Topology refers to the spatial relationships between GIS features or objects. It is important for network routing and maintaining data quality and integrity when features are shared across layers. Geodatabases provide the strongest topological functionality, storing relationships in topology rules and feature classes. The node-arc data model represents the most common topology, with nodes at intersections and endpoints and arcs between nodes forming polygons. Topology allows for analysis without coordinate data but establishing topology is time-consuming.
The document provides an overview of change detection techniques in remote sensing. It defines change detection as the process of identifying differences in objects or phenomena by observing them at different times using remote sensing images. The main goals of change detection are to detect land use and land cover changes over time and understand how the Earth's surface is changing. Several change detection techniques are described, including visual analysis, image differencing, image ratioing, and post-classification comparison. Practical examples of detecting changes in lakes and forests over time are also presented.
Landsat was a joint NASA/USGS satellite program designed to systematically acquire global land surface images. Landsat 1 was launched in 1972 as the first satellite dedicated to observing Earth's land areas. Subsequent Landsat satellites carried improved sensors with higher spatial, spectral, and radiometric resolutions. Landsat provides repetitive coverage of the entire global land mass with images useful for mapping and monitoring land use change over time.
Change detection analysis in land use / land cover of Pune city using remotel...Nitin Mundhe
Lecture delivered in the National Conference entitled “Monitoring Degraded Lands” jointly organized by Agasti Arts, Commerce and Dadasaheb Rupwate Science
College, Akole and Maharashtra Bhugolshastra Parishad Pune to be held on 4 to 6 February 2014.
This document discusses the use of geographic information systems (GIS) in water resource management and assessment. It provides examples of GIS applications in watershed management, groundwater assessment, flood management, and water quality studies. It then describes a case study that developed a GIS-based decision support system to assess watershed runoff in the Kk3 Macro Watershed in India. Key steps included delineating sub-watersheds, creating soil and land use maps, determining hydrologic response units, computing runoff, and generating thematic runoff maps. The system allows users to update rainfall data and evaluate variations in spatial runoff distribution over time.
Trend analysis and time Series Analysis Amna Kouser
Trend analysis uses historical data to predict future movements in stocks. It assumes past performance can indicate future performance when accounting for sector trends, market conditions, and competition. Trend analysis calculates percentage changes over periods of two years or more to identify trends and make short-term, intermediate, and long-term projections. Financial analysts use trend analysis to assess a company's financial health and future performance by examining past performance and current conditions.
This document summarizes three case studies that used remote sensing and GIS techniques to analyze land use and land cover change over time. The first case study analyzed changes from 1990-2010 in Hawalbagh, India using Landsat imagery. It found increases in built-up land and decreases in barren land. The second studied coastal Egypt from 1987-2001 using Landsat, identifying 8 land cover classes. The third examined Simly watershed, Pakistan from 1992-2012 using Landsat and SPOT data, finding increases in agriculture and decreases in vegetation. All three used supervised classification and post-classification comparison to analyze land use/cover changes.
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.
This document provides an overview of geographic information system (GIS) analysis functions. It discusses several types of analysis that GIS is used for, including selection and measurement, overlay analysis, neighbourhood operations, and connectivity analysis. Overlay analysis allows for spatially interrelating multiple data layers and is one of the most important GIS functions. Neighbourhood operations consider characteristics of surrounding areas, such as through buffering or interpolation. Overall, the document outlines the key spatial analysis techniques that GIS provides for examining geographic data patterns and relationships.
DEFINITION :
GIS is a powerful set of tools for collecting, storing , retrieving at will, transforming and displaying spatial data from the real world for a particular set of purposes
APPLICATION AREAS OF GIS
Agriculture
Business
Electric/Gas utilities
Environment
Forestry
Geology
Hydrology
Land-use planning
Local government
Mapping
11. Military
12. Risk management
13. Site planning
14. Transportation
15. Water / Waste water industry
COMPONENTS OF GIS
DATA INPUT
SPATIAL DATA MODEL
Data Model:
It describes in an abstract way how the data is represented in an information system or in DBMS
Spatial Data Model :
The models or abstractions of reality that are intended to have some similarity with selected aspects of the real world
Creation of analogue and digital spatial data sets involves seven levels of model development and abstraction
SPATIAL DATA MODEL
Conceptual model : A view of reality
Analog model : Human conceptualization leads to analogue abstraction
Spatial data models : Formalization of analogue abstractions without any conventions
Database model : How the data are recorded in the computer
Physical computational model : Particular representation of the data structures in computer memory
Data manipulation model : Accepted axioms and rules for handling the data
SPATIAL DATA MODEL
SPATIAL DATA MODEL
Objects on the earth surface are shown as continuous and discrete objects in spatial data models
Types of data models
Raster data model
vector data models
RASTER DATA MODEL
Basic Elements :
Extent
Rows
Columns
Origin
Orientation
Resolution: pixel = grain = grid cell
Ex: Bit Map Image (BMP),Joint Photographic Expert Group (JPEG), Portable Network Graphics(PNG) etc
RASTER DATA MODEL
VECTOR DATA MODEL
Basic Elements:
Location (x,y) or (x,y,z)
Explicit, i.e. pegged to a coordinate system
Different coordinate system (and precision) require different values
o e.g. UTM as integer (but large)
o Lat, long as two floating point numbers +/-
Points are used to build more complex features
Ex: Auto CAD Drawing File(DWG), Data Interchange(exchange) File(DXF), Vector Product Format (VPF) etc
VECTOR DATA MODEL
RASTER vs VECTORRaster is faster but Vector is corrector
TESSELLATIONS OF CONTINUOUS FIELDS
Triangular Irregular Network: (TIN)
TIN is a vector data structure for representing geographical information that is continuous
Digital elevation model
TIN is generally used to create Digital Elevation Model (DEM)
DIGITAL ELEVATION MODEL
DATA STRUCTURES
Data structure tells about how the data is stored
Data organization in raster data structures
Each cell is referenced directly
Each overlay Is referenced directly
Each mapping unit is referenced directly
Each overlay is separate file with general header
GIS is a system for managing and analyzing geographic data. It uses two main data models: vector, representing points, lines and polygons; and raster, representing data as a grid of cells. Common file formats include shapefiles for vector data and GeoTIFF and MrSID for raster. GIS data is referenced using coordinate systems like WGS84 for global latitude/longitude or HK80Grid for Hong Kong. ESRI's ArcGIS software allows viewing, editing, and publishing this geospatial data for mapping and analysis.
Digital cartography involves the generation, storage, and editing of maps using computers. It has advantages over analog cartography like easier storage, updating, and access to data. Data is collected through remote sensing, aerial photography, scanning, and digitizing. GPS is also used. Digital databases store spatial and non-spatial data. Analysis and representation of data is facilitated using GIS tools. Digital cartography has made mapping accessible to non-specialists.
This document discusses GIS data analysis techniques including raster to vector conversion and spatial analysis through vector overlay. It provides information on various data types and models in GIS. Key analysis techniques covered are raster and vector data overlays, terrain mapping and analysis, and spatial interpolation methods. Specific vector and raster overlay methods like point-in-polygon, line-in-polygon and polygon-on-polygon are described. Spatial data editing techniques involving digitization errors and topological/non-topological editing are also summarized.
GIS systems allow for the input, storage, manipulation, analysis and output of geographic data. Spatial data represents location and attributes provide additional data about features. Data can be represented as vectors using points, lines and polygons, or as rasters in a grid cell format. Key properties of spatial data include projection, scale, accuracy, resolution and how it represents real world features. GIS allows for integrated analysis of spatial and attribute data through functions like classification, measurement, overlay and more.
This document discusses GIS topology, which establishes rules for how geographic features share geometry and spatial relationships. Topology ensures data quality, enhances analysis, and manages coincident geometry. It has three components: connectivity between nodes and arcs, area definition using polygon boundaries, and contiguity to determine adjacent features. Topological rules prevent errors like overlaps, gaps, dangles and ensure proper containment of points and boundaries.
The document discusses vector data models in GIS. Vector data models represent geographic features using points, lines, and polygons. The key vector data models are the spaghetti model, which encodes features as strings of coordinates, and the TIN (triangulated irregular network) model, which creates a network of triangles connecting points. Vector models allow for discrete boundaries but complex algorithms, while raster models divide space into a grid but are simpler.
basic concept of geographic data,GIS and its component,data acquisition ,raster, vector formats,spatial data,topology and data model data output ,GIS applications
This document provides an introduction to geographic information systems (GIS). It begins by defining some basic map concepts like features, scale, and symbology. It then discusses what GIS is, how it works, and what makes it special. GIS allows users to capture, store, manipulate, analyze and visualize spatial data. It integrates data from different sources into interactive maps. Users can perform tasks like querying attributes, analyzing networks, modeling 3D surfaces, interpolating between data points, and complex spatial analysis. Overall, the document outlines the core components and capabilities of GIS as a tool for visualizing and solving real-world problems involving geographic data.
Understanding Coordinate Systems and Projections for ArcGISJohn Schaeffer
Everything you need to know to work with coordinate systems and projecting data in ArcGIS. The presentation starts by explaining the terminology, and then discusses the details you need to know to actually work successfully with coordinate systems, use the proper projections, and geographic transformations. This is a very practical look at a complex subject.
This document discusses map projections, which are methods for translating the three-dimensional surface of the Earth onto a two-dimensional map. It describes three types of developable projection surfaces - conic, cylindrical, and planar - that are used to create different map projections. Specific projections are then outlined, including what geometric properties they preserve or distort (shape, area, distance, direction) and their common uses. The document provides a detailed overview of different GIS map projection techniques.
Scanners, image resolution, orbit in remote sensing, pk maniP.K. Mani
This document provides information about different types of satellite orbits and sensors. It discusses polar orbits, geostationary orbits, and examples of weather satellites like METEOSAT, NOAA, and GOES that use these orbit types. It also describes imaging sensors on these satellites and their specifications. Sensors on other platforms like Landsat, SPOT, ERS, and Radarsat are outlined along with their characteristics and applications. Scanning techniques for collecting multispectral data like across-track and along-track scanning are defined.
The document discusses the history and development of GPS and differential GPS (DGPS). It explains that DGPS uses a reference station at a known location to calculate errors in GPS positioning and apply corrections in real-time or post-processing to improve accuracy. The document outlines various DGPS systems, sources of GPS error, DGPS methods like rapid static and traverse, and components of GPS receivers.
Remote sensing involves obtaining information about objects without physical contact. It works by sensing and recording electromagnetic radiation reflected or emitted from targets. The key components are an energy source, sensor, platforms, and data analysis to extract information. Sensors can be optical, thermal, or microwave. Platforms include satellites, aircraft, and ground bases. Applications of remote sensing include agriculture, forestry, geology, hydrology, urban planning, and national security.
The document discusses various methods of georeferencing, which is assigning accurate locations to spatial information. The most comprehensive method is using latitude and longitude, which defines locations based on angles from the equator and Greenwich Meridian. However, the Earth's curved surface poses issues for technologies that work with flat maps and data. Therefore, map projections are used to translate locations on the spherical Earth onto flat planes or surfaces, though all projections introduce some distortion. Common projections include cylindrical, conic, and the Universal Transverse Mercator system.
Raw remote sensing images contain errors that must be corrected through pre-processing before analysis. Pre-processing involves radiometric, geometric, and atmospheric corrections. Radiometric corrections address distortions in pixel values from issues like noise, striping, or dropped scan lines. Geometric corrections rectify distortions caused by terrain, sensor geometry, and platform movement using ground control points. Atmospheric corrections reduce haze effects through techniques like dark object subtraction that assume minimum surface reflectance values. Pre-processing is essential for producing accurate, georeferenced images suitable for analysis and interpretation.
The document summarizes seven categories of change detection techniques:
1. Algebra based approaches include image differencing, regression, ratioing, and change vector analysis. These methods are simple to implement but cannot provide complete change matrices.
2. Transformation techniques apply transformations like PCA and tasseled cap to images before change detection.
3. Classification based techniques perform post-classification comparison or combine classification with other algorithms.
4. Advanced models use techniques like spectral mixture analysis and biophysical parameters.
5. GIS and remote sensing are integrated in some methods.
6. Visual analysis relies on human interpretation of image differences.
7. Other techniques include measures of spatial dependence,
The document discusses Geographic Information Systems (GIS) and presents information on:
- The history and definition of GIS and how it allows users to integrate and analyze spatial data layers.
- Types of GIS software including desktop GIS like QGIS, web-based GIS, and geobrowsers like Google Earth.
- Features of GIS like handling large datasets, data integration and unique analysis methods.
- An example project mapping electrical assets in India using tools like QGIS, Google Earth, and the MAPinr app.
Urban Landuse/ Landcover change analysis using Remote Sensing and GISHarshvardhan Vashistha
This document provides an overview of land cover and land use change detection. It discusses techniques for detecting changes through analyzing satellite images over time. Methods include visual interpretation, image rationing, classification, and indices. Factors to consider include the objective, type of change to detect, and type of changes of interest, like land use, forest, or urban changes. Applications include monitoring land use change, deforestation, fires, wetlands, urban growth, and environmental changes. Proper selection of methods and data depends on the scale and specifics of the changes being examined.
This summarizes a document about change detection techniques in remote sensing for analyzing land use and land cover changes. Remote sensing using aerial photographs and satellite imagery allows efficient monitoring of land cover changes compared to traditional field surveys. Change detection involves identifying transformations of land cover types over time and space using multi-temporal remote sensing data. Common techniques include comparing imagery from Landsat, QuickBird and other satellite sensors to detect changes in agriculture, deforestation, urban growth and other human and natural impacts on the earth's surface.
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.
This document provides an overview of geographic information system (GIS) analysis functions. It discusses several types of analysis that GIS is used for, including selection and measurement, overlay analysis, neighbourhood operations, and connectivity analysis. Overlay analysis allows for spatially interrelating multiple data layers and is one of the most important GIS functions. Neighbourhood operations consider characteristics of surrounding areas, such as through buffering or interpolation. Overall, the document outlines the key spatial analysis techniques that GIS provides for examining geographic data patterns and relationships.
DEFINITION :
GIS is a powerful set of tools for collecting, storing , retrieving at will, transforming and displaying spatial data from the real world for a particular set of purposes
APPLICATION AREAS OF GIS
Agriculture
Business
Electric/Gas utilities
Environment
Forestry
Geology
Hydrology
Land-use planning
Local government
Mapping
11. Military
12. Risk management
13. Site planning
14. Transportation
15. Water / Waste water industry
COMPONENTS OF GIS
DATA INPUT
SPATIAL DATA MODEL
Data Model:
It describes in an abstract way how the data is represented in an information system or in DBMS
Spatial Data Model :
The models or abstractions of reality that are intended to have some similarity with selected aspects of the real world
Creation of analogue and digital spatial data sets involves seven levels of model development and abstraction
SPATIAL DATA MODEL
Conceptual model : A view of reality
Analog model : Human conceptualization leads to analogue abstraction
Spatial data models : Formalization of analogue abstractions without any conventions
Database model : How the data are recorded in the computer
Physical computational model : Particular representation of the data structures in computer memory
Data manipulation model : Accepted axioms and rules for handling the data
SPATIAL DATA MODEL
SPATIAL DATA MODEL
Objects on the earth surface are shown as continuous and discrete objects in spatial data models
Types of data models
Raster data model
vector data models
RASTER DATA MODEL
Basic Elements :
Extent
Rows
Columns
Origin
Orientation
Resolution: pixel = grain = grid cell
Ex: Bit Map Image (BMP),Joint Photographic Expert Group (JPEG), Portable Network Graphics(PNG) etc
RASTER DATA MODEL
VECTOR DATA MODEL
Basic Elements:
Location (x,y) or (x,y,z)
Explicit, i.e. pegged to a coordinate system
Different coordinate system (and precision) require different values
o e.g. UTM as integer (but large)
o Lat, long as two floating point numbers +/-
Points are used to build more complex features
Ex: Auto CAD Drawing File(DWG), Data Interchange(exchange) File(DXF), Vector Product Format (VPF) etc
VECTOR DATA MODEL
RASTER vs VECTORRaster is faster but Vector is corrector
TESSELLATIONS OF CONTINUOUS FIELDS
Triangular Irregular Network: (TIN)
TIN is a vector data structure for representing geographical information that is continuous
Digital elevation model
TIN is generally used to create Digital Elevation Model (DEM)
DIGITAL ELEVATION MODEL
DATA STRUCTURES
Data structure tells about how the data is stored
Data organization in raster data structures
Each cell is referenced directly
Each overlay Is referenced directly
Each mapping unit is referenced directly
Each overlay is separate file with general header
GIS is a system for managing and analyzing geographic data. It uses two main data models: vector, representing points, lines and polygons; and raster, representing data as a grid of cells. Common file formats include shapefiles for vector data and GeoTIFF and MrSID for raster. GIS data is referenced using coordinate systems like WGS84 for global latitude/longitude or HK80Grid for Hong Kong. ESRI's ArcGIS software allows viewing, editing, and publishing this geospatial data for mapping and analysis.
Digital cartography involves the generation, storage, and editing of maps using computers. It has advantages over analog cartography like easier storage, updating, and access to data. Data is collected through remote sensing, aerial photography, scanning, and digitizing. GPS is also used. Digital databases store spatial and non-spatial data. Analysis and representation of data is facilitated using GIS tools. Digital cartography has made mapping accessible to non-specialists.
This document discusses GIS data analysis techniques including raster to vector conversion and spatial analysis through vector overlay. It provides information on various data types and models in GIS. Key analysis techniques covered are raster and vector data overlays, terrain mapping and analysis, and spatial interpolation methods. Specific vector and raster overlay methods like point-in-polygon, line-in-polygon and polygon-on-polygon are described. Spatial data editing techniques involving digitization errors and topological/non-topological editing are also summarized.
GIS systems allow for the input, storage, manipulation, analysis and output of geographic data. Spatial data represents location and attributes provide additional data about features. Data can be represented as vectors using points, lines and polygons, or as rasters in a grid cell format. Key properties of spatial data include projection, scale, accuracy, resolution and how it represents real world features. GIS allows for integrated analysis of spatial and attribute data through functions like classification, measurement, overlay and more.
This document discusses GIS topology, which establishes rules for how geographic features share geometry and spatial relationships. Topology ensures data quality, enhances analysis, and manages coincident geometry. It has three components: connectivity between nodes and arcs, area definition using polygon boundaries, and contiguity to determine adjacent features. Topological rules prevent errors like overlaps, gaps, dangles and ensure proper containment of points and boundaries.
The document discusses vector data models in GIS. Vector data models represent geographic features using points, lines, and polygons. The key vector data models are the spaghetti model, which encodes features as strings of coordinates, and the TIN (triangulated irregular network) model, which creates a network of triangles connecting points. Vector models allow for discrete boundaries but complex algorithms, while raster models divide space into a grid but are simpler.
basic concept of geographic data,GIS and its component,data acquisition ,raster, vector formats,spatial data,topology and data model data output ,GIS applications
This document provides an introduction to geographic information systems (GIS). It begins by defining some basic map concepts like features, scale, and symbology. It then discusses what GIS is, how it works, and what makes it special. GIS allows users to capture, store, manipulate, analyze and visualize spatial data. It integrates data from different sources into interactive maps. Users can perform tasks like querying attributes, analyzing networks, modeling 3D surfaces, interpolating between data points, and complex spatial analysis. Overall, the document outlines the core components and capabilities of GIS as a tool for visualizing and solving real-world problems involving geographic data.
Understanding Coordinate Systems and Projections for ArcGISJohn Schaeffer
Everything you need to know to work with coordinate systems and projecting data in ArcGIS. The presentation starts by explaining the terminology, and then discusses the details you need to know to actually work successfully with coordinate systems, use the proper projections, and geographic transformations. This is a very practical look at a complex subject.
This document discusses map projections, which are methods for translating the three-dimensional surface of the Earth onto a two-dimensional map. It describes three types of developable projection surfaces - conic, cylindrical, and planar - that are used to create different map projections. Specific projections are then outlined, including what geometric properties they preserve or distort (shape, area, distance, direction) and their common uses. The document provides a detailed overview of different GIS map projection techniques.
Scanners, image resolution, orbit in remote sensing, pk maniP.K. Mani
This document provides information about different types of satellite orbits and sensors. It discusses polar orbits, geostationary orbits, and examples of weather satellites like METEOSAT, NOAA, and GOES that use these orbit types. It also describes imaging sensors on these satellites and their specifications. Sensors on other platforms like Landsat, SPOT, ERS, and Radarsat are outlined along with their characteristics and applications. Scanning techniques for collecting multispectral data like across-track and along-track scanning are defined.
The document discusses the history and development of GPS and differential GPS (DGPS). It explains that DGPS uses a reference station at a known location to calculate errors in GPS positioning and apply corrections in real-time or post-processing to improve accuracy. The document outlines various DGPS systems, sources of GPS error, DGPS methods like rapid static and traverse, and components of GPS receivers.
Remote sensing involves obtaining information about objects without physical contact. It works by sensing and recording electromagnetic radiation reflected or emitted from targets. The key components are an energy source, sensor, platforms, and data analysis to extract information. Sensors can be optical, thermal, or microwave. Platforms include satellites, aircraft, and ground bases. Applications of remote sensing include agriculture, forestry, geology, hydrology, urban planning, and national security.
The document discusses various methods of georeferencing, which is assigning accurate locations to spatial information. The most comprehensive method is using latitude and longitude, which defines locations based on angles from the equator and Greenwich Meridian. However, the Earth's curved surface poses issues for technologies that work with flat maps and data. Therefore, map projections are used to translate locations on the spherical Earth onto flat planes or surfaces, though all projections introduce some distortion. Common projections include cylindrical, conic, and the Universal Transverse Mercator system.
Raw remote sensing images contain errors that must be corrected through pre-processing before analysis. Pre-processing involves radiometric, geometric, and atmospheric corrections. Radiometric corrections address distortions in pixel values from issues like noise, striping, or dropped scan lines. Geometric corrections rectify distortions caused by terrain, sensor geometry, and platform movement using ground control points. Atmospheric corrections reduce haze effects through techniques like dark object subtraction that assume minimum surface reflectance values. Pre-processing is essential for producing accurate, georeferenced images suitable for analysis and interpretation.
The document summarizes seven categories of change detection techniques:
1. Algebra based approaches include image differencing, regression, ratioing, and change vector analysis. These methods are simple to implement but cannot provide complete change matrices.
2. Transformation techniques apply transformations like PCA and tasseled cap to images before change detection.
3. Classification based techniques perform post-classification comparison or combine classification with other algorithms.
4. Advanced models use techniques like spectral mixture analysis and biophysical parameters.
5. GIS and remote sensing are integrated in some methods.
6. Visual analysis relies on human interpretation of image differences.
7. Other techniques include measures of spatial dependence,
The document discusses Geographic Information Systems (GIS) and presents information on:
- The history and definition of GIS and how it allows users to integrate and analyze spatial data layers.
- Types of GIS software including desktop GIS like QGIS, web-based GIS, and geobrowsers like Google Earth.
- Features of GIS like handling large datasets, data integration and unique analysis methods.
- An example project mapping electrical assets in India using tools like QGIS, Google Earth, and the MAPinr app.
Urban Landuse/ Landcover change analysis using Remote Sensing and GISHarshvardhan Vashistha
This document provides an overview of land cover and land use change detection. It discusses techniques for detecting changes through analyzing satellite images over time. Methods include visual interpretation, image rationing, classification, and indices. Factors to consider include the objective, type of change to detect, and type of changes of interest, like land use, forest, or urban changes. Applications include monitoring land use change, deforestation, fires, wetlands, urban growth, and environmental changes. Proper selection of methods and data depends on the scale and specifics of the changes being examined.
This summarizes a document about change detection techniques in remote sensing for analyzing land use and land cover changes. Remote sensing using aerial photographs and satellite imagery allows efficient monitoring of land cover changes compared to traditional field surveys. Change detection involves identifying transformations of land cover types over time and space using multi-temporal remote sensing data. Common techniques include comparing imagery from Landsat, QuickBird and other satellite sensors to detect changes in agriculture, deforestation, urban growth and other human and natural impacts on the earth's surface.
PERI-URBAN LAND USE CHANGE IN LAGOS THE MEGA-CITY SEMINAR 2Samuel Dekolo
This document provides background information on a research study investigating peri-urban land use changes in the Lagos megacity between 1976 and 2013. It discusses the global trends of urbanization and issues related to megacity growth such as environmental degradation. The study aims to assess land use changes using remote sensing and GIS, investigate the determinants of change, and evaluate land management policies. The conceptual framework is based on the DPSIR model. A hybrid research design incorporating spatial analysis and field surveys will be used to collect primary and secondary data on the physical, social and policy dimensions of peri-urban land use change in Lagos.
The document discusses the DART (Detecting and Recording Archaeological Traces) project, which aims to improve archaeological detection techniques by taking a scientific approach. It involves intensive ground observation and data collection at sites to better understand how archaeological remains generate detectable contrasts and how those contrasts are influenced by environmental factors over time. The data collected includes spectro-radiometry, soil moisture and temperature probes, weather data, and aerial imagery. Preliminary analysis of temperature, moisture, and resistance data show changes seasonally that could help predict optimal times for detection. The open science approach seeks to further archaeological prospection methods.
Contribution of Satellite Remote Sensing in Environmental Monitoring at Regio...CrimsonpublishersEAES
Since its genesis, satellite remote sensing has enabled observations of environmental changes at inaccessible locations, improving tremendously
many scientific fields like the meteorology, oceanography, agriculture production, glaciology, geology etc. Using the information collected by satellites,
changes in the physical environment can be measured and the information is analyzed to predict future patterns and achieve better environmental
outcomes in different areas. Due to its speed and efficiency in information-gathering, the applications of satellite remote sensing are continually
increasing and becoming a vital part in environmental resource management process.
- The document analyzes changes in land use and land cover in some areas of Anambra State, Nigeria over a 29 year period using satellite imagery from 1986, 2000, and 2013.
- It found significant growth in built-up areas, accompanied by decreasing vegetation and gully areas. Water bodies saw little change.
- Specifically, in Ifite-Ogwari, built-up areas increased from 23% to 37% of the total area between 1986 and 2013, while vegetation declined over the same period.
A Review of Change Detection Techniques of LandCover Using Remote Sensing Dataiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document provides an overview of change detection techniques for landcover using remote sensing data. It begins by discussing issues that can impact change detection accuracy, such as image acquisition parameters, viewing geometry, and radiometric correction. It then categorizes change detection techniques as either pre-classification or post-classification. Pre-classification techniques extract minimal change information, while post-classification techniques separate changes in more detail but may miss subtle within-class changes. Object-based techniques that segment images into objects before analysis are also discussed as a recent development.
This document provides an overview of remote sensing and its applications. It begins with definitions and advantages of remote sensing, including obtaining 3D views from a distance without physical contact and permanently storing data that can be reused. Platforms for remote sensing include airborne and spaceborne options. Applications discussed include topographic mapping, forestry monitoring, land use analysis, and more. Geographic Information Systems are introduced as a way to integrate spatial and non-spatial data for analysis and visualization to aid decision making.
Remote sensing for change detection (presentation) - Prepared by A F M Fakhru...A F M Fakhrul Azam Shaikat
Change detection, in the Remote Sensing discipline, is the analytical process that aims to detect changes, over time and space of the land cover or/and land use....
geo-informatics in land use/land cover studyRohit Kumar
1. The document discusses the role of geo-informatics, which includes remote sensing and GIS, in land use/land cover studies.
2. Remote sensing provides synoptic and multi-temporal land use/land cover data to study changes over time, while GIS provides a platform for analyzing, updating, and retrieving the data.
3. Together, remote sensing and GIS are useful tools for investigating and mapping land use/land cover patterns at various scales, and for monitoring changes in a time-saving and accurate manner.
Role of geo-informatics in land use/land coverRohit Kumar
Geo-informatics, which consists of remote sensing and GIS, plays an important role in land use/land cover studies. Remote sensing provides synoptic and multi-temporal data on land use/cover patterns that can be analyzed using GIS. Together, remote sensing and GIS allow researchers to study land use/cover changes over time in a cost effective and accurate manner. Geo-informatics has been widely used for land use/cover mapping and monitoring due to its time-saving capabilities and ability to store, analyze, and display land use/cover data to support planning, management, and utilization of land resources.
🌍 Remote Sensing (RS) is the science of obtaining information about objects or areas from a distance, typically from aircraft or satellites. This presentation provides a foundational understanding of Remote Sensing, its types, components, satellite data, sensors, and wide-ranging applications in GIS, environmental studies, agriculture, forestry, disaster management, urban planning, and climate monitoring.
In this presentation, you will learn:
The basic principles of Remote Sensing
Different types of remote sensing platforms (active & passive)
Satellite sensors and spectral bands
Image interpretation techniques
Importance of spatial, spectral, radiometric, and temporal resolution
Overview of satellite data sources (Landsat, Sentinel, IRS, MODIS, etc.)
Real-world applications of RS in land use/land cover (LULC) analysis, water resource mapping, vegetation monitoring, and more
The slides are ideal for:
Students and professionals in Geology, Geography, Civil Engineering, Environmental Science, and Urban Planning
Individuals preparing for exams or careers in GIS, Remote Sensing, or Geoinformatics
Institutions and educators delivering foundational RS training
🧠 Whether you're just starting in GIS or want to deepen your understanding of satellite imagery analysis, this presentation is crafted to help you build strong conceptual knowledge in Remote Sensing.
🎓 Presented by:
Geospatial Mission Institute – A dedicated platform offering online training in GIS, Remote Sensing, LiDAR, CAD, and Geospatial Applications.
We provide:
📚 Live instructor-led online classes
✅ Practical hands-on training using ArcGIS, QGIS, Google Earth, Global Mapper, MicroStation, and CAD tools
🎯 Remote Sensing & GIS Projects, Data Analysis, and Interpretation
🤝 Placement assistance for eligible candidates
🧾 Certificate upon course completion
Our courses are suitable for:
Beginners
Students
Government professionals
Working engineers
Academicians looking to upskill in geospatial domains
📌 Follow us for more training presentations, course updates, and GIS tips.
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The presentation was given by Mr. Bas Kempen & Ms. V.L. Mulder, ISRIC, during the GSOC Mapping Global Training hosted by ISRIC - World Soil Information, 6 - 23 June 2017, Wageningen (The Netherlands).
Remote sensing involves acquiring information about the Earth's surface without direct contact using sensors to detect electromagnetic radiation. There are 7 steps in the remote sensing process: illumination, interaction with the atmosphere, interaction with the target, recording by sensors, transmission and processing, interpretation and analysis, and application. Remote sensing uses different parts of the electromagnetic spectrum and has many applications including mapping agriculture, forests, geology, hydrology, and land cover. It is used to monitor crops, estimate yields, assess forest health, explore for minerals and hydrocarbons, map wetlands and floodplains, and create land use maps.
Introduction
Groundwater monitoring involves the systematic collection and analysis of data regarding groundwater levels, quality, and other hydrological parameters. As a crucial resource for drinking water, irrigation, and industrial applications, effective monitoring is essential to manage groundwater sustainably and protect it from contamination.
Room Enough for Everyone? Understanding Human Uses & Interactions in RI Coast...riseagrant
This document discusses understanding human uses and interactions in Rhode Island coastal waters through evaluating social carrying capacity. It begins with an introduction to social carrying capacity and its history. It then discusses mapping and modeling human uses through participatory mapping, aerial surveys, observational studies, and boat-based transect surveys. Lastly, it discusses evaluative studies of social carrying capacity through perceived impacts, standards, and indicators. The goal is to apply these frameworks to understand social carrying capacities related to shellfish aquaculture, harvest and restoration in Rhode Island.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
DART - improving the science. Bradford 21022012DART Project
This document provides an overview of the DART project, which aims to improve the scientific understanding of archaeological detection. DART studies archaeological sites to better understand how their constituents generate observable contrasts and how sensors can detect these contrasts. The project conducts intensive ground observations and measurements at sites to analyze periodic changes in the sites. DART shares its data openly to maximize its impact and further innovation in archaeological detection.
Exploring Substances:
Acidic, Basic, and
Neutral
Welcome to the fascinating world of acids and bases! Join siblings Ashwin and
Keerthi as they explore the colorful world of substances at their school's
National Science Day fair. Their adventure begins with a mysterious white paper
that reveals hidden messages when sprayed with a special liquid.
In this presentation, we'll discover how different substances can be classified as
acidic, basic, or neutral. We'll explore natural indicators like litmus, red rose
extract, and turmeric that help us identify these substances through color
changes. We'll also learn about neutralization reactions and their applications in
our daily lives.
by sandeep swamy
The *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview:
Structure
1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information.
2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing.
3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles.
Functions
1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell.
2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding.
3. *Behavioral responThe *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview:
Structure
1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information.
2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing.
3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles.
Functions
1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell.
2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding.
3. *Behavioral responses*: Insects can exhibit complex behaviors, such as mating, foraging, and social interactions.
Characteristics
1. *Decentralized*: Insect nervous systems have some autonomy in different body parts.
2. *Specialized*: Different parts of the nervous system are specialized for specific functions.
3. *Efficient*: Insect nervous systems are highly efficient, allowing for rapid processing and response to stimuli.
The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive in diverse environments.
The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive
How to Manage Opening & Closing Controls in Odoo 17 POSCeline George
In Odoo 17 Point of Sale, the opening and closing controls are key for cash management. At the start of a shift, cashiers log in and enter the starting cash amount, marking the beginning of financial tracking. Throughout the shift, every transaction is recorded, creating an audit trail.
Geography Sem II Unit 1C Correlation of Geography with other school subjectsProfDrShaikhImran
The correlation of school subjects refers to the interconnectedness and mutual reinforcement between different academic disciplines. This concept highlights how knowledge and skills in one subject can support, enhance, or overlap with learning in another. Recognizing these correlations helps in creating a more holistic and meaningful educational experience.
Odoo Inventory Rules and Routes v17 - Odoo SlidesCeline George
Odoo's inventory management system is highly flexible and powerful, allowing businesses to efficiently manage their stock operations through the use of Rules and Routes.
How to Customize Your Financial Reports & Tax Reports With Odoo 17 AccountingCeline George
The Accounting module in Odoo 17 is a complete tool designed to manage all financial aspects of a business. Odoo offers a comprehensive set of tools for generating financial and tax reports, which are crucial for managing a company's finances and ensuring compliance with tax regulations.
The ever evoilving world of science /7th class science curiosity /samyans aca...Sandeep Swamy
The Ever-Evolving World of
Science
Welcome to Grade 7 Science4not just a textbook with facts, but an invitation to
question, experiment, and explore the beautiful world we live in. From tiny cells
inside a leaf to the movement of celestial bodies, from household materials to
underground water flows, this journey will challenge your thinking and expand
your knowledge.
Notice something special about this book? The page numbers follow the playful
flight of a butterfly and a soaring paper plane! Just as these objects take flight,
learning soars when curiosity leads the way. Simple observations, like paper
planes, have inspired scientific explorations throughout history.
pulse ppt.pptx Types of pulse , characteristics of pulse , Alteration of pulsesushreesangita003
what is pulse ?
Purpose
physiology and Regulation of pulse
Characteristics of pulse
factors affecting pulse
Sites of pulse
Alteration of pulse
for BSC Nursing 1st semester
for Gnm Nursing 1st year
Students .
vitalsign
*Metamorphosis* is a biological process where an animal undergoes a dramatic transformation from a juvenile or larval stage to a adult stage, often involving significant changes in form and structure. This process is commonly seen in insects, amphibians, and some other animals.
A measles outbreak originating in West Texas has been linked to confirmed cases in New Mexico, with additional cases reported in Oklahoma and Kansas. The current case count is 817 from Texas, New Mexico, Oklahoma, and Kansas. 97 individuals have required hospitalization, and 3 deaths, 2 children in Texas and one adult in New Mexico. These fatalities mark the first measles-related deaths in the United States since 2015 and the first pediatric measles death since 2003.
The YSPH Virtual Medical Operations Center Briefs (VMOC) were created as a service-learning project by faculty and graduate students at the Yale School of Public Health in response to the 2010 Haiti Earthquake. Each year, the VMOC Briefs are produced by students enrolled in Environmental Health Science Course 581 - Public Health Emergencies: Disaster Planning and Response. These briefs compile diverse information sources – including status reports, maps, news articles, and web content– into a single, easily digestible document that can be widely shared and used interactively. Key features of this report include:
- Comprehensive Overview: Provides situation updates, maps, relevant news, and web resources.
- Accessibility: Designed for easy reading, wide distribution, and interactive use.
- Collaboration: The “unlocked" format enables other responders to share, copy, and adapt seamlessly. The students learn by doing, quickly discovering how and where to find critical information and presenting it in an easily understood manner.
CURRENT CASE COUNT: 817 (As of 05/3/2025)
• Texas: 688 (+20)(62% of these cases are in Gaines County).
• New Mexico: 67 (+1 )(92.4% of the cases are from Eddy County)
• Oklahoma: 16 (+1)
• Kansas: 46 (32% of the cases are from Gray County)
HOSPITALIZATIONS: 97 (+2)
• Texas: 89 (+2) - This is 13.02% of all TX cases.
• New Mexico: 7 - This is 10.6% of all NM cases.
• Kansas: 1 - This is 2.7% of all KS cases.
DEATHS: 3
• Texas: 2 – This is 0.31% of all cases
• New Mexico: 1 – This is 1.54% of all cases
US NATIONAL CASE COUNT: 967 (Confirmed and suspected):
INTERNATIONAL SPREAD (As of 4/2/2025)
• Mexico – 865 (+58)
‒Chihuahua, Mexico: 844 (+58) cases, 3 hospitalizations, 1 fatality
• Canada: 1531 (+270) (This reflects Ontario's Outbreak, which began 11/24)
‒Ontario, Canada – 1243 (+223) cases, 84 hospitalizations.
• Europe: 6,814
Ultimate VMware 2V0-11.25 Exam Dumps for Exam SuccessMark Soia
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Multi-currency in odoo accounting and Update exchange rates automatically in ...Celine George
Most business transactions use the currencies of several countries for financial operations. For global transactions, multi-currency management is essential for enabling international trade.
2. Remote sensing is a method of obtaining information about the
properties of an object without coming into physical contact with it.
A) Energy Source
B) Atmosphere & Radiation
C) Interaction with the target &
recording of the ref. energy
D) Transmission & ground level
processing
E) Interpretation, Analysis and
Application
Source: GrindGIS
Remote Sensing
3. RS system capture radiation in different wavelength
reflected/emitted by the earth’s surface features and recorded
it either directly on the film as in case of aerial photography or
in digital medium used for generating the images
It provides valuable data over vast area in a short time about
resources, meteorology and environment leading to better
resource management and accelerating national development
Remote Sensing
4. Change detection in GIS is a method of understanding
how a given area has changed between two or more
time periods.
It is helpful in many applications such as:
• Land use changes
• Rate of deforestation
• Coastal change
• Urban sprawl
Change detection involves comparing changes
between aerial photographs taken over different time
periods that cover the exact same geographic area.
Change Detection
5. • Urban change
• Environmental change, drought
monitoring, flood monitoring,
monitoring
• Coastal marine environments,
desertification, and detection of
landslide areas
• Other applications such as crop
monitoring, shifting cultivation
monitoring
• Wetland change
Applications of Change
Detection Techniques
• Land-use and land-cover (LULC)
change
• Forest or vegetation change
• Forest mortality, defoliation and
damage assessment
• Deforestation, regeneration and
selective logging
• Road segments, and change in
glacier mass balance.
• Forest fire and fire-affected area
detection
• Landscape change
6. Change detection can be used to measure four
different types of change:
o Change in the identify of a feature over time
o Change of a feature’s location over time
o Change of a feature’s shape over time
o Change in a feature’s size over time.
Change Detection
8. Good change detection research should
provide the following information's:
Area change and change rate
Spatial distribution of changed types
Change trajectories of land-cover types
Accuracy assessment of change detection results
10. Source: I.R. Hegazy, M.R. Kaloop /
International Journal of Sustainable
Built Environment 4 (2015) 117–124
1985 2000
2010
Mansoura and Talkha land use maps
11. Four land cover maps (A. 2007, B. 2008, C. 2009, and D. 2010) and locations of concession
and conservation areas including deforested landscape observed in 2007e2010 (E).
Source:Soraya Violini, Deforestation: Change Detection in Forest Cover using Remote Sensing
13. o Monitoring urban growth and land use change detection with GIS
and remote sensing techniques in Daqahlia governorate Egypt-
Ibrahim Rizk Hegazy 2015
o PETIT, C. C., and LAMBIN, E. F., 2001, Integration of multi-source
remote sensing data for land cover change detection. International
Journal of Geographical Information Science, 15, 785–803
o Google Time-Lapse video(1984-2016)
References
15. Notes:
Change detection can be used to measure four different types of change:
Change in the identify of a feature over time. For example, the change in
type of retail store at a given location. A local restaurant may go out of
business and be replaced by a toy store. The actual physical building
hasn’t changed, the type of categorized land use hasn’t change
(commercial), but the specific identity of the store has change.
Change of a feature’s location over time. Change detection can be used
to track the movement of a feature.
Change of a feature’s shape over time. Change detection can be used to
understand shrinkage in a specific specie’s habitat over time or the
changes in the shape of a river or lake.
Change in a feature’s size over time. Change detection can also measure
the extent of a feature. Does the urban area grow or shrink between two
time points?