What do we use remote sensing for

A spacecraft in a geostationary orbit. Observing with the Electromagnetic Spectrum Electromagnetic energy, produced by the vibration of charged particles, travels in the form of waves through the atmosphere and the vacuum of space.

Diagram of the Electromagnetic Spectrum Some waves are absorbed or reflected by elements in the atmosphere, like water vapor and carbon dioxide, while some wavelengths allow for unimpeded movement through the atmosphere; visible light has wavelengths that can be transmitted through the atmosphere.

Credit: Jeannie Allen The primary source of the energy observed by satellites, is the Sun. Often, when energy is absorbed, it is re-emitted, usually at longer wavelengths. For example, the energy absorbed by the ocean gets re-emitted as infrared radiation.

Sensors Sensors, or instruments, aboard satellites and aircraft use the Sun as a source of illumination or provide their own source of illumination, measuring the energy that is reflected back. Credit: NASA Applied Remote Sensing Training Program Passive sensors include different types of radiometers instruments that quantitatively measure the intensity of electromagnetic radiation in select bands and spectrometers devices that are designed to detect, measure, and analyze the spectral content of reflected electromagnetic radiation.

Most passive systems used by remote sensing applications operate in the visible, infrared, thermal infrared, and microwave portions of the electromagnetic spectrum. These sensors measure land and sea surface temperature, vegetation properties, cloud and aerosol properties, and other physical properties. Resolution Resolution plays a role in how data from a sensor can be used. Data Processing, Interpretation, and Analysis Remote sensing data acquired from instruments aboard satellites require processing before the data are usable by most researchers and applied science users.

Creating Satellite Imagery Many sensors acquire data at different spectral wavelengths. Image Interpretation Once data are processed into imagery with varying band combinations, they can aid in resource management decisions and disaster assessment; the imagery just needs to be interpreted. Know the scale — there are different scales based on the spatial resolution of the image and each scale provides different features of importance. For example, when tracking a flood, a detailed, high-resolution view will show which homes and businesses are surrounded by water.

The wider landscape view shows which parts of a county or metropolitan area are flooded and perhaps where the water is coming from.

An even broader view would show the entire region—the flooded river system or the mountain ranges and valleys that control the flow. A hemispheric view would show the movement of weather systems connected to the floods. Look for patterns, shapes and textures — many features are easy to identify based on their pattern or shape. For example, agricultural areas are very geometric in shape, usually circles or rectangles.

Straight lines are typically manmade structures, like roads or canals. True- or natural-color images are basically what we would see with our own eyes if looking down from space. Water absorbs light so typically it appears black or blue; however, sunlight reflecting off the surface might make it appear gray or silver. Sediment can affect water color, making it appear more brown, as can algae, making it appear more green.

Bare ground is usually some shade of brown; however, it depends on the mineral composition of the sediment. Urban areas are typically gray from the extensive concrete. Ice and snow are white, but so are clouds.

Consider what you know — having knowledge of the area you are observing aids in the identification of these features. For example, knowing that the area was recently burned by a wildfire can help determine why vegetation may look a bit different. Quantitative Analysis Different land cover types can be discriminated more readily, by using image classification algorithms. Data Pathfinders To aid in getting started with applications-based research using remotely-sensed data, Data Pathfinders provide a data product selection guide focused on specific science disciplines and application areas, such as those mentioned above.

Crime detectives want to narrow down their search before they go on a quest. Remote sensing tools can explore the search area with a fine-tooth comb and pick up anomalies on the ground. This could include anything from a rabbit hole to the crime scene, itself.

This is truly a time-saver if you have a rough idea of the search location. When a boat was dumped illegally with all identification removed in Santa Rosa County, crime investigators took their search to Google Maps. Using historic aerial and satellite imagery, they went on a hunt for its rightful owner. What crime investigators found was the same boat and the address of the illegal dumper. Case closed. Volcanoes form when hot molten rock from the upper mantle finds its way to the surface.

Eruptions are dangerous to humans and the surrounding environment. There are over active volcanoes on Earth. Volcanoes are often inaccessible unless you are Mario or Luigi making remote sensing applications like thermal and mid-infrared clear solutions for understanding volcano activity. Landslides are often under-represented for hazard research. But every year in the United States, landslides cause loss of life and billions of dollars in damage.

The first step in inventorying potential landslides is using stereo and optical images with slope. Slope instability triggers can be several things — earthquakes, erosion, poor drainage, and more. InSAR can provide early warning signs for landslides because of how well it measures ground surface displacements. The result of an earthquake can be catastrophic and at times difficult to assess.

But an earthquake assessment is essential for rescue workers. They need to be done quickly and with accuracy. Object-based image classification using change detection pre- and post-earthquake is a quick way to get damage assessments.

Other remote sensing applications in disaster assessments include casted shadows from buildings and digital surface models. Active sensors use phase difference to measure landscape deformation using interferometry. Industries like the oil and gas sector monitor terrain stability using these types of remote sensing applications for better safety standards.

Over time, continual satellite data means higher safety and ensures pipeline productivity. This means better preparedness for mitigation as well as response and recovery. The integration of Earth observation data and GIS in hazard situations has become the main tool in disaster management. Remote sensing applications for hazards include assessing the extent of damage and assisting dispatch. The sad story about the polar bear is that it is listed as one of the first animals that will become extinct because of global warming.

Ecologists are turning to satellites as their primary source of information because they need a firm count on polar bears for their survival. So… How do you know the difference between a polar bear and a big white rock? In two images, polar bears moved, while rocks stayed in the same spot.

Habitat is important for pandas. This makes roads and infrastructure ecological armageddon for pandas. In order to protect the endangered panda, remote sensing classifies fragmentation and man-made corridors as factors. Birds travel great distances in search of food, climate, and breeding sites. Light-weight GPS telemetry is just one of the tools being used to know where birds migrate. As forests become more limited, migration patterns are important for wildlife managers.

Remote sensing applications like LiDAR, multispectral, and radar can show forest properties like vertical structure and phenology. Habitat suitability models predict the prevalence of bird species using these forest properties. Tanzania hosts one of the greatest migrations on Earth. More than 2 million wildebeest migrate and give birth in the same month. The purpose of migration is to locate food resources. But can we model their movements? Research has shown that variables like vegetation NDVI and relief slope are drivers for wildebeest movement patterns.

However, rainfall may also have an impact on migration patterns as well. Habitat suitability models are making some interesting predictions on the abundance of mosquitoes. Remotely-sensed factors such as greenness, brightness, temperature, and especially moisture positively correlate with the over-occurrence of mosquitoes.

Knowing the location of high concentrations of mosquitoes can guide risk assessment for disease-carrying pathogens and mosquito fogging efforts. If Dr. Evil was a geographer, LiDAR would be his weapon of choice. What makes LiDAR so special is its densely sampled points at laser accuracy. LiDAR generates point clouds for digital surface models , digital elevation models and light intensity models.

Imagine you are a surveyor and your crew chief asks you to survey the whole world. You need to map meter grid cells and are given only 11 days. What would you say?

The secret to its success is Interferometric Synthetic Aperture Radar. Photogrammetry dates back to the mid-nineteenth century. It is used to find the geometric properties of objects by measuring distances between objects. Some of its derived products in GIS include contour mapping, surface models, volumetric surveys, and 3D mapping. The astounding rate of growth in this industry requires extensive planning for optimal network capacity.

Telecommunications companies are using remote sensing as a cost-effective way to optimize capacity requirements. Radiofrequency coverage can be augmented with the appropriate antenna type, location, and direction.

Satellite-derived terrain, land use, and other environmental factors can be modeled to achieve optimal network capacity. A lift irrigation system can improve water supply for agriculture and other industries. Planning the design of lift irrigation systems requires a wide range of data. Satellite stereo image pairs and photogrammetry are particularly useful for generating datasets like digital terrain models.

The engineers can get the full view on the ground before commencing construction. The goal is to achieve a completely autonomous monitoring system. Its purpose is simple — understand the health of the Earth. Biological diversity biodiversity is the wide variety of animals and plants in a geographic location.

With the spatial and spectral resolutions of sensors improving year by year, remote sensing applications in biodiversity are beginning to play a larger role. It remains in the early development stage but strides are being made using hyperspectral and 3D vegetation structures using LiDAR. They are the last line of environmental defense from nutrient runoff for our lakes and rivers. Of all remote sensing applications in the environment, riparian zones perhaps require the highest spatial resolution because of their small width.

A Landsat-8 pixel might not be able to do the job here. As water spans the entire globe, riparian zones are there serving their duty.

Parks provide a home for a large number of animals and species at risk. They often prohibit development and are used for camping and recreation. Parks can be large in scale making them a difficult resource to manage.

Remote sensing data gathered over time can show landscape change. Some remote sensing applications in parks include mapping biodiversity, invasive species, and forest fire risk.

Wildfires cause serious damage to property and even loss of life. For these reasons, there is a need to control wildfires and lessen their impact. Based on satellite data, firefighters can dispatch with pinpoint accuracy. Also, satellites can trace the extent of wildfires using temporal data.

The Surui tribe in Brazil has teamed up with Google to reverse rainforest deforestation. They keep a watchful eye on illegal mining and logging. The good news is that miners and loggers have retreated and illegal activities are at their lowest levels in history. The information potential with satellites for understanding illegal rainforest cutting is enormous as part of Google Earth Outreach to Surui Tribe.

Billions of people depend on healthy forests for their livelihood. The rapid spread of forest disease can have catastrophic effects on ecosystem health and local or national economies. The mountain pine beetle has infested over Because remote sensing monitors for these color changes, forest managers can better track the occurrence of forest diseases such as the mountain pine beetle. The tax revenue agency in Athens, Greece is looking for signs of wealth using satellite data. Not a bad idea where more than 15, swimming pools went unclaimed to tax authorities in The money-strapped country is looking at increasing their tax revenues using remote sensing applications using satellite imagery.

A Landsat pixel spans multiple parcel boundaries and is not a realistic representation of a tree canopy. A mayor would be very embarrassed to know their objective is almost exceeded. Some cities use mobile LiDAR to manage their assets and ensure safety standards. Each year, cities and municipalities issue thousands of permits for construction. This massive volume of permits makes it difficult for cities to manage activities. Using mobile LiDAR collection and comparing it with municipal data, you can ensure construction activity is safe and properly permitted.

Road conditions, utilities, billboards, and sign inventories are some of the other remote sensing applications in asset management. Over 80, graves were captured at 1 cm pixel resolution. Not only was this faster than manually recording each cemetery in the field, but a digital record of the imagery remains.

UAVs were a low-cost and highly accurate solution for cemetery mapping. The result was a spatial database and tax dollars saved. This is why Google and Bing maps have added this functionality to their interfaces. A digital elevation model determines where and how water flows in a watershed. Hydrologists are interested in the hydrologic budget when they study watersheds. Snow appears white, and ash is gray. All of the cabins were damaged and one pavilion is gone altogether.

The area is now inaccessible due to the cut from the harbor to the cabins. These cuts were likely formed when surge flowed over the island from the sound side. The dunes in front of the. Skip to main content. Search Search. Mapping, Remote Sensing, and Geospatial Data. Some examples are: Cameras on satellites and airplanes take images of large areas on the Earth's surface, allowing us to see much more than we can see when standing on the ground. Sonar systems on ships can be used to create images of the ocean floor without needing to travel to the bottom of the ocean.

Cameras on satellites can be used to make images of temperature changes in the oceans. Some specific uses of remotely sensed images of the Earth include: Large forest fires can be mapped from space, allowing rangers to see a much larger area than from the ground.

Tracking clouds to help predict the weather or watching erupting volcanoes, and help watching for dust storms. Tracking the growth of a city and changes in farmland or forests over several years or decades. Discovery and mapping of the rugged topography of the ocean floor e. Apply Filter. What are the band designations for the Landsat satellites? The sensors aboard each of the Landsat satellites were designed to acquire data in different ranges of frequencies along the electromagnetic spectrum View Bandpass Wavelengths for all Landsat Sensors.

What are the acquisition schedules for the Landsat satellites? The Landsat 7 and Landsat 8 satellites orbit the Earth at an altitude of kilometers miles in a kilometer mile swath, moving from north to south over the sunlit side of the Earth in a sun synchronous orbit, following the World Reference System WRS Each satellite makes a complete orbit every 99 minutes, completes about However, this statement is too general.

Remote-sensing big data has several concrete and special characteristics: multi-source, multi-scale, high-dimensional, dynamic-state, isomer, and non-linear characteristics. This survey explains these characteristics in detail. Furthermore, according to whether the characteristics are closely related to the instruments or methods of data acquisition, we points out that the dynamic-state, multi-scale and non-linear characteristics are intrinsic characteristics of remote-sensing big data while the multi-source, high-dimensional and isomer characteristics are extrinsic characteristics of remote- sensing big data.

In addition, we briefly review promising techniques and applications of remote-sensing big data. Remote sensing has become one of the most important methods used to quickly and directly acquire information on the Earths surface. In recent years, with development of environmental information science, remote sensing data have played an important role in many research fields, such as atmospheric science, ecology, soil contamination, water pollution, environmental geology, environmental soil science, volcanic phenomena and evolution of the Earths crust.

The requirements of research have accelerated the development of Earth observation technologies. Many countries have rushed to launch their own satellites. Figure 1 summarizes the number of remote sensing satellites launched by major countries in the period — It is seen that the USA, India and Russia are the three counties that have launched most remote sensing satellites. For most countries and regions, almost all remote sensing satellites have been launched in the period — The requirements of different investigations have increased the specialization and diversity of techniques of acquiring remote sensing data.

Remote sensing data often differ features in terms of their resolution, revisit cycle, spectrum, and mode of imaging. Nowadays, we can choose different remote sensing systems and datasets for different applications. A satellite can be classified as providing low-resolution imaging e.

A satellite can be classified by its mode of imaging as an optical satellite e. A satellite can be classified by its area of application as a terrestrial satellite e. Finally, a satellite can be classified by its ability to revisit an observation area. For example, satellites of the Geostationary Operational Environmental Satellite GOES system can provide continuous, timely and high-quality environmental and atmospheric observations over the surface of the Earth, whereas there are also satellites with a short revisit period of 1 day e.

Table 1 gives a selection of satellites whose data are often used in environmental information science research. Overall, it is seen that there is a tremendous variety of remote sensing data. Table 1. Summary of the characteristics of satellites often used in environmental information research. Another characteristic of remote sensing data is its large volume. The volume of remote sensing data for a single scene is usually on the gigabyte level, the volume of data received by a large ground station [such as China Remote Sensing Satellite Ground Station RSGS in China] is usually on the terabyte level, the volume of the archive of historical data in some countries e.

Additionally, because there are so many satellites orbiting the Earth, the rate of data acquisition is very high. Therefore, remote sensing data are clearly big data. Big data refers to a collection of data sets so large and complex that it is difficult to employ traditional data processing algorithms and models.

Challenges include the acquisition, storage, searching, sharing, transfer, analysis, and visualization of the data. Scientists regularly encounter limitations due to large datasets in many areas, such as geoscience and remote sensing, complex physics simulations, and biological and environmental research.

When we talk about the features of big data, it is popular to refer to the three Vs Laney, : significant growth in the volume, velocity and variety of data. However, the term the three Vs is too general. The big data of remote sensing has several concrete and special characteristics; i.

The multi-source characteristic of remote-sensing big data is obvious. The fundamental reason for the multi-source characteristic is that we often use different instruments to acquire the data. Furthermore, the physical meanings of the multi-source data may be totally different. From the perspective of the imaging mechanism, the main data types are optical data, microwave data, and point cloud data.

Other types of remote sensing data include stereographic pairs created from multiple photographs often used to create three-dimensional or topographic maps and gravity data that show the gravity situation and the amount of water available in one region.

The multi-source data allows us to use and understand information from multiple viewpoints. However, they sometimes cause confusion in that we need to decide which type is the most appropriate and effective for a particular application. Reference is often made to the multiple scales of the big data of remote sensing.

The observation scale, which is also called the measurement scale, refers to the resolution, time interval, spectral range, solid angle or polarization direction Wu and Li, Spatial scale refers to the spatial resolution and can be thought of as the size of the smallest objects that can be distinguished by sensors. A good observation often depends on the appropriate spatial scale.