Establishing a hyperspectral library for Hong Kong mangroves: Species differentiation and leaf decay dynamics

Abstract

Mangroves are the resistant species found in the intertidal zones, providing ecosystem services such as protection of shorelines, provision of habitats to flora and fauna, and contributing to nutrient cycling. Study of their leaf properties has always been challenging, but this has been facilitated by the advent of Hyperspectral Imaging (HSI) systems. In such a context, this study undertook the development of a hyperspectral library offering the reflectance characteristics for adaxial and abaxial surfaces of mangrove species found in Hong Kong, on the temporal scale of seven days to facilitate the species identification and monitor the leaf decay. This library contained species level data, plot level data, and decay level data. Field surveys in fifteen plots (900 m² each) conducted in the Eastern and Western regions of Hong Kong collected hyperspectral data of five mangrove species, namely: Ceriops tagal, Kandelia obovata, Avicennia marina, Avicennia germinans, and Aegiceras corniculatum, using two different types of HSI systems i.e., Specim IQ (in-field data) and NEO Hyspex (in-lab data) hyperspectral cameras. A comparison of sensors unveiled a notably higher reflectance in field collected data than that of the lab-collected data, with a range of 11.8 % (Kandelia obovate) to 73.1 % (Aegiceras corniculatum). The Root Mean Square Error (RMSE) indicated deviation between the two sensors, i.e., 0.211 for Ceriops tagal, followed by Kandelia obovata (0.233), Avicennia marina (0.317), Avicennia germinans, and Aegiceras corniculatum (0.349). This freely available comprehensive hyperspectral library will serve as the foundation for training datasets to achieve automated classification with enhanced accuracy. This open access hyperspectral library will assist the researchers to relate the physiological and anatomical variations in leaves with the changes in hyperspectral reflectance on the temporal scale.