Tech Talk: Understanding Remote Sensing and its Applications
Remote sensing technologies have made significant advances in the digital ecosystem. They provide customised and nuanced solutions to users from diverse domains such as agriculture, insurance, marketing, security, climate change, and more.
These days, Remote Sensing technology combined with Artificial Intelligence, Machine Learning algorithms, and Big Data analytics is providing the much-needed data backbone on which myriad solution-centric applications can be developed.
What is Remote Sensing?
Remote sensing refers to the process of detecting and monitoring an area’s physical characteristics ‘remotely’ by measuring the reflected and emitted radiation from its surface.
For instance, cameras on airplanes capture images of large areas of the Earth’s surface, sonar systems on ships map rugged topographies of the ocean floor, and satellite sensors study temperature variations in oceans.
That’s not all, however. A wide range of sensors that detect not only visible light but also other bands of the electromagnetic spectrum such as infrared, microwaves, and ultraviolet regions are commonly used in remote sensing.
Components and Steps Involved in Remote Sensing
Remote sensing technology primarily involves two components:
- Platform: ‘Carriers’ for remote sensors.
- Platforms can be of three types: ground-based platforms (hand-held devices, tripods, towers, moving vehicles, and total stations), aerial platforms (helicopters, low-altitude, and high-altitude aircrafts, unmanned aerial vehicles/drones), and spaceborne platforms (polar-orbiting satellites, sun-synchronous, and geostationary satellites).
- Sensors: ‘Devices’ that collect data by detecting energy reflected from earth.
- Sensors can be of the following types:
- Active Sensors (emit, reflect, and detect energy produced by their own source) and Passive Sensors (detect the reflected sunlight or energy emitted by the object being studied). LiDAR and RADAR are “active” sensors, while radiometers and spectrometers are “passive”. Passive sensors are known to produce higher quality imagery than active ones.
- Sensors can be of the following types:
Choosing the Right Type of Remote Sensing Technology for Projects
Only well-thought permutations and combinations of both platforms and sensors, depending on the purpose for which remote sensing technology is to be used, can lead to the right alternative. A “one-size-fits-all” approach should never be resorted to.
Shri I M Bahuguna, Deputy Director, Space Applications Centre ISRO, stressed on this very point at the India Geospatial Leadership Summit 2022, “When deciding on the type of remote sensing technology for an application, we begin by defining sensors depending on specificity, solutions, and requirements of the application. Next comes validation of various parameters, followed by Geospatial data visualization, modelling, and assimilation. Finally, applications are developed as per societal needs.”
Close-range characterization of objects (a small patch of land, microclimate of a region) can be better studied using ground-based platforms. Hand-held devices and tripods are best for indoor studies, towers for atmospheric monitoring, and moving vehicles for a fixed stretch of land or sea. Total stations, on the other hand, because of integration capabilities with microprocessors, data collectors and storage systems, are best for wider area survey purposes.
Airborne platforms are best for land and coastal mapping, oceanographic studies, agricultural, disaster management, and urban mapping applications, due to wider coverage. Low-altitude aircraft are good for acquiring high-resolution data on smaller areas inexpensively, while helicopters are best used where the ability to hover is required.
Mid-altitude aircrafts, which operate from above the zone of turbulence, are best when stability, wider and faster coverage are important, while high-altitude aircraft allow for large areal coverage with typically lower spatial resolutions.
Unmanned aerial vehicles (UAVs), or Drones, survey in lesser time, use fewer natural resources, have superior reach and data collection capabilities, and can be used for spraying and precision farming in agriculture, observation and supply distribution during emergencies or to inaccessible areas, etc.
Satellites are the most stable option for remote sensing, often used for base map data acquisition, and present several options for data collection. Geostationary satellites, for instance, always stay over the same location on Earth, and so are best for communications, weather-related, and other real-time applications.
Sun-synchronous satellites, on the other hand, always pass a place at the same local sun time, which can help observe changes, predict patterns, monitor long-term problems like deforestation or rising sea-levels.
Active sensors fully function at any time of the day because they do not require sunlight. However, they do not consider atmospheric scatterings, which means they are less accurate. Passive sensors, on the other hand, can be used only in the daytime, but are more representative of actual surface conditions.
Active remote sensing techniques differ in terms of what they broadcast (light or waves) and what they determine (distance, height, atmospheric conditions, etc.). Microwaves are used in the majority of devices because they are relatively weather resistant.
Radars, that emits radio signals, can detect objects at longer distances and through fog or clouds, making it best for aircraft anti-collision systems, air traffic control, and astronomy. However, it falls short when it comes to accuracy. LiDAR, on the other hand, is a light-based remote sensing technology that enables higher accuracy data capture. This makes LiDAR a better choice for laser altimetry (topography) and contour mapping.
The visible, infrared, thermal infrared, and microwave regions of the electromagnetic spectrum are utilised by most passive sensors in remote sensing applications. This type of sensors includes various types of radiometers and spectrometers.
Radiometers determine power of radiation emitted by the object in specific wavelength ranges (visible, IR, microwave), while spectrometer simply distinguishes and analyses these bands. Radiometers are inexpensive, portable, and provide quick measurements. They can be used for road surface temperature measurement, weather assessment, fires, etc. Spectrometers are more accurate and modular and can be used for research purposes in agriculture, climate change observation, geological studies, etc.
These days, advancements such as hyperspectral radiometer and spectroradiometer have also developed, measuring radiations across multiple wavelength bands for targeted analysis and higher resolution.
Major Applications of Remote Sensing
Disaster Study, Management, and Response
A vivid example of such application is the Forest Fire Monitoring & Analysis Project by the Karnataka State Remote Sensing Applications Centre (KSRSAC), awarded the AGI India Award for Excellence in Disaster Management. They used remote sensing sensors to detect a total of 3,98,774 forest fires since November 2020.
Speaking at the India Geospatial Leadership Summit 2022, Shri Jayachandran Mani, Project Director (GIS), KSRSAC elaborated, “Satellite-based remote sensing technology and GIS tools were used effectively for creating a dynamic information system including an early warning mechanism for fire-prone areas, monitoring fires on a real-time basis, and estimating burn scars. Data was prepared, collated, and disseminated from far and near for use by the Karnataka Forest Department and other concerned departments of the Government of Karnataka.”
Delivering his address at the India Geospatial Leadership Summit 2022 on the theme ‘Harnessing the Power of Remote Sensing Data for Economic Development, Shri Prakash Chauhan, Director, National Remote Sensing Centre (NRSC), ISRO, further elaborated on this aspect of the technology. “Several studies are being conducted using Remote Sensing to study response of Himalayan glaciers to changing climatic systems,” he noted. “20 years of remote sensing data has been converted into a visual of 20 seconds using time lapse technology to study how much glacial retreat has taken place, what will the potential adverse effects be, recent disasters and their impact on riverine flow”.
The Maharashtra Remote Sensing Applications Center (MRSAC) Nagpur, awarded the AGI India Award for Excellence in Governance, demonstrated a similar use case recently. MRSAC conducted a Large-Scale Mapping project at 1: 2000/4000 scale for the entire state of Maharashtra, including mapping of various resources. All outputs are being shared with all State Government departments in the form of a Geo-Portal that acts as a Decision Support, eliminating duplication efforts, saving costs and time, and improving workflows.
Dr. Brijendra Pateriya, Director, Punjab Remote Sensing Centre at IGLS 2022 threw light on the increasing use of remote sensing technology as a key tool for data-driven decision making. “With enhanced awareness and affordable access, governments today rely on location-based information more than ever to support strategic priorities, making decisions, and monitoring outcomes. Remote Sensing plays a pivotal role in this regard with accurate data collection,” he noted.
Interesting advancements are already shaping up. Digital Earth Australia’s Open Data Cube, for instance, combines high-performance computing and data infrastructure to unlock the value of remote sensing data of the Earth’s surface. Similar Data Cubes have also been developed in Africa and Mexico to harness the latest Earth observation data and help governments along with businesses and innovators tackle agricultural, food security, environmental, urbanization, and liveability issues.
The use of Artificial Intelligence (AI), Machine Learning (ML), and Computer Vision has been the most recent innovation in obtaining precise and accurate data utilising Remote Sensing (CV). Large-area analysis, item classification, land use detection and monitoring, data fusion, cloud removal, and spectrum analysis of environmental changes from satellite or aerial photography are all areas where AI can enhance. Computer Vision models can create reports in near real time for assessing vast areas with complex feature distribution.