Information extraction of Ulva Prolifera from coastal landscape using UAV m ultispectral remote sensing images

Since 2007, green tides(also called Ulva prolifera) occurred every summer in the Yellow Sea, causing ecological problems in the coastal environment of Shandong Peninsula . A large number of Ulva prolifera on shore will rot and stink if not handled in time,which seriously affects the tourism and the health of residents in coastal landscape. In order to improve the accuracy of monitoring green tide disasters, and to improve the efficiency of the cleaning up and disposal of Ulva prolifera at key prevention and control area, In this study, the high-precision image of UAV is used to monitor the green tide disaster in Yintan landscape of Rushan City. With the spectral characteristics of Ulva prolifera and coastal vegetation measured by spectroradiometer, four vegetation indices were used to analyze and identify the Ulva prolifera and coastal vegetation, and to verify the extraction of Ulva prolifera and coastal vegetation under different vegetation indices, and based on this extraction method, the biomass of coastal green tide algae was estimated. The results show that in the red-edge band,Ulva prolifera and coastal vegetation can be distinguished. MTCI(MERIS terrestrial chlorophyll index) is more suitale,with the accuracy of 91.3%, followed by SR_(redge),NDVI_(redge) and MSR_(redge),with the accuracy of 85.3%, 83.8% and 81.2%, respectively; Estimation model of biomass based on MTCI index showed that about 600 tons of Ulva prolifera were estimated in 300 m study area. An effective method for dynamic monitoring and cleaning up of green tide disaster is provided

基于视觉显著性的车载单目相机自运动估计及前车尺度估计方法

提出一种基于视觉显著性的车载单目相机自运动估计及前车尺度估计方法。首先，针对车载相机自运动估计，通过视觉显著性计算方法检测并去除含有噪声的单目图像序列中的运动目标，同时考虑图像的纹理区域和平滑区域，利用加权显著图保留有用特征点，进而对车载相机进行鲁棒的自运动估计。其次，将前车距离转化为前车尺度估计问题，通过描述子匹配与李代数中正则化的强度匹配相结合的方法最小化损失函数，通过设计视觉注意力机制选择有纹理无遮挡的图像块，并对选定的图像块中的像素赋权以减轻被噪声破坏像素的影响，从而实现鲁棒、准确的尺度估计。最后，利用多个具有挑战性的数据集对所提方法进行分析验证。结果表明，单目相机自运动估计方法达到了基于立体相机方法的水平，前车尺度估计方法在充分发挥强鲁棒性优势的同时保证了预测精度

心脑血管疾病与气象因素关系预测模型的建立与评估 Establishment and Evaluation of the Prediction Models of the Relationship between Cardiovascular and Cerebrovascular Diseases and Meteorological Factors

目的 探讨心脑血管疾病的发病状况和气象因素之间的关系，运用机器学习方法预测心脑血管疾病发病风险等级，为疾病防控提供科学依据。 方法 以贵州省疾病预防控制中心提供的心脑血管疾病患者为研究对象，通过相关性分析确定模型的预测因子，分别基于支持向量机、极端梯度提升、轻量级梯度提升机、随机森林这4种机器学习模型构建心脑血管疾病发病风险的预测模型。将纳入患者以8∶2的比例分为训练集和测试集。训练集用于模型训练和参数优化，测试集用于评价模型效果。主要以准确率来评价各模型的预测效果。 结果 本研究共纳入60岁以上心脑血管疾病患者16 383例，其中女性6507例，且日发病例数表现为不平衡数据，其中诊断类型包括急性心肌梗死、卒中、心绞痛、心源性猝死。日发病例数与气压、气温、湿度3大类26种气象因素存在相关性，与气压、相对湿度呈正相关，与气温呈负相关。采用GridSearchCV函数找出最优权重的配比后，使用机器学习方法构建模型，并通过测试集验证输出模型指标参数。轻量级梯度提升机模型在预测任务中表现最佳，准确率达到85.68%，精确率为82.56%，召回率为85.68%，F1分数为79.56%（均P＜0.05）。心脑血管疾病患者发病前72 h气温的INP值为63 814，是影响日发病例数最重要的气象因素，排名第2和第3的是发病前48 h气温和发病前24 h气温，对应INP值分别为62 002、43 216。 结论 基于机器学习方法建立的心脑血管疾病发病预测模型具有较高的预测价值，其中轻量级梯度提升机模型的预测效果最好。 Abstract: Objective To explore the relationship between the incidence of cardiovascular and cerebrovascular diseases and meteorological factors, and to predict the incidence risk levels of cardiovascular and cerebrovascular diseases using machine learning methods, with the aim of providing the scientific basis for disease prevention and control. Methods Patients with cardiovascular and cerebrovascular diseases, whose information were provided by the Guizhou Center for Disease Control and Prevention, were selected as subjects. The predictive factors of the model were determined through correlation analysis, and the prediction models for the risk of cardiovascular and cerebrovascular diseases were constructed based on four machine learning models: support vector machine, extreme gradient boosting, light gradient boosting machine, and random forest. The included patients were divided into the training set and the testing set in the ratio of 8∶2. The training set was used for model training and parameter optimization, and the testing set was used to evaluate the effect of the model. The predictive performance of each model was mainly evaluated by accuracy. Results A total of 16 383 patients over 60 years of age with cardiovascular and cerebrovascular diseases were included in this study, including 6507 women. The number of daily cases was unbalanced, in which the diagnostic types included acute myocardial infarction, stroke, angina pectoris, and sudden cardiac death. The number of daily cases was correlated with 26 meteorological factors in 3 categories including air pressure, air temperature, and humidity, and was positively correlated with air pressure and relative humidity, but negatively correlated with air temperature. The GridSearchCV function was used to find the optimal weight ratio, the machine learning method was used to construct the model, and the output model index parameters were verified through the testing set. The light gradient boosting machine model performed best in the prediction task, with an accuracy of 85.68%, a precision of 82.56%, a recall of 85.68%, and the F1 score was 79.56% (all P<0.05). The INP value of the temperature of 72 h before the onset of cardiovascular and cerebrovascular diseases was 63 814, which was the most important meteorological factor affecting the number of daily cases. The temperatures of 48 h before the onset and 24 h before the onset respectively ranked second and third, corresponding to INP values of 62 002 and 43 216. Conclusions The prediction models of cardiovascular and cerebrovascular diseases based on machine learning methods have high predictive value. Among them, the light gradient boosting machine model presented the best performance

改性当地土壤技术修复富营养化水体综合效果研究:Ⅰ.水质改善的应急与长期效果与机制

2010年10月-2011年9月在太湖梅梁湾围隔内研究了改性当地土壤絮凝除藻及其对水质改善的应急和长期效果,并结合室内实验研究了该技术防控底泥再悬浮和减少底泥二次污染的长效机制.现场围隔实验结果表明,改性当地土壤除藻30 min后,TN、NO3--N、NH4+-N、TP、PO34--P和Chl.a的去除率分别为66%、57%、60%、93%、92%和98%;长期监测结果表明,与对照区域相比,围隔内的TN、NH4+-N、NO3--N、TP和PO34--P在处理后11个月内的平均值分别降低了39.83%、52.30%、48.53%、18.75%和60.00%.室内再悬浮实验结果表明,改性土壤和沙子抗再悬浮能力较未改性土壤分别提高了3和5倍.室内柱培养结果表明改性土壤絮凝除藻和沙土覆盖相结合可有效提高表层沉积物-水界面的氧化还原电位和溶解氧,使沉积物向水体的TP和TN通量从源逆转成汇,PO34--P和NH4+-N通量大幅度降低.改性土壤技术在利用絮凝除藻快速改善水质后,可通过改性沙/土分层底泥调控分别达到对藻絮体再悬浮的物理控制和营养盐再释放的化学控制,通过将亚表层底泥中的藻细胞分解并被沉水植物根系吸收,可实现对底泥中水华蓝藻复苏和水体富营养化的长效生态控制

青海湖湖东风成剖面化学元素特征及其环境指示意义/Geochemical Features and Palaeoenvironmental Indications of Aeolian Sediments on the East of Qinghai Lake[J]

通过对青海湖湖东沙地风成沉积剖面化学元素特征的分析,结合光释光测年结果,并和已有研究进行对比,探讨了青海湖区12.5 ka BP以来的气候环境变化过程,将其划分为5个阶段:12.5 ka BP前气候寒冷干燥,青海湖应处于冰川消退的寒冷期,风沙活动强烈；12.5～11.9 ka BP气候向暖湿转变,其中12.2～11.9 ka BP发生一次寒冷事件,对应于新仙女木事件；11.9～8.0 ka BP气候冷暖波动频繁,期间出现了3次寒冷事件；8.0～2.6 ka BP是一个持续时间较长的温暖湿润期；2.6 ka BP至今,气候以干冷为主,与现代气候相近

青海湖湖东风成剖面化学元素特征及其环境指示意义[C]

通过对青海湖湖东沙地风成沉积剖面化学元素特征的分析，结合光释光测年结果，并和已有研究进行对比，探讨了青海湖区12.5 ka BP以来的气候环境变化过程，将其划分为5个阶段：12.5 ka BP前气候寒冷干燥，青海湖应处于冰川消退的寒冷期，风沙活动强烈；12.5～11.9 ka BP气候向暖湿转变，其中12.2～11.9 ka BP发生一次寒冷事件，对应于新仙女木事件；11.9～8.O ka BP气候冷暖波动频繁，期间出现了3次寒冷事件；8.0～2.6 ka BP是一个持续时间较长的温暖湿润期；2.6 ka BP至今，气候以干冷为主，与现代气候相近