This document describes a study that used Landsat-7 ETM+ and NigeriaSat-1 satellite imagery from 2006 to revise the outdated 1964 topographic map of Onitsha Metropolis, Nigeria at a scale of 1:50,000. The satellite images were classified and the classifications were vectorized and integrated with contours generated from 2000 SRTM data to produce a revised 2006 topographic map. Pixel-based analysis found that NigeriaSat-1 classification had slightly higher overall accuracy than Landsat-7 ETM+, making NigeriaSat-1 better suited for revising medium-scale topographic maps in Nigeria. The study recommends completing medium-scale topographic map coverage of Nigeria and adopting 1:25,000 as the standard base
The document describes a regression model developed to predict percent built-up land cover in Pucallpa, Peru using normalized difference vegetation index (NDVI) values derived from Landsat imagery. Landsat and Google Earth imagery were analyzed to determine percent built-up land cover around sample points. NDVI and percent built-up land cover were then used to develop a regression model. The model was able to predict percent built-up land cover with an R-squared value of 0.776, providing planners and managers a low-cost tool for rapid urban area assessment.
This document compares three methods for mapping land cover of Vaderahalli Village, India: analysis of satellite imagery using GIS software MapInfo, analysis of Google Earth images using Google Pro software, and analysis of Google Earth images using MATLAB software. Land cover features mapped included green cover, water bodies, open spaces, paved surfaces and built-up areas. Results from each method were verified on-site using GPS. Analysis with MapInfo using satellite imagery provided the most accurate results but was more expensive and complex. Google Pro analysis was less accurate but simpler and cheaper. MATLAB analysis was least accurate and most complex and time-consuming. Overall, remote sensing with GIS provided the most effective land cover mapping approach.
Remote sensing uses sensors on satellites or aircraft to obtain information about objects without physical contact. A Geographic Information System (GIS) is a computer system for capturing, storing, analyzing and displaying geographical data. GIS integrates remote sensing data with maps to allow analysis of environmental and natural resources. Remote sensing and GIS help monitor natural disasters like floods and droughts in real-time, issue early warnings, and quickly assess damage through analysis of satellite imagery and spatial data.
Land use land cover mapping for smart village using gisSumit Yeole
This document summarizes a presentation on land use and land cover mapping for a smart village in India using GIS. The objectives were to understand GIS and remote sensing technologies and their applications in precision agriculture. The presenter described collecting satellite imagery, classifying land use types, and mapping them for the village of Kundewadi to identify agriculture, settlements, vegetation, water bodies and other land types. Pie charts showed the results, which found people primarily used the land for agriculture and suggested ways to improve wastewater, groundwater, solid waste management and increase agriculture land and trees.
Investigation of the Lake Victoria Region (Africa: Tanzania, Kenya and Uganda)Universität Salzburg
This poster is a student assignment for a course 'GISA 02 GIS: Geographical Information Systems - Advanced Course 0701', a part of the MSc studies. It presents an ArcGIS based spatial analysis of the Victoria Lake region including environmental, biological, social and economic characteristics of the region. The methodology includes data organizing and management in ArcGIS 9.3. Operations and technique: ArcGIS Spatial Analyst. Project architecture: ArcCatalog. Spatial referencing and re-projection: ArcToolbox. Data include DEMs: elevations (USGS). 2 tiles of the USGS DEM, Land cover data (raster), Population data: UNEP, ArcGIS vector.shp files of administrative boundaries fof Uganda, Tanzania, Kenya. Data preprocessing include following data preparation. Initial vector data: UNEP .shp. Spatial reference properties: Africa Albers Equal Area Conic projection, standard parallels 20 and -23, central meridian 25 and Datum WGS-84, Projection GEOGRAPHIC, Spheroid CLARKE1866. Data conversion from ASCII text data format to raster using ArcToolbox / Conversion Tools / ASCII to Raster (Climate precipitation data). Data were projected, processed and several layer formatting and overlays were created. Mapping was created using ArcMap. Victoria Lake has unique environment, important role in the economy of countries supporting 25 M people through fish catchment reaching up to 90-270$ per capita per annum. Kenya, Tanzania and Uganda control 6%, 49% and 45% of the lake surface. Lake catchment provides livelihood of 1/3 of the population of 3 countries with agricultural economy supported by fishing and agriculture (tea and coffee plantations).
This document discusses applications of geographic information systems (GIS) including urban planning, 3D modeling, environmental analysis, and hydrocarbon exploration. It provides examples of how GIS has been used for urban planning tasks like siting a daycare, modeling population change, and analyzing transportation networks. 3D modeling applications include generating high-resolution digital models from laser scanning data for uses like mapping, education, and engineering. Environmental analysis examples include examining the relationship between toxic sites and disadvantaged communities. The document also discusses GIS applications in hydrocarbon exploration like mapping fields and reservoirs, seismic interpretation, and production analysis to optimize resource development.
This document provides an overview of remote sensing and geographic information systems (GIS). It discusses that remote sensing involves gathering information about objects from a distance using sensors, including passive techniques like photography and active techniques like radar. It also outlines key remote sensing concepts and different sensor types. The document then defines GIS as a system for inputting, storing, analyzing and outputting geospatial data to support decision-making. It lists some common GIS functions and applications.
Geospatial technology uses systems like GIS, remote sensing, and photogrammetry to collect and map geographic information. It has evolved with new technologies to provide many socioeconomic benefits. Geospatial technology includes GNSS/GPS for positioning, remote sensing using sensors on planes and satellites, and GIS for analyzing, storing, and visualizing geographic data on maps. Emerging technologies like AI, IoT, AR/VR are also part of the vast and growing geospatial ecosystem, which delivers solutions across many industries.
The document describes a regression model developed to predict percent built-up land cover in Pucallpa, Peru using normalized difference vegetation index (NDVI) values derived from Landsat imagery. Landsat and Google Earth imagery were analyzed to determine percent built-up land cover around sample points. NDVI and percent built-up land cover were then used to develop a regression model. The model was able to predict percent built-up land cover with an R-squared value of 0.776, providing planners and managers a low-cost tool for rapid urban area assessment.
This document compares three methods for mapping land cover of Vaderahalli Village, India: analysis of satellite imagery using GIS software MapInfo, analysis of Google Earth images using Google Pro software, and analysis of Google Earth images using MATLAB software. Land cover features mapped included green cover, water bodies, open spaces, paved surfaces and built-up areas. Results from each method were verified on-site using GPS. Analysis with MapInfo using satellite imagery provided the most accurate results but was more expensive and complex. Google Pro analysis was less accurate but simpler and cheaper. MATLAB analysis was least accurate and most complex and time-consuming. Overall, remote sensing with GIS provided the most effective land cover mapping approach.
Remote sensing uses sensors on satellites or aircraft to obtain information about objects without physical contact. A Geographic Information System (GIS) is a computer system for capturing, storing, analyzing and displaying geographical data. GIS integrates remote sensing data with maps to allow analysis of environmental and natural resources. Remote sensing and GIS help monitor natural disasters like floods and droughts in real-time, issue early warnings, and quickly assess damage through analysis of satellite imagery and spatial data.
Land use land cover mapping for smart village using gisSumit Yeole
This document summarizes a presentation on land use and land cover mapping for a smart village in India using GIS. The objectives were to understand GIS and remote sensing technologies and their applications in precision agriculture. The presenter described collecting satellite imagery, classifying land use types, and mapping them for the village of Kundewadi to identify agriculture, settlements, vegetation, water bodies and other land types. Pie charts showed the results, which found people primarily used the land for agriculture and suggested ways to improve wastewater, groundwater, solid waste management and increase agriculture land and trees.
Investigation of the Lake Victoria Region (Africa: Tanzania, Kenya and Uganda)Universität Salzburg
This poster is a student assignment for a course 'GISA 02 GIS: Geographical Information Systems - Advanced Course 0701', a part of the MSc studies. It presents an ArcGIS based spatial analysis of the Victoria Lake region including environmental, biological, social and economic characteristics of the region. The methodology includes data organizing and management in ArcGIS 9.3. Operations and technique: ArcGIS Spatial Analyst. Project architecture: ArcCatalog. Spatial referencing and re-projection: ArcToolbox. Data include DEMs: elevations (USGS). 2 tiles of the USGS DEM, Land cover data (raster), Population data: UNEP, ArcGIS vector.shp files of administrative boundaries fof Uganda, Tanzania, Kenya. Data preprocessing include following data preparation. Initial vector data: UNEP .shp. Spatial reference properties: Africa Albers Equal Area Conic projection, standard parallels 20 and -23, central meridian 25 and Datum WGS-84, Projection GEOGRAPHIC, Spheroid CLARKE1866. Data conversion from ASCII text data format to raster using ArcToolbox / Conversion Tools / ASCII to Raster (Climate precipitation data). Data were projected, processed and several layer formatting and overlays were created. Mapping was created using ArcMap. Victoria Lake has unique environment, important role in the economy of countries supporting 25 M people through fish catchment reaching up to 90-270$ per capita per annum. Kenya, Tanzania and Uganda control 6%, 49% and 45% of the lake surface. Lake catchment provides livelihood of 1/3 of the population of 3 countries with agricultural economy supported by fishing and agriculture (tea and coffee plantations).
This document discusses applications of geographic information systems (GIS) including urban planning, 3D modeling, environmental analysis, and hydrocarbon exploration. It provides examples of how GIS has been used for urban planning tasks like siting a daycare, modeling population change, and analyzing transportation networks. 3D modeling applications include generating high-resolution digital models from laser scanning data for uses like mapping, education, and engineering. Environmental analysis examples include examining the relationship between toxic sites and disadvantaged communities. The document also discusses GIS applications in hydrocarbon exploration like mapping fields and reservoirs, seismic interpretation, and production analysis to optimize resource development.
This document provides an overview of remote sensing and geographic information systems (GIS). It discusses that remote sensing involves gathering information about objects from a distance using sensors, including passive techniques like photography and active techniques like radar. It also outlines key remote sensing concepts and different sensor types. The document then defines GIS as a system for inputting, storing, analyzing and outputting geospatial data to support decision-making. It lists some common GIS functions and applications.
Geospatial technology uses systems like GIS, remote sensing, and photogrammetry to collect and map geographic information. It has evolved with new technologies to provide many socioeconomic benefits. Geospatial technology includes GNSS/GPS for positioning, remote sensing using sensors on planes and satellites, and GIS for analyzing, storing, and visualizing geographic data on maps. Emerging technologies like AI, IoT, AR/VR are also part of the vast and growing geospatial ecosystem, which delivers solutions across many industries.
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.
This study analyzed land use and land cover changes around a mined area in Kannur district, Kerala, India between 2000 and 2017 using satellite imagery. Support vector machine classification identified five land cover classes: vegetation, barren land, built up area, mining area, and waterbodies. In 2000, vegetation covered 51.34% of the area, followed by barren land at 31.75%. By 2017, vegetation increased to 58.46% while barren land decreased to 19.98%. The mining area saw little change, increasing vertically within the same area. Comparing land cover changes over time can help sustainable environmental management near mined regions.
APPLICATION OF GEOGRAPHIC INFORMATION SYSTEM FOR EXPLORATION ACTIVITIES IN SO...Yudi Syahnur
First published in 2016 Indonesia Petroleum Association (IPA) Technical Symposium, this paper will illustrate how GIS Best Practices have been employed in Saka Indonesia Sesulu. From planning and execution of 550 km square 3D Seismic Survey to Rig Move monitoring activity.
GIS has also helped explorationist to effectively distinct trends, find patterns and anomalies of surface and subsurface structures. GIS allows people from multi-discipline and different backgrounds to collaborate easily, and contribute to the success of Oil & Gas Exploration in South Sesulu PSC.
The document provides an introduction to geographic information systems (GIS) and remote sensing. It discusses how GIS organizes and analyzes spatial data through data management, analysis, and visualization. It describes different data types including vector, raster, and imagery data. It also explains key concepts such as layers, modeling geospatial reality, and coding vector and raster data. The document outlines advantages and disadvantages of vector and raster data models. It introduces remote sensing and describes platforms and sensors used to collect spatial data from aircraft and satellites.
This document discusses the application of Geographic Information Systems (GIS) for various purposes:
1) GIS is used for disaster and emergency management like mapping earthquakes, cyclones, landslides, floods, and fires. It allows for relief and rehabilitation efforts to be planned.
2) GIS enables crime mapping and prediction by analyzing patterns of past crimes.
3) Other applications include GIS-based analysis of parking availability, deforestation over time, and generating contour maps and studying topography.
Geographic information system and remote sensingDhiren Patel
This document provides an overview of remote sensing and geographic information systems. It discusses the history of remote sensing from early aerial photography to modern satellite systems. Both passive and active remote sensing techniques are described, along with common applications in fields like forestry, agriculture, and land use analysis. Optical, radar, and lidar remote sensing systems are outlined. The document also introduces concepts in photogrammetry, surveying, and geographic information systems, including data structures and components of GIS.
This document provides an overview of geography awareness week and GIS. It discusses what GIS is, including how it can be used as a geodatabase, for geovisualization, and spatial analysis. It then discusses several applications of GIS in areas such as natural resource management, emergency response, social science, history, and trends in online GIS, cloud/mobile GIS, 3D-GIS, and volunteered geographic information.
This document provides an overview of geographical information systems (GIS) and remote sensing. It defines GIS and explains its key components, principles, functions, data types, advantages and disadvantages. It also defines remote sensing, describes its principles and stages, and outlines its applications in geology, natural resource management, national security and more. The advantages of remote sensing include large area coverage and permanent data records, while disadvantages include high costs and need for specialized training.
Application of Remote Sensing and GIS in Urban PlanningKARTHICK KRISHNA
This document discusses the application of remote sensing and GIS in urban planning. It outlines how remote sensing provides important data for mapping land use and monitoring environmental changes. GIS allows for spatial analysis and modeling of terrain, watersheds, and growth patterns. The document gives examples of how remote sensing and GIS have been used together for base map preparation, land suitability analysis, delineating sensitive areas, and monitoring urban growth. It concludes that remote sensing and GIS provide an effective tool for data collection, analysis, and innovative planning methodologies.
Use of remote sensing for land cover monitoring servir science applicationsKabir Uddin
This document discusses land cover mapping using remote sensing. It provides background on land cover mapping and monitoring in the Himalayan region, where deforestation and forest degradation have been issues. Remote sensing using satellite imagery and tools like GIS allows accurate land cover mapping over large areas. The document discusses different remote sensing platforms and sensors, as well as image classification techniques including unsupervised, supervised and object-based classification. It provides examples of software used for object-based image analysis, and outlines the steps involved in land cover mapping projects using remote sensing.
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.
geoinformatics handbook:it contains all open source software and commerical software of remote sensing,gis and photogrammerty and also all free data sources.free data sources such as DEM and LIDAR
Application of gis and gps in civil engineeringAvinash Anand
A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present geographical data. GIS integrates geospatial software and tools to enable spatial analysis and the display of large datasets in graphical form. GIS can be used for problem solving, decision making, and visualizing spatial data by mapping locations, quantities, densities, and changes over time for various applications like transportation, watershed analysis, land use planning, and more.
This document describes the production of a geoidal map and three-dimensional surface model for part of Port Harcourt, Nigeria using "Satlevel" collocation modeling. Global Navigation Satellite System data was collected to determine ellipsoidal heights, while leveling provided orthometric heights. These were used in the "Satlevel" collocation model to compute geoidal undulations, which were contoured in Surfer software to generate the geoidal map and 3D surface model. The map depicts the geoid configuration of the study area and can be used with ellipsoidal heights from GNSS to determine orthometric heights, providing a simpler method than conventional techniques.
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 analyze spatial data, facilitate decision making, and support planning and management activities.
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.
Geographic information systems (GIS) are organized collections of computer hardware, software, and geographic data used to capture, store, update, manipulate, analyze, and display geographically referenced information. GIS provides spatial data depicted as points, lines, or polygons with attributes stored in tables, and can take data from various sources and integrate them into multiple layers for analysis. Common applications of GIS include agriculture, natural resource management, disaster management, and urban planning.
Geoinformatics For Precision AgricultureRahul Gadakh
1) Geoinformatics deals with the capture, classification, storage, processing, portrayal, and dissemination of spatial information. It involves technologies like remote sensing, GIS, GPS, and photogrammetry.
2) It is used for applications like land use mapping, precision agriculture, crop discrimination and yield monitoring, soil mapping, and monitoring of land degradation.
3) Hyperspectral data and narrowband vegetation indices derived from it allow detailed analysis of crop biochemical and biophysical properties for improved classification and mapping of agricultural crops.
Geographic Information System(GIS) and Remote Sensing(RS) in Disaster Managem...Our slides
This presentation provides an overview of how Geographic Information Systems (GIS), Global Positioning Systems (GPS), and Remote Sensing (RS) can be applied to disaster management. It discusses the objectives of learning about these tools, the professional skills and expert knowledge gained, and how GIS, GPS, and RS are used in applications like emergency response, infrastructure planning and monitoring terrain. Real-world examples are given of how each technology supports disaster prevention, response and recovery efforts. The presentation concludes that knowledge of GIS, GPS and RS allows for more efficient disaster management by providing precise location data and remote monitoring capabilities.
Remote sensing and GIS for land cover change characterization and rural/urban...Maurizio Pollino
Authors: C. R. Fichera, G. Modica, M. Pollino.
Poster presented at Esri EMEA (Europe, Middle East And Africa) USER CONFERENCE 2010. Rome, October 26–28 2010
The development of the urban areas is able to transform landscapes formed by rural into urban life styles and to make functional changes, from a morphological and structural point of view. Historically, urban expansion (driven by the population increase) has typically take place on former agricultural use.
A multi-temporal image dataset has been analyzed to identify the changing pattern of Land Cover (LC) during a fifty-year period (1954÷2004).
Using the analysis tools of ArcGIS, the results have been synthesized into maps of LC changes, in order to characterize the respective dynamics.
Temporal trend analysis and landscape metrics have been integrated, using ArcGIS and specific extensions: such approach has allowed to characterize landscape patterns through significant indices and to understand the changes therein.
Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis that typically affects the lungs. It is transmitted through droplet infection. Driving forces behind TB include rapid population growth, urbanization, poverty, and poor living conditions. These lead to pressures such as overcrowding, poor sanitation, and increased HIV/AIDS prevalence that worsen the state of TB. Exposure groups with high risk include the elderly, HIV-positive individuals, prisoners, and urban slum residents. TB causes millions of deaths each year and increases poverty. Global and national actions aim to reduce TB prevalence, mortality, and achieve universal access to treatment.
Over 6.5 billion paper cups are used every year, which takes over 500 years to decompose in landfills. The waitress encourages customers to use reusable tumblers by offering rewards like a loyalty card where customers earn stickers for free items after filling it out. The cafe asks customers to keep their reusable tumblers or transform disposable cups into planters to plant tree seeds to help reduce waste and save the earth.
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.
This study analyzed land use and land cover changes around a mined area in Kannur district, Kerala, India between 2000 and 2017 using satellite imagery. Support vector machine classification identified five land cover classes: vegetation, barren land, built up area, mining area, and waterbodies. In 2000, vegetation covered 51.34% of the area, followed by barren land at 31.75%. By 2017, vegetation increased to 58.46% while barren land decreased to 19.98%. The mining area saw little change, increasing vertically within the same area. Comparing land cover changes over time can help sustainable environmental management near mined regions.
APPLICATION OF GEOGRAPHIC INFORMATION SYSTEM FOR EXPLORATION ACTIVITIES IN SO...Yudi Syahnur
First published in 2016 Indonesia Petroleum Association (IPA) Technical Symposium, this paper will illustrate how GIS Best Practices have been employed in Saka Indonesia Sesulu. From planning and execution of 550 km square 3D Seismic Survey to Rig Move monitoring activity.
GIS has also helped explorationist to effectively distinct trends, find patterns and anomalies of surface and subsurface structures. GIS allows people from multi-discipline and different backgrounds to collaborate easily, and contribute to the success of Oil & Gas Exploration in South Sesulu PSC.
The document provides an introduction to geographic information systems (GIS) and remote sensing. It discusses how GIS organizes and analyzes spatial data through data management, analysis, and visualization. It describes different data types including vector, raster, and imagery data. It also explains key concepts such as layers, modeling geospatial reality, and coding vector and raster data. The document outlines advantages and disadvantages of vector and raster data models. It introduces remote sensing and describes platforms and sensors used to collect spatial data from aircraft and satellites.
This document discusses the application of Geographic Information Systems (GIS) for various purposes:
1) GIS is used for disaster and emergency management like mapping earthquakes, cyclones, landslides, floods, and fires. It allows for relief and rehabilitation efforts to be planned.
2) GIS enables crime mapping and prediction by analyzing patterns of past crimes.
3) Other applications include GIS-based analysis of parking availability, deforestation over time, and generating contour maps and studying topography.
Geographic information system and remote sensingDhiren Patel
This document provides an overview of remote sensing and geographic information systems. It discusses the history of remote sensing from early aerial photography to modern satellite systems. Both passive and active remote sensing techniques are described, along with common applications in fields like forestry, agriculture, and land use analysis. Optical, radar, and lidar remote sensing systems are outlined. The document also introduces concepts in photogrammetry, surveying, and geographic information systems, including data structures and components of GIS.
This document provides an overview of geography awareness week and GIS. It discusses what GIS is, including how it can be used as a geodatabase, for geovisualization, and spatial analysis. It then discusses several applications of GIS in areas such as natural resource management, emergency response, social science, history, and trends in online GIS, cloud/mobile GIS, 3D-GIS, and volunteered geographic information.
This document provides an overview of geographical information systems (GIS) and remote sensing. It defines GIS and explains its key components, principles, functions, data types, advantages and disadvantages. It also defines remote sensing, describes its principles and stages, and outlines its applications in geology, natural resource management, national security and more. The advantages of remote sensing include large area coverage and permanent data records, while disadvantages include high costs and need for specialized training.
Application of Remote Sensing and GIS in Urban PlanningKARTHICK KRISHNA
This document discusses the application of remote sensing and GIS in urban planning. It outlines how remote sensing provides important data for mapping land use and monitoring environmental changes. GIS allows for spatial analysis and modeling of terrain, watersheds, and growth patterns. The document gives examples of how remote sensing and GIS have been used together for base map preparation, land suitability analysis, delineating sensitive areas, and monitoring urban growth. It concludes that remote sensing and GIS provide an effective tool for data collection, analysis, and innovative planning methodologies.
Use of remote sensing for land cover monitoring servir science applicationsKabir Uddin
This document discusses land cover mapping using remote sensing. It provides background on land cover mapping and monitoring in the Himalayan region, where deforestation and forest degradation have been issues. Remote sensing using satellite imagery and tools like GIS allows accurate land cover mapping over large areas. The document discusses different remote sensing platforms and sensors, as well as image classification techniques including unsupervised, supervised and object-based classification. It provides examples of software used for object-based image analysis, and outlines the steps involved in land cover mapping projects using remote sensing.
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.
geoinformatics handbook:it contains all open source software and commerical software of remote sensing,gis and photogrammerty and also all free data sources.free data sources such as DEM and LIDAR
Application of gis and gps in civil engineeringAvinash Anand
A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present geographical data. GIS integrates geospatial software and tools to enable spatial analysis and the display of large datasets in graphical form. GIS can be used for problem solving, decision making, and visualizing spatial data by mapping locations, quantities, densities, and changes over time for various applications like transportation, watershed analysis, land use planning, and more.
This document describes the production of a geoidal map and three-dimensional surface model for part of Port Harcourt, Nigeria using "Satlevel" collocation modeling. Global Navigation Satellite System data was collected to determine ellipsoidal heights, while leveling provided orthometric heights. These were used in the "Satlevel" collocation model to compute geoidal undulations, which were contoured in Surfer software to generate the geoidal map and 3D surface model. The map depicts the geoid configuration of the study area and can be used with ellipsoidal heights from GNSS to determine orthometric heights, providing a simpler method than conventional techniques.
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 analyze spatial data, facilitate decision making, and support planning and management activities.
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.
Geographic information systems (GIS) are organized collections of computer hardware, software, and geographic data used to capture, store, update, manipulate, analyze, and display geographically referenced information. GIS provides spatial data depicted as points, lines, or polygons with attributes stored in tables, and can take data from various sources and integrate them into multiple layers for analysis. Common applications of GIS include agriculture, natural resource management, disaster management, and urban planning.
Geoinformatics For Precision AgricultureRahul Gadakh
1) Geoinformatics deals with the capture, classification, storage, processing, portrayal, and dissemination of spatial information. It involves technologies like remote sensing, GIS, GPS, and photogrammetry.
2) It is used for applications like land use mapping, precision agriculture, crop discrimination and yield monitoring, soil mapping, and monitoring of land degradation.
3) Hyperspectral data and narrowband vegetation indices derived from it allow detailed analysis of crop biochemical and biophysical properties for improved classification and mapping of agricultural crops.
Geographic Information System(GIS) and Remote Sensing(RS) in Disaster Managem...Our slides
This presentation provides an overview of how Geographic Information Systems (GIS), Global Positioning Systems (GPS), and Remote Sensing (RS) can be applied to disaster management. It discusses the objectives of learning about these tools, the professional skills and expert knowledge gained, and how GIS, GPS, and RS are used in applications like emergency response, infrastructure planning and monitoring terrain. Real-world examples are given of how each technology supports disaster prevention, response and recovery efforts. The presentation concludes that knowledge of GIS, GPS and RS allows for more efficient disaster management by providing precise location data and remote monitoring capabilities.
Remote sensing and GIS for land cover change characterization and rural/urban...Maurizio Pollino
Authors: C. R. Fichera, G. Modica, M. Pollino.
Poster presented at Esri EMEA (Europe, Middle East And Africa) USER CONFERENCE 2010. Rome, October 26–28 2010
The development of the urban areas is able to transform landscapes formed by rural into urban life styles and to make functional changes, from a morphological and structural point of view. Historically, urban expansion (driven by the population increase) has typically take place on former agricultural use.
A multi-temporal image dataset has been analyzed to identify the changing pattern of Land Cover (LC) during a fifty-year period (1954÷2004).
Using the analysis tools of ArcGIS, the results have been synthesized into maps of LC changes, in order to characterize the respective dynamics.
Temporal trend analysis and landscape metrics have been integrated, using ArcGIS and specific extensions: such approach has allowed to characterize landscape patterns through significant indices and to understand the changes therein.
Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis that typically affects the lungs. It is transmitted through droplet infection. Driving forces behind TB include rapid population growth, urbanization, poverty, and poor living conditions. These lead to pressures such as overcrowding, poor sanitation, and increased HIV/AIDS prevalence that worsen the state of TB. Exposure groups with high risk include the elderly, HIV-positive individuals, prisoners, and urban slum residents. TB causes millions of deaths each year and increases poverty. Global and national actions aim to reduce TB prevalence, mortality, and achieve universal access to treatment.
Over 6.5 billion paper cups are used every year, which takes over 500 years to decompose in landfills. The waitress encourages customers to use reusable tumblers by offering rewards like a loyalty card where customers earn stickers for free items after filling it out. The cafe asks customers to keep their reusable tumblers or transform disposable cups into planters to plant tree seeds to help reduce waste and save the earth.
Consumer Mobile Policy & Security - Lightspeed & KantarMerlien Institute
at Market Research in the Mobile World Europe
23 - 26 September 2014, Belin, Germany
This event is proudly organised by Merlien Institute
Check out our upcoming events by visiting http://www.mrmw.net
The document summarizes key aspects of the film The Shining. It classifies the film as both a mundane and supernatural psychological horror film due to the character of Jack Torrence becoming mad in isolation at the haunted hotel. It identifies traits of horror in the film including a chill atmosphere from the gloomy winter setting, supernatural elements like Danny's psychic abilities, and creepy sound effects. The favorite scene is described as Danny cycling through the hotel hallways, the chasing scene at the end, and Wendy discovering Jack typing strange phrases, showing he is out of control.
Anne Nortcliffe Are we ready for smart device learning surveyAndrew Middleton
This document discusses a survey of staff and students at Sheffield Hallam University on their use of smart devices. It finds that while most staff and students use their devices for tasks like email, calendars, and note-taking, there are still inhibitors to fully integrating smart devices into academic life. Both students and staff cited issues like network connectivity and limitations of apps and websites as challenges. The document concludes that while staff are open to classroom use of smart devices and students use them to supplement learning, digital literacy varies and universities need to address infrastructure and app development to better support smart learning.
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Land use and land cover is very important for appropriate planning and budget of a community. In Jibia several activities are carried on the land without actually knowing the percentage of the land cover of features. Remote sensing and GIS technology are valuable tools in managing land use and land cover. This research described the land use and land cover coverage of the entire LGA land using Remote Sensing and GIS. Landsat Satellite imagery of 2009 of the study area was processed and classified into 5 groups namely Build up sharp sand, Farm Land trees, Vegetation shrubs, Water body and Barren land. Statistical analysis was employed to show the percentage distribution of the land. The study shows the percentage coverage of land physical feature which in turn describe the land use. The research revealed that agriculture is the major activity in the study area. It also concluded by recommending the need for adequate measures to avoid desert encroachment and government support to enhance agricultural produce. Lugga M. S | Babale Z. T | Yamel A. G "Land use Land Cover Highlight for Jibia Local Government, Nigeria" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31134.pdf Paper Url :https://www.ijtsrd.com/other-scientific-research-area/other/31134/land-use-land-cover-highlight-for-jibia-local-government-nigeria/lugga-m-s
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ASSESSMENT AND MAPPING OF WASTELAND CHANGES IN BANDA DISTRICT OF UTTAR PRADES...IRJET Journal
This document analyzes changes in wasteland in the Banda district of Uttar Pradesh, India between 2003-2004 and 2021-2022 using remote sensing and GIS technology. The key findings are:
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4
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IRJET - Digitization of Master Plan Map and Preparation of Different Them...IRJET Journal
This document describes a study that digitized the master plan map of Gorakhpur City, India and created various thematic maps using geospatial techniques. The master plan map was georeferenced and digitized in ArcGIS. Thematic layers for land use/land cover, drainage, transportation, slope, contour, and lithology were produced from satellite imagery and DEM data in ArcGIS and ERDAS Imagine. These vector layers were converted to raster maps. The digitized master plan map and thematic maps allow for easy updating, analysis and planning of Gorakhpur City.
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Comparison of the landsat 7 etm+ and nigeriasat-1 imagery for the revision of 1-50000 topographic map of onitsha metropolis, anambra state, nigeria
1. Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
Vol.4, No.19, 2014
10
Comparison of the Landsat-7ETM+ and NigeriaSat-1 Imagery for
the Revision of 1: 50000 Topographic Map of Onitsha Metropolis,
Anambra State, Nigeria
Ejikeme, J.O., Igbokwe, J.I, Ezeh, F.C Onwuzuligbo, C.U
Department of Surveying and Geoinformatics, Nnamdi Azikiwe University, P.M.B 5025 Awka, Anambra State,
Nigeria
E-mail: ejikemejoseph2@yahoo.com
Abstract
Topographic maps are needed almost in every aspect of public and private sector activities, including general
engineering and construction works, economic and physical planning regulation, environmental management,
general planning and as a base map for land use/land cover mapping. Most of the topographic maps available in
Nigeria are outdated including that of Onitsha and its environs. Consequently, they cannot be used for the
desired needs. This study focused on the use of Remote Sensing and Geographic Information System (GIS) in
revising and analyzing 1:50000 topographic map of Onitsha metropolis. Two satellite images (Landsat-7 ETM+
and NigeriaSat-1) of the same date (2006) were used respectively to revise 1964 topographic map of Onitsha
metropolis. The two imageries which have been georeferenced, in UTM Coordinate System of WGS 84 Zone 32
were sub mapped using the bounding coordinate of the study area. The NigeriaSat-1 and the digitized and
georeferenced topographic map of the study area were co-registered to the 28.5m resolution of Landsat-7 ETM+
using ILWIS 3.3 software. The satellite imageries were classified and the classified images were vectorized in
ArcGIS 9.3 Software and integrated with contour generated from SRTM data of 2000 to produce revised
topographic map of Onitsha Metropolis (2006). The SRTM data of 2000 was used despite the difference in years
(6years), because a sample survey revealed that the topography has not changed significantly between 2000 and
2006 within the study area. Pixel-based image analysis revealed that NigeriaSat-1 has better descriminability
than landsat-7ETM+ based on the overall classification accuracy obtained from each image; 86.90% for
NigeriaSat-1 and 85.77% for landsat-7ETM+. The NigeriaSat-1 was recommended to be used in revising
Medium-Scaled topographic maps of Nigeria. The study recommends among others that Medium-scaled
topographic map coverage of the entire country should be carried out without further delay. Furthermore, the
study recommends that Nigeria should now adopt topographic map of scale 1:25000 as the base map for the
whole country.
Keywords: Topographic Map, GIS, Remote Sensing, Satellite Imagery
1. Introduction
Most topographic map in Nigeria is grossly inadequate and outdated to reflect tremendous development in the
country. A lot of changes have taken place in Onitsha and it’s environ since the 1964 topographic map was
adopted. Efforts have not been made to reflect these changes. This is why any planning especially urban
planning, based on the 1:50000 topographic maps would face serious implementation problems, since most of
the information on features are now outdated (Ejikeme 2013). This inadequacy and out-datedness of 1:50000
topographic map series is indeed a major setback to physical development in Nigeria. These changes can be
reflected on the map through the process of revision. Due to high cost of revision of topographic map using
aerial photograph, all the Nigerian 1:50000 topographic maps are still being used today without revision against
the United Nations Organization (UNO’s) recommended revision period of 10 years for areas of high human
activities and 15 years for remote areas. This means that the 1964 topographic map of Onitsha and environs is 48
years old without revision. The recent availability of satellite imageries have made updating of map easier, faster
and cost effective.
1.1 The Study Area
The study area selected for this study is located between Latitudes 060
021
5611
N and 060
381
3411
N and Longitude
060
371
3011
E and 060
591
3011
E and covers Onitsha North and South Local Government Area and part of Obosi,
Nkpor and Iyiowa Odekpe of Anambra State. It is bounded by Anambra West/East L.G.A. and Oyi in the North,
Idemili-North/South in the East, Ogbaru L.G.A in the South and in the West by the River Niger (See fig. 1a, 1b
and Ic). Onitsha is the largest urban center in Anambra state and is also a major commercial town east of the
Niger.
2. Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
Vol.4, No.19, 2014
11
Fig. 1(a): Map of Nigeria Showing Anambra State. Fig. 1(b): Map of Anambra State Showing Onitsha
metropolis, the Study Area. Fig. 1(c): 1964 Digitized Topographic Map of Onitsha Metropolis, the Study Area.
2. Methodology
The flow chart of the methodology adopted is represented in the figure 2.0.
3. Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
Vol.4, No.19, 2014
12
Figure 2.0: Flow chart for obtaining the revised topographic map of Onitsha metropolis:
The bounding coordinates of the 1964 Onitsha S.E topographic map which were in NTM were
converted to UTM projected coordinate system of WGS 84 zone 32 using Geocalc software. The converted
coordinate was used to georeferenced the scanned topographic map in ArcGIS 9.3 environment. The aim of
converting the coordinate was to bring the topographic map to the same coordinate system of the satellite
imageries which were in UTM coordinate.
The satellite imageries were of different spatial resolution; 28.5m for Landsat-7ETM + (Resampled)
and 32m for NigeriaSat-1. Hence, they were not spatially compatible. The resampled and georeferenced Landsat-
7 ETM + of pixel size of 28.5m was used as a reference image to co-register the target image (NigeriaSat-1) to
the coordinate system of Landsat-7ETM+ image. The essence of co-registration and resampling operation was to
make the imageries compatible in spatial resolution and pixel size as well as in band and at nadir (Gibson et al.
2000). Sub mapping, Resampling, color separation, maplist, sample set and Domain creation as representation of
LULC was carried out in ILWIS 3.3.
Each image set (Landsat-7ETM+ (2006) and NigeriaSat-1(2006)) was classified using the maximum
likelihood classifier. The results are shown in figure 3.1 and 3.2. This produced the landcover and landuse map
for each of the image set. To ensure correct classification, the geographic coordinates of each of the landcover
representative class were plotted into the images and identifies before training. The classified image was
exported into ArcGIS 9.3 where the classes; Waterbodies, Farmland, Built-up area, Vegetation and Open space
were vectorized as polygon.
The use of contour lines generated from topographic map is undoubtedly more accurate than using
SRTM data. Unfortunately, the Nigerian topographic maps which were produced 50 years ago have not been
revised till date (Ihejirika, 2011). Both SRTM elevation and elevation from available 1:50000 topographic maps
could be used to create a good representation of the terrain given the high positive correlation with the more
accurate GPS height data of points within the study area (Ozah & Kufoniyi 2008). Thus, the SRTM Imagery was
Flow Chart of the Revision of 1:50000 Topographic Map Using Landsat-
7ETM+ and NigeriaSat-1 Imageries
SRTM 2000 Topographic Map 1964
Landsat-7 ETM+ and
NigeriaSat-1(2006)
Export to ILWIS 3.3
Image Processing
Export to ArcGIS 9.3
Field Verification
Digital Data Capture
Scanning and Digitizing
of features
Integrate Processed
Data
Image Processing/Geometric
correction
Image Classification
Vectorize Feature Classes
as Polygon
Revised Topographic Map
(2006)
Generate Contour
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the source of elevation points for this study. The downloaded SRTM was used to produce contour of the study
area. The topography of the study area was validated using Ground Control Points (GCPs). The Latitude,
Longitude, Height of randomly selected GCPs within the study area was obtained using handheld GPS. The z or
height value which is in ellipsoidal height was converted to orthometric height using the Global Geoid calculator
available at.
The topography was validated by comparing contour obtained from the SRTM and contour obtained
from Ground Control Points (GCPs) within the area. The result shows that the there is no significant change in
the topography from 2000 to 2012.
3.0 Results and Discussions
Figure 3.1: Classified image of Landsat-7 ETM + of the Study Area (2006)
Figure 3.2: Classified Image of NigeriaSat-1 of the study Area (2006)
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Table 1.0: Result of Landcover and Landuse Classification from Landsat-7 ETM + (2006) and NigeriaSat-1
(2006)
S/N Class Type Area in Hectares Percentage (%)
Landsat-7 ETM+ NigeriaSat-1 Landsat-7ETM+ NigeriaSat-1
1 Built-up Area 85.500 114.000 22.46 30.05
2 Farmland 163.875 96.188 43.05 25.35
3 Open Space 28.512 35.625 7.49 9.39
4 Vegetation 74.813 105.094 19.65 27.70
5 Water bodies 27.989 29.782 7.35 7.82
Total 380.689 380.689 100 100
The red area represent built-up areas, green area were the Vegetation, yellow was for farmland, blue
indicate water bodies and white stood for open space. The result of the Classification as presented in table 1.0
shows a higher level of agreement between the two images in respect of waterbodies and open space but wide
disparities exist in respect of other land cover types. Landsat-7 ETM+ performed better in farmland
discrimination while NigeriaSat-1 performed better in the discrimination of Built-up area and vegetation.
The classified images were tested for accuracy. To assess the accuracy of the classification, the
classified images were compared to their sample set (reference map) and their results were presented in from of
an error matrix sometimes referred to as confusion matrix.
Table 2.0: Error Matrix table for 2006 Landsat-7 ETM + (Supervised Classification)
Classified map Landuse Reference map
Water Open Vegetation Built Farmland Total Error of commission User Accuracy
Water 9846 53 0 332 0 10231 3.76 96.24
Open 0 9489 30 1125 30 10674 11.10 88.90
Vegetation 1707 0 23861 0 128 25696 7.14 92.86
Built 97 97 0 29305 97 29596 0.98 99.02
Farmland 0 549 2470 12105 40955 56079 26.97 73.03
Total 13650 10188 25229 43867 39342 132276
Error of
Omission
27.87 16.68 13.35 33.20 0.65
Producer
Accuracy
72.13 83.32 86.65 66.80 99.35
Overall accuracy = 113456
132276 = 85.77%
Table 3.0: Error matrix table for 2006 NigeriaSat-1 (Supervised Classification)
Classified
map
Landuse Reference map
Water Open Vegetation Built Farmland Total Error of
commission
User
Accuracy
Water 8518 0 0 997 0 9515 10.48 89.52
Open 0 4979 0 6928 62 11969 58.40 41.60
Vegetation 0 0 37088 0 0 37088 0.00 100
Built 90 181 0 39980 23 40274 0.73 99.27
Farmland 0 1666 0 7376 24388 33430 27.05 72.95
Total 8608 6826 37088 55281 24473 132276
Error of
Omission
1.05 27.06 0.00 27.68 0.35
Producer
Accuracy
98.95 72.94 100 72.32 99.65
Overall Accuracy= 114953
132276 = 86.90%
Inspection of the matrix shows how the classification represents actual areas on the landscape.
Examination of the error matrix reveals for each category, error of omission and error of commission. Error of
omission refers to those sample points that were omitted during classification. For example, the assignment of
errors of Open Space on the ground to built up area category on map (in other words, an area of “real” open
space on the ground has been omitted from the map). Using the same example, an error of commission would be
to assign an area of Built-up on the ground to the Open space category on the map. The user accuracy for
farmland is 24388/33430 or 72.95%. This tells the user of the map that of the area labeled farmland, 72.95%
X 100
X 100
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actually correspond to farmland on the ground. The producer’s accuracy for farmland is 24388/24473 or 99.65%.
This informs the analyst who prepared the classification that of the actual farmland, 99.65% was correctly
classified. The overall accuracy for the classified maps of Landsat-7 and NigeriaSat-1 image was 85.77% and
86.90% respectively. Okpala-Okaka (2008) emphasized that the overall accuracy (%) is a function of the scale of
the map and imagery employed in the classification. He stated that for scale 1:50000 -1:250000, an overall
accuracy of 50-70% is acceptable.
The Kappa coefficient or (KHAT) is a measure of the difference between the observed agreement
between two maps (as reported by the diagonal entries in the error matrix) and the agreement that might be
attained solely by change matching of the two maps.
The KHAT statistics is computed as
Where
r = number of rows in the error matrix
xii = number of observations in row i and column i (on the major diagonal)
xi+ = total of observations in row i (shown as marginal total to right of the matrix)
x+i= total of observations in column i (shown as marginal total at bottom of the matrix)
N = total number of observations included in matrix
For LandSat-7 ETM+, the Kappa Coefficient is:
For NigeriaSat-1, the Kappa Coefficient is:
Since the overall accuracy of the classification result from NigeriaSat-1 (86.90%) is greater than that of Landsat-
7 ETM+ (85.77%), so is the K value, we assert that classification result obtained from NigeriaSat-1 is more
efficient and effective than one derived from LandSat-7
Having achieved a reliable classified image, the classified images were exported to ArcGIS 9.3 where
they were vectorized into polygon. The vectorized classes of the images were integrated with contour derived
from SRTM data and roads digitized from the satellite image to obtain the revised topographic map of Onitsha
metropolis using the two image sets (See Figure 3.3 and 3.4)
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Figure 3.3: 2006 Revised Topographic Map of Onitsha Metropolis (Landsat-7 ETM +)
Figure 3.4: 2006 Revised Topographic Map of Onitsha Metropolis (NigeriaSat-1)
4. Conclusion
The use of remote sensing and GIS had been demonstrated in this study as an effective tool for revision of
1:50000 topographic map. 28.5m resolution Resampled Landsat-7 ETM+ and 32m resolution NigeriaSat-1 of
date (2006) were used to revise (1964) 1:50000 topographic map of Onitsha metropolis. Comparative analysis of
the two images showed that NigeriaSat-1 produced better result than Landsat-7ETM+ image based on their
overall classification accuracy of 86.90% and 85.77% for NigeriaSat-1 and Landsat-7ETM+ respectively.
Medium resolution satellite imageries perform better than high resolution satellite imageries in pixel-based
image analysis. This could be the reason why NigeriaSat-1 performed better than Landsat-7ETM+ despite that
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NigeriaSat-1 has higher spatial resolution than Landsat-7ETM+. The result could be different if the image
analysis is object-based. The road networks were better identified in Landsat-7 ETM+ than in NigeriaSat-1. The
result shows a high concentration of human activities close to the river Niger as regards to the Built up Area in
the classification result. Farmland and vegetation occupied large portion of the Northern part of the classification
maps. This area (3.3 towards Nkwelle Ezunaka town) is a relatively growing settlement in Onitsha. Field
verification shows that these areas were characterized by relatively scattered buildings. A conclusion derivable
from these is that there areas can be used for future development and planning.
Further analysis showed that a lot of changes had happened from 1964 to 2006. A major highway
(Onitsha-Enugu Expressways) which appeared as a minor path in 1964 had grown to a major highway linking
Onitsha and Enugu as identified from the landsat-7 image. NigeriaSat-1 performed better in terms of overall
classification accuracy, because of homogeneous spectral characteristics of features. This is not the case with
Landsat-7 ETM+, even though it has higher spatial resolution.
References
Ejikeme, J.O. (2013), “Revision and Analysis of Medium-Scale Topographic Map Using Remote Sensing and
GIS Techniques”, MSc Thesis, Department of Surveying and Geoinformatics, Nnamdi Azikiwe University,
Awka.
Gibson,P.J & Power, C.H (2000), “Introductory Remote Sensing: Digital Image Processing and Applications”,
Routeledge, New Fetter Lane London ECA p 4 EE
Ihejirika, O.A (2011), “Land Administration and Contribution of Survey Organizations to Economic
Development in Nigeria”, Induction Lecture to Newly Qualified Surveyors and Presentation of Awards.
Okpala-Okaka, C (2008), “Revision of Nigerian Topographic Map 1:50000 series using Satellite Imageries: A
Case Study of Nsukka and Environs in Enugu State”, PhD Dissertation, Department of Surveying and
Geoinformatics, Nnamdi Azikiwe University, Awka, Nigeria.
Ozah, A.P & Kufoniyi, O (2008), “ Accuracy Assessment of Contour Interpolation from 1:50000 Topographic
Maps and SRTM Data for 1:25000 Topographical Mapping”, International Archives of the Photogrammetry,
Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7
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