基于无线传感器网络的温室环境监测系统研究

温室环境监删采用基于ZigBee技术的无线传感器网络有着明显的优势.ZigBee网络容量大、功耗低、易于扩充并且支持自组织组网.设计了一种基于ZigBee的温室环境监测系统,简述了ZigBee的特点及温室环境监测系统的特点,包括网络协调器节点和传感器节点的硬件和软件设计.该设计可构架一个较大范围的无线传感器网络,对温室...     

基于ZigBee及ARM9的无线传感器网络实验装置研究

目的针对高校无线传感器网络及ZigBee技术的课程,设计一套无线传感器网络实验装置,以满足传感器网络从感知层、网络层到应用层的多层次教学实践的安排。方法以无线传感器网络及ZigBee技术的课程需要为研究对象,基于ZigBee技术及ARM9的嵌入式开发技术,硬件和软件都采用模块化设计。结果实验装置设计成实验箱的形式,实验箱由多个ZigBee节点和一个网关组成,并提供一个综合教学案例,在教学案例中以网关为中心,网关采用Linux操作系统,网关通过Web的方式呈现ZigBee网络拓扑、传感器数据信息、接收用户指令控制终端节点。结论实验装置结构简单,使用方便,可安排ZigBee单片机基础、传感器、基于ZigBee协议栈Z-Stack的组网、嵌入式系统设计、综合应用等多层次的实验。     

北方草原风电场土壤风蚀无线监测系统的研究

为减小风力发电对北方草原脆弱生态环境的影响,设计了一套基于ZigBee无线传感器网络和GPRS无线通讯技术的异地数据采集传输系统.该系统由ZigBee无线传感网络、传感器组、GPRS终端、上位机监测中心组成;设计的软件可进行草原风电场的空气温湿度、风速、土壤温度水分和风蚀量6个参数异地实时监测和可视化的显示.测试结果表明:在传感器节点和协调器距离在600 m以内,该混合组网的无线监测系统运行稳定,达到设计要求.     

基于ZigBee无线传感器网络的农业环境监测系统研究与设计

针对目前农业环境在线监测的需要,提出了一种基于ZigBee无线传感器网络的农业环境监测系统设计方案,该监测系统由无线监测网络和远程监控中心组成,可对影响作物生长的温度、湿度、光照等环境变量进行实时监测。介绍了系统的总体架构,设计开发了无线传感器网络节点、基站以及软件流程。由于采用了无线传输方式,该系统解决了有线通信方式存在的难以扩展、难以升级等问题,具有低功耗、低成本、扩展灵活等优点,其应用前景非常广阔。     

基于ZigBee无线传感器网络的森林环境监测系统

设计了一种基于ZigBee无线传感器网络的森林环境监测系统;描述了该系统的构成原理与整体结构,以及基于CC2430芯片的传感器节点和网关的硬件设计和系统软件工作流程.采用的星—簇首—路由的拓扑结构,具有低成本、易于部署、使用寿命长等优点.     

基于ZigBee技术的牧场火情监控系统设计研究

设计了一种将ZigBee技术引入到牧场火情监测系统中的方案,构建一个基于zigBee无线传感器网络的牧场火情实时监测系统。该系统可以实时监测牧场的相关参数,如空气湿度、温度及牧场的烟雾浓度变化情况等,为牧场防火灭火提供信息支持。研究了ZigBee无线传感器网络节点的电路设计、节点信息的采集、数据融合、传输以及传感器网络的有效拓扑结构。     

基于ZigBee技术的草地环境信息监测系统设计

设计了一种基于ZigBee无线传感器网络的草地环境监测系统。该监测系统以CC2530芯片为核心,设计开发了适应于草地环境监测的传感器节点、路由节点和协调器节点,并在传感器节点和协调器节点中增加CC2591射频前端,增大节点之间的通信距离;利用ZigBee技术实现节点之间的数据通讯。系统能够实时、远程监测影响草地生长的环境因素,为草地资源保护提供决策依据。     

基于太阳能的无线温室环境监测系统的设计

针对传统有线网络布线繁琐、维护困难等问题,设计了一种基于ZigBee无线网络的温室环境监测系统,该系统由无线传感器网络和监测平台两部分构成.网络内节点均采用太阳能电池供电的方式,能实时监测和记录温室参数信息,并将采集的信息经ZigBee网络传输到监测平台,实现数据显示和存储功能.测试结果表明,该系统组网速度快、节点灵活、显示清晰、运行稳定,能有效地对温室中的参数进行监测,具有较强的可靠性.     

基于ZigBee无线传感器网络的森林火灾监测系统的研究

该文在探讨森林起火因素的基础上,构建了一种基于ZigBee无线传感器网络的森林火灾实时监测系统.该系统给出了森林火灾无线传感器网络监测系统的体系结构,重点设计了基于CC2430芯片的网络节点硬件电路,详尽地讨论了网络的数据传输流程;该系统能够监测林区温湿度等相关环境参数的变化,为有关部门采取相应的防火或灭火措施提供决策依据.      

Deign of Remote Meter Reading Communication Controller for Agriculture Digital Network Based on ZigBEE and GPRS

针对数字化农电网远程抄表网络化的需求,提出将无线传感器网络应用于电能表监测系统中,介绍了基于ZigBee与GPRS无线传感器网络的数字化农电网远程抄表系统的结构组成,设计及实现了GPRS无线终端、ZigBee网络主控站软件与系统应用层协议的规定.     

Agricultural remote sensing big data: Management and applications

Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is one of the backbone technologies for precision agriculture, which considers within-field variability for site-specific management instead of uniform management as in traditional agriculture. The key of agricultural remote sensing is, with global positioning data and geographic information, to produce spatially-varied data for subsequent precision agricultural operations. Agricultural remote sensing data, as general remote sensing data, have all characteristics of big data. The acquisition, processing, storage, analysis and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. This paper overviews available remote sensing data resources, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. A five-layer-fifteen-level (FLFL) satellite remote sensing data management structure is described and adapted to create a more appropriate four-layer-twelve-level (FLTL) remote sensing data management structure for management and applications of agricultural remote sensing big data for precision agriculture where the sensors are typically on high-resolution satellites, manned aircrafts, unmanned aerial vehicles and ground-based structures. The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensing big data management and applications at local regional and farm scale.     

Perspective of Chinese GF-1 high-resolution satellite data in agricultural remote sensing monitoring

High-resolution satellite data have been playing an important role in agricultural remote sensing monitoring. However,the major data sources of high-resolution images are not owned by China. The cost of large scale use of high resolution imagery data becomes prohibitive. In pace of the launch of the Chinese "High Resolution Earth Observation Systems",China is able to receive superb high-resolution remotely sensed images(GF series) that equalizes or even surpasses foreign similar satellites in respect of spatial resolution,scanning width and revisit period. This paper provides a perspective of using high resolution remote sensing data from satellite GF-1 for agriculture monitoring. It also assesses the applicability of GF-1 data for agricultural monitoring,and identifies potential applications from regional to national scales. GF-1's high resolution(i.e.,2 m/8 m),high revisit cycle(i.e.,4 days),and its visible and near-infrared(VNIR) spectral bands enable a continuous,efficient and effective agricultural dynamics monitoring. Thus,it has gradually substituted the foreign data sources for mapping crop planting areas,monitoring crop growth,estimating crop yield,monitoring natural disasters,and supporting precision and facility agriculture in China agricultural remote sensing monitoring system(CHARMS). However,it is still at the initial stage of GF-1 data application in agricultural remote sensing monitoring. Advanced algorithms for estimating agronomic parameters and soil quality with GF-1 data need to be further investigated,especially for improving the performance of remote sensing monitoring in the fragmented landscapes. In addition,the thematic product series in terms of land cover,crop allocation,crop growth and production are required to be developed in association with other data sources at multiple spatial scales. Despite the advantages,the issues such as low spectrum resolution and image distortion associated with high spatial resolution and wide swath width,might pose challenges for GF-1 data applications and need to be addressed in future agricultural monitoring.     

Research advances of SAR remote sensing for agriculture applications: A review

Synthetic aperture radar (SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricultural remote sensing.     

卫星遥感在海洋渔业资源开发、管理与保护中的应用

海洋渔业资源的开发、管理与保护需要大量的海洋环境数据。由于卫星遥感能大面积、长时间、近实时地获取海洋环境数据,其在海洋渔业资源开发、管理与保护中的作用越来越大。本文回顾了卫星遥感数据在海洋渔业资源评估、渔情预报、鱼类栖息地分类与保护、渔船监测、渔业安全、渔具渔法等方面的应用,探讨了在这些应用中可能存在的问题,并对其未来的发展进行了展望,为相关学者了解卫星遥感在海洋渔业资源的开发、管理与保护中的作用提供参考。     

基于遥感技术的土壤质地空间预测方法研究进展

土壤质地影响土壤持水持肥性和透气性,进而驱动一系列与土壤有关的物理化学过程,结合高效快速的遥感技术预测土壤质地空间分布,对土壤质量评价与农业生产规划具有重要的理论和实践意义。本文从遥感预测土壤质地的数据、方法和模型的应用出发,介绍了用于土壤质地遥感预测的雷达、地形和植被指数等辅助数据,提出了光谱响应、特征波长选择和遥感解译这三种基于遥感特征预测土壤质地空间分布的方法,梳理了统计学、地统计学和机器学习这三类模型与遥感结合对土壤质地空间预测的应用效果,总结了几种典型方法的优缺点与适用情况,并分析了遥感预测土壤质地的应用条件和精度验证方法,最后提出未来研究需侧重于深入提取各种遥感光谱特征、利用遥感技术获取多类型环境变量和开发土壤物理属性与数据驱动机器学习特征相结合的多算法混合模型,旨在为开展不同区域尺度下土壤质地空间预测研究提供依据与技术支撑。     

光信息技术在现代农业中的应用研究

以遥感科学与技术的定量化发展为切入点,系统的阐明了光信息与定量遥感技术在我国现代农业中应用现状与发展趋势。详细阐述了遥感科学与技术定量化发展的含义和应用意义,从农业资源调查及动态监测、农作物估产、灾害监测与评估,以及农业气象应用等几方面系统的回顾了遥感技术在我国农业中的应用历史和现状;最后分析了光信息及定量遥感技术与现代农业发展模式之间的关系,给出了促进我国农业信息化的发展趋势与方向。     

农业遥感研究进展与展望

农业遥感是遥感科学的重要分支。文章回顾了农业遥感研究的百年发展历程,认为目前遥感在农业领域的应用广度和深度都在不断扩展,农业遥感从获取传统的总产、面积、单产等要素向更多监测要素不断深入,从传统的资源、环境向植保、农学等方向不断扩展,农业遥感正逐步成为农业科学的基础关键技术。论文从农情遥感、农业灾害遥感、农业资源环境遥感等领域全面总结了近年来中国农业遥感研究取得的成就及重要成果。从农业定量遥感、无人机遥感、作物表型遥感等方面指出了农业遥感研究发展的国际前沿,分析认为,随着传感器、物联网、互联网+、大数据、人工智能等技术的发展及现代农业发展的需求,“十三五”及未来10年,国内农业遥感技术在天空地一体化的农业遥感大数据获取、人工智能与大数据等的信息智能提取和挖掘等方面发展前景巨大。     

Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data

In light of the increasing demand for food production, climate change challenges for agriculture, and economic pressure, precision farming is an ever-growing market. The development and distribution of remote sensing applications is also growing. The availability of extensive spatial and temporal data—enhanced by satellite remote sensing and open-source policies—provides an attractive opportunity to collect, analyze and use agricultural data at the farm scale and beyond. The division of individual fields into zones of differing yield potential (management zones (MZ)) is the basis of most offline and map-overlay precision farming applications. In the process of delineation, manual labor is often required for the acquisition of suitable images and additional information on crop type. The authors therefore developed an automatic segmentation algorithm using multi-spectral satellite data, which is able to map stable crop growing patterns, reflecting areas of relative yield expectations within a field. The algorithm, using RapidEye data, is a quick and probably low-cost opportunity to divide agricultural fields into MZ, especially when yield data is insufficient or non-existent. With the increasing availability of satellite images, this method can address numerous users in agriculture and lower the threshold of implementing precision farming practices by providing a preliminary spatial field assessment.     

基于萤火虫算法的农业遥感图像增强研究

农业遥感图像增强有利于图像信息的提取与分析,萤火虫算法是近年来较为新颖的智能仿生算法,目前国内外关于其能否用于农业遥感图像增强的研究未见报道。文章首先利用非完全Beta函数建立农业遥感图像增强模型,结合人眼最小灰度分辨力函数进行图像细节增强,将每个输入区间的像素灰度值变换到适当的输出灰度级区间,最终生成对比度均衡的图像;然后通过萤火虫优化算法在其动态决策域半径进行伪差分操作更新;最后确定最佳参数的收敛条件,给出了算法流程。试验仿真结果表明,萤火虫算法的农业遥感图像检测在图像细节增强评价指标、相位一致性指标、通用质量评价指标等方面与直方图算法、Retinex算法、小波变换算法、模糊聚类算法等相比数据较优,能够用于农业遥感图像增强。     

农作物种植结构遥感提取研究进展

农作物种植结构信息对农业生产管理、农业可持续发展及国家粮食安全等具有重要意义。本文中概括了农作物种植结构遥感提取的理论基础,归类了近10年间不同农作物种植结构遥感提取技术方法,重点评述了不同技术方法的特点及应用情况,讨论和展望了未来农作物种植结构遥感提取研究的发展方向。当前,光谱特征、时相特征和空间特征是农作物种植结构遥感提取的三大理论基础。基于单一影像源的种植结构提取方法操作简单,但往往难以获取种植结构“最佳识别期”的遥感影像;基于多时序影像源的种植结构提取方法可以充分利用农作物季相节律特征,成为当前农作物种植结构遥感提取的主流方法。在基于多时序影像源的种植结构提取方法中,多特征参量法较单一特征参量法更适用于农作物种植结构复杂区域,基于多特征参量的统计模型法一定程度上解决了混合像元问题,但模型的鲁棒性有待提高。此外,遥感与统计数据融合的农作物种植结构提取法在国家及全球大尺度的农作物种植结构提取中具有优势,但较低的制图分辨率使得数据产品的区域适宜性较差。未来农作物种植结构遥感提取将以区域“作物一张图”为目标,充分发挥多源数据组合利用的优势,围绕多类型作物同步提取和大范围作物种植结构提取开展深入研究,重点加强遥感数据预处理、特征参量提取和分类器高效选择等关键技术研究,从而提升农作物种植结构遥感提取的时空尺度,满足多方位的农业应用需求。