Remote Sensing:. Image Filtering
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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. Filtering is used to enhance the appearance of an image. Spatial filters are designed to highlight or suppress specific features in an image based on their spatial 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 common filtering procedure involves 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. A low-pass filter is 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 filters do the opposite and serve to sharpen the appearance of fine detail in an image. Directional, or edge detection filters are 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.
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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 the colour 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.
푁퐷푉퐼=((푁퐼푅−푅퐸퐷))/((푁퐼푅+푅퐸퐷))
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.
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The main idea of Principal 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.
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Spectral reflectance curve of dead stressed vegetation
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Remote Sensing: Normalized Difference Vegetation Index (NDVI)
Remote Sensing: Normalized Difference Vegetation Index (NDVI)
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International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Feature Extraction of an Image by Using Adaptive Filtering and Morpological S...
Abstract: For enhancing an image various enhancement schemes are used which includes gray scale manipulation, filtering and Histogram Equalization, Where Histogram equalization is one of the well known image enhancement technique. It became a popular technique for contrast enhancement because it is simple and effective. The basic idea of Histogram Equalization method is to remap the gray levels of an image. Here using morphological segmentation we can get the segmented image. Morphological reconstruction is used to segment the image. Comparative analysis of different enhancement and segmentation will be carried out. This comparison will be done on the basis of subjective and objective parameters. Subjective parameter is visual quality and objective parameters are Area, Perimeter, Min and Max intensity, Avg Voxel Intensity, Std Dev of Intensity, Eccentricity, Coefficient of skewness, Coefficient of Kurtosis, Median intensity, Mode intensity. Keywords: Histogram Equalization, Segmentation, Morphological Reconstruction .
Image enhancement lecture
This document discusses image enhancement techniques in digital image processing. It defines image enhancement as modifying image attributes to make an image more suitable for a given task. The main techniques discussed are spatial domain enhancement methods like noise removal, contrast adjustment, and histogram equalization. Examples are provided to demonstrate the effects of these enhancement methods on images.
Animation
Computer animation involves creating animation sequences through object definition, path specification, and key frames. Key techniques include:
1. Raster animation displays pre-computed or real-time animation frames by rapidly presenting them on screen at 30 frames per second or more for a smooth effect.
2. Color-table animation uses a color lookup table to implement simple 2D animations through palette color changes.
3. Tweening and morphing generate intermediate frames between key frames to give the appearance of smooth motion or transition from one image to another. Morphing additionally requires matching areas between images.
International Journal of Computational Engineering Research(IJCER)
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Image processing sw & hw
Image processing involves algorithms that take images as input and output other images. It is used to prepare digital images for viewing or analysis by enhancing structures within images. Common applications of image processing include adjusting properties like brightness, contrast, and gamma; detecting edges; blurring or sharpening; and performing operations like erosion and dilation. Principal component analysis (PCA) is a technique used to reduce the dimensionality of image data for analysis and recognition. Face recognition systems use PCA to extract feature vectors from images, then compare new images to the training set to identify faces.
Ijetr011917
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
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Free Journals, Open access Journals,
erpublication.org,
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The application of image enhancement in color and grayscale images
This is the presentation which was presented at All Pakistan Technical Paper Competition Lahore under the title "The application of image enhancement in color and grayscale images"
Ijcatr04051016
Image enhancement plays an important role in vision applications. Recently a lot of work has been performed in the field of image enhancement. Many techniques have already been proposed till now for enhancing the digital images. This paper has presented a comparative analysis of various image enhancement techniques. This paper has shown that the fuzzy logic and histogram based techniques have quite effective results over the available techniques. This paper ends up with suitable future directions to enhance fuzzy based image enhancement technique further. In the proposed technique, an approach is made to enhance the images other than low-contrast images as well by balancing the stretching parameter (K) according to the color contrast. Proposed technique is designed to restore the degraded edges resulted due to contrast enhancement as well.
ETECH_WEEK7-Image-Manipulation-Techniques-1.pdf
1. The document discusses various image editing techniques like cropping, blurring, adjustments, framing, and adding text overlays using the PicsArt application.
2. It provides step-by-step instructions for performing each technique, including selecting tools and adjusting settings.
3. The document also explains how to combine multiple images using layering and blending modes in PicsArt to design original composite images.
Spatial enhancement techniques
Spatial techniques final ppt. You can ignore my above ppt.
AAKANCHA ANAND
Birla institute of Technology , Mesra Ranchi
Image Segmentation Using Pairwise Correlation Clustering
A pairwise hypergraph based image segmentation framework is formulated in a supervised manner for various images. The image segmentation is to infer the edge label over the pairwise hypergraph by maximizing the normalized cuts. Correlation clustering which is a graph partitioning algorithm, was shown to be effective in a number of applications such as identification, clustering of documents and image segmentation.The partitioning result is derived from a algorithm to partition a pairwise graph into disjoint groups of coherent nodes. In the pairwise correlation clustering, the pairwise graph which is used in the correlation clustering is generalized to a superpixel graph where a node corresponds to a superpixel and a link between adjacent superpixels corresponds to an edge. This pairwise correlation clustering also considers the feature vector which extracts several visual cues from a superpixel, including brightness, color, texture, and shape. Significant progress in clustering has been achieved by algorithms that are based on pairwise affinities between the datasets. The experimental results are shown by calculating the typical cut and inference in an undirected graphical model and datasets.
image-processing-husseina-ozigi-otaru.ppt
The document discusses several functions of image processing, including increasing the contrast of planetary details and reducing noise. It describes techniques for increasing contrast such as unsharp masking and high-pass filters. It also discusses reducing noise through techniques like averaging multiple images, low-pass filters including mean, median and mode filters, and edge detection methods. Further, it covers topics such as texture analysis, texture segmentation, texture synthesis, and deriving shape from texture.
h.pdf
This document discusses image processing and histograms. It covers topics like image restoration, enhancement, and compression. It also discusses representing digital images with matrices and defines spatial and brightness resolution. Finally, it covers image histograms in depth, including defining histograms, properties, types, applications like thresholding and enhancement, and modifications like stretching, shrinking, and sliding histograms. As an example, it shows a histogram for a hypothetical 128x128 pixel image with 8 gray levels.
Animation
The document discusses computer animation techniques such as raster animation, color-table animation, tweening, and morphing. Raster animation involves copying frames from memory to the display very quickly to create the illusion of motion. Color-table animation uses a color lookup table to implement simple 2D animations. Tweening generates intermediate frames between key frames to make the movement between them appear smooth. Morphing transforms one image into another through a seamless transition by gradually warping and dissolving areas between matched images.
Similar to Remote Sensing:. Image Filtering (20)
The Effectiveness and Efficiency of Medical Images after Special Filtration f...
The Effectiveness and Efficiency of Medical Images after Special Filtration f...
COLOUR BASED IMAGE SEGMENTATION USING HYBRID KMEANS WITH WATERSHED SEGMENTATION
COLOUR BASED IMAGE SEGMENTATION USING HYBRID KMEANS WITH WATERSHED SEGMENTATION
Visual Quality for both Images and Display of Systems by Visual Enhancement u...
Visual Quality for both Images and Display of Systems by Visual Enhancement u...
Feature Extraction of an Image by Using Adaptive Filtering and Morpological S...
Feature Extraction of an Image by Using Adaptive Filtering and Morpological S...
International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)
The application of image enhancement in color and grayscale images
The application of image enhancement in color and grayscale images
Image Enhancement Techniques using OpenCV – Python.pptx
Image Enhancement Techniques using OpenCV – Python.pptx
Image Enhancement Techniques using OpenCV – Python.pptx
Image Enhancement Techniques using OpenCV – Python.pptx
Image Segmentation Using Pairwise Correlation Clustering
Image Segmentation Using Pairwise Correlation Clustering
More from Kamlesh Kumar
Migration Profile of Odisha with focus on Bhubaneswar
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.
Population Projection of Khordha District, ODISHA 2021-51
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
DEMOGRAPHIC PROFILE OF CONTINENTAL ODISHA
This document provides demographic information about continental Odisha, India. It includes maps showing the variation in total population, population growth, population density, sex ratio, household size, and literacy rate across districts. The highest population is in Cuttack district, while the lowest is in Debagarh. Malkangiri has the highest population growth at 21.6%, and Bargarh the lowest at 10.02%. Cuttack also has the highest population density. Rayagada has the highest sex ratio at 1048 females per 1000 males, while Nayagarh has the lowest at 916 females per 1000 males. Physical, social, and economic factors contribute to variations between districts.
Fashion
Fashion is a complex social and economic phenomenon that is difficult to define. It involves changes in styles and trends in areas like clothing, but also extends to other domains like music, art, and architecture. While fashion has ancient roots, it developed further with the rise of capitalism in medieval Europe, when changes in clothing styles became more rapid and cultivated for their own sake. Fashion operates within economic and social systems and is shaped by both commercial interests as well as creative forces. It serves as a means of social identification and communication of personal identity. The Indian fashion industry has grown significantly in recent decades due to factors like rising incomes, globalization, and the influence of Bollywood. Fashion in Delhi is strongly status-conscious and
COMMUNAL HARMONY: PUNJABI & TIBETANS IN DELHI
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 stone’s 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 it’s 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.
Remote Sensing: Overlay Analysis
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 analysis—feature 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: Change Detection
In the context of remote sensing, change detection refers 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 of remote sensing software. 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 as NDVI is constructed from each input image, and the difference is taken between the band ratios of different times. In the case of differencing NDVI images, 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: Accuracy Assesment
Accuracy assessment compares a classified image to ground truth data by creating random points and a confusion matrix. It determines overall accuracy and producer's and user's accuracies. Thresholding identifies incorrectly classified pixels statistically based on classification measures. An accuracy assessment was performed on a classified image using 100 random points to generate a confusion matrix and accuracy report showing the image was 75% accurate overall. Post-classification correction was then applied using neighborhood statistics to improve classification accuracy.
Remote Sensing: Interppolation
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. A Regularized method creates 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: Georeferencing
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 class—that resides in the desired map coordinate system. The process involves identifying a series of ground control points—known x,y coordinates—that 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: Resolution Merge
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.
The resolution merge or pan sharpening is the technique used to obtain high resolution multi-spectral images. The color information is collected from the coarse resolution satellite data and the intensity from the high resolution satellite 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.
Mountain ecosystem
Mountainous regions occupy one-fourth of the world’s 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.
Hydrological cycle
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
TEMPERATE ECOSYSTEM
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
Geosystem Approach: El Nino Southern Oscillation Effects
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.
Fire Safety Report, Kirori Mal College
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.
Disaster Prevention & Preparedness: Earthquake in Nepal
This report is detailed study of the field survey conducted in Kathmandu and Sindhupalchowk in Nepal on the earthquake disaster. The basic objective of this report is to get a tough insight in the use of field techniques regarding disaster management. Geography deals with human interaction with nature. This phenomenon can be better understood through field studies. Geography, being a field science, a geographical enquiry always need to be supplemented through well planned field surveys. Field is an essential component of geographic enquire. It is a basic procedure to understand the earth as a home of humankind. It is carried out through observation, sketching, measurement, interviews, etc. Field work takes the children out of the class and enables them to better understand the subject by visiting the areas practically giving an insight into the social, cultural and economic lives of the people. This also adds up the advantage of visiting the grass root levels of the society and ameliorative comprehension of the GLOCAL lives. It also has instilled various research making techniques in the budding geographers and shaping their thinking perspectives. The field surveys facilitate the collection of local level information that is not available through secondary sources.
In this report, various methodologies have been employed such as mapping, digitization, measurement and interviewing (questionnaires designing), the collection and gathering of information at the local level by conducting primary surveys and later, tabulating and computing them is an important part of the field survey.
Furthermore, the field study 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 and the modern technology of navigation, satellite connections, GIS software have been very helpful in the pre-field drills.
The report has the following headings and sub-headings:
Introduction
Study area
Transit: Table & Maps
Disaster scenario of Nepal
Earthquake: Timeline
Causes
Impact
Who is helping Nepal?
Reconstruction and Rehabilitation Status
Objectives & Methodology
Literature review
Data representation and Analysis
Findings and Suggestions
Conclusions
Disaster Prevention & Preparedness: Landslide in Nepal
This report is detailed study of the field survey conducted in Sindhupalchowk, Nepal. The basic objective of this report is to get a tough insight in the use of field techniques regarding disaster management. Geography deals with human interaction with nature. This phenomenon can be better understood through field studies. Geography, being a field science, a geographical enquiry always need to be supplemented through well planned field surveys. Field is an essential component of geographic enquire. It is a basic procedure to understand the earth as a home of humankind. It is carried out through observation, sketching, measurement, interviews, etc. Field work takes the children out of the class and enables them to better understand the subject by visiting the areas practically giving an insight into the social, cultural and economic lives of the people. This also adds up the advantage of visiting the grass root levels of the society and ameliorative comprehension of the GLOCAL lives. It also has instilled various research making techniques in the budding geographers and shaping their thinking perspectives. The field surveys facilitate the collection of local level information that is not available through secondary sources.
In this report, various methodologies have been employed such as mapping, digitization, measurement and interviewing (questionnaires designing), the collection and gathering of information at the local level by conducting primary surveys and later, tabulating and computing them is an important part of the field survey.
Furthermore, the field study 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 and the modern technology of navigation, satellite connections, GIS software have been very helpful in the pre-field drills.
Geographical Information System (GIS) Georeferencing and Digitization, Bihar ...
This work is an effort to share Geographical Information System: Georeferencing, digitization and map making steps through QGIS 2.0.1
Georeferencing
Digitization of Topographical sheet
Point
Line
Area
Bihar Map
District Headquarters
Railway of Bihar
District Boundaries
Thematic Maps (Literacy & Sex Ratio)
Digital image & linear stretching
This work is on Remote sensing consisting the following sub topics:
Scale
Satellite imagery
Annotation
Visual Interpretation
Aerial Photograph
Annotation
Visual Interpretation
Digital image and Linear Stretching
More from Kamlesh Kumar (20)
Migration Profile of Odisha with focus on Bhubaneswar
Migration Profile of Odisha with focus on Bhubaneswar
Population Projection of Khordha District, ODISHA 2021-51
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Geosystem Approach: El Nino Southern Oscillation Effects
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Disaster Prevention & Preparedness: Earthquake in Nepal
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Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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www.StokeNewingtonHistory.com
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Remote Sensing:. Image Filtering
- 2. 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. Filtering is used to enhance the appearance of an image. Spatial filters are designed to highlight or suppress specific features in an image based on their spatial 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 common filtering procedure involves 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. A low-pass filter is 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 filters do the opposite and serve to sharpen the appearance of fine detail in an image. Directional, or edge detection filters are 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.
- 3. 1. Run the app. Right click on the image list on the content tab to add an image. Go to Open Raster Layer. 2. Browse the file and load it. STEPS
- 4. 3. Go to Raster Options after the image has been loaded and change the layer arrangements. 4. Select the second image preferably. Go to Raster and select Spatial and Convolution tab respectively.
- 5. 5. In Convolution, Select the preferred Kernel and set the output folder for the image to be saved and then export it clicking OKAY.
- 6. 6. Load the recently saved convoluted image by clicking Ctrl+N and following the previous steps beside it. Notice the difference in the sharpness of the new image.
- 7. 6. Go to the Raster tab and 7. Select the first image and create a vector layer as instructed.
- 8. 8. Draw a line through the editor in the drawing tab.
- 9. 9. Select the first image and go to Spectral Profile in the Multispectral tab.
- 10. 10. Click the vector shapefile to show the spatial profile of the image. And click on the Show profile for selected vectors to reveal the graph. Repeat the same with the second image with the vector shapefile.
- 11. Repeat the same for the second image and keep changing it ex. 3X3 High pass, 3X3 edge enhanced, 5X5… and record the spatial profile graph for observation.
- 13. 3x3 LOW PASS FILTER
- 14. 3x3 HIGH PASS FILTER
- 17. 3x3 LAPLACIAN EDGE DETECTION FILTER
- 18. 3x3 EDGE DETECTION FILTER
- 20. 3x3 LEFT DIAGONAL FILTER
- 21. 3x3 RIGHT DIAGONAL FILTER
- 22. 5x5 LOW PASS FILTER
- 23. 5x5 HIGH PASS FILTER
- 26. 5x5 EDGE DETECTION FILTER
- 28. Signing off..