Pittsburgh West LiDAR and DEMs used to Create Predictive Models for Hydrologic Flow Nets and Local Land Subsidence
Abstract
This paper explores the use of LiDAR and Digital Elevation Models (DEMs) for analyzing land surfaces with respect to hydrologic discharge and land subsidence. Higher resolution 1-meter DEMs from flown LiDAR data are replacing older 30- and 10-meter resolution DEMs, providing more accurate projections. Rasters created from this high-resolution elevation data can be used to create predictive flow models and land subsidence models using GIS. The National Hydrologic Dataset (NHD) and the Soil Survey Geographic Database (SSURGO) are valuable sources of data for both hydrologic discharge and soil taxonomy. Pittsburgh, a city with steep terrains, is surrounded by mountains and receives a decent amount of precipitation, making it a hotbed for landslides. The paper describes methods used to create flow nets, calculate topographic moisture index (TMI), and create a failure index model for areas at high risk of landslides.
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