Chris Spry Ikonos
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IKONOS was the world's first commercial satellite to provide high-resolution imagery to the public. It was operated by GeoEye and collected over 300 million square kilometers of imagery from 2000 onward. IKONOS had a multispectral sensor with 4 bands at 4m resolution and a panchromatic band at 1m resolution, providing high-quality 11-bit imagery for uses such as national security, mapping, and government applications.
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IKONOS-2.pptx
IKONOS was the first satellite to collect publicly available high-resolution commercial imagery at 1-4 meter resolution. It was successfully launched in 1999 after an initial failed launch, carrying multispectral and panchromatic sensors. IKONOS provides high-quality imagery for applications such as mapping, change detection, and analysis, with sub-meter panchromatic and 4 meter multispectral resolution across a 11km swath. It established the standard for commercial high-resolution satellite imagery.
Remote Sensing_2020-21 (1).pdf
This document provides an overview of remote sensing. It defines remote sensing as acquiring information about an object without physical contact. The history of remote sensing is outlined from early uses of photography from balloons and planes to modern satellite systems. The key principles of remote sensing are described, including the electromagnetic spectrum, energy sources, atmospheric interactions, and how radiation is absorbed, transmitted or reflected when interacting with targets. Remote sensing applications and different sensor types are also mentioned.
REMOTE SENSING PLATFORMS.ppt
This document discusses several remote sensing platforms and satellites used for earth observation. It provides information on satellites such as Landsat, SPOT, Ikonos, RADARSAT, as well as international observation programs from agencies such as ESA, ISRO, and JAXA. The document outlines technical specifications including sensors, spectral bands, spatial resolutions, orbits, and coverage areas of the different systems.
4_REMOTE SENSING PLATFORMS.ppt
The document discusses various remote sensing platforms and Earth observing satellites. It provides information on the characteristics and sensors of satellites operated by different space agencies including Landsat, SPOT, Ikonos, GOES, Meteosat, RADARSAT, IRS series from India, JERS-1 and ADEOS from Japan, and ESA satellites. The document contains detailed tables summarizing the technical specifications of these satellites and their instruments.
remote sensing platforms materials for student
The document discusses several remote sensing platforms and satellites used for earth observation. It provides details on the characteristics and sensors of Landsat, SPOT, Ikonos, RADARSAT, GOES, Meteosat, IRS and Japanese satellites. These satellites collect multi-spectral imagery for applications like land use mapping, environmental monitoring, disaster management and resource exploration. The document compares the spectral bands, resolutions, coverage and revisit times of the different missions.
Remote Sensing fundamentals
This document provides an overview of remote sensing including:
1. The history of remote sensing from early aerial photography to modern satellite systems.
2. The principles of electromagnetic radiation and how different sensors capture radiation in various parts of the spectrum to analyze objects.
3. The various types of remote sensing platforms, sensors, and resolutions including spatial, spectral, temporal, and radiometric and how they provide information.
4. Common applications of remote sensing like land use mapping, change detection, environmental monitoring, and more.
Remote sensing with all fundamentals
This document provides an overview of remote sensing including:
1. The history of remote sensing from early aerial photography to modern satellite systems.
2. The principles of electromagnetic radiation and how different sensors capture radiation in various parts of the spectrum to analyze objects.
3. The various types of remote sensing platforms, sensors, and data products including satellites, spectral resolution, spatial resolution, temporal resolution, and applications like land cover mapping.
Rs fundamentals
This document provides an overview of remote sensing including:
1. The history of remote sensing from early aerial photography to modern satellite systems.
2. The principles of electromagnetic radiation and how different sensors capture radiation in various parts of the spectrum to analyze objects.
3. The various types of remote sensing platforms, sensors, and data products including satellites, spectral resolution, spatial resolution, temporal resolution, and applications like land cover mapping.
Louis: TIROS-1
The launching of TIROS-1, the first weather satellite, in 1960 proved that satellites could successfully observe weather patterns from space. TIROS-1 took over 22,000 pictures in its 78 days in orbit, helping meteorologists better understand weather systems. This satellite launch opened the door to modern satellite observation technology and improved weather forecasting. Today, polar-orbiting and geostationary satellites continue monitoring global weather and environmental conditions. Modern satellites also support GPS navigation and emergency beacons, influencing many aspects of modern life from transportation and communications to search and rescue operations.
Meteorologcal application of satellites
The document discusses the use of meteorological satellites, weather balloons, and sounding rockets for weather forecasting. It provides background on severe weather events that have caused major damage, highlighting the need for improved forecasting. It then describes some of the first meteorological satellites, including TIROS-1, which was the first to transmit cloud photos from space. It also discusses the NIMBUS satellites, which had improved coverage over the TIROS satellites and helped enable long-term observations of phenomena like the ozone hole.
Spaceborne Imagery For Environmental & Disaster Monitoring
The document discusses the use of spaceborne imagery for environmental and disaster monitoring. It provides examples of how satellite imagery has been used to monitor various natural disasters such as flooding, landslides, volcanic eruptions and forest fires. It also discusses how satellites are used to monitor ongoing environmental changes, such as receding glaciers, sand and dust storms, algal blooms, mining, agriculture and deforestation. Different types of satellites are used depending on the specific monitoring needs and situations.
Remote Sensing of Aerosols
The presentation introduces remote sensing technology and how it is used in studying atmospheric aerosols. Remote Sensing technology uses the optical property of aerosols to detect the presence and the type of aerosol. The type or the characteristics of an aerosol is determined by seven factors which are interpreted from the satellite image. The satellite image is retrieved from geosynchronous and polar satellites, of which the latter is preferred for aerosol applications.
In addition, features and terminologies associated with remote sensing, satellite and aerosol optical properties are discussed. This project emphasizes on an interactive material that is best supplemented with lecture video. It is not designed to be conventional lecture slide. Point to note: the question mark appearing in bottom of the slides indicates the author raised a question during the lecture.
This presentation was delivered in coming-of-age lecture style, in contrast to old-school conventional style. This presentation stimulates audiences to think and act than a banal display of abstract data. The lecture videos can be found at:
[1] Part-1/2 (52 minutes): https://youtu.be/-O_mYoeg-us
[2] Part-2/2 (51 minutes): https://youtu.be/IhHHHZYcY0o
This presentation is done as a part of graduate course titled Aerosol Mechanics in Spring 2016. The author was pursuing MS in Environmental Engineering Sciences at University of Florida during the making of this project.
Remote Sensing and its Application
Introduction -Remote means – far away ; Sensing means – believing or observing or acquiring some information.
Remote sensing means acquiring information of things from a distance with sensors. (without touching the things)
Sensors are like simple cameras except that they not only use visible light but also other bands of the electromagnetic spectrum such as infrared, microwaves and ultraviolet regions.
Distance of Remote Sensing, Definition of remote sensing - Remote Sensing is:
“The art and science of obtaining information about an object without being in direct contact with the object” (Jensen 2000).
India’s National Remote Sensing Agency (NRSA) defined as : “Remote sensing is the technique of deriving information about objects on the surface of the earth without physically coming into contact with them.”
Remote Sensing Process, - (A) Energy Source or Illumination.
(B) Radiation and the Atmosphere.
(C) Interaction with the Target.
(D) Recording of Energy by the Sensor.
(E) Transmission, Reception, & Processing.
(F) Interpretation and Analysis.
(G) Application.
Remote sensing platforms , History of Remote Sensing, Applications of remote sensing - In Agriculture, In Geology, Applications of National Priority.
Juno launch
The document provides information about the upcoming Juno mission to Jupiter, including:
- Key mission dates and milestones such as the August 2011 launch window and July 2016 arrival at Jupiter.
- Details about the spacecraft such as its size, mass, and solar arrays that will power it.
- An overview of the science goals such as determining Jupiter's composition and structure.
- Background on Jupiter and previous missions to the planet.
- Contact information for media and the mission's management team.
The operational environment and rotational acceleration of asteroid (101955) ...
During its approach to asteroid (101955) Bennu, NASA’s Origins, Spectral Interpretation,
Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) spacecraft surveyed
Bennu’s immediate environment, photometric properties, and rotation state. Discovery of
a dusty environment, a natural satellite, or unexpected asteroid characteristics would have
had consequences for the mission’s safety and observation strategy. Here we show that
spacecraft observations during this period were highly sensitive to satellites (sub-meter
scale) but reveal none, although later navigational images indicate that further investigation is
needed. We constrain average dust production in September 2018 from Bennu’s surface
to an upper limit of 150 g s–1 averaged over 34 min. Bennu’s disk-integrated photometric
phase function validates measurements from the pre-encounter astronomical campaign.
We demonstrate that Bennu’s rotation rate is accelerating continuously at 3.63 ± 0.52 × 10–6
degrees day–2, likely due to the Yarkovsky–O’Keefe–Radzievskii–Paddack (YORP) effect, with
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This document provides a historical overview of remote sensing and the International Geoscience and Remote Sensing Symposium (IGARSS) from the 1960s to 2010. It discusses the early development of remote sensing technologies and societies, experimental missions like Nimbus and Seasat in the 1970s, and the growth of IGARSS in the 1980s with the launch of new operational sensors. It describes how remote sensing took on new roles in the 1990s related to policy and global change. Finally, it outlines new directions for 2010 and beyond with increased citizen involvement through smartphones, web tools, and crowdsourcing of data.
6%2E2017-2021
The document describes Project ELSA, which aims to design and construct a low-cost spherical probe called the NeoPod to collect and transmit scientific data from the surface of Europa. A team of undergraduate students designed the NeoPod over 8 months to fit within a 25cm diameter sphere and weigh less than 10kg. The NeoPod integrates two sensors (a magnetometer and Geiger counter), an avionics package with an FPGA, a communications system, and a power system to operate for 100 hours. Simulation results show the NeoPod could transmit up to 301MB of data if located at Europa's poles. The project aims to demonstrate the feasibility of using low-cost probes to explore Europa and other planetary
Remote sensing by priyanshu kumar,9608684800
Remote sensing involves obtaining information about objects without physical contact using sensors. It has a long history dating back to the 1840s and became more advanced with satellite technology. Remote sensing is used for applications like urban planning, agriculture, natural resource management, and more. India has developed its own remote sensing program using satellites to support the economy and monitor issues like agriculture, forestry, and disasters. Remote sensing provides large-area coverage and repetitive monitoring but requires specialized training and may have accuracy issues.
thenkabail-uav-germany-final1b
Hyperspectral remote sensing uses narrow, contiguous bands across the electromagnetic spectrum to characterize vegetation. It is useful for studying species composition, crop/vegetation type, biophysical properties like leaf area index and biomass, biochemical properties like chlorophyll and moisture, and stress factors. Hyperspectral data comes from airborne, ground, and spaceborne sensors, with spaceborne providing global continuous coverage but at lower spatial resolution than airborne sensors. Hyperspectral data cubes contain hundreds of bands providing detailed spectral signatures to distinguish vegetation.
Remote sensing
This document provides an overview of remote sensing. It defines remote sensing as acquiring information about objects from a distance using sensors, without direct contact. Remote sensing occurs from various platforms, including ground, airplanes, and satellites. The history of remote sensing is discussed, beginning with aerial photography in 1909 and expanding to use of satellites, hyperspectral imaging, and more advanced technologies. A variety of applications of remote sensing are outlined, including uses in agriculture, forestry, geology, hydrology, land cover/land use mapping, and addressing national priorities such as disaster management and energy management.
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CSpryFinal5
This document presents a storm water modeling project for the Town Creek Watershed in Salisbury, NC using the Curve Number Runoff Method. Land use in the watershed was mapped from aerial imagery from 1978 and satellite imagery from 2009. The Curve Number Runoff equation was used to calculate storm water runoff for a 4-inch rainfall event based on the different land uses and soil types. Results found an 8% increase in urban land use from 1978 to 2009, corresponding increases in storm water runoff, and that while the Curve Number method provided useful insights, it may be better suited to less urbanized areas with greater elevation changes.
Chris Spry
This document presents a storm water modeling project for the Town Creek Watershed in Salisbury, NC using the Curve Number Runoff Method. Land use in the watershed was modeled between 1978 and 2009 using aerial imagery and satellite data. Results found forest and open space decreased by 3% and 5% respectively, while urban area increased by 8%. Storm water runoff increased from 78,592 cubic feet in 1978 to 92,131 cubic feet in 2009 based on a 4 inch rainfall. While the Curve Number method provided useful insights, it may be better suited to areas with greater elevation changes and less urbanization.
Chris Spry
This document presents a storm water modeling project for the Town Creek Watershed in Salisbury, NC using the Curve Number Runoff Method. Land use in the watershed was modeled between 1978 and 2009 using aerial imagery and satellite data. Results found forest and open space decreased by 3% and 5% respectively, while urban area increased by 8%. Storm water runoff increased from 78,592 cubic feet in 1978 to 92,131 cubic feet in 2009 based on a 4 inch rainfall. While the Curve Number method provided useful insights, it may be better suited to areas with greater elevation changes and less urbanization.
Chris Spry
This document summarizes a storm water modeling project for the Town Creek Watershed in Salisbury, NC that used the Curve Number Runoff Method to model land use change between 1978 and 2009 and calculate the resulting changes in storm water runoff. Land use data from aerial imagery and satellite images from 1978 and 2009 were incorporated into a GIS system along with digital elevation models and soil data. The results found increases in urban land use of 8% along with decreases in forest and open space, and a corresponding increase in storm water runoff accumulation at a point in the watershed. The project reinforced the impact of urbanization in increasing storm water runoff but encountered issues applying the method in an already urbanized environment.
Chris Spry Enviro Final
This document summarizes a storm water modeling project for the Town Creek Watershed in Salisbury, NC that analyzed changes in land use and storm water runoff between 1978 and 2009. Digital elevation data, soil data, and aerial/satellite imagery from 1978 and 2009 were used to create land cover maps and calculate storm water runoff for each time period using the Curve Number runoff method. The results showed increases in urban area and storm water runoff over time, reinforcing the impact of urbanization. Issues included limitations of the Curve Number method for less elevated, more urbanized areas.
Group Final Project (Revised-2)
This document summarizes an analysis of emergency blue light phones on the UNCC campus. The analysis used GPS to locate 85 existing phones and analyzed the phone coverage through kernel density analysis, network analysis modeling phone access along paths and streets, Euclidean distance buffers, and visibility analysis considering elevation and building heights. The analysis found areas along Mary Alexander Road and at the intersection of Broaderick Blvd and University Rd had poor phone coverage based on density, network access time, distance, and visibility. The document recommends additional emergency phones be installed in these areas.
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This document summarizes an analysis of emergency blue light phones on the UNCC campus. The analysis used GPS to locate 85 existing phones and analyzed their accessibility through density mapping, network analysis of travel times along paths and streets, euclidean distance buffers, and visibility analysis. The analysis found areas along Mary Alexander Road and at the intersection of Broderick Blvd and University Road had poor phone coverage based on all analysis methods. The document recommends additional emergency phones be placed in these areas.
Group Final project
This group project studies the service areas of the campus emergency phones using several GIS methods.
Final project2
The document summarizes a project to inventory and analyze the accessibility and visibility of emergency blue light phones on the UNCC campus. The goals are to produce maps showing phone locations and coverage density, travel time to phones using the street network, euclidean distance from phones, sight lines to phones, and areas needing additional phones. Methods used include GPS to locate phones, kernel density analysis, network analysis, visibility analysis, and Google Earth. The timeline outlines completing data collection, analysis, and presenting findings over April and May.
El salvador
El Salvador is the smallest and most densely populated country in Central America. It has a mountainous terrain with volcanic activity and is prone to earthquakes. The climate is tropical along the coast and temperate inland, with wet and dry seasons. San Salvador is the capital and largest city, with over 1.6 million people.
Chris Spry Ozone Project
Three students collected ozone, wind speed, and temperature data from four locations on the UNCC campus over four days in September 2009. They created maps in ArcMap showing the ozone and wind speed levels each day. Graphs in Excel displayed the relationship between ozone, wind speed, and temperature. The findings showed higher ozone near traffic and lower ozone near vegetation, but no clear correlation between the factors due to similar site values within the small study area.
Chris Spry Final Rs Project
The document summarizes a study that classified land cover in an area using remote sensing imagery. Three classification methods were tested - parallelepiped, minimum distance, and maximum likelihood. Maximum likelihood classification performed best with 97.39% accuracy. Land cover types were identified and mapped. The percentage and surface area for each type was also calculated, with deciduous vegetation being the largest at 52.55% of the total area.
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