Fires have a noteworthy role to play with regards to ecological and environmental losses in Mediterranean forests. In addition to ecological impacts, fire may create economic, social as well as cultural changes. The d...Fires have a noteworthy role to play with regards to ecological and environmental losses in Mediterranean forests. In addition to ecological impacts, fire may create economic, social as well as cultural changes. The detection of fire-scars has critical importance to help decrease losses.In the present study, forest fires recorded in Antalya, one of the most important ecological and tourist regions within the Western Mediterranean, were clustered and mapped. Since the dominant factors and devastation records derived from the cases had nominal-scaled properties, a categorical databased nonparametric clustering algorithm was performed in this evaluation. The proposed tool, k-modes algorithm,uses modes instead of means for clustering. The algorithm may be implemented quickly and does not make distributional assumptions concerning the available data. It uses a frequency-based method to update the modes of the fires.The derived modes from the maps may be useful information for local authorities to manage. In conclusion, the proposed nonparametric clustering procedure may be employed to build a decision-support system to monitor and identify fire activities and to enhance fire management efficiency.展开更多
Traditional forest-fire recognition based on the characteristics of smoke, temperature and light fails to accurately detect and respond to early fires. By analyzing the characteristics of flame, the methods based on a...Traditional forest-fire recognition based on the characteristics of smoke, temperature and light fails to accurately detect and respond to early fires. By analyzing the characteristics of flame, the methods based on aerial image recognition have been widely used, such as RGB-based and HIS-based methods. However, these methods are susceptible to background factors causing interference and false detection. To alleviate these problems, we investigate two subspace clustering methods based on sparse and collaborative representation, respectively, to detect and locate forest fires. Firstly, subspace clustering segments flame from the whole image. Afterwards, sparse or collaborative representation is employed to represent most of the flame information in a dictionary with l1-regularization or l2-regularization term, which results in fewer reconstruction errors. Experimental results show that the proposed SSC and CSC substantially outperform the state-of-the art methods.展开更多
We investigated the composition of plant communities to quantify their relationships with environmental parameters in the Chitral Hindukush range of Pakistan. We sampled tree vegetation using the Point Centered Quart...We investigated the composition of plant communities to quantify their relationships with environmental parameters in the Chitral Hindukush range of Pakistan. We sampled tree vegetation using the Point Centered Quarter （PCQ） method while understory vegetation was sampled in 1.5-m circular quadrats. Cedrus deodara is the national symbol of Pakistan and was dominant in the sampled communities. Because environmental variables determine vegetation types, we analyzed and evaluated edaphic and topographic factors. DCA-Ordination showed the major gradient as an amalgam of elevation （p〈0.05） and slope （p〈0.01） as the topographic factors correlated with species distribution. Soil variables were the factors of environmental significance along DCA axes. However, among these factors, Mg2＋ , K ＋ and N2＋ contributed not more than 0.054% 0.20% and 0.073%, respectively, to variation along the first ordination axis. We conclude that the principal reason for weak or no correlation with many edaphic variables was the anthropogenic disturbance of vegetation. The understory vegetation was composed of perennial herbs in most communities and was most dense under the tree canopy. The understory vegetation strongly regulates tree seedling growth and regeneration patterns. We recommend further study of the understory vegetation using permanent plots to aid development of forest regeneration strategies.展开更多
Forest health is currently assessed in Europe （ICP Forests monitoring program）. Crown defoliation and dieback, tree mortality, and pathogenic damage are the main aspects considered in tree health assessment. The wor...Forest health is currently assessed in Europe （ICP Forests monitoring program）. Crown defoliation and dieback, tree mortality, and pathogenic damage are the main aspects considered in tree health assessment. The worsening of environmental conditions （i.e., increase of temperature and drought events） may cause large-spatial scale tree mortality and forest decline. However, the role of stand features, including tree species assemblage and diversity as factors that modify environmental impacts, is poorly considered. The present contribution reanalyses the historical dataset of crown conditions in Italian forests from ] 997 to 2014 to identify ecological and structural factors that influence tree crown defoliation, highlighting in a special manner the role of tree diversity. The effects of tree diversity were explored using the entire data set through multivariate cluster analyses and on individual trees, analysing the influence of the neighbouring tree diversity and identity at the local （neighbour） level. Preliminary results suggest that each tree species shows a specific behaviour in relation to crown defoliation, and the distribution of crown defoliation across Italian forests reflects the distribution of the main forest types and their ecological equilibrium with the environment. The potentiality and the problems connected to the possible extension of this analysis at a more general level （European and North American） were discussed.展开更多
The cigarette detection data contains a large amount of true sample data and a small amount of false sample data. The false sample data is regarded as abnormal data, and anomaly detection is performed to realize the i...The cigarette detection data contains a large amount of true sample data and a small amount of false sample data. The false sample data is regarded as abnormal data, and anomaly detection is performed to realize the identification of real and fake cigarettes. Binary particle swarm optimization algorithm is used to improve the isolation forest construction process, and isolation trees with high precision and large differences are selected, which improves the accuracy and efficiency of the algorithm. The distance between the obtained anomaly score and the clustering center of the k-means algorithm is used as the threshold for anomaly judgment. The experimental results show that the accuracy of the BPSO-iForest algorithm is improved compared with the standard iForest algorithm. The experimental results of multiple brand samples also show that the method in this paper can accurately use the detection data for authenticity identification.展开更多
Three indexes including forest pest occurrence area,control area and input fund of 31 provinces from 2003 to 2014 were selected from Forestry Statistical Yearbook,to establish dynamic interaction index evaluation syst...Three indexes including forest pest occurrence area,control area and input fund of 31 provinces from 2003 to 2014 were selected from Forestry Statistical Yearbook,to establish dynamic interaction index evaluation system with clustering robust regression model and Stata 13. 0 software. Total forest pest control efficiency in China was determined according to the computing result of entropy method. Suggestions such as improving forest pest control efficiency,increasing service efficiency and input amount of forest pest control input funds were put forward. It will provide empirical basis for target management evaluation of forest pest control work and accountability system.展开更多
Climate is a major determinant of global vegetation patterns and has a significant influence on the distribution and structure of forest ecosystems. Dong PraYa Yen-KhaoYai Forest Complex has been a UNESCO natural worl...Climate is a major determinant of global vegetation patterns and has a significant influence on the distribution and structure of forest ecosystems. Dong PraYa Yen-KhaoYai Forest Complex has been a UNESCO natural world heritage site since 2007, but little is known about its plant community. Our study aims to identify each plant community within the world heritage area and calculate its potential for carbon content. We determine both the relationship between forest type and both physio-chemical soil properties and climate change impact. We employed allometric equations to calculate aboveground biomass and both cluster analysis and canonical correspondence analysis (CCA) to examine the relationship between forest type and physiochemical soil properties. An equation for each physical parameter was used to predict the forest model. The climate scenario under A2 and B2 was applied to calculate future predominant forest types. Our results reveal that the forest ecosystems at Tab Lan (TL) have the highest species count (332 species) followed by Pang Srida (PD), KhaoYai (KY), Dong Yai (DY), and Tapraya (TY), with 293, 271, 169, and 99 species, respectively. We found KY to have the highest recorded carbon storage value at 2507.6 tC/ha followed by TL, PD, TY, and DY (1613.8, 1269.1, 844 and 810.7 tC/ha, respectively). Cluster analysis results indicated that the dominant species in each forest type is different. Moreover, CCA revealed that soil organic matter (SOM) and soil acid-base indicators are the best parameters to establish correlation for each forest type. Based on our results, future climate predictions show a negative impact on evergreen forests, but a positive one on deciduous ones.展开更多
风力机组的运行状态关乎风电企业的经济效益,低效机组判定对风电场效能提升具有重要意义。现有研究多聚焦单机风电功率特性等运行状态,针对风电场整体效能评估的研究较少。为此,提出一种基于聚类和随机森林的低效风力机组判定方法。首...风力机组的运行状态关乎风电企业的经济效益,低效机组判定对风电场效能提升具有重要意义。现有研究多聚焦单机风电功率特性等运行状态,针对风电场整体效能评估的研究较少。为此,提出一种基于聚类和随机森林的低效风力机组判定方法。首先基于数据采集与监视控制系统(supervisory control and data acquisition,SCADA)获取机组运行数据,引入风能捕获差异率和发电输出差异率指标,对数据进行聚类后添加低效判定值。其次应用随机森林分类算法对低效判定值进行验证分析,最后根据机组低效判定值均值结果识别低效机组。为验证所提方法的准确性和有效性,根据某风电场23台风力机组的全年运行数据进行分析,结果表明该方法能够精确有效识别低效风力机组,能够为风电场针对性技改提供指导。展开更多
文摘Fires have a noteworthy role to play with regards to ecological and environmental losses in Mediterranean forests. In addition to ecological impacts, fire may create economic, social as well as cultural changes. The detection of fire-scars has critical importance to help decrease losses.In the present study, forest fires recorded in Antalya, one of the most important ecological and tourist regions within the Western Mediterranean, were clustered and mapped. Since the dominant factors and devastation records derived from the cases had nominal-scaled properties, a categorical databased nonparametric clustering algorithm was performed in this evaluation. The proposed tool, k-modes algorithm,uses modes instead of means for clustering. The algorithm may be implemented quickly and does not make distributional assumptions concerning the available data. It uses a frequency-based method to update the modes of the fires.The derived modes from the maps may be useful information for local authorities to manage. In conclusion, the proposed nonparametric clustering procedure may be employed to build a decision-support system to monitor and identify fire activities and to enhance fire management efficiency.
文摘Traditional forest-fire recognition based on the characteristics of smoke, temperature and light fails to accurately detect and respond to early fires. By analyzing the characteristics of flame, the methods based on aerial image recognition have been widely used, such as RGB-based and HIS-based methods. However, these methods are susceptible to background factors causing interference and false detection. To alleviate these problems, we investigate two subspace clustering methods based on sparse and collaborative representation, respectively, to detect and locate forest fires. Firstly, subspace clustering segments flame from the whole image. Afterwards, sparse or collaborative representation is employed to represent most of the flame information in a dictionary with l1-regularization or l2-regularization term, which results in fewer reconstruction errors. Experimental results show that the proposed SSC and CSC substantially outperform the state-of-the art methods.
基金supported by the Higher Education Commission of Pakistan
文摘We investigated the composition of plant communities to quantify their relationships with environmental parameters in the Chitral Hindukush range of Pakistan. We sampled tree vegetation using the Point Centered Quarter （PCQ） method while understory vegetation was sampled in 1.5-m circular quadrats. Cedrus deodara is the national symbol of Pakistan and was dominant in the sampled communities. Because environmental variables determine vegetation types, we analyzed and evaluated edaphic and topographic factors. DCA-Ordination showed the major gradient as an amalgam of elevation （p〈0.05） and slope （p〈0.01） as the topographic factors correlated with species distribution. Soil variables were the factors of environmental significance along DCA axes. However, among these factors, Mg2＋ , K ＋ and N2＋ contributed not more than 0.054% 0.20% and 0.073%, respectively, to variation along the first ordination axis. We conclude that the principal reason for weak or no correlation with many edaphic variables was the anthropogenic disturbance of vegetation. The understory vegetation was composed of perennial herbs in most communities and was most dense under the tree canopy. The understory vegetation strongly regulates tree seedling growth and regeneration patterns. We recommend further study of the understory vegetation using permanent plots to aid development of forest regeneration strategies.
基金funded and carried out within SMART4Action LIFE+project“Sustainable Monitoring and Reporting to Inform Forest and Environmental Awareness and Protection”LIFE13 ENV/IT/000813
文摘Forest health is currently assessed in Europe （ICP Forests monitoring program）. Crown defoliation and dieback, tree mortality, and pathogenic damage are the main aspects considered in tree health assessment. The worsening of environmental conditions （i.e., increase of temperature and drought events） may cause large-spatial scale tree mortality and forest decline. However, the role of stand features, including tree species assemblage and diversity as factors that modify environmental impacts, is poorly considered. The present contribution reanalyses the historical dataset of crown conditions in Italian forests from ] 997 to 2014 to identify ecological and structural factors that influence tree crown defoliation, highlighting in a special manner the role of tree diversity. The effects of tree diversity were explored using the entire data set through multivariate cluster analyses and on individual trees, analysing the influence of the neighbouring tree diversity and identity at the local （neighbour） level. Preliminary results suggest that each tree species shows a specific behaviour in relation to crown defoliation, and the distribution of crown defoliation across Italian forests reflects the distribution of the main forest types and their ecological equilibrium with the environment. The potentiality and the problems connected to the possible extension of this analysis at a more general level （European and North American） were discussed.
文摘The cigarette detection data contains a large amount of true sample data and a small amount of false sample data. The false sample data is regarded as abnormal data, and anomaly detection is performed to realize the identification of real and fake cigarettes. Binary particle swarm optimization algorithm is used to improve the isolation forest construction process, and isolation trees with high precision and large differences are selected, which improves the accuracy and efficiency of the algorithm. The distance between the obtained anomaly score and the clustering center of the k-means algorithm is used as the threshold for anomaly judgment. The experimental results show that the accuracy of the BPSO-iForest algorithm is improved compared with the standard iForest algorithm. The experimental results of multiple brand samples also show that the method in this paper can accurately use the detection data for authenticity identification.
基金Supported by Analysis of Forest Pest Cost Responsibility Investigation System(2017-R04)Protection and Development:Coordination Mechanism Research from the Perspective of Community(71373024)
文摘Three indexes including forest pest occurrence area,control area and input fund of 31 provinces from 2003 to 2014 were selected from Forestry Statistical Yearbook,to establish dynamic interaction index evaluation system with clustering robust regression model and Stata 13. 0 software. Total forest pest control efficiency in China was determined according to the computing result of entropy method. Suggestions such as improving forest pest control efficiency,increasing service efficiency and input amount of forest pest control input funds were put forward. It will provide empirical basis for target management evaluation of forest pest control work and accountability system.
文摘Climate is a major determinant of global vegetation patterns and has a significant influence on the distribution and structure of forest ecosystems. Dong PraYa Yen-KhaoYai Forest Complex has been a UNESCO natural world heritage site since 2007, but little is known about its plant community. Our study aims to identify each plant community within the world heritage area and calculate its potential for carbon content. We determine both the relationship between forest type and both physio-chemical soil properties and climate change impact. We employed allometric equations to calculate aboveground biomass and both cluster analysis and canonical correspondence analysis (CCA) to examine the relationship between forest type and physiochemical soil properties. An equation for each physical parameter was used to predict the forest model. The climate scenario under A2 and B2 was applied to calculate future predominant forest types. Our results reveal that the forest ecosystems at Tab Lan (TL) have the highest species count (332 species) followed by Pang Srida (PD), KhaoYai (KY), Dong Yai (DY), and Tapraya (TY), with 293, 271, 169, and 99 species, respectively. We found KY to have the highest recorded carbon storage value at 2507.6 tC/ha followed by TL, PD, TY, and DY (1613.8, 1269.1, 844 and 810.7 tC/ha, respectively). Cluster analysis results indicated that the dominant species in each forest type is different. Moreover, CCA revealed that soil organic matter (SOM) and soil acid-base indicators are the best parameters to establish correlation for each forest type. Based on our results, future climate predictions show a negative impact on evergreen forests, but a positive one on deciduous ones.
文摘风力机组的运行状态关乎风电企业的经济效益,低效机组判定对风电场效能提升具有重要意义。现有研究多聚焦单机风电功率特性等运行状态,针对风电场整体效能评估的研究较少。为此,提出一种基于聚类和随机森林的低效风力机组判定方法。首先基于数据采集与监视控制系统(supervisory control and data acquisition,SCADA)获取机组运行数据,引入风能捕获差异率和发电输出差异率指标,对数据进行聚类后添加低效判定值。其次应用随机森林分类算法对低效判定值进行验证分析,最后根据机组低效判定值均值结果识别低效机组。为验证所提方法的准确性和有效性,根据某风电场23台风力机组的全年运行数据进行分析,结果表明该方法能够精确有效识别低效风力机组,能够为风电场针对性技改提供指导。
