管道泄漏分布式光纤监测时-空大数据分析方法

分布式光纤传感器能够灵敏地感知管道任意位置泄漏导致的局部温度变化,但是海量监测数据却具有显著的时-空非平稳性特征,难以直接根据监测数据进行泄漏诊断。在统计模式识别框架下,提出一种基于滑动窗口离群值分析的时-空大数据分析方法,仅利用分布式温度监测数据的内秉特征即可实现管道泄漏的智能化识别,确定了滑动窗口长度和异常状态诊断窗口长度的取值方法,并且进行了原型保温钢管泄漏监测的物理模拟。结果表明:在管道完好的状态下,该方法不会发生误报警的情况,而管道一旦发生泄漏,该方法能够快速识别管道泄漏事件,并对泄漏位置进行精准定位。该方法是一种无监督的人工智能大数据处理方法,在埋地管道泄漏监测中具有良好的应用前景。     

基于多键相的变转速下旋转叶片振动监测方法

为了解决传统的叶尖定时技术在变转速条件下难以有效监测到叶片振动信息的问题,提出了一种基于多参考键相的变转速下叶片振动监测方法,即在转轴上均匀布置多个键相参考点,实现转速的精确估计,同时在参考键相间按照所囊括叶片数量插入虚拟参考点,以虚拟参考点为标记实现变转速下叶片振动的测量,该方法同样适用于恒转速下叶片振动的测量。基于所提方法推导了变转速状态下叶片振动测量的表达方程,并通过数值建模和实验仿真验证了该方法的可行性和适用性,分析结果表明:基于多键相的变转速下旋转叶片振动监测方法与传统方法相比,平均相对测量误差从200%以上降至7%以下,突破了叶尖定时技术在变转速下难以测量的技术瓶颈。     

Nitrogen fertilizer recommendations based on plant sensing and Bayesian updating

Methods are available to predict nitrogen needs of winter wheat based on plant sensing, but adoption rates by producers are low. Current algorithms that provide nitrogen recommendations based on plant sensing implicitly assume that parameters are estimated without error. A Bayesian updating method was developed that can incorporate precision plant sensing information and is simple enough that it could be computed on-the-go. The method can consider producers prior information and can account for parameter uncertainty. Bayesian updating gives higher nitrogen recommendations than plant sensing recommendations using a plug-in method. These recommendations increase net returns over the previous recommendations, but not enough to make plant sensing profitable in this scenario.     

Systems biology for enhanced plant nitrogen nutrition

Nitrogen (N)-based fertilizers increase agricultural productivity but have detrimental effects on the environment and human health. Research is generating improved understanding of the signaling components plants use to sense N and regulate metabolism, physiology, and growth and development. However, we still need to integrate these regulatory factors into signal transduction pathways and connect them to downstream response pathways. Systems biology approaches facilitate identification of new components and N-regulatory networks linked to other plant processes. A holistic view of plant N nutrition should open avenues to translate this knowledge into effective strategies to improve N-use efficiency and enhance crop production systems for more sustainable agricultural practices.     

Low altitude remote sensing technologies for crop stress monitoring: a case study on spatial and temporal monitoring of irrigated pinto bean

Site-specific crop management is a promising approach to maximize crop yield with optimal use of rapidly depleting natural resources. Availability of high resolution crop data at critical growth stages is a key for real-time data-driven decisions during the production season. The goal of this study was to evaluate the possibility of using small unmanned aerial system (UAS)-based remote sensing technologies to monitor the crop stress of irrigated pinto beans (Phaseolus vulgaris L.) with varied irrigation and tillage treatments. A small UAS with onboard multispectral and infrared thermal imaging sensors was used to collect data from bean field plots on three growth stages in 2015 and 2016, respectively. Indicators including green normalized vegetation index (GNDVI), canopy cover (CC, ratio of ground covered by crop canopy to the total plot area) and canopy temperature (CT, °C) of crops were extracted from imaging data and correlated with ground-reference crop yield and leaf area index (LAI) estimated with a handheld ceptometer. Results show that GNDVI, CC and CT were able to differentiate crops with full and deficit irrigation treatments at each of the three growth stages in both years. Developed indicators were strongly correlated with to the crop yield with Pearson correlation coefficients (r) of approximate 0.71 and 0.72 for GNDVI and CC, respectively, in the early growth stage (54 days after planting) in both years. Canopy temperature showed even stronger correlation (r > 0.8) with yield at early growth stage. Performance of small UAS-based imagery-based indicators in crop stress monitoring and crop yield estimation was better than or comparable to that of the ground-based LAI estimates, indicating the potential of such remote sensing tool in rapid crop stress monitoring and management.     

VI-LST遥感模型在福建省干旱灾害监测中的应用

植被指数(VI)和地表温度(LST)是描述地表特征信息的2个重要参数.本研究采用基于VI和LST特征空间的遥感模型对2000年以来福建省三起比较严重的干旱灾害进行监测分析,结果表明:采用增强型植被指数(EVI)拟合特征空间干边方程的相关系数高于采用归一化植被指数(NDVI)拟合干边方程的相关系数.VI-LST遥感模型能客观地反映地表水分供应状况,较好地反映区域旱情的空间分布和旱情动态发展过程,干旱灾害的卫星遥感监测结果与地面气象监测结果基本一致,在福建省抗旱减灾工作中具有一定的实用价值.     

Automated canopy temperature estimation via infrared thermography: A first step towards automated plant water stress monitoring

Canopy temperature estimation is an important process for plant water status monitoring. In this paper the problem for measurement data of the scene acquired via an infrared (IR) thermography system is considered. An optical image taken from the plant canopy is aligned with the underlying IR image, so that plant leaf area can be extracted via simple colour identification techniques and then the temperature distribution of the leaf area is obtained. The success of this procedure relies on the assumption that both optical-IR image alignment and leaf area extraction are perfect. In practice, such assumptions are rarely justifiable and the computed result can often be found undesirable. Particularly, the simple colour identification technique fails when temperatures of reference leaves, which are embedded in the canopy and provide known conductance to water vapour, are required to be estimated. In this paper, we address this issue and propose a novel algorithm to solve the problem. The underlying plant leaf temperature distribution is considered to be the fusion of two temperature densities separable via a combination of colour identification and Gaussian mixture distribution extraction techniques. A N-average method is tested with moderate success to estimate reference leaf temperatures from the estimated leaf temperature distribution. Our experimental results demonstrate the effectiveness and consistency of the proposed algorithm.     

判断土体应力状态的计算法

本文根据以往采用极限平衡条件式来判断土体应力状态所存在的问题,应用摩尔圆原理,从实用出发,推导出一个既简便又直观的“δ”应用计算法,供判断土体应力状态时使用,此法具有概念清楚,使用简明直观等特点。     

漳江口湿地变化的遥感监测

以漳江口红树林国家级自然保护区(以下简称保护区)为研究对象,2000、2005、2010和2016年的Landsat TM/OLI影像为数据源,通过面向对象随机森林分类法对保护区湿地进行分类,分类结果总体精度达到94.45%,Kappa系数为0.931 2,表明该方法在湿地分类方面具有较大应用潜力。应用遥感技术研究保护区湿地分布及其变化,结果表明:2000—2016年,养殖池分布面积最大,大部分分布在漳江的南北两侧;护花米草主要分布在漳江南部,少量生长在漳江北部;红树林主要分布在漳江南侧,互花米草的东部,漳江北部的养殖池边缘也有少量分布;滩涂与养殖池、互花米草、红树林接壤,分布在漳江两侧。2000—2005年,保护区内的大量天然湿地向人工湿地转化,主要是滩涂湿地及其他类型转换为养殖池,变化发生在保护区南部以及中部,另有部分互花米草转换为养殖池,发生在保护区北部; 2005—2016年,互花米草面积迅速增加,主要发生在漳江南部。引起变化的主要原因是人类活动、气候因素及海平面上升。     

基于数学形态学的植物叶片图像分割方法研究

传统的边缘检测算子均属于线性滤波方法.数学形态学是一种非线性滤波方法,能够保持图像的基本形状特征,在图像处理各个领域广泛应用.仅用边缘检测算子或者数学形态学处理都不能得到理想的分割效果.根据边缘算子检测的结果,进行形态学滤波、数学形态学运算,采取不同的结构元素反复实验对植物叶片边缘信息进行提取.不用传统目测方法而是通过定量的分析实验结果,表明了边缘检测基础上进行形态学处理的分割方法的有效性.     

基于BP神经网络的竹林遥感监测研究

竹林信息提取对利用遥感技术估算竹林碳储量至关重要,高精度地提取竹林信息将有利于降低碳储量估算误差。借助Matlab神经网络模块．采用BP神经网络（back propagation neural network）对ETM＋（enhanced themativ mapper plus）遥感影像提取竹林信息,得到了较高的精度,生产精度和用户精度分别为84．04％和98．75％;同时比较了Levenberg-Marquardt BP算法函数（Trainlm）、自适应学习率BP的梯度递减函数（Traingda）和梯度下降动量BP算法函数（Traingdm）等3种训练函数在分类中的差异。分析表明,Traingda算法函数分类精度最高,而Trainlm算法函数的训练时间最短。     

Hyperspectral remote sensing of grapevine drought stress

Precision Agriculture - In karst landscapes stony soils have little water holding capacity; the rational use of water for irrigation therefore plays an important management role. Because the water...     

植物比较营养学的研究及其应用

回顾了植物营养学研究方法的发展,将其分为：定性阶段、定量阶段、相对定量阶段。在相对定量阶段,以比较方法的运用为其主要特征。本文还综合分析了目前植物营养学中的一些研究方法,将其分为两类,一类是模拟的方法,一类是统计学的方法,并对其进行了初步论述,认为这些方法的实质是比较方法的运用,它既包括两因素的比较,又包括多因素的揉合,并以其在土壤养分盈万的评价及植物硼素营养遗传中的应用为例加以说明。在此基础上,提出了植物比较营养学的初步概念及其特点。     

雷达遥感在水稻生长监测应用中的研究进展

结合雷达遥感特点,分析了水稻后向散射特性的主要影响因素,总结了用于水稻生长监测的主要雷达遥感数据源和特点,在阐述水稻识别与估产、病虫害和水旱灾害监测的雷达遥感研究现状的基础上,初步探讨了未来水稻生长雷达监测研究的发展方向.     

基于遥感的福建闽侯丘陵区农作物种植面积空间抽样方法

以福建闽侯县作为研究区,采用传统抽样(简单随机抽样、系统抽样、分层抽样)、空间抽样(空间随机抽样、空间系统抽样、空间分层抽样)等方法对研究区农作物种植面积进行样本抽选、总体推算及误差估计,结果表明:1 500 m×1 500 m格网为最优抽样单元尺寸,空间分层抽样方法的相对误差为3.86%,变异系数为6.03%,抽样成本为6.03,抽样效率高.与传统抽样方法相比,空间抽样方法显著减少样本容量,节约调查成本.     

Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae)

Detection of crop stress is one of the major applications of hyperspectral remote sensing in agriculture. Many studies have demonstrated the capability of remote sensing techniques for detection of nutrient stress on cotton with only few on pest damage but none so far on leafhopper (LH) severity. Subsequent to introduction of Bt cotton, leafhopper is emerging as a key pest in several countries. In view of its wide host range, geographical distribution and damage potential, a study was initiated to characterise leafhopper stress on cotton, identify sensitive bands, and derive hyperspectral vegetation indices specific to this pest. Cotton plants with varying levels of LH severity were selected from three locations across major cotton growing regions of India. About 57-58 cotton plants from each location exhibiting different levels of LH damage symptoms were selected. Reflectance measurements in the spectral range of 350-2500 nm were made using hyperspectral radiometer. Simultaneously chlorophyll (Chl) and relative water content (RWC) were also estimated from the selected plants. Reflectance from healthy and leafhopper infested plants showed a significant difference in VIS and NIR regions. Decrease in Chl a pigment was more significant than Chl b in the infested plants and the ratio of Chl a/b showed a decreasing trend with increase in LH severity. Regression analysis revealed a significant linear relation between LH severity and Chl (R² = 0.505^∗∗), and a similar fit was also observed for RWC (R² = 0.402^∗∗). Plotting linear intensity curves between reflectance at each waveband with infestation grade resulted in six sensitive bands that exhibited maximum correlation at different regions of the electromagnetic spectrum (376, 496, 691, 761, 1124 and 1457 nm). Regression analysis of several ratio indices formulated with two or more of these sensitive bands led to the identification of new leaf hopper indices (LHI) with a potential to detect leafhopper severity. These new indices along with 20 other stress related hyperspectral indices compiled from literature were further tested for their ability to detect LH severity. Two novel indices LHI 2 and LHI 4 proposed in this study showed significantly high coefficients of determination across locations (R² range 0.521 to 0.825^∗∗) and hence have the potential use for detection of leafhopper severity in cotton.     

一种简便的植物poly(A)~ RNA(多聚腺嘌呤核糖核酸)提取方法

液氮速冻植物材料后,在微碱性条件下用SDS-苯酚法和氯化锂沉淀法,快速从小麦幼苗叶片中分离制备总RNA,所得RNA纯度合格,且未降解.从总RNA经oligo(dT)柱亲和层析分离poly(A)~ RNA,采用重复过柱法,使poly(A)~ RNA纯度较好.此法操作简便,成本低廉,是一种较好的poly(A)~ RNA提纯方法,值得推广.     

应用遥感技术对北京地区不同尺度动态监测方法的研究

简述了在北京地区应用不同空间分辨率的遥感信息源对资源环境动态监测的方法和技术以及精度分析 ,提出北京地区资源环境动态监测应采用不同尺度上的相应空间分辨率的遥感信息源