%0 Articles
%T Remote sensing of floristic patterns in the lowland rain forest landscape
%A Thessler, Sirpa
%D 2008
%J Dissertationes Forestales
%V 2008
%N 59
%R doi:10.14214/df.59
%U http://dissertationesforestales.fi/article/1840
%X Land use and conservation planning urgently need information on floristic variation over large rain forest areas. Floristic variation can not be inventoried in every location and of all the flora, thus inventory is limited in sample sites of a group(s) of indicator species and modelled to predict the floristic composition of non-inventoried sites using spatially continuous information on the environment. Modelling is, however, practicable only if the dimensions of species data can be drastically reduced to a surrogate of floristic composition. The aim was to explore whether remote sensing can be applied to study and map the spatial variation of surrogates in lowland old-growth rain forest.

I studied three surrogates: 1) number of species in ecological categories, 2) vegetation / forest type classification, and 3) species composition, summarized as the scores of three ordination axes. The understorey Melastomataceae and pteridophytes, and tree and palm species were used as indicator species. Landsat TM or ETM+ - satellite images and SRTM digital elevation model were used as a proxy of environmental variation. The prediction methods included a k nearest neighbour method and linear discriminant analysis. The study areas were located in eastern Ecuador, in north-eastern Peru and northern Costa Rica.

The main finding was that floristic patterns in lowland rain forest, expressed as vegetation classes, ordination axis scores or the number of species in ecological categories, can be predicted on the basis of remotely sensed data and field observations. The accuracy of the predictions depended on feature selection and weighting, and on spatial resolution. The k-nn method proved to be a promising method in predicting floristic variation when it was expressed as a continuous variable, such as ordination axis scores or number of species. It also performed better than linear discriminant analysis in distinguishing forest classes using satellite image data.