remote sensing 句子 123
remote例句
remote例句【释义】remoteadj.边远的，偏僻的；（距离或空间上）遥远的；（时间上）久远的；（机会或可能性）渺茫的；差别很大的，很不相同的；不友好的，冷漠的；远亲关系的；（电子设备）遥控的；（计算机）远程的，远程连接的n.遥控装置，遥控器复数remotes比较级remoter或more remote最高级remotest或most remote【短语】1remote sensing遥感遥感;遥感技术2remote control自遥控;远程控制;遥控器;长途节制3remote registry远程管理注册表;远程注册表服务;远程登录服务;长途注册表办事4Wii RemoteWii遥控器;右手柄;遥控器;控制器5Remote Desktop Protocol远程桌面协议;协议;远端桌面通讯协定6remote access远程访问;远端存取;远程接入7remote boot远程引导;长途引导;远程启动;远程自举8routing and remote access我干脆禁用了它;路由和远程访问;路由和远端存取;路由服务9remote evaluation远程求值【例句】1I can't find the remote control.我找不到遥控器。
2It works by remote control.它通过遥控工作。
3The bomb was detonated by remote control.炸弹通过遥控引爆。
4The monastery is in a remote mountain pass.那僧院在一个偏远的山坳通道处。
5The bandits fled to a remote mountain hideaway.这些劫匪逃窜到了一个偏远的大山间藏身。
6He flipped through the channels with the remote.他用遥控器快速浏览了各个频道。
remote短语句子
Remote短语句子
一、短语搭配
remote sensing（遥感）遥感 ; 遥感技术 ; 远距离读出
Remote Interface远程接口 ; 远端接口 ; 端介面 ; 借口类
remote regulating（自）遥调
remote sensor遥感器 ; 传感器
remote machine远程计算机 ; 远程机器 ; 远程主机
remote maintenance遥控维修 ; 远程维护 ; 远距离维修 ; 远程维修
remote indication远距离指示 ; 遥控指示 ; 遥测
Remote Proxy远程代理 ; 本地的代理对象控制一个远程的对象Remote cord遥控线 ; 相机快门线
二、双语例句
If we have the more remote future.
如果我们有更遥远的未来。
With these settings, you should be able to log onto the remote machine without prompting for the password.
完成这些设置之后，就应该能够登录到远程计算机，系统不会提示输入密码。
In this plan, both remote joins run on their respective servers at the same time rather than one after another.
在这个计划中，两个远程连接同时在各自的服务器上运行，而不是一个接一个地运行。
测绘遥感专业英语翻译（1）
1.1 What is Remote Sensing?So, what exactly is remote sensing? For the purposes of this tutorial, we will use thefollowing definition:"Remote sensing is the science (and to some extent, art) of acquiring information about the Earth's surface without actually being in contact with it. This is done by sensing and recording reflected or emitted energy and processing, analyzing, and applying that information."1.1什么是遥感？那么，究竟什么是遥感？这篇教程的目的，我们将使用下面的定义：“遥感科学（在某种程度上，艺术）获取地球表面信息，而不必接触它。
这是通过检测和记录反映或发出能量和处理，进行分析，并应用的信息。
”In much of remote sensing, the process involves an interaction between incident radiation and the targets of interest. This is exemplified by the use of imaging systems where the following seven elements are involved. Note, however that remote sensing also involves the sensing of emitted energy and the use of non-imaging sensors.在许多遥感，过程包括入射辐射和感兴趣的目标之间的相互作用。
Ecological remote sensing
Ecological remote sensingEmergence and spread of infectious diseases in a changing environment require the developmen of new methodologies and tools for risk assessment, early warning and policy making. GISmodelling is routinely used to perform risk assessment for the mitigation of these diseases. We us remote sensing technologies to derive ecological indicators from high temporal resolutionsatellite data time series . Especially the Moderate Resolution Imaging Spectroradiometers (MODIS sensor) which are flown on the Terra and Aqua satellites, deliver an almost complete Earth coverag four times a day at different resolutions (from 250m to 1km pixel resolution). These data are integrated with common GIS data for spatial data analysis. Special focus is on land surfacetemperatures (LST, daily), snow coverage (weekly), leaf area index (LAI, weekly), and vegetatio indices (NDVI, EVI, bi-weekly), all derived from MODIS satellite data. The Enhanced Vegetation Index (EVI) permits to detect seasonal vegetation differences, spring/autumn detection and the length of growing season. Furthermore, the Normalized Difference Water Index (NDWI) can be calculated weekly.Alpine MODIS Vegetation Index example (16 days composite)Sensors/data of ecological relevance and low access costsSensorPeriodSpatial resolution Temporal resolutionFormaAVHRR:●Land Surface Temperature (LST)●Vegetation index (NDVI)1978-today~1km Daily L1BMODIS:●Land Surface Temperature(LST)●Vegetation indices (NDVI, EVI) ●Snow extent ●LAI/FPAR ●...2000-today 1km 500m 250mDaily HDFSPOT Vegetation VGT (NDVI) 1998-today 1km 10 days HDF LANDSAT-TM1-7 (VIS, NIR, TIR)1972-today 15/30/60m16 daysGeoTIF2000-ASTER (VIS, NIR, TIR)today15/30/90m16 days HDF MODISThe launches of the NASA satellite systems Terra (December 1999) and Aqua (May 2002) significantly improve the situation of data availability for scientific purposes and predictive epidemiological studies. The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key instrument on both Terra and Aqua satellites. As they deliver daily two global coverages at 250m (Red, NIR), 500m (MIR) and 1000m resolution (TIR), they are most interesting to support epidemiological studies. Usually one week after acquisition the data are available to the public.For our research, MODIS data are crucial as they help us to derive ecological indicators from MODIS high resolution time series. We are specialised in reconstruction of cloud contaminated LST data which are completely restored using a GIS based methodology. The complex terrain of the Southern Alps is particularly challenging.Further detailsSee MODIS LSTSee MODIS NDVI/EVISee MODIS Sensor SpecificationsRelated publications:●°Roiz D., °Neteler M., Castellani C., Arnoldi D., Rizzoli A. (2011). Climatic Factors DrivingInvasion of the Tiger Mosquito (Aedes albopictus) into New Areas of Trentino, Northern Italy PLoS ONE. 6(4): e14800. [DOI | PDF] (IF: 4.411) - press reactions°The authors contributed equally●He, K.S., Rocchini, D., Neteler, M., Nagendra, H. (2011). Benefits of hyperspectral remotesensing for tracking plant invasions. Diversity and Distributions, 17: 381-392 [DOI | PDF] (IF:4.248)●Rocchini, D., Metz, M., Frigeri, A., Delucchi, L., Marcantonio, M., Neteler, M. (2011). Robusrectification of aerial photographs in an Open Source environment. Computers & Geoscience in press [DOI] (IF: 1.416)●Tonolli, S., Dalponte, M., Gianelle, D., Neteler, M., Rodeghiero, M., and Vescovo, L. (2011).Fusion of airborne LIDAR and satellite multispectral data for the estimation of timber volume an Alpine region. Remote Sensing of Environment, in press [DOI | PDF] (IF: 3.951)●Rocchini, D., McGlinn, D., Ricotta, C., Neteler, M., Wohlgemuth, T. (2011). Landscapecomplexity and spatial scale influence the relationship between remotely sensed spectraldiversity and survey based plant species richness. Journal of Vegetation Science. 22: 688-69 [DOI| PDF] (IF: 2.457)●Neteler, M., 2010: Estimating daily Land Surface Temperatures in mountainous environmentsby reconstructed MODIS LST data. Remote Sensing 2(1), 333-351. (DOI) [ Abstract | PDF ]●Carpi G., Cagnacci F., Neteler M., Rizzoli A, 2008: Tick infestation on roe deer in relation togeographic and remotely-sensed climatic variables in a tick-borne encephalitis endemic area.Epidemiology and Infection,136, pp. 1416-1424. (DOI) (ISI 2007: 1.900) [ PubMed ]● A. Rizzoli, M. Neteler, R. Rosà, W. Versini, A. Cristofolini, M. Bregoli, A. Buckley, and E.A.Gould, 2007: Early detection of TBEv spatial distribution and activity in the Province of Trento assessed using serological and remotely-sensed climatic data. Geospatial Health, 1(2), pp.169-176. [ PubMed | PDF ]●M. Neteler, 2005: Time series processing of MODIS satellite data for landscapeepidemiological applications. International Journal of Geoinformatics, 1(1), pp. 133-138 (PDFFEM-CRI data holdingsMODIS LST data were postprocessed from Terra satellite from 5 mar 2000 - today and from Aqua satellites from 8 jul 2002 - today: more than 13500 maps are in our archive. Aggregation to decade (16 days periods) is performed for epidemiological studies.LANDSATThe recent publication of the USGS LANDSAT archive is a great help for long term studies. With a repeat time of 16 days the entire globe is captured.Further reading●ASPRS guide to land imaging satellites (2008)●MODIS Aqua and Terra products table●MODIS snow & ice products●MODIS on Aqua and Terra operational status●Visible Infrared Imaging Radiometer Suite (VIIRS) - MODIS successor onboard of NPP missio。
Remote sensing, ice&snow and climate change遥感,冰与雪与气候变化;
-deforestation (rainforest, mangrove colonies)
-species inventory -watershed protection (riparian strips) -coastal protection (mangrove forests) -forest health and vigour
Geology
• surficial deposit / bedrock mapping • lithological mapping • structural mapping • sand and gravel (aggregate) exploration/ xploitation • mineral exploration • hydrocarbon exploration • environmental geology • geobotany • baseline infrastructure • sedimentation mapping and monitoring • event mapping and monitoring • geo-hazard mapping • planetary mapping
Forestry
reconnaissance mapping:
-forest cover type discrimination -agroforestry mapping
Commercial forestry:
-clear cut mapping / regeneration assessment -burn delineation -infrastructure mapping / operations support -forest inventory -biomass estimation -species inventory
Resilient Remote Sensing 1
Resilient Remote Sensing1Stephen TaylorTiranee AchalakulJoohan LeeKyung-suk LheeStefan Robila2-106 CST buildingSyracuse UniversitySyracuse, NY 13244{steve, tiranee, jlee, klhee,stefan}@Tel: 315-443-2226, Fax: 315-443-2126AbstractThis invited paper briefly describes our progress in developing aresilient multi-spectral image analysis capability for remote sensingapplications. This capability is intended to allow image streams from acollection of distributed sensors to be disseminated and interpreted bya group of analysts, while under information warfare attack. There arefive component technologies that we are developing: real-time multi-and hyper-spectral camera systems, concurrent algorithms for imageanalysis, high performance networking and computer architectures,algorithms for achieving computational resiliency, and generalmathematical tools for integrating these technologies.1.IntroductionThe end of the cold war signals a radical change in the nature of threats that the United States must now confront. Significant among these threats is the increasing capability of radical regimes abroad and the emergence of international terrorism at home. The key to security in this environment is information. High-quality surveillance and analysis techniques are at a premium, yet surveillance alone is not enough: technologies must be developed that allow sensor information to be fused and disseminated, in real-time, to multiple observers and controllers while under information warfare attack. These technologies must provide a collaborative problem-solving medium with the ability to sense, interpret, analyze, and decide upon dynamically unfolding situations. Our research is concerned with the architectural issues associated with this general research direction. Toward this end we are developing, integrating and evaluating five 1This research is sponsored by the Defense Advanced Research Projects Agency (DARPA) ITO under contract N66001-99-1-8922, and Defensive Information Warfare Branch, AFRL Rome Laboratory, and a BMDO DURIP award under contract N00014-99-1-0525.technologies: real-time multi- and hyper-spectral camera systems, concurrent algorithms for image analysis, high performance networking and computer architectures, algorithms for achieving computational resiliency, and general mathematical tools for integrating these technologies. The sections that follow briefly review our progress to date in each of these areas.2.Real-time Camera SystemIn a joint collaboration with Mobium Enterprises Inc. and Integrated Scientific Imaging we have developed a commercially available real-time, multi-spectral camera system (Figure 1). The system is capable of acquiring and analyzing real-time multi-spectral imagery using concurrent architectures. It incorporates a Kodak Megaplus ES1.0 camera capable of delivering 12 bit/pixel images at 60 frames/sec (FPS) at the full resolution of 1024x1024 pixels, or 120 FPS in a 2x2 binning mode at 512x512 pixels. A motorized filter wheel and interchangeable optical system allow 12 independent spectral bands, from 400-1000nm, to be delivered. Under Air Force contracts, we are exploring designer lenses to fit this system that can be built to suite the optical needs of specific applications.Figure 1. Multi-spectral Camera SystemThe camera system is interfaced directly into the dual-PCI bus of commercial off-the-shelf (COTS) shared-memory multi-processors; these in turn can be connected through gigabit networking for scalability. The system has been operational for several months and is currently being integrated with the other technologies described in this paper.3.Concurrent AlgorithmsWe are investigating two primary concurrent algorithms based on the principal component and independent component transforms [Lee 1998]. In collaboration with Mobium Enterprises we have developed a concurrent spectral-screening PCT algorithm that can be used for hyper-spectral image fusion in remote sensing applications [Achalakul 1999, 2000a, 2000b]. The algorithm combines thePrincipal Component Transform (PCT) [Mackiewicz 1993, Singh 1993] with spectral angle classification [Kruse 1993] and human-centered color mapping [Boynton 1979, Peterson 1993, Poirson 1993]. It can be used on networks of multi-processors consistently with the camera system described in Section 2.The PCT is generally used to summarize and de-correlate images by removing redundancy and packing the residual information into a small set of images, termed principal components. To prevent the PCT from highlighting only the variation that dominates numerically, we augment it with spectral angle classification prior to the de-correlation process. This has the effect of reducing the importance of an object that occurs frequently in a scene. For example, the spectral signature of a mechanized vehicle embedded in a forest scene will be treated as equally important as the signature associated with trees. The final step of the algorithm is to generate a color-composite image from a collection of principal components. To achieve this, we use a human-centered approach that attempts to match the spatial-spectral content of the output image with the spatial-spectral processing capabilities of the human visual system.The concurrent algorithm uses the standard manager/worker decomposition technique [Chandy 1992]. The manager thread partitions the problem and distributes the sub-problems to worker threads. The workers solve each allocated sub-problem, send back a result, and wait for the next sub-problem. Each sub-problem is a sub-cube of the hyper-spectral image set similar to the decompositions used in [Palmer 1998]. To reduce communication overhead, a worker overlaps the request for its next sub-problem with the calculation associated with the current sub-problem.The algorithm was tested using a 210-channel hyper-spectral image collected with the Hyper-spectral Digital Imagery Collection Experiment (HYDICE) sensor, an airborne imaging spectrometer. These images correspond to foliated scenes taken from an altitude of 2000 to 7500 meters at wavelengths between 400nm and 2.5 micron. The scenes contain mechanized vehicles sitting in open fields as well as under camouflage. Figure 2 shows two frames picked from the 210 spectral bands.Figure 2: 400 and 1998 nmFigure 3 shows the resulting image after applying the concurrent spectral-screening PCT to the full 210-frame data set. The color-mapping scheme maps the first principal component to achromatic, the second to red-green opponency, and the third to blue-yellow opponency. The result, when viewed on a high-quality monitor, shows significantly improved contrast levels. The forested areas show significantlyimproved detail and the camouflaged vehicle in the lower left corner is significantly enhanced against its background.Figure 3: Color-Composite ImageThe performance of the algorithm was measured on an 8-way 550MHz Intel shared memory multi-processor. Using this technology it is capable of processing one 210-band image cube in 10 seconds and 12 bands in 1/3 second [Achalakul 2000b]. The algorithm has also been tested on a distributed network of 16 Sun Solaris 300MHz.workstations connected with 100BaseT networking technology [Achalakul 2000a]. The performance improvement was linear with the number of processors to within 5% on multi-processors and 20% on networked workstations. We are currently beginning evaluations of a gigabit network of multi-processors and expect this to be completed by the end of the summer. All of the concurrent aspects of the spectral-screening PCT algorithm carry over directly to the independent component transform that is currently under investigation.working and ArchitecturesIn addition to off-the-shelf networking and multi-processors technologies, we are also exploring the use of special purpose image processing architectures. Our current work is focused on the BMDO VIGILANTE multi-spectral sensing technology [Duong 1997, Padgett 1997].The heart of this project involves the development of the ANTE processor -- a sugarcube sized 3D VLSI chip stack, which performs high-speed convolutions. The ANTE processor uses a neural circuit design based on Multiplying Digital-to-Analog Converter (MDAC) technology. The architecture allows 64 complete inner products, each with a 4096 (i.e., 64x64) input array to be accomplished in 250 nanoseconds (i.e., 1012multiply and add operations in 1 second). We have worked with the Jet Propulsion Laboratory to ensure that the technology can be integrated directly into the dual-buss PCI slots of a COTS multi-processor. This allows the device to be integrated with all of the other component technologies described in this paper. The first prototypes of the sugarcube are expected to be available in the summer of 2000.putational ResiliencyAny system that operates in highly adverse environments, such as battlefield command and control, must be able to tolerate information warfare attacks and networking/processor failures. The Air Force strategy to provide operational survivability is based on the notion of information resiliency: the ability of a system to tolerate, dynamically reconfigure, and repair itself in response to an attack [Giordano 2000]. This strategy incorporates data resiliency for databases and sequential systems, computational resiliency for distributed applications, and real-time attack assessment for Cyberforensics.To understand how computational resiliency operates, consider a distributed application as analogous to an apartment complex inhabited by a new strain of roach (process/thread). The roaches are highly resilient: you can stamp on them, spray them, strike them with a broom but you never kill them all or prevent them from their goal of finding food (resources). To foil your eradication efforts, they use several techniques: they are highly mobile moving from one place in the apartment complex (network) to another with speed and agility. They continually replicate to ensure that it is not possible to kill them all. They sense (attack assessment) their environment to obtain clues that mobility is necessary: if a light is turned on, they scurry away in all directions to hide behind cupboards in places of known safety (secure network zones). If a new roach killer is invented they learn from it, and adapt their behavior to compensate. However, this new strain is particularly aggressive and seeks to live in the daylight (wide-area operation): thus it adopts techniques for camouflage as a form of protection and disinformation.2There are several significant technical challenges involved in developing systems based on this notion. Techniques must be developed for providing policy-driven on-the-fly replication, camouflage, and mobile threads. There are also a number of serious theoretic concerns that relate to race conditions in reconfiguration of a distributed application, resource management, and providing guarantees on message delivery. The techniques must operate on heterogeneous clustered environments that include shared memory multi-processors, high performance networking, and wireless networking. These systems are composed of machines with substantively different memory and processor characteristics, operating systems, floating point representations, and byte orderings.To date we have designed and implemented a prototype mobile thread capability that incorporates dynamic replication and the associated communication protocols.Figure 4 illustrates how this technology is used to organize a distributed computation. Driven by mission policies, critical threads are chosen for replication. In the event that one of the replicated threads is compromised, the remaining replicas are used to dynamically regenerate a new replica, at an alternative location in the network. Communication is automatically reconfigured to the new thread.Figure 4: Replication of Threads2Thanks to Cathy McCollum for providing the analogy.This approach assures operational survivability to the required level of redundancy, subject only to the constraints imposed by the total available resources. Obviously to be successful, the replacement thread must be mapped to an alternative location in the network with sufficient resources.The mobile thread capability has been applied to three applications: a simple fluid dynamics problem based on heat diffusion, a target tracking problem using sonar, and image fusion using the spectral-screening PCT described in a previous section [Achalakul 2000a]. In this latter experiment, the computational overhead associated with the protocols for resiliency was less than 10%, plus the unavoidable cost of replication. The protocols have subsequently been simplified, however, new performance data is not yet available.6.Mathematical ToolsIn collaboration with Mobium Enterprises Inc., we have developed a commercial tool, Mathweb, that provides concurrent image manipulation and linear algebra [Achalakul 1999]. It operates on a single PC, Unix machine, or shared-memory multi-processor. A version for distributed systems, based on web-technologies, is currently under development; This tool provides a collaborative environment for distributed dissemination and analysis of sensor data.MathWeb generalizes the matrix algebra familiar to users of Matlab, IDL, and Mathematica to full tensor algebra. To understand the relevance of this concept to multi- and hyper-spectral image analysis, consider the hierarchy in Figure 5. Using the alternative interpretations, matrix algebra can be generalized to the manipulation of multi- or hyper-spectral image streams. The resulting algebra of images allows these streams to be manipulated using standard well-defined linear algebraic concepts such as matrix inversion, FFT, wavelet transformation, digital filtering, and principal/independent component transformation. MathWeb provides a large variety of operations on tensors that can be used collaboratively through web technologies to operate on real-time multi-spectral image streams.Tensor Dimension Conventional Interpretation Alternative Interpretation0Scalar Real or Complex Number1Vector List of Values2Matrix Table of values or Image3Tensor RGB-ImageN indexed by wave length Tensor Multi-and Hyper-spectral ImageN indexed by time Tensor Video StreamFigure 4. Tensor Hierarchy7.ConclusionThis paper has briefly described our research in exploring an architectural framework for the distributed dissemination and analysis of real-time sensor data. The architecture is intended to allow the fusion of sensor data originating from multi- and hyper-spectral cameras, radar and sonar devices, and other sensorsused in the battlefield environment. The intent is to assure operational survivability of such applications from information warfare attack through resiliency concepts. To date we have focused upon multi- and hyper-spectral imagery as a vehicle to explore these ideas, by combining and adapting well-known approaches to the distributed environment.8.References1.Achalakul T., Haaland P. D., and Taylor S., “Mathweb: A Concurrent Image Analysis Tool Suite forMulti-spectral Data Fusion”, SPIE vol. 3719 Sensor Fusion: Architectures, Algorithms, and Applications III, pp. 351-358, 1999.2.(a) Achalakul T., Lee J., and Taylor S., "Resilient Image Fusion", International Conference onParallel Processing Workshop -HPSECA, 2000.3.(b) Achalakul T. and Taylor S., “A Concurrent Spectral-Screening PCT Algorithm for RemoteSensing Applications”, Journal of Information Fusion, In Press, 2000.4.Boynton T. M., Human Color Vision, Rinehart, and Winston, New York, 1979.5.Chandy L. M. and Taylor S., An Introduction to Parallel Programming, Jones and Bartlett publishers,Boston, 1992.6.Duong T., Thomas T., Daud T., Thakoor A., and Lee B., “64x64 Analog input array for 3-dimensional neural network processor,” Proceedings of the 3rd International Conference on Neural Networks and Their Applications, Marseilles, France, 1997.7.Giordano, J., Taylor, S., and Newland, R. “Information Resiliency”, Submitted to CACM, 2000.8.Kruse F. A., Lefkoff A. B., Boardman J. W., Heidebrecht K. B., Shapiro A. T., Barloon P. J., andGoetz F. H., “The spectral Image Processing System (SIPS) – Interactive Visualization and Analysis of Imaging Spectrometer Data”, Remote Sensing Environment, vol. 44, 1993, pp. 145-163.9.Lee T., Independent Component Analysis: Theory and Applications, Kluwer Academic Publishers,Boston, 1998.10.Mackiewicz A. and Ratajczak W., “Principal Components Analysis (PCA)”, Computers &Geosciences, vol. 19, 1993, pp. 303-342.11.Padgett C., Zhu M., and Suddarth S., “Detection and object identification using VIGILANTEprocessing system”, Proc. SPIE, vol. 3077, 1997.12.Palmer M., Totty B., and Taylor S., “Ray Casting on shared-Memory Architectures: EfficientExploitation of the Memory Hierarchy”, IEEE Concurrency, Vol 6, No. 1, pp 20-36, 1998.13.Peterson H. A., Ahumada A. J., and Watson A. B., “An Improved Detection Model for DCTCoefficient Quantization”, SPIE, vol. 1913, pp. 191-201, 1993.14.Poirson A. B. and Wandell B. A., “Appearance of Colored Patterns: Pattern-color Separability”,Journal of the Optical Society of America, vol. 10, pp 2458-2470, 1993.15.Singh A. and Eklundh L., “A Comparative Analysis of Standardized and Unstandardized PrincipalComponents Analysis in Remote Sensing”, International Journal of Remote Sensing, vol. 14, pp 1359-1370, 1993.。
遥感介绍英文作文
遥感介绍英文作文英文：Remote sensing is a technology that allows us to gather information about the Earth's surface without actually being in physical contact with it. This is typically done using satellite or aircraft-based sensors to capture images and data. These sensors can detect different wavelengths of light, allowing us to see things that are not visible to the naked eye, such as changes in vegetation, pollution levels, and even the temperature of the Earth's surface.One of the most common uses of remote sensing is in agriculture. By using satellite imagery, farmers can monitor the health of their crops, identify areas of pest infestation, and even predict crop yields. This information can help them make more informed decisions about when to irrigate, fertilize, or harvest their crops, ultimately leading to higher yields and better quality produce.Another important application of remote sensing is in environmental monitoring. For example, scientists can use satellite data to track deforestation, monitor the health of coral reefs, and even study the effects of climate change on the Earth's polar ice caps. This information is crucial for understanding the state of our planet and making informed decisions about how to protect it forfuture generations.In addition to these practical applications, remote sensing also plays a crucial role in disaster management. After a natural disaster such as a hurricane or earthquake, satellite imagery can be used to assess the extent of the damage, identify areas that are most in need of assistance, and plan for effective relief efforts. This can help save lives and minimize the impact of these devastating events.Overall, remote sensing is a powerful tool that allows us to better understand and monitor our planet. It has countless applications in fields such as agriculture, environmental science, and disaster management, and it continues to play a crucial role in our efforts to protectand sustain the Earth's resources.中文：遥感技术是一种可以让我们在不与地球表面直接接触的情况下获取信息的技术。
What is remote sensing
MAJOR EARTH OBSERVING SATELLITES (contd.)
• • • • Radarsat ESA Satellites (ERS, ATSR) India Satellites (IRS, LISS, OCM) Japanese Satellites (JERS, ADEOS, AVNIR, OCTS, MOS, ALOS) • Russian Satellites (Priroda, etc)
MAJOR EARTH OBSERVING SATELLITES
• • • • • • • • Landsat SPOT Ikonos AVHRR Seawifs GOES Meteosat Terra EOS Satellite (ASTER, MODIS, CERES, MOPITT, MISR)
Major Areas of Progress in Remote Sensing Since the 1950’s
* Development of new payloads (cameras, sensors, scanners) * Development of new platforms * Development of new electronic data transmission, data receiving, networks, and data processing equipment * Development of data integration systems (e.g. GIS, Spatial Modeling, etc.)
Geographic Information System
An organized collection of computer hardware, software, geographic data and personnel designed to efficiently capture, store, update, manipulate, analyze all forms of geographically referenced information. A layered cake.
remote sensing of environment
Advantage:
Remote sensing of the main advantage is:
can you provide some basic biological
physical information, and these information in
many natural and social economy play a
Science
Evidence for science:
Evidence for art :
Understand the scientific principles better
(更好地理解科学原理)
Are more widely traveled and have seen many landscape objects and geographic areas
The measurement or acquisition of information of some property of an object or phenomenon , by a recording device that is not in physical or intimate contact with the object or phenomenon under study (Colwell,1983)
121403005
黄
姚
Contents :
1、In situ data collection
2、Remote sensing data collection
3、The remote sensing process
Preface:
In situ data collection
Definition OVERVIEW OF REMOTE SENSING
OVERVIEW OF REMOTE SENSING(David Sandwell, Copyright, 2007)DefinitionRemote sensing is an incredibly broad subject ranging from photographic imaging to nuclear magnetic resonance imaging to seismic tomography to multibeam sonars to synthetic aperture radars . . . In this class we'll limit the scope to:•satellite (or aircraft) remote sensing of the Earth (mostly);•information carried by electromagnetic waves; and•no discussion of atmospheric sounding.When you look through the textbook by Rees [2001], you will not find detailed discussion of applications of remote sensing. Instead the focus is on the physical principles of remote sensing. Studying all the applications in a 10-week course would be impossible. Moreover, the methods and applications change frequently so details learned today will be obsolete tomorrow. It is more important to learn the fundamental principles and learn the details later as needed. For the graduate-level course, the students select a scientific problem that can be addressed using one or more remote sensing tools. Then they can become experts on the particular application with scientific guidance from a researcher at SIO. Over the years I have collected perhaps 100 term papers from SIO graduate students. The topics are very diverse. For example a student prepared a term paper on the correlation of offshore bird census information (from ship observations) with ocean surface temperature measured by satellite. My area of expertise uses the tools satellite geodesy to measure the properties of the solid earth. This includes measuring micro variations in the pull of gravity over the oceans using radar altimetry and measuring crustal deformation using synthetic aperture radar interferometry. Each year new remote sensing instrumentation is being developed. This technology development is driven by scientific questions and practical needs.I hope that you can get the following out of this course:• a brief review of some basic physics;• a broad understanding of the methods and limitations of remote sensing systems;•an introduction to image processing and display methods;•an overview of a few of the systems being deployed today; and•finally, throught the term project (graduate students only), I hope you can obtain a detailed understanding of a particular remote sensing system and how it addresses a scientific issue.Most of the information gathered by remote sensing satellites could be obtained by other means. For example if one wanted to measure sea surface temperature (SST) across the Gulf Stream between New York and Bermuda, one could make the measurement from a ship or aircraft that commonly traverse that route. However, if one wanted to measure SST across the Antatctic Circumpolar Current then a satellite is the more appropriate platform. The main advantages of satellite remote sensing are:•global data set of uniform quality;•rapid data acquisition after the satellite is designed, built, and launched;•no need to obtain permission from other countries;•can revisit a site on a regular basis for a long period of time; and•spacacraft provide very stable platforms.Of course the main disadvantages are the high cost of a satellite system, the many years it takes to develop and launch the system, and the possibility of a launch failure or system failure.CryoSat Mission lost due tolaunch failure8 October 2005ESA PR 44-2005. Today at 21.00 CESTMr Yuri Bakhvalov, First DeputyDirector General of the KhrunichevSpace Centre on behalf of the RussianState Commission officially confirmedthat the launch of CryoSat ended in afailure due to an anomaly in the launchsequence and expressed his regret toESA and all partners involved.Preliminary analysis of the telemetrydata indicates that the first stageperformed nominally. The second stageperformed nominally until main enginecut-off was to occur. Due to a missingcommand from the onboard flightcontrol system the main enginecontinued to operate until depletion ofthe remaining fuel.As a consequence, the separation of thesecond stage from upper stage did notoccur. Thus, the combined stack of thetwo stages and the CryoSat satellite fellinto the nominal drop zone north ofGreenland close to the North Pole intohigh seas with no consequences topopulated areas.Space is a risky business, it alwayshas been, it doesn't always goperfectly Prof Duncan Wingham,Cryosat chief scientist"It is a very sad event for manyscientists around Europe and alsofor the teams involved in industrywhich built the satellite," he said. http://www.esa.int/esaCP/SEMR3Q5Y3EE_index_0.htmlApplications of Remote SensingMeterology- profiling of atmospheric temperature, pressure, water vapor, and wind velocity.Oceanography - measuring sea surface temperature, mapping ocean currents, and wave energy spectra.Glaciology- measuring ice cap volumes, ice stream velocity, and sea ice distribution.Geology- geomorphology, identification of rock type, mapping faults and structure.Geodesy - measuring the figure of the earth and its gravity field.Topography and cartography - improving digital elevation models Agriculture, forestry, and botany- monitoring the biomass of land vegetation, monitoring the health of crops, mapping soil moisture, forecasting crop yields. Hydrology- assessing water resources from snow, rainfall and underground aquifers.Disaster warning and assessment - monitoring of floods and landslides, monitoring volcanic activity, assessing damage zones from natural disasters.Planning applications- mapping ecological zones, monitoring deforestation, monitoring urban land use.Oil and mineral exploration- locating natural oil seeps and slicks, mapping geological structures, monitoring oil field subsidence.Military - developing precise maps for planning, monitoring military infrastructure, monitoring ship and troop movements . . . (This is where most of the US funding for remote sensing goes.)Electomagnetic SpectrumBefore starting the course it is useful to review the relevant part of the electromagnetic spectrum. This figure from Rees [2001] shows a wavelength range from ultraviolet through the visible spectrum through infrared to microwave and longer radio waves. Of course, the visible spectrum is subdivided by color (hue). Next lecture we'll discuss how our eyes and brain process visible light. The other major subdivision occurs in the microwave part of this spectrum. As we'll see next, this is a very important part of the spectrum for remote sensing and the electrical engineers have provide cryptic labels that we should all learn (Ka, K, Ku, X, C, S, L, and P).ConstraintsThere are three practical constraints on satellite remote sensing of the Earth.1) There needs to be a source of radiation.Passive remote sensing systems rely thermalemissions from the Sun (mostly visible) whichreflect off the surface of the Earth as well asdirect thermal emissions from the Earth(thermal IR). Active systems can operate in thevisible (laser), IR (laser) and microwave(radar).2) The EM waves must be able to penetrate through the ionosphere and atmosphere. For the Earth there are three main windows in the visible, thermal infrared (IR) and the microwave region.3) There needs to be a platform in space to collect the EM signals, digitize them, and transmit the data back to Earth. The satellite must be in orbit around the earth so the inward force of gravity must be equal to the outward centrifugal force. A variety of orbits are possible as will be discussed in the next lecture. One can tune the orbit and platform characteristics to vary altitude and speed, revist time, phase of the orbit plane with respect to solar illumination or lunar/solar tides, and platform orientation (yaw, pitch and roll).ReferencesRees, W. G., Physical Principles of Remote Sensing, Second Edition, Cambridge University Press, Cambridge, UK, 343 pp., 2001.Baker, D. J., Planet Earth: The View From Space, Harvard University Press, Cambridge, Massachusetts, 191 pp., 1990.。
Active and Passive Remote Sensing
Active and Passive Remote SensingPassive remote sensing systems record EMRthat is reflected(e.g., blue, green, red, andnear-infrared light) or emitted(e.g., thermalinfrared energy) from the surface of the Earth.Active remote sensing systems are not dependent on the Sun’s EMR or the thermal properties of the Earth. Active remote sensors create their own electromagnetic energy that•is transmitted from the sensor toward the terrain•interacts with the terrain producing a backscatter of energy •is recorded by the remote sensor’s receiver.The most widely used active remote sensing systems include:Active microwave(RADAR= RA dio D etection a nd R anging), which is based on the transmission of long-wavelength microwave (e.g., 3-25 cm) through the atmosphere and then recording the amount of energy backscattered from the terrain.The beginning of the RADAR technology was using radio waves. Although radar systems now use microwave wavelength energy almost exclusively instead of radio wave, the acronym was never changed.LIDAR (LI ght D etection A nd R anging),which is based on the transmission of relatively short-wavelength laser light (e.g., 0.90 µm) and then recording the amount of light backscattered from the terrain;SONAR (SO und NA vigation R anging),which is based on the transmission of sound wavesthrough a water column and then recording the amount of energy backscattered from the bottom or from objectswithin the water column.RADARThe “ranging capability is achievedby measuring the time delay fromthe time a signal is transmitted tothe terrain until its echo is received.Because the sensor transmitted asignal of known wavelength, it ispossible to compare the receivedsignal with the transmitted signal.From such comparisons imagingradar detects changes in frequencythat form the basis of capabilitiesnot possible with other sensors.Sending and Receiving a Pulse of Microwave EMR -System ComponentsBrief History of RADAR•1922, Taylor and Young tested radio transmission cross the Anacostia River near Washington D.C.•1935, Young and Taylor combined the antenna transmitter and receiver in the same instrument.•Late 1936, Experimental RADAR were working in the U.S., Great Britain, Germany, and the Soviet Union.•1940, Plane-The circularly scanning Doppler radar (that we watch everyday during TV weather updates to identify the geographic locations of storms)•1950s, Military began using side-looking airborne radar (SLAR or SLR)•1960s, synthetic aperture radar (SAR)•1970s and 1980s, NASA has launched two successful SARs, SEASAT, Shuttle-Imaging Radar (SIR)•1990s, RADARSAT …Active Microwave (RADAR) Commonly Use Frequencies Band Designations(common wavelengths Wavelength (λ) Frequency(ν) shown in parentheses)in cm in GHz_______________________________________________K 1.18 -1.6726.5 to 18.0 Ka(0.86 cm)0.75 -1.1840.0 to 26.5 Ku 1.67 -2.418.0 to 12.5 X(3.0 and 3.2 cm) 2.4 -3.812.5 -8.0C(7.5, 6.0 cm) 3.8 -7.58.0 -4.0S (8.0, 9.6, 12.6 cm)7.5 -15.0 4.0 -2.0L(23.5, 24.0, 25.0cm)15.0 -30.0 2.0 -1.0P(68.0 cm)30.0 -100 1.0 -0.3Airborne Side-Looking Radar (SLAR or SLR)Radar Nomenclature•Nadir•azimuth flight direction•range (near and far)•depression angle (γ)•look angles (φ)•incidence angle (θ)•altitude above-ground-level, H•polarizationAzimuth Direction•The aircraft travels in a straight line that is called the azimuthflight direction .•Pulses of active microwaveelectromagnetic energyilluminate strips of the terrain atright angles (orthogonal) to theaircraft ’s direction of travel,which is called the range or lookdirection .•The terrain illuminated nearestthe aircraft in the line of sight iscalled the near -range . Thefarthest point of terrainilluminated by the pulse ofenergy is called the far -range .a.b.look direction X - band, HH polarization look direction s X - band, HH polarization Look DirectionThe range or look direction for anyradar image is the direction of theradar illumination that is at rightangles to the direction the aircraft orspacecraft is traveling.Generally, objects that trend (or strike) in a direction that isperpendicular to the look directionare enhanced much more than thoseobjects in the terrain that lie parallelto the look direction. Consequently,linear features that appear dark in aradar image using one look directionmay appear bright in another radarimage with a different lookdirection.Depression AngleThe depression angle(γ) is the angle between a horizontal plane extending out from the aircraft fuselage and the electromagnetic pulse of energy from the antenna to a specific point on the ground.The depression angle within a strip of illuminated terrain varies from the near-range depression angle to the far-range depression angle. The average depression angle of a radar image is computed by selecting a point midway between the near and far-range in the image strip. Summaries of radar systems often only report the average depression angle.Incident AngleThe incident angle(θ) is the angle between the radar pulse of EMR and a line perpendicular to the Earth’s surface where it makes contact. When the terrain is flat, the incident angle (θ) is the complement (θ=90 -γ) of the depression angle (γ). If the terrain is sloped, there is no relationship between depression angle and incident angle. The incident angle best describes the relationship between the radar beam and surface slope.Many mathematical radar studies assume the terrain surface is flat (horizontal) therefore, the incident angle is assumed to be the complement of the depression angle.RADAR ResolutionTo determine the spatial resolution at any point in a radar image, it is necessary to compute the resolution in two dimensions: the range and azimuth resolutions.Radar is in effect a ranging device that measures the distance t o objects in the terrain by means of sending out and receiving pulses of active microwave energy.The range resolution in the across-track direction is proportional to the length of the microwave pulse.The shorter the pulse length, the finer the range resolution. Azimuth resolution(Ra) is determined by computing the width of the terrain strip that is illuminated by the radar beam.Azimuth resolution(Ra) is determined by computing the width of the terrain strip that is illuminated by the radar beam.The beam width is inversely proportional to antenna length (L). Where S is the slant-range distance to the point of interest.This means that the longer the radar antenna, the narrower the beam width and the higher the azimuth resolution.Synthetic Aperture Radar (SAR)A major advance in radar remote sensing has been the improvement in azimuth resolution through the development of synthetic aperture radar(SAR) systems.Great improvement in azimuth resolution could be realized if a longer antenna were used.Synthetic Aperture Radar (SAR)Engineers have developed procedures to synthesize a very long antenna electronically. A SAR uses a relatively small antenna (e.g., 1 m) that sends out a relatively broad beam perpendicular to the aircraft. A greater number of additional beams are sent toward t he object. Doppler principles are then used to monitor the returns from all these additional microwave pulses to synthesize the azimuth resolution to become one very narrow beam.Radar MeasurementsRadar MeasurementsExpected surfaceroughness back-scatter fromterrainilluminated with3 cm wavelengthmicrowaveenergy with adepression angleof 45˚.Wavelength and Penetrationof CanopyThe longer the microwavewavelength, the greater thepenetration of vegetationcanopy.Wavelength and Penetration of CanopyThe longer the microwave wavelength, the greater thepenetration of vegetation canopy.Unpolarized energy vibrates in all possible directionsperpendicular to the direction of travel.The transmitted pulse of electromagnetic energy interacts with the terrain and some of it is back-scattered at the speed of light toward the aircraft or spacecraft where it once again must pass through a filter. If the antenna accepts the back-scattered energy, it is recorded. Various types of back-scattered polarized energy may be recorded by the radar.PolarizationRadar antennas send andreceive polarized energy.This means that the pulse ofenergy is filtered so that itselectrical wave vibrationsare only in a single planethat is perpendicular to thedirection of travel. The pulseof electromagnetic energysent out by the antenna maybe vertically or horizontallypolarized .PolarizationIt is possible to:•Send vertically polarized energy and receive only vertically polarized energy (designated VV )•S end horizontal and receivehorizontally polarized energy (HH )•S end horizontal and receivevertically polarized energy (HV )•S end vertical and receivehorizontally polarized energy (VH ) Polarization•HH and VV configurationsproduce like -polarizedradar imagery.•HV and VH configurationsproduce cross -polarizedimagery.An example of radar penetration of dry soil along the Nile River, Sudan. The Space-borne Imaging Radar (SIR)polarized data reveal an ancient, previously unknown channel of the Nile.Space ShuttleColor-InfraredPhotographSIR-C Color Composite:•Red = C-band HV•Green = L-band HV•Blue = L-band HHSIR-C/X-SAR Images of aPortion of Rondonia, Brazil,Obtained on April 10, 1994Geometric Relationship Between Two SAR Systems Used for Interferometry to Extract Topographic Information: InSARShuttleRadarTopographyMission(SRTM)Electrical Characteristics and Relationship with MoistureOne measure of a material's electrical characteristics is the complex dielectric constant, defined as a measure of the ability of a material (vegetation, soil, rock, water, ice) to conduct electrical energy.Dry surface materials have dielectric constants from 3 to 8 in microwave portion of the spectrum.Conversely, water has a dielectric constant of approximately 80.The amount of moisture in soil, on rock surface, or within vegetation tissues may have significant impact on the amount of backscattered radar energy.Electrical Characteristics and Relationship with MoistureMoist soils reflect more radar energy than dry soil. The amount of soil moisture influences how deep the incident energy penetrates into materials.The general rule of thumb for how far microwave energy will penetrate into a dry substance is that the penetration should be equal to the wavelength of the radar system.However,, active microwave energy may penetrate extremely dry soil several meters.Imaging Radar ApplicationsEnvironmental Monitoring•Vegetation mapping•Monitoring vegetation regrowth, timber yields•Detecting flooding underneath canopy, flood plain mapping •Assessing environmental damage to vegetation Hydrology•Soil moisture maps and vegetation water content monitoring •Snow cover and wetness maps•Measuring rain-fall rates in tropical storms Oceanography•Monitoring and routing ship traffic•Detection oil slicks (natural and man-made)•Measuring surface current speeds•Sea ice type and monitoring for directing ice-breakersInSAR study of coastal wetlands over southeastern LouisianaZhong Lu, U.S. Geological SurveyOh-ig Kwoun, Jet Propulsion LaboratoryUsing multi-temporal C-band European Remote-sensing Satellites (ERS)-1/-2 and Canadian Radar Satellite (RADARSAT)-1 synthetic aperture radar (SAR) data over the Louisiana coastal zone to characterize seasonal variations of radar backscattering according to vegetation types and conduct detailed analysis of InSAR imagery to study water level changes of coastal wetlands. Chapter 2: In Remote Sensing of Coastal Environment (Wang, Editor), Taylor & Francis, 2009InSAR-derivedwater-level changes InSAR images showing water-level changes in coastal wetlands over southeastern Louisiana. Each fringe (full color cycle) represents a line-of-sight range change of 11.8 cm and 2.83 cm for ALOS and Radarsat-1 interferograms, respectively. Interferogram phase values are unfiltered for coherence comparison and are draped over the SAR intensity image of the early date. Areas of loss of coherence are indicated by random colors.M –marshes (freshwater, intermediate, brackish, and saline marshes)L –lakeSF –swamp forestBF –bottomland forestAF –agricultural fieldAdvantages of Active Remote Sensing:•Sending and receiving EMR that can pass through cloud, precipitation•Images can be obtained at user-specified times, even at night.•Sending and receiving EMR that can penetrate tree canopy, dry surface, deposits, snow …•Permits imaging at shallow look angles, resulting in different perspectives that cannot always be obtained using aerialphotography.•Providing information on surface roughness, dielectric properties, and moisture content.All weather, day-and-night imaging capacityLIDAR (LIght Detection And Ranging)Is a rapidly emerging technology for determining theshape of the ground surface plus natural and man-made features.Buildings, trees and power lines are individuallydiscernible features. This data is digital and is directly processed to produce detailed bare earth DEMs at vertical accuracies of 0.15 meters to 1 meter.Derived products include contour maps, slope/aspect,three-dimensional topographic images, virtual reality visualizations and more.AS350BA LIDAR PlatformThe LIDAR instrument consists of a system controller and a transmitter and receiver. As the aircraft moves forward along the line-of-flight, a scanning mirror directs pulses of laser light across-track perpendicular to theline-of-flight.Lidar systems generally transmitpulses toward nadir. The lidarpulse is spatially confined toconcentrate energy and getaccurate range estimates.The first and simplest LIDARsystem collects a profile of nearly equidistant points along thesensor's path.The second technology collectsrange samples within a swath by transmitting the LIDAR pulsesaway from nadir, effectivelycollecting a "cloud" of data points.LIDAR ReturnsDepending upon the altitude of the LIDAR instrument and the angle at which the pulse is sent, each pulse illuminates a near-circular area on the ground called the instantaneous laser footprint, e.g., 30 cm in diameter.This single pulse can generate one return or multiple returns. All of the energy within laser pulse A interacts with the ground. One would assume that this would generate only a single return. However, if there are any materials whatsoever with local relief within the instantaneous laser footprint (e.g., grass, small rocks, twigs), then there will be multiple returns.LIDAR ReturnsPost-processing the original data results inseveral LIDAR files commonly referred to as:•1st return;•possible intermediate returns;•last return; and•intensity.The masspoints associated with each return file(e.g., 1st return) are distributed throughout thelandscape at various densities depending uponthe scan angle, the number of pulses persecond transmitted (e.g., 50,000 pps), aircraftspeed, and the materials that the laser pulsesencountered. Areas on the ground that do notyield any LIDAR-return data are referred to asdata voids.First Return Last Return Bare EarthLIDAR-derived TIN Bare Earth DEM overlaid with Contours Classification ofLandcover basedsolely on LIDAR-derived Elevation,Slope, andIntensityblue= buildingsgreen= grasspink= vegetationLIDAR data can be integrated with other data sets, including orthophotos, multispectral, hyperspectral and panchromatic imagery.LIDAR can be combined with GIS data and other surveying information to generate complex geomorphic-structure mapping products, building renderings, advanced three dimensional modeling/earthworks and many more high quality mapping products.。
remote sensing
remote sensingRemote SensingIntroduction:Remote sensing refers to the technology and methods used to collect information about objects or phenomena from a distance, typically from aircraft or satellites. It involves the acquisition of data without having any physical contact with the object or the area being studied. Remote sensing has greatly impacted various fields, including environmental monitoring, agriculture, urban planning, disaster management, and military intelligence. This document will explore the principles, applications, and future prospects of remote sensing.Principles of Remote Sensing:Remote sensing relies on the interaction between electromagnetic radiation and the Earth's surface. The key principle is that objects or materials interact with radiation in specific ways, which can be detected and measured. Different sensors are used to record electromagnetic radiation in various wavelengths, including visible light, infrared, and microwave. The data collected by these sensors can be usedto derive valuable information about land cover, temperatures, vegetation health, pollution levels, and more.Types of Remote Sensing:Remote sensing can be broadly categorized into two types: active and passive. Active remote sensing involves emitting energy towards a target and measuring the amount of energy reflected back. This technique is commonly used in radar or lidar systems. Passive remote sensing, on the other hand, relies on detecting natural or emitted radiation from the target. This approach is widely used in satellite imagery and aerial photography.Applications of Remote Sensing:1. Environmental Monitoring: Remote sensing plays a crucial role in monitoring and managing the Earth's environment. It enables the detection and analysis of land use changes, deforestation, shoreline erosion, and urban sprawl. Remote sensing data also helps in monitoring climate change, analyzing air and water quality, and assessing environmental damage caused by natural disasters.2. Agriculture: Remote sensing has revolutionized the agricultural industry by providing valuable information about crop health, soil moisture, and pest infestations. Farmers canuse this data to optimize irrigation, fertilizer application, and pest control, leading to improved crop yields and reduced environmental impact.3. Urban Planning: Remote sensing techniques aid urban planners in assessing urban growth, infrastructure planning, and monitoring environmental impacts in urban areas. Satellite imagery and LiDAR data are used to create accurate maps, measure elevation, and identify potential development areas.4. Disaster Management: Remote sensing can play a vital role in disaster management, including natural disasters like earthquakes, floods, and wildfires. It helps in mapping affected areas, assessing damages, monitoring post-disaster recovery, and planning rescue and relief operations.5. Military Intelligence: Remote sensing plays a crucial role in military intelligence gathering and surveillance. Satellite imagery and aerial reconnaissance provide valuable information about enemy activities, troop movements, and potential threats. It aids in mission planning, target identification, and monitoring of restricted areas.Future Prospects of Remote Sensing:The future of remote sensing looks promising, with several advancements on the horizon. Some of the key trends include:1. Increased Resolution: Advancements in sensor technology and satellite systems are leading to higher-resolution imagery, enabling a more detailed analysis of objects and phenomena on the Earth's surface.2. Hyperspectral Imaging: Hyperspectral sensors can capture data in hundreds or thousands of narrow spectral bands. This enables the identification of specific materials or compounds on the Earth's surface, opening up new possibilities for precision agriculture, mineral exploration, and environmental monitoring.3. Artificial Intelligence and Machine Learning: Remote sensing data sets are growing exponentially, making it challenging to process and analyze the vast amounts of information. Artificial intelligence and machine learning algorithms hold the potential to automate data analysis, pattern recognition, and classification tasks.4. Integration with Other Technologies: Remote sensing is increasingly being integrated with other technologies such asunmanned aerial vehicles (UAVs), Internet of Things (IoT) devices, and big data analytics, further enhancing its capabilities and applications.Conclusion:Remote sensing is a powerful tool that has revolutionized various fields by providing valuable information about our planet from a distance. Its applications range from environmental monitoring and agriculture to urban planning, disaster management, and military intelligence. With ongoing advancements in technology and data analysis techniques, remote sensing is set to play an even more significant role in the future. The ability to collect and analyze vast amounts of high-resolution data will enable better decision-making, resource management, and environmental protection.。
遥感发展历史(附演讲稿)
World War I trenches in Europe
The And Now…… image
from satellite
ppt1/2: Remote sensing is the acquisition of information about an object or phenomenon, without making physical contact with the object. In modern usage, the term generally refers to the use of aerial sensor technologies to detect and classify objects on Earth (both on the surface, and in the atmosphere and oceans) by means of propagated signals. ppt2: The left image shows us a full view of Death Valley .The surface color and intensity represents the radar reflection properties of the ground cover. The image is arranged like a map. ppt3: Remote sensing makes it possible to collect data on dangerous or inaccessible areas. Remote sensing applications include monitoring deforestation in areas such as the Amazon Basin, glacial features in Arctic and Antarctic regions, and depth sounding of coastal and ocean depths. Military collection during the Cold War made use of stand-off collection of data about dangerous border areas. Remote sensing also replaces costly and slow data collection on the ground, ensuring in the process that areas or objects are not disturbed.
Part one Remote Sensing(lesson one)
七、推断与猜测
1 deduce 推断
Draw the conclusion or make the judgment about the problems which don’t obviously talk about in the article according to the context.
2
surmise
猜测
A complete unit consists of many sentences circling a topic. 由一个主题句和多个辅助句组成
Part one Remote Sensing
Unit one: What is Remote Sensing?
Vocabulary and phase
3 read aloud or by heart 声读和心读
Read silently can more rapidly improve the reading velocity than read aloud or by heart .
4 scatterbrained 注意力不集中
五、影响阅读速度的几个障碍点
5 more understanding but less reading velocity 力求理解，不重速度 6 continually look up dictionary 频繁查字典
Reasons:1)less quantity of glossary;Reading and gripping the word-building can solve the problem.
Illumination/illuminate (n.)照度/(vt.) 照射 electromagnetic energy 电磁能 Radiation 辐射能;放射线,放射物[C][U] 发光;发热;辐射[U] 发射;传播[U] Interpretation 解译
remote sensing
Remote sensing is the science and art of obtaining information about an object,area,or phenomenon through the analysis of data acquired by a device that is not in contact with the object,area,or phenomenon under investigation.遥感：通过不接触的设备，它与正在调查的对象，面积，或现象获得的数据进行分析获得一个对象，领域或现象的有用信息的科学和艺术。
As you view the screen of your computer moniter,you are actively engaged in romote sensing.当你查看你的电脑屏幕上,你正积极从事远程感应。
A light emanates from that screen, whose imaging electronics provides a source of radiation.The radiated light passes over a distance, and thus is “remote” to some extent, until it encounters and captured by a sensor (your eyes).那画面会产生光,其成像电子提供了一个的辐射源。
放射状的光线在一个距离,因此是“遥远国”在某种程度上,直到它碰到后被传感器(眼睛)。
Each eye sends a signal to a processor(your brain)which records the data and interprets this into information.每只眼睛发出信号处理器(大脑)记录数据和解读此信息Collected the remote sensing data can be of many forms, including variations in force distributions, acoustic wave distributions, or electromagnetic energy distributions.收集了遥感数据可以多种形式,包括力分布的变化,声波分布,或电磁能量分布。
遥感技术相关英语资料
Remote SensingRemote Sensing is a process of obtaining information about land, water, or an object, without any physical contact between the sensor and subject of analysis .The term remote sensing most often refers to the collection of data by instruments carried aboard aircraft or satellites. Remote sensing systems are commonly used to survey, map, and monitor the resources and environment of the earth. They also have been used to explore other planets.There are several different types of remote sensing devices. Many system take photographs with cameras, recording reflected energy in the visible spectrum. Other system record electromagnetic energy beyond the range of human sight , such as infrared radiation and microwaves. Multispectral scanners produce images across both the visible and the infrared spectrum.14.1 SensorsThe most familiar form of electromagnetic energy is visible light, which is the portion of electromagnetic spectrum to which human eyes are sensitive. When film in a camera is exposed to light , it records electromagnetic energy. For more than 50 years, photographic images obtained from airborne cameras have been used in urban planning, forest management, topographic mapping ,soil conservation ,military surveillance and many other applications.Infrared sensors and microwave sensors record invisible electromagnetic energy .The heat of an object , for example ,can be measured by the infrared energy it radiates. Infrared sensors create images that show temperature variations in an area ---a difficult or impossible task using conventional photography .Thermal infrared sensors can be used to survey the temperatures of water ,locate damaged underground pipelines, and map geothermal and geologic structures.Microwave sensors ,such as radar, transmit electromagnetic energy toward objects and record how these objects reflect the energy. Microwave sensors operate at very long electromagnetic wavelengths capable of penetrating clouds, are useful when cloud cover prohibits imaging with other sensors .By scanning an area with radar and processing the data in a computer, scientists can create radar maps. The surface of Venus , which is entirely shrouded by dense clouds , has been mapped in this way, Radar imagery is also used in geologic mapping, estimating soil moisture content, and determining sea-ice conditions to aid in ship navigation.Multispectral scanners provide date electronically for multiple portions of the electromagnetic spectrum, Scientists often use computers to enhance the quality of these images or to assist in automated information gathering and mapping. Which computers, scientists can combine several images obtained by multispectral scanners operating at different frequencies.14.2 SatellitesSatellites have proved extremely useful in the development of remote sensing systems. In 1972 the United States launched Landsat-1, the first in a series of satellites designed specifically for remote sensing. Today, Landsat-5 produces images of most of the earth’s surface every 16 days. Each Landsat image covers more than 31,000 sq km (11,970 sq mi). Objects as small as 900sq m (9688 sq ft) can be seen in the images produced by Landsat’s Thematic Mapper, a type of multispectral scanner , Landsat date are used for applications such as mapping land use , managing forested land, estimating crop production, monitoring grazing conditions, assessing water quality, and protecting wildlife.Between 1990 and 1996, almost 50 remote sensing satellites were placed into orbit. Since 1986, France’s SPOT satellites have provided images showing objects as small as 100 sq m (1076sq ft) and have produced stereoscopic images useful for topographic mapping. Earth-observing satellites have also been launched by the European Space Agency and Japan, Russia, India , and other nations.Meteorological satellites, such as other operated by the U.S. National Oceanic and Atmospheric administration, provide images for use in weather forecasting as well as in oceanic and terrestrial applications. Remote sensors on weather satellites can track the movement of clouds and record temperature changes in atmosphere.14.3 OutlookRemote sensing is changing rapidly. Some satellites carry instruments that can images of objects as small as an automobile and constantly improving technology promises even better resolution in the near future. Computer-assisted Image-analysis techniques are leading to many new applications for remote sensing. In the late 1990s, the NASA is scheduled to launch the Earth Observing System, a key program in its Mission to Planet Earth, which involves launching a series of satellites to study environmental changes on the planner.。
专业英语Lesson 14 Remote Sensing
2018/12/13
《测控技术与仪器英语教程》
16
Spectrum in use
• • • • • • • • • • a solar spectrum 太阳光谱 spectrum analysis 光谱分析 the whole spectrum of industry 整个工业领域 absorption spectrum 吸收光谱, 吸收频谱 action spectrum 作用光谱 alpha-ray spectrumα 射线谱 amplitude spectrum 振幅谱 amplitude -frequency spectrum 振幅频谱 atomic spectrum 原子光谱 audible spectrum 声谱, 声频谱
2018/12/13
《测控技术与仪器英语教程》
22
track in use
• track road 纤道
2018/12/13
《测控技术与仪器英语教程》
23
Structure
2018/12/13
《测控技术与仪器英语教程》
24
Reading/writing techniques
2018/12/13
2018/12/13 《测控技术与仪器英语教程》 14
Spectrum
in dictionary
• 【物】谱, 光[波, 能, 质]谱 • 【无】(射频, 无线电信号)频谱 • 【心理】(眼睛的)余象, 残象 • 范围, 领域, 系列
2018/12/13
《测控技术与仪器英语教程》
15
Spectrum in text
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
Generally the GIP(Geo Info System)spatial data that need to be acquisited include the following five kinds:
1)Various statistics investigation data;
一般需要采集的GIS空间数据有以下5种：
（1）各类统计调查数据。
（2）野外调查测量数据，包括调查记录文本。GPS、全站仪等仪器所测得的数字化数据资料。
（3）已有地图(地形图、专题图)数字化。
（4）遥感数字图像
（5）修改或转换已有数据库资料。
GIS数据采集工作的主要任务是将现有的地图、外业测量成果、航空像片、遥感影像数据、文本资料等转换成GIS可以识别和处理的数字化形式。
2)Field investigation and measurement data,including investigation record files.
The digital data information measured with GPS,total station instrument, etc.;
其他回答 共1条
（1）日以继夜地形数据的收藏通过对遥感的用途是可能的。遥感有查出的和开发的数据潜力为许多领域： 农业、林业、水文学和站点规划，命名一些。遥感技术是有用的在紫外、可看见的红外线和电磁波频谱的微波地区。遥感用于测量反射率、emittance、介电常数，表面几何、等效黑体温度植物，土壤和水以地面采样数据极小值。署名是对象的区别的特点。 在电磁波频谱每个对象有在现场被拾起的它自己的署名。 在场面侦查(遥感设备)”立即看见”许多署名
3)Digitalized existing map(topographic map,special subject map);
4)Remote sensing digital images;
5)Modified or transformed existing database information
（2）地形数据共同地被认为为大多数的最重要的依据geo分析。 然而，地球的安心的自动描述是其中一个难题在GIS。 地球的表面是不规则的三维连续流，然后充分地定义和充分地描述以仅点的一个无限数字无法的。 DEMs是的地形表示法手段海拔被记录在具体横拍。 burrough (1986)定义了DEM作为“安心的连续的变异的所有数字表示法在空间”。 方法是需要的夺取和存放海拔数据中间或大区域。 DEMs可以被编组入以下基本的方法： 规则栅格(光栅)， triangulated不规则的网络(锡)和等高。 其中每一有它的好处和限制用不同的应用，但根据DEMs的光栅在应用有更多大众化。
（1）Around the clock collection of terrain data is possible through the use of remote sensing. Remote sensing has the potential of detecting and developing data for many fields: agriculture, forestry, hydrology, and site planning, to name a few. Remote sensing techniques are useful in ultraviolet, visible infrared, and microwave regions of the electromagnetic spectrum. Remote sensing is used to measure reflectance, emittance, dielectric constant, surface geometry, equivalent black-body temperature of plants, soils, and water with a minimum of ground sampling data. The signature is the distinguishing feature of an object. In the electromagnetic spectrum each object has its own signature that is picked up on the scene. In the scene the detection (of the remote sensing device)”sees” many signatures at once
The major task of the work of GIS data collection is to transform the existing maps, fieldwork survey results, aerial photographs, remote sensing image data and text files into digital format that can be identified and processed by GIS.
The main tasks of GIP data acquisition are transforming various information, such as existing maps, fieldwork measurement results, remote sensing images and data, and text files, into digitalized form that GIS can identify and process.
Generally, there are five types of GIS Spatial Data that requires to be collected :
1. Various statistical investigation data.
2. Outdoor survey measurement data, inclusive of survey record files. The digital data information measured with GPS, total station and other instruments.
3. Digitalization of existing maps ( topographic map, thematic map);
4. Remote sensing digital images;
5. Modification or conversion of existing database information;
Hale Waihona Puke . （2）Terrain data is commonly regarded as the most important basis for most of geo-analysis. However, the automatic depiction of earth’s relief is one of the most difficult tasks in GIS. The earth’s surface is an irregular three-dimensional continuum, then it is impossible to be fully defined and fully depicted with an infinite number of points only. DEMs are a means of terrain representation in which elevation is recorded at specific horizontal positions. Burrough (1986) defines DEM as “Any digital representation of continuous variation of relief over space”. The methods are needed to capture and store elevation data over intermediate or large areas. DEMs can be grouped into the following basic approaches: regular grids (raster), triangulated irregular networks (TIN) and contours. Each has its advantages and limitations in different applications, but raster based on DEMs has got more popularity in applications.