[Objective] The aim was to explore the feasibility of using single spectrum image to classify crops based on multi-spectral image and Decision Tree Method. [Method] Taking the typical agriculture plantation area in Hu...[Objective] The aim was to explore the feasibility of using single spectrum image to classify crops based on multi-spectral image and Decision Tree Method. [Method] Taking the typical agriculture plantation area in Hulunbeier area, according to field measured spectrum data, the optimum time of main crops, barley, wheat, rapeseed, based on crops spectrum characteristics, by dint of decision-making tree method, and considering spectral matching method, classification of crops was studied such as SAM. [Result] By dint of Landsat TM image gained in the first half of August, based on geographic and atmospheric proof-reading, decision-making tree was constructed. Plantation information about wheat, barley, and rapeseed and plantation grassland was extracted successfully. The general classification accuracy reached 86.90%. Kappa coefficient was 0.831 1. [Conclusion] Taking typical spectrum image as data source, and applying Decision Tree Method to get crops type＇s information had fine application future.展开更多
目的从患者和医护人员的角度归纳分析2型糖尿病患者初始使用胰岛素的决策参与影响因素的质性研究,为促进患者的决策参与提供参考。方法系统检索CINAHL,Cochrane Library,EMBASE,PubMed,Web of Science,PsycINFO,中国知网,万方,维普和Sin...目的从患者和医护人员的角度归纳分析2型糖尿病患者初始使用胰岛素的决策参与影响因素的质性研究,为促进患者的决策参与提供参考。方法系统检索CINAHL,Cochrane Library,EMBASE,PubMed,Web of Science,PsycINFO,中国知网,万方,维普和SinoMed收录的关于2型糖尿病患者初始胰岛素决策参与影响因素的质性研究,检索日期从建库至2023年9月30日,采用澳大利亚乔安娜布里格斯研究所(Joanna Briggs Institute,JBI)循证卫生保健中心质性研究质量评价工具对纳入文献进行评价,并采用汇集性整合方法对结果进行整合。结果共纳入文献19篇,提炼出20个研究结果,归纳成7个类别(患者决策相关价值观、患者决策参与的角色偏好、患者自身疾病情况、医护人员在患者决策参与中的角色定位、医护人员的专业素质、患者与医护人员的关系、医疗机构支持程度),共4个整合主题(患者个人因素、医护人员因素、患者-医护人员互动因素、医疗机构因素)。结论2型糖尿病患者胰岛素初始使用的决策参与受到患者自身、医护人员和医疗机构的影响,需要多方人员努力,不仅引导患者积极参与决策,医护人员和医疗机构也要提供有效的决策支持。展开更多
基金Supported by the Open Subject of Key Lab of Resources Remote-sensing and Digital Agriculture in Agricultural Department(RDA1008)~~
文摘[Objective] The aim was to explore the feasibility of using single spectrum image to classify crops based on multi-spectral image and Decision Tree Method. [Method] Taking the typical agriculture plantation area in Hulunbeier area, according to field measured spectrum data, the optimum time of main crops, barley, wheat, rapeseed, based on crops spectrum characteristics, by dint of decision-making tree method, and considering spectral matching method, classification of crops was studied such as SAM. [Result] By dint of Landsat TM image gained in the first half of August, based on geographic and atmospheric proof-reading, decision-making tree was constructed. Plantation information about wheat, barley, and rapeseed and plantation grassland was extracted successfully. The general classification accuracy reached 86.90%. Kappa coefficient was 0.831 1. [Conclusion] Taking typical spectrum image as data source, and applying Decision Tree Method to get crops type＇s information had fine application future.
