Jochem Verrelst

Mapping of aggregated floodplain plant communities using image fusion of CASI and LiDAR data.

Combined optical and laser altimeter data offer the potential to map and monitor plant communities based on their spectral and structural characteristics. A problem unresolved is, however, that narrowly defined plant communities, i.e. plant communities at a low hierarchical level of classification in the Braun-Blanquet system, often cannot be linked directly to remote sensing data for vegetation mapping. We studied whether and how a floristic dataset can be aggregated into a few major discrete, mappable classes without substantial loss of ecological meaning. Multi-source airborne data (CASI and LiDAR) and floristic field data were collected for a floodplain along the river Waal in the Netherlands. Mapping results based on floristic similarity alone did not achieve highest levels of accuracy. Ordination of floristic data showed that terrain elevation and soil moisture were the main underlying environmental drivers shaping the floodplain vegetation, but grouping of plant communities based on their position in the ordination space is not always obvious. Combined ordination-based grouping with floristic similarity clustering led to syntaxonomically relevant aggregated plant assemblages and yielded highest mapping accuracies.

Merging the Minnaert-

The Compact High Resolution Imaging Spectrometer (CHRIS) mounted onboard the Project for Onboard Autonomy (PROBA) spacecraft is capable of sampling reflected radiation at five viewing angles over the visible and near-infrared regions of the solar spectrum with high spatial resolution. We combined the spectral domain with the angular domain of CHRIS data in order to map the surface heterogeneity of an Alpine coniferous forest during winter. In the spectral domain, linear spectral unmixing of the nadir image resulted in a canopy cover map. In the angular domain, pixelwise inversion of the Rahman-Pinty-Verstraete (RPV) model at a single wavelength at the red edge (722 nm) yielded a map of the Minnaert-k parameter that provided information on surface heterogeneity at a subpixel scale. However, the interpretation of the Minnaert-k parameter is not always straightforward because fully vegetated targets typically produce the same type of reflectance anisotropy as non-vegetated targets. Merging both maps resulted in a forest cover heterogeneity map, which contains more detailed information on canopy heterogeneity at the CHRIS subpixel scale than is possible to realize from a single-source optical data set.

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In this study we combine the spectral domain with the directional domain of hyperspectral CHRIS (Compact High Resolution Imaging Spectrometer) data to map 3D heterogeneity of an Alpine coniferous forest during wintertime. CHRIS mounted onboard the PROBA (Project for On-board Autonomy) spacecraft is capable of sampling terrestrial reflectance anisotropy over the visible/near-infrared region of the electromagnetic spectrum with high spatial resolution. In the spectral domain, linear unmixing of the nadir image led to a forest cover density map. In the directional domain, the Rahman-Pinty-Verstraete (RPV) model led to Minnaert-k parameter retrieval that provided information of surface heterogeneity at subpixel scale. Due to the bright snow background Minnaert-k yielded the best relationship with forest cover density at the 773 nm spectral band of CHRIS. Fusing both independently derived maps resulted in a forest cover map that includes information of 3D canopy heterogeneity at the subpixel scale.