GAM4water: An R-based method for extracting wetted areas from remotely-sensed images

Abstract

We present ‘GAM4water,’ a R-based method to classify wetted and non-wetted (dry) areas using remotely sensed image indices derived from such images. The GAM4water classification algorithm is built around a Generalized Additive Model (GAM) capable of accounting for non-linear responses. GAM4water can use any type of radiometric data, whether from drones, satellites or other platforms, and can be used with data of different spatial resolutions, geographic extents and spatial reference systems. It is a supervised tool that uses pixel information to distinguish between wetted and dry areas within an image set, extract them and produce a rich output that includes a binary raster, polygons of wetted areas, and a classification performance report. We tested the method in two case-studies, one using high resolution drone images and another using satellite images. The tests show that GAM4water can produce highly accurate classifications of wetted and non-wetted areas, and has the additional benefit of being easily customizable and not requiring complex implementation procedures.

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  This paper introduces the first R based method of wetted area extraction for remotely-sensed images.
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  The method is based on Generalized Additive Models and is applicable to any remotely-sensed data.