Remote Sensing Workflow: From Data to Geospatial Analysis
Remote sensing forms an integral part of Geographic Information System (GIS) applications, environmental monitoring, agriculture, urban planning, disaster management, and land use analysis. Remote sensing allows people to gather information on Earth's surface features in the absence of physical contact through the use of sensors, satellites, aerial photography, drones, among other image acquisition methods.
The process of remote sensing consists of several interconnected steps starting with data acquisition and preprocessing to image classification and generation of useful geospatial information. All the steps are important in ensuring that the end product is accurate and useful.
Knowledge of the remote sensing process enables GIS analysts, researchers, surveyors, and geospatial professionals to derive useful geospatial information from imagery.
What Is a Remote Sensing Workflow?
Remote sensing workflow refers to the procedure used for acquiring, processing, analyzing, and interpreting remotely sensed data. The procedure is made up of remotely sensed data obtained through satellite imagery, aerial imagery, drone imagery, Geographical Information Systems (GIS), and imaging processes to obtain information about geographical locations and environmental conditions.
The workflow process entails the following steps:
Determine the purpose of the study.
Acquisition of remote sensing data
Processing of remote sensing imagery
Enhancing and correcting the imagery
Image classification and feature extraction
Geographic analysis
Results verification
Publishing the results
The specific steps depend on the application, sensor characteristics, spatial resolution, temporal requirements, and accuracy objectives.
Define the Remote Sensing Objective
This step involves determining the goal of the analysis and specifying the area of interest (AOI). A well-defined objective aids in selecting the right data, as well as the processing and analysis methodologies.
Examples of common remote sensing objectives include:
Deforestation and forest degradation monitoring.
Land use/cover change mapping.
Crop monitoring.
Urbanization and development mapping.
Hazard assessment such as floods and wildfires.
Water and environment mapping.
Elevation modeling and terrain mapping.
An example of an objective of a monitoring project may be vegetation health assessment, for which multispectral images from satellites and vegetation indices like NDVI may be required. On the contrary, a terrain mapping project may require either LiDAR data or stereoscopic imaging.
Early definition of the objective is critical, as it reduces wastage of time through unnecessary processing of data.
Acquire Remote Sensing Data
Data acquisition refers to choosing and acquiring imagery or measurements from appropriate remote sensing systems. It depends on requirements concerning spatial resolution, spectral properties, revisit rate, coverage area, and cost.
Common Remote Sensing Data Sources
Satellite images: Images from satellite remote sensing systems are characterized by wide coverage area and high repeatability of observations. Landsat, Sentinel, and commercial Earth observation satellites provide data for land cover classification, environmental monitoring, agriculture, change detection, etc.
Aerial images: Images taken by sensors mounted on aircraft provide high-resolution images useful for surveying, infrastructural inspections, urban mapping, and geographical analysis.
Drone images: High-resolution images collected by drones have limited coverage area. When using ground control points or RTK/PPK positioning systems, drone images may be applied to photogrammetry, creation of orthomosaics, and terrain mapping.
LiDAR: Light Detection and Ranging technology measures distances using laser pulses to create a three-dimensional point cloud. LiDAR is widely used for terrain analysis, mapping of forest structure, and infrastructure.
Synthetic Aperture Radar (SAR): SAR sensors provide radar measurements. SAR sensors may operate at night and under cloud cover. It is useful for flood mapping, surface deformation analysis, and soil moisture analysis.
Important Data Selection Criteria
Before downloading or buying images, consider:
Spatial resolution – Ground area covered by one pixel.
Spectral resolution – The number and bandwidth of spectral bands.
Temporal resolution – How often observations are made.
Radiometric resolution – The capability of the instrument to detect variations in the amount of measured energy.
Cloud cover – Percentage of cloud cover in an optical image.
Coordinate reference system – The spatial reference employed for geographically locating the data.
Date of acquisition – Date and time of year when the imagery was acquired.
The choice of the right data is crucial since low spatial resolution, cloud contamination, or varying dates of acquisition may impact the results of the analysis.
Preprocess the Remote Sensing Data
Remote sensing images can be distorted through geometry, atmospheric distortion, noise from sensors, cloud obscuration, and inconsistent pixel alignment. Preprocessing helps prepare the data for comparison and spatial analysis.
Radiometric and Atmospheric Correction
Radiometric corrections involve correcting the differences in sensor measurements due to characteristics of the sensor, illumination, and other data acquisition factors. Atmospheric corrections involve correcting the effects of atmospheric scattering and absorption on optical images.
For example, surface reflectance images are preferable to optical images before radiometric correction in case of vegetation condition comparison on different dates.
The type of corrections depends on the sensor, image level, and purpose of the analysis. All datasets need not undergo the same corrections.
Geometric Corrections and Orthorectification
Geometric corrections enhance the spatial alignment of the images. Orthorectification helps to adjust for terrain and viewing geometry of the sensor to obtain improved accuracy of positioning of the images.
Georeferencing becomes crucial, especially when the imagery is overlaid on roads, administrative boundaries, and property boundaries, among others.
Cloud and Shadow Masking
Clouds and shadowed clouds can cause problems for land-cover classification, vegetation mapping, and change detection. Cloud masks can help by identifying problem pixels that should be omitted from analysis.
For example, when using Sentinel-2 data, quality information or cloud masking can help to determine which pixels are not appropriate for analysis using optical sensors.
Image Resampling and Alignment
If multiple datasets are going to be used together, then images may need to be aligned to a common coordinate reference system, resolution, alignment of pixel coordinates, and extent.
Resampling approaches include:
Nearest neighbor: Maintains original pixel values and is frequently employed with categorical data.
Bilinear interpolation: Uses neighboring pixels to interpolate new values and can be useful with continuous raster data.
Cubic convolution: Also uses a neighborhood of pixels to produce a smooth surface of interpolated values.
The technique that should be employed varies according to whether the data is continuous data, categorical data, or any other form of raster data.
Perform Geospatial Analysis Using GIS
When the processing is completed and the features have been derived from the images, these datasets may then be combined with other datasets for more thorough analysis.
GIS uses rasters, vectors, terrain models, and attributes to analyze the relationships between these datasets.
Commonly Used Methods of Geospatial Analysis
Overlay analysis: Involves the use of several geographic datasets together to detect the intersections or relationships that exist between them. This could include overlaying regions of flooding on top of regions of buildings and transport routes.
Raster analysis: This involves pixel-based analysis, map algebra, terrain modeling, and suitability analysis.
Buffer analysis: The construction of zones around geographic objects such as roads, rivers, or pipelines.
Zonal statistics: A way to statistically summarize raster values according to geographical zones, including administrative regions, watersheds, and farmlands.
Terrain analysis: An analysis that is based on elevation data in order to calculate such terrain parameters as slope, aspect, hillshade, etc.
Change detection: A method for detecting changes in land use, vegetation, surface water, or urban growth using satellite images taken at different times.
For example, research into urbanization processes may include the analysis of classified satellite images, together with the road network, administrative regions, population, and elevation data.
Visualize and Publish Geospatial Results
This final phase involves the creation of maps, reports, dashboards, and shareable geospatial datasets from analytical results.
Common output formats are:
GeoTIFF: Georeferenced raster imagery and analytics.
Cloud Optimized GeoTIFF (COG): Raster format optimized for efficient partial retrieval over HTTP.
Shapefile and GeoPackage: Vector features with their attributes.
LAS and LAZ: LiDAR point cloud data, with LAZ being the compressed version of the LAS.
NetCDF and Zarr: Multidimensional scientific datasets.
Web map services and tiles: Formats for publishing and displaying geospatial information.
GIS tools such as QGIS and ArcGIS Pro allow visualization of rasters and vectors, perform spatial analysis, and generate maps. Python packages such as Rasterio, GDAL, GeoPandas, Xarray, and Rioxarray facilitate automation and scaling up of the workflow.
There are cloud-based geospatial platforms that can assist teams in storing, processing, visualizing, and distributing large sets of imagery without having to download the entire datasets several times.
In publishing the results, one should provide metadata about the data source, collection date, coordinate reference system, processing steps, classification used, and its limitations.
Challenges in Remote Sensing Workflows
Although much progress has been made in terms of sensors and image processing software, there may be several technical issues facing remote sensing tasks.
Cloud cover interference: Clouds and shadows might limit the amount of useful optical imagery.
Large amount of data: A large amount of data storage and processing might be required due to high-resolution images and lengthy time series.
Spatial and temporal inconsistency: Variations of pixel size, acquisition date, geometry, and sensors might cause problems in comparison of data.
Classification problem: Spectrally similar signatures might cause confusion when distinguishing land-cover types.
Georeferencing problem: Mismatch between the imagery and reference layer might lower the accuracy of spatial data analysis.
These problems may be solved by choosing the right data and using proper procedures and documentation of the process.
The process of remote sensing enables transforming raw satellite, aerial, drone, and LiDAR data into credible geospatial information by systematic acquisition, preprocessing, image enhancement, feature extraction, classification, spatial analysis, validation, and visualization.
All the steps described above play an important role in ensuring that the output is of good quality. Imagery selection, appropriate correction, proper analytical methods application, and results' validation are all crucial for the creation of credible geospatial datasets.
By combining GIS software, Python-based processing, machine learning, and cloud-based data management, geospatial professionals can create efficient workflows for tasks such as environmental monitoring, urban planning, agriculture, disaster management, and infrastructure management.
As new remote sensing technologies continue to emerge, it is clear that efficient data processing and integration with geospatial analysis, with the help of AI, will become an indispensable part of geospatial analysis.
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