Previous studies have shown that the floristic composition of northern peatlands provides important information regarding ecosystem processes and their responses to environmental change. Remote sensing is the most expeditious method of obtaining floristic information at landscape and regional scales, but the spatial complexity of many northern peatlands and the spectral similarity of a number of peatland vegetation species is such that the success of traditional methods of vegetation classification is often limited. Here, we assessed whether ordination and regression analyses may be a useful alternative method for mapping peatland plant communities from remote sensing data. We used isometric feature mapping (Isomap) to describe the community structure of the peatland vegetation and related the identified continuous floristic gradients to hyperspectral imagery (AISA Eagle) using partial least squares regression (PLSR). We performed the same analysis at two hierarchical levels of species aggregation in order to map continuous gradients in the composition of both species and plant functional types (PFTs), the latter of which is the most widely used level of aggregation in northern ecosystems. Isomap was able to transfer 82% and more than 96% of the observed ground-based observations to the ordination space for plots characterised by species and PFT; respectively. The modelled floristic gradients showed good agreement with ground-based species and PFT observations although the strength of the agreement was proportional to the amount of floristic variation explained by each ordination axis (r2 val =0.74, 0.45 and 0.30 for the first three ordination axes and r2 val =0.68 and 0.66 for the first two ordination axes; for species and PFT floristic gradients respectively). We also found that how a PFT is defined has an important influence on the success with which it can be mapped. The resultant mapped floristic gradients enabled visualisation of homogeneous vegetation stands, heterogeneous mixtures of different key species and PFTs, and the presence of continuous and abrupt floristic transitions, without the need for unique spectral signatures or the collection of data characterising ancillary environmental variables.
|Journal||Remote Sensing of Environment|
|Early online date||05 Mar 2015|
|Publication status||Published - 01 Jun 2015|
- partial least square regression
- plant functional type