Predicting mountain plant richness and rarity from space using satellite-derived vegetation indices

Can species richness and rarity be predicted from space? If satellite-derived vegetation indices can provide us with accurate predictions of richness and rarity in an area, they can serve as an excellent tool in diversity and conservation research, especially in inaccessible areas. The increasing availability of high-resolution satellite images is enabling us to study this question more carefully. We sampled plant richness and rarity in 34 quadrats (1000 m²) along an elevation gradient between 300 and 2200 m focusing on Mount Hermon as a case study. We then used 10 Landsat, Aster, and QuickBird satellite images ranging over several seasons, going up to very high resolutions, to examine the relationship between plant richness, rarity, and vegetation indices calculated from the images. We used the normalized difference vegetation index (NDVI), one of the most commonly used vegetation indexes, which is strongly correlated to primary production both globally and locally (in more seasonal and in drier and/or colder environments that have wide ranges of NDVI values). All images showed a positive significant correlation between NDVI and both plant species richness and percentage tree cover (with R² as high as 0.87 between NDVI and total plant richness and 0.89 for annual plant richness). The high resolution images enabled us to examine spatial heterogeneity in NDVI within our quadrats. Plant richness was significantly correlated with the standard deviation of NDVI values (but not with their coefficient of variation) within quadrats and between images. Contrary to richness, relative range size rarity was negatively correlated with NDVI in all images, this result being significant in most cases. Thus, given that they are validated by fieldwork, satellite-derived indices can shed light on richness and even rarity patterns in mountains, many of which are important biodiversity centres.