際際滷shows by User: KamleshKumar265 / http://www.slideshare.net/images/logo.gif 際際滷shows by User: KamleshKumar265 / Thu, 26 Mar 2020 18:03:01 GMT 際際滷Share feed for 際際滷shows by User: KamleshKumar265 Migration Profile of Odisha with focus on Bhubaneswar /slideshow/migration-profile-of-odisha-with-focus-on-bhubaneswar/230921614 migrationprofileofodishawithfocusonbhubaneswar-200326180301
Migration is one the most important demographic component to determine the size, growth and structure of population of a particular region, besides fertility and mortality. For a large country like India, the study of movement of population in different parts of the country helps in understanding the dynamics of the society and societal change better. Bhubaneswar is one of the magnets for migrants in east India attributing to its exponential growth rates. This is an attempt to map the migration pattern in the city and the state.]]>
Migration is one the most important demographic component to determine the size, growth and structure of population of a particular region, besides fertility and mortality. For a large country like India, the study of movement of population in different parts of the country helps in understanding the dynamics of the society and societal change better. Bhubaneswar is one of the magnets for migrants in east India attributing to its exponential growth rates. This is an attempt to map the migration pattern in the city and the state.]]> Thu, 26 Mar 2020 18:03:01 GMT /slideshow/migration-profile-of-odisha-with-focus-on-bhubaneswar/230921614 KamleshKumar265@slideshare.net(KamleshKumar265) Migration Profile of Odisha with focus on Bhubaneswar KamleshKumar265 Migration is one the most important demographic component to determine the size, growth and structure of population of a particular region, besides fertility and mortality. For a large country like India, the study of movement of population in different parts of the country helps in understanding the dynamics of the society and societal change better. Bhubaneswar is one of the magnets for migrants in east India attributing to its exponential growth rates. This is an attempt to map the migration pattern in the city and the state. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/migrationprofileofodishawithfocusonbhubaneswar-200326180301-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Migration is one the most important demographic component to determine the size, growth and structure of population of a particular region, besides fertility and mortality. For a large country like India, the study of movement of population in different parts of the country helps in understanding the dynamics of the society and societal change better. Bhubaneswar is one of the magnets for migrants in east India attributing to its exponential growth rates. This is an attempt to map the migration pattern in the city and the state.
Migration Profile of Odisha with focus on Bhubaneswar from Kamlesh Kumar
]]> 1546 0 https://cdn.slidesharecdn.com/ss_thumbnails/migrationprofileofodishawithfocusonbhubaneswar-200326180301-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Population Projection of Khordha District, ODISHA 2021-51 /slideshow/population-projection-of-khordha-district-odisha-202151/230920777 populationprojectionofkhordhadistrict-200326173655
Work is based on Walter Isard's methods in a simplistic manner. 1. ARITHMATICAL INCREASE METHOD OF PROJECTION 2. GEOMETRIC INCREASE METHOD 3. INCREMENTAL INCREASE METHOD]]>
Work is based on Walter Isard's methods in a simplistic manner. 1. ARITHMATICAL INCREASE METHOD OF PROJECTION 2. GEOMETRIC INCREASE METHOD 3. INCREMENTAL INCREASE METHOD]]> Thu, 26 Mar 2020 17:36:55 GMT /slideshow/population-projection-of-khordha-district-odisha-202151/230920777 KamleshKumar265@slideshare.net(KamleshKumar265) Population Projection of Khordha District, ODISHA 2021-51 KamleshKumar265 Work is based on Walter Isard's methods in a simplistic manner. 1. ARITHMATICAL INCREASE METHOD OF PROJECTION 2. GEOMETRIC INCREASE METHOD 3. INCREMENTAL INCREASE METHOD <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/populationprojectionofkhordhadistrict-200326173655-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Work is based on Walter Isard&#39;s methods in a simplistic manner. 1. ARITHMATICAL INCREASE METHOD OF PROJECTION 2. GEOMETRIC INCREASE METHOD 3. INCREMENTAL INCREASE METHOD
Population Projection of Khordha District, ODISHA 2021-51 from Kamlesh Kumar
]]> 260 0 https://cdn.slidesharecdn.com/ss_thumbnails/populationprojectionofkhordhadistrict-200326173655-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 DEMOGRAPHIC PROFILE OF CONTINENTAL ODISHA /slideshow/demographic-profile-of-continental-odisha/220675350 continentalodisha-200116100759
Although the state is endowed with vast natural resources it has remained on the bottom of the developmental chart of the nation. With such a reserve of natural resources and human resource potential, it is like a hibernating beast which must awake for good. Stealing the limelight of the most favourable smart city, the capital is growing like never before along with a few more cities. Yet the state remains mostly rural and lagging in most aspects except for the coastal regions. My analysis is that the state has not been given its due attention in planning which is the reason for its present backwardness.]]>
Although the state is endowed with vast natural resources it has remained on the bottom of the developmental chart of the nation. With such a reserve of natural resources and human resource potential, it is like a hibernating beast which must awake for good. Stealing the limelight of the most favourable smart city, the capital is growing like never before along with a few more cities. Yet the state remains mostly rural and lagging in most aspects except for the coastal regions. My analysis is that the state has not been given its due attention in planning which is the reason for its present backwardness.]]> Thu, 16 Jan 2020 10:07:59 GMT /slideshow/demographic-profile-of-continental-odisha/220675350 KamleshKumar265@slideshare.net(KamleshKumar265) DEMOGRAPHIC PROFILE OF CONTINENTAL ODISHA KamleshKumar265 Although the state is endowed with vast natural resources it has remained on the bottom of the developmental chart of the nation. With such a reserve of natural resources and human resource potential, it is like a hibernating beast which must awake for good. Stealing the limelight of the most favourable smart city, the capital is growing like never before along with a few more cities. Yet the state remains mostly rural and lagging in most aspects except for the coastal regions. My analysis is that the state has not been given its due attention in planning which is the reason for its present backwardness. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/continentalodisha-200116100759-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Although the state is endowed with vast natural resources it has remained on the bottom of the developmental chart of the nation. With such a reserve of natural resources and human resource potential, it is like a hibernating beast which must awake for good. Stealing the limelight of the most favourable smart city, the capital is growing like never before along with a few more cities. Yet the state remains mostly rural and lagging in most aspects except for the coastal regions. My analysis is that the state has not been given its due attention in planning which is the reason for its present backwardness.
DEMOGRAPHIC PROFILE OF CONTINENTAL ODISHA from Kamlesh Kumar
]]> 2298 3 https://cdn.slidesharecdn.com/ss_thumbnails/continentalodisha-200116100759-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Fashion /slideshow/fashion-220675134/220675134 fashion1-200116100419
Fashion is a notoriously difficult term to pin down, and it is extremely doubtful whether it is possible to come up with necessary and sufficient conditions for something justifiably to be called fashionable. Generally speaking, we can distinguish between two main categories in our notion of fashion: one that fashion refers to clothing or that fashion is a general mechanism, logic or ideology that, among other things, applies to the area of clothing. Adam Smith , who was among the first philosophers to give fashion a central role in his anthropology, claims that fashion applies first and foremost to areas in which taste is a central concept. This applies in particular to clothes and furniture, but also to music, poetry and architecture. Immanuel Kant provides a description of fashion that focuses on general changes in human lifestyles: All fashions are, by their very concept, mutable ways of living. However, trends die quickly and with that comes waste. Clothing produced by fast fashion brands are oftentimes made from cheap materials, like polyester and acrylic, and not built to last: The average American throws away 80 pounds of clothing every year. Weve been conditioned to believe that buying a garment and wearing it once is justifiable. Its not. Due to the growing demand in the fast fashion industry, we see a vast overproduction of clothing; for example, the Copenhagen Fashion Summit reports that fashion is responsible for 92 million tons of solid waste dumped in landfills each year. This cultural shift on how we consume clothing is leaving a huge mark on the planet. Fashion has become much more than representation and being covered.]]>
Fashion is a notoriously difficult term to pin down, and it is extremely doubtful whether it is possible to come up with necessary and sufficient conditions for something justifiably to be called fashionable. Generally speaking, we can distinguish between two main categories in our notion of fashion: one that fashion refers to clothing or that fashion is a general mechanism, logic or ideology that, among other things, applies to the area of clothing. Adam Smith , who was among the first philosophers to give fashion a central role in his anthropology, claims that fashion applies first and foremost to areas in which taste is a central concept. This applies in particular to clothes and furniture, but also to music, poetry and architecture. Immanuel Kant provides a description of fashion that focuses on general changes in human lifestyles: All fashions are, by their very concept, mutable ways of living. However, trends die quickly and with that comes waste. Clothing produced by fast fashion brands are oftentimes made from cheap materials, like polyester and acrylic, and not built to last: The average American throws away 80 pounds of clothing every year. Weve been conditioned to believe that buying a garment and wearing it once is justifiable. Its not. Due to the growing demand in the fast fashion industry, we see a vast overproduction of clothing; for example, the Copenhagen Fashion Summit reports that fashion is responsible for 92 million tons of solid waste dumped in landfills each year. This cultural shift on how we consume clothing is leaving a huge mark on the planet. Fashion has become much more than representation and being covered.]]> Thu, 16 Jan 2020 10:04:19 GMT /slideshow/fashion-220675134/220675134 KamleshKumar265@slideshare.net(KamleshKumar265) Fashion KamleshKumar265 Fashion is a notoriously difficult term to pin down, and it is extremely doubtful whether it is possible to come up with necessary and sufficient conditions for something justifiably to be called fashionable. Generally speaking, we can distinguish between two main categories in our notion of fashion: one that fashion refers to clothing or that fashion is a general mechanism, logic or ideology that, among other things, applies to the area of clothing. Adam Smith , who was among the first philosophers to give fashion a central role in his anthropology, claims that fashion applies first and foremost to areas in which taste is a central concept. This applies in particular to clothes and furniture, but also to music, poetry and architecture. Immanuel Kant provides a description of fashion that focuses on general changes in human lifestyles: All fashions are, by their very concept, mutable ways of living. However, trends die quickly and with that comes waste. Clothing produced by fast fashion brands are oftentimes made from cheap materials, like polyester and acrylic, and not built to last: The average American throws away 80 pounds of clothing every year. Weve been conditioned to believe that buying a garment and wearing it once is justifiable. Its not. Due to the growing demand in the fast fashion industry, we see a vast overproduction of clothing; for example, the Copenhagen Fashion Summit reports that fashion is responsible for 92 million tons of solid waste dumped in landfills each year. This cultural shift on how we consume clothing is leaving a huge mark on the planet. Fashion has become much more than representation and being covered. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fashion1-200116100419-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Fashion is a notoriously difficult term to pin down, and it is extremely doubtful whether it is possible to come up with necessary and sufficient conditions for something justifiably to be called fashionable. Generally speaking, we can distinguish between two main categories in our notion of fashion: one that fashion refers to clothing or that fashion is a general mechanism, logic or ideology that, among other things, applies to the area of clothing. Adam Smith , who was among the first philosophers to give fashion a central role in his anthropology, claims that fashion applies first and foremost to areas in which taste is a central concept. This applies in particular to clothes and furniture, but also to music, poetry and architecture. Immanuel Kant provides a description of fashion that focuses on general changes in human lifestyles: All fashions are, by their very concept, mutable ways of living. However, trends die quickly and with that comes waste. Clothing produced by fast fashion brands are oftentimes made from cheap materials, like polyester and acrylic, and not built to last: The average American throws away 80 pounds of clothing every year. Weve been conditioned to believe that buying a garment and wearing it once is justifiable. Its not. Due to the growing demand in the fast fashion industry, we see a vast overproduction of clothing; for example, the Copenhagen Fashion Summit reports that fashion is responsible for 92 million tons of solid waste dumped in landfills each year. This cultural shift on how we consume clothing is leaving a huge mark on the planet. Fashion has become much more than representation and being covered.
Fashion from Kamlesh Kumar
]]> 256 3 https://cdn.slidesharecdn.com/ss_thumbnails/fashion1-200116100419-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 COMMUNAL HARMONY: PUNJABI & TIBETANS IN DELHI /slideshow/communal-harmony-punjabi-tibetans-in-delhi-220674561/220674561 majnukatila-copy-200116095514
LANDSCAPE AS TEXT Delhi, the majestic, cosmopolitan, sprawling capital of the nation viewed as one of the global nodes bustling with life in haste. It has maintained its identity as a pluralistic amalgamation with myriads of ethno-religious groups and minority communities. Such is the very famous, our own little Tibet- Majnu Ka Tila situated at a stones throw from the Delhi University North Campus. Officially known as Aruna Nagar Colony is the universal gathering place for Tibetans living around Delhi and a transit point for the people of the trans-Himalayan range and conversely a gateway to Tibet for the Indians and foreign tourists alike as the capital city enjoys a status of a flourishing educational and political hub. Tall buildings on either side make the narrow alley so dark its as if the sun never makes it here. Shops on either side sell only exotic Tibetan jewellery, Buddhist artefacts and crockery. In this labyrinth of a colony, the stalls are full of copies of branded shoes and clothes, reflecting the latest in fashion trends across Asia. Many of the tiny outlets sell Buddhist curios and Tibetan literature. Ahead, the alley opens into a bright courtyard facing the monastery. Old ladies sit in the sun, making fresh momos and laphing, pancakes rolled with chilli paste. Besides MKT is a Foodie's paradise, the eateries here are not only popular for its momos, but one can also enjoy authentic Tibetan, Chinese and Korean delicacies along with the yummiest of the English pastries. Majnu Ka Tila not only is limited to Tibetan community but constituted by the Punjabi community as well which has a historical context. The area provides a microcosm of diversified India where there is invisible transition and diffusion of identity, culture of distinct communities and Indianisation of Tibetan lifestyle. For instance, many Tibetans who cannot afford the rising rents of the Tibetan enclave (due to hotels and tourist activities) are forced to live in the Punjabi Basti where renting an apartment is cheaper comparatively. Living in Punjabi zone is seen influencing a cultural and identity loss. To diffuse with the Punjabi population is perceived as a risk of identity loss, and forgetting your Tibetan culture. These frontiers are mental, social and religious. Nonetheless, the ethnic groups interacting and sharing a space is a matter of pride as community harmony.]]>
LANDSCAPE AS TEXT Delhi, the majestic, cosmopolitan, sprawling capital of the nation viewed as one of the global nodes bustling with life in haste. It has maintained its identity as a pluralistic amalgamation with myriads of ethno-religious groups and minority communities. Such is the very famous, our own little Tibet- Majnu Ka Tila situated at a stones throw from the Delhi University North Campus. Officially known as Aruna Nagar Colony is the universal gathering place for Tibetans living around Delhi and a transit point for the people of the trans-Himalayan range and conversely a gateway to Tibet for the Indians and foreign tourists alike as the capital city enjoys a status of a flourishing educational and political hub. Tall buildings on either side make the narrow alley so dark its as if the sun never makes it here. Shops on either side sell only exotic Tibetan jewellery, Buddhist artefacts and crockery. In this labyrinth of a colony, the stalls are full of copies of branded shoes and clothes, reflecting the latest in fashion trends across Asia. Many of the tiny outlets sell Buddhist curios and Tibetan literature. Ahead, the alley opens into a bright courtyard facing the monastery. Old ladies sit in the sun, making fresh momos and laphing, pancakes rolled with chilli paste. Besides MKT is a Foodie's paradise, the eateries here are not only popular for its momos, but one can also enjoy authentic Tibetan, Chinese and Korean delicacies along with the yummiest of the English pastries. Majnu Ka Tila not only is limited to Tibetan community but constituted by the Punjabi community as well which has a historical context. The area provides a microcosm of diversified India where there is invisible transition and diffusion of identity, culture of distinct communities and Indianisation of Tibetan lifestyle. For instance, many Tibetans who cannot afford the rising rents of the Tibetan enclave (due to hotels and tourist activities) are forced to live in the Punjabi Basti where renting an apartment is cheaper comparatively. Living in Punjabi zone is seen influencing a cultural and identity loss. To diffuse with the Punjabi population is perceived as a risk of identity loss, and forgetting your Tibetan culture. These frontiers are mental, social and religious. Nonetheless, the ethnic groups interacting and sharing a space is a matter of pride as community harmony.]]> Thu, 16 Jan 2020 09:55:14 GMT /slideshow/communal-harmony-punjabi-tibetans-in-delhi-220674561/220674561 KamleshKumar265@slideshare.net(KamleshKumar265) COMMUNAL HARMONY: PUNJABI & TIBETANS IN DELHI KamleshKumar265 LANDSCAPE AS TEXT Delhi, the majestic, cosmopolitan, sprawling capital of the nation viewed as one of the global nodes bustling with life in haste. It has maintained its identity as a pluralistic amalgamation with myriads of ethno-religious groups and minority communities. Such is the very famous, our own little Tibet- Majnu Ka Tila situated at a stones throw from the Delhi University North Campus. Officially known as Aruna Nagar Colony is the universal gathering place for Tibetans living around Delhi and a transit point for the people of the trans-Himalayan range and conversely a gateway to Tibet for the Indians and foreign tourists alike as the capital city enjoys a status of a flourishing educational and political hub. Tall buildings on either side make the narrow alley so dark its as if the sun never makes it here. Shops on either side sell only exotic Tibetan jewellery, Buddhist artefacts and crockery. In this labyrinth of a colony, the stalls are full of copies of branded shoes and clothes, reflecting the latest in fashion trends across Asia. Many of the tiny outlets sell Buddhist curios and Tibetan literature. Ahead, the alley opens into a bright courtyard facing the monastery. Old ladies sit in the sun, making fresh momos and laphing, pancakes rolled with chilli paste. Besides MKT is a Foodie's paradise, the eateries here are not only popular for its momos, but one can also enjoy authentic Tibetan, Chinese and Korean delicacies along with the yummiest of the English pastries. Majnu Ka Tila not only is limited to Tibetan community but constituted by the Punjabi community as well which has a historical context. The area provides a microcosm of diversified India where there is invisible transition and diffusion of identity, culture of distinct communities and Indianisation of Tibetan lifestyle. For instance, many Tibetans who cannot afford the rising rents of the Tibetan enclave (due to hotels and tourist activities) are forced to live in the Punjabi Basti where renting an apartment is cheaper comparatively. Living in Punjabi zone is seen influencing a cultural and identity loss. To diffuse with the Punjabi population is perceived as a risk of identity loss, and forgetting your Tibetan culture. These frontiers are mental, social and religious. Nonetheless, the ethnic groups interacting and sharing a space is a matter of pride as community harmony. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/majnukatila-copy-200116095514-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> LANDSCAPE AS TEXT Delhi, the majestic, cosmopolitan, sprawling capital of the nation viewed as one of the global nodes bustling with life in haste. It has maintained its identity as a pluralistic amalgamation with myriads of ethno-religious groups and minority communities. Such is the very famous, our own little Tibet- Majnu Ka Tila situated at a stones throw from the Delhi University North Campus. Officially known as Aruna Nagar Colony is the universal gathering place for Tibetans living around Delhi and a transit point for the people of the trans-Himalayan range and conversely a gateway to Tibet for the Indians and foreign tourists alike as the capital city enjoys a status of a flourishing educational and political hub. Tall buildings on either side make the narrow alley so dark its as if the sun never makes it here. Shops on either side sell only exotic Tibetan jewellery, Buddhist artefacts and crockery. In this labyrinth of a colony, the stalls are full of copies of branded shoes and clothes, reflecting the latest in fashion trends across Asia. Many of the tiny outlets sell Buddhist curios and Tibetan literature. Ahead, the alley opens into a bright courtyard facing the monastery. Old ladies sit in the sun, making fresh momos and laphing, pancakes rolled with chilli paste. Besides MKT is a Foodie&#39;s paradise, the eateries here are not only popular for its momos, but one can also enjoy authentic Tibetan, Chinese and Korean delicacies along with the yummiest of the English pastries. Majnu Ka Tila not only is limited to Tibetan community but constituted by the Punjabi community as well which has a historical context. The area provides a microcosm of diversified India where there is invisible transition and diffusion of identity, culture of distinct communities and Indianisation of Tibetan lifestyle. For instance, many Tibetans who cannot afford the rising rents of the Tibetan enclave (due to hotels and tourist activities) are forced to live in the Punjabi Basti where renting an apartment is cheaper comparatively. Living in Punjabi zone is seen influencing a cultural and identity loss. To diffuse with the Punjabi population is perceived as a risk of identity loss, and forgetting your Tibetan culture. These frontiers are mental, social and religious. Nonetheless, the ethnic groups interacting and sharing a space is a matter of pride as community harmony.
COMMUNAL HARMONY: PUNJABI & TIBETANS IN DELHI from Kamlesh Kumar
]]> 303 3 https://cdn.slidesharecdn.com/ss_thumbnails/majnukatila-copy-200116095514-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Remote Sensing: Overlay Analysis /KamleshKumar265/remote-sensing-overlay-analysis 12-200116092923
An overlay operation is much more than a simple merging of linework; all the attributes of the features taking part in the overlay are carried through. In general, there are two methods for performing overlay analysisfeature overlay (overlaying points, lines, or polygons) and raster overlay. Some types of overlay analysis lend themselves to one or the other of these methods. Overlay analysis to find locations meeting certain criteria is often best done using raster overlay (although you can do it with feature data). Of course, this also depends on whether your data is already stored as features or raster. It may be worthwhile to convert the data from one format to the other to perform the analysis. Weighted Overlay Overlays several raster files using a common measurement scale and weights each according to its importance. The weighted overlay table allows the calculation of a multiple criteria analysis between several raster files. Raster- The raster of the criteria being weighted. Influence- The influence of the raster compared to the other criteria as a percentage of 100. Field- The field of the criteria raster to use for weighting. Remap- The scaled weights for the criterion. In addition to numerical values for the scaled weights in Remap, the following options are available: Restricted- Assigns the restricted value (the minimum value of the evaluation scale set, minus one) to cells in the output, regardless of whether other input raster files have a different scale value set for that cell. No data - Assigns No Data to cells in the output, regardless of whether other input raster files have a different scale value set for that cell. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.]]>
An overlay operation is much more than a simple merging of linework; all the attributes of the features taking part in the overlay are carried through. In general, there are two methods for performing overlay analysisfeature overlay (overlaying points, lines, or polygons) and raster overlay. Some types of overlay analysis lend themselves to one or the other of these methods. Overlay analysis to find locations meeting certain criteria is often best done using raster overlay (although you can do it with feature data). Of course, this also depends on whether your data is already stored as features or raster. It may be worthwhile to convert the data from one format to the other to perform the analysis. Weighted Overlay Overlays several raster files using a common measurement scale and weights each according to its importance. The weighted overlay table allows the calculation of a multiple criteria analysis between several raster files. Raster- The raster of the criteria being weighted. Influence- The influence of the raster compared to the other criteria as a percentage of 100. Field- The field of the criteria raster to use for weighting. Remap- The scaled weights for the criterion. In addition to numerical values for the scaled weights in Remap, the following options are available: Restricted- Assigns the restricted value (the minimum value of the evaluation scale set, minus one) to cells in the output, regardless of whether other input raster files have a different scale value set for that cell. No data - Assigns No Data to cells in the output, regardless of whether other input raster files have a different scale value set for that cell. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.]]> Thu, 16 Jan 2020 09:29:23 GMT /KamleshKumar265/remote-sensing-overlay-analysis KamleshKumar265@slideshare.net(KamleshKumar265) Remote Sensing: Overlay Analysis KamleshKumar265 An overlay operation is much more than a simple merging of linework; all the attributes of the features taking part in the overlay are carried through. In general, there are two methods for performing overlay analysisfeature overlay (overlaying points, lines, or polygons) and raster overlay. Some types of overlay analysis lend themselves to one or the other of these methods. Overlay analysis to find locations meeting certain criteria is often best done using raster overlay (although you can do it with feature data). Of course, this also depends on whether your data is already stored as features or raster. It may be worthwhile to convert the data from one format to the other to perform the analysis. Weighted Overlay Overlays several raster files using a common measurement scale and weights each according to its importance. The weighted overlay table allows the calculation of a multiple criteria analysis between several raster files. Raster- The raster of the criteria being weighted. Influence- The influence of the raster compared to the other criteria as a percentage of 100. Field- The field of the criteria raster to use for weighting. Remap- The scaled weights for the criterion. In addition to numerical values for the scaled weights in Remap, the following options are available: Restricted- Assigns the restricted value (the minimum value of the evaluation scale set, minus one) to cells in the output, regardless of whether other input raster files have a different scale value set for that cell. No data - Assigns No Data to cells in the output, regardless of whether other input raster files have a different scale value set for that cell. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/12-200116092923-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> An overlay operation is much more than a simple merging of linework; all the attributes of the features taking part in the overlay are carried through. In general, there are two methods for performing overlay analysisfeature overlay (overlaying points, lines, or polygons) and raster overlay. Some types of overlay analysis lend themselves to one or the other of these methods. Overlay analysis to find locations meeting certain criteria is often best done using raster overlay (although you can do it with feature data). Of course, this also depends on whether your data is already stored as features or raster. It may be worthwhile to convert the data from one format to the other to perform the analysis. Weighted Overlay Overlays several raster files using a common measurement scale and weights each according to its importance. The weighted overlay table allows the calculation of a multiple criteria analysis between several raster files. Raster- The raster of the criteria being weighted. Influence- The influence of the raster compared to the other criteria as a percentage of 100. Field- The field of the criteria raster to use for weighting. Remap- The scaled weights for the criterion. In addition to numerical values for the scaled weights in Remap, the following options are available: Restricted- Assigns the restricted value (the minimum value of the evaluation scale set, minus one) to cells in the output, regardless of whether other input raster files have a different scale value set for that cell. No data - Assigns No Data to cells in the output, regardless of whether other input raster files have a different scale value set for that cell. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Remote Sensing: Overlay Analysis from Kamlesh Kumar
]]> 1294 0 https://cdn.slidesharecdn.com/ss_thumbnails/12-200116092923-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Remote Sensing: Change Detection /slideshow/remote-sensing-change-detection/220672190 11-200116092710
In the context ofremote sensing,change detectionrefers to the process of identifying differences in the state of land features by observing them at different times. This process can be accomplished either manually (i.e., by hand) or with the aid ofremote sensingsoftware. Manual interpretation of change from satellite images or aerial photos involves an observer or analyst defining areas of interest and comparing them between images from two dates. This may be accomplished either on-screen (such as in a GIS) or on paper. When analyzing aerial photographs, a stereoscope which allows for two spatially-overlapping photos to be displayed in 3D, can aid photo interpretation. Manual image interpretation works well when assessing change between discrete classes (forest openings, land use and land cover maps) or when changes are large (e.g., heavy mechanized maneuver damage, engineering training impacts). Manual image interpretation is also an option when trying to determine change using images or photos from different sources (comparing historic aerial photographs to current satellite imagery). Automated methods of remote sensing change detection usually are of two forms: post-classification change detection and image differencing using band ratios. In post-classification change detection, the images from each time period are classified using the same classification scheme into a number of discrete categories like land cover types. The two (or more) classifications are compared and the area that is classified the same or different is tallied. With image differencing, a band ratio such asNDVIis constructed from each input image, and the difference is taken between the band ratios of different times. In the case of differencingNDVIimages, positive output values may indicate an increase in vegetation, negative values a decrease in vegetation, and values near zero no change. With either post-classification or image differencing change detection, it is necessary to specify a threshold below which differences between the two images is considered to be non-significant. The specification of thresholds is critical to the results of change detection analysis and usually must be found through an iterative process. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.]]>
In the context ofremote sensing,change detectionrefers to the process of identifying differences in the state of land features by observing them at different times. This process can be accomplished either manually (i.e., by hand) or with the aid ofremote sensingsoftware. Manual interpretation of change from satellite images or aerial photos involves an observer or analyst defining areas of interest and comparing them between images from two dates. This may be accomplished either on-screen (such as in a GIS) or on paper. When analyzing aerial photographs, a stereoscope which allows for two spatially-overlapping photos to be displayed in 3D, can aid photo interpretation. Manual image interpretation works well when assessing change between discrete classes (forest openings, land use and land cover maps) or when changes are large (e.g., heavy mechanized maneuver damage, engineering training impacts). Manual image interpretation is also an option when trying to determine change using images or photos from different sources (comparing historic aerial photographs to current satellite imagery). Automated methods of remote sensing change detection usually are of two forms: post-classification change detection and image differencing using band ratios. In post-classification change detection, the images from each time period are classified using the same classification scheme into a number of discrete categories like land cover types. The two (or more) classifications are compared and the area that is classified the same or different is tallied. With image differencing, a band ratio such asNDVIis constructed from each input image, and the difference is taken between the band ratios of different times. In the case of differencingNDVIimages, positive output values may indicate an increase in vegetation, negative values a decrease in vegetation, and values near zero no change. With either post-classification or image differencing change detection, it is necessary to specify a threshold below which differences between the two images is considered to be non-significant. The specification of thresholds is critical to the results of change detection analysis and usually must be found through an iterative process. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.]]> Thu, 16 Jan 2020 09:27:09 GMT /slideshow/remote-sensing-change-detection/220672190 KamleshKumar265@slideshare.net(KamleshKumar265) Remote Sensing: Change Detection KamleshKumar265 In the context ofremote sensing,change detectionrefers to the process of identifying differences in the state of land features by observing them at different times. This process can be accomplished either manually (i.e., by hand) or with the aid ofremote sensingsoftware. Manual interpretation of change from satellite images or aerial photos involves an observer or analyst defining areas of interest and comparing them between images from two dates. This may be accomplished either on-screen (such as in a GIS) or on paper. When analyzing aerial photographs, a stereoscope which allows for two spatially-overlapping photos to be displayed in 3D, can aid photo interpretation. Manual image interpretation works well when assessing change between discrete classes (forest openings, land use and land cover maps) or when changes are large (e.g., heavy mechanized maneuver damage, engineering training impacts). Manual image interpretation is also an option when trying to determine change using images or photos from different sources (comparing historic aerial photographs to current satellite imagery). Automated methods of remote sensing change detection usually are of two forms: post-classification change detection and image differencing using band ratios. In post-classification change detection, the images from each time period are classified using the same classification scheme into a number of discrete categories like land cover types. The two (or more) classifications are compared and the area that is classified the same or different is tallied. With image differencing, a band ratio such asNDVIis constructed from each input image, and the difference is taken between the band ratios of different times. In the case of differencingNDVIimages, positive output values may indicate an increase in vegetation, negative values a decrease in vegetation, and values near zero no change. With either post-classification or image differencing change detection, it is necessary to specify a threshold below which differences between the two images is considered to be non-significant. The specification of thresholds is critical to the results of change detection analysis and usually must be found through an iterative process. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/11-200116092710-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In the context ofremote sensing,change detectionrefers to the process of identifying differences in the state of land features by observing them at different times. This process can be accomplished either manually (i.e., by hand) or with the aid ofremote sensingsoftware. Manual interpretation of change from satellite images or aerial photos involves an observer or analyst defining areas of interest and comparing them between images from two dates. This may be accomplished either on-screen (such as in a GIS) or on paper. When analyzing aerial photographs, a stereoscope which allows for two spatially-overlapping photos to be displayed in 3D, can aid photo interpretation. Manual image interpretation works well when assessing change between discrete classes (forest openings, land use and land cover maps) or when changes are large (e.g., heavy mechanized maneuver damage, engineering training impacts). Manual image interpretation is also an option when trying to determine change using images or photos from different sources (comparing historic aerial photographs to current satellite imagery). Automated methods of remote sensing change detection usually are of two forms: post-classification change detection and image differencing using band ratios. In post-classification change detection, the images from each time period are classified using the same classification scheme into a number of discrete categories like land cover types. The two (or more) classifications are compared and the area that is classified the same or different is tallied. With image differencing, a band ratio such asNDVIis constructed from each input image, and the difference is taken between the band ratios of different times. In the case of differencingNDVIimages, positive output values may indicate an increase in vegetation, negative values a decrease in vegetation, and values near zero no change. With either post-classification or image differencing change detection, it is necessary to specify a threshold below which differences between the two images is considered to be non-significant. The specification of thresholds is critical to the results of change detection analysis and usually must be found through an iterative process. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Remote Sensing: Change Detection from Kamlesh Kumar
]]> 413 0 https://cdn.slidesharecdn.com/ss_thumbnails/11-200116092710-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Remote sensing: Accuracy Assesment /slideshow/remote-sensing-accuracy-assesment/220671881 10accuracyassesment-200116092352
Accuracy assessment is an important part of any classification project. It compares the classified image to another data source that is considered to be accurate or ground truth data. Ground truth can be collected in the field; however, this is time consuming and expensive. Ground truth data can also be derived from interpreting high-resolution imagery, existing classified imagery, or GIS data layers. The most common way to assess the accuracy of a classified map is to create a set of random points from the ground truth data and compare that to the classified data in a confusion matrix. Although this is a two-step process, you may need to compare the results of different classification methods or training sites, or you may not have ground truth data and are relying on the same imagery that you used to create the classification. To accommodate these other workflows, this process uses three geoprocessing tools: Create Accuracy Assessment Points, Update Accuracy Assessment Points, and Compute Confusion Matrix. Thresholding Thresholding is the process of identifying the pixels in a classified image that are the most likely to be classified incorrectly. These pixels are put into another class (usually class 0). These pixels are identified statistically, based upon the distance measures that were used in the classification decision rule. Accuracy Assessment : Error Matrix Accuracy assessment is a general term for comparing the classification to geographical data that are assumed to be true, in order to determine the accuracy of the classification process. Usually, the assumed-true data are derived from ground truth data. It is usually not practical to ground truth or otherwise test every pixel of a classified image. Therefore, a set of reference pixels is usually used. Reference pixels are points on the classified image for which actual data are (or will be) known. The reference pixels are randomly selected. Overall accuracy: Overall accuracy is used to indicate the accuracy of whole classification (i.e. number of correctly classifier pixels divided by the total number of pixels in the error matrix) Users accuracy(commission error): Users accuracy is regarded as the probability that a pixel classified on map actually represents that class on the ground or reference data Producers accuracy(omission error): Producers accuracy represents the probability of reference pixel being correctly classified THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP. ]]>
Accuracy assessment is an important part of any classification project. It compares the classified image to another data source that is considered to be accurate or ground truth data. Ground truth can be collected in the field; however, this is time consuming and expensive. Ground truth data can also be derived from interpreting high-resolution imagery, existing classified imagery, or GIS data layers. The most common way to assess the accuracy of a classified map is to create a set of random points from the ground truth data and compare that to the classified data in a confusion matrix. Although this is a two-step process, you may need to compare the results of different classification methods or training sites, or you may not have ground truth data and are relying on the same imagery that you used to create the classification. To accommodate these other workflows, this process uses three geoprocessing tools: Create Accuracy Assessment Points, Update Accuracy Assessment Points, and Compute Confusion Matrix. Thresholding Thresholding is the process of identifying the pixels in a classified image that are the most likely to be classified incorrectly. These pixels are put into another class (usually class 0). These pixels are identified statistically, based upon the distance measures that were used in the classification decision rule. Accuracy Assessment : Error Matrix Accuracy assessment is a general term for comparing the classification to geographical data that are assumed to be true, in order to determine the accuracy of the classification process. Usually, the assumed-true data are derived from ground truth data. It is usually not practical to ground truth or otherwise test every pixel of a classified image. Therefore, a set of reference pixels is usually used. Reference pixels are points on the classified image for which actual data are (or will be) known. The reference pixels are randomly selected. Overall accuracy: Overall accuracy is used to indicate the accuracy of whole classification (i.e. number of correctly classifier pixels divided by the total number of pixels in the error matrix) Users accuracy(commission error): Users accuracy is regarded as the probability that a pixel classified on map actually represents that class on the ground or reference data Producers accuracy(omission error): Producers accuracy represents the probability of reference pixel being correctly classified THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP. ]]> Thu, 16 Jan 2020 09:23:52 GMT /slideshow/remote-sensing-accuracy-assesment/220671881 KamleshKumar265@slideshare.net(KamleshKumar265) Remote sensing: Accuracy Assesment KamleshKumar265 Accuracy assessment is an important part of any classification project. It compares the classified image to another data source that is considered to be accurate or ground truth data. Ground truth can be collected in the field; however, this is time consuming and expensive. Ground truth data can also be derived from interpreting high-resolution imagery, existing classified imagery, or GIS data layers. The most common way to assess the accuracy of a classified map is to create a set of random points from the ground truth data and compare that to the classified data in a confusion matrix. Although this is a two-step process, you may need to compare the results of different classification methods or training sites, or you may not have ground truth data and are relying on the same imagery that you used to create the classification. To accommodate these other workflows, this process uses three geoprocessing tools: Create Accuracy Assessment Points, Update Accuracy Assessment Points, and Compute Confusion Matrix. Thresholding Thresholding is the process of identifying the pixels in a classified image that are the most likely to be classified incorrectly. These pixels are put into another class (usually class 0). These pixels are identified statistically, based upon the distance measures that were used in the classification decision rule. Accuracy Assessment : Error Matrix Accuracy assessment is a general term for comparing the classification to geographical data that are assumed to be true, in order to determine the accuracy of the classification process. Usually, the assumed-true data are derived from ground truth data. It is usually not practical to ground truth or otherwise test every pixel of a classified image. Therefore, a set of reference pixels is usually used. Reference pixels are points on the classified image for which actual data are (or will be) known. The reference pixels are randomly selected. Overall accuracy: Overall accuracy is used to indicate the accuracy of whole classification (i.e. number of correctly classifier pixels divided by the total number of pixels in the error matrix) Users accuracy(commission error): Users accuracy is regarded as the probability that a pixel classified on map actually represents that class on the ground or reference data Producers accuracy(omission error): Producers accuracy represents the probability of reference pixel being correctly classified THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/10accuracyassesment-200116092352-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Accuracy assessment is an important part of any classification project. It compares the classified image to another data source that is considered to be accurate or ground truth data. Ground truth can be collected in the field; however, this is time consuming and expensive. Ground truth data can also be derived from interpreting high-resolution imagery, existing classified imagery, or GIS data layers. The most common way to assess the accuracy of a classified map is to create a set of random points from the ground truth data and compare that to the classified data in a confusion matrix. Although this is a two-step process, you may need to compare the results of different classification methods or training sites, or you may not have ground truth data and are relying on the same imagery that you used to create the classification. To accommodate these other workflows, this process uses three geoprocessing tools: Create Accuracy Assessment Points, Update Accuracy Assessment Points, and Compute Confusion Matrix. Thresholding Thresholding is the process of identifying the pixels in a classified image that are the most likely to be classified incorrectly. These pixels are put into another class (usually class 0). These pixels are identified statistically, based upon the distance measures that were used in the classification decision rule. Accuracy Assessment : Error Matrix Accuracy assessment is a general term for comparing the classification to geographical data that are assumed to be true, in order to determine the accuracy of the classification process. Usually, the assumed-true data are derived from ground truth data. It is usually not practical to ground truth or otherwise test every pixel of a classified image. Therefore, a set of reference pixels is usually used. Reference pixels are points on the classified image for which actual data are (or will be) known. The reference pixels are randomly selected. Overall accuracy: Overall accuracy is used to indicate the accuracy of whole classification (i.e. number of correctly classifier pixels divided by the total number of pixels in the error matrix) Users accuracy(commission error): Users accuracy is regarded as the probability that a pixel classified on map actually represents that class on the ground or reference data Producers accuracy(omission error): Producers accuracy represents the probability of reference pixel being correctly classified THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Remote sensing: Accuracy Assesment from Kamlesh Kumar
]]> 576 0 https://cdn.slidesharecdn.com/ss_thumbnails/10accuracyassesment-200116092352-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Remote Sensing: Image Classification /KamleshKumar265/remote-sensing-image-classification-220671699 9-200116092151
The objective of image classification is to classify each pixel into only one class (crisp or hard classification) or to associate the pixel with many classes (fuzzy or soft classification). The classification techniques may be categorized either on the basis of training process (supervised and unsupervised) or on the basis of theoretical model (parametric and non-parametric). Unsupervised classificationis where the groupings of pixels with common characteristics are based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorism the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the ground (such as waterbodies, developed areas, forests, etc.). Supervised classificationis based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Input classes are selected based on the knowledge of the user. The user also sets the bounds for howsimilar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the input classes (AOI), plus or minus a certain increment (often based on brightness or strength of reflection in specific spectral bands). The user also designates the number of classes that the image is classified into. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.]]>
The objective of image classification is to classify each pixel into only one class (crisp or hard classification) or to associate the pixel with many classes (fuzzy or soft classification). The classification techniques may be categorized either on the basis of training process (supervised and unsupervised) or on the basis of theoretical model (parametric and non-parametric). Unsupervised classificationis where the groupings of pixels with common characteristics are based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorism the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the ground (such as waterbodies, developed areas, forests, etc.). Supervised classificationis based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Input classes are selected based on the knowledge of the user. The user also sets the bounds for howsimilar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the input classes (AOI), plus or minus a certain increment (often based on brightness or strength of reflection in specific spectral bands). The user also designates the number of classes that the image is classified into. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.]]> Thu, 16 Jan 2020 09:21:50 GMT /KamleshKumar265/remote-sensing-image-classification-220671699 KamleshKumar265@slideshare.net(KamleshKumar265) Remote Sensing: Image Classification KamleshKumar265 The objective of image classification is to classify each pixel into only one class (crisp or hard classification) or to associate the pixel with many classes (fuzzy or soft classification). The classification techniques may be categorized either on the basis of training process (supervised and unsupervised) or on the basis of theoretical model (parametric and non-parametric). Unsupervised classificationis where the groupings of pixels with common characteristics are based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorism the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the ground (such as waterbodies, developed areas, forests, etc.). Supervised classificationis based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Input classes are selected based on the knowledge of the user. The user also sets the bounds for howsimilar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the input classes (AOI), plus or minus a certain increment (often based on brightness or strength of reflection in specific spectral bands). The user also designates the number of classes that the image is classified into. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/9-200116092151-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The objective of image classification is to classify each pixel into only one class (crisp or hard classification) or to associate the pixel with many classes (fuzzy or soft classification). The classification techniques may be categorized either on the basis of training process (supervised and unsupervised) or on the basis of theoretical model (parametric and non-parametric). Unsupervised classificationis where the groupings of pixels with common characteristics are based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorism the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the ground (such as waterbodies, developed areas, forests, etc.). Supervised classificationis based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Input classes are selected based on the knowledge of the user. The user also sets the bounds for howsimilar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the input classes (AOI), plus or minus a certain increment (often based on brightness or strength of reflection in specific spectral bands). The user also designates the number of classes that the image is classified into. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Remote Sensing: Image Classification from Kamlesh Kumar
]]> 1800 1 https://cdn.slidesharecdn.com/ss_thumbnails/9-200116092151-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Remote Sensing: Interppolation /slideshow/remote-sensing-interppolation/220671188 7-200116091708
Interpolation is the process of using points with known values to estimate values at other unknown points.It can be used to predict unknown values for any geographic point data, such as elevation, rainfall, noise levels, atmospheric components and so on. The Inverse Distance Weighting (IDW) assumes each input point to have a local influence that diminishes with distance. It assumes that closer things are more alike than those that are farther apart. It weights the points closer to the processing cell greater than those further away. A specified number of points, or all points within a specified radius can be used to determine the output value of each location. To predict a value for any unmeasured location, IDW will use the measured values surrounding the prediction location. Those measured values closest to the prediction location will have more influence on the predicted value than those farther away. Spline estimates values using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes exactly through the input points. This method is best for gently varying surfaces, such as elevation, water table heights, or pollution concentrations. ARegularized methodcreates a smooth, gradually changing surface with values that may lie outside the sample data range. Kriging is a geostatistical interpolation technique that considers both the distance and the degree of variation between known data points when estimating values in unknown areas.Kriging assumes that the distance or direction between sample points reflects a spatial correlation that can be used to explain variation in the surface. The Kriging tool fits a mathematical function to a specified number of points, or all points within a specified radius, to determine the output value for each location. Kriging is a multistep process; it includes exploratory statistical analysis of the data, variogram modeling, creating the surface, and (optionally) exploring a variance surface. Kriging is most appropriate when you know there is a spatially correlated distance or directional bias in the data. It is often used in soil science and geology. Trend is a statistical method that finds the surface that fits the sample points using a least-square regression fit. It fits one polynomial equation to the entire surface. This results in a surface that minimizes surface variance in relation to the input values. The surface is constructed so that for every input point, the total of the differences between the actual values and the estimated values (i.e., the variance) will be as small as possible. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.]]>
Interpolation is the process of using points with known values to estimate values at other unknown points.It can be used to predict unknown values for any geographic point data, such as elevation, rainfall, noise levels, atmospheric components and so on. The Inverse Distance Weighting (IDW) assumes each input point to have a local influence that diminishes with distance. It assumes that closer things are more alike than those that are farther apart. It weights the points closer to the processing cell greater than those further away. A specified number of points, or all points within a specified radius can be used to determine the output value of each location. To predict a value for any unmeasured location, IDW will use the measured values surrounding the prediction location. Those measured values closest to the prediction location will have more influence on the predicted value than those farther away. Spline estimates values using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes exactly through the input points. This method is best for gently varying surfaces, such as elevation, water table heights, or pollution concentrations. ARegularized methodcreates a smooth, gradually changing surface with values that may lie outside the sample data range. Kriging is a geostatistical interpolation technique that considers both the distance and the degree of variation between known data points when estimating values in unknown areas.Kriging assumes that the distance or direction between sample points reflects a spatial correlation that can be used to explain variation in the surface. The Kriging tool fits a mathematical function to a specified number of points, or all points within a specified radius, to determine the output value for each location. Kriging is a multistep process; it includes exploratory statistical analysis of the data, variogram modeling, creating the surface, and (optionally) exploring a variance surface. Kriging is most appropriate when you know there is a spatially correlated distance or directional bias in the data. It is often used in soil science and geology. Trend is a statistical method that finds the surface that fits the sample points using a least-square regression fit. It fits one polynomial equation to the entire surface. This results in a surface that minimizes surface variance in relation to the input values. The surface is constructed so that for every input point, the total of the differences between the actual values and the estimated values (i.e., the variance) will be as small as possible. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.]]> Thu, 16 Jan 2020 09:17:07 GMT /slideshow/remote-sensing-interppolation/220671188 KamleshKumar265@slideshare.net(KamleshKumar265) Remote Sensing: Interppolation KamleshKumar265 Interpolation is the process of using points with known values to estimate values at other unknown points.It can be used to predict unknown values for any geographic point data, such as elevation, rainfall, noise levels, atmospheric components and so on. The Inverse Distance Weighting (IDW) assumes each input point to have a local influence that diminishes with distance. It assumes that closer things are more alike than those that are farther apart. It weights the points closer to the processing cell greater than those further away. A specified number of points, or all points within a specified radius can be used to determine the output value of each location. To predict a value for any unmeasured location, IDW will use the measured values surrounding the prediction location. Those measured values closest to the prediction location will have more influence on the predicted value than those farther away. Spline estimates values using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes exactly through the input points. This method is best for gently varying surfaces, such as elevation, water table heights, or pollution concentrations. ARegularized methodcreates a smooth, gradually changing surface with values that may lie outside the sample data range. Kriging is a geostatistical interpolation technique that considers both the distance and the degree of variation between known data points when estimating values in unknown areas.Kriging assumes that the distance or direction between sample points reflects a spatial correlation that can be used to explain variation in the surface. The Kriging tool fits a mathematical function to a specified number of points, or all points within a specified radius, to determine the output value for each location. Kriging is a multistep process; it includes exploratory statistical analysis of the data, variogram modeling, creating the surface, and (optionally) exploring a variance surface. Kriging is most appropriate when you know there is a spatially correlated distance or directional bias in the data. It is often used in soil science and geology. Trend is a statistical method that finds the surface that fits the sample points using a least-square regression fit. It fits one polynomial equation to the entire surface. This results in a surface that minimizes surface variance in relation to the input values. The surface is constructed so that for every input point, the total of the differences between the actual values and the estimated values (i.e., the variance) will be as small as possible. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/7-200116091708-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Interpolation is the process of using points with known values to estimate values at other unknown points.It can be used to predict unknown values for any geographic point data, such as elevation, rainfall, noise levels, atmospheric components and so on. The Inverse Distance Weighting (IDW) assumes each input point to have a local influence that diminishes with distance. It assumes that closer things are more alike than those that are farther apart. It weights the points closer to the processing cell greater than those further away. A specified number of points, or all points within a specified radius can be used to determine the output value of each location. To predict a value for any unmeasured location, IDW will use the measured values surrounding the prediction location. Those measured values closest to the prediction location will have more influence on the predicted value than those farther away. Spline estimates values using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes exactly through the input points. This method is best for gently varying surfaces, such as elevation, water table heights, or pollution concentrations. ARegularized methodcreates a smooth, gradually changing surface with values that may lie outside the sample data range. Kriging is a geostatistical interpolation technique that considers both the distance and the degree of variation between known data points when estimating values in unknown areas.Kriging assumes that the distance or direction between sample points reflects a spatial correlation that can be used to explain variation in the surface. The Kriging tool fits a mathematical function to a specified number of points, or all points within a specified radius, to determine the output value for each location. Kriging is a multistep process; it includes exploratory statistical analysis of the data, variogram modeling, creating the surface, and (optionally) exploring a variance surface. Kriging is most appropriate when you know there is a spatially correlated distance or directional bias in the data. It is often used in soil science and geology. Trend is a statistical method that finds the surface that fits the sample points using a least-square regression fit. It fits one polynomial equation to the entire surface. This results in a surface that minimizes surface variance in relation to the input values. The surface is constructed so that for every input point, the total of the differences between the actual values and the estimated values (i.e., the variance) will be as small as possible. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Remote Sensing: Interppolation from Kamlesh Kumar
]]> 125 0 https://cdn.slidesharecdn.com/ss_thumbnails/7-200116091708-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Remote Sensing: Georeferencing /slideshow/remote-sensing-georeferencing/220670764 6-200116091353
Raster data is commonly obtained by scanning maps or collecting aerial photographs and satellite images. Scanned map datasets don't normally contain spatial reference information (either embedded in the file or as a separate file). With aerial photography and satellite imagery, sometimes the location information delivered with them is inadequate, and the data does not align properly with other data one has. Thus, to use some raster datasets in conjunction with other spatial data, we need to align or georeference them to a map coordinate system. A map coordinate system is defined using a map projection (a method by which the curved surface of the earth is portrayed on a flat surface). Georeferencing a raster data defines its location using map coordinates and assigns the coordinate system of the data frame. Georeferencing raster data allows it to be viewed, queried, and analyzed with other geographic data. Generally, we georeference raster data using existing spatial data (target data)such as georeferenced rasters or a vector feature classthat resides in the desired map coordinate system. The process involves identifying a series of ground control pointsknown x,y coordinatesthat link locations on the raster dataset with locations in the spatially referenced data (target data). Control points are locations that can be accurately identified on the raster dataset and in real-world coordinates. Many different types of features can be used as identifiable locations, such as road or stream intersections, the mouth of a stream, rock outcrops, the end of a jetty of land, the corner of an established field, street corners, or the intersection of two hedgerows. The control points are used to build a polynomial transformation that will shift the raster dataset from its existing location to the spatially correct location. The connection between one control point on the raster dataset (the from point) and the corresponding control point on the aligned target data (the to point) is a link. Finally, the georeferenced raster file can be exported for further usage. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.]]>
Raster data is commonly obtained by scanning maps or collecting aerial photographs and satellite images. Scanned map datasets don't normally contain spatial reference information (either embedded in the file or as a separate file). With aerial photography and satellite imagery, sometimes the location information delivered with them is inadequate, and the data does not align properly with other data one has. Thus, to use some raster datasets in conjunction with other spatial data, we need to align or georeference them to a map coordinate system. A map coordinate system is defined using a map projection (a method by which the curved surface of the earth is portrayed on a flat surface). Georeferencing a raster data defines its location using map coordinates and assigns the coordinate system of the data frame. Georeferencing raster data allows it to be viewed, queried, and analyzed with other geographic data. Generally, we georeference raster data using existing spatial data (target data)such as georeferenced rasters or a vector feature classthat resides in the desired map coordinate system. The process involves identifying a series of ground control pointsknown x,y coordinatesthat link locations on the raster dataset with locations in the spatially referenced data (target data). Control points are locations that can be accurately identified on the raster dataset and in real-world coordinates. Many different types of features can be used as identifiable locations, such as road or stream intersections, the mouth of a stream, rock outcrops, the end of a jetty of land, the corner of an established field, street corners, or the intersection of two hedgerows. The control points are used to build a polynomial transformation that will shift the raster dataset from its existing location to the spatially correct location. The connection between one control point on the raster dataset (the from point) and the corresponding control point on the aligned target data (the to point) is a link. Finally, the georeferenced raster file can be exported for further usage. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.]]> Thu, 16 Jan 2020 09:13:53 GMT /slideshow/remote-sensing-georeferencing/220670764 KamleshKumar265@slideshare.net(KamleshKumar265) Remote Sensing: Georeferencing KamleshKumar265 Raster data is commonly obtained by scanning maps or collecting aerial photographs and satellite images. Scanned map datasets don't normally contain spatial reference information (either embedded in the file or as a separate file). With aerial photography and satellite imagery, sometimes the location information delivered with them is inadequate, and the data does not align properly with other data one has. Thus, to use some raster datasets in conjunction with other spatial data, we need to align or georeference them to a map coordinate system. A map coordinate system is defined using a map projection (a method by which the curved surface of the earth is portrayed on a flat surface). Georeferencing a raster data defines its location using map coordinates and assigns the coordinate system of the data frame. Georeferencing raster data allows it to be viewed, queried, and analyzed with other geographic data. Generally, we georeference raster data using existing spatial data (target data)such as georeferenced rasters or a vector feature classthat resides in the desired map coordinate system. The process involves identifying a series of ground control pointsknown x,y coordinatesthat link locations on the raster dataset with locations in the spatially referenced data (target data). Control points are locations that can be accurately identified on the raster dataset and in real-world coordinates. Many different types of features can be used as identifiable locations, such as road or stream intersections, the mouth of a stream, rock outcrops, the end of a jetty of land, the corner of an established field, street corners, or the intersection of two hedgerows. The control points are used to build a polynomial transformation that will shift the raster dataset from its existing location to the spatially correct location. The connection between one control point on the raster dataset (the from point) and the corresponding control point on the aligned target data (the to point) is a link. Finally, the georeferenced raster file can be exported for further usage. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/6-200116091353-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Raster data is commonly obtained by scanning maps or collecting aerial photographs and satellite images. Scanned map datasets don&#39;t normally contain spatial reference information (either embedded in the file or as a separate file). With aerial photography and satellite imagery, sometimes the location information delivered with them is inadequate, and the data does not align properly with other data one has. Thus, to use some raster datasets in conjunction with other spatial data, we need to align or georeference them to a map coordinate system. A map coordinate system is defined using a map projection (a method by which the curved surface of the earth is portrayed on a flat surface). Georeferencing a raster data defines its location using map coordinates and assigns the coordinate system of the data frame. Georeferencing raster data allows it to be viewed, queried, and analyzed with other geographic data. Generally, we georeference raster data using existing spatial data (target data)such as georeferenced rasters or a vector feature classthat resides in the desired map coordinate system. The process involves identifying a series of ground control pointsknown x,y coordinatesthat link locations on the raster dataset with locations in the spatially referenced data (target data). Control points are locations that can be accurately identified on the raster dataset and in real-world coordinates. Many different types of features can be used as identifiable locations, such as road or stream intersections, the mouth of a stream, rock outcrops, the end of a jetty of land, the corner of an established field, street corners, or the intersection of two hedgerows. The control points are used to build a polynomial transformation that will shift the raster dataset from its existing location to the spatially correct location. The connection between one control point on the raster dataset (the from point) and the corresponding control point on the aligned target data (the to point) is a link. Finally, the georeferenced raster file can be exported for further usage. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Remote Sensing: Georeferencing from Kamlesh Kumar
]]> 666 0 https://cdn.slidesharecdn.com/ss_thumbnails/6-200116091353-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Remote Sensing: Resolution Merge /slideshow/remote-sensing-resolution-merge/220670507 5-200116091155
With increasing use of remote sensing, the need for crispier, accurate and enhanced precision has deemed to the improvement in the spectral and spatial resolution of remotely sensed imagery. For most of the systems, panchromatic images typically have higher resolution, while multispectral images offer information in several spectral channels. Resolution merge (also called pan-sharpening) allows us to combine advantages of both kinds of images by merging them into one. Theresolution mergeor pan sharpening is the technique used to obtain highresolutionmulti-spectral images. The color information is collected from the coarseresolutionsatellite data and the intensity from the highresolutionsatellite data. The main constraint is to preserve the spectral information for aspects like land use. Saving theimage from distortion of the spectral characteristics is important in the merged dataset. The most common techniques for spatial enhancement of low-resolution imagery combining high and low resolution data can be used are: Intensity-Hue-Saturation, Principal Component, Multiplicative and Brovey Transform. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.]]>
With increasing use of remote sensing, the need for crispier, accurate and enhanced precision has deemed to the improvement in the spectral and spatial resolution of remotely sensed imagery. For most of the systems, panchromatic images typically have higher resolution, while multispectral images offer information in several spectral channels. Resolution merge (also called pan-sharpening) allows us to combine advantages of both kinds of images by merging them into one. Theresolution mergeor pan sharpening is the technique used to obtain highresolutionmulti-spectral images. The color information is collected from the coarseresolutionsatellite data and the intensity from the highresolutionsatellite data. The main constraint is to preserve the spectral information for aspects like land use. Saving theimage from distortion of the spectral characteristics is important in the merged dataset. The most common techniques for spatial enhancement of low-resolution imagery combining high and low resolution data can be used are: Intensity-Hue-Saturation, Principal Component, Multiplicative and Brovey Transform. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.]]> Thu, 16 Jan 2020 09:11:55 GMT /slideshow/remote-sensing-resolution-merge/220670507 KamleshKumar265@slideshare.net(KamleshKumar265) Remote Sensing: Resolution Merge KamleshKumar265 With increasing use of remote sensing, the need for crispier, accurate and enhanced precision has deemed to the improvement in the spectral and spatial resolution of remotely sensed imagery. For most of the systems, panchromatic images typically have higher resolution, while multispectral images offer information in several spectral channels. Resolution merge (also called pan-sharpening) allows us to combine advantages of both kinds of images by merging them into one. Theresolution mergeor pan sharpening is the technique used to obtain highresolutionmulti-spectral images. The color information is collected from the coarseresolutionsatellite data and the intensity from the highresolutionsatellite data. The main constraint is to preserve the spectral information for aspects like land use. Saving theimage from distortion of the spectral characteristics is important in the merged dataset. The most common techniques for spatial enhancement of low-resolution imagery combining high and low resolution data can be used are: Intensity-Hue-Saturation, Principal Component, Multiplicative and Brovey Transform. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/5-200116091155-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> With increasing use of remote sensing, the need for crispier, accurate and enhanced precision has deemed to the improvement in the spectral and spatial resolution of remotely sensed imagery. For most of the systems, panchromatic images typically have higher resolution, while multispectral images offer information in several spectral channels. Resolution merge (also called pan-sharpening) allows us to combine advantages of both kinds of images by merging them into one. Theresolution mergeor pan sharpening is the technique used to obtain highresolutionmulti-spectral images. The color information is collected from the coarseresolutionsatellite data and the intensity from the highresolutionsatellite data. The main constraint is to preserve the spectral information for aspects like land use. Saving theimage from distortion of the spectral characteristics is important in the merged dataset. The most common techniques for spatial enhancement of low-resolution imagery combining high and low resolution data can be used are: Intensity-Hue-Saturation, Principal Component, Multiplicative and Brovey Transform. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Remote Sensing: Resolution Merge from Kamlesh Kumar
]]> 1462 0 https://cdn.slidesharecdn.com/ss_thumbnails/5-200116091155-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Remote Sensing: Normalized Difference Vegetation Index (NDVI) /slideshow/remote-sensing-normalized-difference-vegetation-index-ndvi/220670240 4-200116090953
The Normalized Difference Vegetation Index (NDVI) is a numerical indicator that uses the visible and near-infrared (NIR) bands of the electromagnetic spectrum to analyze whether the target (image) being observed contains green vegetation or not. Healthy vegetation (chlorophyll) reflects more near-infrared (NIR) and green light compared to other wavelengths. But it absorbs more red and blue light. This is why our eyes see vegetation as thecolour green. If we could see near-infrared, then it would be strong for vegetation too. It is basically measured through the use of Intensity, Hue and saturation of an image and through pixels as well. The density of vegetation (NDVI) at a certain point on the image is equal to the difference in the intensities of reflected light in the red and infrared range divided by the sum of these intensities. 訣=((狩誤))/((狩+誤)) The result of this formula generates a value between -1 and +1. If you have low reflectance (low values) in the red band and high reflectance in the NIR, this will yield a high NDVI value. And vice versa. ]]>
The Normalized Difference Vegetation Index (NDVI) is a numerical indicator that uses the visible and near-infrared (NIR) bands of the electromagnetic spectrum to analyze whether the target (image) being observed contains green vegetation or not. Healthy vegetation (chlorophyll) reflects more near-infrared (NIR) and green light compared to other wavelengths. But it absorbs more red and blue light. This is why our eyes see vegetation as thecolour green. If we could see near-infrared, then it would be strong for vegetation too. It is basically measured through the use of Intensity, Hue and saturation of an image and through pixels as well. The density of vegetation (NDVI) at a certain point on the image is equal to the difference in the intensities of reflected light in the red and infrared range divided by the sum of these intensities. 訣=((狩誤))/((狩+誤)) The result of this formula generates a value between -1 and +1. If you have low reflectance (low values) in the red band and high reflectance in the NIR, this will yield a high NDVI value. And vice versa. ]]> Thu, 16 Jan 2020 09:09:52 GMT /slideshow/remote-sensing-normalized-difference-vegetation-index-ndvi/220670240 KamleshKumar265@slideshare.net(KamleshKumar265) Remote Sensing: Normalized Difference Vegetation Index (NDVI) KamleshKumar265 The Normalized Difference Vegetation Index (NDVI) is a numerical indicator that uses the visible and near-infrared (NIR) bands of the electromagnetic spectrum to analyze whether the target (image) being observed contains green vegetation or not. Healthy vegetation (chlorophyll) reflects more near-infrared (NIR) and green light compared to other wavelengths. But it absorbs more red and blue light. This is why our eyes see vegetation as thecolour green. If we could see near-infrared, then it would be strong for vegetation too. It is basically measured through the use of Intensity, Hue and saturation of an image and through pixels as well. The density of vegetation (NDVI) at a certain point on the image is equal to the difference in the intensities of reflected light in the red and infrared range divided by the sum of these intensities. 訣=((狩誤))/((狩+誤)) The result of this formula generates a value between -1 and +1. If you have low reflectance (low values) in the red band and high reflectance in the NIR, this will yield a high NDVI value. And vice versa. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/4-200116090953-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The Normalized Difference Vegetation Index (NDVI) is a numerical indicator that uses the visible and near-infrared (NIR) bands of the electromagnetic spectrum to analyze whether the target (image) being observed contains green vegetation or not. Healthy vegetation (chlorophyll) reflects more near-infrared (NIR) and green light compared to other wavelengths. But it absorbs more red and blue light. This is why our eyes see vegetation as thecolour green. If we could see near-infrared, then it would be strong for vegetation too. It is basically measured through the use of Intensity, Hue and saturation of an image and through pixels as well. The density of vegetation (NDVI) at a certain point on the image is equal to the difference in the intensities of reflected light in the red and infrared range divided by the sum of these intensities. 訣=((狩誤))/((狩+誤)) The result of this formula generates a value between -1 and +1. If you have low reflectance (low values) in the red band and high reflectance in the NIR, this will yield a high NDVI value. And vice versa.
Remote Sensing: Normalized Difference Vegetation Index (NDVI) from Kamlesh Kumar
]]> 4132 0 https://cdn.slidesharecdn.com/ss_thumbnails/4-200116090953-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Remote Sensing: Principal Component Analysis /slideshow/remote-sensing-principal-component-analysis/220669972 3-200116090753
Principal components analysis is a orthogonal transformational technique (preserving the symmetry between vectors and angles) to reveal new set of data arguably better from the original data set and better capture the essential information as well. It happens often that some variables are highly correlated with a lot of duplication. Instead of discarding the redundant data, principal components analysis condenses the info. in inter-correlated variables into a few variables, called principal components. The main idea ofPrincipal Component Analysis(PCA) is to reduce the dimensionality of a data set consisting of many variables correlated with each other, either heavily or lightly, while retaining the variation present in the dataset, up to the maximum extent. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.]]>
Principal components analysis is a orthogonal transformational technique (preserving the symmetry between vectors and angles) to reveal new set of data arguably better from the original data set and better capture the essential information as well. It happens often that some variables are highly correlated with a lot of duplication. Instead of discarding the redundant data, principal components analysis condenses the info. in inter-correlated variables into a few variables, called principal components. The main idea ofPrincipal Component Analysis(PCA) is to reduce the dimensionality of a data set consisting of many variables correlated with each other, either heavily or lightly, while retaining the variation present in the dataset, up to the maximum extent. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.]]> Thu, 16 Jan 2020 09:07:53 GMT /slideshow/remote-sensing-principal-component-analysis/220669972 KamleshKumar265@slideshare.net(KamleshKumar265) Remote Sensing: Principal Component Analysis KamleshKumar265 Principal components analysis is a orthogonal transformational technique (preserving the symmetry between vectors and angles) to reveal new set of data arguably better from the original data set and better capture the essential information as well. It happens often that some variables are highly correlated with a lot of duplication. Instead of discarding the redundant data, principal components analysis condenses the info. in inter-correlated variables into a few variables, called principal components. The main idea ofPrincipal Component Analysis(PCA) is to reduce the dimensionality of a data set consisting of many variables correlated with each other, either heavily or lightly, while retaining the variation present in the dataset, up to the maximum extent. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/3-200116090753-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Principal components analysis is a orthogonal transformational technique (preserving the symmetry between vectors and angles) to reveal new set of data arguably better from the original data set and better capture the essential information as well. It happens often that some variables are highly correlated with a lot of duplication. Instead of discarding the redundant data, principal components analysis condenses the info. in inter-correlated variables into a few variables, called principal components. The main idea ofPrincipal Component Analysis(PCA) is to reduce the dimensionality of a data set consisting of many variables correlated with each other, either heavily or lightly, while retaining the variation present in the dataset, up to the maximum extent. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Remote Sensing: Principal Component Analysis from Kamlesh Kumar
]]> 1029 0 https://cdn.slidesharecdn.com/ss_thumbnails/3-200116090753-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Remote Sensing:. Image Filtering /slideshow/remote-sensing-image-filtering/220669724 2-200116090558
The advantage of digital imagery is that it allows us to manipulate the digital pixel values in the image. Even after the radiometric corrections image may still not be optimized for visual interpretation. An image 'enhancement' is basically anything that makes it easier or better to visually interpret. An enhancement is performed for a specific application as well. This enhancement may be inappropriate for another purpose, which would demand a different type of enhancement. Filteringis used to enhance the appearance of an image. Spatial filters are designed to highlight or suppress specific features in an image based on theirspatial frequency. Rough textured areas of an image, where the changes in tone are abrupt, have high spatial frequencies, while smooth areas with little variation have low spatial frequencies. A commonfiltering procedureinvolves moving a matrix' of a few pixels in dimension (ie. 3x3, 5x5, etc.) over each pixel in the image, using mathematical calculation and replacing the central pixel with the new value. Alow-pass filteris designed to emphasize larger, homogeneous areas of similar tone and reduce the smaller detail in an image. Thus, low-pass filters generally serve to smooth the appearance of an image. In some cases, like 'low-pass filtering', the enhanced image can actually look worse than the original, but such an enhancement was likely performed to help the interpreter see low spatial frequency features among the usual high frequency clutter found in an image. High-pass filtersdo the opposite and serve to sharpen the appearance of fine detail in an image.Directional, or edge detection filtersare designed to highlight linear features, such as roads or field boundaries. These filters can also be designed to enhance features which are oriented in specific directions. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.]]>
The advantage of digital imagery is that it allows us to manipulate the digital pixel values in the image. Even after the radiometric corrections image may still not be optimized for visual interpretation. An image 'enhancement' is basically anything that makes it easier or better to visually interpret. An enhancement is performed for a specific application as well. This enhancement may be inappropriate for another purpose, which would demand a different type of enhancement. Filteringis used to enhance the appearance of an image. Spatial filters are designed to highlight or suppress specific features in an image based on theirspatial frequency. Rough textured areas of an image, where the changes in tone are abrupt, have high spatial frequencies, while smooth areas with little variation have low spatial frequencies. A commonfiltering procedureinvolves moving a matrix' of a few pixels in dimension (ie. 3x3, 5x5, etc.) over each pixel in the image, using mathematical calculation and replacing the central pixel with the new value. Alow-pass filteris designed to emphasize larger, homogeneous areas of similar tone and reduce the smaller detail in an image. Thus, low-pass filters generally serve to smooth the appearance of an image. In some cases, like 'low-pass filtering', the enhanced image can actually look worse than the original, but such an enhancement was likely performed to help the interpreter see low spatial frequency features among the usual high frequency clutter found in an image. High-pass filtersdo the opposite and serve to sharpen the appearance of fine detail in an image.Directional, or edge detection filtersare designed to highlight linear features, such as roads or field boundaries. These filters can also be designed to enhance features which are oriented in specific directions. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.]]> Thu, 16 Jan 2020 09:05:58 GMT /slideshow/remote-sensing-image-filtering/220669724 KamleshKumar265@slideshare.net(KamleshKumar265) Remote Sensing:. Image Filtering KamleshKumar265 The advantage of digital imagery is that it allows us to manipulate the digital pixel values in the image. Even after the radiometric corrections image may still not be optimized for visual interpretation. An image 'enhancement' is basically anything that makes it easier or better to visually interpret. An enhancement is performed for a specific application as well. This enhancement may be inappropriate for another purpose, which would demand a different type of enhancement. Filteringis used to enhance the appearance of an image. Spatial filters are designed to highlight or suppress specific features in an image based on theirspatial frequency. Rough textured areas of an image, where the changes in tone are abrupt, have high spatial frequencies, while smooth areas with little variation have low spatial frequencies. A commonfiltering procedureinvolves moving a matrix' of a few pixels in dimension (ie. 3x3, 5x5, etc.) over each pixel in the image, using mathematical calculation and replacing the central pixel with the new value. Alow-pass filteris designed to emphasize larger, homogeneous areas of similar tone and reduce the smaller detail in an image. Thus, low-pass filters generally serve to smooth the appearance of an image. In some cases, like 'low-pass filtering', the enhanced image can actually look worse than the original, but such an enhancement was likely performed to help the interpreter see low spatial frequency features among the usual high frequency clutter found in an image. High-pass filtersdo the opposite and serve to sharpen the appearance of fine detail in an image.Directional, or edge detection filtersare designed to highlight linear features, such as roads or field boundaries. These filters can also be designed to enhance features which are oriented in specific directions. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2-200116090558-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The advantage of digital imagery is that it allows us to manipulate the digital pixel values in the image. Even after the radiometric corrections image may still not be optimized for visual interpretation. An image &#39;enhancement&#39; is basically anything that makes it easier or better to visually interpret. An enhancement is performed for a specific application as well. This enhancement may be inappropriate for another purpose, which would demand a different type of enhancement. Filteringis used to enhance the appearance of an image. Spatial filters are designed to highlight or suppress specific features in an image based on theirspatial frequency. Rough textured areas of an image, where the changes in tone are abrupt, have high spatial frequencies, while smooth areas with little variation have low spatial frequencies. A commonfiltering procedureinvolves moving a matrix&#39; of a few pixels in dimension (ie. 3x3, 5x5, etc.) over each pixel in the image, using mathematical calculation and replacing the central pixel with the new value. Alow-pass filteris designed to emphasize larger, homogeneous areas of similar tone and reduce the smaller detail in an image. Thus, low-pass filters generally serve to smooth the appearance of an image. In some cases, like &#39;low-pass filtering&#39;, the enhanced image can actually look worse than the original, but such an enhancement was likely performed to help the interpreter see low spatial frequency features among the usual high frequency clutter found in an image. High-pass filtersdo the opposite and serve to sharpen the appearance of fine detail in an image.Directional, or edge detection filtersare designed to highlight linear features, such as roads or field boundaries. These filters can also be designed to enhance features which are oriented in specific directions. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Remote Sensing:. Image Filtering from Kamlesh Kumar
]]> 4237 0 https://cdn.slidesharecdn.com/ss_thumbnails/2-200116090558-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Mountain ecosystem /slideshow/mountain-ecosystem/146636890 mountainecosystem-190519210218
Mountainous regions occupy one-fourth of the worlds terrestrial surface, most rich in diverse landscapes and hold on to the biodiversity and cultural diversity along with supporting 10% of humankind with their direct life support base. Most mountainous regions have been at the far periphery of mainstream societal concerns for a long time. Remote, relatively inaccessible, they were generally pictured as difficulty, unyielding and unprofitable environments. Very less have focused attention on mountainous people and cultures, primitive religion, marginal survival, unusual adaptation to very high altitude, fraternal polyandry to obliterate informed communication and more meaningful analysis in practical sense. Early research concentrated mainly on specialised studies with little cross disciplinary endeavour. During the last few decades there have been spasmodic accounts of the highland and lowland mainly induced by events of great economic or political significance and due to the degradation of highlands which are potential threats to subjacent lowland population centre. Recent developments, expanding highland research and awareness spread by institutions and governments have shone a new ray of light towards the bright future. However, increased awareness with political advocacy must be pursued further. ]]>
Mountainous regions occupy one-fourth of the worlds terrestrial surface, most rich in diverse landscapes and hold on to the biodiversity and cultural diversity along with supporting 10% of humankind with their direct life support base. Most mountainous regions have been at the far periphery of mainstream societal concerns for a long time. Remote, relatively inaccessible, they were generally pictured as difficulty, unyielding and unprofitable environments. Very less have focused attention on mountainous people and cultures, primitive religion, marginal survival, unusual adaptation to very high altitude, fraternal polyandry to obliterate informed communication and more meaningful analysis in practical sense. Early research concentrated mainly on specialised studies with little cross disciplinary endeavour. During the last few decades there have been spasmodic accounts of the highland and lowland mainly induced by events of great economic or political significance and due to the degradation of highlands which are potential threats to subjacent lowland population centre. Recent developments, expanding highland research and awareness spread by institutions and governments have shone a new ray of light towards the bright future. However, increased awareness with political advocacy must be pursued further. ]]> Sun, 19 May 2019 21:02:18 GMT /slideshow/mountain-ecosystem/146636890 KamleshKumar265@slideshare.net(KamleshKumar265) Mountain ecosystem KamleshKumar265 Mountainous regions occupy one-fourth of the worlds terrestrial surface, most rich in diverse landscapes and hold on to the biodiversity and cultural diversity along with supporting 10% of humankind with their direct life support base. Most mountainous regions have been at the far periphery of mainstream societal concerns for a long time. Remote, relatively inaccessible, they were generally pictured as difficulty, unyielding and unprofitable environments. Very less have focused attention on mountainous people and cultures, primitive religion, marginal survival, unusual adaptation to very high altitude, fraternal polyandry to obliterate informed communication and more meaningful analysis in practical sense. Early research concentrated mainly on specialised studies with little cross disciplinary endeavour. During the last few decades there have been spasmodic accounts of the highland and lowland mainly induced by events of great economic or political significance and due to the degradation of highlands which are potential threats to subjacent lowland population centre. Recent developments, expanding highland research and awareness spread by institutions and governments have shone a new ray of light towards the bright future. However, increased awareness with political advocacy must be pursued further. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mountainecosystem-190519210218-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Mountainous regions occupy one-fourth of the worlds terrestrial surface, most rich in diverse landscapes and hold on to the biodiversity and cultural diversity along with supporting 10% of humankind with their direct life support base. Most mountainous regions have been at the far periphery of mainstream societal concerns for a long time. Remote, relatively inaccessible, they were generally pictured as difficulty, unyielding and unprofitable environments. Very less have focused attention on mountainous people and cultures, primitive religion, marginal survival, unusual adaptation to very high altitude, fraternal polyandry to obliterate informed communication and more meaningful analysis in practical sense. Early research concentrated mainly on specialised studies with little cross disciplinary endeavour. During the last few decades there have been spasmodic accounts of the highland and lowland mainly induced by events of great economic or political significance and due to the degradation of highlands which are potential threats to subjacent lowland population centre. Recent developments, expanding highland research and awareness spread by institutions and governments have shone a new ray of light towards the bright future. However, increased awareness with political advocacy must be pursued further.
Mountain ecosystem from Kamlesh Kumar
]]> 10104 6 https://cdn.slidesharecdn.com/ss_thumbnails/mountainecosystem-190519210218-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Hydrological cycle /slideshow/hydrological-cycle-146636134/146636134 hydrologicalcycle-190519204900
Water is hydrosphere is made up of all the water on Earth. This includes all of the rivers, lakes, streams, oceans, groundwater, polar ice caps, glaciers and moisture in the air (like rain and snow). The hydrosphere is found on the surface of Earth, but also extends down several miles below, as well as several miles up into the atmosphere. So, there is a need for study of water as a scarce resource. WHAT IS HYDROLOGICAL CYCLE SYSTEM APPROACH IN HYDROLOGY HYDROLOGIC INPUT & OUTPUT VARIATION IN HYDROLOGICAL CYCLE COMPONENTS EVAPORATION EVAPOTRANSPIRATION PRECIPITATION INTERCEPTION INFILTRATION GROUND WATER RUN-OFF HUMAN IMPACT EARTH SURFACE CLIMATE CHANGE ATMOSPHERIC POLLUTION MULTI PURPOSE PROJECTS WATER WITHDRAWAL MANAGEMENT AND CONTROL]]>
Water is hydrosphere is made up of all the water on Earth. This includes all of the rivers, lakes, streams, oceans, groundwater, polar ice caps, glaciers and moisture in the air (like rain and snow). The hydrosphere is found on the surface of Earth, but also extends down several miles below, as well as several miles up into the atmosphere. So, there is a need for study of water as a scarce resource. WHAT IS HYDROLOGICAL CYCLE SYSTEM APPROACH IN HYDROLOGY HYDROLOGIC INPUT & OUTPUT VARIATION IN HYDROLOGICAL CYCLE COMPONENTS EVAPORATION EVAPOTRANSPIRATION PRECIPITATION INTERCEPTION INFILTRATION GROUND WATER RUN-OFF HUMAN IMPACT EARTH SURFACE CLIMATE CHANGE ATMOSPHERIC POLLUTION MULTI PURPOSE PROJECTS WATER WITHDRAWAL MANAGEMENT AND CONTROL]]> Sun, 19 May 2019 20:49:00 GMT /slideshow/hydrological-cycle-146636134/146636134 KamleshKumar265@slideshare.net(KamleshKumar265) Hydrological cycle KamleshKumar265 Water is hydrosphere is made up of all the water on Earth. This includes all of the rivers, lakes, streams, oceans, groundwater, polar ice caps, glaciers and moisture in the air (like rain and snow). The hydrosphere is found on the surface of Earth, but also extends down several miles below, as well as several miles up into the atmosphere. So, there is a need for study of water as a scarce resource. WHAT IS HYDROLOGICAL CYCLE SYSTEM APPROACH IN HYDROLOGY HYDROLOGIC INPUT & OUTPUT VARIATION IN HYDROLOGICAL CYCLE COMPONENTS EVAPORATION EVAPOTRANSPIRATION PRECIPITATION INTERCEPTION INFILTRATION GROUND WATER RUN-OFF HUMAN IMPACT EARTH SURFACE CLIMATE CHANGE ATMOSPHERIC POLLUTION MULTI PURPOSE PROJECTS WATER WITHDRAWAL MANAGEMENT AND CONTROL <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hydrologicalcycle-190519204900-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Water is hydrosphere is made up of all the water on Earth. This includes all of the rivers, lakes, streams, oceans, groundwater, polar ice caps, glaciers and moisture in the air (like rain and snow). The hydrosphere is found on the surface of Earth, but also extends down several miles below, as well as several miles up into the atmosphere. So, there is a need for study of water as a scarce resource. WHAT IS HYDROLOGICAL CYCLE SYSTEM APPROACH IN HYDROLOGY HYDROLOGIC INPUT &amp; OUTPUT VARIATION IN HYDROLOGICAL CYCLE COMPONENTS EVAPORATION EVAPOTRANSPIRATION PRECIPITATION INTERCEPTION INFILTRATION GROUND WATER RUN-OFF HUMAN IMPACT EARTH SURFACE CLIMATE CHANGE ATMOSPHERIC POLLUTION MULTI PURPOSE PROJECTS WATER WITHDRAWAL MANAGEMENT AND CONTROL
Hydrological cycle from Kamlesh Kumar
]]> 6709 4 https://cdn.slidesharecdn.com/ss_thumbnails/hydrologicalcycle-190519204900-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 TEMPERATE ECOSYSTEM /slideshow/temperate-ecosystem/146635147 temp2-190519203002
An assessment on the temperate ecosystem with the following sub headings: Geological evolution: Location and Extent Atmospheric changes Hydrological Changes Land Degradation Biodiversity Loss Challenges to Human Community ]]>
An assessment on the temperate ecosystem with the following sub headings: Geological evolution: Location and Extent Atmospheric changes Hydrological Changes Land Degradation Biodiversity Loss Challenges to Human Community ]]> Sun, 19 May 2019 20:30:02 GMT /slideshow/temperate-ecosystem/146635147 KamleshKumar265@slideshare.net(KamleshKumar265) TEMPERATE ECOSYSTEM KamleshKumar265 An assessment on the temperate ecosystem with the following sub headings: Geological evolution: Location and Extent Atmospheric changes Hydrological Changes Land Degradation Biodiversity Loss Challenges to Human Community <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/temp2-190519203002-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> An assessment on the temperate ecosystem with the following sub headings: Geological evolution: Location and Extent Atmospheric changes Hydrological Changes Land Degradation Biodiversity Loss Challenges to Human Community
TEMPERATE ECOSYSTEM from Kamlesh Kumar
]]> 3453 13 https://cdn.slidesharecdn.com/ss_thumbnails/temp2-190519203002-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Geosystem Approach: El Nino Southern Oscillation Effects /slideshow/geosystem-approach-el-nino-southern-oscillation-effects/140339583 geosyst-190410165105
Earth system as a whole is very complex and dynamic, for that matter we prepare models to represent the functioning linkages and processes for better understanding. However, the geo-systems can not be summed up in just one model. Hence, we use system analysis approach, if we see Earth as a giant system, there're many sub-systems for better comprehension representing only a particular component of the system. Here, I've tried to cover the geo-system approach siting a globe affecting example of the El Nino Southern Oscillation (ENSO) phenomena.]]>
Earth system as a whole is very complex and dynamic, for that matter we prepare models to represent the functioning linkages and processes for better understanding. However, the geo-systems can not be summed up in just one model. Hence, we use system analysis approach, if we see Earth as a giant system, there're many sub-systems for better comprehension representing only a particular component of the system. Here, I've tried to cover the geo-system approach siting a globe affecting example of the El Nino Southern Oscillation (ENSO) phenomena.]]> Wed, 10 Apr 2019 16:51:04 GMT /slideshow/geosystem-approach-el-nino-southern-oscillation-effects/140339583 KamleshKumar265@slideshare.net(KamleshKumar265) Geosystem Approach: El Nino Southern Oscillation Effects KamleshKumar265 Earth system as a whole is very complex and dynamic, for that matter we prepare models to represent the functioning linkages and processes for better understanding. However, the geo-systems can not be summed up in just one model. Hence, we use system analysis approach, if we see Earth as a giant system, there're many sub-systems for better comprehension representing only a particular component of the system. Here, I've tried to cover the geo-system approach siting a globe affecting example of the El Nino Southern Oscillation (ENSO) phenomena. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/geosyst-190410165105-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Earth system as a whole is very complex and dynamic, for that matter we prepare models to represent the functioning linkages and processes for better understanding. However, the geo-systems can not be summed up in just one model. Hence, we use system analysis approach, if we see Earth as a giant system, there&#39;re many sub-systems for better comprehension representing only a particular component of the system. Here, I&#39;ve tried to cover the geo-system approach siting a globe affecting example of the El Nino Southern Oscillation (ENSO) phenomena.
Geosystem Approach: El Nino Southern Oscillation Effects from Kamlesh Kumar
]]> 224 5 https://cdn.slidesharecdn.com/ss_thumbnails/geosyst-190410165105-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Fire Safety Report, Kirori Mal College /slideshow/fire-safety-report-kirori-mal-college/96188710 kkfire-copy-180506220659
This report is detailed study of the research conducted in Kirori Mal College. The basic objective of this report is to get a tough insight in the use of research techniques. Geography, being a field science, a geographical enquiry always need to been supplemented through well planned Research. Research is an essential component of geographic enquire. It is a basic procedure to understand the earth as a home of humankind. Disaster management is an inseparable part of the discipline especially which deals with the study of natural phenomena. This research focuses upon the FIRE safety plan of the institution. It is carried out through observation, sketching, measurement, interviews, etc. The Research facilitate the collection of local level information that is not available through secondary sources. In this report, various methodologies have been employed such as my, measurement and interviewing, photographing, examining, the collection and gathering of information at different corners of the institution and later, tabulating and computing them is an important part of the field work. Furthermore, the research report has been prepared in concise form alongside with maps and diagrams for giving visual impressions. Moreover, it contains all the details of the procedures followed, methods, tools and techniques employed. ]]>
This report is detailed study of the research conducted in Kirori Mal College. The basic objective of this report is to get a tough insight in the use of research techniques. Geography, being a field science, a geographical enquiry always need to been supplemented through well planned Research. Research is an essential component of geographic enquire. It is a basic procedure to understand the earth as a home of humankind. Disaster management is an inseparable part of the discipline especially which deals with the study of natural phenomena. This research focuses upon the FIRE safety plan of the institution. It is carried out through observation, sketching, measurement, interviews, etc. The Research facilitate the collection of local level information that is not available through secondary sources. In this report, various methodologies have been employed such as my, measurement and interviewing, photographing, examining, the collection and gathering of information at different corners of the institution and later, tabulating and computing them is an important part of the field work. Furthermore, the research report has been prepared in concise form alongside with maps and diagrams for giving visual impressions. Moreover, it contains all the details of the procedures followed, methods, tools and techniques employed. ]]> Sun, 06 May 2018 22:06:59 GMT /slideshow/fire-safety-report-kirori-mal-college/96188710 KamleshKumar265@slideshare.net(KamleshKumar265) Fire Safety Report, Kirori Mal College KamleshKumar265 This report is detailed study of the research conducted in Kirori Mal College. The basic objective of this report is to get a tough insight in the use of research techniques. Geography, being a field science, a geographical enquiry always need to been supplemented through well planned Research. Research is an essential component of geographic enquire. It is a basic procedure to understand the earth as a home of humankind. Disaster management is an inseparable part of the discipline especially which deals with the study of natural phenomena. This research focuses upon the FIRE safety plan of the institution. It is carried out through observation, sketching, measurement, interviews, etc. The Research facilitate the collection of local level information that is not available through secondary sources. In this report, various methodologies have been employed such as my, measurement and interviewing, photographing, examining, the collection and gathering of information at different corners of the institution and later, tabulating and computing them is an important part of the field work. Furthermore, the research report has been prepared in concise form alongside with maps and diagrams for giving visual impressions. Moreover, it contains all the details of the procedures followed, methods, tools and techniques employed. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/kkfire-copy-180506220659-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This report is detailed study of the research conducted in Kirori Mal College. The basic objective of this report is to get a tough insight in the use of research techniques. Geography, being a field science, a geographical enquiry always need to been supplemented through well planned Research. Research is an essential component of geographic enquire. It is a basic procedure to understand the earth as a home of humankind. Disaster management is an inseparable part of the discipline especially which deals with the study of natural phenomena. This research focuses upon the FIRE safety plan of the institution. It is carried out through observation, sketching, measurement, interviews, etc. The Research facilitate the collection of local level information that is not available through secondary sources. In this report, various methodologies have been employed such as my, measurement and interviewing, photographing, examining, the collection and gathering of information at different corners of the institution and later, tabulating and computing them is an important part of the field work. Furthermore, the research report has been prepared in concise form alongside with maps and diagrams for giving visual impressions. Moreover, it contains all the details of the procedures followed, methods, tools and techniques employed.
Fire Safety Report, Kirori Mal College from Kamlesh Kumar
]]> 1773 10 https://cdn.slidesharecdn.com/ss_thumbnails/kkfire-copy-180506220659-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/migrationprofileofodishawithfocusonbhubaneswar-200326180301-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/migration-profile-of-odisha-with-focus-on-bhubaneswar/230921614 Migration Profile of O... https://cdn.slidesharecdn.com/ss_thumbnails/populationprojectionofkhordhadistrict-200326173655-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/population-projection-of-khordha-district-odisha-202151/230920777 Population Projection ... https://cdn.slidesharecdn.com/ss_thumbnails/continentalodisha-200116100759-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/demographic-profile-of-continental-odisha/220675350 DEMOGRAPHIC PROFILE OF...