Discrimination of lianas and trees with leaf-level hyperspectral data

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Remote Sensing of Environment


Lianas are an important component of the biological diversity in two tropical forests with contrasting moisture regimes in Panama. However, their presence in a tree crown may be a source of confusion in remotely sensed data collected for inventories or assessment of vegetation health. The structural growth form of lianas contrasts with trees in that their proportion of leafy biomass to woody biomass is much higher. In effect, they use trees for structural support and typically form a monolayer of leaves above the crown of the supporting tree. Here, we investigated possible differences between hyperspectral signatures of lianas and trees at the leaf level using pattern recognition techniques. Our method involves principal components analysis followed by training and classification using a selection of supervised parametric and nonparametric classifiers. At a tropical dry forest site (Parque Natural Metropolitano), lianas and trees are distinguishable as groups based on their leaf spectral reflectance characteristics in dry season conditions. Classification was improved using ancillary data on leaf chlorophyll content. Their distinction at this site may be related to drought stress and/or phenological differences between the two groups. At a tropical wet forest site (Fort Sherman), discrimination between the two groups was not as clear. Additional research is required to determine the physiological basis of possible differences as well as to determine if these differences are observable at the canopy level.