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 usi

SUBMITTER: Redana M 

PROVIDER: S-EPMC11462176 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature