Satellites provide us with a unique overview of our planet. As of January 1, 2023, there were 6,718 satellites orbiting Earth, and the number is increasing at an exponential rate. While most of the satellites presently in orbit are communication satellites, 1167 are dedicated to Earth Observation.
Source: Satellite Database | Union of Concerned Scientists (ucsusa.org)
As satellites constantly orbit the earth, they enable us to track physical trends and document changes and surface dynamics over time. In recent years focus has been on increasing spatial resolution in the satellite imagery, and the increasing number of satellites being launched is among other things a consequence of the desire to also increase temporal resolution to get a better idea about the dynamics characterizing the area of interest on the surface.
Essentially, the status of Earth Observation satellite capabilities enables us to create a living map of our planet, and with adequate processing, the satellite data streams can be transformed into information supporting a wide range of applications.
We use the term spatial resolution to describe the level of detail seen in images. A higher spatial resolution enables us to see finer details on in an image.
Temporal resolution is used to describe how often we can acquire a new image from the satellites. Some satellites can capture images of specific locations daily or even more frequently, whereas others may only be able to capture images on a weekly basis.
The footprint of a satellite indicates the area of the earth covered by a single image. For some satellites, the footprint is several thousand square kilometers, whereas for other satellite systems an image acquisition may cover as little as a few square kilometers.
SAR, short for Synthetic Aperture Radar, is an advanced way to build an imaging radar system which ensures that images may be acquired with very high spatial resolution.
Optical satellite sensors capture images by measuring the light reflected by the sun from the surface of the earth. This means that surface properties may only be captured by an optical sensor under cloud free conditions, and that effects of shadowing from clouds or surface objects will be present in the images.
SAR overcomes this challenge by emitting its own radiation, a radar pulse, and measuring the signal reflected by the surface. This permits SAR to “see” in the dark and the wavelength of the radar pulse allows SAR to “look” through the clouds. In other words, SAR imagery will always be governed by the surface properties regardless of cloud cover.
A view through the skies. Port of Hamburg, Germany, August 2018.
Optical Sentinel-2 image (left), and a SAR image from Sentinel-1 (right).
Optical sensors are passive sensors, which depend on an external illumination source, i.e., the sun. The sun emits electromagnetic radiation spanning the ultraviolet, visible light and the shortwave infrared domain. Visible light covers the color spectrum as we know it, such as red, green, and blue colors, including all the shades in between. In this context the notion of color may be extended to also cover ultraviolet and short-wave infrared ‘colors’ that the human eye cannot see.
The number of spectral bands of a satellite sensor is a measure of how many ‘colors’ the sensor is seeing, or in other words how many color layers that are present in the acquired images, including ‘color’ layers in the ultraviolet and short wave infrared spectral domain.
The eruption of the Kilauea Volcano in Hawaii, USA. Image acquired from the Sentinel-2 satellite on May 23rd, 2018. Here shown in true color (bands 4, 3 and 2) in the left image, and false color (bands 12, 11 and 4) in the right image.
Time series refer to a stack of images of the same area of interest, acquired at different times.
Times series analysis of satellite images provide the analyst with powerful information both at image and at pixel-by-pixel level that may be used to describe surface dynamics or pinpoint when specific changes occurred.
Archived satellite images are often available and may be analyzed to show the historic development of certain features or surface properties of an area of interest.
Construction of the Bhadla Solar Park in Rajasthan, India. Animation created from over 100 Sentinel-2 images, acquired between 2015 and 2019.
Use of satellites and satellite image analysis was formerly reserved military purposes, intelligence agencies and a limited number of large publicly funded research groups.
Today, the technological developments and an increased venture capital investment interest has reduced the cost of building and launching satellites and increased competition between commercial providers of satellite data. This has led to significant price reductions and fostered a rapidly expanding interest for implementation of satellite solutions in workflows across the geo-spatial sector.
Surface monitoring with EO satellites is possible for every location around the globe except for the areas close to the poles are poorly covered. However, images of a suburb in Sydney, a forest in Siberia or a farming site in Tanzania, or anywhere else on the globe, are easily tasked and acquired by an EO-satellite.
The wide variety of satellite sensors available today means that you can acquire imagery fit for your specific location, purpose, time frame and budget.
Most satellite data providers will let you choose the areas and dates that you need via an online ordering portal.
Imagery is typically delivered in well-known data formats such as GeoTiff or NetCDF, and projections such as WGS-84 ready for import in your favorite GIS software.
For Danish users, DHI offer access to state-of-the-art pre-processed and structured Analysis Ready Data, which makes it straight forward to work interactively and simultaneously with data from different satellite sensors.
The image below is taken from Digital Globe’s Worldview-3, a very high resolution satellite. The spatial resolution is 30 cm, meaning each pixel in the image corresponds to a real world area of 30 cm x 30 cm.
These type of images can be used to identify archaeological markings or make use of the multi spectral bands in combination with artificial intelligence and machine learning to identify the species of trees and provide information about their overall health or estimate the potential yield of an entire orchard. High resolution images are available from commercial providers such as Digital Globe and Airbus.
The image below is taken from Sentinel-2, a medium resolution multispectral satellite, operated by the European Space Agency. The spatial resolution of this image is 10 m, meaning each pixel in the image corresponds to a real world area of 10 m x 10 m.
Using images such as this, we can clearly see agricultural fields, coastlines, and we can differentiate between urban, agricultural, and forested areas. For this reason, these images are ideal for creating land use and land cover maps. Another widely used medium resolution satellite is NASA’s Landsat 8 (30m).
This image is taken from Sentinel-3 a low resolution satellite, operated by the European Space Agency. The spatial resolution of this image is 300 m, meaning each pixel in the image corresponds to a real world area of 300 m x 300 m.
Low resolution satellite images often have a larger footprint than medium and high resolution images.
Using images such as this, we can differentiate between large forested and non-forested areas. We can see the extent of large floods or droughts, and we can still differentiate between large urban areas and their surrounds. These images are ideal for national, continental, or global scale monitoring. One widely used low resolution sensor is MODIS, on board the Aqua and Terra satellites.
The most advanced commercial satellites today offer images comparable with aerial photographs, with a spatial resolution down to just 30 cm.
High resolution images are often used as basemaps, enabling informed decision-making, but can also be combined with advanced AI for automatic object recognition.
Tasking satellites to acquire images when and where you need them is easy and can be done for most commercial satellites.
A digital elevation model (DEM) is a digital representation of the Earth’s terrain height.
Satellite derived DEMs are a cost-efficient alternative to LiDAR, aerial photography and physical land surveys, and can provide you with topographic maps down to 1-meter resolution.
Satellite derived DEMs can be acquired and used everywhere, but are especially useful for remote locations where mobilizing aircrafts and personnel would be difficult and time-consuming.
Coastal environments are some of the most dynamic regions of the globe.
Monitoring and mapping these changes is critical to environmental studies and construction activities in the coastal zone and shallow off-shore areas.
Satellite derived bathymetry (SDB) uses optical satellite images to process and create an accurate map of the water depth in coastal marine areas.
Visit www.bathymetrics.shop to access bathymetry data.
Medium resolution satellite images, combined with machine learning algorithms can accurately identify and monitor crops and forested areas.
Valuable information on crop status and growth development can be provided throughout the growing season to monitor crop progress and identify any signs of damage or stress.
Near real time alert information on forest disturbance can help mitigate and assess damage from natural events (e.g. forest fires and storm damage) and forest encroachment due to unlicensed mining and agricultural activities.
Based on historical and recent satellite images, we can extract information about coastal dynamics, providing estimates of coastline changes (meters/year) caused by factors such as sand erosion/deposition, or infrastructure developments.
Satellite data dates back to the 1980’s and provides a detailed level of information about changes in shorelines around the world.
This type of analysis provides information about erosion and deposition of sand and sediments, development of vegetation cover in the coastal zone, and information on coastal structures and coastal protection.
Aquatic vegetation is one of the key indicators of ecological status and environmental state of water bodies.
It is therefore widely used as input in Environmental Impact Assessments (EIAs) and used as a factor in various reports, e.g. the EU Water Framework Directive and the Birds Directive.
Using satellite data, machine learning and radiative transfer modelling, we can accurately extract detailed information about submerged aquatic vegetation in marine habitats.
The wide temporal coverage of global satellite images, dating back to the 1980’s, is ideal for creating a simplified land cover change analysis over a long period of time and on a large scale.
The long-term data sets allow for a much more precise understanding of vegetation change over time compared to most climate change models.
Using a combination of low resolution optical satellite images and thermal data, it is possible to map the evaporation and transpiration from the soil.
This helps in understanding water usage in fields, which can give an estimate of crop health and water stress. The estimates are relevant for improving irrigation systems in drought prone areas.
Detection of the quality of marine environments and large inland waters can be eased with the near real-time information from satellite images. Such large areas would otherwise be almost impossible to cover with in-situ sampling alone.
The satellite image archives furthermore enables change detection of chlorophyll concentrations and suspended matter in previously unmeasured or unmonitored water bodies.
DHI was founded in 1964 and has been engaged in Earth Observation since 2000. DHI is specialized in satellite image analysis and data processing across a broad range of applications. Special emphasis is on the water domain including water quality, environmental assessment, Wetland monitoring, habitat monitoring, biodiversity assessment, land cover mapping and climate resilience of urban environments.
We handle the entire satellite data flow for our customers from tasking, reception, processing, analysis to setting up a service or delivery of the final requested product.
We have carried out projects and delivered services in over 75 countries worldwide.
Our long-lasting experience in the EO market and our innovative technologies and solutions enables us to tailor the right solution based on the most suitable data and offer this to our clients at a competitive price. We are also resellers of satellite imagery from market dominating providers, such as Digital Globe and Airbus.
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