How can visual illusions illustrate top down processes in perception Essay Example

How can visual illusions illustrate top down processes in perception Paper There are many different types of visual illusions, many of which can be shown to illustrate different mental processes in perception. Types of illusion including those involving ambiguity, distortion and fiction can be seen to be processed using varied mental methods and can be categorised into physical, physiological, and cognitive illusions. Physiological, or bottom up processing is directly affected by the stimulus input (Eysenck, 2004) as supported by Gibson, where as cognitive or top down theory is a constructivist approach upheld by those such as Bruner and Neisser, stating that processes are influenced by the individuals expectation and knowledge rather than simply the stimulus itself (Eysenck, 2004). Both methods of processing however can be illustrated through the explanation of certain visual illusions. Top down processing is a high level and secondary form of perception and is affected by our expectations and beliefs as well as being hypothesis or expectation driven. Visual illusions can be used to illustrate this type of perceptual processing in a number of ways, one of which is in illusions which involve ambiguity. One example in which this can be seen is the Necker cube (see figure 1). This optical illusion was first published in 1982 in Switzerland by Louis Albert Necker. It has an ambiguous nature as it can be interpreted in more than one way. It the intersection of the two lines, it is unclear which is in the front therefore meaning that it can be understood in two different formats consequently using multi-stable perception. We will write a custom essay sample on How can visual illusions illustrate top down processes in perception specifically for you for only $16.38 $13.9/page Order now We will write a custom essay sample on How can visual illusions illustrate top down processes in perception specifically for you FOR ONLY $16.38 $13.9/page Hire Writer We will write a custom essay sample on How can visual illusions illustrate top down processes in perception specifically for you FOR ONLY $16.38 $13.9/page Hire Writer Upon seeing two different images when looking at clearly only one visual stimulus it can be concluded that the difference in perception has to be attributed to another source. This can be seen to be a result of top down processing as it is the context that the illusion is in and the previous experiences in the same area that mean that the viewer sees more than is really there. The illusion can also be proved top down by the fact that they are somewhat under conscious control in that the perception can be altered by choice. Supporting this approach is Constructivist theory, in which advocates Bruner, Neisser and Gregory all insist the emphasis of the role of top down processes as the one of prominent importance in perception. This theory of indirect perception follows the principle that perception is the end-product of hypotheses expectations and knowledge (Eysenck, 2004). These ideas have been proven for example Palmers kitchen scene experiment in 1975 where items were identified correctly more often if put into their context. When this approach is applied to visual illusions, it supports the top down theory for the explanation of them in that context and previous experiences are used, deceiving people into seeing something that is incorrect. There are however several criticisms of using constructivism to prove top down theory in illusions. One way in which is that one of the main principle of constructivism is that perception reached by hypothesis is prone to error, however in everyday life it can be seen that this is not the case. In addition, many of the experiments that prove the constructivist theory are artificial and inconsistent with normal life. Visual illusions can also be explained from a bottom up perspective. This primary form of cognition is low level and uses serial processing as one process is completed before the next begins (Eysenck, 2004). This form of perceptual information processing is frequently data driven and is predominantly involuntary, not being influenced by context or hypothesis. Visual illusions can be used to explain this type of perceptual processing in a number of ways, one of which is in illusions which involve fiction. One example of which is the illusion of the Mach Bands (see figure 2). This illusion was created by Italian psychologist Ernst Mach and can be seen practical terms in medicine when examining x-rays. Mach Bands is a fictional illusion as the eye tends to interpret either a bright or dark vertical band near the area where there is a strong gradient change in the color, when in fact none exists. This is supposedly due to lateral inhibition of the receptors in the eye however it can also be attributed to the statistical strategy of visual perception (Lotto, Williams, Purves, 1999). This illusion can be explained through bottom up perception processes and constasts sharply with top down theory as it does not use previous knowledge or context to delude the viewer, it is purely an automatic, low-level mechanism without hypothesis or expectation. An argument which supports this approach is the theory of Direct perception, maintained by the American psychologist James Gibson. He claimed there is information available in sensory stimulation (Eysenck, 2004) and his main principles include structured light contains visual information from the environment, and that this provides information about the layout of objects also that perception involves picking up this information with little or no information processing being involved (Eysenck, 2004). These principles were proven by Gibson in 1950 using his pilot experiment in which he observed the pilots processing information from the environment with little or no expectation or hypothesis involved in relation to factors such as direction speed and altitude. When this approach is applied to visual illusions, it supports the bottom up theory for the explanation of them in that it shows visual information being absorbed without considering context. However there are several criticisms of Gibsons direct theory of perception, one of which being that processes are more complex than stated in his experiments, and another that his principles apply much more to some aspects of perception than to others. He also was incorrect in disregarding the use of internal representations to understand perception (Eysenck, 2004) for example memory which was later proved erroneous by the work of Menzel.

Free Essays on Hammurabi’s Code Of Laws

Hammurabis punishments are far more severe than those of today. A good example of this is his first law which imbeds slander. It states,†If anyone ensnare another, putting a ban upon them, but he cannot prove it, then he that ensnared him shall be put to death,† compared with today’s punishment of the same crime this seems a little extreme. Death is punishment for most every crime imaginable in Hammurabi’s time, but I suppose if people are scared to death, they are less likely to do something wrong. I dont know what people thought of these laws back then, but I can guarantee that they didn’t tell him about it. These Code of Laws never gave any person whom committed a crime a second chance, which also refers to not being able to prove the charge, for example â€Å"If anyone brings an accusation of any crime before the elders and does prove what he was charged, he shall, if it be a capital offense charged, be put to death. The word put do death was used in about 94 % of his laws. It seems he used it to give people a fair warning, but never had an explanation for any of them. This automatically shows that all Hammurabi’s written laws were unequal and unfair. To demonstrate some of these examples are â€Å"If a judge try a case, reach a decision, and present his judgment in writing; if later error shall appear in his decision, and it be through his own fault, then he shall pay twelve times the fine set by him in the case, and he shall be publicly removed from the judge’s bench, and never again shall he sit there to render judgment. My second example is â€Å"If anyone buy from the son or the slave of another man, without witnesses or a contract, silver or gold, a male or female slave, an ox or a sheep, an ass or anything, or if he take it in charge, he is considered a thief and shall be put to death. Next is â€Å"If anyone steal cattle or sheep, or an ass, or a pig or a goat, if it belong to a god or to ... Free Essays on Hammurabi’s Code Of Laws Free Essays on Hammurabi’s Code Of Laws Hammurabis punishments are far more severe than those of today. A good example of this is his first law which imbeds slander. It states,†If anyone ensnare another, putting a ban upon them, but he cannot prove it, then he that ensnared him shall be put to death,† compared with today’s punishment of the same crime this seems a little extreme. Death is punishment for most every crime imaginable in Hammurabi’s time, but I suppose if people are scared to death, they are less likely to do something wrong. I dont know what people thought of these laws back then, but I can guarantee that they didn’t tell him about it. These Code of Laws never gave any person whom committed a crime a second chance, which also refers to not being able to prove the charge, for example â€Å"If anyone brings an accusation of any crime before the elders and does prove what he was charged, he shall, if it be a capital offense charged, be put to death. The word put do death was used in about 94 % of his laws. It seems he used it to give people a fair warning, but never had an explanation for any of them. This automatically shows that all Hammurabi’s written laws were unequal and unfair. To demonstrate some of these examples are â€Å"If a judge try a case, reach a decision, and present his judgment in writing; if later error shall appear in his decision, and it be through his own fault, then he shall pay twelve times the fine set by him in the case, and he shall be publicly removed from the judge’s bench, and never again shall he sit there to render judgment. My second example is â€Å"If anyone buy from the son or the slave of another man, without witnesses or a contract, silver or gold, a male or female slave, an ox or a sheep, an ass or anything, or if he take it in charge, he is considered a thief and shall be put to death. Next is â€Å"If anyone steal cattle or sheep, or an ass, or a pig or a goat, if it belong to a god or to ...

Film Critique Movie Review Example | Topics and Well Written Essays - 1250 words - 1

Film Critique - Movie Review Example The issues presented in the movie, regarding concerns of women In the film, almost all the main problems faced by women folk in a male dominated and racially prejudiced society are portrayed in a vivid manner. For instance, the main female characters in the film do not enjoy liberty in the mainstream society. They are forced to act according to the will of the male characters. The most important female character in the film (Daisy Werthan, an elderly Jewish widow) leads a grim life similar to other widows in a male dominated society. The loneliness and alienation faced by Daisy Werthan in her private and public domains is vividly portrayed by the director. The problem of racism is another issue presented in the movie regarding concerns of women. Besides, prejudice against Afro- Americans and the Jews, and marginalization faced by them is another issue presented in the movie regarding concerns of women. Scheuer and Scheuer (2003), opine that â€Å"Driving Miss Daisy tells us, as much more about the difference between North and South, and racial attitudes in America as any sociology textbook will† (p.106). The main female characters in the film are not considered as important in their families. The problem of marginalization in the mainstream society without any decision making capacity leads to mental and emotional problems in female characters. The issues presented in the movie regarding concerns of women are still relevant because male domination, marginalization, alienation, prejudice and racism curb the growth and development of women in the society. Loneliness, racism, and male domination illustrated in the movie The most important issues presented in this movie regarding concerns of women are related to male domination and racial prejudice. So, three issues (loneliness, racism, and male domination), and three female characters (Daisy Werthan, Idella and Florine Werthan), are selected to expose how these issues and characters are illustrated in the m ovie. 1. Loneliness and alienation faced by Daisy Werthan The most important female character in film, Daisy Werthan (Jessica Tandy), an elderly Jewish widow faced alienation in her private (home) and public (society) domains. When Daisy Werthan’s husband was succumbed to death, she gradually got alienated herself from the mainstream society. For instance, her son did not allow her to drive her car. Besides, her maid servant was aware of the alienation felt by Daisy Werthan in her home and society. The director gave ample importance to the issue of loneliness and alienation faced by widows in the society. To be specific, the character of Daisy Werthan represents the sad plight and emotional detachment of widows in the society. 2. Racism and prejudice faced by Idella Idella (Esther Rolle), Daisy Werthan’s Afro-American maid servant is one of the best examples of racial segregation and marginalization faced by the African American community in America. One can easily ide ntify that racism or marginalization from the main stream society is the most important problem faced by African American community in America. Racism is a social evil which hinder the progress of African Americans in the American society. On the other side, Daisy Werthan, a Jew by birth was also a victim of racism. The best example of racism was the

Compare and Contrast the british parlimentary system with the american Term Paper

Compare and Contrast the british parlimentary system with the american presedential system - Term Paper Example In the parliamentary system of politics, the head of state is not the chief executive of the nation. In most cases, the functions of the head of state are merely ceremonial whereas the chief executive heads the legislature (Manuel and Cammisa 64). The comparisons and contrasts between the British and the American political systems will be discussed in this paper. To begin with, it is imperative to first discuss a brief background history of the two democracies. Apparently, The United States of America emerged during a colonial revolutionary war with the British. This colonial war was spearheaded by influential and successful people in America such as large ranch owners and merchants, lawyers and slave owners. They were primarily concerned with getting rid of the colonial rule of the British government as well as the governing structures that had been set up by the colonialists (Munroe 65). These colonial structures did not favor in any way the native farmers, slaves and laborers. The American political system was, therefore, established by revolution. They formed a new constitution, and a new state, in 1789 becoming the oldest democracy to have a functioning constitution. The British state was however not formed by revolution as was the American state. The parliamentary system of the British resulted mainly due to gradual change and evolution. This did not involve the overthrowing of a previous system (Manuel and Cammisa 64). The monarchial and aristocratic rule that was in Britain was just undermined gradually over the centuries to the present day. This means that the institutions and political structures that were there were not done away with. Half a million centuries ago there was a king in Britain, and there is a queen today. Although these structures remained over the years, the powers they hold and their functions have changed significantly. The kingdom today is mostly symbolic as opposed to the powers that the monarch commanded

Government Taxation Research Paper Example | Topics and Well Written Essays - 500 words

Government Taxation - Research Paper Example Income tax Every citizen in the United States is obliged to paying income tax regardless of the place the citizen stays in the country. Each state in the United States has its own state income and sets the amount that the citizens have to pay. A good number of states have extra state income tax while others they at all have no state income tax. Others still apply state income tax to dividend income as well as to interest. Other types of income tax applied in the states are personal income tax, and retirement income tax (Mikesell 2011). Income tax is very useful in the economy of the United States. It permits a progressive taxation on the quantity of cash an individual makes which is an essential scheme that helps in distributing wealth equally. The progressive taxation program also allows the government to stabilize the income stream even in times of depression (Mikesell 2011). Another advantage of income tax is that it is easy to collect since it is automatically r emoved from the paycheck of the citizen. Besides, income tax has its limitations in that the system is very complex. The tax code used is said to be favoring the poor and being unfair to the wealthy. Property tax This is another type of tax in the United States of America which is paid by property owners to the state. Often, the states that which do not have state income tax often have put the burden on the property tax. The rates of the property tax in such states are very high. The rates often vary due to the area in which it is applied, town, city or county. These taxes are very useful since they assist in paying for public services such as community colleges, public schools, and other matters concerned by the local government (Molly 2009). Property owners will be charged tax on the basis of the land in use, any improvement to the land as well as any structures that are not permanent to

Microwave Remote Sensing in Forestry

Microwave Remote Sensing in Forestry BACKGROUND: Microwave remote sensing at wavelengths ranging from 1 cm to 1 m has gained a lot of importance over the past decade for a wide range of scientific applications with the availability of active radar imaging systems. Its potential in spatial applications has been scientifically established in various sectors like forestry, agriculture, land use and land cover, geology and hydrology. A variety of applications have been carried out world over using microwave data like discrimination of crop types, crop condition monitoring, soil moisture retrieval, delineation of forest openings, estimation of forest above ground biomass, forest mapping; forest structure and fire scar mapping, geological mapping, monitoring wetlands and snow cover, sea ice identification, coastal windfield measurement, wave slope measurement, ship detection, shoreline detection, substrate mapping, slick detection and general vegetation mapping (Kasischke et al., 1997). There is an emerging interest on microwave remote sensing, as microwave sensors can image a surface with very fine resolution of a few meters to coarse resolution of a few kilometers. They provide imagery to a given resolution independently of altitude, limited only by the transmitter power available. Fundamental parameters like polarization and look angle can be varied to optimize the system for a specific application. SAR imaging is independent of solar illumination as the system provides its own source of illumination. It can operate independently of weather conditions if sufficiently long wavelengths are chosen. It operates in a band of electromagnetic spectrum different from the bands used by visible and infrared (IR) imageries. Microwave applications in Forestry Applications of microwave remote sensing in forestry have also been reported during the recent past. Recent reviews on the application of radar in forestry show that SAR systems have a good capability in discriminating various types of (tropical) forest cover using multi-temporal and multi-frequency SAR data (Vander Sanden, 1997; Varekamp, 2001; Quinones, 2002; Sgrenzaroli, 2004). These studies showed that the biomass dependence of radar backscatter varies as a function of radar wavelength, polarization and incidence angle. Also recent studies have demonstrated that synthetic aperture radar (SAR) can be used to estimate above-ground standing biomass. To date, these studies have relied on extensive ground-truth measurements to construct relationships between biomass and SAR backscatter (Steininger, 1996; Rignot et al., 1997). Many studies demonstrated the use of Synthetic Aperture Radar (SAR) remote sensing to retrieve biophysical characteristics from forest targets (Richards, 1990). Although radar backscatter from forest is influenced by their structural properties (Imhoff, 1995), earlier studies derived useful relationships between backscattering coefficients and the above-ground biomass (Baker et., 1994; Le Toan et al., 1992; Dobson et el., 1992; Imhoff; 1995). These relationships may provide a method of monitoring forest ecosystems which play such a vital role in carbon storage and NPP. Microwave remote sensing has the advantage of all weather capability coverage overcoming the persistent problem of cloud cover in satellite images like in optical data. Optical remote sensing is being used very successfully in various applications related to earth resources studies and monitoring of the environment. However, optical remote sensing is not suitable for all atmospheric conditions. It cannot penetrate through clouds and haze. In many areas of the world, the frequent cloud conditions often restrain the acquisition of high-quality remotely sensed data by optical sensors. Thus, radar data has become the only feasible way of acquiring remotely sensed data within a given time framework because the radar systems can collect Earth feature data irrespective of weather or light conditions. Due to this unique feature of radar data compared with optical sensor data, the radar data have been used extensively in many fields, including forest-cover identification and mapping, discrimi nation of forest compartments and forest types, estimation of forest stand parameters and monitoring of forests. In areas where vegetation cover is dense, it visually covers the underlying formation and it is very difficult to detect structural limiting the use of optical sensors. Radar however, is sensitive enough to topographic variation that it is able to discern the structural expression reflected in the tree top canopy, and therefore the structure may be clearly defined on the radar imagery. Based on this background, the current thesis work has been carried out to explore the potential of microwave data in addressing core areas of tropical forestry viz., vegetation classification, above ground biomass estimation etc., and to provide the users/researchers a meaningful data base of SAR applications in tropical forestry, specifically over the India region. Research questions: Which SAR wavelength/frequency band is appropriate for vegetation classification in tropical forests? To what extent above ground biomass can be measured in tropical forests? Which frequency band and polarization are suitable for above ground biomass estimation? Is there any enhancement in vegetation classification with polarimetric / interferometric data than stand alone amplitude data? Research hypothesis: Based on the previous studies and earlier mentioned research questions, we understand that the backscatter increases with the increase in above ground biomass and depends on wavelength bands, polarizations used and on the study area, topographic variations and species composition. So, the present study attempts to derive the application potential of airborne and space borne SAR data in the quantification of the forest resources in tropical regions like India, both as a complementary and supplementary role to optical datasets. Different techniques such as Regression analysis, multi-sensor fusion, texture measures and interferometric coherence characterize different biomass ranges of the test sites and classification of major land cover classes. This study would facilitate scope for future research in tropical regions to explore the potentials of SAR data in land cover classification and above ground biomass estimation using the polarimetric and interferometric techniques. OBJECTIVES: Based on this background, the present study aims at the following objectives: Vegetation type classification using polarimetric and interferometric SAR data. Forest above-ground biomass estimation using multi-frequency SAR data and ground inventoried data. Vegetation classification is necessary to understand the diversity of species in a given area which gives above ground biomass with measured parameters. Hence, vegetation classification enhances the estimation of the above ground biomass. Forest biomass is a key parameter in understanding the carbon cycle and determining rates of carbon storage, both of which are large uncertainties for forest ecosystems. Accurate knowledge of biophysical parameters of the ecosystems is essential to develop an understanding of the ecosystem and their interactions, to provide input models of ecosystem and global processes, to test these models and to monitor changes in ecosystem dynamics and processes over time. Thus, it is a useful measure for assessing changes in forest structure, comparing structural and functional attributes of forest ecosystems across a wide range of environmental conditions. Knowing the spatial distribution of forest biomass is important as the knowledge of biomass is required for calculating the sources and sinks of carbon that result from converting a forest to cleared land and vice versa, to know the spatial distribution of biomass which enables measurement of change through time. Field sampling is the most followed conventional method for vegetation type classification. The identification of different species in field yields good results in the estimation of the above ground biomass. It is very time consuming, expensive and very complicated. With the use of multiple sensors, varied data collection and interpretation techniques, remote sensing is a versatile tool that can provide data about the surface of the earth to suit any need (Reene et al, 2001). Remote sensing approach for vegetation classification is cost effective and also time effective. Though the identification of the tree species is possible only from the aerial imagery, major forest types can be identified from the airborne and the spaceborne remote sensing data. Visual image interpretation provides a feasible means of vegetation classification in forests. The image characteristics of shape, size, pattern, shadow, tone and texture are used by interpreters in tree species identification. Phenological correlations are useful in tree species identification. Changes in the appearance of trees in different seasons of the year some times enable discrimination of species that are indistinguishable on single dates. The use of multi-temporal remote sensing data enabl es the mapping of the different forest types. SAR has shown its potential for classifying and monitoring geophysical parameters both locally and globally. Excellent works were carried out on the classification using several approaches such as polarimetric data decomposition (Lee et al., 1998), knowledge based approaches considering the theoretical backscatter modeling and experimental observations ( Ramson and Sun , 1994) ; Backscatter model-related inversion approaches ( Kurvonen et al., 1999), neural networks and data fusion approaches ( Chen et al., 1996). Dong et al. (2001) have shown that the classification accuracy of 95% for the vegetation classes could be achieved through the segmentation and classification of the SAR data using Gaussian Markov Random Field Model (GMRF). Many methods have been employed for classification of polarimetric SAR data, based on the maximum likelihood (ML) (Lee et al. 1994), artificial neural network (NN) (Chen et al. 1996, Ito and Omatu, 1998), support vector machines (SVMs) (Fukuda et al. 2002), fuzzy method (Chen et al. 2003, Du and Lee 1996), or other approaches (Kong et al. 1988, Lee and Hoppel 1992, van Zyl and Burnette 1992, Cloude and Pottier 1997, Lee et al. 1999, Alberqa 2004) Among these methods, the ML classifier (Lee et al. 1994) can be employed for obtaining accurate classification results, but it is based on the assumption of the complex Wishart distribution of the covariance matrix. Assessing the total aboveground biomass of forests (biomass density when expressed as dry weight per unit area at a particular time) is a useful way of quantifying the amount of resource available for all traditional uses. It either gives the quantity of total biomass directly or the quantity by each component (e.g., leaves, branches, and bole) because their biomass tends to vary systematically with the total biomass. However, biomass of each component varies with total biomass by forest type, such as natural or planted forests and closed or open forests. For example, leaves contribute about 3-5% and merchantable bole is about 60% of the total aboveground biomass of closed forests. Many researchers have developed various methods based on field inventory and remote sensing approaches for the estimation of above ground biomass (Kira and Ogawa, 1971). Traditionally, field-measured approach is considered as the most accurate source for above-ground biomass estimation. It has been converted to volume, or biomass, using allometric equations that are based on standard field measurements (tree height and diameter at breast height). Different approaches, based on field measurement (Brown et al. 1989, Brown and Iverson 1992, Schroeder et al.. 1997, Houghton et al., 2001, Brown, 2002); remote sensing (Tiwari 1994, Roy and Ravan 1996, Tomppo et al., 2002, Foody et al., 2003, Santos et al., 2003, Zheng et al., 2004, Lu, 2005); and GIS (Brown and Gaston 1995) have been applied for AGB estimation. Traditional techniques based on field measurement are the most accurate ways for collecting biomass data. A sufficient number of field measurements is a prerequisite for developing AGB estimation models and for evaluating the AGB estimation results. However, these approaches are often time consuming, labour intensive, and difficult to implement, especially in remote areas and are generally limited to 10-year intervals. Also, they cannot provide the spatial distribution of biomass in large areas. For the above reasons, the perspectives of using remote sensing techniques to estimate forest biomass have gained interest. Remote sensing data available at different scales, from local to global, and from various sources, optical to microwave are expected to provide information that could be related indirectly, and in different manners, to biomass information. The possibility that aboveground forest biomass might be determined from space is a promising alternative to ground-based methods (Hese et al., 2005). The advantages of remotely sensed data, such as in repetivity of data collection, synoptic view, digital format that allows fast processing of large quantities of data, and the high correlations between spectral bands and vegetation parameters, make it the primary source for large area AGB estimation, especially in areas of difficult access. Therefore, remote sensing-based AGB estimation has increasingly attracted scientific interest. In general, AGB can be estimated using remotely sensed data with different approaches, such as multiple regression analysis, K nearest-neighbour, and neural network (Roy and Ravan 1996, Nelson et al. 2000a, Steininger 2000, Foody et al. 2003, Zheng et al. 2004), and indirectly estimated from canopy parameters, such as crown diameter, which are first derived from remotely sensed data using multiple regression analysis or different canopy reflectance models (Wu and Strahler 1994, Woodcock et al. 1997, Phua and Saito 2003, Popescu et al. 2003). Spectral signatures or vegetation indices are often used for AGB estimation in optical remote sensing. Many vegetation indices have been developed and applied to biophysical parameter studies (Anderson and Hanson 1992, Anderson et al. 1993, Eastwood et al. 1997, Lu et al. 2004, Mutanga and Skidmore 2004). Vegetation indices have been recommended to remove variability caused by canopy geometry, soil background, sun view angles, and atmospheric conditions when measuring biophysical properties (Elvidge and Chen 1995, Blackburn and Steele 1999). Radar remote sensing has potential to provide information on above ground biomass. The information content of SAR data in terms of the retrieval of biomass parameters will be assessed based on an understanding of the underlying scattering mechanisms, which in turn are derived from observations and modeling results. For this purpose, an analysis of data acquired by multiple frequency, incidence and polarisation systems and by interferometric systems is carried out. It has been proved that the sensitivity to biomass parameters differ strongly at different frequencies, polarisations and incidence angles. In general, long wavelength SAR backscatter (P and L band) is more sensitive to forest biomass than shorter wavelength C-band backscatter and the relationships saturate at certain biomass levels ( Imhoff 1995b). The strength of the relationships and the saturation levels are dependent on the type of forest being analysed (Ferrazoli et al. 1997). The saturation levels for the estimation of above ground biomass depend on the wavelengths (i.e. different bands, such as C, L, P), polarization (such as HV and VV), and the characteristics of vegetation stand structure and ground conditions. C-band can measure forestry biomass up to app. 50 tons/ha, L-band can measure up to 100 tons/ha and P-band can measure up to 200 tons/ha (Floyd et al., 1998). The combination of multiple channels and polarizations provides greater advantage for estimating total biomass (Harry Stern, 1998). RELEVANCE OF THE STUDY: The present study is the part of Radar Imaging satellite Joint Experiment Programme (RISAT-JEP) for forestry applications undertaken by Forestry and Ecology Division of National Remote Sensing Centre (NRSC), as a pilot campaign with specific objectives of above ground biomass estimation and vegetation type classification using airborne DLR (German Aerospace Center) carrying ESAR (Experimental Synthetic Aperture Radar) data for Rajpipla (Gujarat) study site and space borne ENVISAT (Environmental Satellite) carrying Advanced Synthetic Aperture Radar (ASAR) data for three test sites viz., Rajpipla (Gujarat), Dandeli (Karnataka) and Bilaspur (Chattisgarh), India. SCOPE OF THE STUDY: The specific objectives of the present study are above ground biomass estimation and vegetation type classification using airborne DLR (German Aerospace Center) carrying ESAR (Experimental Synthetic Aperture Radar) data for Rajpipla (Gujarat) study site and space borne ENVISAT (Environmental Satellite) carrying Advanced Synthetic Aperture Radar (ASAR) data; ALOS (Advanced Land Observing Satellite) carrying Phased Array L-band Synthetic Aperture Radar (PALSAR) for three test sites viz., Rajpipla (Gujarat), Dandeli (Karnataka) and Bilaspur (Chattisgarh), India. Different techniques such as Regression analysis, multi-sensor fusion, texture measures and interferometric coherence were used to characterize different biomass ranges of the test sites and to classify the major land cover classes using spaceborne C-band ENVISAT-ASAR data and L-band ALOS- PALSAR data. Polarimetric signatures, polarimetric decompositions, multi-sensor fusion techniques etc. were used for the classification of different vegetation types in the Rajpipla study area using the airborne DLR-ESAR data. The study has its uniqueness and gains importance in the application potential of SAR interferometry over tropical regions like India, both in terms of an alternate/substitute to optical data sets due to persisting cloud cover and to the lack of availability of any earlier scientific work over the study region. This study is useful for the applications of to be launched Radar Imaging Satellite (RISAT) in 2010. The study has amply demonstrated the application potential of airborne and space borne SAR data in the quantification of the forest resources in tropical regions like India, both as a complementary and supplementary role to optical datasets. The study would facilitate future research in tropical regions to explore the potentials of SAR data in land cover classification and above ground biomass estimation using the polarimetric and interferometric techniques. LITERATURE SURVEY: During the last decade, many potential applications of SAR in different frequency bands have been studied for forestry applications using data acquired by both airborne and space-borne systems. Various techniques like Polarimetry, Interferometry and Polarimetric-Interferometry enhanced the use of SAR data in forestry applications. The backscatter from vegetation is used to infer information about amplitude data for forest cover mapping and estimation of above ground biomass in regenerating forests. Use of SAR polarimetric data delineated vegetation classes within the forest and also enhanced the capability in estimating the above ground biomass. The use of repeat pass interferometric data enables to calculate the forest stand height and also used for the land cover classification. The emerging Pol-InSAR technique is used to derive the three dimensional forest structures. Forest cover maps were prepared for the boreal, temperate and tropical forests using SAR data. Forest was separated from non-forest regions using multi-temporal C-band ERS SAR data on the test sites of United Kingdom, Poland and Finland (Quegan et al., 2000). The study applied a threshold value to separate forest from other classes. Tropical rainforest of Borneo was mapped from SIR-B data of different incidence angles (Ford and Casey, 1988). Different vegetation covers along with wetlands and clear-cut areas were distinguished. Forest cover mapping was done with JERS-1 SAR data on the coastal regions of Gabon (Simard et al., 2000). The study used decision tree method utilizing both radar amplitude and texture information. Forest cover map was prepared for Southern Chittagong using JERS-1 SAR data (Rahman and Sumantyo, 2007) and the study separated forest, degraded forest, shrubs, coastal plantations, agriculture, shrimp-farms, urban and water. Although radar backscatter from forest is influenced by their structural properties (Imhoff, 1995a), many studies have demonstrated useful relationships between backscattering coefficients and the areal density of above-ground biomass within particular types of forest (Baker et., 1994; Le Toan et al., 1992; Dobson et al., 1992; Imhof et al; 1995b). Many airborne and spaceborne SAR systems have been used to carry out a large amount of experiments for investigating the forest ecosystems. The airborne systems, such as the NASA/JPL AIRSAR, DLR-ESAR, etc., operating at P, L and C band, has been flown over many forest sites (Zebker et al., 1991; Le Toan et al, 1992; Beaudoin et al., 1994; Rignot et al.; 1994; Skriver et al., 1994; Ranson et al., 1996). The experiments of the Canadian CV-580, as well as the European airborne system, mainly operating at C and X band also have been carried out in North America and Europe (Drieman et al., 1989; Hoekman, 1990). Spaceborne SAR is being used from regional to global monitoring in a periodic basis. The spaceborne systems, such as the Seasat SAR, SIR-B, SIR-C/X-SAR and ERS-1, ERS-2, ENVISAT-ASAR, RADARSAT etc., were used for investigations of boreal, temperature and sub-tropical forestry test sites (Ford et al., 1988; Dobson et al., 1992; Ranson et al., 1995; Stofan et al., 1995; Rignotet al., 1995). These experiments and studies have shown that radar is sensitive to forest structural parameters such as diameter at breast height (dbh) and tree mean height including above-ground biomass (Dobson et al., 1992; Pulliainen et al., 1994; Skriver et al., 1994; Ferrazzoli et al., 1995; Ranson et al., 1996). Earlier studies has shown the potential of radar data in estimating AGB (Hussin et al. 1991, Ranson and Sun 1994, Dobson et al. 1995, Rignot et al. 1995, Saatchi and Moghaddam 1995, Foody et al. 1997, Harrell et al. 1997, Ranson et al. 1997, Luckman et al. 1997, 1998, Pairman et al. 1999, Imhoff et al. 2000, Kuplich et al. 2000, Castel et al. 2002, Sun et al. 2002, Santos et al. 2003, Treuhaft et al. 2004). Kasischke et al. (1997) reviewed radar data for ecological applications, including AGB estimation. Lucas et al. (2004) and Kasischke et al. (2004) reviewed SAR data for AGB estimation in tropical forests and temperate and boreal forests, respectively. Different wavelength radar data have their own characteristics in relating to forest stand parameters. Backscatter in P and L bands is highly correlated with major forest parameters, such as tree age, tree height, DBH, basal area, and AGB (Leckie 1998). In particular, SAR L-band data have proven to be valuable for AGB estimation (Sad er 1987, Luckman et al. 1997, Kurvonen et al. 1999, Sun et al. 2002). However, low or negligible correlations were found between SAR C-Band backscatter and AGB (Le Toan et al. 1992). Beaudoin et al. (1994) found that the HH return was related to both trunk and crown biomass, and the VV and HV returns were linked to crown biomass. Harrell et al. (1997) evaluated four techniques for AGB estimation in pine stands using SIR C- and L-Band multi-polarization radar data and found that the L-Band HH polarization data were the critical elements in AGB estimation. Kuplich et al. (2000) used L-band JERS-1/SAR data for AGB estimation of regenerating forests and concluded that these data had the potential to estimate AGB for young, regenerating forests. Sun et al. (2002) found that multi-polarization L-Band SAR data were useful for AGB estimation of forest stands in mountainous areas. Castel et al. (2002) identified the significant relationships between the backscatter coefficient of JERS- 1/SAR data and the stand biomass of a pine plantation. The study observed the improvement in AGB estimation results for young stands, compared to estimation for old stands. Santos et al. (2002) used JERS-1 SAR data to analyse the relationships between backscatter signals and biomass of forest and savanna formations. This study concluded that forest structural-physiognomic characteristics and the radars volume scattering, double bounce scattering are two important factors affecting these relationships. The saturation levels of backscattering co-efficient with respect to AGB depend on the wavelengths (i.e. different Bands, such as C, L, P), polarization (such as HV and VV), and the characteristics of vegetation stand structure and ground conditions. Luckman et al. (1997) found that the longer-wavelength (L-Band) SAR image was more suitable to discriminate different levels of forest biomass up to a certain threshold, indicating that it is suitable for estimating biomass of regenerating forests in tropical regions. Austin et al. (2003) indicated that forest biomass estimation using radar data may be feasible when landscape characteristics are taken into account. The radar backscattering coefficient is correlated with forest biomass and stem volume (Le Toan et al. 1992, Israelsson et al. 1994, Kasischke et al. 1994, Dobson et al. 1995). The sensitivity of Synthetic Aperture Radar (SAR) data to forest stem volume increases significantly as the radar wavelength increases (Israelsson et al. 1997). The imaging process makes SAR suitable for mapping parameters related to forest biomass, like stem volume (Baker et al, 1999; Fransson et al, 1999; Hyyppa et al, 1997; Israelsson et al., 1997; Kurvonen et al, 1999; Pulliainen et al, 1996), total growing stock (Balzter et al, 2000; Schmullius et al, 1997), LAI (Imhoff et al, 1997), or above ground net primary productivity (Bergen et al, 1998). Le Toan et al., (1992) used multi-polarisation L- and P-band airborne radar data, and found that the dynamic range of the radar backscatter corresponded highly with forest growth stages and is maximum at P-band HV polarization. The analysis of P-band data indicated a good correlation between the radar backscatter intensity and the main forest parameters including trunk biomass, height, age, diameter at breast height (dbh), and basal area. Dobson et al., (1992) showed an increasing range of backscatter with changing biomass from C to P-band, as well as higher biomass levels at which backscatter relationships to biomass saturate. Hoekman, (1990) found poor relationships between X- and C-band backscatter and volume and other stand parameters. The spaceborne systems, such as the Seasat SAR, SIR-B, SIR-C/X-SAR and ERS-1, ERS-2, JERS, ENVISAT-ASAR and recently ALOS-PALSAR etc. were used for investigations of boreal, temperature and sub-tropical forestry test sites (Ford et al., 1988; Dobson et al., 1992; Ranson et al., 1995; Stofan et al., 1995; Rignot et al., 1995). These experiments and studies have shown that radar is sensitive to forest structural parameters including above-ground biomass (Dobson et al., 1992; Pulliainen et al., 1994; Skriver et al., 1994; Ferrazzoli et al., 1995; Ranson et al., 1996). Kasischke et al., (1997) reviewed radar data for ecological applications, including AGB estimation. It is being reported in literature that the radar backscatter in the P and L bands is highly correlated with major forest parameters, such as tree age, tree height, DBH, basal area, and AGB. In particular, SAR L-Band data have proven to be valuable for AGB estimation (Sader, 1987; Luckman et al., 1997; Kurvonen et al., 1999; Sun et al., 2002). Kuplich et al., (2000) used JERS-SAR data for AGB estimation of regenerating forests and concluded that these data had the potential to estimate AGB for young, regenerating forests. Luckman et al., (1997) found that the longer-wavelength (L-Band) SAR image was more suitable to discriminate different levels L-Band backscatter shows no sensitivity to increased biomass density after a certain threshold, such as 100 tons ha-1, indicating that it is suitable for estimating biomass of regenerating forests in tropical regions. The radar backscattering coefficient is correlated with forest biomass and stem volume (Le Toan et al. 1992; Israelsson et al., 1994; Kasischke et al., 1994, Dobson et al., 1995). The sensitivity of Synthetic Aperture Radar (SAR) data to forest stem volume increases significantly as the radar wavelength increases (Israelsson et al., 1997). The imaging process makes SAR suitable for mapping parameters related to forest biomass, like stem volume (Baker et al., 1999; Israelsson et al., 1997; Pulliainen et al., 1996), total growing stock (Balzter et al., 2000; Schmullius et al., 1997), LAI (Imhoff et al., 1997), or above ground net primary productivity (Bergen et al., 1998). The dependency of backscatter on above ground biomass was observed and related to the penetration of the radiation into the canopy and interaction with the trunk, where most of the volume, therefore, biomass of the vegetation is concentrated (Sader 1987, Le Toan et al. 1992, Dobson et al. 1992). HV polarization in longer wavelengths (L or P band) is the most sensitive to above ground biomass (Sader 1987, Le Toan et al. 1992, Ranson et al. 1997a) because it originates mainly from canopy volume scattering (Wang et al. 1995), trunk scattering (Le Toan et al. 1992) and is less affected by the ground surface (Ranson and Sun 1994). As forest backscatter in different wavelengths and polarizations originate from separate layers of a canopy, the use of multiple channels or multi-step approaches (e.g., Dobson et al. 1995) could be used to estimate total above-ground biomass (Kasischke et al. 1997). Sun and Ranson (1994) estimated biomass in mixed conifer temperate forest upto 250 Mg/ha. Band ratios (HH/HV and VV/VH) were also used for the above ground biomass estimation. However, Dobson et al. (1995) considered these band ratios too simplistic (as the corresponding backscatter will be much higher for the few tall trees than for the many short ones), although effective in estimating biomass at higher ranges. In spite of this, a combination of bands and polarizations in a multi-step approach made possible the mapping of biomass in a mixed temperate forest upto 250 Mg/ha (Dobson et al. 1995). Establishing a strong link between backscatter and forest variables is an important part of the successful estimation of forest biomass from backscatter. Models are often used to explain the relationship between forest variables, scattering mechanisms and SAR configuration parameters (Richards 1990, Kasischke and Christensen 1990). Another approach is the use of statistical analysis, where forest variables are related to SAR backscatter by regression models (Sader 1987, Le Toan et al. 1992, Rauste et al. 1994). The combination of the two approaches, in most cases to assess the results of the predicted biomass or backscatter via regression (Ranson and Sun 1994, Ferrazzoli et al. 1997, Franson and Israelson 1999). Statistical procedures such as stepwise regression analysis were also used to determine the best set of bands and polarizations to discriminate biomass levels (Ranson et al. 1997a). The three-band (C, L, and P) polarimetric AIRSAR sensor has been used in many forest biomass studies (e.g., Green, 1998; Kasischke et al., 1991, 1995; Moghaddam et al., 1994; Ranson Sun, 1997). The strongest correlation between SAR backscatter and forest biomass has been reported in P-band and the weakest in C-band (e.g., Beaudoin et al., 1992; Dobson et al., 1992; Israelsson et al., 1992; Rauste et al., 1992;

Essay --

We as young adults, deal with many social and emotional issues, which can be seen through depression. Depression, is a state of having low moods and can be described as feeling â€Å"blue†or unhappy. Depressed people may feel so sad at times that they withdraw and lose interest in activities that they once loved to do. In worst cases, some people even commit suicide. Generally, depression does not result from a single event but from a combination of multiple events in life and other personal factors. The number of factors that may cause depression varies from person to person. There could be abuse that happened in the past, conflict from family disputes and personal factors. It could also be from a death or loss and the person cannot get over their grief. Some depression just comes from having an illness. The normal ups and downs in life means that everyone feels sad or "blue" from time to time, but if emptiness and absence of hope have taken place and will not go away, there may be depression in life. When depression is in someone's life they can get very hopeless and not even enjoy thin...