Assessing and predicting biodiversity in a floodplain ecosystem: Assimilation of net primary production derived from imaging spectrometer data into a dynamic vegetation model

New concepts for river management in northwestern Europe are being developed which aim at both flood protection and nature conservation. As a result, methods are required that assess the effect of management activities on the biodiversity of floodplain ecosystems. In this paper, we show that dynamic vegetation models (DVMs) in combination with regional scale derived remote sensing products can be adopted to assess both current and future ecosystem development and biodiversity status of a complex floodplain ecosystem in the Netherlands. The dynamic vegetation model SMART2-SUMO2 in combination with the nature valuation model NTM3 predicting potential floristic diversity was applied to simulate the biodiversity status of the Millingerwaard floodplain along the river Rhine in the Netherlands. Estimates of net primary production (NPP) derived from airborne HyMap imaging spectrometer data were used for validation of the simulated NPP by the DVM at the time of data acquisition in 2004. Imaging spectrometer derived NPP was in good agreement with the SMART2-SUMO2 modeled results. The NTM3 derived nature valuation in 2004 expressed as plant diversity for the floodplain was high and well in agreement with field observations. In a next step, the DVM was re-initialized using imaging spectrometer derived NPP in 2004 and a forecast of plant diversity and biomass development in 2050 was made. A comparison was performed for three pre-defined floodplain management scenarios using a data-assimilation based approach as well as one without. Significant differences in biomass development can be observed between the scenarios. Predicted plant diversity for individual ecosystems in 2050 shows increased variability for forest ecosystems compared to grass ecosystems. This shows that floodplain management should take advantage of spatiotemporal dynamics of the floodplain as a basis for fostering the development of increased biodiversity. The results of this study demonstrate that imaging spectrometer derived products can be used for validation and initialization of DVMs.     

Lidar remote sensing of savanna biophysical attributes: opportunities,progress, and challenges

Despite occupying almost a fifth of the global terrestrial vegetation system, savanna ecosystems are relatively understudied in the Earth observation field. As a result, their contribution to global socioecological functions, such as carbon sequestration, habitat provision, watershed protection, biodiversity, and communal supply of timber and non-timber products, is inadequately accounted for. Since lidar remote sensing has been proved to estimate accurately the three-dimensional structural attributes of vegetation, the author found it insightful to synthesize the application of this technique in the savannas as one of the steps towards addressing this knowledge gap. The synthesis evaluated the progress of current studies that primarily use lidar data in the savannas and identified the associated opportunities and challenges. For each selected application, three main questions are asked: (1) what is typically needed from lidar remote sensing? (2) what have we achieved already? And (3) what is the current status? The last question was further split into two: (a) what is lacking, if any? (b) what challenges need to be addressed? This article concludes by looking into the potential future of lidar remote sensing in the savannas and some recommendations are put forward accordingly.     

Capability representation model for heterogeneous remote sensing sensors: Case study on soil moisture monitoring

Sensor capability information can be used as a basis for the integrated management of vast and heterogeneous remote sensing sensors in Global Earth Observation System of Systems environment. However, the existing representation of this information shows an inconsistent pattern, incomplete capability aspects, and casual expression forms, resulting in information silos among different systems. A sensor capability representation model (SCRM) is proposed in this study. Based on the Meta Object Facility architecture, a five-tuple hierarchical SCRM framework is formulated. Five specific representation element collections for typical remote sensing sensor types are developed to satisfy the requirements of detailed capability expression. The Open Geospatial Consortium Sensor Model Language is used as the expression form of the proposed SCRM. A prototype system is developed and a case study is conducted for a soil moisture monitoring application in Baoxie Town. The SCRM can also be extensively utilised for other environmental monitoring and modelling situations.     

基于机器学习和大数据平台的陆地生态系统碳收支遥感监测

陆地生态系统碳收支是全球碳循环研究的重要指标,也是气候变化的重要参数。针对该指标估测的不确定性,基于陆地生态系统通量观测研究网络的实测碳通量数据及遥感卫星观测数据产品,利用机器学习方法进行建模研究。研究选用随机森林算法自动从高质量的星—地训练数据集中学习特征、挖掘数据中的隐含信息以及时序间依赖关系的差异,建立了基于随机森林算法的碳收支参数GPP（Gross Primary Production）、NEP （Net Ecosystem Production）估算模型,并选择标准指标利用验证数据集对模型进行了客观评价。结果分析表明：与MODIS GPP产品相比,该方法在估算精度上有了提高,其中落叶阔叶林预测结果最优,决策系数为R²为0.82,均方根误差为1.93 gCm^-2 d^-1,在其他植被类型上也明显优于传统光能利用率模型产品,更接近于地面通量观测数据。基于相同方法建立的NEP模型也得到了较好的估测结果,落叶阔叶林预测模型的输出结果与通量塔获得的NEP相关关系R²为0.70,RMSE=1.75 g C m^-2 d^-1。GPP和NEP模型精度差异也表明,在进行机器学习建模时,训练数据集自变量的选择仍然需要机理模型支持。为进行陆地生态系统碳收支大范围快速估算,本研究进行了陆地生态系统碳收支遥感监测平台的搭建,该平台以GEE （Google Earth Engine）大数据平台作为数据存储与计算后端,Django和Nginx作为Web服务框架,OpenLayers和jQuery作为前端框架,从而实现了碳收支参数长时间序列大范围的快速计算、结果实时显示等功能。基于该平台和模型获取的2002—2016年全球（60°N—60°S）逐年GPP结果表明,全球平均GPP存在明显的空间差异,显著增加的区域主要集中在亚洲东部地区及北美洲森林地区等。研究表明,基于机器学习和大数据平台进行碳收支参数遥感监测,能够快速提供与地面真实观测较为一致的陆地生态系统区域和全球尺度碳收支遥感监测结果,该流程在一定程度避免了生理过程模型复杂的参数设置,减少了区域和全球大尺度碳收支监测的不确定性。     

Land cover and global productivity: A measurement strategy for the NASA programme

NASA's Earth science programme is developing an improved understanding of terrestrial productivity and its relationship to global environmental change. Environmental change includes changes that are anthropogenic, caused for example by increasing population and resource use, as well as those that are natural, caused by interannual or decadal variability in climate and intrinsic vegetation dynamics. In response to current science and policy concerns, the Earth science programme has carbon and the major biogeochemical cycles as a primary focus but is broad enough to include related topics such as land-atmosphere interactions associated with the hydrological cycle and the chemical composition of the atmosphere. The research programme includes the study of ecosystems both as respondents to change and as mediators of feedback to the atmosphere. Underlying all the research elements are important questions of natural resources and sustainable land management. The land cover and land use change element of the programme is aimed specifically at studying the causes and effects of land transformation and changes in land use practices. The NASA Earth science programme has a primary focus on using satellite remote sensing systems but also recognizes the need for an integrated approach to achieving its science goals by combining satellite and in situ process measurements and numerical modelling. This paper outlines the programme strategy for addressing its major focus. The approach adopted provides a balance between long-term satellite measurements of the Earth's surface at moderate and high spatial resolutions that are needed to quantify change, and the new experimental satellite missions that are aimed at addressing specific process research questions and testing new sensing technology. In addition to satellite measurements, ground-based in situ measurements are needed to validate the satellite data products, to describe and quantify processes, and to parameterize and validate process models. Numerical models need to be enhanced to provide both the study of processes and a predictive capability for the study of global change.     

成像雷达遥感的生态学应用

成像雷达(SAR)遥感具有独特的全天时、全天候及对地物的穿透性与形态探测能力。尤其是近年来新型成像雷达遥感(极化、干涉)及数据处理技术的发展,从SAR遥感影像上获得的地表信息越来越多,这些为进一步推进成像雷达遥感数据在生态学中的应用提供了基础。总结了成像雷达遥感在陆地生态环境系统中的4类典型应用:①土地覆盖分类与植被制图;②估计森林生物量;③洪泛区与湿地监测;④其它生态环境变化过程的探测。最后分析了不同生态学应用中SAR系统参数的最优配置,这对于我国开展成像雷达遥感在陆地生态环境监测中的应用具有非常重要的参考意义。     

面向森林生态学研究的Web处理服务系统

森林是陆地生态系统的主体,森林生态保护与恢复是生态文明建设的重要组成部分.面对森林生态模型单机化、重复开发部署的现状,本文设计并实现一个面向森林生态学研究的Web处理服务系统.该系统根据开放地理空间信息联盟（Open Geospatial Consortium,OGC）制定的空间数据共享及互操作的接口规范,基于WebService技术,实现了面向森林生态学研究的多模型整合,并将模型计算结果进行展示,减少了重复开发和部署工作,提高了数据与服务的共享性.     

Artificial intelligence for monitoring and supervisory control of process systems

Complex processes involve many process variables, and operators faced with the tasks of monitoring, control, and diagnosis of these processes often find it difficult to effectively monitor the process data, analyse current states, detect and diagnose process anomalies, or take appropriate actions to control the processes. The complexity can be rendered more manageable provided important underlying trends or events can be identified based on the operational data (Rengaswamy and Venkatasubramanian, 1992. An Integrated Framework for Process Monitoring, Diagnosis, and Control Using Knowledge-based Systems and Neural Networks. IFAC, Delaware, USA, pp. 49–54.). To assist plant operators, decision support systems that incorporate artificial intelligence (AI) and non-AI technologies have been adopted for the tasks of monitoring, control, and diagnosis. The support systems can be implemented based on the data-driven, analytical, and knowledge-based approach (Chiang et al., 2001. Fault Detection and Diagnosis in Industrial Systems. Springer, London, Great Britain). This paper presents a literature survey on intelligent systems for monitoring, control, and diagnosis of process systems. The main objectives of the survey are first, to introduce the data-driven, analytical, and knowledge-based approaches for developing solutions in intelligent support systems, and secondly, to present research efforts of four research groups that have done extensive work in integrating the three solutions approaches in building intelligent systems for monitoring, control and diagnosis. The four main research groups include the Laboratory of Intelligent Systems in Process Engineering (LISPE) at Massachusetts Institute of Technology, the Laboratory for Intelligent Process Systems (LIPS) at Purdue University, the Intelligent Engineering Laboratory (IEL) at the University of Alberta, and the Department of Chemical Engineering at University of Leeds. The paper also gives some comparison of the integrated approaches, and suggests their strengths and weaknesses.     

Guest Editors' Introduction: Self-Management through Self-Organization

Given the scale and complexity of today's information systems, it's increasingly important that they handle system management problems and tasks themselves--intelligently and autonomously. This special issue focuses on implementing self-management in a variety of distributed systems by observing the self-managing systems that surround us: multicellular organisms, social insects, market economies, human societies, ecosystems, and so on. These systems are made of components that obey local rules and act on the basis of local observations--often selfishly. Yet the system as a whole exhibits global properties such as self-healing, self-tuning, and self-organization. Distilling the key ideas from these systems and incorporating them into information systems often leads to inexpensive, straightforward, and highly robust solutions.This article is part of a special issue on Self-Managing Systems.     

The potential for estimating chlorophyll content from a vegetation canopy using the Medium Resolution Imaging Spectrometer (MERIS)

In 2000, the European Space Agency aims to launch the Envisat-1 satellite platform which will carry the Medium Resolution Imaging Spectrometer (MERIS), an advanced optical sensor designed to acquire Earth observation data at regional to global scales. Of particular relevance to terrestrial ecosystems functioning, the MERIS offers the potential to estimate the spectral location of the maximum slope, termed the red edge position (REP), which marks the boundary between chlorophyll absorption in the red wavelengths and the high infrared reflectance due to leaf internal scattering. However, although a first derivative transformation of the reflectance spectra will highlight the maximum slope position, the accurate location of the REP is limited by the spectral sampling resolution of the sensor. A theoretical analysis, using a combined leaf-canopy radiative transfer model, demonstrates that the MERIS, having five coarsely spaced wavebands in the region of the REP, can be utilized for monitoring spectral shifts of the REP, resulting from variation in leaf chlorophyll content or leaf area index.     

The ESA Medium Resolution Imaging Spectrometer MERIS a review of the instrument and its mission

Following the successful operations of the ERS-1 and 2 satellites which are mainly dedicated to physical oceanography and ice observations from space, the European Space Agency (ESA) developed a multidisciplinary Earth observation instrument within its polar Earth Observation Programme with a focus on biological ocean observations. The Medium Resolution Imaging Spectrometer (MERIS) will be launched onboard Envisat-1 in the 1999-2000 time frame, providing a European remote sensing capability for observing for example oceanic biology and marine water quality through observations of water colour. MERIS will have a medium spectral and high radiometric resolution and a dual spatial resolution, within a global mission, covering open ocean and coastal zone waters, important aspects of the atmosphere, and large ecosystems over land. The global mission of MERIS will have a major contribution to scientific projects aimed at greater understanding of the role of oceans and ocean productivity in the climate system and our ability to forecast change through models. Secondary objectives of the MERIS mission will be directed to the measurement of atmospheric parameters associated with clouds, water vapour and aerosols in addition to land surface parameters, important in particular for the understanding of vegetation processes. In advance of the launch of MERIS, algorithms are being developed for the interpretation of MERIS observations and dedicated studies are ongoing to establish the means of validating the data products. The aim of this paper is to provide a comprehensive overview of the MERIS concept, its mission and data products in context of the driving scientific requirements.     

A multisensor fusion approach to improve LAI time series

High-quality and gap-free satellite time series are required for reliable terrestrial monitoring. Moderate resolution sensors provide continuous observations at global scale for monitoring spatial and temporal variations of land surface characteristics. However, the full potential of remote sensing systems is often hampered by poor quality or missing data caused by clouds, aerosols, snow cover, algorithms and instrumentation problems. A multisensor fusion approach is here proposed to improve the spatio-temporal continuity, consistency and accuracy of current satellite products. It is based on the use of neural networks, gap filling and temporal smoothing techniques. It is applicable to any optical sensor and satellite product. In this study, the potential of this technique was demonstrated for leaf area index (LAI) product based on MODIS and VEGETATION reflectance data. The FUSION product showed an overall good agreement with the original MODIS LAI product but exhibited a reduction of 90% of the missing LAI values with an improved monitoring of vegetation dynamics, temporal smoothness, and better agreement with ground measurements.     

分布式系统监视器的设计与实现

机载分布式实时系统是由一组节点组成的多处理器系统,它是新一代航空电子系统综合化的基础。对于机载分布式实时系统来说监视是在其开发和调试过程中了解系统行为和状态必不可少的手段。针对机载分布式实时系统的特点设计了分布式系统监视器,实现了对机载分布式实时系统的监视并将监视数据图形化显示。     

Monitoring land cover change and ecological integrity in Canada's national parks

Canada's national parks system includes 43 terrestrial parks covering 3% (276,275 km²) of the country's landmass and representing its full range of natural regions. Considering the vast and often remote areas under protection, Parks Canada Agency envisions Earth Observation technology to be the basis for a Park Ecological Integrity Observing System (Park-EIOS), and integral component of a larger national parks ecological integrity (EI) monitoring program. Park-EIOS is planned for operational use beginning in 2008 and includes coarse filter EI indicators corresponding to landscape pattern, succession and retrogression, net primary productivity, and focal species distributions within parks and their surrounding greater park ecosystems. A primary input to produce all four indicators is a time series of land cover information derived from medium (~ 30 m) resolution, Landsat-class sensors. This paper describes a generic, end-to-end change detection framework developed for Park-EIOS, labelled Automated Multi-temporal Updating through Signature Extension (AMUSE). AMUSE involves radiometric normalization steps, production of a baseline land cover, change vector analysis to identify changed pixels, and a new constrained signature extension approach to update the land cover of changed areas. We present the method and results applied to six pilot parks using time series of Landsat TM/ETM+ imagery from 1985-2005.     

Infrastructures and tools for multiagent systems for the new generation of distributed systems

In order for multiagent systems to be included in real domains (media and Internet, logistics, e-commerce, and health care), infrastructures and tools for multiagent systems should provide efficiency, scalability, security, management, monitoring, and other features related to building real applications. Thus, infrastructures and tools that support multiagent systems are needed, especially those that promote the adoption of agent-based systems by designers and programmers in both academia and industry. This special issue is a selection of contributions whose preliminary versions were presented at the ITMAS 2010 workshop, which was held in conjunction with the International Conference on Autonomous Agents and Multi-agent Systems.     

A digital library framework for biodiversity information systems

Biodiversity Information Systems (BISs) involve all kinds of heterogeneous data, which include ecological and geographical features. However, available information systems offer very limited support for managing these kinds of data in an integrated fashion. Furthermore, such systems do not fully support image content (e.g., photos of landscapes or living organisms) management, a requirement of many BIS end-users. In order to meet their needs, these users—e.g., biologists, environmental experts—often have to alternate between separate biodiversity and image information systems to combine information extracted from them. This hampers the addition of new data sources, as well as cooperation among scientists. The approach provided in this paper to meet these issues is based on taking advantage of advances in digital library innovations to integrate networked collections of heterogeneous data. It focuses on creating the basis for a next-generation BIS, combining new techniques of content-based image retrieval and database query processing mechanisms. This paper shows the use of this component-based architecture to support the creation of two tailored BIS systems dealing with fish specimen identification using search techniques. Experimental results suggest that this new approach improves the effectiveness of the fish identification process, when compared to the traditional key-based method.     

珠江委网络安全态势感知平台设计与应用

为进一步提升珠江委网络安全防护水平,打造全天候主动防御的网络安全防护体系,梳理当前珠江委网络安全防护的短板,从自动告警、攻击行为重塑、脆弱性分析等方面分析态势感知平台功能需求,依托数据融合、事件关联、态势预测等态势感知关键技术,设计一种符合珠江委网络安全防护需求的态势感知平台。平台架构设计为数据采集、存储分析、核心业务和 BI 展示 4 个层次,主要实现资产管理、风险感知、预警管理和安全态势信息专题展示等功能。基于网络安全态势感知平台,珠江委基本形成事先梳理、风险感知、安全监测、事件分析、事件处置的主动防御体系,安全监测和主动防御能力明显提升,重要信息系统防护均未失陷, 取得较好的应用效果。     

Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification

The analysis of remote sensing image areas is needed for climate detection and management, especially for monitoring flood disasters in critical environments and applications. Satellites are mostly used to detect disasters on Earth, and they have advantages in capturing Earth images. Using the control technique, Earth images can be used to obtain detailed terrain information. Since the acquisition of satellite and aerial imagery, this system has been able to detect floods, and with increasing convenience, flood detection has become more desirable in the last few years. In this paper, a Big Data Set-based Progressive Image Classification Algorithm (PICA) system is introduced to implement an image processing technique, detect disasters, and determine results with the help of the PICA, which allows disaster analysis to be extracted more effectively. The PICA is essential to overcoming strong shadows, for proper access to disaster characteristics to false positives by operators, and to false predictions that affect the impact of the disaster. The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches. Two types of proposed PICA systems detect disasters faster and more accurately (95.6%).     

基于可插拔式区块链的生态流量监测系统设计

生态流量保障目标落实情况已纳入最严格水资源管理制度考核，确保生态流量监测数据的真实可信、公开透明尤其关键。本文围绕生态流量监管的公开、透明、多方参与、数据溯源等需求，结合常用的生态流量监测系统，分析了区块链技术在生态流量监测应用中的优势，考虑了可插拔式区块链支撑组件，设计了一种契合生态流量保障工作监督考核需求的可信监测监管系统，在网络层运用点对点传输和拜占庭容错共识机制进行数据通信，在数据资源层采用区块结构进行数据链式存储，将生态流量监管所需的各类算法写入智能合约中，并将计算结果交予业务层进行可信应用或数据溯源，通过智能合约提供区块链节点配置及其权限管理功能。传统监测系统引入区块链技术后，可提升生态流量监测数据的真实透明、难以篡改、可追溯、易扩展等特性，适合多主体、跨部门的监测数据共享与发布。研究成果可为生态流量泄放监管提供一种可靠的技术支撑，亦可为区块链技术在水利行业尤其是水资源管理领域的应用落地提供借鉴。     

Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks

Many biologists, ecologists, and conservationists are interested in the possibilities that remote sensing offers for their daily work and study site analyses as well as for the assessment of biodiversity. However, due to differing technical backgrounds and languages, cross-sectorial communication between this group and remote-sensing scientists is often hampered. Hardly any really comprehensive studies exist that are directed towards the conservation community and provide a solid overview of available Earth observation sensors and their different characteristics. This article presents, categorizes, and discusses what spaceborne remote sensing has contributed to the study of animal and vegetation biodiversity, which different types of variables of value for the biodiversity community can be derived from remote-sensing data, and which types of spaceborne sensor data are available for which time spans, and at which spatial and temporal resolution. We categorize all current and important past sensors with respect to application fields relevant for biologists, ecologists, and conservationists. Furthermore, sensor gaps and current challenges for Earth observation with respect to data access and provision are presented.