Remote Sensing Test 2
Describe the principle and application of using different band combinations for alternative display/scientific visualization, give examples.
Band 1 (Blue): provides increased penetration of water bodies. Underwater terrain survey. Band 2 (Green): green reflectance of healthy vegetation. Vegetation mapping/management. Band 3 (Red): red chlorophyll absorption. Vegetation mapping/management. Band 4 (NIR): vegetation biomass, crop identification, water boundaries. Band 5 (SWIR): turgidity, water amount in plants, crop water stress Band 6 (TIR): locating geothermal activities, vegetation stress analysis Band 7 (SWIR 2): discrimination of geologic rock formation.
List and define the three orbit types according to different ranges of orbital altitude.
Low Earth Orbit (LEO): scientific, earth resource observation -Most scientific satellites and many weather satellites are in a nearly circular, low Earth orbit. 180-2,000 km Medium Earth Orbit (MEO): navigational, communication -It is the orbit used by the Global Positioning System (GPS) satellites. 2,000 - 35,780 km High Earth Orbit (GEO): meteorological, communication -A geostationary orbit is extremely valuable for weather monitoring because satellites in this orbit provide a constant view of the same surface area. >35,780 km
Define sun-synchronous orbit, its characteristics and application.
A Sun-synchronous orbit (SSO), also called a heliosynchronous orbit, is a nearly polar orbit around a planet. The satellite passes over any given point of the planet's surface at the same local mean solar time. More technically, it is an orbit arranged to process through one complete revolution each year and always maintain the same relationship with the Sun.
Define sun-synchronous orbit, its characteristics and application.
A Sun-synchronous orbit is useful for imaging, reconnaissance, and weather satellites because every time the satellite is overhead, the surface illumination angle on the planet underneath it is nearly the same. This consistent lighting is a useful characteristic for satellites that image the Earth's surface in visible or infrared wavelengths, such as weather and spy satellites, and for other remote-sensing satellites, such as those carrying ocean and atmospheric remote-sensing instruments that require sunlight. Satellites in SSO are synchronized so that they are in constant dawn or dusk - this is because by constantly riding a sunset or sunrise, they will never have the Sun at an angle where the Earth shadows them. A satellite in a Sun-synchronous orbit would usually be at an altitude of between 600 to 800 km. At 800 km, it will be traveling at approximately 7.5 km per second.
Describe the major characteristics of AVHRR
AVHRR (Advanced Very High Resolution Radiometer) is a type of instrument that is flown on various Earth observation satellites to collect data on the Earth's surface and atmosphere. Here are some of the major characteristics of AVHRR: Multispectral imaging: AVHRR collects data in multiple spectral bands, including visible, near-infrared, and thermal infrared. This allows for a wide range of applications, including monitoring vegetation, land use, ocean temperature, and atmospheric conditions. Global coverage: AVHRR is flown on several satellites in low Earth orbit, which provides nearly global coverage of the Earth's surface. This allows for continuous monitoring of the Earth's environment and resources. High temporal resolution: AVHRR collects data on a daily basis, which allows for monitoring of short-term changes in the Earth's environment. This is particularly useful for monitoring weather patterns and natural disasters. Moderate spatial resolution: The spatial resolution of AVHRR data is moderate, typically ranging from 1 km to 4 km, depending on the spectral band. This is lower than some other Earth observation instruments, but still provides valuable information on large-scale features such as vegetation cover and ocean temperature. Long record of data: AVHRR has been in operation since the late 1970s, which has resulted in a long record of data on the Earth's environment and resources. This allows for analysis of long-term trends and changes in the Earth's climate and ecosystems. Overall, AVHRR is a valuable instrument for monitoring the Earth's environment and resources, providing multispectral data with high temporal resolution and a long record of observations.
Define hyperspectral remote sensing and describe its applications.
Agriculture: Hyperspectral data can be used to assess crop health and stress, detect disease, monitor growth, and optimize irrigation and fertilizer application. Mineral exploration: Hyperspectral data can be used to identify minerals and mineral assemblages based on their unique spectral signatures, allowing for the detection of new mineral deposits and the mapping of existing ones. Environmental monitoring: Hyperspectral data can be used to map and monitor vegetation, water quality, and land cover changes over time, as well as detect and monitor natural disasters such as wildfires, floods, and landslides. Geology: Hyperspectral data can be used to map lithology, structural geology, alteration, and mineralogy of rocks and soils, enabling geologists to better understand the geologic history of an area. Defense and security: Hyperspectral data can be used to identify and map targets of interest, such as military vehicles, buildings, and infrastructure, as well as detect and monitor environmental and atmospheric conditions that could affect military operations.
Describe the concept of using small satellite constellations vs. traditional large satellites.
Cost: Small satellite constellations are generally much more affordable than traditional large satellites, which can cost billions of dollars to build and launch. This makes it easier for companies and governments to launch multiple satellites, which can provide more frequent and detailed coverage of the Earth's surface. Flexibility: Small satellite constellations can be launched relatively quickly and can be reconfigured or replaced easily if needed. This allows for greater flexibility in responding to changing needs or addressing specific areas of interest. Coverage: Small satellite constellations can provide more frequent and consistent coverage of the Earth's surface than traditional large satellites, which may only pass over a given area once or twice a day. Resolution: While traditional large satellites may have higher spatial resolution than some small satellites, the use of constellations can provide higher temporal resolution, meaning that more frequent images can be obtained. Data management: Small satellite constellations can generate large amounts of data, which can pose challenges for data storage and management. However, advances in cloud computing and data processing technologies have made it easier to manage and analyze large datasets.
List and define the two mechanic systems used in satellite sensor scanning; discuss the pros and cons of each.
Cross track: whisk broom a technology for obtaining satellite images with optical cameras. It is used for passive remote sensing from space. In a whisk broom sensor, a mirror scans across the satellite's path (ground track), reflecting light into a single detector which collects data one pixel at a time. The moving parts make this type of sensor expensive and more prone to wearing out, such as in the Landsat 7. Whisk broom scanners have the effect of stopping the scan, and focusing the detector on one part of the swath width. Because the detector is only focused on a subsection of the full swath at any time, it typically has a higher resolution than a push broom design for the same size of scan swath. Along track: push broom is a device for obtaining images with spectroscopic sensors. The scanners are regularly used for passive remote sensing from space, and in spectral analysis on production lines, for example with near-infrared spectroscopy used to identify contaminated food and feed. A push broom scanner can gather more light than a whisk broom scanner because it looks at a particular area for a longer time, like a long exposure on a camera. One drawback of push broom sensors is the varying sensitivity of the individual detectors. Another drawback is that the resolution is lower than a whisk broom scanner because the entire image is captured at once.
Define and describe EVI.
EVI (Enhanced Vegetation Index) is an index similar to NDVI (Normalized Difference Vegetation Index) used to quantify vegetation health and density. It was developed to overcome some of the limitations of NDVI, including saturation at high vegetation densities and sensitivity to atmospheric interference. EVI is calculated using three spectral bands: blue, red, and near-infrared (NIR). Key characteristics of EVI: Measures vegetation health: EVI is a measure of the health and density of vegetation, similar to NDVI. Overcomes limitations of NDVI: EVI overcomes some of the limitations of NDVI, including saturation at high vegetation densities and sensitivity to atmospheric interference. Sensitivity to changes in vegetation: EVI is sensitive to changes in vegetation density and health, which makes it useful for monitoring changes in vegetation cover over time, such as deforestation or the impact of droughts or wildfires. Widely used: EVI is widely used in remote sensing applications, including monitoring agricultural productivity, forest health, and land use changes. Computationally more complex than NDVI: EVI is more complex to calculate than NDVI, requiring three spectral bands instead of two. Improved accuracy: Studies have shown that EVI provides improved accuracy in estimating vegetation parameters compared to NDVI.
5. Define geostationary orbit, its characteristics and application.
GEO is a kind of GSO. It matches the planet's rotation, but GEO objects only orbit Earth's equator, and from the ground perspective, they appear in a fixed position in the sky. A typical geostationary orbit has the following properties: Inclination: 0° Period: 1436 minutes Eccentricity: 0 Argument of perigee: undefined Semi-major axis: 42,164 km Communications satellites are often placed in a geostationary orbit so that Earth-based satellite antennas do not have to rotate to track them but can be pointed permanently at the position in the sky where the satellites are located. Weather satellites are also placed in this orbit for real-time monitoring and data collection, and navigation satellites to provide a known calibration point and enhance GPS accuracy.
Describe the Sentinel 2 satellites
Here are some key features of the Sentinel 2 satellites: Optical imaging: The Sentinel 2 satellites are equipped with high-resolution cameras that can capture images of the Earth's surface in 13 spectral bands, ranging from the visible to the near-infrared. The cameras have a resolution of up to 10 meters, with some bands having a resolution of 20 meters and others having a resolution of 60 meters. Wide swath: The Sentinel 2 satellites have a wide swath width of 290 km, which means they can cover large areas of the Earth's surface in a single pass. This is particularly useful for monitoring changes in vegetation cover, land use, and water quality over time. Global coverage: The Sentinel 2 constellation consists of two identical satellites in the same orbit, which means they can provide global coverage of the Earth's surface every five days. Open data policy: The data collected by the Sentinel 2 satellites is freely available to the public, making it a valuable resource for researchers, policymakers, and other stakeholders. Multispectral analysis: The Sentinel 2 satellites use multispectral analysis to provide information about vegetation health, land use, water quality, and other environmental parameters. This allows for more accurate monitoring of natural resources and ecosystems.
List and describe the new technologies that SPOT satellites first put into use.
High-resolution optical imaging: The SPOT satellites were equipped with high-resolution cameras that could capture images of the Earth's surface with a resolution of up to 2.5 meters, which was a significant improvement over previous Earth observation satellites. Stereo imaging: SPOT satellites were able to take images of the same area from different angles, allowing them to create 3D images of the Earth's surface. This technology was particularly useful for creating digital elevation models of the Earth's topography. Multispectral imaging: The SPOT satellites were equipped with sensors that could capture images in multiple wavelengths of light, including visible, infrared, and near-infrared. This technology allowed for more accurate analysis of vegetation, geology, and other features on the Earth's surface. Rapid imaging: The SPOT satellites were designed to quickly acquire and transmit images, allowing for near-real-time monitoring of natural disasters, environmental changes, and other events on the Earth's surface.
Describe the major characteristics of high-resolution satellite remote sensing systems and data.
High-resolution satellite remote sensing systems provide detailed imagery and data on the Earth's surface, allowing for a wide range of applications in fields such as agriculture, forestry, urban planning, and environmental monitoring. Here are some of the major characteristics of high-resolution satellite remote sensing systems and data: High spatial resolution: High-resolution satellite remote sensing systems provide imagery with spatial resolution typically ranging from 30 cm to 1 m, allowing for detailed analysis of the Earth's surface. Multispectral imagery: High-resolution satellite remote sensing systems typically capture imagery in multiple spectral bands, ranging from visible to near-infrared and thermal infrared. This allows for detailed analysis of vegetation health, land use, and environmental factors such as temperature and moisture. Rapid revisit times: High-resolution satellite remote sensing systems have short revisit times, allowing for frequent monitoring of the Earth's surface. This is particularly useful for monitoring changes over time, such as crop growth or land use changes. Large coverage areas: High-resolution satellite remote sensing systems can cover large areas in a single pass, allowing for efficient monitoring of large regions. Data processing: High-resolution satellite remote sensing data requires advanced processing techniques to remove noise, atmospheric interference, and other factors that can affect image quality. This typically involves complex algorithms and specialized software. Applications: High-resolution satellite remote sensing data is used for a wide range of applications, including monitoring urban growth, assessing crop health and yield, monitoring deforestation and forest fires, and mapping natural resources such as water and mineral deposits. Commercial availability: High-resolution satellite remote sensing data is commercially available from a number of providers, making it accessible to researchers, policymakers, and other stakeholders.
Define hyperspectral remote sensing and describe its applications.
Hyperspectral remote sensing is a technique that involves measuring the reflectance or emission of energy across a range of narrow and contiguous spectral bands, typically covering a wide range of the electromagnetic spectrum from visible to near-infrared and shortwave-infrared. The resulting hyperspectral data can be used to identify unique spectral signatures or fingerprints of different materials, which can then be used to map and analyze the spatial distribution and properties of those materials across large areas. Overall, hyperspectral remote sensing has the potential to provide a wealth of information about the Earth's surface and its properties, allowing for a better understanding of natural resources, environmental changes, and the impacts of human activities on the planet.
Describe the major characteristics of MODIS.
MODIS (Moderate Resolution Imaging Spectroradiometer) is a key Earth observation instrument flown on two NASA satellites, Terra and Aqua. Here are some of the major characteristics of MODIS: Overall, MODIS is a powerful instrument for monitoring the Earth's environment and resources, providing high spectral resolution data with global coverage and high temporal resolution. MODIS data has been used for a wide range of applications, contributing to our understanding of the Earth's climate and ecosystems. High spectral resolution: MODIS collects data in 36 spectral bands, covering a wide range of wavelengths from visible to thermal infrared. This allows for detailed analysis of the Earth's surface and atmosphere, including vegetation health, land use, and atmospheric composition. Global coverage: MODIS provides nearly global coverage of the Earth's surface every 1-2 days, allowing for continuous monitoring of the Earth's environment and resources. Moderate spatial resolution: The spatial resolution of MODIS data ranges from 250 meters to 1 kilometer, depending on the spectral band. This is lower than some other Earth observation instruments, but still provides valuable information on large-scale features such as vegetation cover and ocean temperature. High temporal resolution: MODIS collects data on a daily basis, which allows for monitoring of short-term changes in the Earth's environment. This is particularly useful for monitoring weather patterns and natural disasters. Open data policy: The data collected by MODIS is freely available to the public, making it a valuable resource for researchers, policymakers, and other stakeholders. Applications: MODIS data is used for a wide range of applications, including monitoring wildfires, detecting oceanic plankton blooms, tracking changes in vegetation cover, and analyzing air pollution.
Here are some key characteristics of NDVI:
Measures vegetation health: NDVI is a measure of the health and density of vegetation, as healthy vegetation reflects more NIR and absorbs more red light than unhealthy or sparse vegetation. Sensitivity to vegetation changes: NDVI is sensitive to changes in vegetation density and health, which makes it useful for monitoring changes in vegetation cover over time, such as deforestation or the impact of droughts or wildfires. Widely used: NDVI is widely used in remote sensing applications, including monitoring agricultural productivity, forest health, and land use changes. Computationally simple: NDVI is easy to calculate, requiring only two spectral bands, and can be applied to a wide range of satellite and aerial imagery. Limitations: NDVI has some limitations, including its sensitivity to atmospheric interference, soil reflectance, and the presence of non-vegetated surfaces such as water or bare soil.
Define and describe NDVI.
NDVI (Normalized Difference Vegetation Index) is a commonly used index in remote sensing to measure the health and density of vegetation on the Earth's surface. It is calculated from the reflectance of the Earth's surface in the red and near-infrared (NIR) parts of the electromagnetic spectrum. The formula for NDVI is: NDVI = (NIR - Red) / (NIR + Red) where NIR is the reflectance in the near-infrared part of the spectrum and Red is the reflectance in the red part of the spectrum. NDVI values range from -1 to 1, with higher values indicating denser and healthier vegetation. A value of 0 indicates no vegetation, while negative values indicate water or other non-vegetated surfaces. Overall, NDVI is a useful index for measuring vegetation health and density, providing a simple and widely used tool for monitoring changes in vegetation cover over time.
Define panchromatic vs. multispectral data/views, from both the sensor perspective and the software perspective.
Panchromatic data refers to imagery that captures light across the entire visible spectrum and sometimes into the ultraviolet or near-infrared range. Panchromatic sensors typically record data in a single band or channel, resulting in a grayscale image. This type of imagery is often used in applications where high-resolution and detail are important, such as urban mapping or military surveillance. On the other hand, multispectral data refers to imagery that captures light across multiple spectral bands or channels. Multispectral sensors can record data in several different wavelength ranges, such as red, green, blue, near-infrared, and shortwave infrared. This type of imagery is often used in applications such as agricultural monitoring, environmental monitoring, and mineral exploration. From a software perspective, panchromatic and multispectral data are typically processed differently. Panchromatic data is often used for high-resolution visual analysis and can be enhanced through various image processing techniques, such as sharpening and contrast adjustment. Multispectral data, on the other hand, is typically analyzed through spectral analysis techniques that involve identifying and quantifying the unique spectral signatures of different materials or features within the image. This can be done through various image classification and image analysis algorithms that use machine learning or statistical techniques.
List and briefly describe the different Landsat satellites by tracing the development of satellite remote sensing technology.
The Landsat program has been in operation since 1972 and has seen the launch of eight different satellites to date, each representing advances in satellite remote sensing technology. Here is a brief overview of each Landsat satellite and its contributions to the development of satellite remote sensing technology: Landsat 1 (launched in 1972): This was the first Landsat satellite and the first Earth observation satellite to provide global coverage of the Earth's land surface. It carried two sensors, a multispectral scanner (MSS) and a return beam vidicon (RBV) camera, and was used to study land use, geology, and other environmental variables. Landsat 2 (launched in 1975): This satellite was very similar to Landsat 1, with the addition of a new sensor, the extended MSS (EMSS), which provided increased spectral resolution and improved radiometric accuracy. Landsat 3 (launched in 1978): Landsat 3 was another incremental improvement over its predecessors, with improved image quality and better calibration of the sensors. Landsat 4 (launched in 1982): This satellite marked a major milestone in the Landsat program, as it was the first to carry the thematic mapper (TM) sensor, which provided increased spectral resolution and better discrimination of different land cover types. Landsat 5 (launched in 1984): Landsat 5 was very similar to Landsat 4, but with improved thermal imaging capabilities and better data transmission. Landsat 6 (launched in 1993): Unfortunately, Landsat 6 never made it to orbit due to a launch failure. Landsat 7 (launched in 1999): Landsat 7 carried the enhanced thematic mapper plus (ETM+) sensor, which provided even higher spectral resolution and better radiometric accuracy than the TM sensor. Landsat 8 (launched in 2013): Landsat 8 is the most recent Landsat satellite and carries the operational land imager (OLI) and thermal infrared sensor (TIRS), which provide even higher spectral resolution and better image quality than the ETM+ sensor.
Describe the major problems encountered in the history of Landsat satellites and how they were overcome/fixed.
The Landsat program has faced several challenges over its history, ranging from launch failures to sensor malfunctions. Here are some of the major problems encountered by Landsat satellites and how they were overcome or fixed: Launch failures: The Landsat program experienced two launch failures, with Landsat 6 failing to reach orbit in 1993 and an earlier satellite, ERTS-B, failing shortly after launch in 1975. These failures led to delays in the launch of subsequent Landsat satellites. Sensor malfunctions: Several Landsat satellites have experienced malfunctions with their sensors, including Landsat 4, which lost the use of its TM sensor in 1993, and Landsat 7, which experienced a scan line error in 2003 that affected about 22% of its images. To address these issues, Landsat 4 was switched to the RBV sensor, which provided lower resolution but still produced useful data, while Landsat 7 continued to operate with the ETM+ sensor, which was unaffected by the scan line error. Data transmission problems: Landsat 7 also experienced problems with data transmission, with some images being lost due to a malfunction in the solid-state recorder. To address this issue, NASA and the US Geological Survey (USGS), which operate the Landsat program, implemented a strategy of prioritizing data acquisition and transmission to ensure that the most valuable data was captured and transmitted. Funding issues: The Landsat program has faced several funding challenges over its history, with the cost of operating and maintaining the satellites and data processing and distribution systems sometimes exceeding available resources. To address this issue, the USGS has worked to streamline operations and reduce costs, while also exploring new funding models and partnerships with other organizations.
Describe the orbital parameters of Landsat satellites.
The Landsat satellites are a series of Earth observation satellites that have been in operation since 1972. Currently, there are eight Landsat satellites that have been launched, and they all share similar orbital parameters. The Landsat satellites orbit the Earth in a polar sun-synchronous orbit, meaning that they pass over each location on Earth at the same local time of day on each orbit. Specifically, the Landsat satellites are in a near-polar orbit, with an inclination of approximately 98 degrees, and an altitude of about 705 kilometers above the Earth's surface. The Landsat satellites have a repeating ground track pattern that takes about 16 days to complete, meaning that they revisit the same location on Earth every 16 days. During each orbit, the Landsat satellites scan the Earth's surface using various sensors, including visible, near-infrared, and thermal sensors, to capture data on land cover, land use, and other environmental variables. Overall, the Landsat satellites are designed to provide consistent, high-quality data over long periods of time, making them a valuable resource for monitoring and understanding changes on Earth's surface.
Describe the Sentinel 2 satellites
The Sentinel 2 satellites are a constellation of Earth observation satellites operated by the European Space Agency (ESA) as part of the Copernicus program. The Sentinel 2 mission aims to provide high-resolution optical imagery of the Earth's surface to support a wide range of applications, including land use mapping, agriculture, forestry, and disaster management. Overall, the Sentinel 2 satellites are a powerful tool for monitoring and understanding our planet's environment, providing high-resolution optical imagery that can be used for a wide range of applications.
Define the geolocation accuracy of high-resolution satellite data using examples.
The geolocation accuracy of high-resolution satellite data refers to the degree which satellite imagery can be accurately located on the Earth's surface. Here are some examples of geolocation accuracy for high-resolution satellite data: DigitalGlobe's WorldView-4 satellite: The WorldView-4 satellite is capable of producing imagery with a geolocation accuracy of 3 meters CE90 (circular error 90), which means that 90% of the locations in the image are within 3 meters of their actual position on the Earth's surface. Airbus Defence and Space's Pleiades satellites: The Pleiades satellites are capable of producing imagery with a geolocation accuracy of 5 meters CE90. Maxar Technologies' GeoEye-1 satellite: The GeoEye-1 satellite is capable of producing imagery with a geolocation accuracy of 5 meters CE90. Planet Labs' Dove satellites: The Dove satellites are capable of producing imagery with a geolocation accuracy of 10 meters CE90. Google Earth: Google Earth is a widely used platform for viewing high-resolution satellite imagery. Its geolocation accuracy varies depending on the source of the imagery, but it generally ranges from 1 to 10 meters. It's important to note that geolocation accuracy can vary depending on a number of factors, including the quality of the satellite sensor, atmospheric conditions, and the processing techniques used to correct for distortions in the imagery. Nonetheless, high-resolution satellite data typically provides a high degree of geolocation accuracy, allowing for precise analysis of the Earth's surface at a local scale.
Describe the concept of using small satellite constellations vs. traditional large satellites.
Traditionally, remote sensing satellites have been large and expensive, designed to operate for several years in orbit. However, recent advances in technology have made it possible to launch smaller and more affordable satellites, which can be operated in constellations or groups of satellites working together. Here are some of the key differences between using small satellite constellations and traditional large satellites: Overall, the use of small satellite constellations represents a significant shift in the way remote sensing data is collected and analyzed. While there are still some limitations to this approach, such as lower spatial resolution and the need for effective data management, the flexibility and cost-effectiveness of small satellite constellations make them a promising option for a wide range of applications, from monitoring natural resources to responding to natural disasters.
