Mangrove classification using airborne hyperspectral AVIRIS-NG and comparing with other spaceborne hyperspectral and multispectral data
Jyoti Prakash Hati, Sourav Samanta, Nilima Rani Chaube, Arundhati Misra, Sandip Giri, Niloy Pramanick, Kaushik Gupta, Sayani Majumdar, Abhra Chanda, Anirban Mukhopadhyay, Sugata Hazra
Abstract
Application of remote sensing makes the assessment and monitoring of mangroves both time and cost-effective. In this study, the capacity of AVIRIS-NG data in discriminating different mangrove species of Lothian Island of Indian Sundarbans has been evaluated and compared with hyperspectral (Hyperion) and multispectral dataset (Landsat 8 OLI and Sentinel-2). Spectral signatures of mangrove species were retrieved, and spectral libraries were created. With the corrected images and spectral libraries, mangroves were classified using appropriate classification techniques. For multispectral datasets (Landsat 8 OLI and Sentinel-2) and hyperspectral coarser-resolution Hyperion datasets, K-means classification followed by knowledge-based classification was adopted. For fine resolution hyperspectral AVIRIS-NG dataset, classification was accomplished using Support Vector Machine (SVM). The overall accuracy for the classification is significantly high in case of AVIRIS-NG data (87.61%) compared to the Landsat 8 OLI (76.42%), Sentinel-2 (79.81%), and Hyperion data (81.98%). The results showed that AVIRIS-NG hyperspectral dataset has the potential to classify not only the genus level but also species-level with satisfactory accuracy in a complex mangrove forest.