Fisheries Research Articles

A novel method for robust marine habitat mapping using a kernelised aquatic vegetation index

Document Type

Article

Publication Date

2-29-2024

Journal Title

ISPRS Journal of Photogrammetry and Remote Sensing

ISSN

Print: 0924-2716 Electronic: 1872-8235

Keywords

Cloud Computing, Remote sensing, Marine habitat, Vegetation indices, Kernel methods, Submerged vegetation

Disciplines

Aquaculture and Fisheries | Marine Biology

Abstract

Efficient and timely mapping and monitoring of marine vegetation are becoming increasingly important as coastal habits have experienced significant declines in spatial extent due to climate change. Recent advances in machine learning and cloud computing, such as Google Earth Engine, have demonstrated that online analysis platforms make global-scale habitat mapping and monitoring possible. However, the mapping and monitoring of marine ecosystems with remote sensing is challenging, and we lack reliable, generalisable and scalable indices such as NDVI to assess spatiotemporal change in marine habitats. Here, we present a novel method for mapping coastal marine habitats using a kernelised aquatic vegetation index (kNDAVI) with spatially balanced in-water ground truthing and compare it to existing indices and mapping approaches. The kernelised vegetation index provides a simple, consistent, scalable and accurate method for mapping shallow marine vegetation across ~ 400 km of coastline along mid-west Australia (31.58◦S − 29.56◦S). This region has significant coastal macroalgae cover that provides critical recruitment habitat for many invertebrates, including commercially valuable fisheries, and has experienced significant loss due to climate-induced heatwaves. We extensively validate kNDAVI and satellite-derived covariates for their utility in mapping SAV using three approaches 1] cross-validation, 2] block cross-validation and 3] site validation. Habitat models that included the kernelised vegetation index achieved excellent agreement (Accuracy > 0.90 and Cohen’s kappa > 0.80) for classifying submerged vegetation. We demonstrate that the kNDAVI index has considerable potential for large-scale vegetation monitoring and provides an applicable metric to map spatiotemporal dynamics and more effectively manage these changing coastal habitats.

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