分布式监测系统的设计与实现

本文分析了分布式监测系统的结构和特点，以吴江发电厂分布式监测系统（WJDS）的具体实施为例，提出了分布式监测系统的设计方法，并针对公布式监测系统实现中的一些问题进行了讨论。     

基于GNSS-IR的土壤湿度监测试验对比分析

开展基于GNSS-IR技术的土壤湿度监测设备观测试验，利用自动土壤水分观测仪，对比分析2021年两种土壤湿度观测设备在相同条件下的观测差异，对GNSS-IR的土壤湿度监测设备的观测性能进行评估。结果表明：GNSS-IR的土壤湿度监测设备基本实现了对土壤体积含水量的自动观测，设备灵敏度更高；当土壤体积含水量大于40%时，对降水的灵敏度更高，设备观测数据偏大；当土壤体积含水量小于30%时，对干旱的灵敏度更高，观测设备数据偏小；观测设备随季节变化明显，冬季>春季>秋季>夏季，冬季的季平均误差为1.8%、季平均绝对误差4.17%、季相关系数0.72,本次试验为GNSS-IR的土壤湿度监测设备后期的业务应用提供了科学依据和参考。     

融合无人机机载激光雷达的变形监测技术研究

为提高变形监测精度，提出一种融合无人机机载激光雷达的变形监测方法。方法通过采用点云差值法(Point cloud difference, PCD)计算两期待监测区域的地表下沉数字表面模型(Digital Surface Model, DSM)差值，并基于无人机机载激光雷达数字正射影像图(Digital Ortho Map, DOM)计算地表水平移动矢量，分别实现了地表下沉监测和水平移动监测。仿真结果表明，所提方法可通过地表下沉监测和水平移动监测，有效、可靠地监测地表变形。在整个监测过程中，像控点精度对变形监测精度具有重要影响，随着像控点中误差的增加，下沉监测与水平移动监测误差增加，误差分布区间占比减小；当平面中误差和高程中误差均为6 cm时，下沉监测误差和水平移动监测误差分别为0.3 cm和0.2 cm,有效提高了变形监测精度，满足了变形监测误差需求，具有一定的实际应用价值。     

苛刻环境嵌入式智能监测平台研究

在煤矿井下、野外露天或环境恶劣的监测现场，使用计算机和数据采集卡来实现监测任务是相当危险和不可靠的。针对监测环境中可能存在易燃易爆条件、湿度大、灰尘多、监测场所分散、距离较远等特殊问题，分析了苛刻监测环境中的平台选择，讨论了监测平台的通信方式和监测数据的压缩编码标准，提出了建立苛刻环境嵌入式智能监测平台的思想，介绍了智能监测平台的设计与实现。     

基于可穿戴式设备的大学生体质健康监测系统设计

针对传统体质健康监测系统对人体活动能耗检测准确率低，导致监测效果不佳的问题，提出设计一个基于可穿戴式设备的大学生体质健康监测系统。其中，采用可穿戴式设备获取加速度信息和大学生运动身体生理指标，将其作为自变量数据，从而构建大学生活动能耗检测模型，以达到体质健康监测的目的。实验结果表明，在男性和女性的能耗检测中，本模型的RMSE值分别为0.12±0.03和0.12±0.04;相较于日常活动检测模型，本模型的检测准确率更高，检测效果更好。将此模型应用到监测系统中后可实现大学生体质健康准确监测，满足系统设计需求。     

基于虚拟仪器的核电站主管道振动监测系统设计

针对主管道振动在线监测的迫切性需要,分析了主管道振动监测的原理;开展了核电站主管道振动监测系统的硬件设计和软件设计;采用LabView为开发平台,集虚拟仪器技术、设备组态图形技术和数据库管理技术于一体,实现了主管道振动的在线实时监测;利用电磁振动台模拟主管道的振动,验证了主管道振动监测系统的性能和基本功能。     

遥感与模糊评判在森林火灾后生态监测评价中的应用

以遥感资料为信息源,针对森林生态的复杂性与模糊性,采用模糊数学中“多层次模糊综合评判”的方法，对森林火灾后生态景观进行评价及动态监测，以期探索森林火灾后生态监测评价的新途径，为森林生态未统的恢复和重建提供科学依据和决策支持。     

基于WPF的物联网监测系统的表现层设计与实现

物联网技术越来越多的用于工业检测系统中。然而，原有的物联网监测系统大多只注重传感器硬件的配置和监测指标的设计，却忽略了表现层的重要性，没有直观的数据展现方式和友好的用户交互界面。针对该问题，本文以一个物联网监测系统--桥梁监测系统为背景，针对其数据特点和监测需求，提出了一个基于WPF的具有良好交互性和数据直观展现的表现层设计方案，并结合数据可视化技术，给出了实现方法。     

卫星干扰监测技术的研究

为了提高卫星干扰监测水平,提出了频谱监测、螺流监测、PID监测、S/N监测和信号矢量图监测等五种监测技术和干扰综合判断方法：根据这些技术手段和方法,并利用计算机网络技术,建立了卫星电视干扰信号自动监测系统;该系统利用网络化设计,采用频谱模板比对,卫星玲标电平校准、信号模式识别、卫星遥测信号提取等技术,提高了卫星干扰监测的准确度和自动化程度;通过大最的仿真测试,设定告警门限,使系统的漏警率和误警率达到实用要求;该系统应用于干扰监测工作中,起到了很好的效果;在一定程度上弥补了国内通信干扰监没的空白。     

空间信息网格下监测系统体系的研究

在网格监测体系结构的基础上，提出一种监测数据源管理系统。该系统给出了底层生产者和数据源之间关系的一个具体实现模型，通过自动控制传感器的运行减小监测系统对被监测者的影响，通过监测数据与元数据分离的架构提高系统的可维护性和灵活性，并且用XML实现了监测对象和监测事件的统一描述。     

基于TM影像的森林病虫灾害遥感监测系统

利用TM影像可以进行森林病虫灾害的监测,但是操作过于复杂,无法在生产单位进行推广应用。为此,本文根据实际的工作流程,采用ERDAS IMAGINE遥感图像处理软件作为基础平台,设计开发了一个森林病虫灾害遥感监测系统。该系统以安徽省作为示范应用区,依据当地的马尾松毛虫灾害监测经验知识,建立了相应的森林病虫灾害模型,通过输入多时相TM影像,提取灾害信息。系统操作简便实用,较好地实现了森林灾害信息的自动处理,具有很大的实用价值。     

森林病虫害遥感监测技术研究的现状与问题

快速准确地监测、预测、评估及防治森林病虫害对国家减灾工程具有重要意义,而遥感技术是森林病虫害监测的重要手段。本文论述了利用遥感技术进行森林病虫害监测研究的内容、技术与方法,并探讨了目前遥感监测病虫害存在的问题及其发展趋势。     

表面水质遥感监测研究

主要讨论了应用多种传感器遥感技术进行表面水质监测研究的有效性。首先论述了纯水和不 同水质的波谱特性,然后以芬兰海湾和芬兰南部湖泊为应用实例,进行多种遥感数据和主要水质参 数之间的相关性分析,从而确定不同波谱段是否可以有效地监测表面水质的变化情况。本研究为新 一代传感器的设计提供水质监测的重要参数,进一步的试验研究仍在进行之中。     

基于面向对象方法的遥感土壤盐渍化信息监测——以渭干河-库车河三角洲绿洲为例

遥感技术由于具有观测范围广、实时强等特点适合用来研究土壤盐渍化现象。利用遥感手段提取盐渍土信息已经取得了一定的成效。利用面向对象方法,以TM卫星图像数据和野外实地数据为数据源进行提取盐渍地信息。首先,对遥感影像进行预处理,预处理包括几何校正和辐射校正,然后对图像进行图像分割,图像分割使用了分割方法的多尺度分割法、特征选择、面向对象分类和分类图像进行精度评价。对面向对象方法和传统的基于像元分类（最大似然法和最小距离法）结果进行对比分析。结果表明：利用面向对象方法对TM遥感图像进行分类,能有效抑制“椒盐现象”的发生,分类精度比传统的分类方法更高,为盐渍地信息的自动提取提供了广阔的前景。     

Lineament mapping in a tropical environment using Landsat imagery

Remote sensing has proved to be a useful tool in lineament identification and mapping. This study demonstrates the use of multispectral Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM?+) satellite data obtained over two acquisition dates in 1990 and 2002 for lineament interpretation in a Malaysian tropical environment. A digital elevation model (DEM) was generated to improve the interpretation. We found that most of the major orientations in the field station could be successfully detected from the remotely sensed imagery. The results from the study show that the remote sensing technique is capable of extracting lineament trends in an inaccessible tropical forest.     

Estimation of Forest Aboveground Biomass and Canopy Heightin Jiangxi Province Using GLAS and Landsat TM Images

Aiming at the problem that optical remote sensing cannot estimate forest biomass exactly because it’s easily affected by the weather and hard to penetrate the canopy of the forest.Using Jiangxi forest as the study area,established forest canopy height and forest biomass model by GLAS waveform data,integrating multispectral data(TM) and filed survey data.The study results show:(1) using waveform feature parameter,terrain feature parameters and field survey data to build forest canopy height model can eliminate the terrain influence and obtain the discrete canopy height.(2) Combined with the NDVI and discrete canopy height can be carried out large scale continuous forest canopy height mapping.(3) Power function relationship between canopy height and forest biomass can be used to estimate forest biomass.In general,large\|footprint LiDAR combined with optical Landsat TM data can give full paly to the advantages of multi\|source remote sensing and improve the precision of forest biomass inversion.     

Dryland observation at local and regional scale — Comparison of Landsat TM/ETM+ and NOAA AVHRR time series

During the last few decades, many regions have experienced major land use transformations, often driven by human activities. Assessing and evaluating these changes requires consistent data over time at appropriate scales as provided by remote sensing imagery. Given the availability of small and large-scale observation systems that provide the required long-term records, it is important to understand the specific characteristics associated with both observation scales. The aim of this study was to evaluate the potentials and limits of remote sensing time series for change analysis of drylands. We focussed on the assessment and monitoring of land change processes using two scales of remote sensing data. Special interest was given to the influence of the spatial and temporal resolution of different sensors on the derivation of enhanced vegetation related variables, such as trends in time and the shift of phenological cycles. Time series of Landsat TM/ETM+ and NOAA AVHRR covering the overlapping time period from 1990 to 2000 were compared for a study area in the Mediterranean. The test site is located in Central Macedonia (Greece) and represents a typical heterogeneous Mediterranean landscape. It is undergoing extensification and intensification processes such as long-term, gradual processes driven by changing rangeland management and the extension of irrigated arable land. Time series analysis of NOAA AVHRRR and Landsat TM/ETM+ data showed that both sensors are able to detect this kind of land cover change in complementary ways. Thereby, the high temporal resolution of NOAA AVHRR data can partially compensate for the coarse spatial resolution because it allows enhanced time series methods like frequency analysis that provide complementary information. In contrast, the analysis of Landsat data was able to reveal changes at a fine spatial scale, which are associated with shifts in land management practice.     

Improvement of tropical vegetation mapping using a remote sensing technique: A case of Khao Yai National Park,Thailand

The forest ecosystems of Thailand are characterized by a diverse and complex vegetation structure. Classification of vegetation types of such forest ecosystems has been experienced as a difficult task, even with large-scale aerial photography. Satellite remote sensing, the digital technique in particular, has not been widely used for vegetation mapping in Thailand until now. The objective of this study was to explore the potential of digital image processing over the existing technique of visual interpretation of Landsat Thematic Mapper (TM) false colour composite (BGR-2, 3, 4) to produce forest cover maps in Thailand. Supervised and unsupervised classification methods were employed with different band combinations to discriminate vegetation types in the Khao Yai National Park using Landsat TM data. The results indicated that thematic classes derived from supervised classification produced higher overall accuracy than unsupervised classification. In addition, the combination of ratio bands R4/3, R5/2, R5/4 and R5/7 ranked the highest in terms of accuracy (65% for unsupervised and 79% for supervised) and the combination of bands 2, 3 and 4 gave the lowest (56% for both methods). Finally, it was concluded that, even within the limit of spectral information available in the image, the digital classification can improve the result of visual interpretation.     

The multispectral separability of Costa Rican rainforest types with support vector machines and Random Forest decision trees

Estimating the extent of tropical rainforest types is needed for biodiversity assessment and carbon accounting. In this study, we used statistical comparisons to determine the ability of Landsat Thematic Mapper (TM) bands and spectral vegetation indices to discriminate composition and structural types. A total of 144 old-growth forest plots established in northern Costa Rica were categorized via cluster analysis and ordination. Locations for palm swamps, forest regrowth and tree plantations were also acquired, making 11 forest types for separability analysis. Forest types classified using support vector machines (SVM), a theoretically superior method for solving complex classification problems, were compared with the random forest decision tree classifier (RF). Separability comparisons demonstrate that spectral data are sensitive to differences among forest types when tree species and structural similarity is low. SVM class accuracy was 66.6% for all forest types, minimally higher than the RF classifier (65.3%). TM bands and the Normalized Difference Vegetation Index (NDVI) combined with digital elevation data notably increased accuracies for SVM (84.3%) and RF (86.7%) classifiers. Rainforest types discriminated here are typically limited to one or two categories for remote sensing classifications. Our results indicate that TM bands and ancillary data combined via machine learning algorithms can yield accurate and ecologically meaningful rainforest classifications important to national and international forest monitoring protocols.     

Remote sensing of industrial impact on Arctic vegetation around Noril'sk,northern Siberia: Preliminary results

This study applies remote sensing techniques for monitoring non-ferrous metal smelting impacts in the extreme environment of northern Siberia. Ground and at-satellite reflectance and normalized difference vegetation index (NDVI) values for different vegetation types have been compared and a hybrid supervised-unsupervised classification of Landsat TM data performed, based on field and ancillary data. This has allowed us to distinguish several degrees of vegetation damage in tundra and forests. However, it was difficult to differentiate between some significant classes, such as damaged grass tundra and sparse dead larch forests with a grass understorey. We suggest possible refinement of our results, including the combination of images taken at different phenological stages and from different sensors. However, it should be noted that the north-Siberian environment presents unusually severe limitations of optical-infrared satellite observation possibilities and problems in imagery interpretation. Standard indicators of vegetation vigour, such as NDVI, widely applied at lower latitudes, become less informative in highly variable tundra and pre-tundra ecosystems.