Mapping estuarine habitats using airborne hyperspectral imagery, with special focus on seagrass meadows. (5th October 2015)

• Record Type: Journal Article
• Authors: Valle, Mireia; Palà, Vicenç; Lafon, Virgine; Dehouck, Aurélie; Garmendia, Joxe Mikel; Borja, Ángel; Chust, Guillem
• Is Part Of: Estuarine, coastal and shelf science. Volume 164(2015)
• Page Start: 433
• Page End: 442
• Publication Date: 2015-10-05
• Subjects: Habitat classification -- Estuaries -- Remote sensing -- Zostera noltii -- Compact airborne spectrographic imager; Estuarine oceanography -- Periodicals; Coasts -- Periodicals; Estuarine biology -- Periodicals; Seashore biology -- Periodicals; Coasts; Estuarine biology; Estuarine oceanography; Seashore biology; Periodicals; 551.461805
• DOI: 10.1016/j.ecss.2015.07.034
• Languages: English
• ISSNs: 0272-7714

Abstract

Abstract: Estuaries and coasts are among the most productive ecosystems and constitute valuable habitats for biodiversity and ecosystem services. Amongst nearshore ecosystems, seagrass beds play a major role enhancing biodiversity and water quality. Consequently, the development of new approaches to create extensive and high-resolution habitat maps is required not only to implement conservation, restoration and management plans, but also to establish adaptation plans to face climate change impacts. This study particularly assesses the capability of hyperspectral airborne imagery acquired with Compact Airborne Spectrographic Imager (CASI) to discriminate and map estuarine habitats, with special focus on Zostera noltii seagrass meadows. To this end, 13 habitats were defined along the supralittoral, intertidal and subtidal zones of an estuary, including Z. noltii seagrass meadows. The CASI sensor was configured to acquire 25 bands in the visible and near infrared wavelengths with a ground sampling distance of 2 m. Spectral bands were selected for species discrimination based on the spectral signature of the different habitat classes. Six different band combinations were tested applying maximum likelihood classification algorithm. The most accurate classification was obtained with 10 band combination (a mean producer accuracy 92% and a mean user accuracy 94%). The classification of Z. noltii beds has been found to be restricted to moderate and high dense meadows, however a vegetation index has been defined which could be applied for mapping Z. noltii meadow cover. These results highlight the value of CASI data to discriminate and map estuarine habitats, providing key information to be used in supporting the implementation of environmental legislation, protection and conservation of coastal habitats. Graphical abstract: Highlights: CASI sensor imagery classification provides reliable mapping of estuarine habitats. 13 habitats in the supralitoral, intertidal and subtidal zones accurately mapped (92–94%). Results will be useful for biodiversity monitoring at spatial and temporal scales. Classification of Zostera noltii is restricted to moderate and high dense meadows. Defined vegetation index can be applied for mapping Z. noltii meadows cover.