Images from the sky

Space-based imagery has, until recently, been available primarily to high-level government leaders and subordinate agencies operating within the government. Rapid advances in communications technology and satellite remote sensing has made it possible for the individual citizen to purchase remote sensing imagery of virtually any area on the globe. No longer the private domain of governments, imagery is readily available to corporate organizations with huge investments in forestry, minerals, crops, or other natural resources. More importantly, imagery can be obtained without the permission of the sensed nation. For example, imagery produced by SPOT Imaging of France was used extensively by the coalition forces and the United States Air Force before and during Operations Desert Shield and Storm. Corporations and governments are not the only groups eager to take advantage of this new technology. One only needs to watch the evening weather report for a glimpse of how low-to-medium resolution satellite imagery is used by the news media to predict and track weather patterns around the globe. For the media to take full advantage however, satellite resolution must be reduced to five meters or less. This paper reviews the history of commercial satellite remote sensing, as well as the U.S. government's highly classified KH-series satellites, and explores the future impact of “Mediasat” with regard to its use by the news media. The satellite images that play an increasing role in TV news and weather reports have a short but revealing history, and are part of a potentially even more revolutionary news reporting technology. What follows is a brief attempt to assess this history and the possible future in terms of technological developments, commercial applications, and social applications.     

Parallel architecture for cotton crop classification using WLI-Fuzzy clustering algorithm and Bs-Lion neural network model

The application of remote sensory images in crop monitoring has been increasing in the recent years due to its high classification accuracy. In this paper, a novel parallel classification methodology is proposed using a new clustering and classification concept. A novel neural network model with the Bs-Lion training algorithm is developed by integrating the Bayesian regularization training with the Lion Algorithm. Here, two levels of parallel processing are performed, namely parallel WLI-Fuzzy clustering and parallel BS-Lion neural network classification. The experimentation of the proposed parallel methodology is carried out using satellite images obtained from the Indian remote sensing satellite IRS-P6. The performance of the proposed system is compared with the existing techniques using validation measures accuracy, sensitivity and specificity. The experimentations resulted in promising results with an accuracy of 0.8994, sensitivity of 0.8682 and specificity of 0.8739, which favour the performance of the proposed parallel architecture in the classification.     

Microneedle Technologies for Food and Crop Health: Recent Advances and Future Perspectives

The global food supply constantly faces the threats of emerging crop diseases initiated by pathogens such as bacteria, fungi, and viruses. Plant diseases can cause significant economic and production losses in the agriculture industry, and early disease detection significantly mitigates losses. Monitoring the food quality and detecting pathogens during the food supply chain is essential in confirming the food's safety and reducing crop loss. This results in lowering production costs and increasing average yield in the agriculture industry. Considering the significant development of nanotechnology in biomedicine for human health monitoring, diagnostics, and treatment, there is an increasing interest in using nanotechnology in crop production, health, and plant science. This technology can allow continuous monitoring of plant health and on-site diagnostics of plant diseases. While many microneedle-based devices are previously reported for human health monitoring, diagnostics, and treatment, the application of this technology to agriculture started relatively recently. This review investigates the recent development of microneedle technology in food and crop health, where the most state-of-the-art microneedle-based devices are utilized for plant drug delivery, disease monitoring, and diagnosis. Finally, the current challenges and future directions in developing microneedle technology for food and crop health are discussed.     

Remote sensing as a tool for achieving and monitoring progress toward sustainability

Airborne and satellite remote sensing have enormous potential for facilitating and monitoring the dynamics of intergenerational natural resource management and built environment sustainability. Based on reviews of (1) the development and current state of the science for deploying remote sensing, and (2) capabilities and limitations of the array of archival and real-time remotely sensed resources as tools for attaining and monitoring sustainability, we suggest several "best practices" for incorporating remote sensing within sustainability plans for selected rural and urban ecosystems. We present sustainability indices for case studies relating to sustainable forestry, agriculture, watersheds, and urbanizing areas, as well as a conceptual prototype for incorporating remotely sensed data within a general purpose decision support system that highlights the benefits of multi-temporal historical change detection in monitoring past and ongoing change, and in visualization of alternative future scenarios. Finally, we examine the sustainability of remote sensing technology, itself, with respect to economic pressures, public policy perturbations, systems resilience, biogenic factors and both inadvertent and deliberate anthropogenic impact.     

Forest survey and management using remote sensing

Some of the important work done in India and other parts of the world in the application of remote sensing for forest survey and management has been reviewed in this paper. The account of work has been given under three main headingsviz airborne remote sensing, satellite remote sensing and multistage approach.     

Remote Sensing Image Classification Algorithm Based on Texture Feature and Extreme Learning Machine

With the development of satellite technology, the satellite imagery of the earth’s surface and the whole surface makes it possible to survey surface resources and master the dynamic changes of the earth with high efficiency and low consumption. As an important tool for satellite remote sensing image processing, remote sensing image classification has become a hot topic. According to the natural texture characteristics of remote sensing images, this paper combines different texture features with the Extreme Learning Machine, and proposes a new remote sensing image classification algorithm. The experimental tests are carried out through the standard test dataset SAT-4 and SAT-6. Our results show that the proposed method is a simpler and more efficient remote sensing image classification algorithm. It also achieves 99.434% recognition accuracy on SAT-4, which is 1.5% higher than the 97.95% accuracy achieved by DeepSat. At the same time, the recognition accuracy of SAT-6 reaches 99.5728%, which is 5.6% higher than DeepSat’s 93.9%.     

Automatic detection of urban area from the remote sensing imagery based on improved D-S evidence theory

Urban area detection from the remote sensing imagery has important significance in dynamic monitoring on the use types of city land and the city development planning. An approach for full-automatic detection of urban area is proposed, which can fuse any number of features. The combination formula of the traditional D-S evidence theory (TDSET) is improved by adding a processing factor for conflicting evidences based on the original fusion function. For the validity of the proposed method, three positive evidences (gradient mean, Harris feature points and spectral homogeneity) as well as one negative evidence (local gradient orientation density) are selected to test three GeoEye images. Experimental results show that the precision of the proposed method achieves 96.89% and its recall is 86.52%, where the recall increases about 50% comparing with the TDSET.     

森林资源遥感估测的重要进展

传统的森林资源调查是一项费时、费力,需大量经费的工作。20世纪70年代末,遥感技术被引入中国。以中国林科院为主的一批林业科学工作者,积极将其应用于森林资源调查工作,特别是森林蓄积量的估测。研究了比值项的作用,确定用定量因子和定性因子联合估测森林蓄积量多元估计方程的形式,以及在中国不同气候带的应用。指出遥感调查方法比传统方法节约经费1/3～1/2。近年来,用近代统计学和非参数方法进行了新的探索和深入研究,解决了一些在林业资源调查中应用遥感技术的重要问题,为建立以遥感技术为主要技术手段的新的森林资源估测体系提供有力支持。     

Detection of gaseous plumes in IR hyperspectral images using hierarchical clustering

The emergence of IR hyperspectral sensors in recent years enables their use in remote environmental monitoring of gaseous plumes. IR hyperspectral imaging combines the unique advantages of traditional remote sensing methods such as multispectral imagery and nonimaging Fourier transform infrared spectroscopy, while eliminating their drawbacks. The most significant improvement introduced by hyperspectral technology is the capability of standoff detection and discrimination of effluent gaseous plumes without need for a clear reference background or any other temporal information. We introduce a novel approach for detection and discrimination of gaseous plumes in IR hyperspectral imagery using a divisive hierarchical clustering algorithm. The utility of the suggested detection algorithm is demonstrated on IR hyperspectral images of the release of two atmospheric tracers. The application of the proposed detection method on the experimental data has yielded a correct identification of all the releases without any false alarms. These encouraging results show that the presented approach can be used as a basis for a complete identification algorithm for gaseous pollutants in IR hyperspectral imagery without the need for a clear background.     

Applications of remote sensing techniques to geology

Remote sensing is a new emerging field of technological development and has made a very significant impact on the geological surveys and studies. The work done so far in geological remote sensing has indicated the scope, utility and limitations of these modern techniques in different geological problems. The utility of airborne surveys and aerial photography has now been well established whereas satellite remote sensing at present has two main constraints—resolution and lack of stereoscopy. With the developments in sensor technology to provide sensors with improved resolution, more spectral bands and stereoscopy, substantial new results are anticipated in the geological remote sensing from space. Brief overview of applications of remote sensing techniques to geology is discussed in this paper.     

海洋光学遥感技术的发展和前沿

空间遥感与信息技术已经发展成为满足人类对海洋资源和环境不同尺度和不同层次连续、动态的信息需求的必要手段。海洋光学遥感在实施大范围海面瞬间信息监测、数年至几十年长序列全球海洋数据采集等方面，发挥了常规调查方法所不能替代的优势。从20世纪70年代开始，国际上发展了利用测量离水辐射率的原理，用现代的卫星遥感技术来实时监测海洋水色环境。80年代以来，我国对该项高技术也十分重视，并有了长足进展。文章回顾我国近20年来海洋光学遥感技术的发展；讨论了当前国家在海洋权益、防灾减灾、海洋资源管理和开发等方面的进一步需求；论述了该技术在环境监测应用导向下的前沿，如：高精度分析和定量化、高频率长序列监测和业务化趋向、多平台监测数据同化和核心技术合成。     

Nanostructures in environmental pollution detection,monitoring, and remediation

We present preliminary results of our joint investigations to monitor and mitigate environmental pollution, a leading contributor to chronic and deadly health disorders and diseases affecting millions of people each year. Using nanotechnology-based gas sensors; pollution is monitored at several ground stations. The sensor unit is portable, provides instantaneous ground pollution concentrations accurately, and can be readily deployed to disseminate real-time pollution data to a web server providing a topological overview of monitored locations. We are also employing remote sensing technologies with high-spatial and spectral resolution to model urban pollution using satellite images and image processing. One of the objectives of this investigation is to develop a unique capability to acquire, display and assimilate these valuable sources of data to accurately assess urban pollution by real-time monitoring using commercial sensors fabricated using nanofabrication technologies and satellite imagery. This integrated tool will be beneficial towards prediction processes to support public awareness and establish policy priorities for air quality in polluted areas. The complex nature of environmental pollution data mining requires computing technologies that integrate multiple sources and repositories of data over multiple networking systems and platforms that must be accurate, secure, and reliable. An evaluation of information security risks and strategies within an environmental information system is presented. In addition to air pollution, we explore the efficacy of nanostructured materials in the detection and remediation of water pollution. We present our results of sorption on advanced nanomaterials-based sorbents that have been found effective in the removal of cadmium and arsenic from water streams.     

信息化农业科技前沿与发展战略

人类进入21世纪的信息社会，信息科学与技术已成为改造传统农业最有效的技术手段之一，以信息化推动我国农业的现代化是必然的趋势，在这样的背景下，信息化农业将成为农业的发展方向。文章论述了信息化农业的基本概念，科学支撑体系，技术体系，科技前沿和我国的发展战略。     

Spatial Filtering Applied to Remote Sensing Imagery

A high-quality optical system has been developed for the optical processing of remote sensing imagery. Picture formats in excess of 10⁷ pixels can be accommodated. Examples are given which involve satellite photography, sonar and airborne radar images.     

Identification of Crop Diseases Based on Improved Genetic Algorithm and Extreme Learning Machine

As an indispensable task in crop protection, the detection of crop diseases directly impacts the income of farmers. To address the problems of low crop-disease identification precision and detection abilities, a new method of detection is proposed based on improved genetic algorithm and extreme learning machine. Taking five different typical diseases with common crops as the objects, this method first preprocesses the images of crops and selects the optimal features for fusion. Then, it builds a model of crop disease identification for extreme learning machine, introduces the hill-climbing algorithm to improve the traditional genetic algorithm, optimizes the initial weights and thresholds of the machine, and acquires the approximately optimal solution. And finally, a data set of crop diseases is used for verification, demonstrating that, compared with several other common machine learning methods, this method can effectively improve the crop-disease identification precision and detection abilities and provide a basis for the identification of other crop diseases.     

Remote Sensing and Control of an Irrigation System Using a Distributed Wireless Sensor Network

Efficient water management is a major concern in many cropping systems in semiarid and arid areas. Distributed in-field sensor-based irrigation systemsoffer a potential solution to support site-specific irrigation management that allows producers to maximize their productivity while saving water. This paper describes details of the design and instrumentation of variable rate irrigation, a wireless sensor network, and software for real-time in-field sensing and control of a site-specific precision linear-move irrigation system. Field conditions were site-specifically monitored by six in-field sensor stations distributed across the field based on a soil property map, and periodically sampled and wirelessly transmitted to a base station. An irrigation machine was converted to be electronically controlled by a programming logic controller that updates georeferenced location of sprinklers from a differential Global Positioning System (GPS) and wirelessly communicates with a computer at the base station. Communication signals from the sensor network and irrigation controller to the base station were successfully interfaced using low-cost Bluetooth wireless radio communication. Graphic user interface-based software developed in this paper offered stable remote access to field conditions and real-time control and monitoring of the variable-rate irrigation controller.     

一种目标提取新方法-光谱排序编码法

为了进一步提高光学遥感目标提取精度,拓展目标提取方法的适用范围,推进遥感智能化信息提取方法的发展,本文从谱带强度与波形特征有机结合的角度出发提出了光谱排序编码法.首先介绍了该方法的基本原理:对端元光谱与图像光谱按谱带强度大小各自进行排序的基础上,通过比较同一顺序上的波段位置的异同来计算两条光谱曲线的相似度;然后开展了与约束能量最小法(CEM)、光谱二阶导数法(SSD)的目标提取精度对比实验,结果表明该方法的目标提取精度为95%,比CEM提高了41.9%,比SSD提高了46.9%;最后利用SPOT多光谱和AVIRIS高光谱数据检验了该方法在目标提取适用范围上的能力,结果表明该方法既适用于多光谱遥感,又适用于高光谱遥感.总之,算法具有速度快、精度高、适用范围广的特点.     

城市大气污染源追踪“广义判识”与Nudging源 同化集成技术途径

提出了城市大气污染源追踪“广义判识”综合应用平台、模式源同化及其卫星遥感应用等新技术。“广义判识”综合应用平台包括气溶胶源主因子分析方法、气溶胶周期变化谱分析法、城市冠层大气污染变化规律同位相特性分析、追溯远距离空气污染源的合成风场相关矢量法、后向轨迹示踪法和边界层影响足痕分析法;另外,为解决长期以来大气污染模式中排放源不确定性的关键技术难点,采用源同化牛顿逼近Nudging技术,获取具有季节、月变化动态变化特征的SO2、NO2区域反演源排放清单。将卫星遥感资料应用在同化模型,探索空气污染预报模式的卫星遥感产品应用技术,卫星遥感—地基观测综合分析平台亦可广泛地应用于城市污染源追踪、城市雾和城市热岛的研究,并提出多圈层水—土—气综合分析方法是一种追踪水污染源的探索性尝试     

可见光和热红外辐射定标方法浅述

随着遥感技术的发展，遥感影像的应用越来越为人们所关注。而遥感影像的处理成为不可缺少的关键环节。在遥感数据的采集过程中，由于受到仪器本身的误差及自然环境诸多因素的影响，不可避免的产生种种误差。为了获得真实反映地物信息的图像，必须进行纠正。本详细介绍了引起辐射畸变的因素以及各种辐射纠正的方法，并以NOAA为例详细分析了其产生畸变的原因，并着重介绍了三种辐射定标方法：反射率法、辐亮度基法和辐照度基法，结果显示辐照度基法较为理想。