The influence of seasonality in estimating mangrove leaf chlorophyll-a content from hyperspectral data

Abstract

Mangrove photosynthetic activity and, consequently, physiological stress can be monitored indirectly using leaf chlorophyll-a (Chla) measurements. Recent studies have demonstrated the feasibility of mangrove leaf Chla content estimation from in situ hyperspectral vegetation indices (VI) but no such research has been conducted using data collected from contrasting seasons (i.e. dry and rainy). In this study, mangrove leaves were collected in a sub-tropical forest of the Mexican Pacific for Chla content determination and in situ hyperspectral measurements (450–1,000 nm). Specifically, we tested 35 VI to estimate Chla content based on a leaf sample of 360 collected from the same trees during both the dry and rainy seasons. The forest examined contained three species of mangrove (Rhizophora mangle, Avicennia germinans and Laguncularia racemosa) exhibiting various conditions of health (dwarf condition, tall and healthy). A principal component analysis, followed by linear regression analyses, were conducted in order to identify those VI that best predict mangrove leaf Chla content during the two seasons. The results indicate that VI derived from hyperspectral measurements collected during the dry season are better at estimating leaf Chla content than those collected during the rainy season. Among the 35 VI, the Vog1 (R740/R720) index was found to be the best predictor of mangrove leaf Chla content, resulting in R 2 values of 0.80 and 0.68 for the dry and rainy season respectively. These results would suggest that for identifying variation in mangrove forest stress (i.e. health) in sub-tropical regions, hyperspectral measurements should be carried out during the dry season.