Remote Sensing Definition
Remote sensing is the science of acquiring information about an object or phenomenon by measuring emitted and reflected radiation. There are two primary types of remote sensing instruments -- active and passive.
What is Remote Sensing?
Remote sensing is a type of geospatial technology that samples emitted and reflected electromagnetic (EM) radiation from the Earth’s terrestrial, atmospheric, and aquatic ecosystems in order to detect and monitor the physical characteristics of an area without making physical contact. This method of data collection typically involves aircraft-based and satellite-based sensor technologies, which are classified as either passive sensors or active sensors.
Passive sensors respond to external stimuli, gathering radiation that is reflected or emitted by an object or the surrounding space. The most common source of radiation measured by passive remote sensing is reflected sunlight. Popular examples of passive remote sensors include charge-coupled devices, film photography, radiometers, and infrared.
Active sensors use internal stimuli to collect data, emitting energy in order to scan objects and areas whereupon a sensor measures the energy reflected from the target. RADAR and LiDAR are typical active remote sensing tools that measure the time delay between emission and return in order to establish the location, direction, and speed of an object. The remote sensing data gathered is then processed and analyzed with remote sensing hardware and computer software (for example energy analytics and energy business intelligence), which is available in a variety of proprietary and open source applications.
What is Remote Sensing Used For?
Remote sensing technology is used in a wide variety of disciplines in thousands of different use cases, including most earth sciences, such as meteorology, geology, hydrology, ecology, oceanography, glaciology, geography, and in land surveying, as well as applications in military, intelligence, commercial, economic, planning, and humanitarian fields. Some typical remote sensing examples include:
- GIS remote sensing: Geographic Information System (GIS) is a system designed to capture, store, manage, analyze, manipulate, and present geographic or spatial data -- satellite remote sensing provides an important source of spatial data. Remote sensing and GIS work together to gather, store, analyze, and visualize data from virtually any geographic position on Earth.
- Irrigation and soil moisture monitoring and management are major components of remote sensing in agriculture.
- Doppler radar measures meteorological events such as wind speed and direction within weather systems as well as precipitation intensity and location. Another application is aerial traffic control.
- AVHRR and MODIS satellites use thermal sensing and mid infrared sensing to monitor active volcanoes.
- INSAR (interferometric synthetic aperture radar) uses interferometry remote sensing technique to predict and provide early warnings for potential landslides.
- A primary application of light detection and ranging (LiDAR) is vegetation management and monitoring, however it is also applied in cases of weapon ranging and laser illuminated homing of projectiles. LiDAR may also be used to detect and measure the concentration of various chemicals in the atmosphere.
- Stereographic pairs of aerial photographs are used to model terrestrial habitat features and make topographic maps by imagery and terrain analysts in trafficability and highway departments for potential routes.
- Spectropolarimetric Imaging is used by researchers at the U.S. Army Research Laboratory for target tracking purposes by identifying man made items by their polarimetric signatures, which are not found in natural objects.
- Remote sensing satellites provide before- and after- remote sensing images in order to quantify post-earthquake damage, which provides vital data for rescue workers.
- Data from laser and radar altimeters on satellites, sonar, and ultrasound measurements can be used for coastal mapping and erosion prevention, to better understand how to manage ocean resources, to assess the impacts of a natural disaster and create disaster response strategies to be used before and after a hazardous event, and to minimize the damage that urban growth has on the environment and help decide how to best protect natural resources.
- Remote sensing for oil and gas is an integral tool for upstream and downstream gas and oil operations through evaluation of infrastructure for well-site planning. Spectral analysis is vital for the evaluation of surface outcrops and surface hydrocarbon seepage.
Importance of Remote Sensing
Remote sensing makes it possible to collect data from dangerous or inaccessible areas, with growing relevance in modern society. It replaces slower, costly data collection on the ground, providing fast and repetitive coverage of extremely large areas for everyday applications, ranging from weather forecasts to reports on natural disasters or climate change.
Remote sensing is also an unobstructive method, allowing users to collect data and perform data processing and GIS analysis offsite without disturbing the target area or object. Monitoring floods and forest fires, deforestation, polar bears, chemical concentrations, and earthquakes are just a few cases in which geospatial remote sensing provides a global perspective and actionable insights that would otherwise be unattainable.
Advantages of Microwave Remote Sensing
Microwave remote sensing encompasses both passive and active remote sensing, covering wavelengths ranging from one centimeter to one meter -- the microwave’s longer wavelength is an important feature in remote sensing as it can penetrate haze, rainfall, dust, and cloud cover more effectively than visible and infrared.
Remote sensing of the environment using microwave remote sensing is therefore unaffected as the longer wavelengths are not susceptible to atmospheric scattering. Microwave energy can be detected and data can be gathered under most environmental conditions. Applications include sea ice monitoring and global soil moisture mapping.
Application of Remote Sensing to Climate Change
Application of remote sensing in the studies of climate change has provided major advances in understanding the climate system and its changes, by quantifying spatio-temporal states and processes of the atmosphere, oceans, and lands. Satellite sensors have aided in the detection and measurement of the cooling effects of increased stratospheric aerosols and the spatial pattern of sea-level rise, which otherwise went unobserved by conventional climate models observations.
Global climate change research uses big data from Earth observation platforms, in which remote multi-satellite, multi-sensor, and long-term time series data methods are implemented. This has facilitated the detection of climate sensitivity factors, advanced the study of the spatial variability of terrestrial ecosystems, and aided in the development of global climate change response strategies.
Limitations of Remote Sensing Data
Remote sensing is ultimately managed by human operators that make crucial decisions regarding which sensors should be used to collect data and when, resolution specifications for the collected data and sensor calibration, and the selection of the platform that will carry the sensor, all of which expose this method to a certain degree of human error.
Inaccuracy may also be introduced by the electromagnetic spectrum radiation emitted from powerful active remote sensing systems, which can be intrusive and affect the target phenomenon being investigated. Remote sensing instruments may contribute inaccurate, un-calibrated data if the hardware system becomes un-calibrated. There may also be cost related limitations. It is an expensive method that requires extensive, special training for image analysis
History of Remote Sensing
The earliest practices of modern remote sensing consisted of primitive photographs of the earth’s surface taken from tethered balloons for the purpose of topographic mapping in the 1840s. Systematic aerial photography using modified aircrafts was developed for military surveillance and reconnaissance purposes during the first World War and through the Cold War.
With the emergence of the space program in the 1960s, instrumentation on Earth observing and weather satellites such as the Nimbus and Landsat provided global measurements of various data for military, civil, and research purposes. IKONOS, the first commercial satellite built to collect very high resolution imagery, was commissioned by Lockheed Martin, launched in 1999, and decommissioned in 2015.
Does HEAVY.AI Provide Remote Sensing Solutions?
Remote sensing data is a major source of spatial data used in Geographic Information Systems (GIS). Geospatial-specific processes in GIS tools are becoming too slow for the enormous data volumes provided by modern remote sensing technologies.
HEAVY.AI bridges this divide, providing an accelerated analytics platform that allows geospatial analysts to cross-filter billions of location data records and polygons alongside other features in milliseconds. The HEAVY.AIDB SQL engine natively stores geographic and geometric data types, enabling users to run geo calculations with the massively parallel processing power of CPUs and GPUs.