When and How People Learned to Make Cloth

Textiles, to archaeologists anyway, can mean woven cloth, bags, nets, basketry, string-making, cord impressions in pots, sandals, or other objects created out of organic fibers. This technology is at least 30,000 years old, although preservation of the textiles themselves is rare in prehistory, so it may be quite a bit older still. Because textiles are perishable, often the oldest evidence of the use of textiles is implied from impressions left in burned clay or the presence of weaving-related tools such as awls, loom weights, or spindle whorls. Preservation of intact fragments of cloth or other textiles has known to occur when archaeological sites are in extreme conditions of cold, wet or dry; when fibers come into contact with metals such as copper; or when textiles are preserved by accidental charring. Discovery of Early Textiles The oldest example of textiles yet identified by archaeologists is at the Dzudzuana Cave in the former Soviet state of Georgia. There, a handful of flax fibers was discovered that had been twisted, cut and even dyed a range of colors. The fibers were radiocarbon-dated to between 30,000-36,000 years ago. Much of the early use of cloth began with making string. The earliest string-making to date was identified at the Ohalo II site in modern Israel, where three fragments of twisted and plied plant fibers were discovered and dated to 19,000 years ago. The Jomon culture in Japan — believed to be among the earliest pottery makers in the world — shows evidence of cord-making in the form of impressions in ceramic vessels from Fukui Cave that are dated to roughly 13,000 years ago. Archaeologists chose the word Jomon to refer to this ancient hunter-gather culture because it means cord-impressed. The occupation layers discovered at Guitarrero Cave in the Andes mountains of Peru contained agave fibers and textile fragments that were dated to about 12,000 years ago. Thats the oldest evidence of textile use in the Americas to date. The earliest example of cordage in North America is at Windover Bog in Florida, where the special circumstances of the bog chemistry preserved textiles (among other things) dated to 8,000 years ago. Silk making, which is made from thread derived from insect cases rather than plant material, was invented during the Longshan period in China, ca 3500-2000 BCE. Finally, one extremely important (and unique in the world) use of string in South America was as quipu, a system of communication composed of knotted and dyed cotton and llama wool string used by many South American civilizations at least 5,000 years ago.

Trends And Aspects Of Policing Models - 776 Words

Trends in Policing Models Due to the ever-changing political climate and needs of the community, policing models frequently change (McElvain, Kposowa, Gray 2012, 1). During the periods of enactment, numerous policing models served specific purposes but would quickly become outdated due to research or differing approaches to problem solving. Researchers McElvan, Kposowa, and Gray introduce a number of past policing models that have become outdated. Introduced around the mid-1900s the Professional model sought to fight corruption within the police culture and asked officers to gather facts and nothing more. The effectiveness of the strategy was based upon crime statistics and response times to calls (Ibid, 1). Over the years, policing models have gone through periods of evolution and reform, leading to the establishment of numerous modes by either law enforcement agencies or universities. In time, the Professional model was transformed by public criticism of the police being out of to uch with the public they were serving. The new model required a different approach-one that humanized police officers. Beginning in the 1980’s, Community Policing, heavily relying upon a partnership from law enforcement agencies and public partnership, grew in popularity. The researchers theorize the policing initiative was developed to strengthen community relations, specifically those with underprivileged minority communities. The idea was to re-humanize the police force from theShow MoreRelatedOrganizational Management and Operations Paper1690 Words   |  7 PagesGregory Baugh * Criminal Justice Trends Paper * The Policing component is only one part of the criminal justice system (triad of justice) that includes Policing, Courts, and Corrections, and it is the primary tier that generates criminal arrests, prosecutions, restraints, criminal activity, and protects the peace. * This paper will review the policing function of the criminal justice system and will identify, compare, and contrast the policing function at the local, state, and federalRead MoreCrime Rates And Gathering Intelligence Information866 Words   |  4 Pagesof Literature Reforming Police Models Within the progressive social climate where community needs are important and the political atmosphere is ever changing, policing modes have adapted over time to meet the demands (McElvain, Kposowa, Gray 2012, 1). 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It has been slowly reforming to meet the needs of the people, reduce crime, and make policing more efficient. Some of the reforms that will probably take place in the future include, better educated police officers and police managers, consolidation of police departments to save on money and resources, upgraded technology, race and gender equality, better testing techniques t o recruit and promote within the department, andRead MorePolice Departments : Police And Problem Oriented Policing936 Words   |  4 PagesTo start off, there are trends and techniques that police departments implement in order to minimize crime as much as possible. The two most common techniques used by police officers are community-oriented police and problem-oriented policing. This paper will describe the both methods and highlight the pros and cons of each. 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Special attentionRead MoreEffective Reduction Of Crime And Quality Of Life Abatement1574 Words   |  7 Pagescollection and analysis of criminal intelligence data which in turn assists commanders in developing effective strategies and tactics to combat crime. The effective reduction of crime and quality of life abatement is of prime importance in the COMPSTAT model is and is one of the four core principles in the COMPSTAT paradigm. This essay will examine the impact that the implementation of COMPSTAT has had. In doing so it will determine what changes in strategy, structure and operations have taken place asRead MoreEssay On Trends And Issues In Alberta1129 Words   |  5 PagesTrends and Issues in Alberta Alberta is an extraordinary province, and Alberta’s police are facing extraordinary problems. Police are challenged with increased crime rates, staffing, and diversified populations. The government of Alberta declared an astonishing 9% unemployment rate as of late 2016. Increased crime has a historical correlation with economic crisis, which is consistent with Alberta currently. (â€Å"Economic crises may trigger rises in crime†, 2017) believed that societies undergoing economic

Blod Bank free essay sample

It will explain the purpose and features of the system, the interfaces of the system, what the system will do, the constraints under which it must operate, specific requirements and other supporting information. This document is intended for both the stakeholders and the developers of the system. This document is intended for developers, project coordinator, project guide, users and testers. The developer can use the document to view the analysis and design part, to understand requirements specification. The project coordinator can use this document to review the idea of project whether it is innovative and usable or not. The project guide can use this document to analyze and manage the flow of project development. The tester can use this document to get clear idea about system, to understand the exact input domain of system and to generate respective test cases for the testing purpose. 1. 1 Purpose This Software Requirements Specification provides a complete description of all the functions and specifications of the Blood bank Management System software. We will write a custom essay sample on Blod Bank or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page The purpose of this document is to present a detailed description of the Blood Bank System. This document is useful for Blood Bank employees who use this system on the daily basis. This document will also serve as a reference to incorporate any changes made in the system to the future. It will also prove to be useful to the managerial staff which is responsible for the smooth functioning of the counter transactions that are performed on daily basis. This document is primarily intended to provide a detailed specification of the functional requirements for developers of the product. It also provides an overview of the product for the users. It will explain the purpose and features of the system, what the systems do and the constraints under which it will operate. This SRS document contains the complete software requirement for Post Office Transaction System and describes the design decisions, architectural design and the detail design needed to implement the system. It provides the visibility in the design and provides information needed for software support. 1. 2 Scope This software is used by System Administrator, Supervisor, Assistant and Lab technicians. The system is designed to provide user authentication by maintaining the user database which contains all the details of user along with login details such as username and password. Each user has a different level of operation for the authentication purpose. The solution will provide a well-organized and coordinated system where the blood and donor records are managed more efficiently with better security. The major goal of the system is to provide synchronized and centralized donor and blood stock database to the blood bank. It also improves the efficiency of blood stock management by alerting the blood bank administrator. System will provides traceability of donor in case of emergency. It also provides performance evaluation using report generation. 1. 3 Definitions, Acronyms, and Abbreviations This subsection should provide the definitions of all terms, acronyms, and abbreviations required to properly interpret the SRS. This information may be provided by reference to one or more appendixes in the SRS or by reference to other documents. 1. 4 References 1. www. bharatbloodbank. com 2. www. bloodbankindia. com 1. 5 Overview This document contains a brief history and insight into the problem as well as what the system is designed to do, the scope of the solution. It gives a brief summary of whom the system is targeted at, its capabilities, and its positioning in the device development process. It also explains requirements of the system and also where exactly the user and stakeholders are placed in the scheme of things. It elaborates on security issues, hardware and software requirement and also the priorities of various functionalities. The document helps the user to understand the Blood Bank System. The first part of the document provides the purpose, objectives and scope of the proposed system. It includes important definition, the problem statement specify the needed details to avoid any kind of ambiguity from user’s perspective. The document also states the feature set and capabilities of the system. The document also highlights the feature of proposed system and the advantages over the existing system. The document also highlights the various issues, error handling and the modification to be done in the system. The document is helpful to the user as it gives them opportunities to state what they expect from the system. In addition, the hardware specification required for the system is also mentioned. The user can read the document and make sure that his machine is equipped to run the system to avoid any inconvenience The user should make use of the document to understand the system better. The rest of this document provides a full description of the project for the blood bank. It lists all the functions performed by the system. It gives the details of each of the system functions and actions in full for the software developer’s assistance. It also gives the overview of the problems currently faced by the system and provides solutions for the same. 2. General Description The project deals with the problem of automated management system that is keeping track of the actual amount of blood stock in the blood bank and to minimize human errors. The solution will provide a well-organized and coordinated system for blood bank where the blood and donor records are managed more efficiently with better security and added automation functions in alerting the administrator when the blood quantity is below par level and about the expired samples. In addition, the administrator can also trace back the donor responsible in case of reactive sample in blood transfusion and blood screening result. This system also creates a convenient and paperless environment for the blood bank in managing donors and processing blood stocks. The registration time for old donor is shortened as the records are retrievable from the system. Also the performance evaluation is possible using report generation facility. 2. 1 Product Perspective This system keep the track of all the actual amount of the blood stock in the blood bank to minimize the human errors. 2. 2 Product Functions 1. System will also maintain Blood Stock records and Discard register. 2. System will provide levels of operators for authentication purpose. 3. System will provide an authentication form where the users will fill the details. 4. System will process the request of login. 5. System will check for the entered details in the login database. 6. If the entered username and password are valid then the system will grant access to the user else will display an error message. 7. System will provide five interfaces such as Testing Interface, Blood Stock Interface, Issue Interface, Donor Registration Interface and Statistics Interface. 8. Donor Registration Interface will take all the personal and medical details of donor and generate a unique id for each donor. 9. If the donor already has an id then will retrieve its details. 10. In Testing Interface, if the results are positive then discard register is updated with sample details. 11. If the results are negative Blood Stock Repository is updated with sample details. 12. In Blood Stock Interface, system will generate unique bag numbers for each product. 13. It will generate alerts when the blood quantity is below par level and about the expired samples. 14. The System will move expired sample details to discard register and update blood stock repository. 15. In Issue Repository, system will check blood stock repository for availability. 16. If stocks are available then system will place an issue order. 7. It will generate a label which includes bag number and details of sample. 18. In Statistics analysis, system will generate various reports. 19. The whole transaction of the blood will be recorded in a system 20. The software will perform the stock management amp; bill generation. 21. All the transaction of the blood will be updated within a small period of t ime. 22. The software will generate all the annual record of the blood bank.. 23. It will also maintain the record of the blood donor. 24. It will also maintain the record of the rejected blood and the rejection for the blood rejection. 2. User Characteristics The user must have the basic knowledge of the computer system. It is not necessary that the user must have the knowledge of the computer hardware. System administrator, supervisor, assistant and lab technicians. 2. 4 General Constraints * Blood bank employees ,donors 2. 5 Assumptions and Dependencies * The System will not allow any user with invalid username and invalid password. * The system should not provide administrative privilege to any customer. 3. Specific Requirements The systems should satisfy the below given requirements. 1. Operating System: Windows XP Professional 2. Database Management System (DBMS): MySQL 5. 0 3. Peer to peer connection. This can be done by connecting both the systems using a cross cable and giving different IP addresses to them. 3. 1 External Interface Requirements System Interfaces The systems should satisfy the below given requirements. 4. Operating System: Windows XP Professional 5. Database Management System (DBMS): MySQL 5. 0 6. Peer to peer connection. This can be done by connecting both the systems using a cross cable and giving different IP addresses to them. User Interfaces User Type| Intended Interaction| 1. DONOR REGISTRATION a. New donor b. Regular donor| * The system will take all the personal and medical details of donor and generate an unique id for the donor. * The system will take donors id as input, validate it and retrieve all its details including last donations. | 2. Administrator Login | * This login page will take input as username and password from the administrator * The system will validate the inputs and provide access to the system with more privileges to system and administrator| Testing | * This will take the input as blood group, donor id, date of collection; test carried out and its result. If the test results are positive then sample details are moved to discard register otherwise to blood stock repository. | Blood stock| * It will take all the details of the samples including blood group, its product details, date of collection, date of preparation, date of expiry, result of testing and generate a bag number. * It will generate alerts when the blood quantity is below par level and about the expired samples. * The expired sample details are moved to discard register and removed from blood stock repository. | Issue| * It will take input as product name, quantity, blood group and validate it with blood stock repository. If the stock is available then issue order will be placed. * It will generate a label which includes bag number, details of sample etc. | Statistics Display Page| * The administrator has access to this page. It displays all statistical data regarding the various issue orders placed, blood stock repository and testing screening. * Report generation. | 3. 5 Non-Functional Requirements Goal To provide authentication to donors. Objective 1 – Create unique id for donor’s. a. Accept donor personal and medical information for new donors. b. Generate unique id for each donor. Objective 2 – Process authentication request. a. Accept donor id in case of regular donor. b. Retrieve donor’s profile. Goal To manage Blood Stocks. Objective 1 – Process blood stock details. a. Accept product details like collection date, expiry date, blood group etc. b. Generates a unique bag number. Objective 2 – Establish connection with Issue interface and testing interface. a. Validate stock details for issue. b. Update blood stock repository after testing is negative. Goal To generate reports and label. Objective 1 – To generate report. a. Generate product statistics b. Generate yearly, monthly reports and provide it to the administrator. Objective 1 – To generate labels for issue. a. Generate labels for the issue order placed. b. Provide it to the customer along with the date of delivery 3. 5 Non-Functional Requirements 7. 1. 1 Availability * For the customers, the services available are: Registration as a donor, placement of issue order. * For the administrator, the services available are: Manipulation of databases, change the functionalities provided by the system and monthly and yearly reports generation. . 1. 2 Security * This system maintains consistency and security as the blood stock details can’t be modified by the administrator or the other user. The system analyses the issue order placed by the customer and accordingly fulfills it. The system will also provide authentication by using username and password. * The donor’s database will have its own security to prevent unauthorized write/de lete access. The donor’s details will not be disclosed while issuing. 3. 8 Logical Database Requirements The databases used by the system are * Donor database Blood stock database * Discard database * Account database User database: The purpose of this database is to store the required information of the donors and their id’s. This database will be used for traceability of donor. The donors will fill their information while registration. Their credentials will be saved in this particular database. Blood Stock database: This database is used to maintain blood stock information. It maintains the details of valid samples. Discard database: This database is used to store details of blood samples which have to be discarded. This will contain information of expired samples and those samples which reacted positively in testing screening. Accounts database: The purpose of this database is to store the account details regarding issue of blood sample and to maintain the history of all the previous transactions. This database will be used by the administrator so that he can calculate the total cost of the issue orders placed. Accessing capabilities: 1. Only administrator can set the role of users and their access capabilities 2. No user can access anything outside its accessing capability .

Cornell note taking system

Problems In Remote Sensing †Graphic Arts Essay

Problems In Remote Sensing – Graphic Arts Essay Free Online Research Papers Problems In Remote Sensing Graphic Arts Essay The predicaments in most remotely sensed data is affected by several common factors, such as error, uncertainty and scale. â€Å"The goal of remote sensing is to infer information about objects from measurements† (Curtis Woodcock,2002) from various â€Å"locations†, however the process is not always perfect as â€Å"there is an element of uncertainty† concerning data results. Remote sensing studies have always shown various discrepancies as researchers have always identified errors and uncertainties in image interpretations. It also highlights that these properties can be human induced due to mis-interpretation of the data, miss handling of equipment (calibration problems, poor decision making in when to take data, if cloud problems. However, these issues can be technological problems in the sub-orbital and orbital craft; wrong flight path, poor sensor range. The objective of this essay is to highlight the certain types of errors, the causes of uncertainties and scale problems in remote sensing. The extent of this area of terms is substantial in terms. Background Error Error, according to Heuvelink(1991) is defined as â€Å"the difference between reality and our representation of reality†. Error produces ramifications, as â€Å"error is a bad thing†. However Heuvelinks definition does not account for subtle and random errors â€Å"in a statistical way†. The concept of error is illustrated by Jensen’s belief that error is based on two factors internal and external geometric error. Jensen (2005) backs this up by stating it is important to recognize the problem sources of internal and external error, â€Å"whether it is systematic or nonsystematic†. Apparently, geometric error of the systematic approach is generally easier to define and correct. The problems of internal geometric errors are caused by the Earth’s curvature characteristics and remote sensing system being used. Jensen breaks down where internal geometric errors happen 1. Skew 2. Scanning system 3. Relief Displacement Skew Skew effects happen because of the remote sensing data being affected by the earth’s rotation on the axis and the remote sensors orbit of the planet whether it is phased, sun synchronous or geosynchronous. This combination of factors causes the image of the IFOV being examined to be skewed, this was based on using the LandSat enhanced thematic plus using its linear array of 16 lines, which scanned 3 times. This skew effect could be the result of a faulty scanner. Jensen (2005, states if the image is not deskewed, the data will be displayed incoherently. The belief is that the image is skew in an eastwardly approach. The deskewed images being scanned will have an abrupt change on the pixels being read by the scanner. Every orbital sensor that collects image from the Earth will incur skewed images due the spectral curve of the land and also because of the overlaps in images. Scanning Systems Since a large amount of data is retrieved from various scanning sensors aboard orbital and sub-orbital craft. Jensen makes the comparison that multi-spectral orbital craft have minimal distortion to multi-spectral sub-orbital craft. This distortion is reduced in orbital craft because of their nadir equipment, altitude and IFOV in the terms of swath. Jensen(2005, p 227-235) places the problems of geometric distortion on aircraft because of there AGL and their operating height. The only way that this distortion is reduced is only by using the central 70% of the swath width, because scientists noticed â€Å"ground resolution elements have larger cell sizes the farther way from the nadir† Relief displacement The use of aerial photography illustrate that photographs are exposed to perspective geometry , where all the objects are displaced from their plan metric positions outwardly from the principal point. The greater planimetric distance the greater, the reliefs distance(Jensen, 2005, p 227-235). The displacement occurs because of the direction that is perpendicular to the line of flight of each scan. The problems of one-dimensional relief displacement cause objects from their terrain to be displaced from their true position. This causes maps created from imagery to contain plainimetric errors. External Errors Externals errors happen because of unexpected ramifications â€Å"in nature through space and time†. The widely known errors of this are; altitude alterations and attitude changes, which can be yaw pitch and roll. Altitude changes The majority of remote systems operate well above AGL so that images can produce a uniform pattern. If the orbital or suborbital craft changes any of its orbits along the designated flight path, this will result in the scale of the image to change. Jensen(2005, p 227-235)), points out that these changes happen due to the elevation of the aircraft and the terrain. The only ways these issues are corrected by are the use geometric rectification algorithms. Attitude changes The problem of sub-orbital craft are that they affected by issues of turbulence and wind. This happens when sub-orbital craft are collecting data and have to contend with up and down drafts, cross, tail and head winds. This results in the aircraft changing its flight path by rotating its various axes (roll, pitch and yaw. This intern causes geometric distortions to image by introducing compression and expansion of the image. Most satellite and aircraft used in remote sensing have gyro-stabilization equipment to offset these errors in their flight paths. The problem can also be located in mishandling of equipment, as sensors can be miss-calibrated for recording information. Uncertainty The remotely sensed data can attribute to uncertainties in the processing outcomes, which in turn can affect the sound decision-making. Uncertainty pertains to areas of inference and prediction (May,2001). Researchers often confuse these terms in remote sensing. Uncertainty plays a large part in remote sensing as it causes problems especially the classification of land types, Measurement of Sea Surface Temperature, Image interpretation, Image mapping and many other areas of remote sensing. Uncertainty comes from many sources such as ignorance, â€Å"through measurement of prediction†. Uncertainty relates to being not 100% sure of something. The problems in uncertainty are that sometimes it is the most exciting in remote sensing. This is where remote sensing can explored â€Å"to find things so that a base is made for better understanding of how the world operates. The problems of uncertainty can be located in AVHRR data used for measurements of vegetation can have levels of i naccuracy. The best example where uncertainty can cause calamities was the Kyoto protocol in 2000, which collapsed due to high levels of uncertainties in the measurement and understanding of carbon emissions. Woodcock(2002) relates uncertainty to three areas Accuracy Accuracy is often described as the closeness of results, observations that correspond to values being accepted as being true Bias Bias is often seen as an over-estimation of a true value. Bias is often model based. Precision Precision is often the exact value expressed whether the value is right or wrong. These three terms are often seen in projects of measurement of changes in ice sheets, Tropical forests and land classification. Grant Leavenworth (1988) emphasis â€Å"during the life cycle of remotely sensed data, uncertainties are introduced and propagated in an often unknown way†. By constructing a listing where an uncertainty occurs provides the factors of how uncertainty is found in remote sensing. The sensor system. Using the landsat as an example, the velocity of the scanning optical mirror, the number of spectral bands, orbit and altitude height are, parameters that determine the signal noise and â€Å"goodness† of the measurement. In addition to this, all these factors are translated in the terms of resolution; spatial, spectral, temporal. The data gathered from the sensor are affected by these characteristics. This happens in multi temporal systems, especially tropical regions, where there is high cloud cover, this results in incomplete data sets. Coverage Area The problem relates to a sensor trying to correctly capture areas with high complexity. The detection of objects in mixed classifications can be difficult with areas of land and urban together. These objects can affect the area being examined as spectral influence could offset sensors reading in IFOV, although this can be improved by high spatial resolution. In all, this causes the image to become fuzzy and results in uncertainty if the pixels values in the image do not correspond to the classification structure, as two pixels of different values represent the same type of land cover. Geometric and atmospheric distortions. Jensen (2005) illustrates that geometric cause many errors in raw data interpretation especially when affected by the height and altitude of a sensor or by the ground control points(GCP). The concern with atmospheric distortions is that electromagnetic radiation interaction with the atmosphere can diminish information signal of a sensor. This happens, as radiation is subjected to back scattering and absorption. This causes some uncertainty in capturing a truly perfect image of a flat representation. Image distortions The processing of data causes a lot of uncertainty as the amount uncertainty allowed in data is based on Success of the radiometric and geometric corrections The loss of data during conversion Scale Scale variations have long been a thorn in remote sensing, as scale can be constraints for detail in which information needs to be observed and analyzed. Altering the scale in image causes the representation of patterns to differ from the actual size. Scale (Maher, 1997) is used as a basis for measurement as a scale of 1:1,000 means for every one unit on the map, you would need to measure one-thousand of those units on the earth. For example, 1mm on the map represents 1,000 mm on the ground and 1 metre represents 1,000 metres. Although these relations of unit measurement can cause problems as some users may miss-interpret a measurement unit, as 1:10000 is a large scale in comparison to a 1:100000(l. Lillesand(2004, p 617-622) belief is that scale can be very problematic in remote sensing as the definition and understanding varies from researchers. Lillesand breaks down scale in to two areas temporal and spatial. In remote sensing scale is an ambiguous term and is often defined as the relationship between the size of feature on a map or image to the corresponding dimensions on the ground. However ecologists understanding of spatial scale is based on two factors, grain (finest resolution of data) and extent (area under observation). Other problems of spatial scale are the understanding of large and small scales in an image. This is evident as small scale represents coarser spatial objects while large scale represents clearer objects, however individualist (ecologists) reverse the meanings of small and large scale for image analyzing. The only way ensuring the scale of the image is correct for research is using the three principles that Lillesand(2004,p 622) created. 1. Spatial resolution of the sensor 2. The spatial area under observation 3. The nature of information sought in any given image processing operation These key factors should always be relevant in deciding which type of sensor to be used for image analyzing of an area in spatial scale. However, scale varies have changed since the deployment of imaging systems on satellites. Sabins (1997) show this. Small scale 1:500,000 as 1cm= 5km or moirà © These descriptions are different from the aerial photography. However, Harvey and Hill (2003) see that detailed large-scale date extracted from aerial photography was superior as similar scale data could not be extract from the Landsat TM, SPOT XS satellite. This was due to errors of spectral data found in the classification for different vegetation covers. The best available photographic data are panchromatic and at scales of 1:25 000 or 1:50 000. It is generally believed that all environmental processes are scale dependent. The different scale measurements manifest a homogenous side to one scale and a heterogeneous to another side(Atkinson Tate,2000). This can be evident as scale dependence on spatial variation can be problematic in the processing of data, as the techniques of averaging, smoothing and extrapolation can be dangerous for replacing missing datasets. In spatial scale it is often desirable to focus on the particular scale of spatial variation (mean and sample of data), as not all scales need to utilized for specific process(Atkinson Tate,2000). However that can problem if using a specific scale such as the drainage basin scale to represent contours on a topography map, however it would be useless for sheet flow on a hill slope. The general belief is, that it is widely accepted that scales of measurement are determined by the sampling strategy, the sampling generally refers to spatial pattern of the sample obse rvation. Unfortunately, all samples of spatial data are not stationary and can affect the spatial scale of the area under study(Atkinson Tate,2000). That is the fundamental reason for the continuing interest in scale in remote sensing is that spatial resolution is the primary scale of measurement (Atkinson and Aplin). Discussion Zhou Liu(2004) says that classification of land produces errors in multi- temporal data acquisition. The use of classification causes many problems in mapping of land and urban areas which results in uncertainty of accuracy. Using spatial resolution, a decrease in pixel size can cause major ramifications in an image being inferred for a prediction model (H. Liu Q. Zhou). This is evident as pixels being inferred in a spatial swath may represent more than one classification. This can found by comparing different satellites spatial resolution. As landsat TM uses a 30 * 30 metre resolution that is acceptable for some classifications, however if the resolution was needed for small buildings (5 x5 metres), it would fail. This also highlights that uncertainties can enter in to classification of land types as distinguish uncertainties can happen (Atkinson Foody), Page 14). The occurrence of ambiguous definition of classes The problems of land transitions over period due to rapid changes of building into new land types. There also more sources of uncertainty that can occur during the post classification stage as different users can create different conclusions from the IFOV under observation. This is a result of their map reading skills, understanding of raster information and the ability to distinguish significant objects. Neels (2005) defines that uncertainty is found in most of the big ice sheets. This happens due to poor observation of spatial coverage of measuring their variable changes in changes in surface elevation. This was due to the implementation of the budget method. This method is insufficient because of the fluctuation changes in ice sheet volume are determined by the snakk residual alterations in the large terms. This causes temporal and spatial fluctuations, which then cause uncertainty in measuring the ice sheet volume. Not all these measurements in high accuracy and dense data can provide a reliable estimate of ice sheet volume and this highlight errors in analysis of the surface elevation measurements. It also manifests the scale problem of obtaining this volume of data. However Harvey and Hill (2003) notice, that errors are common in image acquisition in remote sensing studies of wetland areas. They believe that spectral overlap may cause a reduction in the utility of imagery collection of a certain season, although spectral classification can be enhanced by image optimizing. This is manifested by their work in tropical wetland environments, as tropical wetlands have large variations in the nature of rainfall. These factors can affect the overall classifications and cause various errors to the image been inferred. Errors can be found in most areas as Czaplewski (2003) questions the methods used in remote sensing especially in monitoring the of global deforestation trends. He manifests this by agreeing with the arguments of Townsend and Tucker over the 10% stratified random samples used by FAO to estimate tropical deforestation. These samples are quite questionable as tropical deforestation is spatially concentrated. The belief here is that these samples can have significant problems for estimations for the FAO. This was seen in study models of dense tropical regions and showed that some regions are less spatially concentrated. Therefore this provided that sampling errors from Landsat sensor scenes in these studies are higher that other regions. In Rajeev and Saxena (2004) argument, they conclude that large scale soil mapping of different scales are dependent upon the requirements of the user. However the uses of satellite have being problem for large scaling mapping because of their coarse resolution. The only way this scale of operation was feasible was to employ conventional methods, which were costly and time consuming. This has changed with the introduction of high resolution PAM and LISS III data from IRS-1C/CD satellite. Although scale is seen mostly as a measurement, scale can be used as a term in remote sensing as the area of data to be analyzed .This was seen with the central America dataset as the scale of coverage was 619 048km2 and includes the countries of Belize, Guatemala, El Salvador, Nicaragua, Costa Rica and Panama. However the assessment of this scale of mapping proved to be difficult, as it show the limits of AVHRR data for classification of NDVI in comparison to Landsat TM data, which has higher classif ication accuracies (Friedl et al, 2000). It also evident that the temporal scales of these studies have been relatively short (days, weeks, months), and few studies have exceeded years in duration. Despite this, researchers are now being called upon to lengthen their studies to longer temporal scales. Conclusion The use of these terms illustrate the problems that are found in remote sensing and highlights the problems they cause to images being taken from orbital and sub-orbital craft. It demonstrates the issues of pixel classification of data in images for the representation of land, woodland, urban and water classes. It also raises the concern about the understanding of these terminologies in remote sensing as some individuals lack the true meaning of what these terms are. That is why Woodcock (2002) states that â€Å"where relevant, adopt the terminology used within statistics and otherwise should adopt terms that convey clearly the authors meaning.† Scale apparently has being problem for researchers as they are concerned in deciding which scale will provide the most accurate measurement in spatial. The problems of uncertainty and error is also seen as new techniques such as using different types of sensors, prediction models are still not capable of yielding the trends of ice sheet mass balance, unless decades of observation are made. Error can also be human induced to poor understanding and misinterpretation of data, technology and the area of observation. It appears that it is by far unfeasible to assess all errors , uncertainties and scale problems as they will always appear in studi es, observations and new types of equipment in remote sensing, as nothing can be 100% perfect in any environment. Finally the biggest concern is the constant changes of electromagtic radiation as this causes major problems to remote sensing when attaining information on a large scale region of interest. these changes in the spectrum will cause uncertainties for the researcher and the scientist. References Books Sabins F Floyd, 1997, Remote Sensing Principles and Interpretation (Third Edition), W.H . Freeman and Company, New York. Jensen R John, 2005, Introductory Digital Image Processing, Pearson Prentice Hall, New Jersey. Lillesand M Thomas, Kiefer W Ralph, Chipman W Jonathan,2004, Remote Sensing and Image Interpretation, The Lehigh Press, United States of America. Atkinson M.P Foody M.G, 2002,Uncertainty in Remote Sensing and GIS: Fundamentals, John Wiley Sons Ltd. Journals K. R. Harvey and G. J. E. Hill, Vegetation mapping of a tropical freshwater swamp in the Northern Territory, Australia: a comparison of aerial photography, Landsat TM and SPOT satellite imagery, int. j. remote sensing, 2001, vol. 22, no. 15, 2911–2925 Reeh Niels, Mass balance of the Greenland Ice sheet: can modern observation methods reduce the uncertainty, int. j. remote sensing, 735-742 H. Liu, Q. Zhou, Accuracy analysis of remote sensing change detection by rule-based rationality evaluation with post-classification comparison, int. j. remote sensing, 10 MARCH, 2004, vol. 25, no. 5, 1037–1050 Czaplewski R. L., Can a sample of Landsat sensor scenes reliably estimate the global extent of tropical deforestation?,International Journal of Remote Sensing, vol 24, no 6, March 20, 2003, 1409-1412. Rajeev Saxena, Technique of large scale soil mapping in basaltic terrain using satellite remote sensing data, int. j. remote sensing, 10 March, 2004,vol. 25,no. 5, 1037–1050 Friedl M. A., Woodcock C., Gopal S., Muchoney D.,Strahler A. H., Barker-Schaaf C,A note on procedures used for accuracy assessment in land cover maps derived from AVHRR data, International Journal of Remote Sensing, 20 March 2000,vol 21, no 5, pp. 1073-1077(5) Atkinson M.P Tate J Nicholas, Spatial Scale Problems and Geostatistical Solutions: A review, Nov 2000, No 4, 607-620. Websites May Robert, Risk and Uncertainty, nature, 21 june 2001,vol 41 Grant Leavenworth,Heuvalink, library.uu.nl/digiarchief/dip/diss/1903229/c4.pdf#search=Uncertainty%2C%20remote%20sensing Visual Exploration of Uncertainty in Remote-sensing Classification, 1997, Frans J.M. van der Wel, Linda C. van der Gaag, library.uu.nl/digiarchief/dip/dispute/2001-0226-133022/1997-29.pdf Research Papers on Problems In Remote Sensing - Graphic Arts EssayIncorporating Risk and Uncertainty Factor in CapitalResearch Process Part OneRiordan Manufacturing Production PlanThe Project Managment Office SystemBionic Assembly System: A New Concept of SelfInfluences of Socio-Economic Status of Married MalesAnalysis Of A Cosmetics AdvertisementThe Relationship Between Delinquency and Drug UseOpen Architechture a white paperMind Travel

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