Redana Matteo, Lancaster Lesley T, Gibbins Chris
School of Informatics, Computing and Cyber System, Northern Arizona University, United States.
School of Biological Science, University of Aberdeen, United Kingdom.
MethodsX. 2024 Sep 10;13:102955. doi: 10.1016/j.mex.2024.102955. eCollection 2024 Dec.
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.
我们展示了“GAM4water”，这是一种基于R语言的方法，用于使用从此类图像派生的遥感图像指数对湿润和非湿润（干燥）区域进行分类。GAM4water分类算法围绕能够考虑非线性响应的广义相加模型（GAM）构建。GAM4water可以使用任何类型的辐射数据，无论是来自无人机、卫星还是其他平台的数据，并且可以与具有不同空间分辨率、地理范围和空间参考系统的数据一起使用。它是一种监督工具，使用像素信息来区分图像集中的湿润和干燥区域，提取这些区域并生成丰富的输出，包括二进制栅格、湿润区域的多边形以及分类性能报告。我们在两个案例研究中测试了该方法，一个使用高分辨率无人机图像，另一个使用卫星图像。测试表明，GAM4water可以对湿润和非湿润区域进行高度准确的分类，并且具有易于定制且不需要复杂实施程序的额外优点。
•本文介绍了第一种基于R语言的遥感图像湿润区域提取方法。
•该方法基于广义相加模型，适用于任何遥感数据。
