3S技术在有蹄类动物栖息地监测中的应用

3S技术是以遥感(RS)、地理信息系统(GIS)、全球定位系统(GPS)为基础,与其他高新技术有机地整合而形成一项新的有蹄类栖息地监测综合方法。全球定位系统(GPS)数据可以显著地提高RS识别地物的能力,为GIS提供数据源。应用地理信息系统(GIS)可以对有蹄类动物栖息地的状况进行评估,自动绘制有蹄类动物的分布图等。遥感(RS)是GIS数据库的数据源。3S技术在将来的野生动物栖息地监测研究中会有更大的发展潜力。     

田间作物信息监测技术的研究现状与展望

利用田间监测技术采集作物信息,可以实时获取田间作物的生长情况,并做出相应决策,对提高作物的产量和品质有重要作用。针对依赖于人工采样测量的传统作物田间监测方法存在效率低下、主观性强、特征单一等缺点,田间作物的快速监测、信息获取及分析技术成为了当下的研究热点。本文从采集目标、监测平台以及不同数据(信息)分析方法 3个方面分析了国内外田间作物监测技术的研究现状,总结了目前我国田间作物监测中存在的问题,并对今后在监测技术创新、信息解析技术、数据(信息)标准化与共享化,以及基础设施及推广方面的发展提出了建议,以期为我国田间作物监测技术的创新和产业发展提供参考。     

北疆水库富营养化污染遥感快速监测方法研究

水库受富营养化污染后,水生植物群落结构变的简单,主要以藻类为主。北疆污染水库冬季冰封加排污,使水体含氧量急剧降低,导致绿色光合植物大量死亡或以孢子形式越冬。春季冰雪融化后水体表现为无明显的绿色植物光合色素吸收峰和反射峰,利用二阶衰减指数函数对污染水库水体光谱曲线进行拟合,其拟合优度(R~2)值高。正常水库水生植物群落结构完整,冬季水体含氧量高,植物不会因缺氧死亡,在春季冰雪融化后水体表现为有明显植物光合色素吸收峰和反射峰,利用二阶衰减指数函数对正常水库光谱曲线进行拟合,其R~2值低。本文利用冬季到春季不同水库光谱曲线的二阶衰减指数函数R~2值斜率的变化,衡量了水库富营养化污染状况,即污染程度重,该段R~2的斜率值高,反之该段R~2的斜率值低,污染程度轻。研究结果经水质抽样分析验证,监测结果正确。该研究方法为今后北疆水库水质快速监测提供了新的思路。     

区域水土保持生态修复模式及效果评价——以长江流域两当河上游为例

根据长江流域两当河上游的环境特点,提出了自然力与人工辅助相结合的水土保持生态修复技术及建设模式,包括退化林地修复模式、荒山荒坡修复模式、农地修复模式和生态辅助措施.并应用遥感技术和地面人工调查相结合的方法,对生态修复模式实施的效果进行评价.结果表明,两当河退化生态系统修复效果明显,农田退化系统恢复效果显著,植被盖度提高,种群数量增加,生态系统向良性演变.     

依据物联网的树干径流自动监测系统

针对森林水分监测中树干径流数据收集单一、不准确、严重依赖人力操作等问题,研究设计基于物联网的树干径流自动监测系统。系统由翻斗式传感器、数据采集和处理模块、无线通信模块、服务器、客户端等组成。嵌入式系统接受和储存翻斗式脉冲信号次数,利用4G无线网络将数据上传到服务器,通过上位机对原始数据进行处理,最终达到在手机或者电脑上直观读取数据。测试表明该系统长期在野外运行稳定、数据收集准确,节约了大量人力成本和时间。     

高校学业预警机制运作中对象监测方式研究——以西北农林科技大学X学院为例

西北农林科技大学X学院在对学业预警机制构建的实践中,将传统的"事后处理型"转变为"事前、事中预防型",预警对象的监测跟踪方式从以班级学生骨干、政工干部为主导的粗放式监测管理,转变为以制度为依托,以学生党员、政工干部、学生家长为主体的"一对一"精细化预警监测方式,全面提高了学业预警效果。     

Prevalence of VTEC O157 in dairy and veal herds and risk factors for veal herds

The aim of this study was to determine the herd prevalence of veal and dairy herds and to identify risk factors for VTEC O157 positive veal herds. The study was based on monitoring data from November 1996 through July 2005 of 1051 dairy herds and 930 veal herds. The herd level prevalence (95% CI) was 8.0% (6.4–9.6) for dairy herds and 12.6% (10.5–14.7) for veal herds. Within the population of veal herds, a prevalence of 39.8% (33.9–45.6) was found for pink veal herds (n = 269) and 1.5% (0.7–2.8) for white veal herds (n = 661).Multivariable logistic regression showed that the type of veal (pink vs. white; OR = 21.6; 95% CI: 10.4–45.0), ventilation (mechanical vs. natural; OR = 0.4; 95% CI: 0.2–0.8), time between arrival in the herd and sampling (3–5 months vs. 0–2 months: OR = 2.33; 95% CI: 1.1–5.1, ≥6 months vs. 0–2 months: OR = 4.11; CI: 1.9–8.9), other feed than the 7 most common (yes vs. no; OR = 2.1; 95% CI: 1.2–3.7) and at least one dog present in the stable (yes vs. no; OR = 2.6; 95% CI: 1.5–4.6) were significantly (P < 0.05) associated with the presence of VTEC O157. The large difference in the VTEC O157 prevalences for pink veal and white veal production might have been caused by a very different management of these type of herds. However, this could not be studied with the data collected.     

Validating a Time Series of Annual Grass Percent Cover in the Sagebrush Ecosystem

We mapped yearly (2000–2016) estimates of annual grass percent cover for much of the sagebrush ecosystem of the western United States using remotely sensed, climate, and geophysical data in regression-tree models. Annual grasses senesce and cure by early summer and then become beds of fine fuel that easily ignite and spread fire through rangeland systems. Our annual maps estimate the extent of these fuels and can serve as a tool to assist land managers and scientists in understanding the ecosystem’s response to weather variations, disturbances, and management. Validating the time series of annual maps is important for determining the usefulness of the data. To validate these maps, we compare Bureau of Land Management Assessment Inventory and Monitoring (AIM) data to mapped estimates and use a leave-one-out spatial assessment technique that is effective for validating maps that cover broad geographical extents. We hypothesize that the time series of annual maps exhibits high spatiotemporal variability because precipitation is highly variable in arid and semiarid environments where sagebrush is native, and invasive annual grasses respond to precipitation. The remotely sensed data that help drive our regression-tree model effectively measures annual grasses’ response to precipitation. The mean absolute error (MAE) rate varied depending on the validation data and technique used for comparison. The AIM plot data and our maps had substantial spatial incongruence, but despite this, the MAE rate for the assessment equaled 12.62%. The leave-one-out accuracy assessment had an MAE of 8.43%. We quantified bias, and bias was more substantial at higher percent cover. These annual maps can help management identify actions that may alleviate the current cycle of invasive grasses because it enables the assessment of the variability of annual grass ? percent cover distribution through space and time, as part of dynamic systems rather than static systems.     

安徽省池州市拟建蔬菜生产基地土壤重金属含量的监测分析与评价

[目的]了解安徽省池州市拟建蔬菜基地的土壤重金属含量情况。[方法]选取30个拟建蔬菜基地,对其土壤进行重金属镉、铅、汞、砷含量的抽样监测与分析,将被测区域内土壤各项重金属的含量与GB/T 18407.1—2001《农产品质量安全无公害蔬菜产地环境要求》规定的限量值进行比较,对池州市拟建蔬菜基地土壤重金属的环境质量状况进行监测分析与评价。[结果]监测区域内土壤重金属铅、砷、汞的平均含量均低于GB/T 18407.1—2001《农产品质量安全无公害蔬菜产地环境要求》规定的限量值,但是部分监测点区域内的重金属镉处于超标状况,特别是贵池区沿江地区重金属镉超标比较普遍,不适合建无公害蔬菜生产基地。[结论]该研究可为池州市蔬菜基地的发展与定位提供技术依据。