[目的/意义]叶面积指数(Leaf Area Index,LAI)作为量化作物冠层结构、光合潜力和群体长势的核心农学参数,对其精准栽培管理至关重要。旨在探索从无人机多光谱影像中提取植被指数和纹理特征,结合机器学习方法实现高精度估测冬油菜LAI的...[目的/意义]叶面积指数(Leaf Area Index,LAI)作为量化作物冠层结构、光合潜力和群体长势的核心农学参数,对其精准栽培管理至关重要。旨在探索从无人机多光谱影像中提取植被指数和纹理特征,结合机器学习方法实现高精度估测冬油菜LAI的可行性。[方法]以甘蓝型油菜作为研究对象,从多光谱影像中提取植被指数和纹理特征作为输入。通过最小冗余最大相关性(Minimum Redundancy Maximum Relevance,mRMR)算法降低特征维度,选择10个最具有代表性和最小冗余的特征并采用3种回归模型进行建模,使用分组交叉验证(Group KFold Cross Validation)评估模型性能,分组依据是油菜样本所属的小区(将来自同一小区的样本视为一组)。[结果和讨论]输入特征为植被指数与纹理特征的机器学习模型优于输入为单一特征模型。其中基于植被指数与纹理特征融合的支持向量机回归(Support Vector Regression,SVR)模型在全生育期的估算精度最优,决定系数R^(2)=0.90;均方根误差(Root Mean Square Error,RMSE)和平均绝对误差(Mean Absolute Error,MAE)分别为0.39和0.29。[结论]综上所述,融合无人机多光谱植被指数与纹理特征可高精度地反演冬油菜复杂冠层在全生育期的LAI,以期为油菜长势无损监测与精准管理提供高效技术支撑。展开更多
探究总氮(Total Nitrogen,TN)质量浓度时空分布特征,揭示其与土地利用的相关性,对维护流域水环境健康具有重要意义。文章采用北江流域Sentinel-2 MSI多光谱遥感数据,结合流域内59个水质自动监测站的同步水质监测数据,构建随机森林TN遥...探究总氮(Total Nitrogen,TN)质量浓度时空分布特征,揭示其与土地利用的相关性,对维护流域水环境健康具有重要意义。文章采用北江流域Sentinel-2 MSI多光谱遥感数据,结合流域内59个水质自动监测站的同步水质监测数据,构建随机森林TN遥感反演模型,并基于该模型分析2019—2023年流域水体TN质量浓度的时空变化规律。同时收集ESRI(Environmental Systems Research Institute)发布的土地利用数据,采用皮尔逊相关系数开展水体TN质量浓度与各种土地利用类型在不同空间尺度的单因子相关性分析。结果表明:2019—2023年流域水体TN质量浓度呈现下降趋势,年均质量浓度在2021年最高,2023年最低。上游至三角洲入海口TN质量浓度逐渐上升。TN质量浓度与水体、草地、林地面积占比呈显著负相关,与农业用地和建筑用地面积占比呈显著正相关。流域内城市化程度逐年上升,这可能导致未来流域水体TN的来源更多地转向生活源和工业源。并针对北江流域不同区域TN质量浓度分布特征和土地利用格局对流域TN削减工程提出建议。展开更多
Population migration,especially population inflow from epidemic areas,is a key source of the risk related to the coronavirus disease 2019(COVID-19)epidemic.This paper selects Guangdong Province,China,for a case study....Population migration,especially population inflow from epidemic areas,is a key source of the risk related to the coronavirus disease 2019(COVID-19)epidemic.This paper selects Guangdong Province,China,for a case study.It utilizes big data on population migration and the geospatial analysis technique to develop a model to achieve spatiotemporal analysis of COVID-19 risk.The model takes into consideration the risk differential between the source cities of population migration as well as the heterogeneity in the socioeconomic characteristics of the destination cities of population migration.It further incorporates a time-lag process based on the time distribution of the onset of the imported cases.In theory,the model will be able to predict the evolutional trend and spatial distribution of the COVID-19 risk for a certain time period in the future and provide support for advanced planning and targeted prevention measures.The research findings indicate the following:(1)The COVID-19 epidemic in Guangdong Province reached a turning point on January 29,2020,after which it showed a gradual decreasing trend.(2)Based on the time-lag analysis of the onset of the imported cases,it is common fora time interval to exist between case importation and illness onset,and the proportion of the cases with an interval of 1-14 days is relatively high.(3)There is evident spatial heterogeneity in the epidemic risk;the risk varies significantly between different areas based on their imported risk,susceptibility risk,and ability to prevent the spread.(4)The degree of connectedness and the scale of population migration between Guangdong’s prefecture-level cities and their counterparts in the source regions of the epidemic,as well as the transportation and location factors of the cities in Guangdong,have a significant impact on the risk classification of the cities in Guangdong.The first-tier cities-Shenzhen and Guangzhou-are high-risk regions.The cities in the Pearl River Delta that are adjacent to Shenzhen and Guangzhou,including Dongguan,Foshan,Huizhou,Zhuhai,Zhongshan,are medium-risk cities.The eastern,northern,and western parts of Guangdong,which are outside of the metropolitan areas of the Pearl River Delta,are considered to have low risks.Therefore,the government should develop prevention and control measures that are specific to different regions based on their risk classification to enable targeted prevention and ensure the smooth operation of society.展开更多
文摘探究总氮(Total Nitrogen,TN)质量浓度时空分布特征,揭示其与土地利用的相关性,对维护流域水环境健康具有重要意义。文章采用北江流域Sentinel-2 MSI多光谱遥感数据,结合流域内59个水质自动监测站的同步水质监测数据,构建随机森林TN遥感反演模型,并基于该模型分析2019—2023年流域水体TN质量浓度的时空变化规律。同时收集ESRI(Environmental Systems Research Institute)发布的土地利用数据,采用皮尔逊相关系数开展水体TN质量浓度与各种土地利用类型在不同空间尺度的单因子相关性分析。结果表明:2019—2023年流域水体TN质量浓度呈现下降趋势,年均质量浓度在2021年最高,2023年最低。上游至三角洲入海口TN质量浓度逐渐上升。TN质量浓度与水体、草地、林地面积占比呈显著负相关,与农业用地和建筑用地面积占比呈显著正相关。流域内城市化程度逐年上升,这可能导致未来流域水体TN的来源更多地转向生活源和工业源。并针对北江流域不同区域TN质量浓度分布特征和土地利用格局对流域TN削减工程提出建议。
基金National Key Research and Development Program,No.2019YFB2103101GDAS Special Project of Science and Technology Development,No.2020GDASYL-20200301003Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou),No.GML2019ZD0301。
文摘Population migration,especially population inflow from epidemic areas,is a key source of the risk related to the coronavirus disease 2019(COVID-19)epidemic.This paper selects Guangdong Province,China,for a case study.It utilizes big data on population migration and the geospatial analysis technique to develop a model to achieve spatiotemporal analysis of COVID-19 risk.The model takes into consideration the risk differential between the source cities of population migration as well as the heterogeneity in the socioeconomic characteristics of the destination cities of population migration.It further incorporates a time-lag process based on the time distribution of the onset of the imported cases.In theory,the model will be able to predict the evolutional trend and spatial distribution of the COVID-19 risk for a certain time period in the future and provide support for advanced planning and targeted prevention measures.The research findings indicate the following:(1)The COVID-19 epidemic in Guangdong Province reached a turning point on January 29,2020,after which it showed a gradual decreasing trend.(2)Based on the time-lag analysis of the onset of the imported cases,it is common fora time interval to exist between case importation and illness onset,and the proportion of the cases with an interval of 1-14 days is relatively high.(3)There is evident spatial heterogeneity in the epidemic risk;the risk varies significantly between different areas based on their imported risk,susceptibility risk,and ability to prevent the spread.(4)The degree of connectedness and the scale of population migration between Guangdong’s prefecture-level cities and their counterparts in the source regions of the epidemic,as well as the transportation and location factors of the cities in Guangdong,have a significant impact on the risk classification of the cities in Guangdong.The first-tier cities-Shenzhen and Guangzhou-are high-risk regions.The cities in the Pearl River Delta that are adjacent to Shenzhen and Guangzhou,including Dongguan,Foshan,Huizhou,Zhuhai,Zhongshan,are medium-risk cities.The eastern,northern,and western parts of Guangdong,which are outside of the metropolitan areas of the Pearl River Delta,are considered to have low risks.Therefore,the government should develop prevention and control measures that are specific to different regions based on their risk classification to enable targeted prevention and ensure the smooth operation of society.
