NIR Hyperspectral Imaging Combined with Chemometrics for Mapping Water Patterns During Dehydration of Nonvascular Epiphytic Communities

In recent years, nonvascular epiphytic communities have been increasingly subjected to extreme climatic conditions, with heavy rains and prolonged droughts. Therefore, understanding their management of water resources provides insight into their ecosystem-level contributions. However, until now, little has been done to assess this feature at a micro-scale level considering species-species interactions. In this context, this study develops an analytical strategy based on hyperspectral imaging (HSI) and chemometrics to map the water content (WC) of nonvascular epiphytic communities during a dehydration process, while considering interactions among life forms. Exploratory analysis of data by means of principal component analysis (PCA) demonstrates that the highest source of variability along the process is due to water loss, though differences among communities can be observed as well. Indeed, the generation of false color RGB score maps enables the evaluation of different life forms' responses, giving an initial understanding of facilitation and competition mechanisms based on community composition. Moreover, the use of multivariate regression using partial least squares (PLS) regression to predict water content at a pixel level, with a final error in prediction around 3%, leads to the visualization of maps representing the WC of each pixel composing the sample, permitting the evaluation of communities' response at a detailed scale, providing a valuable method for recovering spatial information while monitoring dehydration. The analytical impact and novelty of the approach are supported by the consistency in results obtained from developing the model with two different strategies, image-based and pixel-based, and by the complementarity of the information obtained by the two strategies themselves.