Earth observation data is used for sustainable resource management in a wide variety of areas including water resource management, forestry, wetlands, mineral exploration, mine reclamation and waste management, agriculture, and soils mapping. Decision-makers in these areas rely on consistent interpretations of EO data to influence the sustainable development of the resources they manage.

However, Earth observation data is often acquired over large areas, on different dates, and from different sensor systems resulting in image artifacts, or distortions in EO images. When these artifacts are present, erroneous interpretations of EO data may occur. Many of these image artifacts can be removed using mathematical models. Geometric processing, then, or the correction of errors of skew, rotation, and perspective in raw, remotely sensed data is a key issue in multi-source data integration, management and analysis for geomatic applications, including those that influence the sustainable development of natural resources.

To date, mathematical correction models have not been consistently or reliably developed, which has slowed the use of EO data for the sustainable development of natural resources. To resolve this problem, GSDNR's Image Processing Standards for Earth Observation Data project is working to validate, calibrate, and standardize EO data.

This project produces correction algorithms and tools to remove artifacts from raw Earth observation data and provides supporting quality assessment reports that reflect improvements resulting from the application of these new correction tools on EO data products.

The project emphasizes new EO sensor systems that are of importance in sustainable resource management strategies including:

• Spaceborne hyperspectral systems, which use reflected solar radiation to capture the unique spectral signature of an object to identify and quantify its composition using contiguous narrow bands and the whole of the electromagnetic spectrum
• High spatial resolution and advanced radar systems, which can acquire data at horizontal (HH), vertical (VV) and cross (HV & VH) polarizations over a range of resolutions from three to 100 metres, and
• Ground-based (in situ) sensorwebs, which are networks of automated, intra-communicating sensor pods that are deployed to monitor and explore environments and that share the information they collect with each other and react to information they receive from each other.

Project Priorities

• The generation of new tools for the characterization, correction and integration of EO data.
• Assessment of the improvements resulting from application of the new correction tools on EO data products.
• Improvements to remote sensing data and the standardization of new EO products through use of ground-based (in situ) sensorwebs.
• Validation of users' needs for calibrated EO data products.

Report

Importance of Data Standardization for Generating High Quality Earth Observation Products for Natural Resource Management

For more information, please contact the project leader:Phil Teillet

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