the sandstorm area of Jingbian County,Shaanxi Province.[Methods]The distribution characteristics and abundance of 0-20 cm shallow soil organic carbon in 5 towns in the sandstorm area in the north of Jingbian County were studied by field sampling and indoor detection.[Results]The average soil organic carbon contents in Hongdunjie Town,Haizetan Town,Huanghaojie Town,Ningtiaoliang Town and Dongkeng Town were 2.93,3.21,2.53,2.54 and 4.08 g/kg,respectively,which were all lower than the national background value(31.00 g/kg).The coefficients of variation of soil organic carbon content in Hongdunjie Town,Huanghaojie Town and Dongkeng Town were 59.04%,35.97%and 47.55%,respectively,with higher coefficients of variation and larger differences in spatial distribution.The organic carbon content of Haizetan Town and Dongkeng Town was above the abundance,accounting for 70%and 50%,which were relatively rich,while the soil organic carbon content of Hongdunjie was relatively scarce.The average content of soil organic carbon in the sandstorm area was 3.03 g/kg,which was also lower than the national background value.The coefficient of variation was 46.53%,showing high coefficient of variation and large difference in spatial distribution.In addition,20.41%of the average content of soil organic carbon in the sandstorm area was in the deficient level,and 79.59%were in the medium or above level.[Conclusions]The study of distribution characteristics of soil organic carbon in degraded forest land in the sandstorm area of Jingbian County will better serve the precise management of soil resources.展开更多
基于激光雷达(Light Detection And Ranging,LiDAR)数据重建树体三维模型并精准获取林木空间枝干结构参数对林木性状评价、森林动态经营管理与可视化研究具有重要意义。为此提出一种基于骨架细化提取的树木模型重建方法。首先,采用Focus...基于激光雷达(Light Detection And Ranging,LiDAR)数据重建树体三维模型并精准获取林木空间枝干结构参数对林木性状评价、森林动态经营管理与可视化研究具有重要意义。为此提出一种基于骨架细化提取的树木模型重建方法。首先,采用FocusS350/350 PLUS三维激光扫描仪获取3块不同树龄橡胶树的样地数据。然后,作为细化建模的重点,将枝干点云从原始树点中分离出来,再将其过度分割为若干点云簇,通过相邻点云簇判断是否有分枝以及动态确定骨架点间距,并将其运用在空间殖民算法以此来生成树的三维骨架点和骨架点连通性链表,根据连通链表结构自动识别树木中的主枝干和各个一级分枝,再通过广义圆柱体生成树干完成树木三维重建。最后,利用数字孪生技术对这3块不同树龄样地树木进行三维实景建模,使其穿越时空在同一空间中重现,以便更为直观地观察树木在生长过程中的形态变化。该算法得到的橡胶树胸径与实测值比对为,决定系数(R^(2))>0.91,均方根误差(root mean square Error,RMSE)<1.00 cm;主枝干与一级枝干的分枝角为,R^(2)>0.91,RMSE<2.93;一级枝干直径为,R^(2)>0.90,RMSE<1.41 cm;将3个树龄放在一起计算其生长参数,并与实测值进行对比,发现该算法同样适用于异龄林样地的各个生长参数计算。同时发现橡胶树的一级枝条的直径越大,其相对应的叶团簇体积就越大。运用人工智能的理论模型来处理林木的激光点云数据,旨在为森林的可视化以及树木骨架结构的智能化分析与处理等研究领域提供有价值的参考。展开更多
工业过程层析成像使用非干扰传感器获取过程容器、反应器等内部状态的二维或三维图像,因在工业应用中获取的电容层析成像资料和信息量有限,难以对影像进行精准、稳定的重构。为此,设计基于彩色-深度(Red Green Blue-Depth,RGB-D)传感器...工业过程层析成像使用非干扰传感器获取过程容器、反应器等内部状态的二维或三维图像,因在工业应用中获取的电容层析成像资料和信息量有限,难以对影像进行精准、稳定的重构。为此,设计基于彩色-深度(Red Green Blue-Depth,RGB-D)传感器的电容层析成像图像重建方法。利用RGB-D传感器采集电容层析成像图像,采用非局部均值-权重法剔除图像中的噪声后,将图像输入到随机森林分类器中,提取图像的轮廓特征。通过快速投影Landweber算法对轮廓特征求解后,利用电容物质分布的重组完成电容层析成像图像的重建。实验结果显示:所提方法的峰值信噪比数值在35 dB附近波动,结构相似性数值在0.89~1.03之间,重建耗时在4.1 s以下,具有较好的重建效果、质量和结构相似性,能够有效提高重建效率。展开更多
心室颤动是导致心搏骤停最常见的病理生理机制,心搏骤停若能得到及时救助,就能大幅度提高患者存活率,因此,快速准确识别心室颤动极为重要。该研究提出一种基于BP(back propagation)神经网络和随机森林的心室颤动自动检测算法。将心电信...心室颤动是导致心搏骤停最常见的病理生理机制,心搏骤停若能得到及时救助,就能大幅度提高患者存活率,因此,快速准确识别心室颤动极为重要。该研究提出一种基于BP(back propagation)神经网络和随机森林的心室颤动自动检测算法。将心电信号通过6 s的移动窗口,根据信号的时频域信息,计算出6种特征参数,将这6种特征参数作为分类器的输入,进行分类和测试,并以数据库中权威专家给定的标签作为参考输出,共使用了44例相关数据对该方法进行了评估。十折交叉验证法结果表明,该方法在CU数据库(Creighton University Ventricular Tachyarrhythmia Database)和AHA数据库(The American Heart Association Database)中心室颤动分类准确率达到了96.38%和99.45%,具有一定的可应用性。展开更多
基金the following grants:The National Key R&D Program of China(2019YFA0606600)the Natural Science Foundation of China(31971577)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)。
文摘Historical forest fire risk databases are vital for evaluating the effectiveness of past forest management approaches,enhancing forest fire warnings and emergency response capabilities,and accurately budgeting potential carbon emissions resulting from fires.However,due to the unavailability of spatial information technology,such databases are extremely difficult to build reliably and completely in the non-satellite era.This study presented an improved forest fire risk reconstruction framework that integrates a deep learning-based time series prediction model and spatial interpolation to address the challenge in Sichuan Province,southwestern China.First,the forest fire danger index(FFDI)was improved by supplementing slope and aspect information.We compared the performances of three time series models,namely,the autoregressive integrated moving average(ARIMA),Prophet and long short-term memory(LSTM)in predicting the modified forest fire danger index(MFFDI).The bestperforming model was used to retrace the MFFDI of individual stations from 1941 to 1970.Following this,the Anusplin spatial interpolation method was used to map the distributions of the MFFDI at five-year intervals,which were then subjected to weighted overlay with the distance-to-river layer to generate forest fire risk maps for reconstructing the forest fire danger database.The results revealed LSTM as the most accurate in fitting and predicting the historical MFFDI,with a fitting determination coefficient(R^2)of 0.709,mean square error(MSE)of0.047,and validation R^2 and MSE of 0.508 and 0.11,respectively.Independent validation of the predicted forest fire risk maps indicated that 5 out of 7 historical forest fire events were located in forest fire-prone areas,which is higher than the results determined from the original FFDI(2 out of 7).This proves the effectiveness of the improved MFFDI and indicates a high level of reliability of the historical forest fire risk reconstruction method proposed in this study.
基金Supported by Internal Scientific Research Project of Shaanxi Provincial Land Engineering Construction Group Co.,Ltd.(DJNY2022-21)Shaanxi Provincial Youth Talent Promotion Program(NYKJ202228)+1 种基金Technology Innovation Center for Land Engineering and Human Settlements,Shaanxi Provincial Land Engineering Construction Group Co.,Ltd.and Xi an Jiaotong University(2021WHZ0094)Shaanxi Provincial Enterprise Innovation Striving for the First Young Talents Support Program Project(2021-1-2).
文摘[Objectives]The paper was to investigate the distribution characteristics of soil total nitrogen in low-efficiency forest land in the northern windy desert area of Jingbian County.[Methods]The distribution of soil total nitrogen in the 0-40 cm soil layer of 5 towns in the northern windy desert area of Jingbian County was studied through field sampling and laboratory detection.[Results]The average soil total nitrogen contents of Hongdunjie Town,Haizetan Town,Huanghaojie Town,Ningtiaoliang Town,Dongkeng Town and windy desert area in the 0-20 cm soil layer were 0.259,0.224,0.242,0.248,0.431 and 0.275 g/kg,respectively.The soil total nitrogen content in Dongkeng Town was higher than those in other towns,while there was little difference among other regions.The average total nitrogen contents in the 20-40 cm soil layer were 0.239,0.285,0.113,0.262,0.349 and 0.241 g/kg,respectively.The soil total nitrogen content in Huanghaojie Town was slightly lower than those in other towns,while that in Dongkeng town was higher.The variation coefficient of soil total nitrogen content in the survey area was greater than 30%,and there was great difference in spatial distribution.With the increase of soil depth,there was little change in soil total nitrogen content,namely the soil total nitrogen contents in 5 towns and windy desert area were not statistically different in the 0-40 cm soil layer.According to the nutrient grading standard of the second national soil survey,the soil total nitrogen content in the survey area was in the deficiency grade.[Conclusions]The research will provide a scientific guidance for the healthy and sustainable development of vegetation in arid regions.
基金supported by the Science and Technology Innovation Program of the Shaanxi Academy of Forestry (SXLK2022-02)the National Natural Science Foundation of China (42077452)the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA23070201).
文摘The construction of artificial shelter forests(ASFs)has resulted in substantial ecological,economic,and societal benefits to the Chinese Loess Plateau(CLP).However,the health and benefits of ASFs are being increasingly threatened by the formation of low-efficiency artificial shelter forests(LEASFs).In this study,LEASFs are systematically analyzed in terms of their status,formation mechanisms,and developmental obstacles.The key restoration techniques and schemes were summarized to improve the quality and efficiency of LEASFs.LEASFs are formed by relatively complex mechanisms,but they arise mainly due to poor habitat conditions,improper tree species selections,mismatch between stands and habitat,extensive forest management measures,and human interferences.The restoration and improvement of LEASFs are hindered by water deficits,mismatch between stands and habitat,single management purpose,and low efficiency.LEASFs are becoming more complex due to their wide range,the challenges associated with their restoration,and insufficient technological measures for their restoration.The key techniques of the quality and efficiency improvement of LEASFs include basic forest tending methods,near-natural restoration,multifunction-oriented improvement,and systematic restoration.An understanding on the formation mechanisms of LEASFs and a scientific approach toward their restoration are urgently needed and critical for the ecological protection and high-quality development of LEASFs on the CLP.Based on these analyses,we recommend strengthening the monitoring and supervision of LEASFs,considering the bearing capacity of regional water resources,implementing multiple restoration techniques,promoting multifunction-oriented ecological development,and exploring new management concepts to achieve the sustainable development of ASFs on the CLP.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA19030402)the Key Special Projects for International Cooperation in Science and Technology Innovation between Governments(Grant No.2017YFE0133600the Beijing Municipal Natural Science Foundation Youth Project 8214066:Application Research of Beijing Road Visibility Prediction Based on Machine Learning Methods.
文摘We propose a novel machine learning approach to reconstruct meshless surface wind speed fields,i.e.,to reconstruct the surface wind speed at any location,based on meteorological background fields and geographical information.The random forest method is selected to develop the machine learning data reconstruction model(MLDRM-RF)for wind speeds over Beijing from 2015-19.We use temporal,geospatial attribute and meteorological background field features as inputs.The wind speed field can be reconstructed at any station in the region not used in the training process to cross-validate model performance.The evaluation considers the spatial distribution of and seasonal variations in the root mean squared error(RMSE)of the reconstructed wind speed field across Beijing.The average RMSE is 1.09 m s^(−1),considerably smaller than the result(1.29 m s^(−1))obtained with inverse distance weighting(IDW)interpolation.Finally,we extract the important feature permutations by the method of mean decrease in impurity(MDI)and discuss the reasonableness of the model prediction results.MLDRM-RF is a reasonable approach with excellent potential for the improved reconstruction of historical surface wind speed fields with arbitrary grid resolutions.Such a model is needed in many wind applications,such as wind energy and aviation safety assessments.
基金Supported by the Scientific Research Project of Shaanxi Provincial Land Engineering Construction Group(DJNY2022-21)Shaanxi Province Youth Talent Promotion Program(YKJ202228).
文摘[Objectives]To explore the distribution characteristics of soil organic carbon in degraded forest land in the sandstorm area of Jingbian County,Shaanxi Province.[Methods]The distribution characteristics and abundance of 0-20 cm shallow soil organic carbon in 5 towns in the sandstorm area in the north of Jingbian County were studied by field sampling and indoor detection.[Results]The average soil organic carbon contents in Hongdunjie Town,Haizetan Town,Huanghaojie Town,Ningtiaoliang Town and Dongkeng Town were 2.93,3.21,2.53,2.54 and 4.08 g/kg,respectively,which were all lower than the national background value(31.00 g/kg).The coefficients of variation of soil organic carbon content in Hongdunjie Town,Huanghaojie Town and Dongkeng Town were 59.04%,35.97%and 47.55%,respectively,with higher coefficients of variation and larger differences in spatial distribution.The organic carbon content of Haizetan Town and Dongkeng Town was above the abundance,accounting for 70%and 50%,which were relatively rich,while the soil organic carbon content of Hongdunjie was relatively scarce.The average content of soil organic carbon in the sandstorm area was 3.03 g/kg,which was also lower than the national background value.The coefficient of variation was 46.53%,showing high coefficient of variation and large difference in spatial distribution.In addition,20.41%of the average content of soil organic carbon in the sandstorm area was in the deficient level,and 79.59%were in the medium or above level.[Conclusions]The study of distribution characteristics of soil organic carbon in degraded forest land in the sandstorm area of Jingbian County will better serve the precise management of soil resources.
文摘基于激光雷达(Light Detection And Ranging,LiDAR)数据重建树体三维模型并精准获取林木空间枝干结构参数对林木性状评价、森林动态经营管理与可视化研究具有重要意义。为此提出一种基于骨架细化提取的树木模型重建方法。首先,采用FocusS350/350 PLUS三维激光扫描仪获取3块不同树龄橡胶树的样地数据。然后,作为细化建模的重点,将枝干点云从原始树点中分离出来,再将其过度分割为若干点云簇,通过相邻点云簇判断是否有分枝以及动态确定骨架点间距,并将其运用在空间殖民算法以此来生成树的三维骨架点和骨架点连通性链表,根据连通链表结构自动识别树木中的主枝干和各个一级分枝,再通过广义圆柱体生成树干完成树木三维重建。最后,利用数字孪生技术对这3块不同树龄样地树木进行三维实景建模,使其穿越时空在同一空间中重现,以便更为直观地观察树木在生长过程中的形态变化。该算法得到的橡胶树胸径与实测值比对为,决定系数(R^(2))>0.91,均方根误差(root mean square Error,RMSE)<1.00 cm;主枝干与一级枝干的分枝角为,R^(2)>0.91,RMSE<2.93;一级枝干直径为,R^(2)>0.90,RMSE<1.41 cm;将3个树龄放在一起计算其生长参数,并与实测值进行对比,发现该算法同样适用于异龄林样地的各个生长参数计算。同时发现橡胶树的一级枝条的直径越大,其相对应的叶团簇体积就越大。运用人工智能的理论模型来处理林木的激光点云数据,旨在为森林的可视化以及树木骨架结构的智能化分析与处理等研究领域提供有价值的参考。
文摘工业过程层析成像使用非干扰传感器获取过程容器、反应器等内部状态的二维或三维图像,因在工业应用中获取的电容层析成像资料和信息量有限,难以对影像进行精准、稳定的重构。为此,设计基于彩色-深度(Red Green Blue-Depth,RGB-D)传感器的电容层析成像图像重建方法。利用RGB-D传感器采集电容层析成像图像,采用非局部均值-权重法剔除图像中的噪声后,将图像输入到随机森林分类器中,提取图像的轮廓特征。通过快速投影Landweber算法对轮廓特征求解后,利用电容物质分布的重组完成电容层析成像图像的重建。实验结果显示:所提方法的峰值信噪比数值在35 dB附近波动,结构相似性数值在0.89~1.03之间,重建耗时在4.1 s以下,具有较好的重建效果、质量和结构相似性,能够有效提高重建效率。
文摘心室颤动是导致心搏骤停最常见的病理生理机制,心搏骤停若能得到及时救助,就能大幅度提高患者存活率,因此,快速准确识别心室颤动极为重要。该研究提出一种基于BP(back propagation)神经网络和随机森林的心室颤动自动检测算法。将心电信号通过6 s的移动窗口,根据信号的时频域信息,计算出6种特征参数,将这6种特征参数作为分类器的输入,进行分类和测试,并以数据库中权威专家给定的标签作为参考输出,共使用了44例相关数据对该方法进行了评估。十折交叉验证法结果表明,该方法在CU数据库(Creighton University Ventricular Tachyarrhythmia Database)和AHA数据库(The American Heart Association Database)中心室颤动分类准确率达到了96.38%和99.45%,具有一定的可应用性。
