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Multispectral applications

Multispectral Multisensor Remote Sensing

The Sentinel 2 false colour composite shows the geological characteristics of an area near Prieska/ Northern Cape/ South Africa. The colors represent the different lithological units of the Precambrian Banded Iron Stones (BIF) in dark purple and brown, the Carbonates in purple and the Quarzites in light blue. Prominent are the Orange River and the green field along the river in the image center. Source: BGR

Analysis of space- and airborne multispectral- and multisensor data contribute significantly to the understanding of the earth coupled system, by investigating the interactions of different earth sciences. The addressed topics range from geology, natural resources, soil, groundwater, national and international cooperation to geohazard assessment and earthquakes.

Multispectral/ multisensor remote sensing focuses on:

mapping of geology, lithology, mineralogy and tectonics
exploration of new natural resources
sustainable and environmentally friendly use of natural resources
monitoring of abundant mining sites
registration of regional topography and earth´s surface features, e.g. soil and vegetation, as well as
surface changes due to geological-related subsurface processes.

Remote sensing data are processed and investigated by professional software and algorithms as well as by in-house developed methods for application oriented results.
Applications are for example geological-, hydrogeological-, geological environmental-, soil- and land use mapping, investigation of geohazards and exploration of natural resources.
Open data (e.g. Landsat, Sentinel, ASTER…) as well as commercial data (e.g. SPOT, RapidEye, World View, TerraSAR-X, …) are applied.
Data processing on the one hand is based on standard methods e.g. image enhancement, filtering, classification, geocoding, GIS-analysis and map production. Furthermore, special methods are applied for the processing and interpretation of radar and hyperspectral data, as for example radar interferometry, spectral unmixing and multifeature extraction. For certain questions digital terrain models (DTM) are derived from remote sensing data e.g. SAR- and LiDAR data.