{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Raster Analytics: Calculate wildfire landslide risk\n", "\n", "> * 🔬 Data Science\n", "* 🖥️ Requires ArcGIS Image Server\n", "* 👟 Ready To Run!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In October 2017, wildfires raged through Sonoma and Napa counties, devastating surrounding communities. In the wake of these fires, the burn scars could cause further risk to public safety from a different kind of disaster: landslides. Post-fire landslides are particularly hazardous because there is more erosion and weaker soil in burned areas without vegetation to anchor the topsoil.\n", "\n", "Groups handling rehabilitation, emergency planning and mitigation after a wildfire need to assess the vulnerability of the landscape to landslides. In this notebook, we will provide local emergency management teams a summary of post-wildfire landslide risk, so officials can target mitigation efforts to the most vulnerable watershed basins.\n", "\n", "We will use the imagery layers to assess landslide risk per watershed within the burn area. We will create a landslide risk map and then summarize the landslide risk based on watershed sub-basins. We will use raster function chains to derive a burn severity map, a topographic slope map, and a landcover index map. These individual processing chains will be combined into one processing chain for distributed processing on the Raster Analytics server and then be summarized by watershed sub-basins." ] }, { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "