HazMapper: a global open-source natural hazard mapping application in Google Earth Engine

Abstract

Abstract. Modern satellite networks with rapid image acquisition cycles allow for near-real-time imaging of areas impacted by natural hazards such as mass wasting, flooding, and volcanic eruptions. Publicly accessible multi-spectral datasets (e.g., Landsat, Sentinel-2) are particularly helpful in analyzing the spatial extent of disturbances, however, the datasets are large and require intensive processing on high-powered computers by trained analysts. HazMapper is an open-access hazard mapping application developed in Google Earth Engine that allows users to derive map and GIS-based products from Sentinel or Landsat datasets without the time- and cost-intensive resources required for traditional analysis. The first iteration of HazMapper relies on a vegetation-based metric, the relative difference in the normalized difference vegetation index (rdNDVI), to identify areas on the landscape where vegetation was removed following a natural disaster. Because of the vegetation-based metric, the tool is typically not suitable for use in desert or polar regions. HazMapper is not a semi-automated routine but makes rapid and repeatable analysis and visualization feasible for both recent and historical natural disasters. Case studies are included for the identification of landslides and debris flows, wildfires, pyroclastic flows, and lava flow inundation. HazMapper is intended for use by both scientists and non-scientists, such as emergency managers and public safety decision-makers.

An Overview of Geoinformatics State-of-the-Art Techniques for Landslide Monitoring and Mapping

“…However, mapping landslides using satellite-based datasets is reducing the time and costs for such tasks, especially with the use of freely available images from NASA's Landsat and ESA's Sentinel missions. Due to their medium spatial resolution and relatively high revisit time, scholars are mainly implementing change detection approaches relying on presence of vegetation to map landslide extents after the failure using both multispectral and radar imagery (e.g., (Plank et al, 2016;Scheip and Wegmann, 2021). However, using only optical dataset may have its limitations especially when it is present a constant cloud cover, which obstructs timely detection.…”

Using ALOS-2 synthetic aperture radar (SAR) and interferometric SAR to detect landslides on the mountainous island of Pohnpei, Federated States of Micronesia

“…The study relied on the HazMapper tool for the mapping of land-cover change on Pohnpei (Scheip and Wegmann, 2021) and on observations from field reconnaissance of damages from landslides following an intense rainfall event in mid-March 2018 (Federated States of Micronesia, 2019b). Although the mapping and field reconnaissance observations of landslides occurred shortly after the rainfall event, the dates of occurrence for most landslides were not known and may not have been related to the rainfall event.…”

Ten simple rules for researchers who want to develop web apps

“…Web apps are powerful tools for increasing the accessibility and approachability of research data and findings because they leverage recent advancements in interactive web browsers to support browser-based user interfaces without the need for download and installation. Examples of web apps range from interactive maps depicting disease transmission (e.g., [ 9 ]), marine health (e.g., [ 10 , 11 ]), natural hazards (e.g., [ 12 , 13 ]), and pest infestations (e.g., [ 14 , 15 ]) to bioinformatics resource collections (e.g., [ 16 ]), to omics data analysis platforms (e.g., [ 17 ]), and to citation visualization tools (e.g., [ 18 ]), among others. Throughout this ten simple rules paper, we focus on web apps that help stakeholders make decisions by improving their access to information.…”

“…However, web apps can be beneficial beyond their use by stakeholders. As an example, web apps can support research by improving the sharing of results and facilitating communications between scientists (e.g., [ 12 , 13 , 19 ]). In this ten simple rules paper, we reflect on our experiences developing the aforementioned decision support tool and web app, called “ShellCast” ( https://go.ncsu.edu/shellcast ), as well as how our experience applies more broadly to researchers venturing into web development.…”