Mangrove monitoring in Lothian Island using airborne hyperspectral AVIRIS-NG data

Abstract

Constant monitoring of the mangroves is important for understanding their health species assemblage and undertaking policies to rehabilitate or restore them. In this study, an attempt was made to classify and detect health and carbon stock in mangroves through a Remote Sensing perspective. The AVIRIS-NG dataset with high spectral (5 nm) and spatial (5 m) resolution proved to be capable of identifying different species of the same mangrove genus along with other morphological features. Using Spectral Angle Mapper (SAM) classification technique, 75% accuracy has been achieved over 12 separate classes. The health of the Lothian Island mangroves was evaluated by using eight different vegetation indices, namely ACI, ARI, MARI, ARVI, CAI, CARI, Chl-red-edge, and REPI. The vegetation indices were normalized and then used as input in a Multi-Criteria Decision Support System (MCDSS) model. The MCDSS model output health map clearly identifies the degraded and healthy mangroves on Lothian Island. The Lothian Island mangroves were found to be degraded in most of the region, which should be of major concern. Above Ground Biomass (AGB), which is another indicator of health, was calculated using Field measured data and AVIRIS-NG hyperspectral data. The highest, lowest, and mean biomass recorded in field transact is 507.19 and 7.39 and 126.43 tonnes, respectively. These three parameters, namely species assemblage of mangroves, their health, and AGB combined can provide much insight into the ecosystem health of Lothian Island.