New publication on Hyperspectral Data for Mapping Fractional Cover

New publication on Hyperspectral Data for Mapping Fractional Cover

written by Martin Wegmann

September 29, 2015

The outcome of one of our MSc students, Sarah Malec, got published in Remote Sensing titled “Capability of Spaceborne Hyperspectral EnMAP Mission for Mapping Fractional Cover for Soil Erosion Modeling”. Soil erosion can be linked to relative fractional cover of photosynthetic-active vegetation (PV), non-photosynthetic-active vegetation (NPV) and bare soil (BS), which can be integrated into erosion models as the cover-management C-factor. This study investigates the capability of EnMAP imagery to map fractional cover in a region near San Jose, Costa Rica, characterized by spatially extensive coffee plantations and grazing in a mountainous terrain. Simulated EnMAP imagery is based on airborne hyperspectral HyMap data. Fractional cover estimates are derived in an automated fashion by extracting image endmembers to be used with a Multiple End-member Spectral Mixture Analysis approach. The C-factor is calculated based on the fractional cover estimates determined independently for EnMAP and HyMap. Results demonstrate that with EnMAP imagery it is possible to extract quality endmember classes with important spectral features related to PV, NPV and soil, and be able to estimate relative cover fractions. This spectral information is critical to separate BS and NPV which greatly can impact the C-factor derivation. From a regional perspective, we can use EnMAP to provide good fractional cover estimates that can be integrated into soil erosion modeling.

 

Malec, S.; Rogge, D.; Heiden, U.; Sanchez-Azofeifa, A.; Bachmann, M.; Wegmann, M. Capability of Spaceborne Hyperspectral EnMAP Mission for Mapping Fractional Cover for Soil Erosion Modeling. Remote Sens. 2015, 7, 11776-11800.

you may also like:

Call for Papers for the Joint Urban Remote Sensing Event (JURSE)

Call for Papers for the Joint Urban Remote Sensing Event (JURSE)

The Joint Urban Remote Sensing Event (JURSE) ( http://jurse.org/ ) is a forum of excellence where researchers, practitioners and students present, share, and discuss their latest findings and results. A very dynamic version of the Joint Urban Remote Sensing Event...

public talk by Hannes Taubenböck

public talk by Hannes Taubenböck

Deutschland – Wie und wo wir wohnen (wollen) Vortrag von Hannes Taubenböck (publich talk in german by Hannes Taubenböck on "How and where we like to live") Jede:r von uns wohnt – irgendwie. Ob in ländlichen oder urbanen Gefilden, ob in Ein- oder Mehrfamilienhäusern,...

new publication: on population disaggregation

new publication: on population disaggregation

A new article by Hannes Taubenböck and his team got published "Empiric recommendations for population disaggregation under different data scenarios" in PLOS One. From the abstract: "High-resolution population mapping is of high relevance for developing and...

most recent news:

Call for Papers for the Joint Urban Remote Sensing Event (JURSE)

Call for Papers for the Joint Urban Remote Sensing Event (JURSE)

The Joint Urban Remote Sensing Event (JURSE) ( http://jurse.org/ ) is a forum of excellence where researchers, practitioners and students present, share, and discuss their latest findings and results. A very dynamic version of the Joint Urban Remote Sensing Event...

public talk by Hannes Taubenböck

public talk by Hannes Taubenböck

Deutschland – Wie und wo wir wohnen (wollen) Vortrag von Hannes Taubenböck (publich talk in german by Hannes Taubenböck on "How and where we like to live") Jede:r von uns wohnt – irgendwie. Ob in ländlichen oder urbanen Gefilden, ob in Ein- oder Mehrfamilienhäusern,...

new publication: on population disaggregation

new publication: on population disaggregation

A new article by Hannes Taubenböck and his team got published "Empiric recommendations for population disaggregation under different data scenarios" in PLOS One. From the abstract: "High-resolution population mapping is of high relevance for developing and...