This document discusses GIS file formats, conversions between formats, and common vector and raster formats. It provides the following key points:
1. GIS file formats encode geographic information into files, with main formats created by mapping agencies and GIS software developers, including vector and raster formats.
2. The GDAL/OGR libraries support conversions between 142 raster formats and 84 vector formats and are used in many GIS software programs.
3. Common vector formats include ESRI Shapefiles and CSV files, while common raster formats include TIFF and GeoTIFF files.
The basic intention of this presentation is to help the beginners in GIS to understand what GIS is? It is a simple presentation about GIS, i mean an introductory one. Hope anyone finds it useful.
This document provides an overview of key concepts in GIS including shapefiles, grids, rasters, vectors, DEM, TIN, coordinate systems, and common file formats. It discusses the differences between raster and vector data, and explains that shapefiles are commonly used to store vector data while grids are used for raster data. DEM and TIN are introduced as methods for representing elevation data. The document also covers projected and unprojected coordinate systems and provides examples of coordinate systems. Common file formats for both raster and vector data are listed.
Geographical Information System (GIS) is a computer system for capturing, storing, analyzing, and displaying spatially-referenced data. GIS allows users to visualize relationships and patterns in data through maps, globes, reports, and charts. The key components of GIS are data capture, database management, geographic analysis, and result preparation. GIS data comes in vector and raster formats, with vector being better for representing real-world features precisely and raster being better for dense data like elevation or land cover. GIS provides accurate data, better analysis and predictions, and helps answer questions by visualizing spatial relationships. However, GIS software can be expensive and difficult to integrate with traditional maps.
This document provides an overview of databases and WebGIS. It discusses different types of databases including MySQL, PostgreSQL, and spatially-enabled databases. It compares MySQL and PostgreSQL, covering when each would be used. It also covers database data conversions between formats like JSON, GeoJSON, CSV, SHP, and KML/KMZ. For WebGIS, it defines it as a distributed information system comprising a server and client, where the server is a GIS server and client a web browser. It discusses purposes, technologies, languages/frameworks like Python, JavaScript, GeoDjango, and case studies for building WebGIS systems.
GIS and Remote Sensing to study urban-rural transformation during a fifty-yea...Maurizio Pollino
C. R. Fichera, G. Modica, M. Pollino (2011).
Presented at "Computational Science and Its Applications - ICCSA 2011 International Conference", Santander, Spain, June 20-23, 2011.
A relevant issue in Remote Sensing (RS) and GIS is related to the analysis and the characterization of Land Use Land Cover (LULC) changes, very useful for a wide range of environmental applications and to efficiently undertake landscape planning and management policies. The methodology described has been applied to a case-study conducted in the area of the Province of Avellino (Southern Italy). Firstly, aerial photos and Landsat imagery have been classified to produce LULC maps for a fifty-year period (1954÷2004). Then, through a GIS approach, change detection and spatiotemporal analysis has been integrated to characterize LULC dynamics, focusing on the urban-rural gradient. This study has shown that LULC patterns and their changes are linked to both natural and social processes whose driving role has been clearly demonstrated: after the disastrous Irpinia earthquake (1980), local specific zoning laws and urban plans have significantly addressed landscape changes.
The document discusses properties of Landsat satellites and remote sensing data. It provides details on:
- The history and timeline of Landsat satellites and their sensors from Landsat 1 to Landsat 7.
- How Landsat data is processed to convert digital numbers to radiances and reflectances and apply atmospheric corrections.
- How different surface features like vegetation, soil and water absorb electromagnetic radiation differently, enabling their identification in remote sensing imagery.
This document discusses GIS tools and techniques for watershed analysis including DEM, fill sinks, flow direction, flow accumulation, conditional elevation, stream ordering, snapping pour points, watershed delineation, basin delineation, and calculating flow length. Key steps are opening a DEM, preprocessing the DEM with fill and flow tools, defining streams and pour points, delineating watersheds, and calculating attributes like flow length. The overall goal is to use GIS to analyze watersheds, drainage patterns, and water flow across landscapes.
This document discusses GIS file formats, conversions between formats, and common vector and raster formats. It provides the following key points:
1. GIS file formats encode geographic information into files, with main formats created by mapping agencies and GIS software developers, including vector and raster formats.
2. The GDAL/OGR libraries support conversions between 142 raster formats and 84 vector formats and are used in many GIS software programs.
3. Common vector formats include ESRI Shapefiles and CSV files, while common raster formats include TIFF and GeoTIFF files.
The basic intention of this presentation is to help the beginners in GIS to understand what GIS is? It is a simple presentation about GIS, i mean an introductory one. Hope anyone finds it useful.
This document provides an overview of key concepts in GIS including shapefiles, grids, rasters, vectors, DEM, TIN, coordinate systems, and common file formats. It discusses the differences between raster and vector data, and explains that shapefiles are commonly used to store vector data while grids are used for raster data. DEM and TIN are introduced as methods for representing elevation data. The document also covers projected and unprojected coordinate systems and provides examples of coordinate systems. Common file formats for both raster and vector data are listed.
Geographical Information System (GIS) is a computer system for capturing, storing, analyzing, and displaying spatially-referenced data. GIS allows users to visualize relationships and patterns in data through maps, globes, reports, and charts. The key components of GIS are data capture, database management, geographic analysis, and result preparation. GIS data comes in vector and raster formats, with vector being better for representing real-world features precisely and raster being better for dense data like elevation or land cover. GIS provides accurate data, better analysis and predictions, and helps answer questions by visualizing spatial relationships. However, GIS software can be expensive and difficult to integrate with traditional maps.
This document provides an overview of databases and WebGIS. It discusses different types of databases including MySQL, PostgreSQL, and spatially-enabled databases. It compares MySQL and PostgreSQL, covering when each would be used. It also covers database data conversions between formats like JSON, GeoJSON, CSV, SHP, and KML/KMZ. For WebGIS, it defines it as a distributed information system comprising a server and client, where the server is a GIS server and client a web browser. It discusses purposes, technologies, languages/frameworks like Python, JavaScript, GeoDjango, and case studies for building WebGIS systems.
GIS and Remote Sensing to study urban-rural transformation during a fifty-yea...Maurizio Pollino
C. R. Fichera, G. Modica, M. Pollino (2011).
Presented at "Computational Science and Its Applications - ICCSA 2011 International Conference", Santander, Spain, June 20-23, 2011.
A relevant issue in Remote Sensing (RS) and GIS is related to the analysis and the characterization of Land Use Land Cover (LULC) changes, very useful for a wide range of environmental applications and to efficiently undertake landscape planning and management policies. The methodology described has been applied to a case-study conducted in the area of the Province of Avellino (Southern Italy). Firstly, aerial photos and Landsat imagery have been classified to produce LULC maps for a fifty-year period (1954÷2004). Then, through a GIS approach, change detection and spatiotemporal analysis has been integrated to characterize LULC dynamics, focusing on the urban-rural gradient. This study has shown that LULC patterns and their changes are linked to both natural and social processes whose driving role has been clearly demonstrated: after the disastrous Irpinia earthquake (1980), local specific zoning laws and urban plans have significantly addressed landscape changes.
The document discusses properties of Landsat satellites and remote sensing data. It provides details on:
- The history and timeline of Landsat satellites and their sensors from Landsat 1 to Landsat 7.
- How Landsat data is processed to convert digital numbers to radiances and reflectances and apply atmospheric corrections.
- How different surface features like vegetation, soil and water absorb electromagnetic radiation differently, enabling their identification in remote sensing imagery.
This document discusses GIS tools and techniques for watershed analysis including DEM, fill sinks, flow direction, flow accumulation, conditional elevation, stream ordering, snapping pour points, watershed delineation, basin delineation, and calculating flow length. Key steps are opening a DEM, preprocessing the DEM with fill and flow tools, defining streams and pour points, delineating watersheds, and calculating attributes like flow length. The overall goal is to use GIS to analyze watersheds, drainage patterns, and water flow across landscapes.
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.
Georeferencing is the process of associating a map or aerial photo with geographic coordinates. It involves relating the internal coordinate system of a map to real-world locations. The georeferencing process in QGIS involves enabling the Georeferencer GDAL plugin, opening a raster image, collecting ground control points by clicking points on the image and entering their coordinates, specifying transformation settings like resampling method, and starting the georeferencing which warps the image using the ground control points. When complete, the georeferenced layer is loaded into QGIS.
Mobile GIS allows field workers to capture and edit geographic data on mobile devices. It integrates GPS, mobile devices, and wireless communications to access GIS data from the field. The main benefits are improved field efficiency and data accuracy. ESRI provides several mobile GIS apps, including ArcPad for data collection, and apps for Windows, iOS, and Android devices that can access maps and perform analysis in the field. Mobile GIS systems connect mobile devices running GIS software via wireless networks to central GIS servers to share and sync field data.
This document provides a short introduction to Geographic Information Systems (GIS). It discusses the purposes of GIS, including using GIS to understand phenomena that have both geographic and temporal dimensions. It also describes how GIS allows users to enter, analyze, and present georeferenced data. The document outlines how GIS represents real world features through models like maps and databases and discusses spatial databases specifically. It positions GIS as existing at the intersection of geography and information science and technology.
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
A Geographic Information System (GIS) integrates hardware, software and data to capture, store, analyze and display spatially-referenced information. GIS allows users to view, understand, question, interpret, and visualize data in many ways that reveal relationships, patterns, and trends. Key components of a GIS include hardware, software, data, methods, and personnel with GIS expertise. GIS differs from other graphics systems in its ability to geo-reference data, use relational databases to link spatial and non-spatial data, and overlay multiple data layers in a single map.
This document provides an overview of remote sensing technology presented at a training seminar. It discusses the basics of remote sensing including history, platforms like airborne and spaceborne sensors, and organizations like ISRO and NRSC involved in remote sensing. It also describes GPS technology and how coordinates are determined. Geographic information systems and how they integrate remote sensing data and GPS coordinates into databases is outlined. Image processing techniques like enhancement, restoration and compression are summarized along with applications. The linear model of integrating GPS, remote sensing, GIS and image processing is presented. Advantages and applications of remote sensing are highlighted along with challenges. A case study on mapping various resources in Sirohi district using remote sensing data is briefly described.
Geographical Information System (GIS) Georeferencing and Digitization, Bihar ...Kamlesh Kumar
This work is an effort to share Geographical Information System: Georeferencing, digitization and map making steps through QGIS 2.0.1
Georeferencing
Digitization of Topographical sheet
Point
Line
Area
Bihar Map
District Headquarters
Railway of Bihar
District Boundaries
Thematic Maps (Literacy & Sex Ratio)
The document discusses spatial data and spatial analysis. It defines spatial data as data connected to locations on Earth, with three main components - geometric data describing location, thematic data providing attribute values, and identifiers linking the geometric and thematic components. Spatial analysis in GIS involves functions like measurements, queries, classifications and modeling to analyze spatial relationships in the data and address real-world problems. Common analysis functions in GIS include measurements, queries, extractions, proximity analysis, and network analysis.
GIS can be applied to various urban planning problems, such as master planning, area monitoring, regional potential analyses, site selection studies, and the documentation and approval of development plans. It is useful for interpreting and formulating land use policy, modeling likely land use changes, and assessing the impacts of predicted land use changes. GIS is also significant for environmental planning, such as developing natural resource inventories, identifying pollution sources, assessing constraints, and determining suitability for waste treatment techniques. It can also help with wetland applications like regional inventories.
This document provides an overview of GIS, raster data formats, and spatial analysis tools. It defines GIS as an automated system for capturing, storing, analyzing and displaying spatial data linked to tabular data on maps. Raster data represents real-world phenomena through a matrix of cells, with each cell storing a value like elevation or satellite imagery. Spatial analysis tools in GIS like Euclidean distance and point density perform cell-based raster analyses, calculating distances from raster cells to points or densities of points within neighborhoods.
The document provides an introduction to ArcGIS. It outlines that it will discuss what GIS is, how geographic data is represented in GIS, how data is stored in ArcGIS, GIS maps, GIS analysis processes, what ArcGIS is, and planning a GIS project. It then proceeds to define GIS, explain how geographic data is modeled in vector and raster formats, describe how data is organized and stored in an ArcGIS geodatabase, discuss GIS mapping and visualization, and overview spatial analysis tools in ArcGIS.
Government has huge amounts of information but how can this be effectively managed and delivered through the web? This session will ‘lift the lid’ on web mapping technology and identify some of the key issues that must be addressed to achieve a successful outcome.
The NSW government SIX Viewer web mapping portal will be used as a case study to demonstrate how terabytes of data can be integrated and delivered via the Internet.
Flood risk mapping using GIS and remote sensing and SARRohan Tuteja
This document summarizes a presentation on using synthetic aperture radar (SAR) data from RADARSAT-1 to map flooding in Kendrapara District, India. SAR data from four dates in September 2008 were used to map the spatial extent and temporal progression of flooding over time. Traditional flood mapping methods are time-consuming and difficult during floods, while SAR data can penetrate clouds and capture flooding regardless of weather conditions. The methodology involved preprocessing the SAR data, removing noise, correcting geometrically, and classifying images to map flooding and analyze how floodwaters spread over the four dates. Peak flooding occurred on September 22nd, affecting over 37,400 hectares. The results demonstrate how SAR data can effectively monitor flooding and inform disaster response
The document discusses the components of ArcGIS software. It describes ArcMap as the application for viewing, editing, creating, and analyzing geospatial data. ArcToolbox contains tools for tasks like data management and analysis. ArcCatalog provides tools for managing data, folders, metadata, and more. It also discusses concepts like map projections, spatial data formats, attribute tables, and performing selections and joins on data.
This document provides an overview of geographic information systems (GIS). It discusses the history of GIS, defines what GIS is, describes what types of geographical data are used in GIS, and outlines the key GIS processes of capture, manage, analyze and present. It also provides some examples of GIS applications such as crime mapping, hydrology and health services. The overall document provides a high-level introduction to what GIS is and how it works.
This document discusses the various applications of geographic information systems (GIS). It begins by introducing GIS and its capabilities, such as data input, management, analysis and modeling. It then examines 10 specific applications of GIS: 1) geological mapping, 2) mining and mineral exploration, 3) groundwater exploration, 4) environmental analysis, 5) disaster management, 6) transportation systems, 7) demographic analysis, 8) agricultural development, 9) forestry, and 10) tourism. For each application, it provides details on how GIS is used to input, store, analyze and output geospatial data to support decision making in that domain.
Automated features extraction from satellite images.HimanshuGupta1081
This is the final year civil engineering project presentation in which different features i.e. Buildings, Road Network, Vegetation and Water are extracted automatically from satellite images with the help of Ecognition software. We have done our analysis on satellite images of sikar, rajasthan. In this project object based image analysis (OBIA) approach are used.
Total station and its application to civil engineeringTushar Dholakia
Total stations are surveying instruments that combine an electronic theodolite, electronic distance meter, and on-board computer. They allow users to measure horizontal and vertical angles as well as slope distances to calculate coordinates. Modern total stations can store thousands of data points, perform computations, and transfer data remotely via memory cards or wireless connections. They have largely replaced standalone theodolites and distance meters due to greater accuracy, automation, and data processing capabilities. Total stations find wide application in civil engineering, mining, accident reconstruction, and other fields requiring precise spatial measurements and positioning.
Radio Mobile is a free software tool used to design and simulate wireless networks. It predicts radio link performance using digital elevation maps and terrain data. The document provides instructions on how to use Radio Mobile to model wireless networks, including how to acquire terrain data, create network topologies, and perform coverage simulations and predictions. It also describes some wireless networks in Merida, Venezuela that have been implemented using Radio Mobile and outdoor wireless routers.
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.
Georeferencing is the process of associating a map or aerial photo with geographic coordinates. It involves relating the internal coordinate system of a map to real-world locations. The georeferencing process in QGIS involves enabling the Georeferencer GDAL plugin, opening a raster image, collecting ground control points by clicking points on the image and entering their coordinates, specifying transformation settings like resampling method, and starting the georeferencing which warps the image using the ground control points. When complete, the georeferenced layer is loaded into QGIS.
Mobile GIS allows field workers to capture and edit geographic data on mobile devices. It integrates GPS, mobile devices, and wireless communications to access GIS data from the field. The main benefits are improved field efficiency and data accuracy. ESRI provides several mobile GIS apps, including ArcPad for data collection, and apps for Windows, iOS, and Android devices that can access maps and perform analysis in the field. Mobile GIS systems connect mobile devices running GIS software via wireless networks to central GIS servers to share and sync field data.
This document provides a short introduction to Geographic Information Systems (GIS). It discusses the purposes of GIS, including using GIS to understand phenomena that have both geographic and temporal dimensions. It also describes how GIS allows users to enter, analyze, and present georeferenced data. The document outlines how GIS represents real world features through models like maps and databases and discusses spatial databases specifically. It positions GIS as existing at the intersection of geography and information science and technology.
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
A Geographic Information System (GIS) integrates hardware, software and data to capture, store, analyze and display spatially-referenced information. GIS allows users to view, understand, question, interpret, and visualize data in many ways that reveal relationships, patterns, and trends. Key components of a GIS include hardware, software, data, methods, and personnel with GIS expertise. GIS differs from other graphics systems in its ability to geo-reference data, use relational databases to link spatial and non-spatial data, and overlay multiple data layers in a single map.
This document provides an overview of remote sensing technology presented at a training seminar. It discusses the basics of remote sensing including history, platforms like airborne and spaceborne sensors, and organizations like ISRO and NRSC involved in remote sensing. It also describes GPS technology and how coordinates are determined. Geographic information systems and how they integrate remote sensing data and GPS coordinates into databases is outlined. Image processing techniques like enhancement, restoration and compression are summarized along with applications. The linear model of integrating GPS, remote sensing, GIS and image processing is presented. Advantages and applications of remote sensing are highlighted along with challenges. A case study on mapping various resources in Sirohi district using remote sensing data is briefly described.
Geographical Information System (GIS) Georeferencing and Digitization, Bihar ...Kamlesh Kumar
This work is an effort to share Geographical Information System: Georeferencing, digitization and map making steps through QGIS 2.0.1
Georeferencing
Digitization of Topographical sheet
Point
Line
Area
Bihar Map
District Headquarters
Railway of Bihar
District Boundaries
Thematic Maps (Literacy & Sex Ratio)
The document discusses spatial data and spatial analysis. It defines spatial data as data connected to locations on Earth, with three main components - geometric data describing location, thematic data providing attribute values, and identifiers linking the geometric and thematic components. Spatial analysis in GIS involves functions like measurements, queries, classifications and modeling to analyze spatial relationships in the data and address real-world problems. Common analysis functions in GIS include measurements, queries, extractions, proximity analysis, and network analysis.
GIS can be applied to various urban planning problems, such as master planning, area monitoring, regional potential analyses, site selection studies, and the documentation and approval of development plans. It is useful for interpreting and formulating land use policy, modeling likely land use changes, and assessing the impacts of predicted land use changes. GIS is also significant for environmental planning, such as developing natural resource inventories, identifying pollution sources, assessing constraints, and determining suitability for waste treatment techniques. It can also help with wetland applications like regional inventories.
This document provides an overview of GIS, raster data formats, and spatial analysis tools. It defines GIS as an automated system for capturing, storing, analyzing and displaying spatial data linked to tabular data on maps. Raster data represents real-world phenomena through a matrix of cells, with each cell storing a value like elevation or satellite imagery. Spatial analysis tools in GIS like Euclidean distance and point density perform cell-based raster analyses, calculating distances from raster cells to points or densities of points within neighborhoods.
The document provides an introduction to ArcGIS. It outlines that it will discuss what GIS is, how geographic data is represented in GIS, how data is stored in ArcGIS, GIS maps, GIS analysis processes, what ArcGIS is, and planning a GIS project. It then proceeds to define GIS, explain how geographic data is modeled in vector and raster formats, describe how data is organized and stored in an ArcGIS geodatabase, discuss GIS mapping and visualization, and overview spatial analysis tools in ArcGIS.
Government has huge amounts of information but how can this be effectively managed and delivered through the web? This session will ‘lift the lid’ on web mapping technology and identify some of the key issues that must be addressed to achieve a successful outcome.
The NSW government SIX Viewer web mapping portal will be used as a case study to demonstrate how terabytes of data can be integrated and delivered via the Internet.
Flood risk mapping using GIS and remote sensing and SARRohan Tuteja
This document summarizes a presentation on using synthetic aperture radar (SAR) data from RADARSAT-1 to map flooding in Kendrapara District, India. SAR data from four dates in September 2008 were used to map the spatial extent and temporal progression of flooding over time. Traditional flood mapping methods are time-consuming and difficult during floods, while SAR data can penetrate clouds and capture flooding regardless of weather conditions. The methodology involved preprocessing the SAR data, removing noise, correcting geometrically, and classifying images to map flooding and analyze how floodwaters spread over the four dates. Peak flooding occurred on September 22nd, affecting over 37,400 hectares. The results demonstrate how SAR data can effectively monitor flooding and inform disaster response
The document discusses the components of ArcGIS software. It describes ArcMap as the application for viewing, editing, creating, and analyzing geospatial data. ArcToolbox contains tools for tasks like data management and analysis. ArcCatalog provides tools for managing data, folders, metadata, and more. It also discusses concepts like map projections, spatial data formats, attribute tables, and performing selections and joins on data.
This document provides an overview of geographic information systems (GIS). It discusses the history of GIS, defines what GIS is, describes what types of geographical data are used in GIS, and outlines the key GIS processes of capture, manage, analyze and present. It also provides some examples of GIS applications such as crime mapping, hydrology and health services. The overall document provides a high-level introduction to what GIS is and how it works.
This document discusses the various applications of geographic information systems (GIS). It begins by introducing GIS and its capabilities, such as data input, management, analysis and modeling. It then examines 10 specific applications of GIS: 1) geological mapping, 2) mining and mineral exploration, 3) groundwater exploration, 4) environmental analysis, 5) disaster management, 6) transportation systems, 7) demographic analysis, 8) agricultural development, 9) forestry, and 10) tourism. For each application, it provides details on how GIS is used to input, store, analyze and output geospatial data to support decision making in that domain.
Automated features extraction from satellite images.HimanshuGupta1081
This is the final year civil engineering project presentation in which different features i.e. Buildings, Road Network, Vegetation and Water are extracted automatically from satellite images with the help of Ecognition software. We have done our analysis on satellite images of sikar, rajasthan. In this project object based image analysis (OBIA) approach are used.
Total station and its application to civil engineeringTushar Dholakia
Total stations are surveying instruments that combine an electronic theodolite, electronic distance meter, and on-board computer. They allow users to measure horizontal and vertical angles as well as slope distances to calculate coordinates. Modern total stations can store thousands of data points, perform computations, and transfer data remotely via memory cards or wireless connections. They have largely replaced standalone theodolites and distance meters due to greater accuracy, automation, and data processing capabilities. Total stations find wide application in civil engineering, mining, accident reconstruction, and other fields requiring precise spatial measurements and positioning.
Radio Mobile is a free software tool used to design and simulate wireless networks. It predicts radio link performance using digital elevation maps and terrain data. The document provides instructions on how to use Radio Mobile to model wireless networks, including how to acquire terrain data, create network topologies, and perform coverage simulations and predictions. It also describes some wireless networks in Merida, Venezuela that have been implemented using Radio Mobile and outdoor wireless routers.
Real-Time Map Building using Ultrasound ScanningIRJET Journal
This document describes a device that uses an ultrasonic sensor to build maps of environments in real-time. The device scans the sensor across a 180 degree range to collect distance data points and generates a 2D map visualization. It is intended to enable robots to autonomously map and navigate environments. The device was able to generate basic maps of test environments but had some inaccuracies, depicting flat surfaces as arcs rather than straight lines due to the wide beam of the ultrasonic sensor. Improving the sensor resolution could help address these inaccuracies. The real-time mapping ability, low cost, and small size make the device suitable for mobile robot applications.
The document provides an overview of photogrammetry, which is the science and technology of obtaining reliable spatial information about physical objects and the environment through analyzing photographs. It discusses the different types of photogrammetry including aerial/spaceborne photogrammetry and close-range photogrammetry. It also summarizes the key techniques, applications, and products of photogrammetry such as digital terrain models, orthophotos, and 3D models.
The document describes the ShortWave Radiation Balance (SWRB) component in the JGrass-NewAge modeling system. The SWRB component calculates shortwave radiation on complex terrain by accounting for elevation, slope, aspect, and shadowing effects. It uses parameterizations to estimate clear sky irradiance and models the direct and diffuse radiation components. The document provides details on the component's algorithms, inputs, outputs, and examples of running it within the OMS modeling framework.
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This document discusses the Galileo PVT app, which provides positioning, velocity, and time solutions using Galileo and GPS satellite constellations. It retrieves ephemeris and clock data from a SUPL server and applies ionospheric corrections using the NeQuick model. The app was tested in static, pedestrian, and vehicular scenarios and provides satellite visibility information, computed positions on a map, and augmented reality views. It aims to demonstrate assisted GNSS capabilities on Android devices using Galileo signals.
H2020 Jupiter Project's webinar, hold on February 23rd 2016.
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Low cost L1 GPS system suitable for PPK systems and precise navigation for Drones. Geomos and other structural monitoring systems. Safety systems where positioning is critical. Autonomous machine control, surface as well as Underground
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Modern surveying techniques provide accurate measurements for civil engineering projects. Techniques like total stations, digital levels, GPS, and data collectors allow surveyors to measure horizontal and vertical angles as well as distances electronically. This improves accuracy and efficiency over traditional methods. Specific applications in civil engineering include road, tunnel, and bridge alignment, infrastructure mapping for urban planning, and construction site layout and monitoring. The summarized data can be imported into CAD software for modeling and analysis. Overall, modern surveying techniques enhance accuracy and productivity for civil engineering design and construction.
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2. Key steps include data preparation with local mapping parties, UAV flights and imagery collection, focus groups to identify issues, initial flood simulations, and revisions based on community feedback.
3. Examples are given of community decision making, such as organizing to clean wastewater canals at the neighborhood level and removing advertising boards claiming credit for local work.
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- Formats like flipbook volumes and Contoured Frequency by Altitude Diagrams (CFADs) collapse dimensions to visualize patterns in rainfall and erosivity over space and time.
- These previews make it easier to identify data errors, periods of intense weather, and ensure only reliable data is used for modeling and analysis.
Digitization and 3d modelling of a mine planSafdar Ali
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Similar to The GRASS GIS software (with QGIS) - GIS Seminar (20)
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The Geographic Resources Analysis Support System (http://grass.osgeo.org/), commonly referred to as GRASS GIS, is an Open Source Geographic Information System providing powerful raster, vector and geospatial processing capabilities in a single integrated software suite. GRASS GIS includes tools for spatial modeling, visualization of raster and vector data, management and analysis of geospatial data, and the processing of satellite and aerial imagery. It also provides the capability to produce sophisticated presentation graphics and hardcopy maps. GRASS GIS has been translated into about twenty languages and supports a huge array of data formats. It can be used either as a stand-alone application or as backend for other software packages such as QGIS and R geostatistics. It is distributed freely under the terms of the GNU General Public License (GPL). GRASS GIS is a founding member of the Open Source Geospatial Foundation (OSGeo).
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European and other countries are at increasing risk for new or re-emerging vector-borne diseases. Among the top ten vector-borne diseases with greatest potential to affect European citizens are Dengue fever, Chikungunya, Hantavirus, and Crimean-Congo hemorrhagic fever. Despite the risk of disease transmission, many vectors like the Asian tiger mosquito or ticks are also a nuisance in daily life. The examination of disease vector spread and a better understanding of spatio-temporal patterns in disease transmission and diffusion is greatly facilitated by Geoinformatics. New methods including the use of high resolution time series from space in spatial models enable us to predict species invasion and survival, and to assess potential health risks. Geoinformatics is able to address the increasing challenge for human and veterinary public health not only in Europe, but across the globe, assisting decision makers and public health authorities to develop surveillance plans and vector control.
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Geospatial Analytics Forum at North Carolina State University, 4 Sept 2014 - http://geospatial.ncsu.edu/about/geoforum/
See also: http://opensource.com/education/14/9/back-school-grass-gis
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GRASS GIS (Geographic Resources Analysis Support System) looks back to the longest development history in the FOSS4G community. Having been available for 30 years, a lot of innovation has been put into the new GRASS GIS 7 release. After six years of development it offers a lot of new functionality, e.g. enhanced vector network analysis, voxel processing, a completely new engine for massive time series management, an animation tool for raster and vector map time series, a new graphic image classification tool, a "map swiper" for interactive maps comparison, and major improvements for massive data analysis (see also http://grass.osgeo.org/grass7/). The development was driven by the rapidly increasing demand for robust and modern free analysis tools, especially in terms of massive spatial data processing and processing on high-performance computing systems. With respect to GRASS GIS 6.4 more than 10,000 source code changes have since been made.
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GRASS GIS 7 bietet neue Module zur Vektornetzwerk-, Voxelanalyse, Zeitreihenspeicherung und -management, dazu ein Animationstool für Raster-und Vektorkartenzeitreihen, ein graphisches Bildklassifikationtool, "Map Swiper" zum interaktiven Kartenvergleich nebst verbesserter massiver Datenanalyse.
GRASS GIS (Geographic Ressourcen Analysis Support System) blickt mit nun 30 Jahren auf die längste Entwicklungsgeschichte in der FOSSGIS Community zurück. Die stark ansteigende Nachfrage nach robusten und modernen freien Analysewerkzeugen, v.a. im Hinblick auf die heutzutage enormen räumlichen Datenmengen führte 2008 zum Beginn der GRASS GIS 7 Entwicklung. In Bezug auf GRASS GIS 6.4 wurden inzwischen mehr als 10.000 Verbesserungen vorgenommen.
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Darüber hinaus wurde insbesondere die topologische Vektorbibliothek in Bezug auf die Unterstützung von großen Dateien verbessert. Des weiteren gibt es eine Reihe von neuen Analysefunktionen und auch im Raster-/Bildbereich die Unterstützung für massive Datenanalyse. Auch werden nun Projektionen andere Planeten unterstützt. Viele Module wurden in Bezug auf Geschwindigkeit signifikant optimiert. Der Vortrag illustriert die interessantesten Neuerungen und zeigt, wie Benutzer auf einfache Weise auf die kommende GRASS GIS 7 Version migrieren können. Testversionen stehen für alle üblichen Betriebssysteme zur Verfügung (http://grass.osgeo.org/download/software/).
News in GRASS GIS7. Plenary talk at FOSS4G-CEE 2013, RomaniaMarkus Neteler
GRASS GIS, commonly referred to as GRASS (Geographic Resources Analysis Support System), is the free Geographic Information System (GIS) software with the longest record of development as FOSS4G community project. The increasing demand for a robust and modern analytical free GIS led to the start of GRASS GIS 7 development in April 2008. Since GRASS 6 more than 10,000 changes have been implemented with a series of new modules for vector network analysis, image processing, voxel analysis, time series management and improved graphical user interface (http://trac.osgeo.org/grass/wiki/Grass7/NewFeatures). The core system offers a new Python API and large file support for massive data analysis. Many modules have been undergone major optimization also in terms of speed. The presentation will highlight the advantages for users to migrate to the upcoming GRASS GIS 7 release.
This document discusses the history and capabilities of GRASS GIS, an open source geospatial software suite. GRASS was first developed in 1984 and continues to be improved through contributions from its development team and user community. It provides tools for geospatial data management, spatial modeling, map production, spatial analysis, and visualization. GRASS can process large LIDAR datasets, connect to external data sources, perform 3D modeling and visualization, and is increasingly integrated with web-based tools and programming interfaces like Python.
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OGRS 2009: International Opensource Geospatial Research Symposium
www.ogrs2009.org
From a niche to a global user community: Open Source GIS and OSGeo
Markus Neteler
IASMA Research and Innovation Centre
Fondazione Edmund Mach
Environment and Natural Resources Area
GIS and Remote Sensing Unit, Trento, Italy
Web: http://gis.fem-environment.eu/
Email: markus.neteler . iasma.it
Geographical Information Systems (GIS) have evolved from a highly specialized niche to a technology that affects nearly every aspect of our lives, from finding driving directions to managing natural disasters. The masses have discovered geospatial data and technologies through the availability of popular globes; wiki-fied street mapping which was started by a few individuals has grown to weekly mapping parties around the globe. Today almost everybody can create customized maps or overlay GIS data. Current GIS technology covers viewing maps and images on the web, simple and complex spatial analysis, modeling and simulations.
In our presentation we'll present highlights of the last 20 years of Open Source GIS developments. Many projects are born as initiative of individuals when the lack of available software for a specific application is solved by own development and the result is then made available to the public on the Internet for further collaborative development. In the early 80's, the first Open Source GIS (MOSS and GRASS GIS) reached production status followed by the PROJ4 library project, a first crucial library for many Open Source GIS applications. In 1995 the UMN MapServer project was started to implement OGC standard. The second cross-project library GDAL/OGR was born in 1998. While these projects became mature, new applications were started with partially extraordinary success (OpenEV, OSSIM, MapBuilder, PostGIS, Geoserver, Quantum GIS, uDIG, MapGuide Open Source, MapBender, gvSIG, Geonetwork and OpenLayers).
The wealth of available but partially unconnected projects suggested to establish an umbrella foundation to foster source code and knowledge sharing. Hence, in February 2006, the Open Source Geospatial Foundation (OSGeo, www.osgeo.org) has been created to support and promote worldwide use and collaborative development of Open Source geospatial technologies and data. The foundation supports outreach and advocacy activities to promote Open Source concepts. It also builds shared infrastructure for improved cross-project collaboration. OSGeo has been a stimulating force for cooperative developments of sister projects, leveraging each other efforts by developing shared architecture components and expanding interoperability.
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In our opinion, Open Source GIS is an appropriate choice for scientific computing as it is developed in a peer review process. We will show some case studies for GRASS GIS usage in research which illustrates its academic roots especially in environmental applications. This covers analysis of spatio-temporal data sets such as multi-temporal Lidar and remote sensing data including processing of large amounts of geospatial data on a cluster.
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http://www.archeo-foss.org/
Abstract:
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GRASS 6.4.0, the new stable release after more than one year of development and testing, brings a number of exciting enhancements to the GIS. Besides the hundreds of new module features, supported data formats, and language translations. The 6.4.0 release also runs in MS-Windows, a new installer is provided. A new graphical user interface with integrated location wizard and new vector digitizer is also included.
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The GRASS GIS software (with QGIS) - GIS Seminar
1. The GRASS GIS software GIS Seminar Politecnico di Milano Polo Regionale di Como M. Neteler neteler at osgeo.org http://grass.itc.it ITC-irst, Povo (Trento), Italy (Document revised November 2006)
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4. GRASS GIS Brief Introduction Developed since 1984, always Open Source , since 1999 under GNU GPL Written in C programming language, portable code (multi-OS, 32/64bit) International development team , since 2001 coordinated at ITC-irst GRASS master Web site: http://grass.itc.it GNU/Linux MacOSX MS-Windows iPAQ
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6. Spatial Data Types Supported Spatial Data Types 2D Raster data incl. image processing 3D Voxel data for volumetric data 2D/3D Vector data with topology Multidimensional points data http://grass.itc.it Orthophoto Distances Vector TIN 3D Vector buildings Voxel
7. Raster data model Raster geometry cell matrix with coordinates resolution: cell width / height (can be in kilometers, meters, degree etc.) y resolution x resolution
8. Vector data model Vector geometry types Point Centroid Line Boundary Area (boundary + centroid) face (3D area) [kernel (3D centroid)] [volumes (faces + kernel)] Geometry is true 3D: x, y, z Line Faces not in all GIS! Node Node Vertex Vertex Segment Segment Segment Node Boundary Vertex Vertex Vertex Vertex Centroid Area
9. OGC Simple Features versus Vector Topology Simple Features ... - points, lines, polygons - replicated boundaries for adjacent areas Advantage: - faster computations Disadvantage: - extra work for data maintenance - in this example the duplicated boundaries are causing troubles Switzerland slivers Map generalized with Douglas-Peucker algorithm in non-topological GIS gaps (Latitude-longitude)
10. OGC Simple Features versus Vector Topology ... versus Vector Topology - points, centroids, lines, boundaries - in topology centroid and boundary form an area - single boundaries for adjacent areas Advantage: - less maintenance, high quality Disadvantage: - slower computations Switzerland Original Pruned each boundary is a single line, divided by two polygons (UTM32N projection) Map generalized with v.clean “prune” algorithm in topological GIS GRASS
11. Italy: Gauss-Boaga Coordinate System Gauss-Boaga Transverse Mercator projection 2 zones ( fuso Ovest, Est ) with a width of 6º30' longitude Each zone is an own projection! False easting: Fuso Ovest: 1500000m (1500km) Fuso Est: 2520000m (2520km) False northing: 0m Scale along meridian: 0.9996 – secante case, not tangent case Ellipsoid: international (Hayford 1909, also called International 1924) Geodetic datum : Rome 1940 (3 national datums; local datums to buy from IGM). National datum values available at: http://crs.bkg.bund.de/crs-eu/
17. WebGIS: Integration of data sources GRASS in the Web Real-time monitoring of Earthquakes (provided in Web by USGS) with GRASS/PHP: http://grass.itc.it/spearfish/php_grass_earthquakes.php
23. Spearfish Sample Dataset Spearfish (SD) sample data location Maps: raster, vector and point data covering two 1:24000 topographic maps (quadrangles Spearfish and Deadwood North) UTM zone 13N, transverse mercator projection, Clarke66 ellipsoid, NAD27 datum, metric units, boundary coordinates: 4928000N, 4914000S, 590000W, 609000E DATA download: http://mpa.itc.it/markus/osg05/ SD Spearfish
24. Practical GIS Usage Start a “terminal” to enter commands Start GRASS 6 within the terminal: grass61 -help grass61 -gui 1. 2. 3.
25. GRASS user interface: QGIS Start QGIS within GRASS terminal: qgis http://qgis.org GRASS Toolbar
26. QGIS: further key functionality Creating a paper map GRASS toolbox GRASS raster maps GRASS vector maps GRASS vector digitizer
27. New GRASS user interface: QGIS Excercise: Please reproduce this map view! Raster: - elevation.dem - aspect Vector: - roads - fields
28. QGIS map composer: prepare map with layout Creating a paper map for printing or saving into a file (SVG, PNG, Postscript) Transfer map view into map composer (printer symbol)
31. QGIS-GRASS Exercises: Noise impact 1/4 1) Simple noise impact map: Extract interstate (highway) from roads vector map into new map and buffer interstate for 3km in each direction GRASS commands: a) first look at the table to get column name and ID of interstate: v.db.select roads b) we extract only 'interstate' (cat = 1, cat is the GRASS standard column name for ID): v.extract in=roads out=interstate where=”cat = 1” c) we buffer the interstate (give buffer in map units which is meters here): v.buffer interstate out=interstate_buf3000 buffer=3000
32. QGIS-GRASS Exercises: Noise impact 2/4 2) Verify affected areas: Look at landcover.30m raster map, overlay extracted interstate and overlay buffered interstate_buf3000 (use transparency to make it nice)
33. # set current region to landcover map, '-p' prints the settings: g.region rast= landcover.30m -p Info: Command line versus graphical user interface On the next slide we either use the following command line: or these settings in the graphical user interface:
34. QGIS-GRASS Exercises: Noise impact 3/4 How to get statistics on influenced landcover-landuse units? -> needs generalization of original landcover.30m map (originates from satellite map) Approach 1: Raster based generalization : “mode” operator in moving window # set current region to landcover map, '-p' prints the settings: g.region rast= landcover.30m -p r.neighbors in= landcover.30m out= landcover.smooth method= mode size= 3 3x3 moving window
35. QGIS-GRASS Exercises: Noise impact 4/4 ... Generalization cont'ed: Approach 2: Vector based generalization : “rmarea” tool: merges small areas into bigger a. # zoom to map: g.region rast= landcover.30m -p # raster to vector conversion: r.to.vect in= landcover.30m out= landcover_30m f=area # filter perimeter of 3x3 pixels ( threshold=(30 * 3)^2 = 8100) v.clean in= landcover_30m out= landcover_30m_gen tool= rmarea thresh= 8100
38. GRASS: Geographic Resources Analysis Support System Example for Location and Mapsets /home/user/grassdata /europa /hannover /world hist dbln coor sidx topo Mapset Location /PERMANENT GRASS Database /prov_trentino /PERMANENT /trento Geometry and attribute data streets parks lakes poi streets.dbf parks.dbf poi.dbf lakes.dbf fcell hist colr cell_misc cellhd cell cats vector dbf /silvia
39. Raster map analysis DEM analysis Raster map algebra Geocoding of scanned map Volume data processing
40. GRASS Command Classes d.* display graphical output (screen) r.* raster raster data processing r3.* raster3D raster voxel 3D data processing i.* imagery image processing v.* vector vector data processing g.* general general file operations (copy, rename of maps, ...) m.* misc miscellaneous commands ps.* postscript map creation in Postscript format Prefix Class Functionality
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42. Raster data analysis: Geomorphology DEM: r.param.scale # set region/resolution to the input map: g.region rast=elevation.10m -p # generalize with size parameter r.param.scale elevation.10m out=morph param=feature size=25 # with legend d.rast.leg morph # view with aspect/shade map (or QGIS) d.his h=morph i=aspect.10m Spearfish DEM: 10m Moving window size: 25x25 nviz elev=elevation.10m col=morph
43. Raster data analysis: Water flows - Contributing area Topographic Index: ln(a/tan(beta)) g.region rast=elevation.10m -p r.topidx in=elevation.10m out=ln_a_tanB d.rast ln_a_tanB d.vect streams col=yellow # ... the old vector stream map nicely deviates from the newer USGS DEM nviz elevation.10m col=ln_a_tanB
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47. Working with vector data Vector map import Attribute management Buffering Extractions, selections, clipping, unions, intersections Conversion raster-vector and vice verse Digitizing in GRASS and QGIS Working with vector geometry
48. GRASS 6 Vector data Vector geometry types Point Centroid Line Boundary Area (boundary + centroid) face (3D area) [kernel (3D centroid)] [volumes (faces + kernel)] Geometry is true 3D: x, y, z Line Faces Node Node Vertex Vertex Segment Segment Segment Node Boundary Vertex Vertex Vertex Vertex Centroid Area
49. Raster-Vector conversion – extraction 1/2 Extraction of residential areas from raster landuse map # set current region to map; look at the landuse/landcover map with legend: g.region rast=landcover.30m -p d.erase d.rast.leg -n landcover.30m # Automated vectorization of the landuse/landcover map: r.to.vect -s landcover.30m out=landcover30m feature=area # see attribute table ('-p' prints the current connection between vector # geometry and attribute table – note that GRASS can link to various DBMS): v.db.connect -p landcover30m # ... will tell you that it is a DBF table v.db.select landcover30m
50. Raster-Vector conversion – extraction 2/2 Extraction of residential areas from raster landuse map # generate list of unique landuse/landcover types from text legend output: v.db.select landcover30m | sort -t '|' -k2 -n -u #display selected categories: d.erase d.vect landcover30m where="value=21 or value=22" fcol=orange # Extract residential area into a new vector map: v.extract landcover30m out=residential where="value=21 or value=22" d.frame -e d.vect residential fcol=orange type=area d.vect roads d.barscale -mt This pipe '|' character is a nice way of combining Unix commands. The output of the first command is sent into the second and so forth... sort is here sorting by second column on numbers (-n) and extracts unique (-u) rows only
56. Vector network analysis methods Vector network with one way roads Generic vector directions One attribute column for each direction Value -1 closes direction (for one way streets) drawn in ps.map Street direction open closed
57. Vector networking Shortest path with d.path d.vect roads d.path roads # or: # v.net.path Further vector network exercises: http://mpa.itc.it/corso_dit2004/grass04_4_vector_network_neteler.pdf
58. Working with own data - Import/Export/Creating Locations Import of LANDSAT-7 data Creating a new location external data files Creating from EPSG code/interactively a new location http://mpa.itc.it/markus/mum3/
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62. Image processing Image classification Image fusion with Brovey transform Natural color composites Calculating a degree Celsius map from the LANDSAT thermal channel
63. Import of LANDSAT-7 Erdas/Img Unsupervised & Supervised Image Classification classification methods in GRASS: all image data must be first listed in a group ( i.group ) See handout for unsupervised classification example Image Classification radiometric, radiometric, supervised radio- and geometric unsupervised supervised Preprocessing i.cluster i.class (monitor) i.gensig (maps) i.gensigset (maps) Computation i.maxlik i.maxlik i.maxlik i.smap
65. Image fusion: Brovey transform We use the earlier imported LANDSAT-7 scene to perform image fusion of the channels 2 (red), 4 (NIR), and 5 (MIR): g.region -dp i.fusion.brovey -l ms1=tm.2 ms2=tm.4 ms3=tm.5 pan=pan out=brovey # zoom to fused channel g.region -p rast=brovey.red # color composite: r.composite r=brovey.red g=brovey.green b=brovey.blue n out=tm.brovey d.rast tm.brovey nviz elevation.10m col=tm.brovey # Increase visual resolution in NVIZ # with Panel -> Surface # -> Polygon resolution # (lower! the value)
66. Natural color composites: LANDSAT-7 RGB The i.landsat.rgb script performs a histogram-area based color optimization: http://plantsci.sdstate.edu/woodardh/Soils_and_Ag/ Black_Hills/Soil_Characteristics_Profiles/landscape_pine.htm Photo: H.J. Woodard, SD Stae Univ. Standard RGB Enhanced RGB
67. TM61: Conversion of temperature first to Kelvin, then to degree Celsius g.region rast=tm6.1 -p #DN: digital numbers (coded temperatures) r.info -r tm6.1 min=131 max=175 # Conversion of DN to spectral radiances: r.mapcalc "tm61rad=((17.04 - 0.)/(255. - 1.))*(tm6.1 - 1.) + 0." r.info -r tm61rad min=8.721260 max=11.673071 # Conversion of spectral radiances to absolute temperatures (Kelvin): # T = K2/ln(K1/L_l + 1)) r.mapcalc "temp_kelvin=1260.56/(log (607.76/tm61rad + 1.0))" r.info -r temp_kelvin min=296.026722 max=317.399879 Recalibrating the LANDSAT-7 thermal channel 1/2
68. TM61: ... conversion to degree Celsius # We currently have the land surface temperature map in Kelvin. # Conversion to degree Celsius: r.mapcalc "temp_celsius=temp_kelvin - 273.15" r.info -r temp_celsius min=22.876722 max=44.249879 # New color table: r.colors temp_celsius col=rules << EOF -10 blue 15 green 25 yellow 35 red 50 brown EOF d.rast.leg temp_celsius g.region rast=elevation.dem -p nviz elevation.dem col=temp_celsius Recalibrating the LANDSAT-7 thermal channel 2/2
70. R-stats is a powerful statistical language Spatial extentions available for all kinds of geostatistics, spatial pattern analysis, time series etc Interface to exchange raster and point data between GRASS and R-stats Rdbi: connects R-stats to PostgreSQL PostgreSQL Spatial data Tables Geostatistics Predictive Models http://www.r-project.org http://grass.itc.it/statsgrass/ GRASS/R-stats interface - R-stats/PostgreSQL interface
74. GRASS: User map Who is using GRASS? AMTI/NASA Ames Research Center USA Austrian Institute for Avalanche and Torrent Research Bank of America Bombardier Aerospace Canada Brenner Railway Austria BR-NetProduction (Bavarian Television) Germany Canadian Forest Service CEA Monte Bondone Census USA CERN Switzerland CICESE Mexico CNR Italia Colorado State University Comune di Prato, Italy Comune Milano, Italy Comune Modena, Italy Comune di Torino, Italy Cornell University USA CSIRO Australia Deutsche Bank Germany DLR Germany Dubai Municipality DuPont Spain EDF France Ericsson Sweden ETH Zuerich Switzerland FED USA Finnish Meteorological Institute Forschungszentrum Juelich Germany Forschungszentrum Karlsruhe Germany GFZ Potsdam Germany Global Environmental Technology Nigeria Limited Graz Technical University Austria Harvard University Hokkaido University HPCC NECTEC Bangkok Thailand Iceland Forest Service Iceland Inst.of Earthquake Engineering & Seismology (ITSAK) Greece ISMAA - Centro Agrometeorologico, Istituto Agrario San Michele JPL NASA JSC NASA Purdue University Qualcomm USA Regione Toscana Rutgers University Sevilla University Spain South African Weather Bureau (METSYS) Stockholm Environment Institute-Boston Teledetection France Telefónica Spain TU Berlin TU Muenchen UC Davis UFRGS Brasilia University of Costa Rica University of Sydney University of Toronto Canada University of Trento, Italy US Army US Bureau of Reclamation US Dep. of Agriculture VA Linux Systems USA Landesmuseum Linz Austria La Poste France Lawrence Laboratories USA Lockheed Martin Space USA Los Alamos National Laboratory Meteo Poland MIT Lincoln Laboratory Nanjing University National Botanic Garden of Belgium National Museum Japan National Radio Astronomy Observatory USA National Research Center of Soils USA NCSA Illinois USA NCSU USA NIMA USA NOAA USA (GLOBE DEM generated with GRASS) NRSA USA Onera France (running SPOT etc.) Politecnico di Milano Politecnico di Torino Princeton University Procergs Brasilia
75. New OSGeo Foundation: Proposed founding projects Founded 4 th February 2006, Chicago http://www.osgeo.org GRASS GIS
77. Capacity building Communities growing together... GRASS GIS Spatial Computing http:// grass.itc.it GDAL - Geospatial Data Abstraction Library http://www.gdal.org ... AND MANY OTHERS! http://www.osgeo.org (General) statistical computing environment: http://www.r-project.org / Rgeo: spatial data analysis in R, unified classes and interfaces (e.g, RGRASS) http://r-spatial.sourceforge.net/ QGIS: user friendly Open Source GIS http://www.qgis.org Spatially-enabled Internet applications http:// mapserver.gis.umn.edu / PostGIS : support for geographic objects to the PostgreSQL object-relational database http://postgis.refractions.net PostgreSQL Most advanced open source relational database http://www.postgresql.org/
78. Closure of the Seminar Thanks for your interest and your attention! M. Neteler neteler at osgeo.org http://grass.itc.it ITC-irst, Povo (Trento), Italy