Peer review artikel

Mapping a priori defined plant associations using remotely sensed vegetation characteristics


“Incorporation of a priori defined plant associations into remote sensing products is a major challenge that has
only recently been confronted by the remote sensing community.Wepresent an approach tomap the spatial distribution
of such associations by using plant indicator values (IVs) for salinity, moisture and nutrients as an intermediate
between spectral reflectance and association occurrences. For a 12km2 study site in the Netherlands, the
relations between observed IVs at local vegetation plots and visible and near-infrared (VNIR) and short-wave infrared
(SWIR) airborne reflectance data were modelled using Gaussian Process Regression (GPR) (R2 0.73, 0.64
and 0.76 for salinity, moisture and nutrients, respectively). These relations were applied to map IVs for the complete
study site. Association occurrence probabilities were modelled as function of IVs using a large database of
vegetation plots with known association and IVs. Using the mapped IVs, we calculated occurrence probabilities
of 19 associations for each pixel, resulting in both a crisp association map with the most likely occurring association
per pixel, as well as occurrence probability maps per association. Association occurrence predictions were
assessed by a local vegetation expert, which revealed that the occurrences of associations situated at frequently
predicted indicator value combinations were over predicted. This seems primarily due to biases in the GPR predicted
IVs, resulting in associations with envelopes located in extreme ends of IVs being scarcely predicted.
Although the results of this particular study were not fully satisfactory, the method potentially offers several advantages
compared to current vegetation classification techniques, like site-independent calibration of association
probabilities, site-independent selection of associations and the provision of IV maps and occurrence
probabilities per association. If the prediction of IVs can be improved, this method may thus provide a viable
roadmap to bring a priori defined plant associations into the domain of remote sensing.”
© 2013 Elsevier Inc. All rights reserved.
(Citaat: Roelofsen, H.D., Kooistra, L., et al. – Mapping a priori defined plant associations using remotely sensed vegetation characteristics – Remote Sensing of Environment 140(2014), p.639-651)

Heeft u een vraag over deze publicatie?