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
Radiometric corrections include correcting the data for sensor irregularities and unwanted sensor or atmospheric noise, and converting the data so they accurately represent the reflected or emitted radiation measured by the sensor.
Radiometric corrections include correcting the data for sensor irregularities and unwanted sensor or atmospheric noise, and converting the data so they accurately represent the reflected or emitted radiation measured by the sensor.
Content- Introduction to surveying and leveling
Object and Uses of Surveying, Fundamental Principles of Surveying, Introduction to conventional methods and equipment used for surveying and Leveling
Introduction to modern equipment’s used in surveying- EDM, Total Station, GIS,GPS, Remote sensing, planimeter.
Introduction to Topo sheets and use of maps.
The figure of the Earth can be modelled either by a cartesian plane, a sphere or an (oblate) ellipsoid, in decreasing order with respect to the approximation quality. The shortest path between two points on such a surface is called a geodesic. Studying geodesic problems on ellipsoids dates back to Newton. However, the majority of open-source GIS systems today use methods on the cartesian plane. The main advantages of those approaches are simplicity of implementation and performance. On the other hand, those approaches come with a handicap: accuracy.
We experimentally study the accuracy-performance trade-offs of various methods for distance computation (as well as similar geodesic problems such as azimuth and area computation). We test projections paired with cartesian computations, spherical-trigonometric computations and a number of ellipsoidal methods such as [Andoyer'65] and [Thomas'70] formulas, [Vincenty'75] iterative method, great elliptic arc's method, and [Karney'15] series approximation. We also show that some methods from the bibliography (e.g. [Tseng'15]) are neither faster nor more accurate compared to the above list of methods and thus become redundant. For our experiments we use the open source libraries Boost Geometry and GeographicLib.
Our results are of independent interest since we are not aware of a similar experimental study. More interestingly, they can be used as a reference for practitioners that want to use the most efficient method with respect to some given accuracy.
Geodesic computations (such as distance computations) apart from being a fundamental problem in computational geometry and geography/geodesy are also building blocks for many higher level algorithms such as k-nearest neighbour problems, line interpolation, densification of geometries, area and buffer, to name a few.
# References
* Some experimental results can be found here: https://github.com/vissarion/geometry/wiki/Accuracy-and-performance-of-geographic-algorithms
* A related talk (with some graphs on performance and accuracy) can be found here https://fosdem.org/2019/schedule/event/geo_boostgeometry
* The source code of most of the algorithms of the study is in Boost Geometry: https://github.com/boostorg/geometry but we contain to our study GeographicLib https://geographiclib.sourceforge.io
One of most important topics in ArcGIS and GIS, is coordinate system, the slides will cover this topic in order to understand the difference between various coordinate systems.
Land Cover maps supply information about the physical material at the surface of the Earth (i.e. grass, trees, bare ground, asphalt, water, etc.). Usually they are 2D representations so to present variability of land covers about latitude and longitude or other type of earth coordinates. Possibility to link this variability to the terrain elevation is very useful because it permits to investigate probable correlations between the type of physical material at the surface and the relief. This paper is aimed to describe the approach to be followed to obtain 3D visualizations of land cover maps in GIS (Geographic Information System) environment. Particularly Corine Land Cover vector files concerning Campania Region (Italy) are considered: transformed raster files are overlapped to DEM (Digital Elevation Model) with adequate resolution and 3D visualizations of them are obtained using GIS tool. The resulting models are discussed in terms of their possible use to support scientific studies on Campania Land Cover.
Calculation of the Curvature Expected in Photographs of a Sphere's HorizonJames Smith
A formula is derived for the curvature of the horizon's image in photos of a sphere of radius R , taken by a camera with horizontal view angle alpha from height h above the sphere's surface. The formula is validated by means of an interactive GeoGebra construction: a key angle calculated from the formula derived here is compared to the angle actually present in the construction. Using the validated formula, the amount of curvature expected to be present in a photo of the Earth's horizon from an altitude of 3 m is calculated. The result is an order of magnitude smaller than typical degrees of barrel distortion present in consumers' digital cameras. Therefore, claims that "flat horizons in photos of waterscapes prove that the Earth is flat" are untenable.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Content- Introduction to surveying and leveling
Object and Uses of Surveying, Fundamental Principles of Surveying, Introduction to conventional methods and equipment used for surveying and Leveling
Introduction to modern equipment’s used in surveying- EDM, Total Station, GIS,GPS, Remote sensing, planimeter.
Introduction to Topo sheets and use of maps.
The figure of the Earth can be modelled either by a cartesian plane, a sphere or an (oblate) ellipsoid, in decreasing order with respect to the approximation quality. The shortest path between two points on such a surface is called a geodesic. Studying geodesic problems on ellipsoids dates back to Newton. However, the majority of open-source GIS systems today use methods on the cartesian plane. The main advantages of those approaches are simplicity of implementation and performance. On the other hand, those approaches come with a handicap: accuracy.
We experimentally study the accuracy-performance trade-offs of various methods for distance computation (as well as similar geodesic problems such as azimuth and area computation). We test projections paired with cartesian computations, spherical-trigonometric computations and a number of ellipsoidal methods such as [Andoyer'65] and [Thomas'70] formulas, [Vincenty'75] iterative method, great elliptic arc's method, and [Karney'15] series approximation. We also show that some methods from the bibliography (e.g. [Tseng'15]) are neither faster nor more accurate compared to the above list of methods and thus become redundant. For our experiments we use the open source libraries Boost Geometry and GeographicLib.
Our results are of independent interest since we are not aware of a similar experimental study. More interestingly, they can be used as a reference for practitioners that want to use the most efficient method with respect to some given accuracy.
Geodesic computations (such as distance computations) apart from being a fundamental problem in computational geometry and geography/geodesy are also building blocks for many higher level algorithms such as k-nearest neighbour problems, line interpolation, densification of geometries, area and buffer, to name a few.
# References
* Some experimental results can be found here: https://github.com/vissarion/geometry/wiki/Accuracy-and-performance-of-geographic-algorithms
* A related talk (with some graphs on performance and accuracy) can be found here https://fosdem.org/2019/schedule/event/geo_boostgeometry
* The source code of most of the algorithms of the study is in Boost Geometry: https://github.com/boostorg/geometry but we contain to our study GeographicLib https://geographiclib.sourceforge.io
One of most important topics in ArcGIS and GIS, is coordinate system, the slides will cover this topic in order to understand the difference between various coordinate systems.
Land Cover maps supply information about the physical material at the surface of the Earth (i.e. grass, trees, bare ground, asphalt, water, etc.). Usually they are 2D representations so to present variability of land covers about latitude and longitude or other type of earth coordinates. Possibility to link this variability to the terrain elevation is very useful because it permits to investigate probable correlations between the type of physical material at the surface and the relief. This paper is aimed to describe the approach to be followed to obtain 3D visualizations of land cover maps in GIS (Geographic Information System) environment. Particularly Corine Land Cover vector files concerning Campania Region (Italy) are considered: transformed raster files are overlapped to DEM (Digital Elevation Model) with adequate resolution and 3D visualizations of them are obtained using GIS tool. The resulting models are discussed in terms of their possible use to support scientific studies on Campania Land Cover.
Calculation of the Curvature Expected in Photographs of a Sphere's HorizonJames Smith
A formula is derived for the curvature of the horizon's image in photos of a sphere of radius R , taken by a camera with horizontal view angle alpha from height h above the sphere's surface. The formula is validated by means of an interactive GeoGebra construction: a key angle calculated from the formula derived here is compared to the angle actually present in the construction. Using the validated formula, the amount of curvature expected to be present in a photo of the Earth's horizon from an altitude of 3 m is calculated. The result is an order of magnitude smaller than typical degrees of barrel distortion present in consumers' digital cameras. Therefore, claims that "flat horizons in photos of waterscapes prove that the Earth is flat" are untenable.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Definition of Surveying
Objects of Surveying
Uses of Surveying
Primary Divisions of Surveying
Principles of Surveying
List of Classification of Surveying
Definitions : Plan and Map, scales :Plain Scale and Diagonal Scale,
Migration Profile of Odisha with focus on BhubaneswarKamlesh Kumar
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 2021-51Kamlesh Kumar
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 ODISHAKamlesh Kumar
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.
‘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. We’ve been conditioned to believe that buying a garment and wearing it once is justifiable. It’s 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.
COMMUNAL HARMONY: PUNJABI & TIBETANS IN DELHIKamlesh Kumar
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.
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.
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.
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)
User’s accuracy(commission error): User’s accuracy is regarded as the probability that a pixel classified on map actually represents that
class on the ground or reference data
Producer’s accuracy(omission error): Producer’s accuracy represents the probability of reference pixel being correctly classified
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 classification is 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 classification is 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 how similar 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.
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.
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.
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.
Remote Sensing: Normalized Difference Vegetation Index (NDVI)Kamlesh Kumar
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.
Remote Sensing: Principal Component AnalysisKamlesh Kumar
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 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.
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.
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.
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.
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
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 EffectsKamlesh Kumar
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.
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.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
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Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
1. REMOTE SENSING
SCALE
The shape of the earth is geoid (three-dimensional) and a globe represents it the best way. While
a map is a simplified depiction of whole or part of the earth on a piece of paper (two-dimensional).
To show the 3-D earth on the 2-D surface we use a system of map projections. As it is impossible
to represent all the features of the earth’s surface in their actual size and form, a map is drawn at a
reduced scale.
Systems of Measurements
There are two different systems of measurement of the distances used in different countries of the
world.
A. Metric System of measurement (in use in India)
B. English System of measurement
Metric System of Measurement
1 km = 1000 Metres
1 Metre = 100 Centimetres
1 Centimetre = 10 Millimetres
English System of Measurement
1 Mile = 8 Furlongs
1 Furlong = 220 Yards
1 Yard = 3 feet
1 Foot = 12 Inches
Scale: It’s the first step in map making. It shows the ratio between the distances of two points on
the map, image or photograph and the actual distance between the same two points on the ground.
The scale of a map sets limits of information contents and the degree of reality with which it can
be delineated on the map.
Photograph scale: It is the ratio between the distance on the aerial photograph or map and the
actual distance on the ground or the land surface. There are two types of scale:
a) Large Scale Photo: A map or photo which depicts a small territory is referred to as a large-
scale map. This is because the area of land being represented by the map has been scaled
down or in other words, the scale is larger. It only shows a small area, but in great detail.
b) Small Scale Photo: A map or photo depicting a large area, such as an entire country is
considered a small-scale map. In order to show the entire country, the map must be scaled
down until it is much smaller. A small-scale photograph similarly shows more territory,
but it is less detailed.
There are at least three methods of representation of scale:
1. Statement of Scale
2. Representative Fraction (R. F.)
3. Graphical Scale
Statement of Scale: It is the simplest of the three methods. Indicated in the form of a written
statement. For example, “1 cm represents 10 km” means that on that map 1 cm equals 10 km on
the ground. It may also be expressed in any other system of measurement i.e. 1 inch represents 10
miles
Limitations:
2. • The people who are familiar with one system may not understand the statement of scale in
another system of measurement.
• If the map is reduced or enlarged, the scale will become superfluous and a new scale is to
be worked out.
Graphical or Bar Scale: This scale shows map distances and the corresponding ground distances
using a line bar with primary and secondary divisions marked on it. Unlike the statement of the
scale method, the graphical scale stands valid even when the map is reduced or enlarged.
Representative Fraction (R. F.): The most versatile method representing the relationship between
the map distance and the corresponding ground distance in units of length. It is generally shown
in fraction because it shows how much the real world is reduced to fit on the map. For example, a
fraction of 1: 25,000 shows that one unit of length on the map represents 25,000 of the same units
on the ground. It may, however, be noted that while converting the fraction of units into Metric
or English systems, units in centimeter or inch are normally used by convention. This quality of
expressing scale in units in R. F. makes it a universally acceptable and usable method.
Relationship Between Photo Distance and Map Distance
Photo scale: Map scale = Photo distance: Map distance
Focal Length (f): Flying Height(H) = Photo distance (PD): Ground distance (GD)
Formulae
𝑷𝒉𝒐𝒕𝒐 𝑺𝒄𝒂𝒍𝒆 =
𝒇𝒐𝒄𝒂𝒍 𝒍𝒆𝒏𝒈𝒕𝒉
𝑯𝒆𝒊𝒈𝒉𝒕 𝒐𝒇 𝒕𝒉𝒆 𝒂𝒊𝒓𝒄𝒓𝒂𝒇𝒕
[𝑷. 𝑺. =
𝒇
𝑯
]
𝑷𝒉𝒐𝒕𝒐 𝑺𝒄𝒂𝒍𝒆
=
𝒇𝒐𝒄𝒂𝒍 𝒍𝒆𝒏𝒈𝒕𝒉
𝑯𝒆𝒊𝒈𝒉𝒕 𝒐𝒇 𝒕𝒉𝒆 𝒂𝒊𝒓𝒄𝒓𝒂𝒇𝒕 − 𝒉𝒆𝒊𝒈𝒉𝒕 𝒐𝒇 𝒕𝒆𝒓𝒓𝒂𝒊𝒏
[𝑷. 𝑺. =
𝒇
𝑯 − 𝒉
]
𝑷𝒉𝒐𝒕𝒐 𝑺𝒄𝒂𝒍𝒆 =
𝑷𝒉𝒐𝒕𝒐 𝑫𝒊𝒔𝒕𝒂𝒏𝒄𝒆
𝑮𝒓𝒐𝒖𝒏𝒅 𝑫𝒊𝒔𝒕𝒂𝒏𝒄𝒆
𝑴𝒂𝒑 𝑺𝒄𝒂𝒍𝒆 =
𝑴𝒂𝒑 𝑫𝒊𝒔𝒕𝒂𝒏𝒄𝒆
𝑮𝒓𝒐𝒖𝒏𝒅 𝑫𝒊𝒔𝒕𝒂𝒏𝒄𝒆
𝑮𝒓𝒐𝒖𝒏𝒅 𝑫𝒊𝒔𝒕𝒂𝒏𝒄𝒆 =
𝑷𝒉𝒐𝒕𝒐 𝑫𝒊𝒔𝒕𝒂𝒏𝒄𝒆
𝑷𝒉𝒐𝒕𝒐 𝑺𝒄𝒂𝒍𝒆
3. SAMPLE QUESTION & ANSWERS
Question 1: If the focal length of the camera is 151.8mm and the aircraft is at 2000meters, with
the given terrain height of 500m. Find the photo scale of the aerial photograph.
Solution:
Given: Focal length = 151.8 mm.~ 0.1518 m. (∵ 1m=1000mm.)
Height of the aircraft =2000m
Terrain height =500m
To find: Photo scale
𝑃. 𝑆. =
𝑓
𝐻 − ℎ
=
0.1518
2000 − 500
=
0.1518
1500
=
1
9881.42
~
1
10,000
∴ P.S.= 1:10,000
Question 2. Find the scale of the aerial photograph, if focal length of the camera is 151.8mm and
height of the flying aircraft 600 ft.
Solution:
Given: Focal length = 151.8mm ~ 0.1518 m. (∵ 1m=1000mm.)
Height of the aircraft (H)= 600ft. ~ 181.81 (∵ 1m=3.3 ft.)
To Find: Photo scale (P.S.)
𝑃𝑆. =
𝑓𝑜𝑐𝑎𝑙 𝑙𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑡ℎ𝑒 𝑐𝑎𝑚𝑒𝑟𝑎
𝐹𝑙𝑦𝑖𝑛𝑔 ℎ𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑡ℎ𝑒 𝑎𝑖𝑟𝑐𝑟𝑎𝑓𝑡
[𝑃. 𝑆. =
𝑓
𝐻
]
=
0.1518
181.81
=
1
1197.7
∴ P.S.= 1:1200
4. QUESTIONS WITH RF 1:25,000
Question 3. On a topographical map with RF= 1:25,000, the distance between two points was found to be
7 cms. and the distance between the same points on an aerial photograph was found to be 1cms. Calculate
scale of aerial photograph.
Solution:
Given: Map scale = 1: 25,000
Map distance = 7cm
Photo distance = 1cm
To find: Photo scale
𝑃ℎ𝑜𝑡𝑜 𝑠𝑐𝑎𝑙𝑒 =
𝑃ℎ𝑜𝑡𝑜 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒
𝐺𝑟𝑜𝑢𝑛𝑑 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒
Map scale =
Map Distance
Ground Distance
⇒ 𝐺𝑟𝑜𝑢𝑛𝑑 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 =
Map Distance
Map Scale
=
7
1
25000
= 7 ×
25000
1
G.D. = 1,75,000 cm.
𝑃ℎ𝑜𝑡𝑜 𝑠𝑐𝑎𝑙𝑒 =
1
175000
∴ P.S.= 1:1,75,000
Question 4. On a topographical map with RF= 1:25,000, the distance between two points was found to be
5 cms. and the distance between the same points on an aerial photograph was found to be 2cms. Calculate
scale of aerial photograph.
Solution:
Given: Map scale = 1: 25,000
Map distance = 5cm
Photo distance = 2cm
To find: Photo scale
𝑃ℎ𝑜𝑡𝑜 𝑠𝑐𝑎𝑙𝑒 =
𝑃ℎ𝑜𝑡𝑜 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒
𝐺𝑟𝑜𝑢𝑛𝑑 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒
Map scale =
Map Distance
Ground Distance
⇒ 𝐺𝑟𝑜𝑢𝑛𝑑 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 =
Map Distance
Map Scale
=
5
1
25000
= 5 ×
25000
1
G.D. = 1,25,000 cm.
𝑃ℎ𝑜𝑡𝑜 𝑠𝑐𝑎𝑙𝑒 =
2
125000
⇒
1
62500
5. ∴ P.S.= 1:62,500
Question 5. The scale of the aerial photograph is 1: 25,000, ground distance is 3.5 km. Find the photo
distance.
Solution:
Given: Photo scale (P.S.) = 1:25,000
Ground distance (G.D.) = 3.5 km~ 3,50,000 cms. (∵ 1km. = 1000 m.= 1,00,000 cm.)
To find: Photo distance (P.D.)
𝑃ℎ𝑜𝑡𝑜 𝑠𝑐𝑎𝑙𝑒 =
𝑃ℎ𝑜𝑡𝑜 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒
𝐺𝑟𝑜𝑢𝑛𝑑 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒
⇒ 𝑃ℎ𝑜𝑡𝑜 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = 𝑃ℎ𝑜𝑡𝑜 𝑆𝑐𝑎𝑙𝑒 × 𝐺𝑟𝑜𝑢𝑛𝑑 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒
=
1
25000
× 350000
∴ P.S. = 14cms. ~ 0.14m.
QUESTIONS WITH RF 1:50,000
Question 6. On a topographical map with RF= 1:50,000, the distance between two points was found to be
5 cms. and the distance between the same points on an aerial photograph was found to be 2cms. Calculate
scale of aerial photograph.
Solution:
Given: Map scale = 1: 50,000
Map distance = 5cm
Photo distance = 2cm
To find: Photo scale
𝑃ℎ𝑜𝑡𝑜 𝑠𝑐𝑎𝑙𝑒 =
𝑃ℎ𝑜𝑡𝑜 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒
𝐺𝑟𝑜𝑢𝑛𝑑 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒
Map scale =
Map Distance
Ground Distance
⇒ 𝐺𝑟𝑜𝑢𝑛𝑑 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 =
Map Distance
Map Scale
=
5
1
50000
= 5 ×
50000
1
G.D. = 2,50,000 cm.
𝑃ℎ𝑜𝑡𝑜 𝑠𝑐𝑎𝑙𝑒 =
2
250000
⇒
1
125000
∴ P.S.= 1:1,25,000
6. Question 7. The scale of the aerial photograph is 1: 50,000, ground distance is 3.5 km. Find the photo
distance.
Solution:
Given: Photo scale (P.S.) = 1:50,000
Ground distance (G.D.) = 3.5 km~ 3,50,000 cms. (∵ 1km. = 1000 m.= 1,00,000 cm.)
To find: Photo distance (P.D.)
𝑃ℎ𝑜𝑡𝑜 𝑠𝑐𝑎𝑙𝑒 =
𝑃ℎ𝑜𝑡𝑜 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒
𝐺𝑟𝑜𝑢𝑛𝑑 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒
⇒ 𝑃ℎ𝑜𝑡𝑜 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = 𝑃ℎ𝑜𝑡𝑜 𝑆𝑐𝑎𝑙𝑒 × 𝐺𝑟𝑜𝑢𝑛𝑑 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒
=
1
50000
× 350000
∴ P.S. = 7 cms. ~ 0.07m.
Question 8. On a topographical map with RF= 1:50,000, the distance between two points was found to be
7 cms. and the distance between the same points on an aerial photograph was found to be 1cms. Calculate
scale of aerial photograph.
Solution:
Given: Map scale = 1: 50,000
Map distance = 7cm
Photo distance = 1cm
To find: Photo scale
𝑃ℎ𝑜𝑡𝑜 𝑠𝑐𝑎𝑙𝑒 =
𝑃ℎ𝑜𝑡𝑜 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒
𝐺𝑟𝑜𝑢𝑛𝑑 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒
Map scale =
Map Distance
Ground Distance
⇒ 𝐺𝑟𝑜𝑢𝑛𝑑 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 =
Map Distance
Map Scale
=
7
1
50000
= 7 ×
50000
1
G.D. = 3,50,000 cm.
𝑃ℎ𝑜𝑡𝑜 𝑠𝑐𝑎𝑙𝑒 =
1
350000
∴ P.S.= 1:3,50,000
9. ANNOTATIOOM OF SATELLITE IMAGERY
The data recorded/captured by the satellite sensors is used for information derivation related to the
forms, and patterns of the area, objects and phenomena of the earth’s surface. The derivation of
both qualitative and quantitative properties of the features is carried out either through visual
interpretation methods or digital image processing techniques. The visual interpretation involves
observation of the images of objects for their identification. On the other hand, digital images
require a combination of hardware and software to extract the information.
Annotation is a note which provides background information and baseline data of the time at
which the imagery was captured. It is used foe explanatory purpose and to indicate items or areas
of spatial importance.
Annotation Table
SL. No. Parameter Description
1 Satellite name IRS-1B
2 Country India
3 Date of Acquisition 14 October 1996
4 Date of Processing 24 September 1997
5 Date of Printing 29 September 1997
6 Projection POL
7 Sampling Method CC (cubic consultation)
8 Sensor LISS-1
9 Product type SD (Standard)
10 Bands B-2 (Green), 3 (Red), 4(Near Infrared)
11 Path number PO28
12 Row number R46
13 Format centre FN 30°25’N & 78°53’E
14 Latitudinal Extent 29°30’N & 34°00’N
15 Longitudinal Extent 78°00’E & 79°30’E
16 Generation Agency ISRO-NRSA/NRSC
17 Exposure Time 5:48 AM
18 Colour Scale YES
19 Grain Setting 63
20 Station XT
21 Negative number XT:85705
22 Project Type NA
23 Colour Scheme LGLUT (Linear OpenGL Utility Toolkit)
24 Imagery number 026138
10. VISUAL INTERPRETATION OF SATELLITE IMAGERY
Elements of Visual Interpretation
In our day-to-day life we use the form, size, location of the objects and their relationships with the
surrounding objects to identify them. These characteristics of objects are termed as elements of visual
interpretation.
Tone or Colour: The reflected amount of the EMR energy that is received and recorded by the sensor in
tones of grey, or hues of colour in black and white, and colour images depending upon the orientation of
incoming radiations, surface properties and the composition of the objects. Smooth and dry object surfaces
reflect more energy in comparison to the rough and moist surfaces. For example, healthy vegetation reflects
strongly in the infrared region because of the multiple-layered leaf structure and appears in a light tone or
bright red colour in standard false colour composite and the scrubs appear in greyish red colour). Similarly,
a fresh water body absorbs much of the radiations received by it and appears in dark tone or black colour,
whereas the turbid water body appears in light tone or light bluish colour.
Texture: The texture refers to the minor variations in tones of grey or hues of colour. These variations are
primarily caused by an aggregation of smaller unit features such as high density (fine texture) and low
density (coarse texture) of features. The textural differences in the images of certain objects vary from
smooth to coarse textures.
Size: The size of an object as observed from the resolution or scale of an image is another important
characteristic of the features. It helps in distinctively identifying the similar features.
Shape: The general form and configuration or an outline of a feature provides important clues in the
interpretation of remote sensing images. The shape of some of the objects is so distinctive that make them
easy to identify. For example, religious places, a railway line can be readily distinguished from a road due
to its long continuous linearity in shape with gradual change in its course.
Shadow: Shadow of an object is a function of the sun’s radiance angle and the height of the object itself.
Shadow also adversely affects the identifiability of the objects by producing a dark tone, which dominates
the original tone or colour of the features lying under the shadow of tall buildings. The shadow as an element
of image interpretation is of less use in
satellite images. However, it serves a useful purpose in large-scale aerial photography.
Site: The position on the landscape with reference to direction as well as the latitudinal and longitudinal
values enabling to locate the exact location of the feature.
Pattern: The spatial arrangements of natural and man–made features show repetitions of form and
relationship. The arrangements can easily be identified from the images through the utilization of the pattern
they form. For example, planned residential areas with the same size and layout in an urban area can easily
be identified; same goes for orchards and plantations, drainage system etc.
Resolution: Photographic resolution is the maximum number of line-pairs per mm that can be distinguished
on a film when taken from a resolution target. It describes the distance between distinguishable patterns or
objects in an image that can be separated from each other. Resolution depends mainly on granularity.
Stereoscopic Resolution: Distinguishable pattern of the shadows formed by any feature on the surface
with a particular height.
11. COMPARISON
The Codrington port is located at 17°38′N 61°50′W in the Caribbean sea. As evident the ‘after’
image of the port, Barbuda gives a stark contrast to the ‘before’ image. The before image provides
a sunny image of the area, the sinuous and well-connected roads form trellis and dendritic pattern
as a river basin, the settlements are spread across the whole area and vegetation has grown in close
proximity. The port is in the central western part on the shore, a strip of vegetation cover parallels
the coastline, the concentration of settlement and vegetation increases as one moves towards the
east. In the ‘after image’ the area has been ransacked by the Irma cyclone, most of the road network
and settlements have been destroyed and all that which is left is just the debris. The port has been
ransacked, most part of the image is not clear due to the presence of clouds/cyclonic wind.
CODRINGTON PORT, BARBUDA
LOCATION: 17°38′N 61°50′W
Before After
13. VISUAL INTERPRETATION
Sl.
No
.
ROADS SETTLEMENT WATER PORT
1 Image
2 Location Spread all
over the
area except
the Western
part.
Spread all
over the area
excluding the
Western part
with increase
in
concentration
as one moves
towards East
Spread all
over the area
except the
water covered
western part
Western part:
broader
towards NW
direction and
tapered
towards the
SW part
West central
part of the
image along
the coast
3 Recognitio
n
Form a
elongated
network
with well
connectivity
Proximity to
the
settlements,
lush and deep
green colour.
Polygonal
with different
shades, spread
all over the
area except
the ocean.
Spread over a
large area
with
dominant
bluish colour.
Located at the
shore.
4 Shape Elongated
and curvy
Spotty,
Circular
Patches
Polygonal as
well as
irregular
Irregularly
enclosed
Rectangular
and elongated
5 Size Elongated
with
variable
width.
Medium-
Small size as
discerned
from the tree
crowns.
Small
compared to
one large one
to the north-
eastern of the
port
Comparativel
y larger than
all other
features
Longer than
any other
settlements
extended into
the ocean.
6 Tone Mostly grey
and a little
of white
tone
Dark Green Varying from
Grey to cream
Deep Blue Cream
7 Shadow No Yes Yes No Yes, on the
ocean surface
8 Pattern Dendritic Heterogeneou
s
Heterogeneou
s
Homogeneou
s
Homogeneous
9 Texture Rugged Rough Rough blocks Smooth Smooth on the
roof but rough
VEGETATION
14. at the
outstretches
10 Resolution High High High Medium Medium
11 Stereoscop
ic
Appearanc
e
No Yes Yes No Yes
12 Feature
Remark
Road:
Because
elongated,
curvy and
connected
Vegetation:
because are
of similar
shape and
deep green
colour
Settlement:
Spread all
over the area
with rough
polygonal
roof as seen
from top.
Water body:
includes
Shore and
port
Port: Location
along the water
body
16. VISUAL INTERPRETATION
Sl.
No
.
ROADS
VEGETATION SETTLEMENT
CLOUDS/
CYCLONI
C WIND
PORT
1 Image
2 Location Spread all
over the
area
except the
Western
part.
Spread all
over the area
excluding the
Western part
with increase
in
concentration
as one moves
towards East
Spread all
over the area
except the
water covered
western part
South-
Western,
South-
Central
and
Eastern
part
West central
part of the
image along
the coast
3 Recognitio
n
Form a
elongated
network
with well
connectivit
y
Proximity to
the
settlements,
lush and deep
green colour.
Polygonal with
different
shades, spread
all over the
area except the
ocean.
Appears
smoky/
feather like
Located at
the shore.
4 Shape Elongated
and curvy
Spotty,
Circular
Patches
Polygonal as
well as
irregular
Irreguular Rectangular
and
elongated
5 Size Elongated
with
variable
width.
Medium-
Small size as
discerned
from the tree
crowns.
Small
compared to
one large one
to the north-
eastern of the
port
Comparativ
ely larger
than all
other
features
(Covering
almost half
of the area)
Longer than
any other
settlements
extended into
the ocean.
6 Tone Mostly
grey and a
little of
white tone
Dark Green Varying from
Grey to cream
Light ash Cream
7 Shadow No No Yes No Yes, on the
ocean
surface
8 Pattern Dendritic Heterogeneou
s
Heterogeneous Homogeneo
us
Homogeneou
s
17. 9 Texture Rugged Rough Rough blocks Smooth Smooth on
the roof but
rough at the
outstretches
10 Resolution High High High Medium Medium
11 Stereoscopi
c
Appearanc
e
No Yes Yes No Yes
12 Feature
Remark
Road:
Because
elongated,
curvy and
connected
Vegetation:
because are
of similar
shape and
deep green
colour
Settlement:
Spread all over
the area with
rough
polygonal roof
as seen from
top.
Cloud/
cyclonic
wind: due to
the smoky
and feather
like
appearance
Port:
Location
along the
water body
18. AERIAL PHOTOGRAPH
The photographs taken of ground from an elevated position from an aircraft or any other flying
object using a precision camera are termed aerial photographs. Aerial photographs are used in
topographical mapping and interpretation.
History of Aerial Photography in India
Aerial photography in India goes back to 1920 when large-scale aerial photographs of Agra city
were obtained. Subsequently, several similar surveys were carried out and advanced methods of
mapping from aerial photographs were used. Today, aerial photography in India is carried out
under the supervision of the Directorate of Air Survey (Survey of India) New Delhi. Three flying
agencies, i.e. Indian Air Force, Air Survey Company, Kolkata and
National Remote Sensing Agency (NRSA), Hyderabad have been officially authorized to take
aerial photographs in India.
Difference between Maps and Aerial Photographs
Aerial Photograph Map
1 It is a central Projection. It is an orthogonal Projection.
2 An aerial photograph is geometrically
incorrect. The distortion in the geometry
minimum at the centre and
increases towards the edges of the
photographs.
A map is a geometrically correct representation
of the part of the earth is projected.
3 The scale of the photograph is not uniform. The scale of the map is uniform throughout the
map extent.
4 Enlargement/reduction does not change the
contents of the photographs and can easily
be carried out.
Enlargement/reduction of the maps involves
redrawing it afresh.
5 Aerial photography holds good for
inaccessible and inhospitable areas.
The mapping of inaccessible and inhospitable
areas
is very difficult and
sometimes it becomes impossible.
19.
20. ANNOTATION OF AERIAL PHOTOGRAPH
Annotations are preliminary information about an aerial photograph. The aim is to improve
information content of the image description. Annotations can add information that can't be easily
included in descriptions. These generally highlight features, details, or points of interest within an
imagery.
Need of Annotation:
• Provides crucial prerequisite information on the imagery.
• Identifies places/objects/locations in panoramic/aerial photographs.
• Highlighting important hard-to-notice details of the image.
• Identify the various elements of a composition of physical earth.
• Transcribing inscriptions, signs, or words in the image for better understanding.
SL. No. Parameter Description
1 Size of the Photograph 23 x 23 cm
2 Photograph Number 1296 − 𝐵
𝐴3 − 3
3 Negative Number 0700
4 Secret Yes
5 Kodak Safety Film No
6 Instrument Number UAg479
7 Focal length 151.80
8 Watch 1:55:48 pm
9 Resolution 3000m
10 Fiducial Marks 04
21. VISUAL INTERPRETATION OF AERIAL PHOTOGRAPHY
Image Interpretation: An act of identifying the images of the objects and judging their relative
significance. The principles of image interpretation are applied to obtain qualitative information
from the aerial photographs such as land use/land cover, topographical forms, soil types, etc.
Sl. No. ROADS VEGETATION SETTLEMENT
1 Image
2 Location A major road passes through
the centre of the section and
smaller roads are connected
to it
Located along the roads and
near the settlements.
Located along the roads.
3 Recognition Form an elongated network
with well connectivity.
Proximity to the settlements
and roads, lush and deep
green colour.
Polygonal with different shades,
sizes located along the road.
4 Shape Elongated, sinuous and
curvy
Spotty, Amorphous Patches Polygonal as well as irregular
5 Size Elongated with variable
width.
Medium- Small size as
discerned from the tree
crowns.
Varying from small to long
rectangular to large cubicle.
6 Tone Light ash/dusty (Middle
shade)
Dark Green Mostly dark grey
7 Shadow No Yes Yes
8 Pattern Trellis Heterogeneous Heterogeneous
9 Texture Rugged Rough Rough blocks
10 Resolution High High High
11 Stereoscopic
Appearance
No Yes Yes
12 Feature Remark Road: Because elongated,
curvy and connected
Vegetation: because are of
similar shape and deep green
colour
Settlement: Spread along the road
with rough polygonal roof as seen
from top.
22. DIGITAL IMAGE & LINEAR STRETCHING
The electromagnetic energy may be detected either photographically or electronically. The
photographic process uses light sensitive film to detect and record energy variations. On
the other hand, a scanning device obtains images in digital mode. It is important to distinguish
between the terms images and photographs. An image refers to pictorial representation, regardless
of what regions of energy have been used to detect and record it. A photograph refers specifically
to images that have been recorded on photographic film.
Hence, it can be said that all photographs are images, but all images are not photographs.
Based upon the mechanism used in detecting and recording, the remotely sensed data products
may be broadly classified into two types:
• Photographic Images
• Digital Images
Photographic Images: Photographs are acquired in the optical regions of electromagnetic
spectrum, i.e. 0.3 – 0.9 µm. In aerial photography black and white film is normally used.
Photographs may be enlarged to any extent without losing information contents or the contrast.
Digital Images: A digital image consists of discrete picture elements called pixels. Pixels are the
smallest size of picture element on an image. Each pixel in an image represents an intensity value
and an address in form of rows and columns in two-dimensional image space. A digital number
(DN) represents the average intensity value of a pixel. It is determined by the electromagnetic
energy received by the sensor and the intensity levels used to describe its range. The details in the
images of the features are governed by the size of the pixel. However, zooming of the digital image
beyond certain extent produces loss of information and the appearance of pixels only. Basically,
23. Digital image are an array of digital numbers (DN) arranged in rows and columns, having the
property of an intensity value and their locations.
Formula:
Perform the linear stretch of the following 5X5 matrix of an 8-bit system
8-Bit system matrix (5X5)
31 38 45 49 89
73 75 85 89 100
95 95 93 89 110
110 111 93 89 150
110 111 93 89 150
DN (Maximum)=150
DN (Minimum)=31
DN (Input) Frequency
31 1
38 1
45 1
49 1
73 1
75 1
85 1
89 5
93 3
95 2
100 1
110 3
111 2
150 2
DN (Output) = [
𝐷𝑁 (𝐼𝑛𝑝𝑢𝑡) − 𝐷𝑁(𝑀𝑖𝑛𝑖𝑚𝑢𝑚)
𝐷𝑁 (𝑀𝑎𝑥𝑖𝑚𝑢𝑚) − 𝐷𝑁 (𝑀𝑖𝑛𝑖𝑚𝑢𝑚)
] × 255