AI Article Synopsis
- GAM4water is a new R-based method designed to classify wetted and non-wetted areas in remotely sensed images using image indices.
- The algorithm employs a Generalized Additive Model (GAM) that effectively handles non-linear responses and is compatible with various types of radiometric data, including drone and satellite imagery.
- Tests in two case studies demonstrate that GAM4water can accurately classify these areas while providing flexible outputs, including binary rasters and detailed reports, without needing complex setup.
Article 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.•This paper introduces the first R based method of wetted area extraction for remotely-sensed images.•The method is based on Generalized Additive Models and is applicable to any remotely-sensed data.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11462176 | PMC |
| http://dx.doi.org/10.1016/j.mex.2024.102955 | DOI Listing |
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