A hierarchical method and workflow for the semi-automated mapping of valley bottom geomorphic units using publicly available remote sensing datasets

Abstract

Geomorphic units (GUs) are the landforms that make up the valley bottom and are produced by fluvial processes that determine river structure and function. Mapping of GUs can be used to interpret river type and behaviour and to analyse river condition and recovery processes. The advancement of remote sensing technologies and big-data acquisition are enabling the development and operationalisation of tools to semi-automate the mapping of assemblages of GUs across large spatial areas. In this study, we develop a hierarchical method that combines a landscape classification approach (Geomorphons) with supervised classification using light detection and ranging data (LiDAR) and satellite images to semi-automate the mapping of GUs across valley bottoms. We have also produced a new method for identifying and mapping pools in the absence of bathymetry data. We applied our method on 78 river sections in coastal catchments of NSW, Australia. We were able to identify 20 refined GU types and four further sub-types of bank-attached bars. Our method produced GU maps that are consistent with desktop manual delineation from aerial images and digital elevation models. Our hierarchical method offers GU maps with varying accuracy and resolution, accommodating a user's decisions regarding amount of effort invested relative to map quality and accuracy required. Initial runs produce maps with 12 preliminary GUs with 61%–75% consistency when compared to desktop manual mapping. With additional effort and manual corrections, a higher level of GU identification is possible (i.e. refined GU mapping increases the consistency to 70%–81%). The delineation of more intricate sub-types of GU or sub-units on compound GUs, which is essential for interpretation of river behaviour, condition, and recovery, still requires on-site field verification to achieve the best results.