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Plant Species Richness (PSR) is one of the most widely used metrics to estimate alpha diversity in ecology. Several approaches have been developed to estimate PSR with Remote Sensing (RS) data. Among them, the Spectral Diversity Hypothesis (SDH) approach can be successfully applied to airborne hyperspectral data. Although effective, these data are limited in space and time due to high aerial acquisition costs. Satellite multispectral data are continuously acquired on a global scale, but their spatial and spectral resolutions are not comparable to those of hyperspectral data. Although some studies compared different optical data for estimating PSR using SDH, the impact of the spatial and spectral resolutions on the assessment of this biodiversity indicator is not clear. Moreover, most of the studies focus on dense tropical forest areas or wetlands, while little has been done to test the SDH approach in open forests located in Mediterranean regions. For all these reasons, the present work aims to: (1) apply and interpret PSR estimated with the SDH approach in open Mediterranean forest, and (2) evaluate the impact of the spatial and spectral resolutions on PSR estimation using real and simulated RS data. The PSR was estimated applying the SDH approach on a 4m hyperspectral data (373 bands), 30m multispectral satellite data (7 bands), synthetic 16m and 30m hyperspectral data (373 bands), and synthetic 4m multispectral data (7 bands). Preliminary results carried out in the San Joaquin Experimental Range (SJER) indicate that: (1) there is a weak correlation between spectral and species diversity in the less dense forest areas (R2 =-0.13 for the hyperspectral and R2 =0.14 for the multispectral data), while revealing good correlation in the more dense forest areas (R2 =0.68 for the hyperspectral and R2 =0.65 for the multispectral data), (2) the number of identified spectral species is more influenced by the spectral resolution than the spatial one, and (3) high spatial resolution data tends to overestimate the PSR in less dense forest areas because of the influence of background and understory vegetation.
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