Substrate mapping of three rivers in a Ramsar wetland in Jamaica: a comparison of data collection (hydroacoustic v. grab samples), classification and kriging methods

Abstract

Most developing countries have failed to adopt hydroacoustics to aid with the management of their aquatic natural resources. We tested the ability of single-beam sonar (SBES) to discern and map substrates in three rivers from the largest wetland in Jamaica, the Black River Lower Morass (BRLM). We used five supervised classification methods (including C5.0; random forest, RF; and naïve Bayes, NB) and four interpolation algorithms (indicator kriging (iks), fixed path simulation (fpth), random path simulations (rpth) and multinomial categorical simulation (mcs) based on transitional rates and incorporated into Markov Chain). Irrespective of the classifier used, mcs consistently produced higher overall classification accuracies (OAC) and kappa statistics; however, rpth interpolation produced the lowest balanced error rate (BER) recorded. For all three rivers, OAC, kappa and BER statistics were 49.7–87.1, 32.8–81.0 and 15.3–45.1% respectively. All interpolation algorithms produced maps with higher OAC and kappa indices from data classified using the tree-based classifiers (C5.0 and RF) in the absence of gravel-free substrates. At a lower spatial resolution, comparable maps were obtained by interpolating discrete sample points acquired by grab samples. Given that most of rivers in island states are small, sinuous, shallow and sometimes non-navigable by boat, the use of SBES as the most cost-effective and efficient way of mapping river substrates is questionable, but the interpolation of grab samples might suffice.