Urban air pollution is a commonly concerned environmental problem in the world.Identification of air quality trend using long-term monitoring data is helpful to understand the effectiveness of pollution control strate...Urban air pollution is a commonly concerned environmental problem in the world.Identification of air quality trend using long-term monitoring data is helpful to understand the effectiveness of pollution control strategies.This study,using data from six monitoring stations in Zhengzhou City,analyzed the changing trend in concentrations of SO 2,NO x /NO 2 and TSP/PM 10 in 1996-2008,based on non-parametric Mann-Kendall test and Sen's slope estimator,and evaluated the comprehensive air pollution level using Multi-Pollutant Index(MPI).It was found that the concentration of each pollutant exceeded obviously the World Health Organization(WHO) guideline value,but the changing trend varied:SO 2 and NO 2 were significantly increased mainly due to an increase in coal consumption and vehicle number,while NO x,TSP and PM 10 decreased.The air pollution was serious,and differed markedly among the three functional regions:it is the most severe in the Industrial and Residential Area(IRA),followed by the Transportation Hub and Business District(THBD),and then the High-tech,Cultural and Educational Area(HCEA).Different from NO 2 concentration that had a similar change trend/rate among the function regions,the change rate of PM 10 concentration differed spatially,decreased much more obviously in THBD than other two regions.For the whole city,the comprehensive air pollution level declined gradually,illustrating that the air quality in Zhengzhou was improved in the last decade.展开更多
为探究无人机多源遥感影像估算玉米叶面积指数(Leaf area index,LAI)垂直分布,在田间设置了密度和播期试验,在7个生育时期利用无人机采集了可见光、多光谱和热红外影像并同步获取玉米LAI垂直分布数据。同时,为合理制定无人机飞行任务,...为探究无人机多源遥感影像估算玉米叶面积指数(Leaf area index,LAI)垂直分布,在田间设置了密度和播期试验,在7个生育时期利用无人机采集了可见光、多光谱和热红外影像并同步获取玉米LAI垂直分布数据。同时,为合理制定无人机飞行任务,分析了不同飞行高度和不同太阳高度角下获取的无人机影像对估算玉米LAI的影响。基于无人机影像提取的与玉米LAI相关性较高的植被指数、纹理信息和冠层温度等特征,利用7种机器学习方法分别构建了玉米冠层不同高度LAI估算模型,从中选取鲁棒性强的2个模型用于分析在不同飞行高度和不同太阳高度角下估算LAI的差异。研究结果表明,MLPR和RFR模型对玉米LAI估算鲁棒性最强,全生育期下模型rRMSE为11.31%(MLPR)和11.42%(RFR)。玉米冠层LAI垂直分布估算误差,所有模型的平均rRMSE分别为9.1%(LAI-1)、14.19%(LAI-2)、18.62%(LAI-3)、23.29%(LAI-4)和26.7%(LAI-5)。对于玉米穗位叶及以下部位的LAI估算误差均在20%以下,得到了较好精度。同时,在不同飞行高度和太阳高度角试验中可以得出,当飞行高度为30 m时LAI估算精度最高,R^(2)为0.73,rRMSE为10.97%,在09:00—10:00观测的玉米LAI估算精度最高。无人机多源遥感影像数据可以准确估算玉米冠层LAI垂直分布,及时掌握玉米功能叶片LAI长势差异,可为玉米品种筛选提供辅助。展开更多
基金Under the auspices of National Natural Science Foundation of China (No. 41071063)
文摘Urban air pollution is a commonly concerned environmental problem in the world.Identification of air quality trend using long-term monitoring data is helpful to understand the effectiveness of pollution control strategies.This study,using data from six monitoring stations in Zhengzhou City,analyzed the changing trend in concentrations of SO 2,NO x /NO 2 and TSP/PM 10 in 1996-2008,based on non-parametric Mann-Kendall test and Sen's slope estimator,and evaluated the comprehensive air pollution level using Multi-Pollutant Index(MPI).It was found that the concentration of each pollutant exceeded obviously the World Health Organization(WHO) guideline value,but the changing trend varied:SO 2 and NO 2 were significantly increased mainly due to an increase in coal consumption and vehicle number,while NO x,TSP and PM 10 decreased.The air pollution was serious,and differed markedly among the three functional regions:it is the most severe in the Industrial and Residential Area(IRA),followed by the Transportation Hub and Business District(THBD),and then the High-tech,Cultural and Educational Area(HCEA).Different from NO 2 concentration that had a similar change trend/rate among the function regions,the change rate of PM 10 concentration differed spatially,decreased much more obviously in THBD than other two regions.For the whole city,the comprehensive air pollution level declined gradually,illustrating that the air quality in Zhengzhou was improved in the last decade.
文摘为探究无人机多源遥感影像估算玉米叶面积指数(Leaf area index,LAI)垂直分布,在田间设置了密度和播期试验,在7个生育时期利用无人机采集了可见光、多光谱和热红外影像并同步获取玉米LAI垂直分布数据。同时,为合理制定无人机飞行任务,分析了不同飞行高度和不同太阳高度角下获取的无人机影像对估算玉米LAI的影响。基于无人机影像提取的与玉米LAI相关性较高的植被指数、纹理信息和冠层温度等特征,利用7种机器学习方法分别构建了玉米冠层不同高度LAI估算模型,从中选取鲁棒性强的2个模型用于分析在不同飞行高度和不同太阳高度角下估算LAI的差异。研究结果表明,MLPR和RFR模型对玉米LAI估算鲁棒性最强,全生育期下模型rRMSE为11.31%(MLPR)和11.42%(RFR)。玉米冠层LAI垂直分布估算误差,所有模型的平均rRMSE分别为9.1%(LAI-1)、14.19%(LAI-2)、18.62%(LAI-3)、23.29%(LAI-4)和26.7%(LAI-5)。对于玉米穗位叶及以下部位的LAI估算误差均在20%以下,得到了较好精度。同时,在不同飞行高度和太阳高度角试验中可以得出,当飞行高度为30 m时LAI估算精度最高,R^(2)为0.73,rRMSE为10.97%,在09:00—10:00观测的玉米LAI估算精度最高。无人机多源遥感影像数据可以准确估算玉米冠层LAI垂直分布,及时掌握玉米功能叶片LAI长势差异,可为玉米品种筛选提供辅助。