Satellites provide us with a unique overview of our planet.
There are about 5000 satellites in orbit around the earth, and about 700 of these have been sent up with the purpose of recording earth observation data.
As satellites constantly orbit the earth, they enable us to track physical trends, changes, and developments over long periods of time.
Essentially, they can provide us with a living map of our planet, and can be used for a wide range of applications.
We use the term spatial resolution to describe the level of detail seen in images. Generally speaking, a higher spatial resolution enables us to see finer details on the surface.
Temporal resolution is used to describe how often we can get a new image from the satellites. Some satellites will capture images of specific locations each day, some only once a week.
The footprint of a satellite indicates the area of the earth covered by a single image. For some satellites, the footprint is several hundreds of square kilometers, and for some it is only a few square kilometers.
SAR, short for Synthetic Aperture Radar, is an advanced way to build a radar which ensures images are of a very high resolution compared to traditional radars.
Optical satellites capture images by measuring the light reflected by the sun from the surface of the earth. This means that they do not work without the sun’s radiation (at night), or when something blocks the suns radiation (clouds).
SAR overcomes this challenge by emitting its own radiation, a radar pulse, and measuring the returning signal. This permits SAR to “see” in the dark and look through 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).
Visible light is electromagnetic radiation, and colors such as red or blue, are small parts of this radiation, which are also called a spectral band.
The number of spectral bands of a satellite tells us how many colors the camera on board the satellite can see.
These colors are sometimes the same as the ones we can see with our own eyes, such as red and blue, but can also be extended to colors that we cannot see naturally, such as infrared or ultraviolet.
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 large collection of images of the same area, taken at different times.
Times series of images often provide analysts with valuable information about the transformation of the area they are observing.
Access to historical satellite images can show how an area has developed over several years.
Construction of the Bhadla Solar Park in Rajasthan, India. Animation created from over 100 Sentinel-2 images, acquired between 2015 and 2019.
All places around the globe are equally accessible. Whether you need images of a suburb in Sydney or a forest in Siberia, these can be easily acquired by an imaging satellite.
The wide variety of satellites means you can get the perfect images for your location, time frame and budget.
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 GRAS is specialised in satellite image and data processing for hydrology, water quality, environmental assessment and land cover mapping.
We handle the entire data flow from the reception and processing of satellite images to the delivery of the final requested information product.
The company was established in 2000 and has completed projects and delivered services in over 75 countries worldwide for a range of different clients.
Our long experience in the market enables us to access the right data at the right price for our clients, and we are proud resellers of satellite images from the biggest providers around the world such as Digital Globe and Airbus.
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