International Journal of Applied Earth Observations and Geoinformation (Dec 2022)
Cloud-based interactive susceptibility modeling of gully erosion in Google Earth Engine
Abstract
The gully erosion susceptibility literature is largely dominated by contributions focused on model comparison. This has led to prioritize certain aspects and leave others underdeveloped as compared to other natural hazard applications. For instance, in gully erosion data-driven modeling most studies use different platforms when it comes to data management, modeling and conversion into predictive maps. This in turn has limited the scope to catchment-scales. In this manuscript, we opt to propose a tool where the whole modeling procedure is unified within the same cloud computing system, allowing one to get rid of potential errors caused by input/output operations but also to extend the study areas indefinitely, as cloud data-management tools easily offer access to global data. Specifically, we present an interactive tool for susceptibility modeling in Google Earth Engine (GEE), the Susceptibility Tool for GEE (STGEE). Our tool requires few input data and makes use of the breadth of predictors’ information available in GEE. In this cloud computing environment, binary classifiers typical of susceptibility models can be called and fed with information related to mapping units and any natural hazards’ distribution over the geographic space. We tested our tool to generate susceptibility estimates for gully erosion occurrences in a study area located in Sicily (Italy). The tool we propose is equipped with a series of functions to aggregate the predictors’ information in space and time over a mapping unit of choice. Here we chose a Slope Unit partition but any polygonal structure can be chosen by the user. Once this information is derived, our tool calls for a Random Forest classifier to distinguish locations prone to gully erosion from locations where this process is not probabilistically expected to develop. This is done while providing a modeling performance overview, accessible via a separate panel. Such performance can be calculated on the basis of a exploratory analysis where all the information is used to fit a benchmark model as well as a spatial k-fold cross-validation scheme. Ultimately, the predictive function can be interactively used to generate susceptibility maps in real time, for the study area as well as any study area of interest. To promote the use of our tool, we are sharing it in a GitHub repository accessible at this link: https://github.com/giactitti/STGEE.
