Abstract
AbstractAn estimated 76% of global stream area is occupied by channels with widths above 30 m. Sentinel‐2 imagery with resolutions of 10 m could supply information about the composition of river corridors at national and global scales. Fuzzy classification models that infer sub‐pixel composition could further be used to compensate for small channel widths imaged at 10 m of spatial resolution. A major challenge to this approach is the acquisition of suitable training data useable in machine learning models that can predict land‐cover type information from image radiance values. In this contribution, we present a method which combines unmanned aerial vehicles (UAVs) and Sentinel‐2 imagery in order to develop a fuzzy classification approach capable of large‐scale investigations. Our approach uses hyperspatial UAV imagery in order to derive high‐resolution class information that can be used to train fuzzy classification models for Sentinel‐2 data where all bands are super‐resolved to a spatial resolution of 10 m. We use a multi‐temporal UAV dataset covering an area of 5.25 km2. Using a novel convolutional neural network (CNN) classifier, we predict sub‐pixel membership for Sentinel‐2 pixels in the fluvial corridor as divided into classes of water, vegetation and dry sediment. Our CNN model can predict fuzzy class memberships with median errors from −5% to +3% and mean absolute errors from 10% to 20%. We also show that our CNN fuzzy predictor can be used to predict crisp classes with accuracies from 95.5% to 99.9%. Finally, we use an example to show how a fuzzy CNN model trained with localized UAV data can be applied to longer channel reaches and detect new vegetation growth. We therefore argue that the novel use of UAVs as field validation tools for freely available satellite data can bridge the scale gap between local and regional fluvial studies. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd
Highlights
Fluvial remote sensing at sub‐metric resolutions has been the focus of a significant body of research in recent years (e.g. Marcus and Fonstad, 2008, 2010; Carbonneau et al, 2012; Piégay et al, 2012, 2020; Bizzi et al, 2016; Dugdale et al, 2019)
When we compare the accuracies of desk‐based models validated with desk‐based data against the accuracies of desk‐based models validated with unmanned aerial vehicles (UAVs) data, we see that desk‐based validation always over‐estimates the quality of a classification, sometimes by as much as 16%
We have demonstrated that classification models, both crisp and fuzzy, trained with the benefit of ground‐truth data derived from low‐altitude UAV flights systematically deliver better performance
Summary
Fluvial remote sensing at sub‐metric resolutions has been the focus of a significant body of research in recent years (e.g. Marcus and Fonstad, 2008, 2010; Carbonneau et al, 2012; Piégay et al, 2012, 2020; Bizzi et al, 2016; Dugdale et al, 2019). Vannote et al, 1980; Fausch et al, 2002) In this context, it has been argued that very high‐resolution imagery, sometimes called hyperspatial imagery (Carbonneau and Piégay, 2012), could allow for a process‐based analysis of image data that could be used to advance fundamental ideas in fluvial ecology and geomorphology (Carbonneau et al, 2012). It has been argued that very high‐resolution imagery, sometimes called hyperspatial imagery (Carbonneau and Piégay, 2012), could allow for a process‐based analysis of image data that could be used to advance fundamental ideas in fluvial ecology and geomorphology (Carbonneau et al, 2012) Such process‐focussed monitoring over regional, national or continental scales remains largely out of reach.
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