Remote Sensing data is collected from a distance using sensors. When collecting this data, no direct contact is made with the earth or water surface being mapped. Hence the name Remote Sensing.
The collection of Remote Sensing data is done using satellites, manned or unmanned aircraft and drones. The sensors detect and record electromagnetic radiation reflected, absorbed or emitted by the Earth's surface, oceans and atmosphere.
Which ArcGIS applications support Remote Sensing functionalities?
Remote Sensing functionalities are mainly used in practice in ArcGIS Pro and in the Drone2Map application. But there are more ArcGIS tools that support the use of advanced sensor data:
The Image Analyst extension provides additional capabilities for Remote Sensing data in ArcGIS Pro. Add this extension to the software and you can get more out of your aerial photos, satellite images and other raster data.
ArcGIS Online also offers some Remote Sensing functionalities, such as displaying and analysing images and performing simple image processing tasks. Using ArcGIS Image for ArcGIS Online, you can also get more out of your Remote Sensing data in the cloud. This Saas solution supports orthorectification, image classification and change detection.
ArcGIS Enterprise supports Remote Sensing functionalities via the Image Server, a component that specialises in processing and serving large-scale image data.
Software Developer can use ArcGIS APIs and SDKs to build their own APIs that integrate Remote Sensing functionalities.
To get the most out of Remote Sensing data, it is recommended to work with ArcGIS Pro. Especially when combined with the Image Analyst extension, this package offers the most advanced capabilities.
What capabilities does ArcGIS Pro offer in terms of Remote Sensing?
Remote Sensing data in particular is widely used in ArcGIS Pro and in the App Drone2Map. The number of possibilities is large, so here we list some common applications:
ArcGIS Pro can correct errors caused by the angle of incidence of the sun and the varying distance between the earth and the sun.
It is also possible to classify landscape, vegetation, land cover using Remote Sensing data;
The software provides multiple options to enhance the visual quality of Remote Sensing images, such as histogram extraction, local contrast enhancement and filtering.
Pan-sharpening allows you to improve the spatial resolution of multispectral images by combining them with panchromatic images.
ArcGIS Pro provides various image transformations, such as vegetation indexes, principal component analysis (PCA) and tasseled cap transformations, to highlight specific features and patterns in Remote Sensing images.
The change in land use, land cover, vegetation and other outdoor features can be analysed over a given time period. ArcGIS provides multiple change detection techniques, such as image subtraction, image stratification and cross-correlation.
What is Remote Sensing used for?
There are lots of uses for Remote Sensing data in your GIS. We list the most relevant ones here:
You can study large geographical areas faster and more efficiently. Especially when creating digital maps with a high level of detail, this is a big advantage.
Remote Sensing provides multispectral imagery. The sensors capture data in different parts of the electromagnetic spectrum, giving you, as a GIS Analyst, a wide range of information at your disposal.
Automatic classification allows you to recognise key features and patterns from the Remote Sensing data. This speeds up your GIS process as well as increasing the accuracy of your analysis.
You can integrate the data collected by sensors with other GIS data, such as elevation data, soil information and demographic data, giving your analyses even more insights.
Efficient use of Remote Sensing can be cost-saving. Traditional fieldwork surveys can be labour- and cost-intensive, especially when covering a large area. Using Remote Sensing, less fieldwork is needed.
How do I use Remote Sensing with point cloud data in ArcGIS Pro?
You can follow the following steps to work with point cloud data in ArcGIS Pro:
First collect relevant point cloud data (such as Lidar data).
Import the point cloud data into ArcGIS Pro as a LAS dataset.
Create a LAS dataset. In ArcGIS Pro, go to: Catalog pane, right-click on a folder or geodatabase and 'choose New > Las Dataset. Then add the LAS files to the dataset by clicking 'Add Files'.
Visualise the added point cloud data by adding the LAS dataset to your project by dragging it to a 2D or 3D view. In the view, you can visualise and edit the point cloud.
Go to the contents page and right-click on the point cloud layer to set the layer's properties. Choose 'properties' and adjust the symbology, filters and other properties to get the desired view.
You can then use the Point Cloud toolset to analyse the data. The toolset can be found in the 3D Analyst toolbox and contains a wide range of digital tools that allow you to perform very specific analyses.
Then you can use the analysed point cloud data to create terrain models, such as Digital Elevation Models (DEM), Digital Surface Models (DSM) and Canopy Height Models (CHM).
Would you like to then combine the analysis you have done with point cloud data with data from other sources, such as multispectral images or radar images? You can do that with the Image Analyst toolbox.
Have you completed the steps above? Then you are ready to share your analysis and your terrain models with other ArcGIS users, for example in the form of a digital map, a web application or a report.
How do I use Remote Sensing techniques in combination with raster data?
Please go through the following steps to use Remote Sensing with raster data:
First collect all relevant raster data, e.g. satellite images or aerial photos. Then import these into ArcGIS Pro.
Add the raster data to your map view by dragging it to the Contents pane. Adjust the symbology and other layer properties to get the desired view.
Use tools such as 'Contrast Stretch', 'Histogram Equalisation' and 'Gamma Stretch' to improve image quality and make important details more visible.
Correct atmospheric and sensor-related distortions using radiometric correction tools such as 'Dark Object Subtraction', 'Flat Field Correction' or 'Empirical Line Correction'.
: Correct geometric distortions using tools such as 'Georeferencing' or 'Orthorectification'.
Use image classification tools to classify raster data different land use types (or other desired categories). ArcGIS Pro provides a wide range of tools for this, such as the 'Iso Cluster Unsupervised Classification', 'Maximum Likelihood Classification' and the 'Random Trees'.
With the Spatial Analyst toolbox, you can then analyse your data. Two commonly used components of this toolbox are the 'Zonal Statistics', 'Cost Distance' and the 'Flow Direction'.
Optional: combine raster data with vector data such as administrative boundaries, points of interest or infrastructure data, to perform more complex analyses.
Have you completed the above steps? Then you are ready to share your analysis and your terrain models with other ArcGIS users, for example in the form of a digital map, a web application or a report.
Aerial imagery and Remote Sensing
How do I integrate aerial photos into ArcGIS to improve the accuracy of GIS data?
The number of applications for allowing aerial imagery to increase data quality is high. We list some common applications:
Use orthorectification to correct aerial images. Deviations caused by lens distortion, by the angle of the camera when taking the photo or errors caused by the relief of the terrain can be corrected automatically using orthorectification. The ortho-photos in question can then be combined with other GIS data layers, such as elevation models, street maps and land use information, to improve the accuracy of base maps.
Object recognition is a technique in which objects are recognised as buildings, roads or vegetation based on an algorithm. The resulting information can then be extracted and stored as vector data, which can be integrated with other GIS data layers for spatial analyses and visualisations.
Air photographs allow you to extensively analyse the texture and pattern of the Earth's surface. Using this data, you can improve the accuracy of land use and land cover classifications in your GIS.
By comparing aerial photographs from different time points, changes in the landscape, such as urbanisation, deforestation and coastal erosion, over time can be analysed and visualised. Using this, you can identify and predict trends.
Finally, aerial photos continue to offer higher resolution than satellite images, providing more detailed information about the Earth's surface. This benefits the quality of your GIS analyses and maps.
How do I generate point cloud data based on aerial photos?
Please go through the following steps to generate point cloud data based on aerial photos:
Collect high-quality aerial photos, preferably with overlapping images, to generate the most accurate point cloud dataset based on stereo pairs.
Import the data into ArcGIS Pro and generate point cloud data. ArcGIS Pro itself does not provide direct functionality for generating point clouds from aerial photos, but there are external software packages that can do this, such as Pix4D, Agisoft Metashape, Bentley ContextCapture or Trimble Business Center.
Do a camera and lens calibration to correct lens distortions and other errors. This improves the accuracy of the final point cloud.
Determine the orientation of the aerial images relative to each other and record the images to ensure accurate reconstruction of the scene.
Next, ArcGIS Pro performs "dense image matching" or "dense photogrammetry" to generate the point cloud data. This process uses the overlapping parts of the images and known orientations to extract depth information and generate 3D points. The result is a point cloud in a file format such as LAS or LAZ.
You can georeference the generated point cloud if necessary using ground control measurements or known reference points to link the point cloud to a specific coordinate system.
In conclusion, import the generated point cloud into ArcGIS Pro and add it to the 3D view for further analysis and visualisation.
How do I improve the quality of my raster data using aerial photographs?
Follow the following steps to increase the quality of raster data using aerial photographs:
Choose high-quality aerial photos. Collect aerial photos that cover the area of the raster data. The better the quality of the aerial photos, the better the improvement of the grid data will be.
Make sure the georeferencing of the aerial photos is good so that they are correctly aligned with the existing raster data. The Georeferencing Toolbar in ArcGIS Pro can help you do this.
Execute colour correction and radiometric enhancement on the aerial photos to improve the visual quality and clarity of the images. In ArcGIS Pro, you can do this using raster functions and geoprocessing tools.
For images with panchromatic and multispectral bands, you can apply Pan-sharpening to improve the spatial resolution of the multispectral bands. The Create Pan-sharpened Raster Dataset" geoprocessing tool in ArcGIS Pro can help you do this.
Fusing two datasets can also help increase quality. To do this, you combine high-quality aerial photos with the existing raster data using fusion techniques, such as the arithmetic mean, maximum or minimum of the two datasets. You can also perform this operation using geoprocessing tools such as "Mosaic to New Raster" or "Weighted Overlay".
Continue to check if the quality of the combined raster has improved and look for any errors or inconsistencies in data. Has the quality visibly improved? Then your goal has been achieved. If not, go through the above steps again to achieve the desired quality.
How do I use infrared images to map vegetation development?
In ArcGIS Pro, it is possible to map vegetation growth using infrared imagery. To do this, follow the following steps:
Collect aerial or satellite images with infrared bands. Preferably images with both near-infrared (NIR) and red (R) bands, as these bands are widely used for vegetation analysis.
Import these infrared images into ArcGIS Pro and make sure the georeferences are correct.
Next, calculate the Normalised Difference Vegetation Index (NDVI). This is a widely used index to assess the presence and health of vegetation. You calculate the NDVI using this formula: (NIR - R) / (NIR + R). By the way, you don't have to apply this formula yourself. In ArcGIS Pro, you can use the Raster Calculator tool to make the calculation based on the infrared and red bands of the images.
Use the NDVI values to classify vegetation based on their health or growth stage. You can set your own threshold values for different classes of vegetation, such as healthy, moderately healthy and unhealthy. The Reclassify tool in ArcGIS Pro lets you classify vegetation based on the chosen threshold values.
Optional: it is possible to increase the number of vegetation classifications in your project with the ArcGIS tools Maximum Likelihood Classification and ISO Cluster Unsupervised Classification tools. As the titles of the tools suggest, it is possible to apply machine learning as well as manual classification for this purpose.
Have you completed the above steps? Then it's time to create the vegetation analysis that suits your organisational goals.
Drone imagery and ArcGIS
What are the benefits of using drones to collect GIS data?
There are a number of advantages to using drone footage:
Drones fly closer to the ground than planes. Drone images therefore have higher resolution and are more accurate. This is especially useful for mapping small objects and other details in the terrain.
The cost of deploying drones is generally lower than other photo-taking vehicles.
Drones can be deployed quickly. This enables real-time data collection, for example. This makes it easier to keep up with the evolution of the landscape and thus the decision-making process can be shortened.
Changing sensor types and cameras is a lot easier with a drone than with an aircraft. This makes it easier for one to adjust data requirements relatively quickly according to your project goals.
A drone can capture images in areas that are difficult for other vehicles to reach, such as densely forested areas or in high mountains.
Drones are more environmentally friendly than other vehicles and cause less noise pollution while collecting data.
Drones images can be used to create very accurate 3d models, enabling GIS analysis such as volumetric calculations and slope analysis.
How to ensure the accuracy of drone data?
There are a number of considerations when deploying drone imagery to get the maximum value out of the extracted data. We list them for you:
Choose the right type of drone and the right sensors. Select a drone that is suitable for the data required for your specific project, such as high-resolution cameras, LiDAR, or multispectral sensors.
Place clearly visible markers on the ground with known coordinates. These so-called GCPs (ground control markers) improve data accuracy. Capturing GCPs is done in ArcGIS Online and Esri Fieldmaps. The GCPs are used to correct and georeference data collected by the drone.
Plan the drone flight well in advance and ensure a good overlap between images (usually between 60% and 80%). This improves the quality of the final 3d model of the terrain.
Consider using Real-time kinematic (RTK) or Post-processed kinematic technology to improve GPS data accuracy. These technologies correct GPS data using ground stations.
Think about the weather. An ideal day for drone flight is as clear as possible with a weak wind.
Use Drone2Map. This ArcGIS application provides the best results for your drone images. Drone2Map helps perform orthorectification, DEM generation and 3D modelling.
Finally, check the accuracy of the retrieved data. You can do this by comparing the data with known reference points, such as GCPs, or by performing independent measurements with traditional surveying instruments.
How can photogrammetry improve your GIS processes?
Photogrammetry uses the principles of perspective and stereoscopy to extract three-dimensional (3D) information from two or more overlapping images taken from different viewpoints. There are a number of reasons why photogrammetry can improve your GIS processes:
Photogrammetry generates highly accurate and reliable geographical data that can serve as the basis for your GIS analyses. You can think of: orthophotos, digital terrain models (DTM) and digital surface models (DSM).
Photogrammetry makes it easier to collect data over large areas. Compared to traditional fieldwork methods, it is time- and cost-saving.
Photogrammetry enables the creation of 3D models of objects and landscapes. This provides a better understanding of the spatial characteristics of the area under analysis.
By comparing images taken at different times, you can detect and analyse changes in the landscape, such as erosion, urbanisation, deforestation and land use.
Photogrammetry can also play a role in infrastructure maintenance. It enables efficient monitoring of the condition of bridges, dams, roads and railways, for example.
What photogrammetry functionalities are supported by ArcGIS?
The most commonly used photogrammetry functionalities in ArcGIS Pro are:
ArcGIS can generate Orthophotos by correcting images for lens distortion, perspective and terrain variations. The georeferencing of Orthophotos is corrected so that they can be used for accurate analysis.
With the Ortho Mapping workflow, you can correct images, perform block adjustments, process raw images and generate orthophotos.
ArcGIS Pro supports generating digital elevation models (DEM) and digital surface models (DSM) from stereo imagery or LiDAR data. You can use these models for analysing terrain, measuring volumes and modelling surfaces.
Automatic image registration and alignment is possible using photogrammetric techniques. This improves the accuracy of the data you use.
ArcGIS supports image adjustment to minimise colour, brightness and contrast differences. It is also possible to merge multiple images.
In addition to the above functionalities, ArcGIS also supports other Remote Sensing described on this page, such as 3d modelling and change detection.